Identification of a new RNA-binding proteins-based signature for prognostic prediction in gastric cancer

Abstract

Gastric cancer (GC) is one of the most common cancers with high incidence and mortality worldwide. Recently, RNA-binding proteins (RBPs) have drawn more and more attention for its role in cancer pathophysiology. However, the function and clinical implication of RBPs in GC have not been fully elucidated. RNA sequencing data along with the corresponding clinical information of GC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA-binding proteins (DERBPs) between tumor and normal tissues were identified by “limma” package. Functional enrichment analysis and the protein-protein interaction (PPI) network were harnessed to explore the function and interaction of DERBPs. Next, univariate and multiple Cox regression were applied to screen prognosis-related hub RBPs and to construct a signature for GC. Meanwhile, a nomogram was built on the basis of the independent factors. A total of 296 DERBPs were found, and most of them mainly related to post-transcriptional regulation of RNA and ribonucleoprotein. A PPI network of DERBPs was constructed, consisting of 262 nodes and 2567 edges. A prognostic signature was built depending on 7 prognosis-related hub RBPs that could divide GC patients into high-risk and low-risk groups. Survival analysis showed that high-risk group had a worse prognosis compared with the low-risk group and the time-dependent receiver operating characteristic (ROC) curves suggested that signature existed moderate predictive capacities of survival for GC patients. Similar results were obtained from another independent set GSE62254, confirming the robustness of signature. Besides, the genetic variation and immune heterogeneity differences were identified between the high-risk and low-risk groups by bioinformatics methods. These findings would provide evidence of the effect of RBPs and offer a novel potential biomarker in prognostic prediction and clinical decision for GC.

Identification of Signature Genes and Construction of an Artificial Neural Network Model of Prostate Cancer

“…To compare genes differentially expressed in PCa and NP groups, we performed differential expression analysis between groups by statistical tests using the R packages limma, pheatmap, and ggplot [ 15 , 16 ] based on the gene expression matrix of the training set. Results were presented in a heatmap and a volcano plot.…”

The Musashi RNA-binding proteins in female cancers: insights on molecular mechanisms and therapeutic relevance

“…Notably, these proteins are often dysregulated during carcinogenesis [ 10 ]. Based on their trigger function for the disease, RBP can serve as prognostic markers as well as potential therapeutic targets [ 11 – 15 ].…”