Value Of RNA-binding Proteins Research Articles

Nucleolin Regulates Pulmonary Artery Smooth Muscle Cell Proliferation under Hypoxia by Modulating miRNA Expression

Hypoxia induces the abnormal proliferation of vascular smooth muscle cells (VSMCs), resulting in the pathogenesis of various vascular diseases. RNA-binding proteins (RBPs) are involved in a wide range of biological processes, including cell proliferation and responses to hypoxia. In this study, we observed that the RBP nucleolin (NCL) was downregulated by histone deacetylation in response to hypoxia. We evaluated its regulatory effects on miRNA expression under hypoxic conditions in pulmonary artery smooth muscle cells (PASMCs). miRNAs associated with NCL were assessed using RNA immunoprecipitation in PASMCs and small RNA sequencing. The expression of a set of miRNAs was increased by NCL but reduced by hypoxia-induced downregulation of NCL. The downregulation of miR-24-3p and miR-409-3p promoted PASMC proliferation under hypoxic conditions. These results clearly demonstrate the significance of NCL-miRNA interactions in the regulation of hypoxia-induced PASMC proliferation and provide insight into the therapeutic value of RBPs for vascular diseases.

Comprehensive landscape of the functions and prognostic value of RNA binding proteins in uterine corpus endometrial carcinoma.

Background: The dysregulation of RNA binding proteins (RBPs) is involved in tumorigenesis and progression. However, information on the overall function of RNA binding proteins in Uterine Corpus Endometrial Carcinoma (UCEC) remains to be studied. This study aimed to explore Uterine Corpus Endometrial Carcinoma-associated molecular mechanisms and develop an RNA-binding protein-associated prognostic model. Methods: Differently expressed RNA binding proteins were identified between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues by R packages (DESeq2, edgeR) from The Cancer Genome Atlas (TCGA) database. Hub RBPs were subsequently identified by univariate and multivariate Cox regression analyses. The cBioPortal platform, R packages (ggplot2), Human Protein Atlas (HPA), and TIMER online database were used to explore the molecular mechanisms of Uterine Corpus Endometrial Carcinoma. Kaplan-Meier (K-M), Area Under Curve (AUC), and the consistency index (c-index) were used to test the performance of our model. Results: We identified 128 differently expressed RNA binding proteins between Uterine Corpus Endometrial Carcinoma tumor tissues and normal tissues. Seven RNA binding proteins genes (NOP10, RBPMS, ATXN1, SBDS, POP5, CD3EAP, ZC3H12C) were screened as prognostic hub genes and used to construct a prognostic model. Such a model may be able to predict patient prognosis and acquire the best possible treatment. Further analysis indicated that, based on our model, the patients in the high-risk subgroup had poor overall survival (OS) compared to those in the low-risk subgroup. We also established a nomogram based on seven RNA binding proteins. This nomogram could inform individualized diagnostic and therapeutic strategies for Uterine Corpus Endometrial Carcinoma. Conclusion: Our work focused on systematically analyzing a large cohort of Uterine Corpus Endometrial Carcinoma patients in the The Cancer Genome Atlas database. We subsequently constructed a robust prognostic model based on seven RNA binding proteins that may soon inform individualized diagnosis and treatment.

Transcriptomic signature associated with RNA-binding proteins for survival stratification of laryngeal cancer.

RNA-binding proteins (RBPs) have been suggested as important prognostic indicators in different human cancers. This study was designed to search the prognostic value of RBPs of laryngeal squamous cell carcinoma (LSCC). Differentially expressed RBPs (DERBPs) were screened via The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify prognostic DERBPs. Expression profiling of training cohort were calculated to develop a transcriptomic signature, which was validated by three independent cohorts (TCGA cohort, GSE65858 cohort and GSE27020 cohort). We identified DERBPs and a set of signatures (GTPBP3, KHDRBS3 and RBM38) were confirmed as prognosis-related hub DERBPs in LSCC, which was also tested and verified by bioinformatics method and molecular biology experiment. The role of immune cell infiltration and drug resistance between subgroups was explored. Furthermore, the risk score based on transcriptomic signature was turned out to be an independent prognostic indicator for LSCC. Finally, a nomogram for further clinical application was established. Our study demonstrated that the transcriptomic signature we constructed could serve as a novel therapeutic target and biomarker for LSCC from the perspective of RBPs.

Construction and Validation of an RNA-Binding Protein-Related Bladder Cancer Prognostic Model Based on Bioinformatics

Objective: This study aimed to construct a bladder cancer prognostic model using bioinformatics to predict the survival of bladder cancer patients. Methods: RNA sequences and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database, and the differentially expressed RNA-binding proteins (RBPs) were screened for analysis using the limma software package. Then, GO enrichment analysis and KEGG pathway analysis were performed on these differentially expressed RNA-binding proteins, and a PPI network was constructed. Finally, a risk model was constructed based on the screened central RBP, and a Kaplan-Meier survival curve was drawn to evaluate the prognostic value of central RBP and predict the prognosis of bladder Cancer(BLCA) patients with this model. Finally, the human protein atlas (HPA) online database (http://www.proteinatlas.org/) was used to further detect the differential expression of central hub RBP at the protein level between tumor tissue and normal tissue. Results: The bladder cancer prognostic model constructed with these six central RBPs had good sensitivity and specificity in predicting the prognosis of bladder cancer patients. Conclusion: This study explored the genes and regulatory networks of bladder cancer prognosis-related RNA-binding protein and bladder cancer, and constructed a bladder cancer prognosis model, which provides a theoretical basis for the development of new bladder cancer prognosis biomarkers in the future.

The predictive value of RNA binding proteins in colon adenocarcinoma

RNA binding proteins (RBPs) play an important role in regulating post-transcriptional gene expression and have been reported to be closely associated with the occurrence and development of tumors. However, the effect of RBPs in colon cancer remains unclear. We downloaded clinical information and transcriptome data of colon adenocarcinoma (COAD) from The Cancer Genome Atlas database (TCGA) database. After combining this data, we identified differentially expressed RBPs in normal and cancer tissues and subsequently performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Prognosis-related RBPs were identified via Cox regression analysis. The samples were randomly divided into two groups; an experimental group and a control group. A predictive model was constructed by dividing the experimental group into high- and low-risk subgroups based on the scores of the prognostic-related RBPs, and the prognosis of samples in these two subgroups was compared. Then, this model was applied to the control group. Finally, the model results were verified based on an online survival database and the Human Protein Atlas (HPA) database. A total of 469 differentially expressed RBPs were identified in normal and cancer tissues. Ten prognosis-related RBPs were determined by Cox regression analysis. In the prognostic prediction model, the prognosis of high-risk patients in the experimental group was worse than that in the low-risk group, and the same result was obtained in the control group. In addition, the risk score in the Cox regression analysis showed that the model could be used as an independent prognostic factor (P<0.001). The results of the online survival analysis tool, HPA database, and the model were consistent. Some specific RBPs are significantly associated with the prognosis of patients with COAD, and this finding may provide important information for the future diagnosis and treatment of patients with COAD.

A Novel Prognostic Model for Oral Squamous Cell Carcinoma: The Functions and Prognostic Values of 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.

Integrated Analysis of the Roles of RNA Binding Proteins and Their Prognostic Value in Clear Cell Renal Cell Carcinoma.

Methods We downloaded the RNA sequencing data of ccRCC from the Cancer Genome Atlas (TCGA) database and identified differently expressed RBPs in different tissues. In this study, we used bioinformatics to analyze the expression and prognostic value of RBPs; then, we performed functional analysis and constructed a protein interaction network for them. We also screened out some RBPs related to the prognosis of ccRCC. Finally, based on the identified RBPs, we constructed a prognostic model that can predict patients' risk of illness and survival time. Also, the data in the HPA database were used for verification. Results In our experiment, we obtained 539 ccRCC samples and 72 normal controls. In the subsequent analysis, 87 upregulated RBPs and 38 downregulated RBPs were obtained. In addition, 9 genes related to the prognosis of patients were selected, namely, RPL36A, THOC6, RNASE2, NOVA2, TLR3, PPARGC1A, DARS, LARS2, and U2AF1L4. We further constructed a prognostic model based on these genes and plotted the ROC curve. This ROC curve performed well in judgement and evaluation. A nomogram that can judge the patient's life span is also made. Conclusion In conclusion, we have identified differentially expressed RBPs in ccRCC and carried out a series of in-depth research studies, the results of which may provide ideas for the diagnosis of ccRCC and the research of new targeted drugs.

The Function and Prognostic Value of RNA-Binding Proteins in Colorectal Adenocarcinoma Were Analyzed Based on Bioinformatics of Smart Medical Big Data.

Colon cancer is the third most frequent cancer in the world and is mainly adenocarcinoma in terms of pathological type. It has been confirmed that the dysregulation of RNA-binding proteins (RBPs) significantly participates in the occurrence and development of numerous malignant tumors. Therefore, we analyzed the RBPs associated with colon adenocarcinoma (COAD) to assess their possible biological effects and prognostic value. A total of 398 COAD tissue datasets and 39 normal tissue datasets were retrieved from the TCGA data resource and screened out the RBPs, which are differentially expressed between tumor tissues and nontumor tissues. Then, bioinformatics analyses based on smart medical big data were conducted on these RBPs. Overall, 181 differentially expressed RBPs were uncovered, consisting of 121 upregulated RBPs and 60 downregulated RBPs. Finally, we selected 7 prognostic-related RBPs with research prospects and constructed a prognostic model according to the median risk score. There were remarkable differences in OS between the high-risk and low-risk groups. In addition, the performance of the prognostic model was evaluated and verified with other COAD patient data in the TCGA database. The results showed that the area under the ROC curve (AUC) for the train group was 0.744 and the one for the test group was 0.661, confirming that the model assesses patients' prognosis to some extent. And based on 7 hub RBPs, we constructed a nomogram as a reference for evaluating the survival rate of COAD patients.

Integrated Analysis of RNA-Binding Proteins Associated With the Prognosis and Immunosuppression in Squamous Cell Carcinoma of Head and Neck.

RNA-binding proteins (RBPs) interacting with target RNAs play essential roles in RNA metabolism at the post-transcription level. Perturbations of RBPs can accelerate cancer development and cause dysregulation of the immune cell function and activity leading to evade immune destruction of cancer cells. However, few studies have systematically analyzed the potential prognostic value and functions of RBPs in squamous cell carcinoma of head and neck (SCCHN). Here, for the first time, we comprehensively identified 92 differentially expressed RBPs from The Cancer Genome Atlas (TCGA) database. In the training set, a prognosis risk model was constructed with six RBPs, including NCBP2, MKRN3, MRPL47, AZGP1, IGF2BP2, and EZH2, and validated by the TCGA test set, the TCGA all set, and the GEO data set. In addition, the risk score was related to the clinical stage, T classification, and N classification. Furthermore, the high-risk score was significantly correlated with immunosuppression, and low expression of EZH2 and AZGP1 and high expression of IGF2BP2 were the main factors. Thus, the risk model may serve as a prognostic signature and offer highlights for individualized immunotherapy in SCCHN patients.

Systematic analysis of the function and prognostic value of RNA binding proteins in Colon Adenocarcinoma.

Background: Abnormal expression of RNA-binding proteins (RBPs) is closely related to tumorigenesis, progression, and prognosis. This study performed systematic bioinformatic analysis of RBPs abnormally expressed in colon adenocarcinoma (COAD) using the Cancer Genome Atlas (TCGA) database to screen prognostic markers and potential therapeutic targets.Methods: First, the gene expression data from COAD samples were used to screen out differentially expressed RBPs for functional enrichment analysis and to visualize interaction relationships. Second, RBPs that were significantly related to prognosis were screened through univariate and multivariate Cox regression analysis to construct a prognostic model. The prediction performance of the prognostic model was evaluated by survival analysis and receiver operating characteristic (ROC) curve analysis. It addition, it was verified in the test cohort. The Human Protein Atlas (HPA) online database was used to verify the expression levels of RBPs in the prognostic model.Results: The study identified 181 differentially expressed RBPs and analyzed their interaction and functional enrichment, which were mainly related to non-coding RNA processing, ribosome biogenesis, RNA metabolic processes, RNA phosphodiester bond hydrolysis, and alternative mRNA splicing. Five RBPs related to prognosis were used to construct a prognostic model, and its predictive ability was verified by the test cohort. ROC curve analysis showed that the prognostic model had good sensitivity and specificity. Independent prognostic analysis showed that risk scores could be used as independent prognostic factors for COAD.Conclusion: This study constructed a reliable prognostic model by analyzing COAD differentially expressed RBPs, facilitating the screening of COAD prognostic markers and therapeutic targets.

The expression and prognostic value of RNA binding proteins in clear cell renal cell carcinoma.

BackgroundRNA binding proteins (RBPs) have previously been demonstrated to be involved in the initiation and development of human cancers. However, its role in clear cell renal cell carcinoma (ccRCC) is not yet clear. The study was intended to explore the diagnostic and prognostic value of RBPs in ccRCC via bioinformatics methods of public datasets.MethodsData download from the Cancer Genome Atlas (TCGA) database was used to identify differentially expressed RBPs between normal renal samples and cancerous samples. Then, we performed the gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of differentially expressed genes (DEGs) using the ClusterProfiler package. Next, the protein-protein interaction (PPI) network was built by the online tool STRING database and Cytoscape software. The significant module and hub genes were screened by MCODE and Cytohubba plugin, respectively. Lastly, we performed a systematical analysis to investigate the diagnostic and prognostic value of candidate RBPs.ResultsA total of 133 DEGs, including 39 upregulated RBPs and 94 downregulated RBPs, were screened between ccRCC samples and noncancerous samples. From these data, eight candidate RBPs (RPS2, GAPDH, RPS20, EIF4A1, RPL18, RPL13, RPL18A, and RPS19) were identified.ConclusionsIn summary, we screened differentially expressed RBPs of ccRCC, which were enriched mainly in various biological processes and signaling pathways. Furthermore, we identified eight candidate RBPs, which could serve as potential biomarkers of ccRCC.

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

BackgroundHepatocellular 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.MethodsIn 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.ResultMost 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.ConclusionGene signatures based on RNA-binding proteins can be independent risk factors for hepatocellular carcinoma patients.

Identification of the Six-RNA-Binding Protein Signature for Prognosis Prediction in Bladder Cancer.

RNA-binding proteins (RBPs) are a kind of gene regulatory factor that presents a significant biological effect in the initiation and development of various tumors, including bladder cancer (BLCA). However, the RBP-based prognosis signature for BLCA has not been investigated. In this study, we attempted to develop an RBP-based classifier to predict overall survival (OS) for BLCA based on transcriptome analysis. We extracted data of BLCA patients from The Cancer Genome Atlas database (TCGA) and UCSC Xena. Finally, a total of 398 cases without missing clinical data were enrolled and six RBPs (FLNA, HSPG2, AHNAK, FASTKD3, POU5F1, and PCSK9) associated with OS of BLCA were identified through univariate and multivariate Cox regression analysis. Online analyses and immunohistochemistry validated the prognostic value and expression of six RBPs. Risk scores were calculated to divide patients into high-risk and low-risk level, and patients in the high-risk group tended to have a poor prognosis. In addition, the receiver operating characteristic (ROC) curve analysis was performed to assess the prognostic value of RBPs, and the area under the curve (AUC) values were 0.711 and 0.706, respectively, in the training set and validating set. The findings were further validated in an external validation set. Subsequently, the 6-RBP-based signature and pathological stage were used to construct the nomogram to predict the 3- and 5-years OS of BLCA patients. Also, this 6-RBP-based signature was highly related to recurrence-free survival of BLCA. Weighted co-expression network analysis (WGCNA) combined with functional enrichment analysis contributed to study the potential pathways of six RBPs, including keratinocyte differentiation, RHO GTPases activate PNKs, epithelial tube morphogenesis, establishment or maintenance of cell polarity, and so on. In summary, the 6-RBP-based signature holds the potentiality to serve as a novel prognostic predictor of OS for BLCA.

Integrated analysis of RNA-binding proteins in human colorectal cancer

BackgroundAlthough RNA-binding proteins play an essential role in a variety of different tumours, there are still limited efforts made to systematically analyse the role of RNA-binding proteins (RBPs) in the survival of colorectal cancer (CRC) patients.MethodsAnalysis of CRC transcriptome data collected from the TCGA database was conducted, and RBPs were extracted from CRC. R software was applied to analyse the differentially expressed genes (DEGs) of RBPs. To identify related pathways and perform functional annotation of RBP DEGs, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the database for annotation, visualization and integrated discovery. Protein-protein interactions (PPIs) of these DEGs were analysed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Based on the Cox regression analysis of the prognostic value of RBPs (from the PPI network) with survival time, the RBPs related to survival were identified, and a prognostic model was constructed. To verify the model, the data stored in the TCGA database were designated as the training set, while the chip data obtained from the GEO database were treated as the test set. Then, both survival analysis and ROC curve verification were conducted. Finally, the risk curves and nomograms of the two groups were generated to predict the survival period.ResultsAmong RBP DEGs, 314 genes were upregulated while 155 were downregulated, of which twelve RBPs (NOP14, MRPS23, MAK16, TDRD6, POP1, TDRD5, TDRD7, PPARGC1A, LIN28B, CELF4, LRRFIP2, MSI2) with prognostic value were obtained.ConclusionsThe twelve identified genes may be promising predictors of CRC and play an essential role in the pathogenesis of CRC. However, further investigation of the underlying mechanism is needed.

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

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.

Biological functions and prognostic value of RNA Binding Proteins in clear cell Renal Cell Carcinoma.

Early detection and accurate evaluation were both critical to improving the prognosis of clear cell Renal Cell Carcinoma (ccRCC) patients. More importantly, RNA Binding Proteins (RBPs) play a vital role in the tumorigenesis and progression of numerous cancers. However, the relationship between RBPs and ccRCC is still unclear. Exploring the potential biological functions of RBPs in ccRCC and establishing a prognostic signature to predict the survival probability remains meaningful. In this study, transcriptome profiling and the corresponding clinical information were obtained from the TCGA database, GEO database, and ICGC database. By using the "edgeR" R package, 200 DERBPs were found, including 128 up-regulated and 72 down-regulated RBPs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that DERBPs were mainly involved in regulating transcriptional processes and metabolism. Furthermore, there were 4 hub genes (RPS2, RPS14, RPS20, and RPLP0) were found in the PPI network, which may play vital biological roles among those DERBPs. Then we used LASSO regression to construct a prognostic signature and validated the signature in the GEO and ICGC cohort. The time-dependent receiver operating characteristic (ROC) curve showed that the signature could accurately predict the prognosis of ccRCC patients. Then we established a nomogram, and the calibration curve and ROC curve showed that the nomogram could accurately predict 1-year, 3-year, and 5-year overall survival (OS) of ccRCC patients (The AUC value: 0.871, 0.829, and 0.816). In conclusion, we constructed a 10-RBPs-based prognostic signature integrating clinical parameters to predict the prognosis of ccRCC patients. The prognostic signature based on the differentially expressed RBPs (DERBPs) might serve as promising diagnostic and prognostic biomarkers in ccRCC.