Identification of the Functions and Prognostic Values of RNA Binding Proteins in Bladder Cancer.
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ABSTRACT:
SUBMITTER: Wu Y
PROVIDER: S-EPMC8258248 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
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Project description:BackgroundHepatocellular carcinoma (HCC), one of the most common malignant tumors worldwide, ranks as the fifth most common cancer and has been the second most frequent cause of cancer-related death. RNA binding proteins (RBPs) are proteins that interact with different classes of RNA and are commonly detected in cells.MethodsWe used RNA sequencing data from TCGA to display dysfunctional RBPs microenvironments and provide potential useful biomarkers for HCC diagnosis and prognosis.Results330 differently expressed RBPs (208 upregulated and 122 downregulated) were identified. KEGG were mainly enriched in RNA degradation, Influenza A, Hepatitis C, RIG-I-like receptor signaling pathway, Herpes simplex virus 1 infection and RNA transport. CBioPortal results demonstrated that these genes were altered in 50 samples out of 357 HCC patients (14%) and the amplification of BRCA1 was the largest frequent copy-number alteration.ConclusionBased on the online database, we identified novel RBPs markers for the prognosis of hepatocellular carcinoma.
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Project description:PurposeThe biological roles and clinical significance of RNA-binding proteins (RBPs) in oral squamous cell carcinoma (OSCC) are not fully understood. We investigated the prognostic value of RBPs in OSCC using several bioinformatic strategies.Materials and methodsOSCC data were obtained from a public online database, the Limma R package was used to identify differentially expressed RBPs, and functional enrichment analysis was performed to elucidate the biological functions of the above RBPs in OSCC. We performed protein-protein interaction (PPI) network and Cox regression analyses to extract prognosis-related hub RBPs. Next, we established and validated a prognostic model based on the hub RBPs using Cox regression and risk score analyses.ResultsWe found that the differentially expressed RBPs were closely related to the defense response to viruses and multiple RNA processes. We identified 10 prognosis-related hub RBPs (ZC3H12D, OAS2, INTS10, ACO1, PCBP4, RNASE3, PTGES3L-AARSD1, RNASE13, DDX4, and PCF11) and effectively predicted the overall survival of OSCC patients. The area under the receiver operating characteristic (ROC) curve (AUC) of the risk score model was 0.781, suggesting that our model exhibited excellent prognostic performance. Finally, we built a nomogram integrating the 10 RBPs. The internal validation cohort results showed a reliable predictive capability of the nomogram for OSCC.ConclusionWe established a novel 10-RBP-based model for OSCC that could enable precise individual treatment and follow-up management strategies in the future.
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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.
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Project description:RBPs in the development and progression of BC remains unclear. Here, we elucidated the role of RBPs in predicting the survival of patients with BC. Clinical information and RNA sequencing data of the training and validation cohorts were downloaded from the Cancer Genome Atlas and Gene Expression Omnibus databases, respectively. Survival-related differentially expressed RBPs were identified using Cox regression analyses. A total of 113 upregulated and 54 downregulated RBPs were observed, with six showing prognostic values (AHNAK, MAP1B, LAMA2, P4HB, FASN, and GSDMB). In both the GSE32548 and GSE31684 datasets, patients with low-risk scores in survival-related six RBPs-based prognostic model showed longer overall survival than those with high-risk scores. AHNAK, MAP1B, P4HB, and FASN expression were significantly upregulated in both BC tissues and cell lines. BC tissues from high-risk group showed higher proportions of naive CD4+ T cells, M0 and M2 macrophages, and neutrophils and lower proportions of plasma cells, CD8+ T cells, and T-cell follicular helper compared to low-risk group. AHNAK knockdown significantly inhibited the proliferation, invasion, and migration of BC cells in vitro and inhibited the growth of subcutaneous tumors in vivo. We thus developed and functionally validated a novel six RBPs-based prognostic model for BC.
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Project description:RNA binding proteins (RBPs) have been proved to play pivotal roles in a variety types of tumors. However, there is no convincible evidence disclosing the functions of RBPs in thyroid cancer (THCA) thoroughly and systematically. Integrated analysis of the functional and prognostic effect of RBPs help better understanding tumorigenesis and development in thyroid and may provide a novel therapeutic method for THCA. In this study, we obtained a list of human RBPs from Gerstberger database, which covered 1,542 genes encoding RBPs. Gene expression data of THCA was downloaded from The Cancer Genome Atlas (TCGA, n = 567), from which we extracted 1,491 RBPs' gene expression data. We analyzed differentially expressed RBPs using R package "limma". Based on differentially expressed RBPs, we constructed protein-protein interaction network and the GO and KEGG pathway enrichment analyses were carried out. We found six RBPs (AZGP1, IGF2BP2, MEX3A, NUDT16, NUP153, USB1) independently associated with prognosis of patients with thyroid cancer according to univariate and multivariate Cox proportional hazards regression models. The survival analysis and risk score analysis achieved good performances from this six-gene prognostic model. Nomogram was constructed to guide clinical decision in practice. Finally, biological experiments disclosed that NUP153 and USB1 can significantly impact cancer cell proliferation and migration. In conclusion, our research provided a new insight of thyroid tumorigenesis and development based on analyses of RBPs. More importantly, the six-gene model may play an important role in clinical practice in the future.
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Project description:PurposeTo investigate the prognostic value and biological function of RNA binding proteins (RBPs) in stomach adenocarcinoma (STAD).Materials and methodsDatasets of the differentially expressed genes (DEGs) were downloaded from the TCGA-based (The Cancer Genome Atlas) GEPIA database, from which the differentially expressed RBPs were determined. Functions and prognostic values of these determined RBPs were systematically investigated by a series of methods in bioinformatics analysis. In addition, transwell assays were performed to explore the effect of PTBP1 in STAD cells.ResultsThree hundred and sixty-two differentially expressed RBPs were determined, with 331 up-regulated and 31 down-regulated. Seven RBPs (PTBP1, PPIH, SMAD5, MSI2, RBM15, MRPS17, and ADAT3) were identified to be prognosis-related and adopted to construct a prognostic model. Compared with low-risk patients in TCGA training cohort, TCGA testing cohort and GEO cohort, high-risk patients had poorer overall survival (OS). The area under the ROC curves of this prognostic model were 0.804, 0.644 and 0.581 for TCGA training cohort, TCGA testing cohort and GEO cohort, respectively, justifying itself as a good prognostic model with reliable predictive ability. Using the seven identified RBPs, we then constructed a nomogram to generate a clinical utility model. The regulatory networks and functions of the seven RBPs were then investigated, the results of which demonstrated that MRPS17 and PTBP1 reduced the number of infiltrated immune cells. In-vitro experiments showed that the downregulation of PTBP1 weakened the migration and invasion capability of AGS and HGC27 cells.ConclusionThe seven-gene signature can be used as a reliable STAD prognostic biomarker, and these findings help us better understand the prognostic roles and functions of RBPs in STAD.
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Project description:Bladder cancer can range from noninvasive to invasive tumors. When non-muscle invasive bladder cancer (NMIBC) recurs, patients could endure long-term invasive malignancies with a high disease-specific death rate. Immune escape frequently results in tumor development, metastases, unfavorable prognosis, and failure of immunotherapy. Based on the median immune score, this study used ESTIMATE scores to evaluate 411 bladder cancer cases from TCGA-BLCA. Two hundred two ncRNAs were differentially expressed in two groups, where 29 candidates appeared to be associated with the overall survival of bladder cancer patients. LASSO algorithm was performed to establish the risk score model of 13-ncRNA. Risk scores were computed for cases in the training set, validation set, and TCGA-BLCA set; Poor prognosis in cases with higher risk scores was based on the training set, validating set, and TCGA-BLCA set. Among the 13 ncRNAs, IGF2BP2-AS1, MAGF-AS1, ARHGAP5-AS1, and LINC00942 were significantly correlated with the overall survival of bladder cancer patients. Pearson's correlation analysis based on TCGA-BLCA identified 2093, 3107, 386, and 936 mRNAs co-expressed with IGF2BP2-AS1, MAGF-AS1, ARHGAP5-AS1, and LINC00942, respectively. Conclusively, the 13 ncRNA signature exhibited a feasible predictive prognostic value for bladder cancer patients. IGF2BP2-AS1 expression was higher in bladder cancer tissues and significantly correlated to immune-related factors, suggesting that IGF2BP2-AS1 represents a promising immune-related target for treating bladder cancer patients.
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Project description:BackgroundEndometrial cancer (EC) is a common gynecological malignancy, and the prognosis of advanced EC is unsatisfactory. The deregulated expression of RNA-binding proteins (RBPs) is closely associated with the occurrence and development of cancer. However, the role of RBPs in EC remains unclear. The aim of this study was to validate the prognostic values of RBPs combined with clinical factors.MethodsWe downloaded the RNA sequencing and clinical data for EC from The Cancer Genome Atlas (TCGA) database. R software was used to identify the differentially expressed RBPs. Univariate and multivariate Cox proportional hazards regression analyses were performed to predict the 4 overall survival (OS)-related RBPs. We then constructed a nomogram combining the 4-RBP signature with clinical risk factors to assess the prognostic power. Furthermore, we validated the expression of 4 RBPs in our patient samples using quantitative real-time polymerase chain reaction (qRT-PCR) and explored the effect of cold-inducible RNA-binding protein (CIRBP) on EC tumor growth using cell proliferation experiments.ResultsIt is found that Shwachman-Bodian-Diamond syndrome (SBDS), CIRBP, MRPL15, and CELF4 were significantly related to the prognosis of EC patients. In addition, the nomogram showed better performance in OS predictions than the International Federation of Gynecology and Obstetrics (FIGO) stage. The qRT-PCR results showed that low CIRBP expression was associated with cell proliferation.ConclusionsIn our study, we constructed a 4-RBP signature-based nomogram combined with clinical factors in EC that could effectively predict the prognosis of EC patients. The results provide novel insights into the development of treatment targets and prognostic molecular markers in EC.
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Project description:AbstractGastric 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.
| S-EPMC8878810 | biostudies-literature