Dataset Information

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma.

ABSTRACT: Lung cancer is the top cause of carcinoma-associated deaths worldwide. RNA binding proteins (RBPs) dysregulation has been reported in various malignant tumors, and that dysregulation is closely associated with tumorigenesis and tumor progression. However, little is known about the roles of RBPs in lung adenocarcinoma (LUAD). In this study, we downloaded the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and cancer tissues. We then performed an integrative analysis to explore the expression and prognostic significance of these RBPs. A total of 164 differently expressed RBPs were identified, including 40 down-regulated and 124 up-regulated RBPs. Pathway and Gene ontology (GO) analysis indicated that the differently expressed RBPs were mainly related to RNA processing, RNA metabolic process, RNA degradation, RNA transport, splicing, localization, regulation of translation, RNA binding, TGF-beta signaling pathway, mRNA surveillance pathway, and aminoacyl-tRNA biosynthesis. Survival analysis revealed that the high expression of BOP1 or GNL3 or WDR12 or DCAF13 or IGF2BP3 or IGF2BP1 were associated with poor overall survival (OS). Conversely, overexpression of KHDRBS2/SMAD predicted high OS in these patients. ROC curve analysis showed that the eight hub genes with a better diagnostic accuracy to distinguish lung adenocarcinoma. The results provided novel insights into the pathogenesis of LUAD and the development of treatment targets and prognostic molecular markers.

SUBMITTER: Li W

PROVIDER: S-EPMC7007976 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma.

Li Wei W Li Na N Gao Lina L You Chongge C

PeerJ 20200206

Lung cancer is the top cause of carcinoma-associated deaths worldwide. RNA binding proteins (RBPs) dysregulation has been reported in various malignant tumors, and that dysregulation is closely associated with tumorigenesis and tumor progression. However, little is known about the roles of RBPs in lung adenocarcinoma (LUAD). In this study, we downloaded the RNA sequencing data of LUAD from The Cancer Genome Atlas (TCGA) database and determined the differently expressed RBPs between normal and ca ...[more]

PMID: 32071816

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Project description:Background and Purpose: Breast cancer is one of the leading causes of death among women. RNA binding proteins (RBPs) play a vital role in the progression of many cancers. Functional investigation of RBPs may contribute to elucidating the mechanisms underlying tumor initiation, progression, and invasion, therefore providing novel insights into future diagnosis, treatment, and prognosis. Methods: We downloaded RNA sequencing data from the cancer genome atlas (TCGA) by UCSC Xena and identified relevant RBPs through an integrated bioinformatics analysis. We then analyzed biological processes of differentially expressed genes (DEGs) by DAVID, and established their interaction networks and performed pathway analysis through the STRING database to uncover potential biological effects of these RBPs. We also explored the relationship between these RBPs and the prognosis of breast cancer patients. Results: In the present study, we obtained 1092 breast tumor samples and 113 normal controls. After data analysis, we identified 90 upregulated and 115 downregulated RBPs in breast cancer. GO and KEGG pathway analysis indicated that these significantly changed genes were mainly involved in RNA processing, splicing, localization and RNA silencing, DNA transposition regulation and methylation, alkylation, mitochondrial gene expression, and transcription regulation. In addition, some RBPs were related to histone H3K27 methylation, estrogen response, inflammatory mediators, and translation regulation. Our study also identified five RBPs associated with breast cancer prognosis. Survival analysis found that overexpression of DCAF13, EZR, and MRPL13 showed worse survival, but overexpression of APOBEC3C and EIF4E3 showed better survival. Conclusion: In conclusion, we identified key RBPs of breast cancer through comprehensive bioinformatics analysis. These RBPs were involved in a variety of biological and molecular pathways in breast cancer. Furthermore, we identified five RBPs as a potential prognostic biomarker of breast cancer. Our study provided novel insights to understand breast cancer at a molecular level.

Project description: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.

Project description:Increasing evidence has shown competitive endogenous RNAs (ceRNAs) play key roles in numerous cancers. Nevertheless, the ceRNA network that can predict the prognosis of lung adenocarcinoma (LUAD) is still lacking. The aim of the present study was to identify the prognostic value of key ceRNAs in lung tumorigenesis. Differentially expressed (DE) RNAs were identified between LUAD and adjacent normal samples by limma package in R using The Cancer Genome Atlas database (TCGA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway function enrichment analysis was performed using the clusterProfiler package in R. Subsequently, the LUAD ceRNA network was established in three steps based on ceRNA hypothesis. Hub RNAs were identified using degree analysis methods based on Cytoscape plugin cytoHubba. Multivariate Cox regression analysis was implemented to calculate the risk score using the candidate ceRNAs and overall survival information. The survival differences between the high-risk and low-risk ceRNA groups were determined by the Kaplan-Meier and log-rank test using survival and survminer package in R. A total of 2,989 mRNAs, 185 lncRNAs, and 153 miRNAs were identified. GO and KEGG pathway function enrichment analysis showed that DE mRNAs were mainly associated with "sister chromatid segregation," "regulation of angiogenesis," "cell adhesion molecules (CAMs)," "cell cycle," and "ECM-receptor interaction." LUAD-related ceRNA network was constructed, which comprised of 54 nodes and 78 edges. Top ten hub RNAs (hsa-miR-374a-5p, hsa-miR-374b-5p, hsa-miR-340-5p, hsa-miR-377-3p, hsa-miR-21-5p, hsa-miR-326, SNHG1, RALGPS2, and PITX2) were identified according to their degree. Kaplan-Meier survival analyses demonstrated that hsa-miR-21-5p and RALGPS2 had a significant prognostic value. Finally, we found that a high risk of three novel ceRNA interactions (SNHG1-hsa-miR-21-5p-RALGPS2, SNHG1-hsa-miR-326-RALGPS2, and SNHG1-hsa-miR-377-3p-RALGPS2) was positively associated with worse prognosis. Three novel ceRNAs (SNHG1-hsa-miR-21-5p-RALGPS2, SNHG1-hsa-miR-326-RALGPS2, and SNHG1-hsa-miR-377-3p-RALGPS2) might be potential biomarkers for the prognosis and treatment of LUAD.

Project description:BackgroundNeuroblastoma (NB) is the most common solid tumor in children. NB treatment has made significant progress; however, given the high degree of heterogeneity, basic research findings and their clinical application to NB still face challenges. Herein, we identify novel prognostic models for NB.MethodsWe obtained RNA expression data of NB and normal nervous tissue from TARGET and GTEx databases and determined the differential expression patterns of RNA binding protein (RBP) genes between normal and cancerous tissues. Lasso regression and Cox regression analyses identified the five most important differentially expressed genes and were used to construct a new prognostic model. The function and prognostic value of these RBPs were systematically studied and the predictive accuracy verified in an independent dataset.ResultsIn total, 348 differentially expressed RBPs were identified. Of these, 166 were up-regulated and 182 down-regulated RBPs. Two hubs RBPs (CPEB3 and CTU1) were identified as prognostic-related genes and were chosen to build the prognostic risk score models. Multivariate Cox analysis was performed on genes from univariate Cox regression and Lasso regression analysis using proportional hazards regression model. A five gene prognostic model: Risk score = (-0.60901*expCPEB3)+(0.851637*expCTU1) was built. Based on this model, the overall survival of patients in the high-risk subgroup was lower (P = 2.152e-04). The area under the curve (AUC) of the receiver-operator characteristic curve of the prognostic model was 0.720 in the TARGET cohort. There were significant differences in the survival rate of patients in the high and low-risk subgroups in the validation data set GSE85047 (P = 0.1237e-08), with the AUC 0.730. The risk model was also regarded as an independent predictor of prognosis (HR = 1.535, 95% CI = 1.368-1.722, P = 2.69E-13).ConclusionsThis study identified a potential risk model for prognosis in NB using Cox regression analysis. RNA binding proteins (CPEB3 and CTU1) can be used as molecular markers of NB.

Project description:Background Bone morphogenetic proteins (BMPs) regulate tumor progression via binding to their receptors (BMPRs). However, the expression and clinical significance of BMPs/BMPRs in lung adenocarcinoma remain unclear due to a lack of systematic studies. Methods This study screened differentially expressed BMPs/BMPRs (deBMPs/BMPRs) in a training dataset combining TCGA-LUAD and GTEx-LUNG and verified them in four GEO datasets. Their prognostic value was evaluated via univariate and multivariate Cox regression analyses. LASSO was performed to construct an initial risk model. Subsequently, after weighted gene co-expression network analysis (WGCNA), differential expression analysis, and univariate Cox regression analysis, hub genes co-expressed with differentially expressed BMPs/BMPRs were filtered out to improve the risk model and explore potential mechanisms. The improved risk model was re-established via LASSO combining hub genes with differentially expressed BMPs/BMPRs as the core. In the testing cohort including 93 lung adenocarcinoma patients, immunohistochemistry (IHC) was performed to verify BMP5 protein expression and its association with prognosis. Results BMP2, BMP5, BMP6, GDF10, and ACVRL1 were verified as downregulated in lung adenocarcinoma. Survival analysis identified BMP5 as an independent protective prognostic factor. We also found that BMP5 was significantly correlated with EGFR expression and mutations, suggesting that BMP5 may play a role in targeted therapy. The initial risk model containing only BMP5 showed a significant correlation (HR: 1.71, 95% CI: 1.28−2.28, p: 3e-04) but low prognostic accuracy (AUC of 1-year survival: 0.6, 3-year survival: 0.6, 5-year survival: 0.63). Seventy-nine hub genes co-expressed with BMP5 were identified, and their functions were enriched in cell migration and tumor metastasis. The re-established risk model showed greater prognostic correlation (HR: 2.58, 95% CI: 1.92–3.46, p: 0) and value (AUC of 1-year survival: 0.72, 3-year survival: 0.69, and 5-year survival: 0.68). IHC results revealed that BMP5 protein was also downregulated in lung adenocarcinoma and higher expression was markedly associated with better prognosis (HR: 0.44, 95% CI: 0.23–0.85, p: 0.0145). Conclusion BMP5 is a potential crucial target for lung adenocarcinoma treatment based on significant differential expression and superior prognostic value.

Dataset Information

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma.

Publications

Integrated analysis of the roles and prognostic value of RNA binding proteins in lung adenocarcinoma.

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