Development and Validation of a Novel Gene Signature for Predicting the Prognosis by Identifying m5C Modification Subtypes of Cervical Cancer - Yu, Jing; Liang, Lei-Lei; Liu, Jing; Liu, Ting-Ting; Li, Jian; Xiu, Lin; Zeng, Jia; Wang, Tian-Tian; Wang, Di; Liang, Li-Jun; Xie, Da-Wei; Chen, Ding-Xiong; et al. (Frontiers in Genetics, 2021)
Background: 5-Methylcytidine (m5C) is the most common RNA modification and plays critical roles in several biological functions. Our purpose is to perform bioinformatics analysis of m5C RNA modification combined with immune infiltration to predict the prognosis of cervical cancer (CC). Methods: We extracted 13 m5C regulatory factors from the TCGA database and determined the subtype of m5C modification by a nonnegative matrix clustering algorithm. MCPcounter was used to evaluate the scores of 10 immune cells and the scores of 28 immune cells were evaluate by the ssGSEA method and then to compare the differences between the molecular subtypes. We also analyzed the expression of 13 m5c-related genes in the two subtypes. According to the differentially expressed genes in the two subtypes and survival data, we carried out univariate Cox and lasso Cox regression analyses to establish a 4-gene signature related to m5C modification. Then the patients were divided into high-risk and low-risk groups, and Kaplan-Meier survival analysis was performed to evaluate the prognostic value of the risk score. A nomogram was established to predict the individual survival rate by combining clinicopathological features and prognostic gene features. Finally, the expression and function of the signature genes were explored by Western blotting, qRT-PCR, immunohistochemistry and proliferation, colony formation, migration and invasion assays. Results: Based on the consistent clustering of m5C-modified genes, CC was divided into two subtypes (C1 and C2). There was a significant difference in immune infiltration between the two subtypes, and the C1 subtype had a worse prognosis. Based on 714 differentially expressed genes (DEGs), we constructed a 4-gene signature related to m5C modification. We verified the prognostic value of the signature in the training set and 3 validation sets, and then constructed a nomogram for clinical risk prediction. The expression levels of FNDC3A, VEGFA, OPN3 and CPE were all high in CC tissues. Downregulation of FNDC3A, VEGFA or CPE expression suppressed the proliferation, migration and invasion of SiHa cells. Conclusions: Two m5C modification subtypes of CC were identified and then a 4-gene signature was established, which provide new feasible methods for clinical risk assessment and targeted therapies for CC.