Expression profile of RNA binding protein in cervical cancer using bioinformatics approach

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 proportional hazard cox regression and multivariate proportional 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 model showed that there were ten DEGs significantly associated with overall survival of cervical cancer 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 stratified 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.

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Expression Profile of RNA Binding Protein in Cervical Cancer

10.21203/rs.3.rs-655697/v1 ◽

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.

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Expression profiling analysis of autophagy-related genes in cervical cancer

10.21203/rs.3.rs-595676/v1 ◽

2021 ◽

Author(s):

Zhiyuan Huang ◽

He Wang ◽

Min Liu ◽

Xinrui Li ◽

Lei Zhu ◽

...

Keyword(s):

Risk Factors ◽

Cervical Cancer ◽

Overall Survival ◽

Risk Score ◽

Roc Curve ◽

Normal Tissue ◽

Cox Regression ◽

Risk Group ◽

Differentially Expressed ◽

Tissue Samples

Abstract Background: It has been demonstrated by studies globally that autophagy took part in the development of cervical cancer (CC). Few studies concentrated on the correlation between overall survival and CC patients. We retrieved significant autophagy-related genes (ARGs) correlated to the process of cervical cancer. They may be used as prognosis marker or treatment target for clinical application.Methods: Expressions level of genes in cervical cancer and normal tissue samples were obtained from GTEx and TCGA database. Autophagy-related genes (ARGs) were retrieved accroding to the gene list from HaDB. Differentially expressed autophagy related genes (DE-ARGs) related to cervical cancer were identified by Wilcoxon signed-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 DE-ARGs 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 constructed accroding to multivariate cox regression. Correlations between Differentially expressed autophagy related genes (DE-ARGs) and other clinical features were investigated by t test or Cruskal wallis analysis. Correlation between Immune and autophagy in cervical cancer was investigated by ssGSEA and TIMER database. Results: Fifty-six differentially expressed ARGs (DE-ARGs) were retrieved from cervical cancer tissue and normal tissue samples. GO enrichment analysis showed that these ARGs involved in autophagy, ubiquitination of protein and apoptosis. Cox regression medel showed that there were six ARGs significantly associated with overall survival of cervical caner patients. VAMP7 (HR = 0.599, P= 0.033) and TP73 (HR = 0.671, P= 0.014) played protective roles in survival among these six genes. Stage (Stage IV vs Stage I HR = 3.985, P<0.001) and risk score (HR = 1.353, 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 six predictor ARGs were correlated significantly in statistic with tumor grade or stage, clinical T stage, clinical N stage, pathology or risk score (all P< 0.05). The 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 autophagy genes involved in the progress of cervical cancer. Many autophagy-related genes could probably serve as prognostic biomarkers and accelerate the discovery of treatment targets for CC patients.

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

10.21203/rs.3.rs-379855/v1 ◽

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.

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

Frontiers in Oncology ◽

10.3389/fonc.2021.592614 ◽

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.

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Genome-Wide Profiling of Cervical RNA-Binding Proteins Identifies Human Papillomavirus Regulation of RNASEH2A Expression by Viral E7 and E2F1

mBio ◽

10.1128/mbio.02687-18 ◽

2019 ◽

Vol 10 (1) ◽

pp. e02687-18 ◽

Cited By ~ 15

Author(s):

Junfen Xu ◽

Habin Liu ◽

Yanqin Yang ◽

Xiaohong Wang ◽

Poching Liu ◽

...

Keyword(s):

Cervical Cancer ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Cancer Tissue ◽

Cervical Tissue ◽

Tissue Samples ◽

Altered Expression ◽

Cervical Cancer Tissue ◽

E6 And E7

ABSTRACTRNA-binding proteins (RBPs) control mRNA processing, stability, transport, editing, and translation. We recently conducted transcriptome analyses comparing normal (i.e., healthy) cervical tissue samples with human papillomavirus (HPV)-positive cervical cancer tissue samples and identified 614 differentially expressed protein-coding transcripts which are enriched in cancer-related pathways and consist of 95 known RBPs. We verified the altered expression of 26 genes with a cohort of 72 cervical samples, including 24 normal cervical samples, 25 cervical intraepithelial neoplasia grade 2 (CIN2) and CIN3 samples, and 23 cervical cancer tissue samples. LY6K (lymphocyte antigen 6 complex locus K), FAM83A (family member with sequence similarity 83), CELSR3, ASF1B, IQGAP3, SEMA3F, CLDN10, MSX1, CXCL5, ASRGL1, ELAVL2, GRB7, KHSRP, NOVA1, PTBP1, and RNASEH2A were identified as novel candidate genes associated with cervical lesion progression and carcinogenesis. HPV16 or HPV18 infection was found to alter the expression of 8 RBP genes (CDKN2A, ELAVL2, GRB7, HSPB1, KHSRP, NOVA1, PTBP1, and RNASEH2A) in human vaginal and foreskin keratinocytes. Both viral E6 and E7 decreased NOVA1 expression, but only E7 increased the expression of RNASEH2A in an E2F1-dependent manner. Proliferating cell nuclear antigen (PCNA) directs RNASEH2 activity with respect to DNA replication by removing the RNA primers to promote Okazaki fragment maturation, and two factors are closely associated with neoplasia progression. Therefore, we predict that the induction of expression of RNASEH2A via viral E7 and E2F1 may promote DNA replication and cancer cell proliferation.IMPORTANCEHigh-risk HPV infections lead to development of cervical cancer. This study identified the differential expression of 16 novel genes (LY6K, FAM83A, CELSR3, ASF1B, IQGAP3, SEMA3F, CLDN10, MSX1, CXCL5, ASRGL1, ELAVL2, GRB7, KHSRP, NOVA1, PTBP1, and RNASEH2A) in HPV-infected cervical tissue samples and keratinocytes. Eight of these genes (CDKN2A, ELAVL2, GRB7, HSPB1, KHSRP, NOVA1, PTBP1, and RNASEH2A) encode RNA-binding proteins. Further studies indicated that both HPV16 and HPV18 infections lead to the aberrant expression of selected RBP-encoding genes. We found that viral E6 and E7 decrease NOVA1 expression but that E7 increases RNASEH2A expression via E2F1. The altered expression of these genes may be utilized as biomarkers for high-risk (HR)-HPV carcinogenesis and progression.

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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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Development and validation of a six-RNA binding proteins prognostic signature and candidate drugs for prostate cancer

10.1101/2020.06.28.175984 ◽

2020 ◽

Author(s):

Lei Gao ◽

Jialin Meng ◽

Yong Zhang ◽

Junfei Gu ◽

Zhenwei Han ◽

...

Keyword(s):

Prostate Cancer ◽

High Risk ◽

Binding Proteins ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Risk Group ◽

Life Quality ◽

High Risk Group ◽

Prognostic Signature

AbstractThe dysregulation of RNA binding proteins (RBPs) play critical roles in the progression of several cancers. However, the overall functions of RBPs in prostate cancer (PCa) remain poorly understood. Therefore, we first identified 144 differentially expressed RBPs in tumors compared to normal tissues based on the TCGA dataset. Next, six RBP genes (MSI1, MBNL2, LENG9, REXO2, RNASE1, PABPC1L) were screened out as prognosis hub genes by univariate, LASSO and multivariate Cox regression and used to establish the prognostic signature. Further analysis indicated that high risk group was significantly associated with poor RFS, which was validated in the MSKCC cohort. Besides, patients in high risk group was closely associated with dysregulation of DNA damage repair pathway, copy number alteration, tumor burden mutation and low-respond to cisplatin (P < 0.001), bicalutamide (P < 0.001). Finally, three drugs (ribavirin, carmustine, carbenoxolone) were predicted using Connectivity Map. In summary, we identified a six-RBP gene signature and three candidate drugs against PCa, which may promote the individualized treatment and further improve the life quality of PCa patients.

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Systematic Construction and Validation of an RNA Binding Proteins-Based Signature for Prognostic Prediction in Gastric Cancer

10.21203/rs.3.rs-52319/v1 ◽

2020 ◽

Author(s):

Yi Zhang ◽

Yuzhi Wang ◽

Chengwen Li ◽

Tianhua Jiang

Keyword(s):

Gastric Cancer ◽

Binding Proteins ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Risk Group ◽

Low Risk ◽

Functional Enrichment ◽

Ppi Network ◽

Incidence And Mortality

Abstract Background: Gastric cancer (GC) is one of the most common cancers with high incidence and mortality worldwide. Recently, RNA-binding proteins (RBPs) have drawn more and more attention for its role in cancer pathophysiology. In this study, we aim to explore the function and clinical implication of RBPs in GC. Methods: RNA sequencing data along with the corresponding clinical information of GC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed RNA-binding proteins (DERBPs) between tumor and normal tissues were identified by ‘limma’ package. Functional enrichment analysis and the protein-protein interaction (PPI) network were harnessed to explore the function and interaction of DERBPs. Next, Univariate and multiple Cox regression were applied to screen prognosis-related hub RBPs and to construct a signature for BC. Meanwhile, a nomogram was built based on the same RBPs. Results: A total of 296 DERBPs were found, and most of them mainly related to post-transcriptional regulation of RNA and ribonucleoprotein. A PPI network of DERBPs was constructed, consisting of 262 nodes and 2567 edges. A prognostic signature was built depended on seven prognosis-related hub RBPs that could divide GC patients into high- and low-risk groups. Survival analysis showed that the high-risk group had a worse prognosis compared to the low-risk group and the time-dependent receiver operating characteristic (ROC) curves suggested that the signature existed moderate predictive capacities of survival for GC patients. Similar results were obtained from another independent set GSE84437, confirming the robustness of signature. Calibration plots reported good consistency between overall survival (OS) prediction by nomogram and actual observation. Conclusion: The findings of this study would provide evidence of the effect of RBPs on GC as well as offering novel potential biomarkers in prognosis prediction and clinical decision for GC patients.

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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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