Development and Validation an Epigenetic Modification-related Signals for Diagnosis and Prognosis of Hepatocellular Carcinoma

Abstract Background: Increasing evidence has indicated that abnormal epigenetic modification such as RNAm6a modification, histone modification, DNA methylation modification, RNA binding proteins and transcription factors, is correlated with Hepatocarcinogenesis. However, it is unknown how epigenetic modification associated genes contribute to the occurrence and clinical outcome of hepatocellular carcinoma (HCC). Thus, we constructed epigenetic modification associated model that may enhance the diagnosis and prognosis of HCC.METHODS: In this study, we focused on the clinical values of epigenetic modification associated genes for HCC. Our gene expression data were collected from TCGA and a HCC datasets from GEO dataset in order to ensure the reliability of data. Their function was analyzed by bioinformatics methods. We used lasso regression, SUV, logistic regression and cox regression to construct the diagnosis and prognosis models. We also constructed a nomogram for the practicability of the above-mentioned prognosis model. The above results have been verified in an independent liver cancer dataset from ICGC database. Furthermore, we carried out pan cancer analysis to verify the specificity of the above model.RESULT: A large number of epigenetic modification associated genes were significantly different in HCC and normal liver tissues. The gene signatures showed good performance for predicting the occurrence and survival of HCC patients verified by DCA and ROC curve.CONCLUSION: Gene signatures based on epigenetic modification associated genes can be used to identify the occurrence and prognosis of liver cancer.

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Development and Validation of Epigenetic Modification-Related Signals for the Diagnosis and Prognosis of Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.649093 ◽

2021 ◽

Vol 11 ◽

Author(s):

Maoqing Lu ◽

Sheng Qiu ◽

Xianyao Jiang ◽

Diguang Wen ◽

Ronggui Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cancer ◽

Rna Binding ◽

Epigenetic Modification ◽

Support Vector ◽

Data Set ◽

Gene Signatures ◽

Cancer Data ◽

Diagnosis And Prognosis ◽

Wide Range

BackgroundIncreasing evidence has indicated that abnormal epigenetic factors such as RNA m6A modification, histone modification, DNA methylation, RNA binding proteins and transcription factors are correlated with hepatocarcinogenesis. However, it is unknown how epigenetic modification-associated genes contribute to the occurrence and clinical outcome of hepatocellular carcinoma (HCC). Thus, we constructed the epigenetic modification-associated models that may enhance the diagnosis and prognosis of HCC.MethodsIn this study, we focused on the clinical value of epigenetic modification-associated genes for HCC. Our gene expression data were collected from TCGA and HCC data sets from the GEO database to ensure the reliability of the data. Their functions were analyzed by bioinformatics methods. We used lasso regression, Support vector machine (SVM), logistic regression and Cox regression to construct the diagnostic and prognostic models. We also constructed a nomogram of the practicability of the above-mentioned prognostic model. The above results were verified in an independent liver cancer data set from the ICGC database and clinical samples. Furthermore, we carried out pan-cancer analysis to verify the specificity of the above model and screened a wide range of drug candidates.ResultsMany epigenetic modification-associated genes were significantly different in HCC and normal liver tissues. The gene signatures showed a good ability to predict the occurrence and survival of HCC patients, as verified by DCA and ROC curve analysis.ConclusionGene signatures based on epigenetic modification-associated genes can be used to identify the occurrence and prognosis of liver cancer.

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

Molecular Medicine ◽

10.1186/s10020-020-00252-5 ◽

2020 ◽

Vol 26 (1) ◽

Author(s):

Li Wang ◽

Na Zhou ◽

Jialin Qu ◽

Man Jiang ◽

Xiaochun Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Binding Proteins ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Characteristic Curve ◽

Gene Signature ◽

Clinicopathological Parameters ◽

Lasso Regression ◽

Bioinformatics Analyses

Abstract Background Hepatocellular carcinoma (HCC) is a common malignant primary cancer with high mortality. Previous studies have demonstrated that RNA binding proteins (RBPs) are involved in the biological processes of cancers, including hepatocellular cancer. Methods In this study, we aimed to identify the clinical value of RNA-binding proteins for hepatocellular carcinoma. We obtained gene expression and clinical data of hepatocellular carcinoma patients from the TCGA and ICGC databases. The prognostic value of RBP-related genes in patients with hepatocellular carcinoma and their function were studied by comprehensive bioinformatics analyses. The gene signature of SMG5, EZH2, FBLL1, ZNF239, and IGF2BP3 was generated by univariate and multivariate Cox regression and LASSO regression analyses. We built and verified a prognostic nomogram based on RBP-related genes. The gene signature was validated by the ICGC database. The expression of RBP-related genes was validated by the Oncomine database, the Human Protein Atlas and Kaplan–Meier plotter. Result Most RBP-related genes were significantly different in cancer and normal tissues. The survival of patients in the different groups was significantly different. The gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters. Conclusion Gene signatures based on RNA-binding proteins can be independent risk factors for hepatocellular carcinoma patients.

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Construction and Validation of a Potent Epigenetic Modification-Related Prognostic Signature for Osteosarcoma Patients

Journal of Oncology ◽

10.1155/2021/2719172 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Siyu Liu ◽

Bing Wu ◽

Xiaomin Li ◽

Lulu Zhao ◽

Wen Wu ◽

...

Keyword(s):

Risk Score ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Epigenetic Modification ◽

Prognostic Models ◽

Clinical Prognosis ◽

Diagnosis And Prognosis ◽

Therapeutic Decision Making

Background. Increasing evidence has shown that tumorigenesis correlates with aberrant epigenetic factors, such as DNA methylation, histone modification, RNA m6A modification, RNA binding proteins, and transcription factors. However, it is unclear that how epigenetic genes linked with alteration contribute to osteosarcoma’s incidence and clinical prognosis. We developed an epigenetic modification-related prognostic model that may improve the diagnosis and prognosis of osteosarcoma. Methods. We investigated the epigenetic modification-associated genes and their clinical significance in osteosarcoma in this research. Our gene transcriptome data were obtained from the TARGET database and the GEO database. Bioinformatics techniques were used to investigate their functionalities. The diagnostic and prognostic models were constructed using univariate and multivariate Cox regression. In addition, we developed a nomogram indicating the practicability of the prognostic model described above. Results. A risk score model constructed based on four epigenetic modification-related genes (MYC, TERT, EIF4E3, and RBM34) can effectively predict the prognosis of patients with osteosarcoma. Based on the risk score and clinical features, we constructed a nomogram. Conclusion. Epigenetic modification-related genes have been identified as important prognostic markers that may assist in osteosarcoma therapy therapeutic decision-making.

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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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Development and validation of an RNA binding protein-associated prognostic model for hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-020-07625-3 ◽

2020 ◽

Vol 20 (1) ◽

Author(s):

Min Wang ◽

Shan Huang ◽

Zefeng Chen ◽

Zhiwei Han ◽

Kezhi Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Low Risk ◽

Functional Enrichment ◽

Differentially Expressed ◽

Regression Analyses ◽

Kaplan Meier

Abstract Background Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized. Methods Data from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Finally, find protein-coding genes (PCGs) related to hub RBPs were used to construct a hub RBP-PCG co-expression network. Results In total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS. Conclusion We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes.

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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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Development and Validation of an Rna Binding Protein-associated Prognostic Model for Hepatocellular Carcinoma

10.21203/rs.3.rs-40802/v1 ◽

2020 ◽

Author(s):

Min wang ◽

Shan Huang ◽

Zefeng Chen ◽

Zhiwei Han ◽

Kezhi Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Low Risk ◽

Functional Enrichment ◽

Differentially Expressed ◽

Regression Analyses ◽

Kaplan Meier

Abstract Background: Hepatocellular carcinoma (HCC) is among the deadliest forms of cancer. While RNA-binding proteins (RBPs) have been shown to be key regulators of oncogenesis and tumor progression, their dysregulation in the context of HCC remains to be fully characterized. Methods: Data from the Cancer Genome Atlas - liver HCC (TCGA-LIHC) database were downloaded and analyzed in order to identify RBPs that were differentially expressed in HCC tumors relative to healthy normal tissues. Functional enrichment analyses of these RBPs were then conducted using the GO and KEGG databases to understand their mechanistic roles. Central hub RBPs associated with HCC patient prognosis were then detected through Cox regression analyses, and were incorporated into a prognostic model. The prognostic value of this model was then assessed through the use of Kaplan-Meier curves, time-related ROC analyses, univariate and multivariate Cox regression analyses, and nomograms. Lastly, the relationship between individual hub RBPs and HCC patient overall survival (OS) was evaluated using Kaplan-Meier curves. Results: In total, we identified 81 RBPs that were differentially expressed in HCC tumors relative to healthy tissues (54 upregulated, 27 downregulated). Seven prognostically-relevant hub RBPs (SMG5, BOP1, LIN28B, RNF17, ANG, LARP1B, and NR0B1) were then used to generate a prognostic model, after which HCC patients were separated into high- and low-risk groups based upon resultant risk score values. In both the training and test datasets, we found that high-risk HCC patients exhibited decreased OS relative to low-risk patients, with time-dependent area under the ROC curve values of 0.801 and 0.676, respectively. This model thus exhibited good prognostic performance. We additionally generated a prognostic nomogram based upon these seven hub RBPs and found that four other genes were significantly correlated with OS. Conclusion: We herein identified a seven RBP signature that can reliably be used to predict HCC patient OS, underscoring the prognostic relevance of these genes.

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Noncoding RNAs Serve as Diagnosis and Prognosis Biomarkers for Hepatocellular Carcinoma

Clinical Chemistry ◽

10.1373/clinchem.2018.301150 ◽

2019 ◽

Vol 65 (7) ◽

pp. 905-915 ◽

Cited By ~ 15

Author(s):

Chang Tan ◽

Jingyi Cao ◽

Lu Chen ◽

Xiaochen Xi ◽

Siqi Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Rna Binding ◽

Rna Binding Proteins ◽

Clinical Performance ◽

Survival Rates ◽

Noncoding Rnas ◽

Area Under The Curve ◽

Small Rna Sequencing ◽

Diagnosis And Prognosis ◽

Noninvasive Biomarkers

Abstract BACKGROUND Reliable noninvasive biomarkers for hepatocellular carcinoma (HCC) diagnosis and prognosis are urgently needed. We explored the potential of not only microRNAs (miRNAs) but other types of noncoding RNAs (ncRNAs) as HCC biomarkers. METHODS Peripheral blood samples were collected from 77 individuals; among them, 57 plasma cell-free RNA transcriptomes and 20 exosomal RNA transcriptomes were profiled. Significantly upregulated ncRNAs and published potential HCC biomarkers were validated with reverse transcription (RT)-qPCR in an independent validation cohort (60–150 samples). We particularly investigated the diagnosis and prognosis performance and biological function for 1 ncRNA biomarker, RN7SL1, and its S fragment. RESULTS We identified certain circulating ncRNAs escaping from RNase degradation, possibly through binding with RNA-binding proteins: 899 ncRNAs were highly upregulated in HCC patients. Among them, 337 genes were fragmented long noncoding RNAs, 252 genes were small nucleolar RNAs, and 134 genes were piwi-interacting RNAs. Forty-eight candidates were selected and validated with RT-qPCR, of which, 16 ncRNAs were verified to be significantly upregulated in HCC, including RN7SL1, SNHG1, ZFAS1, and LINC01359. Particularly, the abundance of RN7SL1 S fragment discriminated HCC samples from negative controls (area under the curve, 0.87; 95% CI, 0.817–0.920). HCC patients with higher concentrations of RN7SL1 S fragment had lower survival rates. Furthermore, RN7SL1 S fragment alone promoted cancer cell proliferation and clonogenic growth. CONCLUSIONS Our results show that various ncRNA species, not only miRNAs, identified in the small RNA sequencing of plasma are also able to serve as noninvasive biomarkers. Particularly, we identified a domain of srpRNA RN7SL1 with reliable clinical performance for HCC diagnosis and prognosis.

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

10.21203/rs.3.rs-86109/v1 ◽

2020 ◽

Author(s):

Li Wang ◽

Na Zhou ◽

Jialin Qu ◽

Man Jiang ◽

Xiaochun Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Binding Proteins ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Characteristic Curve ◽

Gene Signature ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters

Abstract Background: Hepatocellular carcinoma (HCC) is a leading cause of cancer-related morbidity and mortality among all human cancers. Studies have demonstrated that RNA binding proteins (RBPs) involved in the biological process of cancers including hepatocellular cancer. In this study, we aim to identify clinical value of RNA binding proteins for hepatocellular carcinoma.Methods: We analyses the data of HCC that downloaded from the Cancer Genome Atlas (TCGA) database and determined the differently expressed of RBPs between cancer and normal tissues. We further elucidate the function of RBPs by utilized Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Gene signature of SMG5, EZH2, FBLL1, ZNF239, IGF2BP3 were generated by performed the univariate and multivariate Cox regression and LASSO regression analysis. CIBERSORT analysis was used to evaluation of tumor-infiltrating immune cells in different group. We built and verify a prognosis nomogram base on RBPs-related genes. Gene signature was validated by the International Cancer Genome Consortium (ICGC) database. The expressions of RBPs-related genes were validated by using Oncomine database, and the Human Protein Atlas.Result: Most of RBPs-related genes were significantly different in cancer and normal tissue. The survival of patients in the different group was statistically different. The Gene signature showed good performance for predicting the survival of HCC patients by having a better area under the receiver operating characteristic curve than other clinicopathological parameters (AUC=0.758). The patients in the high-risk group were more likely to have a higher Macrophages M0. Conclusion: Gene signature constructed by RNA binding proteins can be independent risk factors for hepatocellular carcinoma patients.

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