{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:32:04Z","timestamp":1760711524745,"version":"3.41.2"},"reference-count":41,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Colorectal adenocarcinoma (COAD) is a prevalent malignant tumor associated with a high mortality rate. Within the tumor microenvironment, CD8<jats:sup>+<\/jats:sup> T cells play a pivotal role in the anti-tumor immune response within the human body. Fibrosis directly and indirectly affects the therapeutic response of tumor immunotherapy. However, the significance of regulatory genes associated with tumor-associated fibrosis and CD8<jats:sup>+<\/jats:sup> T cell infiltration remains uncertain. Therefore, it is imperative to identify biomarkers with prognostic value and elucidate the precise role of CD8<jats:sup>+<\/jats:sup> T cells and tumor-associated fibrosis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We performed a single-cell transcriptome analysis of COAD samples from the GEO database. To evaluate immune infiltration in COAD samples, we utilized CIBERSORT and ESTIMATE. Furthermore, we analyzed the correlation between CD8<jats:sup>+<\/jats:sup> T cells and immune infiltration. To analyze COAD expression\u2019s quantitative immune cell composition data, we conducted a Weighted Gene Correlation Network Analysis and utilized a deconvolution algorithm. The data for these analyses were obtained from the GEO database. We utilized univariate Cox regression and LASSO analysis to create a prognostic model. The predictive model was assessed through Kaplan-Meier analysis, and a survival prediction nomogram was created. Additionally, we analyzed the correlation between the prognostic model and chemotherapy drug sensitivity. To estimate the expression of hub genes, we employed immunohistochemistry, real-time PCR, and western blot techniques.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Single-cell transcriptome analysis has indicated a higher prevalence of CD8<jats:sup>+<\/jats:sup> T cells in COAD tumor samples. The connection between COAD and CD8<jats:sup>+<\/jats:sup> T cells was further confirmed by WGCNA and deconvolution analysis using the GEO database. The Protein-Protein Interaction network analysis revealed three hub genes: <jats:italic>LARS2<\/jats:italic>, <jats:italic>SEZ6L2<\/jats:italic>, and <jats:italic>SOX7<\/jats:italic>. A predictive model was subsequently created using LASSO and univariate COX regression, which included these three genes. Two of these hub genes (<jats:italic>LARS2<\/jats:italic> and <jats:italic>SEZ6L2<\/jats:italic>) were found to be upregulated in COAD cell lines and tissues, while <jats:italic>SOX7<\/jats:italic> was observed to be downregulated. The prognostic model demonstrated a significant association with CD8<jats:sup>+<\/jats:sup> T cells, suggesting that these genes could serve as potential biomarkers and targets for gene therapy in treating COAD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>This study has identified three key genes associated with CD8<jats:sup>+<\/jats:sup> T cells and the prognosis of COAD, providing new prognostic biomarkers for diagnosing and treating COAD.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fimmu.2025.1496640","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T05:56:32Z","timestamp":1743400592000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Discovering biomarkers associated with infiltration of CD8+ T cells and tumor-associated fibrosis in colon adenocarcinoma using single-cell RNA sequencing and gene co-expression 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