Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer

Abstract

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

A risk model of gene signatures for predicting platinum response and survival in ovarian cancer

“…Specifically, PNLDC1, a PARN-like 3′-to-5′ exonuclease located at the membrane of the mitochondria in a mouse, is critical to the processing of piRNAs and PNLDC1 disrupted in mice would lead to azoospermia and male infertility ultimately [38,39]. Former studies pointed that PNLDC1 was related to survival in CRC patients [40], and PNLDC1 expressed higher in normal colorectal tissues than in cancer tissues [41]. VSTM2L could induce adverse survival outcomes in rectal cancer [42].…”

Whole genome sequencing reveals the independent clonal origin of multifocal ileal neuroendocrine tumors

“…Both the deletion and the breakpoint affected CELF4 , which encodes an RNA-binding protein (RBP) implicated in the regulation of pre-mRNA alternative splicing [ 41 ]. The protein is used as a part of RBP-based models to predict the survival of colorectal cancer (CRC) patients [ 42 ]. Also, a rare intronic germline variant in CELF4 has been associated with CRC risk [ 43 ].…”

Exonucleases: Degrading DNA to Deal with Genome Damage, Cell Death, Inflammation and Cancer

“…High levels of gene expression, together with other genes, can predict recurrence [ 40 ]. Similarly, enhanced expression of AEN was used as a prognostic marker in an RNA-binding protein signature for colorectal cancer [ 41 ]. Given its response to genotoxins and its links to p53 and apoptosis, it is rather surprising that there is little existing knowledge on the role of AEN in cancer.…”