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

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.

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

10.21203/rs.3.rs-846096/v1 ◽

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.

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

PLoS ONE ◽

10.1371/journal.pone.0260876 ◽

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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The construction and validation of an RNA binding protein-related prognostic model for bladder cancer

BMC Cancer ◽

10.1186/s12885-021-07930-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Fengxia Chen ◽

Qingqing Wang ◽

Yunfeng Zhou

Keyword(s):

Bladder Cancer ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Sequence Data ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Differentially Expressed ◽

Depth Analysis

Abstract Background RNA-binding proteins (RBPs) play crucial and multifaceted roles in post-transcriptional regulation. While RBPs dysregulation is involved in tumorigenesis and progression, little is known about the role of RBPs in bladder cancer (BLCA) prognosis. This study aimed to establish a prognostic model based on the prognosis-related RBPs to predict the survival of BLCA patients. Methods We downloaded BLCA RNA sequence data from The Cancer Genome Atlas (TCGA) database and identified RBPs differentially expressed between tumour and normal tissues. Then, functional enrichment analysis of these differentially expressed RBPs was conducted. Independent prognosis-associated RBPs were identified by univariable and multivariable Cox regression analyses to construct a risk score model. Subsequently, Kaplan–Meier and receiver operating characteristic curves were plotted to assess the performance of this prognostic model. Finally, a nomogram was established followed by the validation of its prognostic value and expression of the hub RBPs. Results The 385 differentially expressed RBPs were identified included 218 and 167 upregulated and downregulated RBPs, respectively. The eight independent prognosis-associated RBPs (EFTUD2, GEMIN7, OAS1, APOBEC3H, TRIM71, DARS2, YTHDC1, and RBMS3) were then used to construct a prognostic prediction model. An in-depth analysis showed lower overall survival (OS) in patients in the high-risk subgroup compared to that in patients in the low-risk subgroup according to the prognostic model. The area under the curve of the time-dependent receiver operator characteristic (ROC) curve were 0.795 and 0.669 for the TCGA training and test datasets, respectively, showing a moderate predictive discrimination of the prognostic model. A nomogram was established, which showed a favourable predictive value for the prognosis of BLCA. Conclusions We developed and validated the performance of a prognostic model for BLCA that might facilitate the development of new biomarkers for the prognostic assessment of BLCA patients.

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

10.21203/rs.3.rs-46791/v1 ◽

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.

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Construction and Validation of a Prognostic Model of Gastric Cancer with 10RBPs Based on 6 Microarrays

10.21203/rs.3.rs-117996/v1 ◽

2020 ◽

Author(s):

Liqiang Zhou ◽

Hao Lu ◽

Lin Xin ◽

Qi Zhou ◽

You Wu ◽

...

Keyword(s):

Gastric Cancer ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Differentially Expressed ◽

Lasso Regression ◽

Operating Characteristics ◽

Systematic Analysis ◽

Cox Regression Analysis

Abstract For explore the potential connection of RNA binding proteins (RBPs) to the expression function of gastric cancer (GC). We download the GPL10558 and GPL6947 platform mircroarray data from Gene Expression Omnibus (GEO) and Express database. Then the system integrates and analyzes the differentially expressed RBPs. And enrich the differentially expressed RBPs to understand the mechanism of its influence on tumors. Univariate Cox, lasso regression and multivariate Cox regression analysis were used to screen independent prognostic parameters to construct prognostic model, and calculate aera under time-dependent receiver operating characteristics (AUC) and survival analysis were used to evaluate their prognostic ability. GSE15459, GSE62254 cohorts were used to verify hub signature. Finally, we also verified the prognosis and expression of hub-RBPs. Systematic analysis identified 23 up-regulated and 30 down-regulated RBPs, and enrichment analysis showed that they mainly affect their modification by binding to mRNA, and their stability affects the progression of GC. After multiple statistical analyses, we obtained the prognostic signature constructed by 10 RBPs and determined that it has better predictive performance (AUC = 0.685). Through comprehensive bioinformatics analysis, we have obtained 10 key gastric cancer RBPs as potential prognostic biomarkers, providing new perspectives for the treatment and prognostic of GC.

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Integrated Analysis of the Prognosis-Associated RNA-Binding Protein Genes and Candidate Drugs in Renal Papillary Cell Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.627508 ◽

2021 ◽

Vol 12 ◽

Author(s):

Silin Jiang ◽

Xiaohan Ren ◽

Shouyong Liu ◽

Zhongwen Lu ◽

Aiming Xu ◽

...

Keyword(s):

Cell Carcinoma ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Biological Effects ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Integrated Analysis ◽

Poor Overall Survival ◽

Cox Regression Analysis

RNA-binding proteins (RBPs) play significant roles in various cancer types. However, the functions of RBPs have not been clarified in renal papillary cell carcinoma (pRCC). In this study, we identified 31 downregulated and 89 upregulated differentially expressed RBPs on the basis of the cancer genome atlas (TCGA) database and performed functional enrichment analyses. Subsequently, through univariate Cox, random survival forest, and multivariate Cox regression analysis, six RBPs of SNRPN, RRS1, INTS8, RBPMS2, IGF2BP3, and PIH1D2 were screened out, and the prognostic model was then established. Further analyses revealed that the high-risk group had poor overall survival. The area under the curve values were 0.87 and 0.75 at 3 years and 0.78 and 0.69 at 5 years in the training set and test set, respectively. We then plotted a nomogram on the basis of the six RBPs and tumor stage with the substantiation in the TCGA cohort. Moreover, we selected two intersectant RBPs and evaluate their biological effects by GSEA and predicted three drugs, including STOCK1N-28457, pyrimethamine, and trapidil by using the Connectivity Map. Our research provided a novel insight into pRCC and improved the determination of prognosis and individualized therapeutic strategies.

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Integrated Analysis of Nine Prognostic RNA-Binding Proteins in Soft Tissue Sarcoma

Frontiers in Oncology ◽

10.3389/fonc.2021.633024 ◽

2021 ◽

Vol 11 ◽

Author(s):

Lu-Lu Lin ◽

Zi-Zhen Liu ◽

Jing-Zhuo Tian ◽

Xiao Zhang ◽

Yan Zhang ◽

...

Keyword(s):

Soft Tissue ◽

Soft Tissue Sarcoma ◽

Risk Score ◽

Binding Proteins ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Functional Enrichment ◽

Differentially Expressed ◽

Integrated Analysis

RNA-binding proteins (RBPs) have been shown to be dysregulated in cancer transcription and translation, but few studies have investigated their mechanism of action in soft tissue sarcoma (STS). Here, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases were used to identify differentially expressed RBPs in STS and normal tissues. Through a series of biological information analyses, 329 differentially expressed RBPs were identified. Functional enrichment analysis showed that differentially expressed RBPs were mainly involved in RNA transport, RNA splicing, mRNA monitoring pathways, ribosome biogenesis and translation regulation. Through Cox regression analyses, 9 RBPs (BYSL, IGF2BP3, DNMT3B, TERT, CD3EAP, SRSF12, TLR7, TRIM21 and MEX3A) were all up-regulated in STS as prognosis-related genes, and a prognostic model was established. The model calculated a risk score based on the expression of 9 hub RBPs. The risk score could be used for risk stratification of patients and had a high prognostic value based on the receiver operating characteristic (ROC) curve. We also established a nomogram containing risk scores and 9 key RBPs to predict the 1-year, 3-year, and 5-year survival rates of patients in STS. Afterwards, methylation analysis showed significant changes in the methylation degree of BYSL, CD3EAP and MEX2A. Furthermore, the expression of 9 hub RBPs was closely related to immune infiltration rather than tumor purity. Based on the above studies, these findings may provide new insights into the pathogenesis of STS and will provide candidate biomarkers for the prognosis of STS.

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Comprehensive Analysis of the Functions and Prognostic Value of RNA-Binding Proteins in Thyroid Cancer

Frontiers in Oncology ◽

10.3389/fonc.2021.625007 ◽

2021 ◽

Vol 11 ◽

Author(s):

Yue Ma ◽

Shi Yin ◽

Xiao-feng Liu ◽

Jing Hu ◽

Ning Cai ◽

...

Keyword(s):

Gene Expression ◽

Thyroid Cancer ◽

Gene Expression Data ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Integrated Analysis ◽

Expression Data

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

10.21203/rs.3.rs-105341/v1 ◽

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.

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Identification of RNA-Binding Proteins with Prognostic Prediction in Colorectal Cancer

BioMed Research International ◽

10.1155/2021/7114445 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Xiao-fen Bai ◽

Jing-wen Liu

Keyword(s):

Colorectal Cancer ◽

High Risk ◽

Prognostic Model ◽

Binding Proteins ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Risk Group ◽

The Cancer Genome Atlas ◽

Risk Scores

Colorectal cancer (CRC) is one of the most common malignancies of the digestive system. Recent studies have revealed the importance of RNA-binding proteins (RBPs) in tumorigenesis, but their role in CRC remains unclear. The present study systematically analyzed the relationships between RBPs and CRC using data from The Cancer Genome Atlas. We detected 483 differentially expressed RBPs and identified a series of pathways and processes using GO (Gene Ontology) analysis and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis. Analyzing protein–protein interactions and modules identified the edges and modules of RBPs. Univariate and multivariate Cox regression analyses were then used to construct a prognostic model that included 13 RBPs. Survival analyses indicated that the overall survival (OS) was significantly lower for CRC patients in the high-risk group than for those in the low-risk group, and that high risk scores were associated with poor OS. Finally, we constructed a nomogram that included 13 RBPs for calculating the estimated survival probabilities of CRC patients at 1, 2, and 3 years. Calibration plots indicated good conformity between the predicted and observed outcomes. This study has revealed that the expression of RBPs differs between CRC and normal tissues. A prognostic model based on 13 RBP coding genes has been developed that can provide independent prognoses of CRC.

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