From a molecular classification perspective, p53-mutated EC is categorized as an immunosuppressive subtype. This group typically displays elevated levels of Tregs, M2 macrophages, PD-L1⁺CD68⁺ macrophages, and CD8⁺PD-1⁺ T cells, which correlate with the poorest survival outcomes. In contrast, the NSMP subtype of EC demonstrates an immune-desert phenotype, with minimal infiltration by immune cells such as CD8⁺ cytotoxic T lymphocytes and low expression of immune checkpoint molecules. Although generally well-differentiated, NSMP tumors also exhibit high levels of tumor-associated macrophages (TAMs) and are associated with unfavorable prognoses (19–21).

Various stromal and immune-related cells within the tumor microenvironment contribute to immunosuppression and tumor progression. Cancer-associated fibroblasts secrete cytokines such as TGF-β and IL-6, which suppress immune responses and restrict immune cell infiltration (22). Tumor-associated neutrophils, particularly the N2 subtype, promote tumor growth and impair cytotoxic T cell activity (23). Additionally, mesenchymal stromal cells modulate immune function through paracrine signaling, further enhancing immunosuppression within the tumor niche (24).

Additionally, low immunogenicity correlates with hormone receptor status. The absence of both ER and PR expression independently predicts poor prognosis in EC, even among low-grade tumors. Specifically, ER-negative tumors tend to have reduced FOXP3⁺ Treg infiltration, while PR-negative cases show increased TAM presence, potentially promoting immune tolerance and worse outcomes (25, 26).

The tumor immune microenvironment in immune-nonresponsive EC is typically characterized by dysfunctional or sparse immune infiltration. While p53-mutated tumors exhibit features of an immunosuppressive microenvironment, NSMP tumors generally lack a competent anti-tumor immune response (27).

In EC, immune checkpoint blockade proves less effective for about 70% of MSS or pMMR cases. The KEYNOTE-028 study supports this, reporting just 13% response and 13% disease stabilization with pembrolizumab (28, 29). Patients with immune-nonresponsive EC generally have a poor prognosis. Specifically, p53-mutated EC is associated with the lowest survival due to its immunosuppressive microenvironment. Likewise, NSMP EC also demonstrates poor clinical outcomes, likely due to ineffective antitumor immune responses (30, 31).

Among immune cell-associated markers, CXCL8_hi IL1B_hi macrophages have been identified as predictors of poor and non-durable responses to ICIs in EC. A high-risk score derived from six associated genes correlates with shorter survival and decreased ICI efficacy. Moreover, the presence of dysfunctional (CD8⁺PD-1⁺) or terminally exhausted (CD8⁺PD-1⁺TOX⁺) T cells, along with their interactions with PD-L1⁺ cells, independently predicts 24-month progression-free survival (PFS) in dMMR uterine or ovarian cancer patients receiving nivolumab (32, 33). In the context of the tumor immune microenvironment, TIL-low tumors—often seen in ECs with either mutant or wild-type p53—lack distinct immunologic features. Though they may also appear in MMR-deficient or POLE-mutated tumors, these TIL-low tumors generally indicate an immune-cold phenotype (34). Additionally, molecules linked to immunosuppression, such as PD-L1, may be related to poor immune reactivity in EC, although further studies are needed to confirm this association.

The main workflow involves sample processing and cell preparation. After obtaining EC tissues and matched adjacent normal tissues, they must be dissociated into single-cell suspensions. This step aims to maintain cell viability and integrity while minimizing cell death and contamination. Due to the high heterogeneity of tumor tissues, it is essential to ensure that all cell types are represented in the suspension (38). For single-cell RNA sequencing, the 10x Genomics Chromium platform offers high throughput at a relatively low cost. Yet, it requires careful experimental handling and involves intricate operations. Additionally, variations among platforms lead to inconsistent data, complicating cross-study comparisons (46). Data analysis includes quality control, normalization, dimensionality reduction, and clustering, relying on diverse bioinformatics tools and algorithms. Cell type annotation depends on known marker genes, but the limited availability of markers hinders comprehensive and accurate identification. Additionally, data analysis demands significant computational resources and expertise, with workload increasing exponentially as experiment size grows (47). Integrating and sharing data across studies can improve accuracy, but differences in experimental conditions, sequencing platforms, and analysis methods pose challenges. Therefore, more effective integration methods and standardized workflows are needed, along with data sharing platforms to promote communication and accelerate EC research.

Single-cell sequencing reveals immune cell changes associated with EC treatment resistance. Notably, exhausted CD8+ T cells increase, expressing more inhibitory receptors that reduce anti-tumor activity. Meanwhile, regulatory T cells (Tregs) and myeloid-derived suppressor cells (MDSCs) also rise, creating an immunosuppressive environment that promotes resistance to immunotherapy (48–50). Potential biomarkers for evaluating immune cell status and forecasting immunotherapy response include molecular indicators of immune cell dysfunction, such as improved levels of the exhaustion markers PD-1, CTLA-4, and LAG-3 on T cells, as well as M2 macrophage polarization markers CD163 and CD206 (51). By analyzing immune cell infiltration, functional states, and intercellular interactions in the tumor microenvironment (TME) through single-cell data, researchers identify potential immunotherapy targets. For example, therapies targeting molecules involved in immune dysfunction or drugs promoting macrophage polarization toward the M1 phenotype enhance anti-tumor immunity (52). Furthermore, integrating clinical features and treatment responses helps screen targets related to therapy efficacy, thereby improving clinical trial success rates (53). Standard endpoints such as survival and tumor reduction may not accurately represent early immunotherapy effects. Alternatively, single-cell approaches detect immune cell dynamics and functional shifts in the TME, enabling early response evaluation and optimized care (54).

ST integrates into early clinical trials for EC. For instance, in a trial targeting the Netrin-1 antibody (NP137), researchers collect biopsy samples and analyze them, revealing that the treatment reduces the expression of genes related to epithelial-mesenchymal transition (EMT). This finding not only offers new insights for clinical therapy but also supports subsequent drug development and clinical trial design (39). In research on high-grade serous ovarian cancer, spatial analysis comparing tumors from long-term and short-term survivors shows that tumors from long-term survivors contain a more diverse infiltration of immune cells. This observation provides a reference for studying the immune microenvironment in EC and helps clarify the role of immune cells in tumor progression (55).

ST reveals detailed immune microenvironment profiles in EC at various phases. Immune cell infiltration and spatial distribution, along with intercellular interactions, undergo significant changes as the tumor advances. Early-stage tumors show localized immune presence, while later stages have widespread immune infiltration and enriched suppressive cells, which influence therapeutic results and prognosis (56). In addition, the immune landscapes of primary tumors and metastatic lesions present notable differences. ST studies indicate that immune cell composition and activity in metastatic sites may not mirror those in primary tumors. Some immune subtypes may be more active in metastases, or the interactions between immune and tumor cells may shift. Understanding these variations is important for deciphering metastasis and formulating precise therapeutic strategies (57, 58).

The development of tailored immunotherapy approaches is made possible by the spatial analysis of the immune microenvironment within individual tumors. For instance, combining immune checkpoint inhibitors with medications that target immunosuppressive cells may be taken into consideration if a tumor has a high concentration of these cells. Conversely, when immune cell infiltration appears limited, it becomes essential to explore strategies aimed at enhancing immune cell activation and recruitment (58). Spatial data helps identify exact targets and synergistic actions of drugs in combination immunotherapies. For instance, it guides whether immune checkpoint inhibitors boost immune cell activity and if chemotherapy enhances immune infiltration by analyzing the spatial layout of immune and tumor cells within the TME (59).

Combining spatial and single-cell transcriptomic technologies reveals both cellular heterogeneity and tissue architecture in EC. This helps develop accurate biomarkers for early diagnosis and guides therapy in advanced or recurrent cases (60). Additionally, multi-omics profiles enable precise risk classification and treatment stratification, which serve as the foundation for personalized therapeutic strategies. Early identification of high-risk patients permits proactive monitoring and intervention to minimize relapse, and specific therapies for distinct risk groups improve treatment efficacy and patient survival.

The combined use of single-cell and ST technologies enables a deep understanding of the immune microenvironment and molecular mechanisms of tumor cells in EC, providing a theoretical foundation for designing new immunotherapies. For instance, identifying the critical roles of TAMs and MDSCs in tumor immune evasion offers potential targets for developing immune-modulating drugs. Moreover, clinical trials employ single-cell and spatial transcriptomic analyses to monitor and evaluate drug efficacy in real time, while also revalidating drug targets to ensure the direction and effectiveness of drug development. By examining changes in immune cell subsets and functions before and after treatment, researchers assess immunomodulatory effects, which guide drug optimization and improvement.

In the clinical application of single-cell and ST technologies, strict adherence to relevant laws and regulations is essential. Moreover, protecting patient privacy and managing data security are critical to prevent unauthorized access or misuse of personal and genetic information. Techniques such as anonymization and encryption help safeguard privacy, while robust data security systems prevent breaches and illegal use. Furthermore, following rigorous clinical trial and regulatory protocols ensures the safety and efficacy of these new technologies, thereby protecting patients’ health and rights. Before clinical implementation, comprehensive preclinical studies and clinical trials must verify their safety and effectiveness, and approval from regulatory authorities is required in accordance with applicable laws.