The predictive value of RNA binding proteins in colon adenocarcinoma

Luan, Lipeng; Lu, Feng; Wang, Xiaochuan; Wang, Yunliang; Wang, Wei; Yang, Yong; Chen, Guoliang; Yao, Heng‐Chen; Shi, Xinyu; Yuan, Zihan; Zhang, Haitao; He, Songbing

doi:10.21037/jgo-21-318

Cited by 2 publications

(1 citation statement)

References 34 publications

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“…According to a previous study, RBPs are abnormally expressed in colorectal cancer, affecting the translation of mRNAs into proteins and leading to carcinogenesis ( 6 ). The prognostic model based on RBPs also shows good predictive efficacy in colorectal cancer ( 7 , 8 ). Several studies have established prognostic models for ESCA, such as m6A methylation-associated genes-based model, immune-related gene prognostic model and etc.…”

Section: Introductionmentioning

confidence: 96%

Development and validation of a prognostic model based on RNA binding proteins in patients with esophageal cancer

Du,

Li,

Pang

et al. 2023

J Thorac Dis

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Background RNA-binding proteins (RBPs) play a crucial role in regulating RNA turnover and are associated with cancer development. However, little is known about the role of RBPs in esophageal cancer (ESCA). The present study focuses on the association between RBP gene expression and survival in ESCA, addressing the clinical relevance of an RBPs-based prediction model for prognosis. Methods RNA-sequencing data and clinical information of patients with ESCA were obtained from The Cancer Genome Atlas (TCGA) database. We identified differentially expressed genes in ESCA and intersected them with RBP-encoding genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed with the identified differentially expressed RBPs. Then, a protein-protein interaction (PPI) network was constructed through the STRING database to determine the hub RBPs. Univariate Cox regression analysis and multivariate Cox regression analysis were applied to construct a novel prognostic model based on RBPs. Based on the R package “Caret”, we divided patients into the training set and validation set. The efficacy of the prognostic model was evaluated by the area under the receiver operating characteristic (ROC) curve. A nomogram was developed for the prediction of patient survival outcomes. Results A total of 158 ESCA patients from the TCGA database were included in our analysis. We screened out five prognostic RBPs (CLK1, CIRBP, MRPL13, TNRC6A, and TYW3) through univariate and multivariate Cox regression analysis. CLK1, CIRBP, TNRC6A and TYW3 were downregulated in tumor samples, while MRPL13 was upregulated. A prognostic model constructed with these five RBPs in the training data set accurately stratified ESCA patients into high- and low-risk groups. When the same prognostic model was applied to the test data set and entire cohort, the 5-RBP signature remained an independent prognostic factor in multivariate analysis. The areas under the time-dependent ROC curve of the prognostic model for predicting one-year survival in the training data set, test data set, and entire cohort were 0.789, 0.753, and 0.764, respectively, confirming that this model is a good prognostic model. The nomogram based on the five RBPs and clinical variables could improve individualized outcome predictions and highlight the importance of RBPs in the outcomes of patients with ESCA. Conclusions Our study provides a potential prognostic model for predicting the prognosis of ESCA patients. The prognostic nomogram could improve individualized outcome predictions for patients with ESCA, therefore providing novel insights into future diagnosis and treatment.

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Section: Introductionmentioning

confidence: 96%

Development and validation of a prognostic model based on RNA binding proteins in patients with esophageal cancer

Du,

Li,

Pang

et al. 2023

J Thorac Dis

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Comprehensive Characterization of RNA-Binding Proteins in Colon Adenocarcinoma Identifies a Novel Prognostic Signature for Predicting Clinical Outcomes and Immunotherapy Responses Based on Machine Learning

Miao

Yuan

Wang

et al. 2023

CCHTS

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Background: RNA-binding proteins (RBPs) are crucial factors that function in the post-transcriptional modification process and are significant in cancer. Objective: his research aimed for a multigene signature to predict the prognosis and immunotherapy response of patients with colon adenocarcinoma (COAD) based on the expression profile of RNA-binding proteins (RBPs). Methods: COAD samples retrieved from the TCGA and GEO datasets were utilized for a training dataset and a validation dataset. Totally, 14 shared RBP genes with prognostic significance were identified. Non-negative matrix factorization clusters defined by these RBPs could stratify COAD patients into two molecular subtypes. Cox regression analysis and identification of 8-gene signature acted to categorize COAD patients into high- and low-risk populations with significantly different prognosis and immunotherapy responses. Results: Our prediction signature was superior to another five well-established prediction models. A nomogram was generated to quantificationally predict the overall survival (OS) rate, which was validated by calibration curves. Our findings also indicated that high-risk populations possessed an enhanced immune evasion capacity and low-risk population might be beneficial for immunotherapy, especially for the joint combination of PD-1 and CTLA4 immunosuppressants. DHX15 and LARS2 were detected with significantly different expression in both datasets, which were further confirmed by qRT-PCR and immunohistochemical staining Conclusion: Our observations supported an eight-RBp-related signature that could be applied for survival prediction and immunotherapy response of patients with COAD.

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The predictive value of RNA binding proteins in colon adenocarcinoma

Cited by 2 publications

References 34 publications

Development and validation of a prognostic model based on RNA binding proteins in patients with esophageal cancer

Development and validation of a prognostic model based on RNA binding proteins in patients with esophageal cancer

Comprehensive Characterization of RNA-Binding Proteins in Colon Adenocarcinoma Identifies a Novel Prognostic Signature for Predicting Clinical Outcomes and Immunotherapy Responses Based on Machine Learning

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