Integrated Analysis of the Functions and Prognostic Values of RNA Binding Proteins in Lung Squamous Cell Carcinoma

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide. Dysregulation of RNA binding proteins (RBPs) has been found in a variety of cancers and is related to oncogenesis and progression. However, the functions of RBPs in lung squamous cell carcinoma (LUSC) remain unclear. In this study, we obtained gene expression data and corresponding clinical information for LUSC from The Cancer Genome Atlas (TCGA) database, identified aberrantly expressed RBPs between tumors and normal tissue, and conducted a series of bioinformatics analyses to explore the expression and prognostic value of these RBPs. A total of 300 aberrantly expressed RBPs were obtained, comprising 59 downregulated and 241 upregulated RBPs. Functional enrichment analysis indicated that the differentially expressed RBPs were mainly associated with mRNA metabolic processes, RNA processing, RNA modification, regulation of translation, the TGF-beta signaling pathway, and the Toll-like receptor signaling pathway. Nine RBP genes (A1CF, EIF2B5, LSM1, LSM7, MBNL2, RSRC1, TRMU, TTF2, and ZCCHC5) were identified as prognosis-associated hub genes by univariate, least absolute shrinkage and selection operator (LASSO), Kaplan-Meier survival, and multivariate Cox regression analyses, and were used to construct the prognostic model. Further analysis demonstrated that high risk scores for patients were significantly related to poor overall survival according to the model. The area under the time-dependent receiver operator characteristic curve of the prognostic model was 0.712 at 3 years and 0.696 at 5 years. We also developed a nomogram based on nine RBP genes, with internal validation in the TCGA cohort, which showed a favorable predictive efficacy for prognosis in LUSC. Our results provide novel insights into the pathogenesis of LUSC. The nine-RBP gene signature showed predictive value for LUSC prognosis, with potential applications in clinical decision-making and individualized treatment.

Keywords: RNA-binding proteins; bioinformatics; lung squamous cell carcinoma; prognostic signature; survival analysis.

FIGURE 1

Framework for analyzing the RBPs in LUSC.

FIGURE 2

PPI network and module analysis. (A) PPI network for RBPs; (B) critical module 1 in PPI network; (C) critical module 2 in PPI network.

FIGURE 3

Validation of protein expression of hub genes in normal lung tissue and LUSC using the HPA database. (A) MRPL15; (B) MRPL13; (C) MRPL4; (D) MRPL3; (E) MRPL24; (F) MRPS12; (G) MRPL11; (H) MRPL21; (I) MRPL36; (J) MRPL47.

FIGURE 4

ROC analysis of 10 hub RBPs based on the TCGA dataset. (A) MRPL15; (B) MRPL13; (C) MRPL4; (D) MRPL3; (E) MRPL24; (F) MRPS12; (G) MRPL11; (H) MRPL21; (I) MRPL36; (J) MRPL47.

FIGURE 5

Hub RBP expression and alteration analysis in LUSC. (A) Mutation frequency of hub genes; (B) mutation frequency of each gene; (C) interaction network.

FIGURE 6

Prognostic value of key nine RBPs in LUSC. (A) LSM1; (B) MBNL2; (C) A1CF; (D) EIF2B5; (E) TTF2; (F) TRMU; (G) LSM7; (H) ZCCHC5; (I) RSRC1.

FIGURE 7

Risk score analysis of nine-gene prognostic model in TCGA LUSC cohort. (A) Survival analysis according to risk score; (B) ROC analysis; (C) heat map and survival status of patients.

FIGURE 8

Risk score analysis of nine-gene prognostic model in GSE73403 LUSC cohort. (A) Survival analysis according to risk score; (B) ROC analysis; (C) heat map and survival status of patients.

FIGURE 9

Nomogram and calibration plots of nine RBPs. (A) Nomogram to predict 3- and 5-year OS in the TCGA cohort. (B,C) Calibration plots of the nomogram to predict OS at 3 and 5 years.