Identification of an E2Fs-based gene signature for predicting prognosis and therapeutic response in colorectal cancer

Abstract E2F family genes are common transcription factors, abnormal in several malignant tumors. However, their complex involvement in colorectal cancer, particularly in prognosis, immune infiltration, and mutational landscape, remains unclear. We conducted a study using gene expression data from the TCGA and GEO datasets to examine the abnormal expression of E2Fs in colorectal cancer. And we performed consensus clustering and differential gene expression analyses to identify E2Fs-related genes. Then, we used Lasso regression and multivariate Cox regression to create a prognostic risk model for colorectal cancer. We analyzed the differences between the E2Fs-based gene risk and various clinical characteristics, gene mutations, immune cell infiltration, immunotherapy responses, and drug sensitivity using clinicopathological data, single-cell RNA sequences, multiple immune algorithms. Finally, we have developed a prognostic risk model that includes FMO5, NDUFA11, LIPG, FIGNL1, MOGAT2, and GZMB. We observed significant differences in clinical characteristics, immune cell infiltration, gene mutation landscapes, immunotherapy responses, and drug sensitivity between the high-risk and low-risk groups. The novel E2Fs-based gene risk model shows significant potential for contributing to the evaluation of prognosis and predicting immunotherapeutic outcomes for colorectal cancer patients.