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

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 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 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 colon adenocarcinoma

10.21203/rs.3.rs-29330/v1 ◽

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.

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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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Exploring an RNA binding proteins-associated prognostic model for Gastric Cancer

10.21203/rs.3.rs-33114/v1 ◽

2020 ◽

Author(s):

Ming Liu ◽

Jiayi Xie ◽

Xiaobei Luo ◽

Yaxin Luo ◽

Side Liu ◽

...

Keyword(s):

Gastric Cancer ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Roc Curves ◽

Low Risk ◽

Lasso Regression

Abstract Background: Gastric cancer (GC) is one of the most prevalent malignant cancers around the world. Given that abnormal RNA binding proteins (RBPs) are involved in the tumorigenesis, we aimed to explore the potential value of RBPs-associated genes in gastric cancer.Methods: RNA-seq and clinical data were retrieved from The Cancer Genome Atlas (TCGA) database and differentially expressed RBPs genes were screened. GO and KEGG pathway enrichment analyses were implemented to elucidate the roles of RBPs in GC. The protein-protein interaction (PPI) networks of RBPs were carried out, and the hub genes were determined by MCODE built in Cytoscape. The TCGA-STAD dataset was randomly divided into training and testing groups. A prognostic signature including five RBPs was developed within the training cohort after Cox regression and Lasso regression analyses. We used Kaplan–Meier (KM) and receiver operating characteristic (ROC) curves to evaluate the capacity of the model in both groups. Then, a nomogram based on hub RBPs expression was established. Gene Set Enrichment Analysis was performed between the high-risk and low-risk group.Results: A total of 166 up-regulated RBPs and 130 down-regulated RBPs were identified. Via Cox regression and Lasso regression analysis within the training group, five hub RBPs (RNASE1, SETD7, BOLL, PPARGC1B, MSI2) were screened and the prognostic model was constructed. The risk score was calculated and gastric cancer patients were divided into high-risk and low-risk groups. In multivariate analysis, risk score was still an independent prognostic indicator (HR = 1.80, 95% CI = 1.45-2.22, P < 0.01). Patients with low risk had favorable survival rate in both training and testing group compared to those at high risk (P < 0.001). The areas under the ROC curves (AUC) of the prognostic model are 0.718 in the training cohort and 0.651 in the testing cohort. The hub RBPs-based nomogram model exhibited excellent ability to predict the OS of GC. GSEA illustrated that several cancer-related signaling pathways were enriched in patients with a high-risk score.Conclusions: This study discovered a five RBPs signature which might provide a potential prognostic value to GC patients.

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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 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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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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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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A Five Autophagy-Related Long Non-Coding RNA Prognostic Model for Patients with Lung Adenocarcinoma

10.21203/rs.3.rs-543033/v1 ◽

2021 ◽

Author(s):

Boxuan Liu ◽

Yun Zhao ◽

Shuanying Yang

Keyword(s):

Lung Adenocarcinoma ◽

Risk Score ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Predictive Ability ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Lasso Regression ◽

Cox Regression Analysis

Abstract Background: Lung adenocarcinoma is the most occurred pathological type among non-small cell lung cancer. Although huge progress has been made in terms of early diagnosis, precision treatment in recent years, the overall 5-year survival rate of a patient remains low. In our study, we try to construct an autophagy-related lncRNA prognostic signature that may guide clinical practice.Methods: The mRNA and lncRNA expression matrix of lung adenocarcinoma patients were retrieved from TCGA database. Next, we constructed a co-expression network of lncRNAs and autophagy-related genes. Lasso regression and multivariate Cox regression were then applied to establish a prognostic risk model. Subsequently, a risk score was generated to differentiate high and low risk group and a ROC curve and Nomogram to visualize the predictive ability of current signature. Finally, gene ontology and pathway enrichment analysis were executed via GSEA.Results: A total of 1,703 autophagy-related lncRNAs were screened and five autophagy-related lncRNAs (LINC01137, AL691432.2, LINC01116, AL606489.1 and HLA-DQB1-AS1) were finally included in our signature. Judging from univariate(HR=1.075, 95% CI: 1.046–1.104) and multivariate(HR =1.088, 95%CI = 1.057 − 1.120) Cox regression analysis, the risk score is an independent factor for LUAD patients. Further, the AUC value based on the risk score for 1-year, 3-year, 5-year, was 0.735, 0.672 and 0.662 respectively. Finally, the lncRNAs included in our signature were primarily enriched in autophagy process, metabolism, p53 pathway and JAK/STAT pathway. Conclusions: Overall, our study indicated that the prognostic model we generated had certain predictability for LUAD patients’ prognosis.

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