Application of RNA processing factors for predicting clinical outcomes in colon cancer

Liujin Hou; Fan Huang; Guanghou Chen; Jian Qiu; Yuyao Liu; Hongchuan Zhao; Zhengguang Wang

doi:10.3389/fgene.2022.979001

Application of RNA processing factors for predicting clinical outcomes in colon cancer

Front Genet. 2022 Sep 23:13:979001. doi: 10.3389/fgene.2022.979001. eCollection 2022.

Authors

Liujin Hou¹, Fan Huang¹, Guanghou Chen¹, Jian Qiu¹, Yuyao Liu¹, Hongchuan Zhao¹, Zhengguang Wang¹

Affiliation

¹ Department of General Surgery, The First affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.

Abstract

Background: Colon cancer is the fifth most common cause of cancer-related death worldwide, and despite significant advances in related treatment, the prognosis of colon cancer patients remains poor. Objective: This study performs systematic bioinformatics analysis of prognostic-associated RNA processing factor genes in colon cancer using the Cancer Related Genome Atlas database to explore their role in colon carcinogenesis and prognosis and excavate potential therapeutic targets. Methods: Data sets of colon cancer patients were obtained from GEO and TCGA databases. Univariate cox analysis was performed on the GSE39582 training set to identify prognosis-associated RNA processing factor genes and constructed a muticox model. The predictive performance of the model was validated by Correlation curve analysis. Similar results were obtained for the test dataset. Functional analyses were performed to explore the underlying mechanisms of colon carcinogenesis and prognosis. Results: A constructed muticox model consisting of βi and prognosis-related RNA processing factor gene expression levels (Expi) was established to evaluate the risk score of each patient. The subgroup with a higher risk score had lower overall survival (OS), higher risk factor, and mortality. We found that the risk score, age, gender, and TNM Stage were strongly associated with OS, and the 13-gene signature as an independent prognostic factor for colon cancer. The model has good accuracy in predicting patient survival and is superior to traditional pathological staging. Conclusion: This study proposes 13 RNA processing factor genes as a prognostic factor for colon cancer patients, which can independently predict the clinical outcome by risk score. The gene expression profile in this model is closely related to the immune status and prognosis of colon cancer patients. The interaction of the 13 RNA processing factor genes with the immune system during colon carcinogenesis provides new ideas for the molecular mechanisms and targeted therapies for colon cancer.

Keywords: GEO database; RNA processing factors; TCGA data base; colon cancer; prediction model; prognosis.