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Background  Multiple genes were previously identified to be associated with cervical cancer; however, the genetic architecture of cervical cancer remains unknown and many potential causal genes are yet to be discovered.    Methods  To explore potential causal genes related to cervical cancer, a two-stage causal inference approach was proposed within the framework of Mendelian randomization, where the gene expression was treated as exposure, with methylations located within the promoter regions of genes serving as instrumental variables. Five prediction models were first utilized to characterize the relationship between the expression and methylations for each gene; then, the methylation-regulated gene expression (MReX) was obtained and the association was evaluated via Cox mixed-effect model based on MReX. We further implemented the aggregated Cauchy association test (ACAT) combination to take advantage of respective strengths of these prediction models while accounting for dependency among the  p- values.    Results  A total of 14 potential causal genes were discovered to be associated with the survival risk of cervical cancer in TCGA when the five prediction models were separately employed. The total number of potential causal genes was brought to 23 when conducting ACAT. Some of the newly discovered genes may be novel (e.g.,  YJEFN3 ,  SPATA5L1 ,  IMMP1L ,  C5orf55 ,  PPIP5K2 ,  ZNF330 ,  CRYZL1 ,  PPM1A ,  ESCO2 ,  ZNF605 ,  ZNF225 ,  ZNF266 ,  FICD , and  OSTC ). Functional analyses showed that these genes were enriched in tumor-associated pathways. Additionally, four genes (i.e.,  COL6A1 ,  SYDE1 ,  ESCO2 , and  GIPC1 ) were differentially expressed between tumor and normal tissues.    Conclusion  Our study discovered promising candidate genes that were causally associated with the survival risk of cervical cancer and thus provided new insights into the genetic etiology of cervical cancer.

frontiers in genetics

Leveraging Methylation Alterations to Discover Potential Causal Genes Associated With the Survival Risk of Cervical Cancer in TCGA Through a Two-Stage Inference Approach