Kaili Liao ¹ Jinting Cheng ² Minqi Zhu ² Zijun Gao ³ Bing Sun ⁴ Yihui Qian ³ Bingying Lin ⁴ Jingyan Zhang ⁴ Tingyi Qian ⁵ Yixin Jiang ⁴ Lei Guo ¹ Xiaozhong Wang ^1*

• ¹ Second Affiliated Hospital of Nanchang University, Nanchang, China
• ² School of Public Health, Jiangxi Medical College,Nanchang University, Nanchang, Jiangxi Province, China
• ³ Second Clinical Medical College, Nanchang University, Nanchang, Jiangxi Province, China
• ⁴ Queen Mary college, Nanchang University, Nanchang, Jiangxi Province, China
• ⁵ Nanchang University, Nanchang, Jiangxi Province, China

OBJECTIVE: To build a new prognostic risk assessment model based on immune cell co-expression networks for predicting overall survival and evaluating the efficacy of immunotherapy for colon cancer patients.The Cancer Genome Atlas (TCGA) database was used to obtain mRNA expression profiling data, clinical information, and somatic mutation data from colorectal cancer patients. The degree of tumor immune cell infiltration of the samples was analyzed using the CIBERSORT algorithm. Co-expression of immune-related genes was analyzed using weighted correlation network analysis (WGCNA) and gene modules were identified. Prognosis-related genes were screened and models were constructed using LASSO-Cox analysis. The models were validated by survival analysis. The prognostic potential of the models was quantitatively assessed using Cox regression analysis and the development of column line plots. Immunotherapy sensitivity analysis was performed using CIBERSORT and TIMER algorithms. Gene biofunction analysis was performed using Gene set enrichment analysis (GSEA) and Gene set variation analysis (GSVA).And the chemotherapeutic response to different drugs was assessed.RESULTS: We established a novel prognostic model utilizing the WGCNA method,which demonstrated robust predictive accuracy for patient survival. The high-risk subgroup in our model exhibited elevated immune cell infiltration coupled with a higher tumor mutation burden, but the difference in response to immunotherapy was not significant compared to the low-risk group. Furthermore, we identified distinct chemotherapy responses to 39 drugs between these risk subgroups.CONCLUSION: This study revealed a significant correlation between high levels of immune infiltration and unfavorable prognosis in patients with colon cancer. Furthermore, an accurate prognostic risk prediction model based on the co-expression of relevant genes by immune cells was developed, enabling precise prediction of survival of colon cancer patients. These findings offer valuable insights for accurate prognostication and comprehensive management of individuals diagnosed with colon cancer.