scholarly journals Systematic Analysis of the Function and Prognostic Value of RNA-Binding Proteins in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
TONG WU ◽  
Zhiyun Yang ◽  
Yuying Yang ◽  
Yuyong Jiang ◽  
Peipei Meng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Roc Curve ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Curve Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Roc Curve Analysis

Abstract Background: RNA-binding proteins (RBPs) are abnormally expressed in a variety of malignant tumors and are closely related to tumorigenesis, tumor progression, and prognosis. The role of RBPs in hepatocellular carcinoma (HCC) is unclear. Based on the cancer genome atlas (TCGA) database, we conducted a systematic bioinformatics analysis of abnormally expressed RBPs in HCC, with the aim of identifying the prognostic markers and potential therapeutic targets.Methods: HCC RNA sequencing data downloaded from TCGA database were used to determine the differentially expressed RBPs in livery cancer and normal tissues, followed by performing functional enrichment analysis and visualization of interaction relationships. Univariate and multivariate Cox regression analyses were subsequently used to identify RBPs that were significantly related to the prognosis to construct a prognostic model. The predictive performance of the prognostic model was evaluated by survival analysis and receiver operating characteristic (ROC) curve analysis and verified in the test cohort. Human protein atlas online database was used to verify the expression level of RBPs in the prognostic model.Results: In total, 82 differentially expressed RBPs were identified, including 55 upregulated and 27 downregulated RBPs. Further functional enrichment and interaction analyses showed that the differentially expressed RBPs were mainly related to regulating of mRNA metabolic process, RNA catabolic, mRNA catabolic process, and macromolecule methylation. Five RBP genes, LIN28B, SMG5, PPARGC1A, LARP1B, and ANG were identified as prognostic-related genes and used to construct the prognostic model. The predictive ability of the prognostic model was verified in the test cohort. ROC curve analysis showed that the prognostic model had good sensitivity and specificity. Independent prognostic analysis showed that the risk score may be an independent prognostic factor for HCC.Conclusion: This study constructed a reliable prognostic prediction model by analyzing the differentially expressed RBPs of HCC, facilitating the identification of HCC prognostic biomarkers and therapeutic targets.

scholarly journals Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Min Wang ◽  
Shan Huang ◽  
Zefeng Chen ◽  
Zhiwei Han ◽  
Kezhi Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Regression Analyses ◽  
Kaplan Meier

Abstract Background Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized. Methods Data from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Finally, find protein-coding genes (PCGs) related to hub RBPs were used to construct a hub RBP-PCG co-expression network. Results In total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS. Conclusion We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes.

scholarly journals A Novel Prognostic Model In Oral Squamous Carcinoma: The Functions And Prognostic Values Of Rna Binding Proteins

2020 ◽  
Author(s):  
Yingjuan Lu ◽  
Yongcong Yan ◽  
Mo Liu ◽  
Yancan Liang ◽  
Yushan Ye ◽  
...  
Keyword(s):  
Risk Score ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Squamous Carcinoma ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Oral Squamous Carcinoma

Abstract Background: The 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 by several bioinformatic strategies.Methods: OSCC 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 by Cox regression and risk score analyses.Results: We found that the differentially expressed RBPs were closely related to the defence response to virus and multiple RNA processes. We obtained ten 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 ROC curve (AUC) of the risk score model was 0.781, suggesting that our model had good prognostic performance. Finally, we built a nomogram integrating the ten RBPs. The internal validation cohort results showed a reliable predictive capability of the nomogram for OSCC.Conclusions: We established a novel ten-RBP-based model for OSCC that could enable precise therapeutic targets in the future.

scholarly journals Development and Validation of an Rna Binding Protein-associated Prognostic Model for Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Min wang ◽  
Shan Huang ◽  
Zefeng Chen ◽  
Zhiwei Han ◽  
Kezhi Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Regression Analyses ◽  
Kaplan Meier

Abstract Background: Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized. Methods: Data from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Results: In total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS. Conclusion: We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes.

scholarly journals Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma

2020 ◽  
Author(s):  
Min wang ◽  
Shan Huang ◽  
Zefeng Chen ◽  
Zhiwei Han ◽  
Kezhi Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Regression Analyses ◽  
Kaplan Meier

Abstract Background: Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized. Methods: Data from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Finally, find protein-coding genes (PCGs) related to hub RBPs were used to construct a hub RBP-PCG co-expression network.Results: In total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS.Conclusion: We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes.

scholarly journals Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma

2020 ◽  
Author(s):  
Min wang ◽  
Shan Huang ◽  
Zefeng Chen ◽  
Zhiwei Han ◽  
Kezhi Li ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Low Risk ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Regression Analyses ◽  
Kaplan Meier

Abstract Background: Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized.Methods: Data from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Finally, find protein-coding genes (PCGs) related to hub RBPs were used to construct a hub RBP-PCG co-expression network.Results: In total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS.Conclusion: We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes.

scholarly journals Construction And Verification of A Prognostic Model Related To RNA-Binding Proteins In Uterine Corpus Endometrial Carcinoma

2021 ◽  
Author(s):  
Yukun Jia ◽  
Zhan Peng ◽  
Guangye Wang
Keyword(s):  
Endometrial Carcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Binding Proteins ◽  
Rna Binding ◽  
Cox Regression ◽  
Rna Binding Proteins ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: RNA binding proteins (RBP) plays an important role in post-transcriptional regulation. Although the dysregulation of RBP expression is closely related to the occurrence and metastasis of a variety of tumors, there are few reports on RBP in endometrial carcinoma (UCEC). This study aims to establish a RBP-related prognostic model of UCEC. Methods: We downloaded UCEC gene expression and clinical information data from the Cancer Genome Atlas (TCGA) and GEO database, and determined RBPs that are differentially expressed between tumors and normal tissues. Then, used functional enrichment analysis to analyze the biological functions of the differentially expressed RBP. Used univariate Cox regression analysis to screen prognostic-related RBP and construct a prognostic model. Subsequently, Kaplan-Meier and recipient operating characteristic (ROC) curves were drawn to evaluate the model. Finally, established a nomogram. Results: This study identified 531 differentially expressed RBPs, including 325 up-regulated and 206 down-regulated RBPs, respectively. Then six independent prognostic-related RBPs (REXO2, MARS2, XPO5, YBX1, YBX2, and CELF4) were used to construct a prognostic model. According to this model, the overall survival (OS) of patients in the high-risk score group was significantly lower than that of the low-risk score group. In the training queue and the test queue, the areas under the ROC curve were 0.799 and 0.669, respectively, showing the moderate predictive value of the model. Conclusion: We have developed and validated the RBP-related prognostic model.

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 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 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.

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