scholarly journals Identification of an RNA binding protein-related gene signature in hepatocellular carcinoma patients

2020 ◽  
Vol 26 (1) ◽  
Author(s):  
Li Wang ◽  
Na Zhou ◽  
Jialin Qu ◽  
Man Jiang ◽  
Xiaochun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Characteristic Curve ◽  
Gene Signature ◽  
Clinicopathological Parameters ◽  
Lasso Regression ◽  
Bioinformatics Analyses

Abstract Background Hepatocellular carcinoma (HCC) is a common malignant primary cancer with high mortality. Previous studies have demonstrated that RNA binding proteins (RBPs) are involved in the biological processes of cancers, including hepatocellular cancer. Methods In this study, we aimed to identify the clinical value of RNA-binding proteins for hepatocellular carcinoma. We obtained gene expression and clinical data of hepatocellular carcinoma patients from the TCGA and ICGC databases. The prognostic value of RBP-related genes in patients with hepatocellular carcinoma and their function were studied by comprehensive bioinformatics analyses. The gene signature of SMG5, EZH2, FBLL1, ZNF239, and IGF2BP3 was generated by univariate and multivariate Cox regression and LASSO regression analyses. We built and verified a prognostic nomogram based on RBP-related genes. The gene signature was validated by the ICGC database. The expression of RBP-related genes was validated by the Oncomine database, the Human Protein Atlas and Kaplan–Meier plotter. Result Most RBP-related genes were significantly different in cancer and normal tissues. The survival of patients in the different groups was significantly different. The gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters. Conclusion Gene signatures based on RNA-binding proteins can be independent risk factors for hepatocellular carcinoma patients.

scholarly journals Identification of an RNA binding protein-related gene signature in hepatocellular carcinoma patients

2020 ◽  
Author(s):  
Li Wang ◽  
Na Zhou ◽  
Jialin Qu ◽  
Man Jiang ◽  
Xiaochun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Characteristic Curve ◽  
Gene Signature ◽  
Cancer Genome ◽  
The Cancer Genome Atlas ◽  
Clinicopathological Parameters

Abstract Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related morbidity and mortality among all human cancers. Studies have demonstrated that RNA binding proteins (RBPs) involved in the biological process of cancers including hepatocellular cancer. In this study, we aim to identify clinical value of RNA binding proteins for hepatocellular carcinoma.Methods: We analyses the data of HCC that downloaded from the Cancer Genome Atlas (TCGA) database and determined the differently expressed of RBPs between cancer and normal tissues. We further elucidate the function of RBPs by utilized Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Gene signature of SMG5, EZH2, FBLL1, ZNF239, IGF2BP3 were generated by performed the univariate and multivariate Cox regression and LASSO regression analysis. CIBERSORT analysis was used to evaluation of tumor-infiltrating immune cells in different group. We built and verify a prognosis nomogram base on RBPs-related genes. Gene signature was validated by the International Cancer Genome Consortium (ICGC) database. The expressions of RBPs-related genes were validated by using Oncomine database, and the Human Protein Atlas.Result: Most of RBPs-related genes were significantly different in cancer and normal tissue. The survival of patients in the different group was statistically different. The Gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters (AUC=0.758). The patients in the high-risk group were more likely to have a higher Macrophages M0. Conclusion: Gene signature constructed by RNA binding proteins can be independent risk factors for hepatocellular carcinoma patients.

scholarly journals Identification of Novel RNA Binding Proteins Influencing Circular RNA Expression in Hepatocellular Carcinoma

2021 ◽  
Vol 22 (14) ◽  
pp. 7477
Author(s):  
Rok Razpotnik ◽  
Petra Nassib ◽  
Tanja Kunej ◽  
Damjana Rozman ◽  
Tadeja Režen
Keyword(s):  
Hepatocellular Carcinoma ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Regulatory Protein ◽  
Circular Rna ◽  
Differentially Expressed ◽  
Circular Rnas ◽  
Bioinformatics Analyses ◽  
Published Evidence

Circular RNAs (circRNAs) are increasingly recognized as having a role in cancer development. Their expression is modified in numerous cancers, including hepatocellular carcinoma (HCC); however, little is known about the mechanisms of their regulation. The aim of this study was to identify regulators of circRNAome expression in HCC. Using publicly available datasets, we identified RNA binding proteins (RBPs) with enriched motifs around the splice sites of differentially expressed circRNAs in HCC. We confirmed the binding of some of the candidate RBPs using ChIP-seq and eCLIP datasets in the ENCODE database. Several of the identified RBPs were found to be differentially expressed in HCC and/or correlated with the overall survival of HCC patients. According to our bioinformatics analyses and published evidence, we propose that NONO, PCPB2, PCPB1, ESRP2, and HNRNPK are candidate regulators of circRNA expression in HCC. We confirmed that the knocking down the epithelial splicing regulatory protein 2 (ESRP2), known to be involved in the maintenance of the adult liver phenotype, significantly changed the expression of candidate circRNAs in a model HCC cell line. By understanding the systemic changes in transcriptome splicing, we can identify new proteins involved in the molecular pathways leading to HCC development and progression.

scholarly journals Development and Validation an Epigenetic Modification-related Signals for Diagnosis and Prognosis of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Diguang Wen ◽  
Sheng Qiu ◽  
Zuojin Liu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Cancer ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Epigenetic Modification ◽  
Lasso Regression ◽  
Cancer Dataset ◽  
Gene Signatures ◽  
Diagnosis And Prognosis

Abstract Background: Increasing evidence has indicated that abnormal epigenetic modification such as RNAm6a modification, histone modification, DNA methylation modification, RNA binding proteins and transcription factors, is correlated with Hepatocarcinogenesis. However, it is unknown how epigenetic modification associated genes contribute to the occurrence and clinical outcome of hepatocellular carcinoma (HCC). Thus, we constructed epigenetic modification associated model that may enhance the diagnosis and prognosis of HCC.METHODS: In this study, we focused on the clinical values of epigenetic modification associated genes for HCC. Our gene expression data were collected from TCGA and a HCC datasets from GEO dataset in order to ensure the reliability of data. Their function was analyzed by bioinformatics methods. We used lasso regression, SUV, logistic regression and cox regression to construct the diagnosis and prognosis models. We also constructed a nomogram for the practicability of the above-mentioned prognosis model. The above results have been verified in an independent liver cancer dataset from ICGC database. Furthermore, we carried out pan cancer analysis to verify the specificity of the above model.RESULT: A large number of epigenetic modification associated genes were significantly different in HCC and normal liver tissues. The gene signatures showed good performance for predicting the occurrence and survival of HCC patients verified by DCA and ROC curve.CONCLUSION: Gene signatures based on epigenetic modification associated genes can be used to identify the occurrence and prognosis of liver cancer.

scholarly journals Integrated analysis of the functions and prognostic values of RNA-binding proteins in neuroblastoma

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260876
Author(s):  
Jun Yang ◽  
Jiaying Zhou ◽  
Cuili Li ◽  
Shaohua Wang
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Risk Model ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Differentially Expressed ◽  
Lasso Regression

Background Neuroblastoma (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. Methods We 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. Results In 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). Conclusions This 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.

scholarly journals Integrated Analysis of the Functions and Prognostic Values of RNA Binding Proteins and Candidate Drugs in Renal Papillary Cell Carcinoma

2020 ◽  
Author(s):  
Silin Jiang ◽  
Xiaohan Ren ◽  
Shouyong Liu ◽  
Zhongwen Lu ◽  
Aiming Xu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Integrated Analysis ◽  
Sequencing Data ◽  
Bioinformatics Analyses ◽  
Cox Regression Analysis ◽  
Cox Analysis

Abstract Background: Roles of RNA binding proteins in renal papillary cell carcinoma (KIRP) remain undiscovered. We thus conducted a series of bioinformatics analyses to elucidate the associations between RBPs and prognosis of renal papillary cell carcinoma.Methods: RNA sequencing data and clinical of KIRP were downloaded from the TCGA database. The differentially expressed RBP coding genes (DEGs) were sorted out by R software and Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were then performed to evaluate the functional pathways. Protein-protein interaction (PPI) network of DEGs was formed through the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape. Subsequently, a prognostic model was constructed by the uses of univariate Cox regression analysis, random survival forest analysis and multivariate Cox analysis. Validations of Receiver Operating Characteristic (ROC) analysis, KM analysis of overall survival and nomogram were performed as follow. Furthermore, CMap database was used to predict potential drugs.Results: A prognostic OS-predictive model based on six RBPs (SNRPN, RRS1, INTS8, RBPMS2, IGF2BP3 and PIH1D2) was constructed. STOCK1N-28457, pyrimethamine and trapidil were determined as potential drugs according to the CMap database.Conclusion: This study constructed a prognosis-related six-RBP signature and made a prediction of three small molecular drugs, which provided a novel insight into KIRP and assisted the development of individualized therapeutic strategies.

Comprehensive Analysis of Autophagy-related Genes in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Li Wang ◽  
Jialin Qu ◽  
Na Zhou ◽  
Man Jiang ◽  
Xiaochun Zhang
Keyword(s):  
Hepatocellular Carcinoma ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Survival Model ◽  
Survival Models ◽  
Clinicopathological Parameters ◽  
Lasso Regression ◽  
Kaplan Meier ◽  
Human Protein Atlas ◽  
Independent Risk Factors

Abstract Background: Hepatocellular carcinoma (HCC) is the common type of cause of cancer-related death among human cancers. There are ample evidences to showing that autophagy-related genes (ARGs) may play a significant role in the biological process of HCC. Methods: In this study, we aim to identify survival model and nomogram that could effectively predict the prognosis of HCC based on ARGs. First, we download the data of HCC patients from TCGA database. Second, we analysis the function of ARGs by utilized GO and the KEGG analysis. Finally, we screen 5 ARGs (SQSTM1, CAPN10, EIF2S1, ATIC, RHEB) for survival model by performed the Cox regression and Lasso regression analysis. We further built and verified a prognostic nomogram base on prognostic ARGs. Moreover, its efficacy was validated by the ICGC database. The expressions level of 5 ARGs was performed using Oncomine database, the Human Protein Atlas and Kaplan-Meier plotter.Result: We found patients the survival of patients in the different groups was significantly different both in the TCGA cohort and ICGC cohort. The survival model showed good performance for predicting the prognosis of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters. Conclusion: our survival models and prognostic ARGs nomogram can be independent risk factors for hepatocellular carcinoma patients.

scholarly journals Expression Profile of RNA Binding Protein in Cervical Cancer

2021 ◽  
Author(s):  
Zhiyuan Huang ◽  
Fang Li ◽  
Qinchuan Li
Keyword(s):  
Risk Factors ◽  
Cervical Cancer ◽  
Risk Score ◽  
Normal Tissue ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Risk Group ◽  
Tissue Samples

Abstract Background: It has been demonstrated by studies globally that RNA binding proteins (RBPs) took part in the development of cervical cancer (CC). Few studies concentrated on the correlation between RBPs and overall survival of CC patients. We retrieved significant DEGs (differently expressed genes, RNA binding proteins) correlated to the process of cervical cancer development. Methods: Expressions level of genes in cervical cancer and normal tissue samples were obtained from GTEx and TCGA database. Differently expressed RNA binding proteins (DEGs) were retrieved by Wilcoxon sum-rank test. ClusterProfiler package worked in R software was used to perform GO and KEGG enrichment analyses. Univariate propotional hazard cox regression and multivariate propotional hazard cox regressions were applied to identify DEGs equipped with prognostic value and other clinical independent risk factors. ROC curve was drawn for comparing the survival predict feasibility of risk score with other risk factors in CC patients. Nomogram was drawn to exhibit the prediction model and validated by C-index and calibration curve. Correlations between Differentially expressed RNA binding proteins (DEGs) and other clinical features were investigated by t test or Cruskal wallis analysis. Correlation between Immune and DEGs in cervical cancer was investigated by ssGSEA. Results: 347 differentially expressed RBPs (DEGs) were retrieved from cervical cancer tissue and normal tissue samples. GO enrichment analysis showed that these DEGs involved in RNA splicing, catabolic process and metabolism. Cox regression medel showed that there were ten DEGs significantly associated with overall survival of cervical caner patients. WDR43 (HR = 0.423, P=0.008), RBM38 (HR = 0.533, P<0.001), RNASEH2A (HR=0.474, P=0.002) and HENMT1 (HR=0.720, P=0.071) played protective roles in survival among these ten genes. Stage (Stage IV vs Stage I HR = 3.434, P<0.001) and risk score (HR = 1.214, P< 0.001) were sorted as independent prognostic risk factors based on multivariate cox regression. ROC curve validated that risk score was preferable to predict survival of CC patients than other risk factors. Additionally, we found some of these ten predictor DEGs were correlated significantly in statistic with tumor grade or stage, clinical T stage, clinical N stage, pathology or risk score (all P< 0.05). Part of immune cells and immune functions showed a lower activity in high risk group than low risk group which is distincted by median risk score. Conclusion: Our discovery showed that many RNA binding proteins involved in the progress of cervical cancer, which could probably serve as prognostic biomarkers and accelerate the discovery of treatment targets for CC patients.

scholarly journals Mining TCGA Database to Construct a RNA Binding Proteins-Related Prognostic Model for GBM

2021 ◽  
Author(s):  
Wenjing GUO ◽  
Rui Chen ◽  
Hui Deng ◽  
Mengxian Zhang
Keyword(s):  
Risk Score ◽  
Roc Curve ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Functional Enrichment ◽  
Good Prediction ◽  
Cox Regression Analysis ◽  
Prediction Function

Abstract Background: Glioblastoma(GBM) is a common primary malignant brain tumor with poor prognosis, and currently effective therapeutic strategies are still limited. RNA binding proteins(RBPs) dysregulation has been reported in various cancers and is closely related to tumor initiation and progression. However, little is known about the role of RBPs in GBM.Methods: We downloaded RNA-seq transcriptome from TCGA database and differently expressed RBPs were screened between tumor and normal tissues. Then we performed functional enrichment analysis of these RBPs and based on univariate and multivariate cox regression analysis, hub RBPs were identified. Furthermore, we constructed a risk model based on hub RBPs and divided patients into high- and low-risk groups based on the median risk score. To validate the model, CGGA database were conducted as a training set and then both survival analysis and ROC curve were conducted. We also developed a nomogram based on five RBPs, which made more convenient to observe each patient’s prognosis and validated the connection between patients survival and each hub RBP . Finally, we used GEPIA website to further explore the value of these hub RBPs. Results: A total 309 differently expressed RBPs were identified, including 145 downregulated and 164 upregulated RBPs. and the result indicated that they were mainly enriched in mRNA processing, RNA splicing, RNA catabolic process, RNA transport, spliceosome, ribosome and mRNA surveillance pathway. Five hub RBPs were identified and we observed that patients with high risk score were related to poor overall survival and the AUC of ROC curve was 0.752 in TCGA. The result was subsequently proved by CGGA, showing the good prediction function of the model. Then GEPIA website suggested that MRPL41, MRPL36 and FBXO17 were closely associate with OS in GBM. Conclusion: Our result may provide novel insights into pathogenesis of GBM and development of new therapeutic targets. However, further experiments in vitro and in vivo will be warranted.

scholarly journals Integrated analysis of the functions and prognostic values of RNA binding proteins in colon adenocarcinoma

2020 ◽  
Author(s):  
Xinhong Liu ◽  
Fang Tan ◽  
Xingyao Long ◽  
Ruokun Yi ◽  
Dingyi Yang ◽  
...  
Keyword(s):  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Prediction Models ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Cox Regression Analysis

Abstract Background RNA binding proteins (RBPs) play an important role in a variety of cancers. However, the role of RBPs in colorectal adenocarcinoma (COAD) has not been studied. Integrated analysis of RBPs will provide a better understanding of disease genesis and new insights into COAD treatment. Methods The gene expression data and corresponding clinical information for COAD were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis was used to screen for RBPs associated with COAD recurrence, and multivariate Cox proportional hazards regression analyses were used to identify genes that were associated with COAD recurrence. A nomogram was constructed to predict the recurrence of COAD, and a receiver operating characteristic (ROC) curve analysis was performed to determine the accuracy of the prediction models. The Human Protein Atlas database was used in prediction models to confirm the expression of key genes in COAD patients. Result A total of 177 differentially expressed RBPs was obtained, comprising 123 upregulated and 54 downregulated. GO and KEGG enrichment analysis showed that the differentially expressed RBPs were mainly related to mRNA metabolism, RNA processing and translation regulation. Seven RBP genes (TDRD6, POP1, TDRD7, PPARGC1A, LIN28B, LRRFIP2 and PNLDC1) were identified as prognosis-associated genes and were used to construct the prognostic model. Conclusion We constructed a COAD prognostic model through bioinformatics analysis, which indicated that prognostic model RBPs have a potential role in the diagnosis and prognosis of COAD. Moreover, the nomogram can effectively predict the 1-year, 3-year, and 5-year survival rate for COAD patients.

scholarly journals A Novel Prognostic Model for Oral Squamous Cell Carcinoma: The Functions and Prognostic Values of RNA-Binding Proteins

2021 ◽  
Vol 11 ◽  
Author(s):  
Yingjuan Lu ◽  
Yongcong Yan ◽  
Bowen Li ◽  
Mo Liu ◽  
Yancan Liang ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins

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.

Sign in / Sign up

Export Citation Format

Share Document