4.4 Article

RNA Binding Protein-Based Model for Prognostic Prediction of Colorectal Cancer

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SAGE PUBLICATIONS INC
DOI: 10.1177/15330338211019504

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RNA binding protein; colorectal cancer; prognostic signature; survival analysis

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The study focused on the association between RNA-binding proteins (RBPs) disorder and colorectal cancer, identifying an 8-RBP signature as a potential prognostic biomarker for CRC patients. High-risk score based on this signature was significantly correlated with poor prognosis. The model showed accurate and effective prognostic value in validation dataset, illustrating its potential as an independent prognostic indicator for CRC.
Background: Colorectal cancer (CRC) is a kind of gastrointestinal tumor with serious high morbidity and mortality. Several reports have implicated the disorder of RNA-binding proteins (RBPs) in plenty of tumors, associating it to tumorigenesis and disease progression. The study is intended to construct novel prognostic biomarkers associated with CRC patients. Methods: Data of gene expression was acquired from the TCGA database, prognosis-related genes were selected. Besides, we analyzed GO and KEGG pathways. Univariate and multivariate Cox analyses were performed to generate a prognostic-related gene signature, which was evaluated by the Kaplan-Meier (K-M) and the Receiver Operating Characteristic (ROC) curve. The independent prognostic factor was established by survival analysis. GSE38832 dataset was used to validate the signature. Finally, expression of 8 genes was further confirmed by qRT-PCR in SW480 and SW620 cell lines. Results: We obtained 224 differentially expressed RBPS in total, of which 78 were downregulated and 146 were upregulated. Univariate COX analysis was conducted in the TCGA cohort to select 13 RBPs with P < 0.005, stepwise multivariate COX regression analysis was used to construct an 8-RBP signature (TERT, PPARGC1A, BRCA1, CELF4, TDRD7, LUZP4, PNLDC1, ZC3H12C). Based on the model, systematic analysis illustrated that a high risk score was obviously connected to a poor prognosis. The prognostic value of the risk score was validated in GSE38832 dataset, indicating that the risk model was accurate and effective. The prognostic signature-based risk score was identified as an independent prognostic indicator for CRC. The expression results of qRT-PCR were consistent with the results of differential expression analysis. Conclusions: The eight-RBP signature can predict the survival of CRC patients and potentially act as CRC prognostic biomarker.

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