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<journal-id journal-id-type="publisher-id">Front. Immunol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Immunology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Immunol.</abbrev-journal-title>
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<issn pub-type="epub">1664-3224</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-meta>
<article-id pub-id-type="doi">10.3389/fimmu.2026.1744845</article-id>
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<article-categories>
<subj-group subj-group-type="heading">
<subject>Review</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Single-cell dissection of hepatocellular carcinoma immunity: from heterogeneous subtypes to precision therapeutics</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Liang</surname><given-names>Yuni</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="author-notes" rid="fn003"><sup>&#x2020;</sup></xref>
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<contrib contrib-type="author">
<name><surname>Wu</surname><given-names>Hemeng</given-names></name>
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<contrib contrib-type="author">
<name><surname>Qiu</surname><given-names>Yunshan</given-names></name>
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<contrib contrib-type="author">
<name><surname>Mo</surname><given-names>Qiulian</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author">
<name><surname>Chen</surname><given-names>Peipei</given-names></name>
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<contrib contrib-type="author">
<name><surname>Li</surname><given-names>Mingfen</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Lin</surname><given-names>Hongsheng</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>*</sup></xref>
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<aff id="aff1"><label>1</label><institution>Department of Clinical Laboratory, First Affiliated Hospital of Guangxi University of Chinese Medicine</institution>, <city>Nanning</city>,&#xa0;<country country="cn">China</country></aff>
<aff id="aff2"><label>2</label><institution>The First Clinical Faculty of GuangXi University of Chinese Medicine</institution>, <city>Nanning</city>,&#xa0;<country country="cn">China</country></aff>
<author-notes>
<corresp id="c001"><label>*</label>Correspondence: Hongsheng Lin, <email xlink:href="mailto:linhs@gxtcmu.edu.cn">linhs@gxtcmu.edu.cn</email></corresp>
<fn fn-type="equal" id="fn003">
<label>&#x2020;</label>
<p>These authors have contributed equally to this work</p></fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-11">
<day>11</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="corrected" iso-8601-date="2026-03-06">
<day>06</day>
<month>03</month>
<year>2026</year></pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>17</volume>
<elocation-id>1744845</elocation-id>
<history>
<date date-type="received">
<day>12</day>
<month>11</month>
<year>2025</year>
</date>
<date date-type="accepted">
<day>26</day>
<month>01</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Liang, Wu, Qiu, Mo, Chen, Li and Lin.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Liang, Wu, Qiu, Mo, Chen, Li and Lin</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-11">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<p>Hepatocellular carcinoma (HCC) represents one of the most prevalent malignancies worldwide and poses a critical public health challenge due to difficulties in early diagnosis, therapy resistance, and high mortality rates. The complex tumor microenvironment (TME) of HCC plays a pivotal role in tumor progression, immune evasion, metastasis, and treatment resistance. Single-cell sequencing (scRNA-seq) has emerged as a revolutionary tool for resolving the intricacies and cellular heterogeneity of the TME, with its applications in advancing therapeutic research attracting considerable attention. As the primary battleground for antitumor immune responses, the HCC tumor TME warrants comprehensive analysis of immune cell subsets at distinct developmental and functional states to elucidate the complexity of tumor immunology. This review synthesizes extensive research on TME immune cellular subpopulations, in order to summarize mainstream classifications of immune subsets at single-cell resolution and analyze their functional significance and therapeutic value through biomarker gene profiling.</p>
</abstract>
<kwd-group>
<kwd>hepatocellular carcinoma</kwd>
<kwd>heterogeneity</kwd>
<kwd>single-cell sequencing</kwd>
<kwd>subsets</kwd>
<kwd>tumor immune microenvironment</kwd>
<kwd>tumor microenvironment</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. The Natural Science Foundation of Guangxi, grant numbers 2025GXNSFAA069372, 2025GXNSFDA069035.</funding-statement>
</funding-group>
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<ref-count count="222"/>
<page-count count="16"/>
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<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Cancer Immunity and Immunotherapy</meta-value>
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</front>
<body>
<sec id="s1" sec-type="intro">
<label>1</label>
<title>Introduction</title>
<sec id="s1_1">
<label>1.1</label>
<title>Epidemiology and current treatment landscape of liver cancer</title>
<p>Primary liver cancer represents one of the most prevalent malignant tumors worldwide and poses a significant global health challenge. Globally, it ranks as the sixth most commonly diagnosed cancer and the third leading cause of cancer-related death (<xref ref-type="bibr" rid="B1">1</xref>). According to 2022 global epidemiological data, approximately 865,000 new liver cancer cases and 758,000 deaths occur annually (<xref ref-type="bibr" rid="B2">2</xref>). As reported by China&#x2019;s National Cancer Center, 368,000 new cases occurred in China in 2022, accounting for approximately 42% of the global burden and establishing the country as the most heavily impacted by liver cancer (<xref ref-type="bibr" rid="B3">3</xref>). A recent 2025 global projection predicts that without intervention, new annual cases will surge to 1.52 million by 2025, while deaths will rise sharply to 1.37 million (<xref ref-type="bibr" rid="B4">4</xref>). HCC comprising 90% of liver cancer cases, primarily arises from risk factors including hepatitis B and C infections. Notably, non-infectious etiologies such as non-alcoholic steatohepatitis linked to metabolic syndrome or diabetes are becoming increasingly prevalent causal factors in Western nations (<xref ref-type="bibr" rid="B5">5</xref>).Clinical management faces substantial hurdles due to limitations in early diagnosis, specifically the persistent lack of highly sensitive and specific serum biomarkers for screening (<xref ref-type="bibr" rid="B6">6</xref>), alongside the tendency for ultrasound and imaging modalities to miss small lesions against cirrhotic backgrounds (<xref ref-type="bibr" rid="B7">7</xref>). Consequently, most patients present with advanced disease at diagnosis, rendering them ineligible for curative surgery. Although tyrosine kinase inhibitors (e.g., sorafenib, lenvatinib) and immune checkpoint inhibitors(ICIs) targeting PD-1/CTLA-4 have advanced treatment paradigms, high tumor heterogeneity in HCC restricts their efficacy to subsets of patients. Frequent development of therapeutic resistance further leaves many without viable options (<xref ref-type="bibr" rid="B8">8</xref>, <xref ref-type="bibr" rid="B9">9</xref>). The intrinsically aggressive nature of HCC which is marked by high malignancy, recurrence, and metastasis, compounds these challenges, establishing it as a major therapeutic challenge globally. Nevertheless, the rapid evolution of multi-omics technologies and intensified research into novel early diagnostic biomarkers offer promising avenues (<xref ref-type="bibr" rid="B10">10</xref>). Metabolites, protein signatures, genetic markers, and circulating cells represent emerging candidates to enhance early diagnostic accuracy (<xref ref-type="bibr" rid="B11">11</xref>, <xref ref-type="bibr" rid="B12">12</xref>). Since its advent, scRNA-seq has enabled the resolution of gene expression at individual cell resolution, overcoming tissue-level heterogeneity limitations. This breakthrough allows unprecedented exploration of cellular behavior, underlying mechanisms, and tissue (<xref ref-type="bibr" rid="B13">13</xref>). Tumor heterogeneity is the core to HCC treatment resistance which is now being deconvoluted through scRNA-seq. By delineating the heterogeneity of the HCC tumor TME, this technology provides unprecedented insights into tumor growth, metastasis, and intercellular crosstalk mechanisms (<xref ref-type="bibr" rid="B14">14</xref>). Collectively, these advances offer new hope for transformative therapeutic strategies in HCC. In recent years, researchers have been attempting to summarize the roles and clinical value of various cell types in the tumor microenvironment of liver cancer (<xref ref-type="bibr" rid="B15">15</xref>&#x2013;<xref ref-type="bibr" rid="B18">18</xref>). Standing on the shoulders of giants, we have synthesized a large number of review studies and experimental articles to further explore the application value of scRNA-seq in HCC. We systematically integrated the mainstream subclustering of five types of immune cells in scRNA-seq studies within the tumor microenvironment. We emphasize that the TME is not composed of static cell types, but rather a continuous spectrum of functional states that continuously shift under tumor stress, metabolic stress, and treatment exposure. Based on this foundation, we utilized a new analytical framework of marker genes - cellular developmental/functioning states - resistance mechanisms/therapeutic value translation to provide new perspectives for precise and personalized tumor interventions.</p>
</sec>
</sec>
<sec id="s2">
<label>2</label>
<title>Advancements and applications of scRNA-seq technology</title>
<p>scRNA-seq represents a revolutionary advancement in biological research. By enabling the extraction and sequencing of RNA from individual isolated cells to obtain cell-specific expression profiles, scRNA-seq overcomes the limitations of traditional transcriptomics which only measures averaged gene expression across cell populations. This technology provides unprecedented precision in monitoring cellular activity at single-cell resolution (<xref ref-type="bibr" rid="B19">19</xref>). By revealing gene expression patterns in individual cells through high-throughput sequencing, scRNA-seq has been widely adopted in developmental biology, neuroscience, immunology, and oncology research (<xref ref-type="bibr" rid="B20">20</xref>&#x2013;<xref ref-type="bibr" rid="B22">22</xref>).In cancer studies specifically, scRNA-seq precisely characterizes the complexity of the TME, decoding dynamic interactions among tumor cells, immune cells, and stromal cells while offering unparalleled resolution for dissecting tumor heterogeneity (<xref ref-type="bibr" rid="B23">23</xref>). For example, Izar et&#xa0;al. at Columbia University constructed multiomic single-cell atlases of untreated human melanoma brain metastases and extracranial metastases via scRNA-seq, revealing distinct TME features between these metastatic sites (<xref ref-type="bibr" rid="B24">24</xref>). In colorectal cancer (CRC), scRNA-seq has revealed the identification of associations between point mutations and gene expression patterns and uncovered prognostic gene biomarkers, providing critical insights into CRC&#x2019;s molecular mechanisms (<xref ref-type="bibr" rid="B25">25</xref>, <xref ref-type="bibr" rid="B26">26</xref>). A non-small cell lung cancer study leveraged large-scale single-cell profiling to uncover heterogeneity within the TME during anti-PD-1 therapy, stratifying five distinct TME subtypes with unique immune compositions and therapeutic response signatures. These findings guide clinical stratification and treatment optimization (<xref ref-type="bibr" rid="B27">27</xref>). Similarly, a scRNA-seq-based triple-negative breast cancer study delineated immune landscapes shaped by T-cell/B-cell crosstalk, exposing microenvironmental differences in immune cell dynamics and interactions (<xref ref-type="bibr" rid="B28">28</xref>).The implementation of scRNA-seq in liver cancer research is particularly extensive. Researchers recently established the largest snRNA-seq-derived hepatocyte dataset to date, capturing key cellular populations and transcriptomic profiles in HCC. This work uncovered evolutionary genomic trajectories in hepatocarcinogenesis and established a molecular classification system based on signature genes, leading to novel diagnostic frameworks and precision therapeutic strategies (<xref ref-type="bibr" rid="B29">29</xref>).As scRNA-seq permeates research across cancer types, it uniquely captures intercellular transcriptomic variation.scRNA-seq identifies subclonal gene expression patterns and mutational signatures, and deconstructs tissue composition via cellular proportion mapping. Combined with spatial transcriptomics, scRNA-seq-driven multiomics approaches are revealing disease progression and drug resistance mechanisms across increasingly multidimensional frameworks. These efforts concurrently identify clinically actionable biomarkers for early diagnosis and tailored therapies, establishing a new paradigm for precision medicine.</p>
</sec>
<sec id="s3">
<label>3</label>
<title>Deciphering the immune cell subsets of HCC TMEvia scRNA-seq</title>
<p>The immune cellular subpopulations in liver cancer comprises natural killer (NK) cells, dendritic cells (DCs), T cells, macrophages, and B cells. As a highly heterogeneous malignancy, HCC&#x2019;s complex TME critically drives disease progression and therapeutic resistance by modulating tumor cell proliferation, TME promotes immune evasion and metastasis, and contributes to drug resistance mechanisms (<xref ref-type="bibr" rid="B30">30</xref>, <xref ref-type="bibr" rid="B31">31</xref>). Throughout the oncogenic continuum, the TME fundamentally influences tumor evolution and treatment responses. Resolution of the liver cancer TME at single-cell precision allows researchers to reveal functional dynamics profiling of immune cell states, and identify key immunosuppressive cellular subsets. By analyzing functional interrogation of novel cell type-specific gene signatures, researchers reveal variations across patients and disease stages. This multifaceted approach holds far-reaching implications for overcoming current therapeutic bottlenecks and identifying novel treatment opportunities (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure S1</bold></xref>).</p>
<sec id="s3_1">
<label>3.1</label>
<title>NK cells</title>
<p>NK cells, as innate lymphocytes, serve as primary responders initiating immune activation. NK cells play pivotal roles in host defense and immune surveillance. These cells directly eliminate tumor cells not only through cytotoxic effector functions, but also by acting as regulatory lymphocytes, which secrete cytokines and interact with both innate and adaptive immune cells (<xref ref-type="bibr" rid="B32">32</xref>). Traditional classification stratifies NK cells by CD56 expression density into cytotoxicity-dominant CD56<sup>dim</sup> and cytokine-secreting CD56<sup>bright</sup> subpopulations. Recent studies using scRNA-seq refine this framework by delineating developmental trajectories and functional states, identifying critical subsets including cytotoxic NK cells, regulatory NK cells, and memory-like NK cells (<xref ref-type="bibr" rid="B33">33</xref>, <xref ref-type="bibr" rid="B34">34</xref>).</p>
<p>Perforin-1 (PRF1), encoded by the <italic>PRF1</italic> gene, is a pore-forming protein stored in secretory vesicles. PRF1 acts as a key cytotoxic molecule in NK cells and plays an essential role in their tumor-killing function (<xref ref-type="bibr" rid="B35">35</xref>). During target cell engagement, PRF1 is released and forms pores in the target cell membrane, disrupting intracellular calcium balance. This enables Granzyme B (GZMB) and other cytotoxic agents to enter the cytoplasm, triggering an enzymatic cascade that ultimately induces tumor cell death (<xref ref-type="bibr" rid="B36">36</xref>). Given the critical function of cytotoxic NK cells in directly lysing cancer cells, their antitumor efficacy becomes severely limited when NK cells exhibit an exhausted phenotype. Thus, research on stimulating sustained secretion of PRF1 and GZMB by NK cells holds significant importance (<xref ref-type="bibr" rid="B37">37</xref>). Adoptive cell therapy using chimeric antigen receptor NK cells offers unique advantages over chimeric antigen receptor T cells therapy. It avoids triggering severe cytokine release syndrome, graft-versus-host disease, or neurotoxicity. It also permits diverse cell sourcing and enables cytotoxic enhancement through genetic engineering. Its favorable safety and efficacy profiles have been consistently validated across multiple hematologic and solid tumors (<xref ref-type="bibr" rid="B38">38</xref>).</p>
<p>Regulatory NK cells exhibit high <italic>XCL1</italic> gene expression, with the chemokine XCL1 being a critical component in the NK cell-mediated antitumor signaling network (<xref ref-type="bibr" rid="B39">39</xref>). As the primary source of XCL1, NK cells precisely recruit conventional type 1 dendritic cells (cDC1) into the TME via XCL1 secretion. This initiates a coordinated &#x201c;NK-DC-T cell&#x201d; immune attack axis, where cDC1 infiltration density directly dictates T-cell activation efficacy and subsequent tumor cell clearance (<xref ref-type="bibr" rid="B40">40</xref>). Functioning as cellular communication hubs, NK cells deficiency renders the TME an &#x201c;immune-deserted island&#x201d;. It permits tumor immune escape through failed immune cell activation. Notably, <italic>XCL1</italic> is a signature gene of immunoregulatory NK cells which is aberrantly expressed on certain tumor cells, potentially contributing to immune evasion and chemotherapy resistance (<xref ref-type="bibr" rid="B41">41</xref>). Given the pivotal role of the XCR1-XCL1 axis in antitumor responses, modulating NK-cDC1 crosstalk to boost cDC1 recruitment. Intratumoral XCL1 injection enhances infiltration of antigen-specific CD8<sup>+</sup> T cells and NK cells, thereby suppressing tumor growth (<xref ref-type="bibr" rid="B42">42</xref>). XCL1 expression is significantly positively correlated with the number of tumor-infiltrating CD8<sup>+</sup> T cells and PD-L1 expression, potentially inducing PD-1/PD-L1 interactions and dysfunction of CD8<sup>+</sup> T cells through the XCL1-XCR1 axis, thereby predicting response to anti-PD-1/PD-L1 therapy (<xref ref-type="bibr" rid="B43">43</xref>). Spatial transcriptomics has confirmed the colocalization characteristics of XCL1<sup>+</sup> CD8<sup>+</sup> T cells with immune cells, suggesting that targeting this cell population may enhance the efficacy of immunotherapy for hepatocellular carcinoma (<xref ref-type="bibr" rid="B44">44</xref>).</p>
<p>Memory-like NK cells highly express the immunostimulatory receptor NKG2C (encoded by <italic>KLRC2</italic>), a type II transmembrane protein (<xref ref-type="bibr" rid="B45">45</xref>). NKG2C transmits activation signals upon binding HLA-E on target cells, promoting NK cell degranulation and cytokine secretion to enhance infected cell elimination. <italic>NKG2C<sup>+</sup></italic> NK cells demonstrate potent antibody-dependent cellular cytotoxicity (ADCC) facilitating sustained clearance of infected cells. Chronic infections drive clonal expansion of <italic>NKG2C<sup>+</sup></italic> NK cells, forming persistent &#x201c;memory-like&#x201d; populations with augmented effector functions (<xref ref-type="bibr" rid="B46">46</xref>). The competitive binding of NKG2C and the inhibitory receptor NKG2A to HLA-E dynamically regulates NK cell reactivity balance (<xref ref-type="bibr" rid="B47">47</xref>). In the TME, HLA-E overexpression often impairs T/NK cell effector function. To address this, novel chimeric receptors termed &#x201c;NKG2A/C-swapping receptors&#x201d; have been engineered. These receptors confer enhanced and specific cytotoxicity against tumors with moderate-to-high HLA-E expression while sparing normal tissues, representing a promising therapeutic strategy (<xref ref-type="bibr" rid="B48">48</xref>).</p>
<p>Current NK cell classification relies predominantly on CD56 and CD16, lacking resolution for the complexity of NK cell development and stress-responsive states. Broader tissue coverage remains limited. Integrating spatial transcriptomics can comprehensively map tissue-specific NK cell distribution and function. Future efforts should leverage epigenomic and transcriptomic datasets within spatiotemporal multi-omics approaches to dissect real-time microenvironmental regulation of NK plasticity, thereby refining subtype classification and advancing precision immunotherapies.</p>
</sec>
<sec id="s3_2">
<label>3.2</label>
<title>Dendritic cells</title>
<p>DCs represent the most potent professional antigen-presenting cells in the human immune system and serve as the exclusive activators of the na&#xef;ve T cells. Functioning as critical bridges between innate and adaptive immunity, DCs play pivotal roles in initiating and sustaining T-cell antitumor responses. DCs involve in antigen presentation in tumor and lymph nodes, primary T-cell activation, and maintenance of T-cell survival and effector function within the TME. Deficiency or functional impairment of DCs severely limits antitumor immunity, thereby facilitating tumor progression (<xref ref-type="bibr" rid="B49">49</xref>). DCs are broadly classified into conventional DCs (cDCs, encompassing cDC1 and cDC2) and plasmacytoid DCs (pDCs).</p>
<p>cDCs constitute central hubs of the cancer immunity cycle. cDC1s, as master cross-presenting cells, drive CD8<sup>+</sup> T cell responses against malignancies via MHC-I&#x2013;restricted tumor antigen presentation,. While cDC2s predominantly prime CD4<sup>+</sup> T-cell responses through MHC-II and modulate Th1/Th2/Th17/Treg immunity (<xref ref-type="bibr" rid="B50">50</xref>). cDC1s uniquely express <italic>XCR1</italic> which is the sole receptor for XCL1 enables their recruitment to inflammatory/neoplastic sites (<xref ref-type="bibr" rid="B51">51</xref>). Engineered T cells overexpressing XCL1 have been shown to increase DC abundance, significantly inducing antigen spreading and endogenous polyclonal T-cell responses to amplify antitumor immunity (<xref ref-type="bibr" rid="B52">52</xref>).</p>
<p>Signal regulatory protein alpha (SIRP&#x3b1;), a hallmark of cDC2s, interacts with CD47 to maintain immune homeostasis by restraining DC overactivation (<xref ref-type="bibr" rid="B53">53</xref>). <italic>SIRP&#x3b1;<sup>+</sup></italic> DC2s reduce cross-presentation efficiency and impair CD8<sup>+</sup> T-cell responses through CD47 signaling (<xref ref-type="bibr" rid="B54">54</xref>). In the TME, they mature into regulatory DCs that phagocytose apoptotic tumor cells via the AXL-SIRP&#x3b1; axis, upregulate PD-L1, suppress CD8<sup>+</sup> T cell function, and promote Treg differentiation which critically enforcing immune tolerance. Tumor cells overexpressing CD47 evade immune clearance by engaging SIRP&#x3b1; on DCs to inhibit phagocytosis (<xref ref-type="bibr" rid="B55">55</xref>). Anti-SIRP&#x3b1; antibodies blocking this axis restore DC function, enhance antigen presentation and tumor phagocytosis by macrophages. Dual targeting of SIRP&#x3b1; and PD-L1 synergistically activates T cells, offering novel combination immunotherapy strategies (<xref ref-type="bibr" rid="B56">56</xref>).</p>
<p>pDCs, historically named for their plasma cell-like morphology, pDCs are specialized DC subsets that function as primary producers of type I interferons (IFN-I) and modulate antitumor immunity by regulating NK/T-cell activity in the TME (<xref ref-type="bibr" rid="B57">57</xref>). Blood dendritic cell antigen 2 (BDCA2), a C-type lectin receptor exclusively expressed on pDCs, marks this population (<xref ref-type="bibr" rid="B57">57</xref>). <italic>BDCA2<sup>+</sup></italic> pDCs exhibit robust proinflammatory functions through IFN-&#x3b1;/TNF secretion, they can also drive immune suppression by inducing Tregs via the IDO1 pathway to dampen Th1/Th17 responses (<xref ref-type="bibr" rid="B58">58</xref>). Current <italic>BDCA2</italic> research prioritizes autoimmune diseases, focusing on IFN-I overproduction as a key pathophysiology in systemic lupus erythematosus. Therapies crosslinking BDCA2 potently inhibit IFN-I production by pDCs, presenting a promising treatment strategy for systemic lupus erythematosus (<xref ref-type="bibr" rid="B59">59</xref>, <xref ref-type="bibr" rid="B60">60</xref>). Beyond systemic lupus erythematosus, <italic>BDCA2</italic> also emerges as a compelling therapeutic target for other immune disorders and hematologic malignancies.</p>
<p>Collectively, advances in single-cell profiling of DC subsets illuminate key mechanisms of immune regulation and therapeutic resistance in HCC.</p>
</sec>
<sec id="s3_3">
<label>3.3</label>
<title>T cells</title>
<p>T cells constitute a critical lymphocyte subset within the TME, playing a central role in antitumor immunity and serving as the foundation for most immunotherapies. CD4<sup>+</sup> T cells encompass subsets including TH1, TH2, TH17, TFH, and Treg. They are all differentiated from na&#xef;ve CD4<sup>+</sup> T cells while each exhibiting distinct effector functions essential for shaping adaptive immune responses (<xref ref-type="bibr" rid="B61">61</xref>).</p>
<p>CC chemokine receptor 7 (CCR7), a lymph node-homing receptor expressed on dendritic cells (DCs) and T cells, guides antigen-presenting DCs and T-cell subsets to lymphoid organs via interactions with ligands CCL21/CCL19 to initiate immune responses (<xref ref-type="bibr" rid="B62">62</xref>). <italic>CCR7</italic> is highly expressed in na&#xef;ve CD4<sup>+</sup> T cells (<xref ref-type="bibr" rid="B63">63</xref>), with elevated levels observed in patients with lymph node-metastatic cancers (<xref ref-type="bibr" rid="B64">64</xref>). CCR7 facilitates lymphatic metastasis across malignancies as CCR7-expressing tumor cells mimic immune cell behavior by following chemokine gradients toward lymph nodes (<xref ref-type="bibr" rid="B65">65</xref>). Anti-CCR7 antibodies are being explored to inhibit CLL migration to lymph nodes, though clinical efficacy remains under investigation (<xref ref-type="bibr" rid="B66">66</xref>).</p>
<p>CXCR3 is a G protein-coupled receptor enriched in Th1 cells (<xref ref-type="bibr" rid="B67">67</xref>). CXCR3 mediates targeted migration and immune responses through ligand binding on endothelial cells, playing key roles in infections, autoimmune disorders, and tumor immunity (<xref ref-type="bibr" rid="B68">68</xref>). The CXCR3 axis comprises ligands CXCL9, CXCL10, and CXCL11 and shows strong cancer relevance (<xref ref-type="bibr" rid="B69">69</xref>). Notably, CXCR3 drives immunogenic cell death in HCC, where its expression positively correlates with T-cell infiltration. HCC patients with high CXCR3 expression display improved responses to immunotherapy (<xref ref-type="bibr" rid="B70">70</xref>).</p>
<p>CCR4, initially characterized as a TH2-selective chemokine receptor, is now recognized across multiple T-cell subsets (<xref ref-type="bibr" rid="B71">71</xref>). Its interaction with the atypical ligand CXCL12 drives cellular migration and proliferation in physiological and cancerous contexts (<xref ref-type="bibr" rid="B72">72</xref>). CXCR4 overexpression in over 20 cancer types is associated with poor prognosis and cancer stemness (<xref ref-type="bibr" rid="B73">73</xref>), while CCR4 antagonists suppress distant metastasis in liver cancer models (<xref ref-type="bibr" rid="B74">74</xref>).</p>
<p>The interleukin-23 receptor (<italic>IL-23R</italic>), primarily expressed on TH17 cells, transmits signals enhancing TH17 differentiation while promoting IL-22 secretion from CD4<sup>+</sup> T cells (<xref ref-type="bibr" rid="B75">75</xref>, <xref ref-type="bibr" rid="B76">76</xref>). IL-23R signaling induces multiple highly proinflammatory cytokines and serves as a key pathogenic driver in chronic inflammation and autoimmune diseases (<xref ref-type="bibr" rid="B77">77</xref>). Expanding beyond their immunological roles, TH17 cells exhibit tumor-promoting properties (<xref ref-type="bibr" rid="B78">78</xref>). During acute hepatic inflammation, these cells contribute to inflammatory cascades and induce HCC recurrence through dual mechanisms: facilitating cancer cell migration/invasion and enhancing tumor stemness (<xref ref-type="bibr" rid="B79">79</xref>).However,IL-23R involvement in this process warrants investigation.</p>
<p><italic>CXCR5</italic> features selective expression on follicular helper T (TFH) cells. Together with its ligand CXCL13, this receptor-ligand pair governs TFH development and functional specialization (<xref ref-type="bibr" rid="B80">80</xref>). TFH cells are indispensable for germinal center formation and maintenance (<xref ref-type="bibr" rid="B81">81</xref>), thereby modulating humoral immunity. The CXCL13-CXCR5 axis contributes to tumor progression through multifaceted mechanisms involving direct tumor stimulation, immune cell recruitment, and TME modulation (<xref ref-type="bibr" rid="B82">82</xref>).</p>
<p>FOXP3 serves as the master transcriptional regulator for regulatory T cells (Tregs), orchestrating genes controlling their differentiation and immunosuppressive activity (<xref ref-type="bibr" rid="B83">83</xref>, <xref ref-type="bibr" rid="B84">84</xref>). <italic>FOXP3<sup>+</sup></italic> Tregs employ diverse mechanisms, including secretion of anti-inflammatory cytokines (TGF-&#x3b2;, IL-10, IL-35) and perforin/granzyme-mediated cytolysis (<xref ref-type="bibr" rid="B85">85</xref>). <italic>FOXP3<sup>+</sup></italic> Tregs suppress antitumor immunity and facilitate tumor escape (<xref ref-type="bibr" rid="B86">86</xref>). Clinically, elevated <italic>FOXP3<sup>+</sup></italic> Treg infiltration correlates with metastatic progression in HCC patients (<xref ref-type="bibr" rid="B87">87</xref>). Therapeutic inhibition enhances IFN-&#x3b3; production in CD8<sup>+</sup> T cells and restricts tumor growth (<xref ref-type="bibr" rid="B88">88</xref>). A recent paradigm-shifting approach demonstrates that <italic>FOXP3<sup>+</sup></italic> -overexpressing chimeric antigen receptor T cells exhibit potent antitumor efficacy, which lack immunosuppressive functions (<xref ref-type="bibr" rid="B89">89</xref>).</p>
<p>As an immune checkpoint molecule, <italic>CTLA-4</italic> is expressed on activated T cells and Tregs. It both inhibits T-cell activation and delivers essential co-stimulatory signals to Tregs (<xref ref-type="bibr" rid="B90">90</xref>). Functionally, it suppresses immune responses through intrinsic negative regulation of effector T cells and extrinsic modulation via Treg-mediated suppression (<xref ref-type="bibr" rid="B91">91</xref>, <xref ref-type="bibr" rid="B92">92</xref>). <italic>CTLA-4</italic> overexpression compromises antitumor immunity to enable immune escape (<xref ref-type="bibr" rid="B93">93</xref>) In HCC, <italic>CTLA-4</italic> blockade with neutralizing antibodies enhances tumor clearance (<xref ref-type="bibr" rid="B94">94</xref>), while genetic silencing augments antitumor responses (<xref ref-type="bibr" rid="B95">95</xref>). Therapeutically, the dual immune checkpoint regimen combining CTLA-4 inhibitor ipilimumab with PD-1 blocker <italic>nivolumab</italic> has achieved landmark status, which becomes China&#x2019;s first approved first-line dual-immunotherapy for HCC.</p>
<p>CD8<sup>+</sup> T cells serve as the terminal effectors of adaptive immunity, functioning as primary cytotoxic executors in antitumor immunity (<xref ref-type="bibr" rid="B96">96</xref>). Research focuses on their effector mechanisms, with increasing attention to CD8<sup>+</sup> T cell exhaustion in tumor responses.</p>
<p><italic>LEF1</italic> demonstrates elevated expression in na&#xef;ve CD8<sup>+</sup> T cells (<xref ref-type="bibr" rid="B97">97</xref>). Collaborating with <italic>TCF1</italic>, it provides sustained surveillance of CD8<sup>+</sup> T cell identity and function by promoting T-lineage genes while suppressing non-T-lineage programs (<xref ref-type="bibr" rid="B98">98</xref>). Notably, LEF1 enhances self-renewal capacity, drug resistance, dedifferentiation, and invasiveness in HCC cells (<xref ref-type="bibr" rid="B99">99</xref>). For instance, it confers lenvatinib resistance by advancing Epithelial-mesenchy maltransition, migration, and invasion in HCC cells, thereby establishing <italic>LEF1</italic> as a novel therapeutic target to overcome acquired lenvatinib resistance (<xref ref-type="bibr" rid="B100">100</xref>).</p>
<p>Chemokine receptor <italic>CX3CR1</italic> express across multiple immune cell types.<italic>CX3CR1<sup>+</sup>CD8<sup>+</sup></italic> T cells exhibit potent antitumor effector functions (<xref ref-type="bibr" rid="B101">101</xref>) and manifest exceptional cytotoxicity (<xref ref-type="bibr" rid="B102">102</xref>).CX3CR1 expression displays a graded transcriptional pattern reflecting CD8<sup>+</sup> T cell differentiation status, with levels positively correlating with increased <italic>GZMB</italic>, perforin <italic>1</italic>, and granzyme A expression (<xref ref-type="bibr" rid="B103">103</xref>). While their antitumor efficacy is validated in diverse cancers, further experimental studies are required to delineate their role in HCC.</p>
<p><italic>CD69</italic>, a C-type lectin characteristically expressed on CD8<sup>+</sup> tissue-resident memory T cells, inhibits T-cell egress from tissues and participates in tissue retention alongside other lectins (<xref ref-type="bibr" rid="B104">104</xref>). Tissue-resident memory CD8<sup>+</sup> T cells provide infection protection at barrier sites (<xref ref-type="bibr" rid="B105">105</xref>) and local immune defense against tumor rechallenge (<xref ref-type="bibr" rid="B106">106</xref>). Studies indicate that <italic>CD69</italic> expression modulates CD8<sup>+</sup> T-cell exhaustion. Within the TME, <italic>CD69<sup>+</sup>CD8<sup>+</sup></italic> T cells progressively lose effector functions, adopting an exhausted phenotype. Blocking CD69 or its signaling pathways enhances their antitumor activity (<xref ref-type="bibr" rid="B107">107</xref>). Notably, CD69<sup>+</sup> tissue-resident memory CD8<sup>+</sup> T cells expressing unique signature genes were identified in HCC patient tumors, exhibiting correlation with patient survival outcomes (<xref ref-type="bibr" rid="B108">108</xref>).</p>
<p>The granzyme K gene (<italic>GZMK</italic>) is enriched specifically in innate-like lymphocytes and certain CD8<sup>+</sup> T-cell subsets. <italic>GZMK<sup>+</sup> T cells</italic> typically represent central and effector memory T-cell populations (<xref ref-type="bibr" rid="B109">109</xref>). <italic>GZMK<sup>+</sup> T cells</italic> display inflammatory potential, correlating positively with plasma IL-6, TNF&#x3b1;, and IL-8 levels (<xref ref-type="bibr" rid="B110">110</xref>). Recent research reveals GZMK can instigate inflammation through complement activation mediation (<xref ref-type="bibr" rid="B111">111</xref>). In HCC, <italic>GZMK<sup>+</sup>CD8<sup>+</sup> T cells</italic> demonstrate cytotoxicity (<xref ref-type="bibr" rid="B14">14</xref>), and higher <italic>GZMK</italic> expression associates with favorable prognosis across multiple HCC cohorts (<xref ref-type="bibr" rid="B112">112</xref>).</p>
<p>Lymphocyte-activation gene 3 <italic>(LAG3)</italic> is expressed on CD4<sup>+</sup> T cells, CD8<sup>+</sup> T cells, and Tregs (<xref ref-type="bibr" rid="B113">113</xref>), functioning as an immune checkpoint inhibitory receptor (<xref ref-type="bibr" rid="B114">114</xref>). Combined LAG-3 and PD-1 signaling drives T-cell exhaustion while impeding IFN-&#x3b3;-mediated autocrine antitumor immunity (<xref ref-type="bibr" rid="B115">115</xref>). LAG3 engagement with MHC class II and other ligands transduces T-cell inhibitory signals leading to dysfunction, which notable upregulation on exhausted T cells within the TME (<xref ref-type="bibr" rid="B116">116</xref>).<italic>LAG3</italic> correlates with poor prognosis in HCC patients when expressed at high levels (<xref ref-type="bibr" rid="B117">117</xref>). Whilst anti-LAG3 monotherapy yields suboptimal efficacy, combination strategies particularly with PD-1 inhibitors show superior therapeutic promise (<xref ref-type="bibr" rid="B118">118</xref>). Indeed, dual <italic>LAG-3/PD-1</italic> blockade demonstrates manageable safety and objective efficacy in advanced HCC treatment (<xref ref-type="bibr" rid="B119">119</xref>).</p>
<p>Layilin (LAYN), a transmembrane protein functioning as a C-type lectin. It participates primarily in cellular adhesion and modulates diverse including immune cell activation and T-cell subset differentiation, exerting pro-tumorigenic roles in HCC (<xref ref-type="bibr" rid="B120">120</xref>). <italic>LAYN</italic> is upregulated in tumor-infiltrating CD8<sup>+</sup> T cells and Tregs within HCC and associates with their suppressive functions (<xref ref-type="bibr" rid="B97">97</xref>). <italic>LAYN</italic>-overexpressing CD8<sup>+</sup> T cells display hallmark exhaustion features and diminished antitumor capacity. Patients with high <italic>LAYN</italic> levels exhibit poorer overall survival (<xref ref-type="bibr" rid="B121">121</xref>).</p>
<p>T-cell subset profiling remains a sustained research focus. Despite considerable efforts on CD8<sup>+</sup> T cells due to their prominence in tumor immunity, this field nears investigational saturation with diminishing novel discoveries. Emerging paradigms now emphasize exploring untapped CD4<sup>+</sup> T cell subsets. Notably, Tregs&#x2014;whose discoverers received the <italic>2025 Nobel Prize in Physiology or Medicine</italic>,show exceptional promise as a prime candidate for next-generation investigations. Their discovery underscores the transformative potential of regulatory T cells in cancer immunology.</p>
</sec>
<sec id="s3_4">
<label>3.4</label>
<title>Macrophages</title>
<p>Landmark advances in macrophage functional studies originated from the 1990s-established M1/M2 polarization model. In this model, IFN-&#x3b3;/LPS-induced classically activated M1 macrophages exhibit pro-inflammatory/antitumor functions, while IL-4/IL-13-induced alternatively activated M2 macrophages display anti-inflammatory/pro-repair activities. This paradigm, inspired by Th1/Th2 differentiation concepts, significantly advanced our understanding of macrophage roles in tumorigenesis and infections (<xref ref-type="bibr" rid="B122">122</xref>), revealing polarization&#x2019;s centrality in immune regulation. However, scRNA-seq and spatial transcriptomics have exposed limitations in this binary model. It oversimplifies <italic>in vivo</italic> macrophage heterogeneity, depicts context-dependent polarization states, and fails to incorporate developmental origin diversity. The M1/M2 polarization framework has essential differences in conceptual assumptions and research methods compared to single-cell scRNA-seq. The M1/M2 polarization model originates from <italic>in vitro</italic> stimulation experiments. It assumes that macrophages differentiate into two relatively stable, opposing functional states under specific stimuli (e.g., IFN-&#x3b3;/LPS or IL-4/IL-13) to explain immune regulation during inflammation or repair processes (<xref ref-type="bibr" rid="B123">123</xref>). However, this framework implies the premises of discrete states and single dominant signals, making it difficult to reflect the true cellular landscape shaped by multiple overlapping signals, metabolic reprogramming, and developmental origin differences in complex <italic>in vivo</italic> microenvironments (<xref ref-type="bibr" rid="B124">124</xref>). In contrast, scRNA-seq is a data-driven, hypothesis-free single-cell analysis method. By simultaneously measuring the expression levels of thousands of genes at single-cell resolution, it directly reveals the continuous spectrum of macrophage functional states, dynamic transition trajectories, and spatial correlations within tissues (<xref ref-type="bibr" rid="B125">125</xref>, <xref ref-type="bibr" rid="B126">126</xref>). At the methodological level, M1/M2 relies on a limited number of markers and artificially defined polarization conditions, while scRNA-seq characterizes a multidimensional, highly heterogeneous network of transcriptional states through clustering analysis, pseudotime inference, and cell communication modeling. Contemporary research integrates multi-dimensional approaches including single-cell subpopulation definition, spatial functional mapping, and pseudotime trajectory inference (<xref ref-type="bibr" rid="B127">127</xref>, <xref ref-type="bibr" rid="B128">128</xref>), providing insights beyond static classifications. Current representative categories include inflammatory macrophages, angiogenic macrophages, interferon macrophages, regulatory macrophages, lipid-associated macrophages, and tissue-resident macrophages (<xref ref-type="bibr" rid="B124">124</xref>).</p>
<p><italic>CCL3</italic> (macrophage inflammatory protein-1&#x3b1;, <italic>MIP-1&#x3b1;</italic>), a CC-chemokine engaging CCR1/CCR5/CCR9 receptors, is highly expressed by macrophages upon inflammatory stimulation (<xref ref-type="bibr" rid="B129">129</xref>). This pro-inflammatory chemokine actively recruits monocytes/macrophages to inflammatory sites (<xref ref-type="bibr" rid="B130">130</xref>). In human HCC, CCL3 orchestrates antitumor immunity, including enhancing phagocytic activity, upregulating MHC molecules for antigen presentation, recruiting/activating T cells, and reprogramming the TME to restore adaptive immunity (<xref ref-type="bibr" rid="B131">131</xref>). Therapeutic CCL3 administration enhances TME immunogenicity. Mice with CCL3-enriched tumors showed delayed growth post anti-PD-1 monoclonal antibody treatment, suggesting combinational &#x3b1;PD-1/CCL3 strategies may augment clinical efficacy (<xref ref-type="bibr" rid="B132">132</xref>).</p>
<p>Secreted phosphoprotein 1 (SPP1), a pleiotropic phosphoglycoprotein, regulates immune responses while promoting tumor proliferation, invasion, and therapy resistance (<xref ref-type="bibr" rid="B133">133</xref>). Although produced in multiple organs, SPP1 expression is restricted to osteoblasts, fibroblasts, macrophages, dendritic cells, lymphocytes, and monocytes. Cancer cells also express <italic>SPP1</italic>, and elevated circulating SPP1 or tumor SPP1 correlates with poor prognosis across cancers (<xref ref-type="bibr" rid="B134">134</xref>). <italic>SPP1<sup>+</sup></italic> macrophages promote tumor angiogenesis and hypoxic microenvironments by secreting extracellular matrix proteins (e.g., CD44, MMPs) and activating HIF-1 signaling (<xref ref-type="bibr" rid="B122">122</xref>). In CRC, <italic>SPP1<sup>+</sup></italic> macrophage/FAP<sup>+</sup> fibroblast interactions through TGF-&#x3b2; and IL-17 signaling exacerbate pro-angiogenic and immunosuppressive milieus. <italic>SPP1<sup>+</sup></italic> TAMs across tumor types overexpress angiogenesis-related genes (e.g., VEGF pathway members) and correlate with adverse outcomes (<xref ref-type="bibr" rid="B135">135</xref>). Compared with normal tissues, tumors exhibit significantly enriched <italic>SPP1<sup>+</sup></italic> macrophage infiltration. High <italic>SPP1</italic> expression induces resistance to anti-PD-L1 immunotherapy (<xref ref-type="bibr" rid="B122">122</xref>) and potentiates liver cancer stemness, growth, migration, and chemoresistance (<xref ref-type="bibr" rid="B136">136</xref>). Mechanistically, SPP1<sup>-</sup>CD44 interactions activate multiple exhaustion-associated pathways in CD8<sup>+</sup> T cells, with SPP1 transcriptionally regulating T cell exhaustion (<xref ref-type="bibr" rid="B137">137</xref>), thereby facilitating tumor immune evasion. High expression of SPP1 is significantly associated with poor prognosis in various cancers, including colorectal cancer and liver cancer. Anti-SPP1 combined with PD-1 inhibitors (e.g., NCT05230901) can reverse the immunosuppressive functions of TAMs (<xref ref-type="bibr" rid="B138">138</xref>). SPP1<sup>+</sup> TAMs secrete MMP9 and collagen, promoting tumor invasion and metastasis, and the expression level of SPP1 can predict resistance to PD-1 therapy in esophageal squamous cell carcinoma (<xref ref-type="bibr" rid="B139">139</xref>). Despite the enormous potential of SPP1 as a therapeutic target in cancer, fibrosis, and other diseases, and with several drug discovery projects underway, there are currently no approved SPP1-targeted drugs globally.</p>
<p>ISG15 (Interferon-Stimulated Gene 15) encodes the first identified ubiquitin-like protein modifier, functioning both as a free intracellular/extracellular molecule and as a post-translational modifier during ISGylation (<xref ref-type="bibr" rid="B140">140</xref>). During viral infections, IFN-I induction is a key component of innate immunity which triggers <italic>ISG15</italic> expression, which participates in cancer cell apoptosis via IFN production (<xref ref-type="bibr" rid="B141">141</xref>). While <italic>ISG15</italic> upregulation enhances IFN-mediated macrophage phagocytosis and antiviral activity (<xref ref-type="bibr" rid="B142">142</xref>), tumor-secreted <italic>ISG15</italic> acts as a TME factor that induces M2-like polarization, promotes tumor progression, and suppresses cytotoxic T-lymphocyte responses (<xref ref-type="bibr" rid="B143">143</xref>). Consequently, ISG15 exhibits context-dependent roles in cancer. It functions as either a tumor suppressor or oncogene by altering distinct biological pathways across cancer types. Elevated <italic>ISG15</italic> expression in HCC cell lines and clinical specimens correlates with cell cycle progression, cancer cell proliferation/migration, and poor 5-year survival (<xref ref-type="bibr" rid="B144">144</xref>).</p>
<p>Arginase 1 (Arg1), encoded by the <italic>ARG1</italic> gene, hydrolyzes arginine as a rate-limiting enzyme of the urea cycle. Produced by tumor-infiltrating myeloid cells including macrophages, granulocytes, DCs, immature progenitors. High Arg1 activity depletes L-arginine in TME, suppressing T-cell proliferation and reducing effector T-cell populations (<xref ref-type="bibr" rid="B145">145</xref>). Macrophage polarization toward M2 phenotypes involves metabolic reprogramming in which <italic>Arg1</italic> plays key roles (<xref ref-type="bibr" rid="B146">146</xref>). <italic>Arg1</italic>-expressing macrophages inhibit CD4<sup>+</sup> T-cell proliferation/cytokine production (<xref ref-type="bibr" rid="B147">147</xref>), thereby supporting tumor growth. Notably, tumor cells supply arginine to macrophages to enhance polyamine biosynthesis, driving immunosuppressive polarization and CD8<sup>+</sup> T-cell dysfunction (<xref ref-type="bibr" rid="B148">148</xref>). Targeting Arg1 effectively reprograms macrophages; Arg1-derived peptide vaccines activate <italic>Arg1</italic><sup>+</sup> CD4<sup>+</sup> T cells, promoting inflammatory macrophage phenotypes and generating antitumor immune responses to inhibit growth (<xref ref-type="bibr" rid="B149">149</xref>).</p>
<p>Apolipoprotein E (APOE), a plasma cholesterol-transport protein and Alzheimer&#x2019;s disease (AD) risk factor, has been predominantly studied in AD pathology (<xref ref-type="bibr" rid="B150">150</xref>). Highly expressed in macrophages, macrophage-derived APOE exerts immunomodulatory functions beyond lipid transport (<xref ref-type="bibr" rid="B151">151</xref>). Macrophage <italic>APOE</italic> critically regulates extracellular vesicle production, influencing T-lymphocyte proliferation, activation, and IFN-&#x3b3; secretion (<xref ref-type="bibr" rid="B152">152</xref>). Enriched in M2 macrophages, exosomal APOE mediates macrophage-gastric cancer crosstalk to promote metastasis (<xref ref-type="bibr" rid="B153">153</xref>). <italic>APOE<sup>+</sup></italic> macrophages interact with exhausted CD8<sup>+</sup> T cells, attenuating immune checkpoint inhibitor efficacy, whereas <italic>APOE</italic> blockade enhances immunotherapy responses (<xref ref-type="bibr" rid="B154">154</xref>). Pan-cancer analyses reveal differential <italic>APOE</italic> expression between tumors and normal tissues, correlating with clinical phenotypes and serving as a prognostic biomarker across cancers (<xref ref-type="bibr" rid="B155">155</xref>). APOE is a strong genetic risk factor associated with Alzheimer&#x2019;s disease, and APOE genotyping can be used for risk stratification and early diagnosis (<xref ref-type="bibr" rid="B156">156</xref>). APOE may serve as a biomarker for HER2-negative breast cancer, aiding in predicting responses to immunotherapy. APOE<sup>+</sup> TAMs promote the expression of genes related to lipid metabolism and are associated with an immunosuppressive tumor microenvironment and combination immunotherapy may enhance anti-tumor immune responses (<xref ref-type="bibr" rid="B157">157</xref>). While research on APOE has primarily focused on Alzheimer&#x2019;s disease, its role in tumor biology remains highly controversial and faces multiple methodological challenges.</p>
<p>LYVE-1 (Lymphatic Vessel Endothelial Hyaluronan Receptor-1), a marker of lymphangiogenesis, is expressed on lymphatic endothelial cells and macrophages (<xref ref-type="bibr" rid="B158">158</xref>). Associated with tumor lymphangiogenesis and TME modulation (<xref ref-type="bibr" rid="B159">159</xref>), macrophage-expressed LYVE-1 mediates leukocyte docking/transmigration (<xref ref-type="bibr" rid="B160">160</xref>) and cancer cell-endothelial adhesion. In melanoma metastasis studies, <italic>LYVE-1</italic> blockade enhanced pro-inflammatory states in pre-metastatic livers, altering TME to reduce hepatic metastasis (<xref ref-type="bibr" rid="B161">161</xref>). <italic>LYVE-1<sup>+</sup></italic> macrophages represent a protumorigenic, anti-inflammatory subset promoting hyaluronan remodeling in TME, and their depletion delayed mammary tumor growth (<xref ref-type="bibr" rid="B162">162</xref>). Anti-LYVE-1 monoclonal antibodies inhibit lymphangiogenesis, suppressing both primary tumor formation and metastasis, highlighting <italic>LYVE-1</italic> as a promising therapeutic target (<xref ref-type="bibr" rid="B163">163</xref>).</p>
<p>Although the M1/M2 paradigm remains influential, growing evidence urges its abandonment in single-cell contexts (<xref ref-type="bibr" rid="B164">164</xref>, <xref ref-type="bibr" rid="B165">165</xref>). This binary model is established from <italic>in vitro</italic> polarization&#x2014;oversimplifies tissue macrophage states, ignoring their continuous, dynamic, and multidimensional <italic>in vivo</italic> landscapes. Persisting with rigid classifications obscures critical biological insights and impedes precision therapeutics. As scRNA-seq becomes increasingly accessible, multidimensional interrogation of functional subsets will provide comprehensive blueprints for tumor immunotherapy.</p>
</sec>
<sec id="s3_5">
<label>3.5</label>
<title>B cells</title>
<p>B cells, long recognized as core components of the adaptive immune system, play pivotal roles within the TME. Beyond antibody secretion, antigen presentation, and cytokine production, they modulate immune responses to influence tumor progression (<xref ref-type="bibr" rid="B166">166</xref>). Based on developmental trajectories and functional states, major B-cell subsets include na&#xef;ve B cells, memory B cells, germinal center (GC) B cells, plasma cells, and regulatory B cells (Bregs) (<xref ref-type="bibr" rid="B167">167</xref>, <xref ref-type="bibr" rid="B168">168</xref>).</p>
<p><italic>TCL1A</italic>, a marker of na&#xef;ve B cells, regulates signaling during early B-cell development; its downregulation signifies B-cell maturation (<xref ref-type="bibr" rid="B169">169</xref>). TCL1A promotes proliferation by activating stem cell expansion pathways (<xref ref-type="bibr" rid="B170">170</xref>), enhancing <italic>KI67</italic> expression and modulating the cell cycle (reducing G1 phase, increasing S/G2 phases). It also upregulates <italic>CR2</italic> expression on B cells (<xref ref-type="bibr" rid="B154">154</xref>), enabling efficient antigen presentation (<xref ref-type="bibr" rid="B155">155</xref>). Notably, TCL1A promotes tertiary lymphoid structure formation (<xref ref-type="bibr" rid="B171">171</xref>), which correlates with positive immunotherapy responses and survival outcomes (<xref ref-type="bibr" rid="B172">172</xref>&#x2013;<xref ref-type="bibr" rid="B174">174</xref>), underscoring its value as a therapeutic target.</p>
<p><italic>CD27</italic>, a member of the TNF receptor superfamily and key memory B-cell marker, interacts with its ligand CD70 to regulate humoral immunity and tolerance (<xref ref-type="bibr" rid="B175">175</xref>). CD27 sustains memory B-cell survival by activating NF-&#x3ba;B signaling to inhibit apoptosis and guiding migration to secondary lymphoid organs for survival factors. Upon antigen re-exposure, CD27-CD70 engagement activates NF-&#x3ba;B and MAPK/ERK pathways, driving rapid memory B-cell activation and proliferation. Subsequently, CD27 signaling induces plasma cell differentiation via downstream transcriptional activators, facilitating high-affinity antibody secretion during secondary immune responses (<xref ref-type="bibr" rid="B176">176</xref>). The CD27-CD70 axis represents a promising target across hematologic and solid tumors: the agonistic anti-CD27 antibody varlilumab shows clinical efficacy (<xref ref-type="bibr" rid="B177">177</xref>), soluble CD27 predicts anti-PD-1 monotherapy response (<xref ref-type="bibr" rid="B178">178</xref>), and serves as an HCC risk biomarker (<xref ref-type="bibr" rid="B179">179</xref>).</p>
<p>The transcriptional repressor <italic>BCL6</italic>, predominantly expressed in GC B cells, is essential for GC formation and B-cell differentiation (<xref ref-type="bibr" rid="B180">180</xref>). By repressing differentiation-related genes, it maintains GC B cell proliferation/survival and governs TFH-dependent somatic hypermutation/class-switch recombination, ultimately generating high-affinity antibody-secreting plasma cells and memory B cells (<xref ref-type="bibr" rid="B181">181</xref>). BCL6 also acts as an HBV promoter suppressor in hepatocytes, enhancing chemokine production and immune cell liver infiltration (<xref ref-type="bibr" rid="B182">182</xref>). However, <italic>BCL6</italic> is oncogenic: it epigenetically represses pro-apoptotic genes via promoter binding to promote cancer cell survival (<xref ref-type="bibr" rid="B183">183</xref>), making its targeted degradation therapeutically valuable (<xref ref-type="bibr" rid="B184">184</xref>).</p>
<p><italic>MZB1</italic> (Marginal Zone B and B1 cell-specific protein 1), upregulated during plasma cell differentiation (<xref ref-type="bibr" rid="B185">185</xref>), encodes an ER-resident co-chaperone critical for antibody secretion capacity (<xref ref-type="bibr" rid="B186">186</xref>). <italic>MZB1</italic> deficiency impairs plasmablast migration (via &#x3b2;1-integrin dysregulation) and terminal plasma cell differentiation (<xref ref-type="bibr" rid="B187">187</xref>). Overexpressed in hematologic malignancies, <italic>MZB1</italic> sustains malignant B-cell protein folding and chemoresistance in multiple myeloma (<xref ref-type="bibr" rid="B188">188</xref>, <xref ref-type="bibr" rid="B189">189</xref>). As a key regulator of ER homeostasis and antibody production, <italic>MZB1</italic> targeting may modulate plasma cell function to mitigate inflammation (<xref ref-type="bibr" rid="B190">190</xref>).</p>
<p>Regulatory B cells (<italic>Bregs</italic>) suppress immunity primarily through IL-10 production (<xref ref-type="bibr" rid="B191">191</xref>). <italic>CD38</italic>high Bregs secrete IL-10/TGF-&#x3b2; to convert na&#xef;ve TH cells into FoxP3<sup>+</sup> Tregs (<xref ref-type="bibr" rid="B192">192</xref>), while suppressing antigen-specific T-cell effector functions and Th17 differentiation (<xref ref-type="bibr" rid="B193">193</xref>),collectively facilitating tumor immune evasion. CD38 governs multiple inflammatory processes (migration, adhesion, phagocytosis, antigen presentation/release) and is a therapeutic target in hematologic malignancies (<xref ref-type="bibr" rid="B194">194</xref>). Anti-CD38 antibodies (e.g., isatuximab, daratumumab) induce antibody-dependent cytotoxicity, with clinical efficacy validated in multiple myeloma, NK-cell lymphoma, and CD19<sup>-</sup> B-cell malignancies (<xref ref-type="bibr" rid="B195">195</xref>, <xref ref-type="bibr" rid="B196">196</xref>).</p>
<p>While B-cell biology in hematologic tumors is well-characterized, their roles in solid tumors (breast, lung, colorectal cancers) remain limited. B-cell subsets exhibit context-dependent plasticity across tumor types, disease stages, and treatments. Current insights represent only partial functional delineations; deeper investigations into TME-specific mechanisms and subset-targeted immunomodulation are warranted.</p>
</sec>
<sec id="s3_6">
<label>3.6</label>
<title>Perspectives on scRNA-seq-driven cell subtype dissection</title>
<p>In the cascade of antitumor immune responses, which spanning antigen presentation, phagocytosis, cytotoxicity, and antibody secretion. NK cells, DCs, T cells, macrophages, and B cells each execute distinct roles in tumor cell elimination. However, this process is frequently hijacked by tumors to facilitate their growth and immune evasion. Beyond these, neutrophils, endothelial cells, and fibroblasts also exert critical functions within the TME. Critically, these cell types are not independent entities; they engage in intense intercellular crosstalk through diverse signaling networks. Thus, elucidating cell-cell communication is paramount for decoding TME complexity. Research on cell interactions supported by scRNA-seq is no longer limited to average signals from specific cell types (<xref ref-type="bibr" rid="B197">197</xref>); it can precisely identify individual cells&#x2019; gene expression, protein secretion, and signaling characteristics, thereby highlighting cellular heterogeneity (<xref ref-type="bibr" rid="B198">198</xref>). The integration of spatial transcriptomics further reveals the spatiotemporal dynamics and functional diversity of these interactions. Researchers have used scRNA-seq and spatial transcriptomics to discover that MRC1<sup>+</sup> tumor-associated TAMs interact with cancer-associated fibroblasts through WNT5A and HGF signaling to promote the metastasis of hepatocellular carcinoma (<xref ref-type="bibr" rid="B124">124</xref>). Microfluidic chip analyses combined with scRNA-seq secretion profiling showed that only a subset of CD8<sup>+</sup> T cells secretes IFN-&#x3b3; after contacting tumor cells, while most cells enter an exhausted state due to PD-1/PD-L1 signaling (<xref ref-type="bibr" rid="B199">199</xref>).Additionally, researchers found through ligand-receptor analysis that NK cells recruit cDC1 to the tumor core by secreting XCL1 and CCL5, forming a positive feedback loop that enhances anti-tumor immunity (<xref ref-type="bibr" rid="B200">200</xref>). In summary, single-cell technologies have transformed the study of cell interactions from a blurry average relationship to a finely regulated network with spatial, temporal, and state resolution. While not all discussed cell subtypes have been extensively studied in HCC, mechanisms observed in other cancers may exhibit comparable or opposing effects in HCC which finds awaiting further experimental validation. Meanwhile, we have listed the subgroups and marker genes discussed in detail  in <xref ref-type="table" rid="T1"><bold>Table 1</bold></xref> and some potential subgroup marker genes that are not mentioned in <xref ref-type="table" rid="T2"><bold>Table&#xa0;2</bold></xref>. scRNA-seq enables precise characterization of transcriptional profiles that define cellular states. Yet &#x201c;signature gene&#x201d; expression is inherently multifaceted: genes may be expressed across multiple cell types or exhibit functional reversals during different developmental stages of the same lineage. Under pathological stresses, which including tumor hypoxia, inflammatory stimulation, metabolic stress, or therapeutic exposure&#x2014;virtually any functional gene may be induced, silenced, or reprogrammed, thereby sculpting novel cellular states with unique phenotypic and biological significance. The exploration of functional subtypes seems to hold near-limitless potential (<xref ref-type="supplementary-material" rid="SM1"><bold>Supplementary Figure S2</bold></xref>).</p>
<table-wrap id="T1" position="float">
<label>Table&#xa0;1</label>
<caption>
<p>Immune cell subtype marker genes and clinical applications.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="center">Marker Gene</th>
<th valign="top" align="center">Clinical Applications</th>
<th valign="top" align="center">References</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="top" colspan="3" align="center">NK Cells</th>
</tr>
<tr>
<td valign="top" align="left">XCL1</td>
<td valign="top" align="left">Intratumoral XCL1 injection recruits cDC1 cells, boosting antigen-specific CD8<sup>+</sup> T cell infiltration</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B42">42</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">NKG2C</td>
<td valign="top" align="left">NKG2A/C switch receptor engineered to target HLA-E-overexpressing tumors</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B48">48</xref>)</td>
</tr>
<tr>
<th valign="top" colspan="3" align="center">DCs</th>
</tr>
<tr>
<td valign="top" align="left">XCR1</td>
<td valign="top" align="left">Engineered XCL1-overexpressing T cells enhance DC-mediated antitumor immunity</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B52">52</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">SIRP&#x3b1;</td>
<td valign="top" align="left">Anti-SIRP&#x3b1; antibody blocks CD47-SIRP&#x3b1; axis to improve DC antigen presentation efficiency</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B56">56</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">BDCA2</td>
<td valign="top" align="left">Litifilimab targets BDCA2 to inhibit IFN-&#x3b1; production by pDCs (treating systemic lupus erythematosus)</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B59">59</xref>)</td>
</tr>
<tr>
<th valign="top" colspan="3" align="center">T cells</th>
</tr>
<tr>
<td valign="top" align="left">CXCR3</td>
<td valign="top" align="left">CXCR3-high expression positively correlates with T cell infiltration and predicts immunotherapy response</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B70">70</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">CCR4</td>
<td valign="top" align="left">CCR4 antagonists suppress HCC distant metastasis</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B74">74</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">CTLA-4</td>
<td valign="top" align="left">Ipilimumab plus nivolumab becomes China's first dual-checkpoint inhibitor regimen for first-line HCC treatment</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B94">94</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">LAG3</td>
<td valign="top" align="left">LAG3-PD-1 bispecific antibody (relatlimab) demonstrates objective response in advanced HCC</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B119">119</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">FOXP3</td>
<td valign="top" align="left">FOXP3-overexpressing CAR-T cells enhance antitumor efficacy (preclinical)</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B89">89</xref>)</td>
</tr>
<tr>
<th valign="top" colspan="3" align="center">Macrophages</th>
</tr>
<tr>
<td valign="top" align="left">CCL3</td>
<td valign="top" align="left">CCL3 synergizes with anti-PD-1 to enhance antitumor efficacy (preclinically validated)</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B132">132</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">SPP1</td>
<td valign="top" align="left">SPP1<sup>+</sup> TAMs cause resistance to PD-L1 blockade,serve as detrimental prognostic biomarkers</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B122">122</xref>, <xref ref-type="bibr" rid="B137">137</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">APOE</td>
<td valign="top" align="left">APOE inhibitors potentiate checkpoint inhibitor efficacy</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B154">154</xref>)</td>
</tr>
<tr>
<th valign="top" colspan="3" align="center">B cells</th>
</tr>
<tr>
<td valign="top" align="left">BCL6</td>
<td valign="top" align="left">Targeted degradation of BCL6 for B-cell lymphoma therapy</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B184">184</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">CD38</td>
<td valign="top" align="left">Daratumumab/isatuximab treat multiple myeloma via ADCC mechanisms</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B192">192</xref>, <xref ref-type="bibr" rid="B196">196</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">CD27</td>
<td valign="top" align="left">Soluble CD27 (sCD27) as biomarker for PD-1 therapy response and HCC risk prediction in HCV-sustained virological response patients</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B179">179</xref>)</td>
</tr>
</tbody>
</table>
</table-wrap>
<table-wrap id="T2" position="float">
<label>Table&#xa0;2</label>
<caption>
<p>Supplementary marker genes for immune cell subtypes unlisted in main text.</p>
</caption>
<table frame="hsides">
<thead>
<tr>
<th valign="top" align="center">Marker Gene</th>
<th valign="top" align="center">Biological Function</th>
<th valign="top" align="center">References</th>
</tr>
</thead>
<tbody>
<tr>
<th valign="top" colspan="3" align="center">NK Cells</th>
</tr>
<tr>
<td valign="top" align="left">GZMB</td>
<td valign="top" align="left">GZMB serves as a core cytotoxic effector molecule in NK cells, inducing target cell apoptosis through caspase pathway activation upon release from cytoplasmic granules.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B209">209</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">TIGIT</td>
<td valign="top" align="left">TIGIT overexpression significantly impairs IFN-&#x3b3; and TNF production, diminishing NK cell cytotoxic function against tumor targets. It induces NK cell dysfunction and reduces immune response capacity.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B210">210</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">CD160</td>
<td valign="top" align="left">CD160 engages both classical and non-classical MHC class I molecules, thereby triggering NK cell cytotoxicity and cytokine production to promote NK cell activation and degranulation; conversely, CD160 is essential for efficient IFN-&#x3b3; production in activated NK cells</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B211">211</xref>)</td>
</tr>
<tr>
<th valign="top" colspan="3" align="center">DCc</th>
</tr>
<tr>
<td valign="top" align="left">BATF3</td>
<td valign="top" align="left">Collaborates with IRF8 to support cDC1 precursor development and maturation. Drives effector gene expression programs enabling cDC1-specific molecules required for cross-presentation and T cell co-stimulation.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B212">212</xref>, <xref ref-type="bibr" rid="B213">213</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">IRF8</td>
<td valign="top" align="left">Orchestrates cDC1 development and function, thereby enhancing tumor antigen cross-presentation competence and potentiating antitumor immunity.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B214">214</xref>)</td>
</tr>
<tr>
<th valign="top" colspan="3" align="center">T Cells</th>
</tr>
<tr>
<td valign="top" align="left">GNLY</td>
<td valign="top" align="left">Synergizes with perforin and granzymes to directly eliminate pathogens/tumor cells upon release by T/NK cells, while augmenting inflammatory responses.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B215">215</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">CD6</td>
<td valign="top" align="left">Costimulatory receptor on T cells that binds ALCAM on APCs and epithelia, delivering activation signals to T lymphocytes.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B216">216</xref>, <xref ref-type="bibr" rid="B217">217</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">CXCR6</td>
<td valign="top" align="left">The CXCR6+CD8+ T cell subset exhibits enhanced activation and cytotoxicity; as a principal responder population mediating immune checkpoint therapeutics, intratumoral CXCR6+CD8+ T cells are indispensable for driving cancer regression.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B218">218</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">CXCL13</td>
<td valign="top" align="left">The CXCL13:CXCR5 axis orchestrates intercellular interactions to modulate lymphocyte infiltration in the tumor microenvironment, thereby dictating tumor responsiveness to cytotoxic and immune-targeted therapeutics; concurrently, CXCL13 drives recruitment of CXCR5+CD8+ T cells to the liver&#x2014;a subset critically enabling enhanced viral control in chronic hepatitis B infection.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B219">219</xref>, <xref ref-type="bibr" rid="B220">220</xref>)</td>
</tr>
<tr>
<th valign="top" colspan="3" align="center">Macrophages</th>
</tr>
<tr>
<td valign="top" align="left">VEGFA</td>
<td valign="top" align="left">Promotes angiogenesis and tissue repair; functionally linked to tumor progression. Function description pending validation. (Note: Source paper describes FAP<sup>+</sup>-SPP1<sup>+</sup> interactions)</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B122">122</xref>)</td>
</tr>
<tr>
<td valign="top" align="left">FOLR2</td>
<td valign="top" align="left">Marks protumoral M2-like macrophages that drive immunosuppression via DC recruitment and CD4<sup>+</sup>FOXP3<sup>+</sup> Treg differentiation.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B221">221</xref>)</td>
</tr>
<tr>
<th valign="top" colspan="3" align="center">B Cells</th>
</tr>
<tr>
<td valign="top" align="left">MKI67</td>
<td valign="top" align="left">Essential for B cell proliferation/differentiation. Ki-67 overexpression facilitates rapid cell cycle entry post-antigen stimulation, generating plasma/memory B cells for humoral immunity.</td>
<td valign="middle" align="center">(<xref ref-type="bibr" rid="B222">222</xref>)</td>
</tr>
</tbody>
</table>
</table-wrap>
</sec>
</sec>
<sec id="s4" sec-type="discussion">
<label>4</label>
<title>Discussion</title>
<p>HCC remains a global health challenge due to high incidence, mortality, and treatment resistance. Despite diagnostic advances, the complex TME severely limits therapeutic efficacy. scRNA-seq has revolutionized our understanding of HCC TME heterogeneity, mapping immune cell dynamics and cellular interactions. These insights reveal spatiotemporal complexity and communication networks, guiding precision treatment strategies and target discovery. This review synthesizes scRNA-seq advances in profiling HCC TME immune architecture, including T cells, B cells, macrophages, NK cells, and dendritic cells. The technology surpasses bulk RNA limitations to map antitumor (e.g., <italic>GZMK<sup>+</sup></italic> T, <italic>CCL3</italic><sup>+</sup>) and protumor subsets (Tregs, Bregs, suppressive). Key regulatory mechanisms identified involve chemokine receptors (CXCR3), checkpoints (LAG-3), functional modulators (FOXP3/SPP1), and transcription factors (BCL6) that govern immune function, treatment resistance, and metastasis. These findings reveal high-value therapeutic targets beyond diagnostic utility. Although scRNA-seq has significantly improved our ability to analyze the heterogeneity of the HCC TME, there are still clear limitations in its studies. Differences in tissue dissociation and transcript capture efficiency may lead to the underestimation of certain immune subsets and the omission of key signaling genes (<xref ref-type="bibr" rid="B201">201</xref>). Additionally, conventional scRNA-seq only provides static transcriptional snapshots, making it difficult to accurately reflect the spatiotemporal dynamics of immune cells during tumor progression and treatment responses (<xref ref-type="bibr" rid="B202">202</xref>). The results of single-cell clustering are highly sensitive to algorithms and thresholds, leading to a lack of standardized definitions for subsets across different studies, and transcript levels do not always equate to functional states (<xref ref-type="bibr" rid="B203">203</xref>). Therefore, single-cell discoveries still require integration with spatial genomics, multi-omics approaches, and functional and clinical validations to achieve reliable mechanistic explanations and clinical translation (<xref ref-type="bibr" rid="B201">201</xref>, <xref ref-type="bibr" rid="B204">204</xref>).The development of spatially resolved transcriptomics (SRT) significantly complements the loss of spatial information in scRNA-seq. The combined application of scRNA-seq and SRT technologies provides unprecedented solutions for studying the functional genomics of individual cells and their spatial environments within tissues (<xref ref-type="bibr" rid="B205">205</xref>). High-resolution techniques, such as Stereo-seq and 10&#xd7; Visium HD, enhance spatial resolution to subcellular levels through <italic>in situ</italic> capture technology, allowing for the direct localization of gene expression at tumor boundaries and within heterogeneous regions (<xref ref-type="bibr" rid="B203">203</xref>). SRT enables near-complete transcriptome analysis (&gt;20,000 genes) on formalin-fixed paraffin-embedded samples, identifying the spatial exclusion of PD-L1<sup>+</sup> TAMs and CD8<sup>+</sup> T cells, thereby revealing potential mechanisms of resistance to immunotherapy (<xref ref-type="bibr" rid="B206">206</xref>). Researchers have constructed tumor cell lineage states, clonal structures, and spatial maps of the tumor microenvironment by analyzing primary and metastatic tumor samples from patients, discovering significant transcriptional differences in tumors between primary sites and various metastatic sites (<xref ref-type="bibr" rid="B207">207</xref>). In these studies, SRT technology not only retains single-cell precision but also reconstructs the spatial context of tissues, making it an indispensable complementary tool in cancer exploration. Clinical translation studies targeting modulation of the TME remain at a nascent stage. From a clinical perspective, while scRNA-seq has revealed the high heterogeneity of the HCC immune microenvironment, it still faces practical challenges in explaining mechanisms of immune therapy resistance and guiding patient stratification. In patients resistant to immune ICIs, immunosuppressive myeloid cell subsets, functionally exhausted T cell states, and abnormal cellular communication networks are often enriched. However, these characteristics exhibit significant individual variance among different patients, making it difficult to translate them into unified, actionable resistance biomarkers. Additionally, the cell subsets and gene features defined by scRNA-seq are primarily retrospective associations and lack simplified indicators that are strongly correlated with clinical outcomes and detectable in routine pathology or liquid biopsies, thereby limiting their practical application in patient stratification and treatment decisions. Therefore, there is an urgent need for future efforts to combine single-cell analyses with clinical. Critically, research disproportionately prioritizes T cells and macrophages, overlooking pivotal contributions from B, NK, and dendritic cells. Moving forward, research should prioritize multi-omics integration using spatial transcriptomics coupled with scRNA-seq to construct &#x201c;spatial-cell type-functional state&#x201d; atlases of HCC TME. Longitudinal tracking via TCR/BCR sequencing and epigenetics will elucidate temporal evolution of immune subsets during tumor progression/therapy. Developing rational combination therapies, such as immune ICIs with Treg/TAM/Breg-targeting agents, CAR-T/NK against impaired molecules (BCL6, SPP1), or cell therapies combined with cytokines (XCL1) remains essential. AI-driven analytics will enhance scRNA-seq data processing, phenotype identification, and biological network interpretation, enabling predictive treatment-response models for true personalized therapy. While T cell/macrophage research nears saturation, focused efforts on understudied B/NK/DC subsets hold untapped potential. By establishing a single-cell-level prognostic model to predict targeted therapy responses based on cancer patients&#x2019; cellular subtypes, we will significantly improve the precision of personalized treatment (<xref ref-type="bibr" rid="B208">208</xref>).Through sustained innovation and cross-disciplinary collaboration, the profound biological understanding derived from single-cell technologies will ultimately herald a new era of precision immunotherapy in HCC&#x2014;delivering transformative survival benefits for patients worldwide.</p>
</sec>
</body>
<back>
<sec id="s5" sec-type="author-contributions">
<title>Author contributions</title>
<p>YL: Investigation, Visualization, Writing &#x2013; original draft, Conceptualization. HW: Data curation, Formal Analysis, Investigation, Writing &#x2013; review &amp; editing. YQ: Data curation, Validation, Writing &#x2013; review &amp; editing. QM: Data curation, Supervision, Visualization, Writing &#x2013; review &amp; editing. PC: Project administration, Supervision, Writing &#x2013; review &amp; editing. ML: Funding acquisition, Project administration, Resources, Writing &#x2013; review &amp; editing. HL: Investigation, Methodology, Supervision, Writing &#x2013; review &amp; editing, Conceptualization, Funding acquisition.</p></sec>
<ack>
<title>Acknowledgments</title>
<p>The authors extend their sincere gratitude to all researchers who have dedicated themselves to unveiling the heterogeneity of hepatocellular carcinoma and to the development and application of single-cell RNA sequencing technologies. Special thanks go to the patients and their families who have participated in the studies cited in this review, as their contributions are invaluable to advancing medical knowledge and improving clinical outcomes. Thanks also go to my supervisor, Mr. Lin Hongsheng, for his professional and enthusiastic guidance Additionally, we appreciate the contributions of the reviewers and editors at Frontiers in Immunology for their guidance and support throughout the process.</p>
</ack>
<sec id="s7" sec-type="COI-statement">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p></sec>
<sec id="s8" sec-type="correction-statement">
<title>Correction note</title>
<p>A correction has been made to this article. Details can be found at: <ext-link xlink:href="https://doi.org/10.3389/fimmu.2026.1817873" ext-link-type="uri">10.3389/fimmu.2026.1817873</ext-link>.</p></sec>
<sec id="s9" sec-type="ai-statement">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p></sec>
<sec id="s10" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p></sec>
<sec id="s11" sec-type="supplementary-material">
<title>Supplementary material</title>
<p>The Supplementary Material for this article can be found online at: <ext-link ext-link-type="uri" xlink:href="https://www.frontiersin.org/articles/10.3389/fimmu.2026.1744845/full#supplementary-material">https://www.frontiersin.org/articles/10.3389/fimmu.2026.1744845/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Image1.tif" id="SF1" mimetype="image/tiff"><label>Supplementary Figure S1</label>
<caption>
<p>Architecture of the TME.</p>
</caption></supplementary-material>
<supplementary-material xlink:href="Image2.tif" id="SF2" mimetype="image/tiff"><label>Supplementary Figure S2</label>
<caption>
<p>Antitumor immune response orchestrated by immune cells. Legend: &#x2460;NK cells serve as the "primary initiators" of antitumor immunity. The loss or downregulation of MHC class I molecules on tumor cells fails to deliver inhibitory signals to NK cells. Instead, NKG2D activation receptors recognize and bind stress-induced ligands (e.g., MICA/B) on malignant cells, triggering cytotoxic responses. Activated NK cells release perforin-1 and granzyme B to directly lyse tumor cells. Additionally, they secrete CCL5 and XCL1, guiding cDC1 recruitment for optimal tumor antigen uptake and cross-presentation. A subset of NK cells differentiates into memory-like NK cells, exhibiting enhanced antibody-dependent cellular cytotoxicity (ADCC) for long-term tumor surveillance&#x2461;Tumor cells express mutated neoantigens or tumor-associated antigens (TAAs). cDC1 captures tumor antigens via pattern-recognition receptors (PRRs, e.g., TLRs), internalizes them, and processes peptides onto MHC class I for CD8+ T cell priming. Meanwhile, cDC2 presents antigens via MHC class II to activate CD4+ T cells, orchestrating broader immune coordination.&#x2462;Macrophages engulf tumor cells via PRR recognition, followed by antigen processing and MHC class I/II presentation to CD8+ and CD4+ T cells, respectively. This bridges innate and adaptive immunity.&#x2463;Upon recognizing MHC I-presented tumor antigens, CD8+ T cells differentiate into cytotoxic T lymphocytes (CTLs). CTLs eliminate tumors via perforin-1/granzyme B-induced apoptosis and generate long-lived memory CD8+ T cells for rapid recall responses.Activated by MHC II-antigen complexes, CD4+ T cells differentiate into Th1 subsets, secreting IFN-&#x3b3; to enhance CTL function, or Th2 cells, which support B cell responses.&#x2464;BCR engagement triggers tumor antigen internalization, processing, and MHC II presentation to CD4+ T cells. T-B cell interaction via CD40-CD40L and Th2-derived cytokines (e.g., IL-4) drive affinity-matured antibody production by plasma cells. Concurrently, memory B cells are generated for secondary responses. Antibodies tag tumor cells for complement-mediated lysis and NK/macrophage-mediated ADCC, amplifying tumor clearance.</p>
</caption></supplementary-material>
<supplementary-material xlink:href="DataSheet1.pdf" id="SM1" mimetype="application/pdf"/></sec>
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<p><ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/1713415">Yanying Yang</ext-link>, Fudan University, China</p></fn>
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