Systematic Analysis Identifies a Specific RNA-Binding Protein-Related Gene Model for Prognostication and Risk-Adjustment in HBV-Related Hepatocellular Carcinoma

ObjectiveIncreasing evidence shows that dysregulated RNA binding proteins (RBPs) modulate the progression of several malignancies. Nevertheless, their clinical implications of RBPs in HBV-related hepatocellular carcinoma (HCC) remain largely undefined. Here, this study systematically analyzed the associations of RBPs with HBV-related HCC prognosis.MethodsBased on differentially expressed RBPs between HBV-related HCC and control specimens, prognosis-related RBPs were screened by univariate analyses. A LASSO model was then created. Kaplan-Meier curves, ROCs, multivariate analyses, subgroup analyses and external verification were separately applied to assess the efficacy of this model in predicting prognosis and recurrence of patients. A nomogram was created by incorporating the model and clinical indicators, which was verified by ROCs, calibration curves and decision curve analyses. By CIBERSORT algorithm, the association between the risk score and immune cell infiltrations was evaluated.ResultsTotally, 54 RBPs were distinctly correlated to prognosis of HBV-related HCC. An 11-RBP model was created, containing POLR2L, MRPS12, DYNLL1, ZFP36, PPIH, RARS, SRP14, DDX41, EIF2B4, and NOL12. This risk score sensitively and accurately predicted one-, three- and five-year overall survival, disease-free survival, and progression-free interval. Compared to other clinical parameters, this risk score had the best predictive efficacy. Also, the clinical generalizability of the model was externally verified in the GSE14520 dataset. The nomogram may predict patients’ survival probabilities. Also, the risk score was related to the components in the immune microenvironment.ConclusionCollectively, RBPs may act as critical elements in the malignant progression of HBV-related HCC and possess potential implications on prognostication and therapy decision.

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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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RNA-Binding Proteins Play an Important Role in the Prognosis of Patients With Testicular Germ Cell Tumor

Frontiers in Genetics ◽

10.3389/fgene.2021.610291 ◽

2021 ◽

Vol 12 ◽

Author(s):

Liangyu Yao ◽

Rong Cong ◽

Chengjian Ji ◽

Xiang Zhou ◽

Jiaochen Luan ◽

...

Keyword(s):

Germ Cell ◽

Risk Score ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Expression Profiles ◽

Germ Cell Tumors ◽

Disease Free Survival ◽

Testicular Germ Cell ◽

Model Based

Testicular germ cell tumors (TGCTs) are common urological neoplasms in young adult males. The outcome of TGCT depends on pathologic type and tumor stage. RNA-binding proteins (RBPs) influence numerous cancers via post-transcriptional regulation. The prognostic importance of RBPs in TGCT has not been fully investigated. In this study, we set up a prognostic risk model of TGCT using six significantly differentially expressed RBPs, namely, TRMT61A, POLR2J, DIS3L2, IFIH1, IGHMBP2, and NPM2. The expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression datasets. We observed by performing least absolute shrinkage and selection operator (LASSO) regression analyses that in the training cohort, the expression of six RBPs was correlated with disease-free survival in patients with TGCT. We assessed the specificity and sensitivity of 1-, 3-, 5-, and 10-year survival status prediction using receiver operating characteristic curve analysis and successfully validated using the test cohorts, the entire TCGA cohort, and Gene Expression Omnibus (GEO) datasets. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analyses were carried out to seek the possible signaling pathways related with risk score. We also examined the association between the model based on six RBPs and different clinical characteristics. A nomogram was established for TGCT recurrence prediction. Consensus clustering analysis was carried out to identify the clusters of TGCT with different clinical outcomes. Ultimately, external validations of the six-gene risk score were performed by using the GSE3218 and GSE10783 datasets downloaded from the GEO database. In general, our study constructed a prognostic model based on six RBPs, which could serve as independent risk factor in TGCT, especially in seminoma, and might have brilliant clinical application value.

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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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Integrated analysis of the functions of RNA binding proteins in clear cell renal cell carcinoma

10.21203/rs.3.rs-28762/v1 ◽

2020 ◽

Author(s):

Xiaoliang Hua ◽

Juan Chen ◽

Shengdong Ge ◽

Haibing Xiao ◽

Li Zhang ◽

...

Keyword(s):

Risk Score ◽

Rna Binding ◽

Cox Regression ◽

Clear Cell ◽

Rna Binding Proteins ◽

Diagnostic Value ◽

Cell Renal Cell Carcinoma ◽

Kaplan Meier ◽

Score Model ◽

Cluster 2

Abstract Background: RNA binding proteins (RBPs) dysregulation is involved in the process es of various tumor. However, the roles of RBPs in clear cell renal cell carcinoma (ccRCC) remain poorly understand. Systematic exploration of the roles of RBPs in ccRCC may provide new insights for the treatments of ccRCC. Methods: Expression data of RBPs was obtained from The Cancer Genome Atlas database. The roles of RBPs in ccRCC were systematically investigated using consensus clustering methods. Differentially expressed RBPs between normal and tumor tissues were obtained. Protein-protein interaction (PPI) network was constructed using “STRING” software. The expression levels of hub genes were validated in The Human Protein Atlas (HPA) database and receiver operating characteristic (ROC) curves were used to evaluate diagnostic value . Univariate and Lasso Cox regression and Kaplan–Meier curves were used to screen the most useful prognostic genes. Multivariate Cox regression was performed to construct a risk score model. The efficiency of the model was evaluated using time-dependent ROC and Kaplan–Meier curves, and validated in E-MTAB-3267 set. Results: Two clusters were identified based on the expression similarity of RBPs, and the cluster 2 was closely correlated with the malignancy of ccRCC. Several oncogenic pathways, including epithelial mesenchymal transition, G2M checkpoint, KRAS signaling and IL6 JAK STAT3 signaling were enriched in cluster 2. In addition, we obtained 115 differently expressed RBPs in ccRCC, comprising 71 up-regulated and 44 down-regulated ones. Ten hub RBPs with good diagnostic value were obtained from PPI network and validated in HPA database. Ten RBPs were identified as survival-related genes and used to construct a risk score model. The model could be used to stratify patients with different prognosis. We found high-risk patients tended to be advanced stage, high grade, high pathological T staging and could be an independent risk factor for overall survival of ccRCC patients. Conclusions: We identified ten RBPs with diagnostic value, which might be the potential diagnostic biomarkers for ccRCC. A risk score model was established to stratify patients and could be used as a complementation for clinical factors to guide clinical practice in the future.

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

10.21203/rs.3.rs-40802/v3 ◽

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

10.21203/rs.3.rs-40802/v2 ◽

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. 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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Transcript levels of spindle and kinetochore-associated complex 1/3 as prognostic biomarkers correlated with immune infiltrates in hepatocellular carcinoma

Scientific Reports ◽

10.1038/s41598-021-89628-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

De-Chen Yu ◽

Xiang-Yi Chen ◽

Xin Li ◽

Hai-Yu Zhou ◽

De-Quan Yu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Protein Complex ◽

Immune Cell ◽

Prognostic Significance ◽

Tumor Development ◽

Disease Free Survival ◽

Tumor Grade ◽

Tumor Stage ◽

Prognostic Biomarkers ◽

High Expression

AbstractThe spindle and kinetochore-associated protein complex (Ska) is an essential component in chromosome segregation. It comprises three proteins (Ska1, Ska2, and Ska3) with theorized roles in chromosomal instability and tumor development, and its overexpression has been widely reported in a variety of tumors. However, the prognostic significance and immune infiltration of Ska proteins in hepatocellular carcinoma (HCC) are not completely understood. The bioinformatics tools Oncomine, UALCAN, gene expression profiling interactive analysis 2 (GEPIA2), cBioPortal, GeneMANIA, Metascape, and TIMER were used to analyze differential expression, prognostic value, genetic alteration, and immune cell infiltration of the Ska protein complex in HCC patients. We found that the mRNA expression of the Ska complex was markedly upregulated in HCC. High expression of the Ska complex is closely correlated with tumor stage, patient race, tumor grade, and TP53 mutation status. In addition, high expression of the Ska complex was significantly correlated with poor disease-free survival, while the high expression levels of Ska1 and Ska3 were associated with shorter overall survival. The biological functions of the Ska complex in HCC primarily involve the amplification of signals from kinetochores, the mitotic spindle, and (via a MAD2 invasive signal) unattached kinetochores. Furthermore, the expression of the complex was positively correlated with tumor-infiltrating cells. These results may provide new insights into the development of immunotherapeutic targets and prognostic biomarkers for HCC.

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Identification of Novel RNA Binding Proteins Influencing Circular RNA Expression in Hepatocellular Carcinoma

International Journal of Molecular Sciences ◽

10.3390/ijms22147477 ◽

2021 ◽

Vol 22 (14) ◽

pp. 7477

Author(s):

Rok Razpotnik ◽

Petra Nassib ◽

Tanja Kunej ◽

Damjana Rozman ◽

Tadeja Režen

Keyword(s):

Hepatocellular Carcinoma ◽

Binding Proteins ◽

Rna Binding ◽

Rna Binding Proteins ◽

Regulatory Protein ◽

Circular Rna ◽

Differentially Expressed ◽

Circular Rnas ◽

Bioinformatics Analyses ◽

Published Evidence

Circular RNAs (circRNAs) are increasingly recognized as having a role in cancer development. Their expression is modified in numerous cancers, including hepatocellular carcinoma (HCC); however, little is known about the mechanisms of their regulation. The aim of this study was to identify regulators of circRNAome expression in HCC. Using publicly available datasets, we identified RNA binding proteins (RBPs) with enriched motifs around the splice sites of differentially expressed circRNAs in HCC. We confirmed the binding of some of the candidate RBPs using ChIP-seq and eCLIP datasets in the ENCODE database. Several of the identified RBPs were found to be differentially expressed in HCC and/or correlated with the overall survival of HCC patients. According to our bioinformatics analyses and published evidence, we propose that NONO, PCPB2, PCPB1, ESRP2, and HNRNPK are candidate regulators of circRNA expression in HCC. We confirmed that the knocking down the epithelial splicing regulatory protein 2 (ESRP2), known to be involved in the maintenance of the adult liver phenotype, significantly changed the expression of candidate circRNAs in a model HCC cell line. By understanding the systemic changes in transcriptome splicing, we can identify new proteins involved in the molecular pathways leading to HCC development and progression.

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NDRG3 overexpression is associated with a poor prognosis in patients with hepatocellular carcinoma

Bioscience Reports ◽

10.1042/bsr20180907 ◽

2018 ◽

Vol 38 (6) ◽

Cited By ~ 1

Author(s):

Ji-sheng Jing ◽

Hongbo Li ◽

Shun-cai Wang ◽

Jiu-ming Ma ◽

La-qing Yu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Survival Curve ◽

Disease Free Survival ◽

Prognostic Indicator ◽

Clinicopathological Characteristics ◽

Pcr Analysis ◽

Kaplan Meier ◽

Tumor Tissues ◽

The Relationship

N-myc downstream-regulated gene 3 (NDRG3), an important member of the NDRG family, is involved in cell proliferation, differentiation, and other biological processes. The present study analyzed NDRG3 expression in hepatocellular carcinoma (HCC) and explored the relationship between expression of NDRG3 in HCC patients and their clinicopathological characteristics. We performed quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) analysis and immunohistochemistry (IHC) analyses on HCC tissues to elucidate NDRG3 expression characteristics in HCC patients. Kaplan–Meier survival curve and Cox regression analyses were used to evaluate the prognoses of 102 patients with HCC. The results revealed that compared with non-tumor tissues, HCC tissues showed significantly higher NDRG3 expression. In addition, our analyses showed that NDRG3 expression was statistically associated with tumor size (P=0.048) and pathological grade (P=0.001). Survival analysis and Kaplan–Meier curves revealed that NDRG3 expression is an independent prognostic indicator for disease-free survival (P=0.002) and overall survival (P=0.005) in HCC patients. The data indicate that NDRG3 expression may be considered as a oncogenic biomarker and a novel predictor for HCC prognosis.

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

10.21203/rs.3.rs-137969/v1 ◽

2021 ◽

Author(s):

Diguang Wen ◽

Sheng Qiu ◽

Zuojin Liu

Keyword(s):

Hepatocellular Carcinoma ◽

Liver Cancer ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Epigenetic Modification ◽

Lasso Regression ◽

Cancer Dataset ◽

Gene Signatures ◽

Diagnosis And Prognosis

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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