RNA Binding Protein-Based Model for Prognostic Prediction of Colorectal Cancer

Background: Colorectal cancer (CRC) is a kind of gastrointestinal tumor with serious high morbidity and mortality. Several reports have implicated the disorder of RNA-binding proteins (RBPs) in plenty of tumors, associating it to tumorigenesis and disease progression. The study is intended to construct novel prognostic biomarkers associated with CRC patients. Methods: Data of gene expression was acquired from the TCGA database, prognosis-related genes were selected. Besides, we analyzed GO and KEGG pathways. Univariate and multivariate Cox analyses were performed to generate a prognostic-related gene signature, which was evaluated by the Kaplan-Meier (K-M) and the Receiver Operating Characteristic (ROC) curve. The independent prognostic factor was established by survival analysis. GSE38832 dataset was used to validate the signature. Finally, expression of 8 genes was further confirmed by qRT-PCR in SW480 and SW620 cell lines. Results: We obtained 224 differentially expressed RBPS in total, of which 78 were downregulated and 146 were upregulated. Univariate COX analysis was conducted in the TCGA cohort to select 13 RBPs with P < 0.005, stepwise multivariate COX regression analysis was used to construct an 8—RBP signature (TERT, PPARGC1A, BRCA1, CELF4, TDRD7, LUZP4, PNLDC1, ZC3H12C). Based on the model, systematic analysis illustrated that a high risk score was obviously connected to a poor prognosis. The prognostic value of the risk score was validated in GSE38832 dataset, indicating that the risk model was accurate and effective. The prognostic signature-based risk score was identified as an independent prognostic indicator for CRC. The expression results of qRT-PCR were consistent with the results of differential expression analysis. Conclusions: The eight-RBP signature can predict the survival of CRC patients and potentially act as CRC prognostic biomarker.

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Identification of Prognostic RBPs in Osteosarcoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211004918 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110049

Author(s):

Bei Li ◽

Long Fang ◽

Baolong Wang ◽

Zengkun Yang ◽

Tingbao Zhao

Keyword(s):

Regression Analysis ◽

Prognostic Factors ◽

Differential Expression ◽

Ribosome Biogenesis ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Malignant Tumors ◽

Differential Expression Analysis ◽

Cox Regression Analysis

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman’s correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

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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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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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A Six CT83-Related Genes Based Prognostic Signature for Lung Adenocarcinoma

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1871520621666210713112630 ◽

2021 ◽

Vol 24 ◽

Author(s):

Yongmei Wang ◽

Guimin Zhang ◽

Ruixian Wang

Keyword(s):

Regression Analysis ◽

Lung Adenocarcinoma ◽

Differential Expression ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Differential Expression Analysis ◽

Functional Enrichment ◽

Risk Scores ◽

Cox Regression Analysis

Background: This study aims to explore the prognostic values of CT83 and CT83-related genes in lung adenocarcinoma (LUAD). Methods: We downloaded the mRNA profiles of 513 LUAD patients (RNA sequencing data) and 246 NSCLC patients (Affymetrix Human Genome U133 Plus 2.0 Array) from TCGA and GEO databases. According to the median expression of CT83, the TCGA samples were divided into high and low expression groups, and differential expression analysis between them was performed. Functional enrichment analysis of differential expression genes (DEGs) was conducted. Univariate Cox regression analysis and LASSO Cox regression analysis were performed to screen the optimal prognostic DEGs. Then we established the prognostic model. A Nomogram model was constructed to predict the overall survival (OS) probability of LUAD patients. Results: CT83 expression was significantly correlated to the prognosis of LUAD patients. A total of 59 DEGs were identified, and a predictive model was constructed based on six optimal CT83-related DEGs, including CPS1, RHOV, TNNT1, FAM83A, IGF2BP1, and GRIN2A, could effectively predict the prognosis of LUAD patients. The nomogram could reliably predict the OS of LUAD patients. Moreover, the six important immune checkpoints (CTLA4, PD1, IDO1, TDO2, LAG3, and TIGIT) were closely correlated with the Risk Score, which was also differentially expressed between the LUAD samples with high and low-Risk Scores, suggesting that the poor prognosis of LUAD patients with high-Risk Score might be due to the immunosuppressive microenvironments. Conclusion: A prognostic model based on six optimal CT83 related genes could effectively predict the prognosis of LUAD patients.

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Development of an Immune Infiltration-Related Eight-Gene Prognostic Signature in Colorectal Cancer Microenvironment

BioMed Research International ◽

10.1155/2020/2719739 ◽

2020 ◽

Vol 2020 ◽

pp. 1-43

Author(s):

Beilei Wu ◽

Lijun Tao ◽

Daqing Yang ◽

Wei Li ◽

Hongbo Xu ◽

...

Keyword(s):

Colorectal Cancer ◽

Risk Score ◽

Immune Cells ◽

Clinical Characteristics ◽

Cox Regression ◽

Roc Curves ◽

Functional Enrichment ◽

Cox Regression Analysis ◽

Immune Genes ◽

Score Model

Objective. Stromal cells and immune cells have important clinical significance in the microenvironment of colorectal cancer (CRC). This study is aimed at developing a CRC gene signature on the basis of stromal and immune scores. Methods. A cohort of CRC patients (n=433) were adopted from The Cancer Genome Atlas (TCGA) database. Stromal/immune scores were calculated by the ESTIMATE algorithm. Correlation between prognosis/clinical characteristics and stromal/immune scores was assessed. Differentially expressed stromal and immune genes were identified. Their potential functions were annotated by functional enrichment analysis. Cox regression analysis was used to develop an eight-gene risk score model. Its predictive efficacies for 3 years, 5 years, overall survival (OS), and progression-free survival interval (PFI) were evaluated using time-dependent receiver operating characteristic (ROC) curves. The correlation between the risk score and the infiltering levels of six immune cells was analyzed using TIMER. The risk score was validated using an independent dataset. Results. Immune score was in a significant association with prognosis and clinical characteristics of CRC. 736 upregulated and two downregulated stromal and immune genes were identified, which were mainly enriched into immune-related biological processes and pathways. An-eight gene prognostic risk score model was conducted, consisting of CCL22, CD36, CPA3, CPT1C, KCNE4, NFATC1, RASGRP2, and SLC2A3. High risk score indicated a poor prognosis of patients. The area under the ROC curves (AUC) s of the model for 3 years, 5 years, OS, and PFI were 0.71, 0.70, 0.73, and 0.66, respectively. Thus, the model possessed well performance for prediction of patients’ prognosis, which was confirmed by an external dataset. Moreover, the risk score was significantly correlated with immune cell infiltration. Conclusion. Our study conducted an immune-related prognostic risk score model, which could provide novel targets for immunotherapy of CRC.

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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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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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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 Signature Constructed by Immune-Related LncRNA Predicts the Immune Landscape of Colorectal Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.695130 ◽

2021 ◽

Vol 12 ◽

Author(s):

Mengyu Sun ◽

Tongyue Zhang ◽

Yijun Wang ◽

Wenjie Huang ◽

Limin Xia

Keyword(s):

Colorectal Cancer ◽

High Risk ◽

Immune Cell ◽

Cox Regression ◽

Risk Group ◽

Univariate Analysis ◽

The Cancer Genome Atlas ◽

High Morbidity ◽

Cox Regression Analysis ◽

Chemotherapy Drugs

Colorectal cancer (CRC) has the characteristics of high morbidity and mortality. LncRNA not only participates in the progression of CRC through genes and transcription levels, but also regulates the tumor microenvironment and leads to the malignant phenotype of tumors. Therefore, we identified immune-related LncRNAs for the construction of clinical prognostic model. We searched The Cancer Genome Atlas (TCGA) database for original data. Then we identified differentially expressed irlncRNA (DEirlncRNA), which was paired and verified subsequently. Next, univariate analysis, Lasso and Cox regression analysis were performed on the DEirlncRNA pair. The ROC curve of the signature was drawn, and the optimal cut-off value was found. Then the cohort was divided into a high-risk and a low-risk group. Finally, we re-evaluated the signature from different perspectives. A total of 16 pairs of DEirlncRNA were included in the construction of the model. After regrouping according to the cut-off value of 1.275, the high-risk group showed adverse survival outcomes, progressive clinicopathological features, specific immune cell infiltration status, and high sensitivity to some chemotherapy drugs. In conclusion, we constructed a signature composed of immune-related LncRNA pair with no requirement of the specific expression level of genes, which shows promising clinical predictive value in CRC 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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