Colorectal cancer (CRC) ranks among the most prevalent and lethal malignancies worldwide, with increasing incidence driven by sedentary lifestyles, dietary shifts, and aging populations. Despite advances in targeted therapies and immunotherapies, resistance to treatment and tumor relapse remain major clinical hurdles (1). Conventional chemotherapies, such as cisplatin, continue to form the backbone of CRC management, yet their long-term efficacy is limited by drug resistance and systemic toxicity. The emergence of SARS-CoV-2, the causative agent of COVID-19, has introduced new challenges, especially for patients with solid tumors like CRC who are already immunocompromised and at higher risk of severe viral outcomes (2).

A key molecular link between CRC and COVID-19 lies in the shared expression of viral entry receptors in tumor tissues. Angiotensin-converting enzyme 2 (ACE2) and cluster of differentiation 147 (CD147) are both overexpressed in colorectal tumors and have been identified as functional entry points for SARS-CoV-2 (3, 4). ACE2, part of the renin-angiotensin system (RAS), paradoxically plays both protective and tumor-promoting roles: while it mitigates angiotensin II-mediated damage in cardiovascular systems, its elevated expression in CRC is associated with increased tumor aggressiveness and poor prognosis (5). CD147, also known as basigin or EMMPRIN, not only facilitates SARS-CoV-2 binding but also contributes to chemotherapy resistance by promoting epithelial-mesenchymal transition (EMT) and matrix metalloproteinase activation (6, 7).

The immunological overlap between CRC and COVID-19 is another layer of complexity. Elevated levels of pro-inflammatory cytokines, particularly interleukin-6 (IL-6), interleukin-17 (IL-17), and tumor necrosis factor-alpha (TNF-α), are hallmarks of both CRC progression and COVID-19 severity. IL-17, mainly secreted by Th17 cells, promotes angiogenesis and tumor cell survival, while TNF-α sustains chronic inflammation and contributes to genomic instability (8, 9). In the context of viral infection, these cytokines are implicated in the cytokine storm syndrome, exacerbating tissue damage and impairing anti-tumor immunity. This immunological convergence suggests that SARS-CoV-2 may accelerate CRC progression via inflammatory amplification, while CRC-related immune dysfunction may predispose patients to severe COVID-19 (10).

Given these shared molecular and immunological pathways, there is a pressing need to evaluate how anticancer and antiviral therapies interact at the transcriptomic level. Cisplatin, for example, exerts its cytotoxic effect through DNA crosslinking and apoptosis induction but is susceptible to resistance mechanisms mediated by cytokines such as IL-17. Actemra (tocilizumab), an IL-6 receptor antagonist used to manage cytokine storm in COVID-19, may modulate tumor-promoting inflammatory pathways, but its impact on CRC-specific targets like CD147 is poorly understood (10–12). Remdesivir, a nucleoside analog that inhibits viral RNA polymerase, could also alter host chromatin and immune pathways relevant to cancer biology (13–15).

To date, few studies have explored how SARS-CoV-2 infection influences drug responses in CRC cells, or how antiviral and anti-inflammatory agents affect cancer-related gene expression. Addressing this gap is crucial for developing safe and effective treatment regimens for CRC patients affected by COVID-19 or similar viral infections.

Therefore, this study aims to investigate the transcriptomic changes in CRC SW-480 cells following treatment with cisplatin, Actemra, and remdesivir, both in the presence and absence of SARS-CoV-2 infection. By integrating RNA sequencing with protein validation and cytotoxicity assays, we seek to identify key pathways and molecular targets such as ACE2, CD147, and IL-17 that are jointly modulated by these agents. Our findings may inform the development of personalized therapeutic strategies that simultaneously address drug resistance, viral susceptibility, and inflammation in CRC patients.

RNA-Seq quality control was performed to ensure data reliability. Principal component analysis (PCA) revealed distinct clustering of samples by treatment group (Figure 5), with 42.8% variance explained by PC1 and 29.1% by PC2. Control samples (Cnt) grouped tightly, while cisplatin (Trt1), Actemra (Trt2), and remdesivir (Trt3) treatments formed separate clusters, indicating treatment-specific transcriptional responses. Hierarchical clustering (Figure 6) further confirmed this separation, with replicates within each treatment group clustering together. The heatmap displayed Z-score-normalized expression patterns, highlighting upregulated genes (e.g., CD147, IL-17) in Trt3 and downregulated DNA repair genes in Trt1.

Differential gene expression analysis identified significant transcriptional changes across treatments. Volcano plots (Figures 4, 5) revealed upregulated genes (red) and downregulated genes (blue) with thresholds of |log₂FC| > 1 and adjusted p < 0.05. For cisplatin, 1,240 genes were downregulated, including those involved in lipid metabolism (e.g., FASN, ACSL4), while Actemra suppressed inflammatory cytokines (e.g., IL-6, IL-23). Remdesivir induced the highest number of DEGs (2,010), particularly in chromatin organization (e.g., HIST1H2BK, TOP2A). The top 10 DEGs for each treatment are listed in Table 1, with CD147 (logFC = 3.2, p = 1.2e-5) and TNF-α (logFC = −2.8, p = 4.5e-6) showing the most significant changes.

Pathway enrichment analysis highlighted dysregulated biological processes. KEGG analysis (Figure 7) revealed significant enrichment of downregulated DEGs in the focal adhesion pathway (FDR = 0.003) and lipid metabolism pathway (FDR = 0.012) in cisplatin-treated cells, driven by genes such as FASN (log₂FC = −2.5, p = 1.2e-5) and ACSL4 (log₂FC = −2.0, p = 3.4e-5) (Table 2). In contrast, remdesivir-treated cells showed enrichment of upregulated DEGs in the DNA replication pathway (FDR = 0.001), driven by genes such as HIST1H2BK (log₂FC = 3.0, p = 2.1e-6) and TOP2A (log₂FC = 2.8, p = 4.5e-6) (Table 2). To confirm the directionality of pathway regulation, Gene Set Enrichment Analysis (GSEA) was performed, demonstrating negative enrichment of focal adhesion (NES = −1.8, FDR = 0.002) and lipid metabolism (NES = −1.6, FDR = 0.009) pathways in cisplatin-treated cells, and positive enrichment of DNA replication (NES = 2.1, FDR = 0.001) in remdesivir-treated cells (Figure 8). GO analysis (Figure 9) revealed enrichment of “nucleosome assembly” (p = 1.8e-10) and “chromatin remodeling” (p = 3.4e-9) in Trt3, consistent with histone gene upregulation. Actemra-treated cells showed enrichment of downregulated DEGs in IL-17 signaling (FDR = 0.007), aligning with reduced cytokine levels.

Table 3 summarizes the enrichment of GO Cellular Component terms among significantly upregulated and downregulated genes following treatment. Upregulated genes are predominantly associated with nuclear structures, including the protein–DNA complex, nucleosome, chromatin, and chromosomal regions, suggesting enhanced transcriptional and epigenetic activity. In contrast, downregulated genes are enriched in components related to the plasma membrane, extracellular matrix, and cell junctions, indicating reduced cell adhesion and signaling activity. Statistical significance is based on adjusted p-values (FDR < 0.05).

Functional validation via ELISA confirmed RNA-Seq trends. CD147 levels decreased under Actemra (2,000 μg/μL) compared to control (1.2 vs. 3.8 μg/μL, p < 0.05) (Figure 9), while cisplatin + Actemra combinations showed no significant change. ELISA assays further validated reduced FASN protein levels in cisplatin-treated cells (15.6–500 μg/μL) compared to controls (e.g., 1.2 μg/μL at 500 μg/μL vs. 3.5 μg/μL in control, p < 0.01), confirming suppression of lipid metabolism (Figure 10). IL-17 concentrations peaked in remdesivir + cisplatin (500 μg/μL) but dropped at higher doses (2,000 μg/μL: 5.6 vs. 12.4 μg/μL in NT, p < 0.01). TNF-α levels were highest in untreated cells (18.9 μg/μL) and reduced by remdesivir (2000 μg/μL: 6.3 μg/μL, p < 0.001). SARS-CoV-2 infection increased ACE2 expression (250 μg/μL remdesivir + C19, 4.8 μg/μL vs. NT + C19: 2.1 μg/μL, p < 0.05) (Figure 11).

Table 2 lists key DEGs driving pathway enrichment across treatments, including FASN and ACSL4 for focal adhesion and lipid metabolism in cisplatin-treated cells, and HIST1H2BK and TOP2A for chromatin remodeling in remdesivir-treated cells. These findings provide clarity on the molecular drivers of pathway enrichment and support the integration of transcriptomic and functional data to infer pathway regulation.

This table presents the top 10 DEGs for cisplatin (Trt1), Actemra (Trt2), and remdesivir (Trt3) in SW-480 colorectal cancer cells, treated with 15.6–500 μg/mL cisplatin, 62.5–2,000 μg/mL Actemra, or 62.5–2,000 μg/mL remdesivir for 24–48 h. RNA-Seq data (Illumina NovaSeq 6,000, ≥30 million reads/sample) were analyzed using STAR (v2.7.9a), FeatureCounts (v2.0.1), and DESeq2 (v1.38.0) with |log₂FC| > 1 and adjusted p < 0.05 (Benjamini-Hochberg). Key genes include FASN and ACSL4 (lipid metabolism) for cisplatin, IL-6 and IL-23 (IL-17 signaling) for Actemra, and HIST1H2BK and TOP2A (DNA replication) for remdesivir.

This study provides a comprehensive transcriptomic analysis of SW-480 colorectal cancer (CRC) cells exposed to cisplatin, Actemra (tocilizumab), remdesivir, and SARS-CoV-2 infection, elucidating molecular interactions between anticancer and antiviral therapies under comorbid conditions. To address concerns regarding the interpretation of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, we clarify that these analyses indicate significant overrepresentation of differentially expressed genes (DEGs) in specific pathways, such as focal adhesion, lipid metabolism, and IL-17 signaling, rather than direct upregulation or downregulation of pathway activity. To infer the directionality of pathway regulation, we integrated Gene Set Enrichment Analysis (GSEA) and functional assays, including ELISA, as detailed below.

KEGG analysis revealed significant enrichment of downregulated DEGs in the focal adhesion (FDR = 0.003) and lipid metabolism (FDR = 0.012) pathways in cisplatin-treated cells, driven by genes such as FASN (log₂FC = −2.5, p = 1.2e-5) and ACSL4 (log₂FC = −2.0, p = 3.4e-5) (Table 2). GSEA confirmed negative enrichment of these pathways (NES = −1.8, FDR = 0.002 for focal adhesion; NES = −1.6, FDR = 0.009 for lipid metabolism) (Figure 8), indicating pathway suppression. ELISA assays further validated reduced FASN protein levels (p < 0.01) (Figure 10), consistent with cisplatin-induced metabolic reprogramming that disrupts lipid metabolism and tumor cell proliferation (16). Additionally, upregulated DEGs were enriched in DNA repair pathways (FDR = 0.015) in cisplatin-treated cells, with GSEA showing positive enrichment (NES = 1.9, FDR = 0.008), suggesting enhanced DNA damage response, a potential target for combination therapies (17).

Actemra, an IL-6 receptor antagonist, demonstrated enrichment of downregulated DEGs in IL-17 signaling (FDR = 0.007), driven by genes such as IL-6 (log₂FC = −2.3, p = 2.8e-6) and IL-23 (log₂FC = −2.1, p = 4.0e-6) (Table 2). GSEA confirmed negative enrichment of IL-17 signaling (NES = −1.7, FDR = 0.005), supported by ELISA assays showing reduced IL-17 protein levels (p < 0.01) (Figure 9). These findings suggest Actemra’s potential to mitigate tumor-promoting inflammation in CRC patients with COVID-19-related hyperinflammation (18). Notably, Actemra reduced CD147 protein expression (1.2 vs. 3.8 μg/μL in controls, p < 0.05) (Figure 9), a receptor implicated in both SARS-CoV-2 entry and chemotherapy resistance, indicating a dual therapeutic role (12).

Remdesivir treatment induced the most extensive transcriptomic changes, with 2,010 DEGs significantly enriched in DNA replication pathways (FDR = 0.001), driven by upregulated genes such as HIST1H2BK (log₂FC = 3.0, p = 2.1e-6) and TOP2A (log₂FC = 2.8, p = 4.5e-6) (Table 2). GSEA validated positive enrichment of DNA replication (NES = 2.1, FDR = 0.001) (Figure 8), suggesting epigenetic reprogramming that may influence tumor biology (19). However, remdesivir increased ACE2 expression under SARS-CoV-2 infection (4.8 μg/μL at 250 μg/μL vs. 2.1 μg/μL in untreated infected controls, p < 0.05) (Figure 11), raising concerns about enhanced viral entry potential (3). Conversely, remdesivir reduced TNF-α levels (6.3 μg/μL at 2000 μg/μL vs. 18.9 μg/μL in controls, p < 0.001), aligning with its immunomodulatory effects (20).

The dual role of CD147 was particularly evident, with remdesivir upregulating its transcript levels (log₂FC = 3.2, p = 1.2e-5) while Actemra suppressed its protein expression (8). This discrepancy underscores the importance of multi-level validation (RNA and protein) for therapeutic targets. Similarly, the dose-dependent increase in IL-17 under remdesivir/cisplatin combinations (12.4 μg/μL at 500 μg/μL vs. 5.6 μg/μL at 2,000 μg/μL, p < 0.01) highlights CD147’s role in inflammation and viral entry, corroborating previous findings (11). The regulation of TGF-β signaling, a shared pathway in fibrosis and tumor progression, under these treatments warrants further exploration due to its role in immune evasion and tumor microenvironment modulation (15).

While GO and KEGG analyses effectively identify pathways enriched with DEGs, they do not directly confirm pathway activity, a limitation we addressed using GSEA and functional assays to infer directionality, as evidenced by NES values and ELISA results. This study utilized only the SW480 cell line, which may not represent the biological diversity and drug responses across colorectal cancer subtypes. Future studies incorporating additional cell lines, such as HCT116 and HT-29, and 3D models like spheroids, are recommended to validate the findings. We plan to include these additional colorectal cancer cell lines in future investigations to assess a broader range of drug responses and gene expression profiles, including ACE2 and TMPRSS2, enhancing the generalizability of the results and supporting the hypotheses presented. However, the in vitro design using SW-480 monocultures does not fully capture the tumor microenvironment or host immune responses. Additionally, the absence of proteomic and metabolomic profiling may overlook post-transcriptional regulation. Future studies using co-culture models, patient-derived organoids, or in vivo systems are essential to validate these findings and assess therapeutic safety and efficacy.

This study highlights CD147, IL-17, and ACE2 as critical molecular nodes linking inflammation, viral susceptibility, and drug resistance in CRC. By integrating transcriptomic profiling with GSEA and functional validation, we provide a framework for developing personalized therapeutic strategies that combine antivirals (e.g., remdesivir) with cytokine inhibitors (e.g., Actemra) to enhance chemosensitivity and mitigate hyperinflammation in CRC patients with COVID-19. These findings support further exploration of combinatory approaches targeting DNA repair, lipid metabolism, and TGF-β signaling for improved clinical outcomes (21).

We believe these revisions, by acknowledging the current limitation of using a single cell line and proposing future studies with diverse cell lines, address the reviewer’s concern and lay the groundwork for more comprehensive research. We welcome any specific suggestions from the reviewer regarding particular cell lines or additional methodologies.

This study demonstrates that RNA-Seq is a powerful tool for dissecting the interplay between CRC therapeutics and SARS-CoV-2 infection. The data underscore CD147 and IL-17 as critical nodes linking viral entry, inflammation, and drug resistance, while pathway-level insights (e.g., lipid metabolism, chromatin remodeling) highlight vulnerabilities for targeted therapy. These findings advocate for personalized strategies that combine antivirals (e.g., remdesivir) with cytokine inhibitors (e.g., actemra) to mitigate hyperinflammation and enhance chemosensitivity in CRC patients with COVID-19. Validated by ELISA (e.g., CD147 trends in Figures 3–9) and MTT assays, these results provide a framework for optimizing dosing regimens and prioritizing pathways like TGF-β signaling [25]for future clinical trials.