The exploration of mitochondrial-related features helps to reveal the prognosis and immunotherapy methods of colorectal cancer.

BACKGROUND

Cancer cell survival, proliferation, and metabolism are all intertwined with mitochondria. However, a complete description of how the features of mitochondria relate to the tumor microenvironment (TME) and immunological landscape of colorectal cancer (CRC) has yet to be made. We performed subgroup analysis on CRC patient data obtained from the databases using non-negative matrix factorization (NMF) clustering. Construct a prognostic model using the mitochondrial-related gene (MRG) risk score, and then compare it to other models for accuracy. Comprehensive analyses of the risk score, in conjunction with the TME and immune landscape, were performed, and the relationship between the model and different types of cell death, radiation and chemotherapy, and drug resistance was investigated. Results from immunohistochemistry and single-cell sequencing were utilized to verify the model genes, and a drug sensitivity analysis was conducted to evaluate possible therapeutic medicines. The pan-cancer analysis is utilized to further investigate the role of genes in a wider range of malignancies.

METHODS AND RESULTS

We found that CRC patients based on MRG were divided into two groups with significant differences in survival outcomes and TME between groups. The predictive power of the risk score was further shown by building a prognostic model and testing it extensively in both internal and external cohorts. Multiple immune therapeutic responses and the expression of immunological checkpoints demonstrate that the risk score is connected to immunotherapy success. The correlation analysis of the risk score provide more ideas and guidance for prognostic models in clinical treatment.

CONCLUSION

The TME, immune cell infiltration, and responsiveness to immunotherapy in CRC were all thoroughly evaluated on the basis of MRG features. The comparative validation of multiple queues and models combined with clinical data ensures the effectiveness and clinical practicality of MRG features. Our studies help clinicians create individualized treatment programs for individuals with cancer.