Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma

BackgroundEvidence from prevailing studies show that hepatocellular carcinoma (HCC) is among the top cancers with high mortality globally. Gene regulation at post-transcriptional level orchestrated by RNA-binding proteins (RBPs) is an important mechanism that modifies various biological behaviors of HCC. Currently, it is not fully understood how RBPs affects the prognosis of HCC. In this study, we aimed to construct and validate an RBP-related model to predict the prognosis of HCC patients.MethodsDifferently expressed RBPs were identified in HCC patients based on the GSE54236 dataset from the Gene Expression Omnibus (GEO) database. Integrative bioinformatics analyses were performed to select hub genes. Gene expression patterns were validated in The Cancer Genome Atlas (TCGA) database, after which univariate and multivariate Cox regression analyses, as well as Kaplan-Meier analysis were performed to develop a prognostic model. Then, the performance of the prognostic model was assessed using receiver operating characteristic (ROC) curves and clinicopathological correlation analysis. Moreover, data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Finally, a nomogram combining clinicopathological parameters and prognostic model was established for the individual prediction of survival probability.ResultsThe prognostic risk model was finally constructed based on two RBPs (BOP1 and EZH2), facilitating risk-stratification of HCC patients. Survival was markedly higher in the low-risk group relative to the high-risk group. Moreover, higher risk score was associated with advanced pathological grade and late clinical stage. Besides, the risk score was found to be an independent prognosis factor based on multivariate analysis. Nomogram including the risk score and clinical stage proved to perform better in predicting patient prognosis.ConclusionsThe RBP-related prognostic model established in this study may function as a prognostic indicator for HCC, which could provide evidence for clinical decision making.

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Integrated analysis of iron metabolic-related genes in hepatocellular carcinoma

10.21203/rs.3.rs-147085/v1 ◽

2021 ◽

Author(s):

Li Wang ◽

Jialin Qu ◽

Man Jiang ◽

Na Zhou ◽

Zhixuan Ren ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Clinicopathological Parameters ◽

Integrated Analysis ◽

Consensus Clustering ◽

Cox Regression Analysis

Abstract Background Iron is a nutrient essential for hemoglobin synthesis, DNA synthesis, and energy metabolism in all mammals. Iron metabolic involved in numerous types of cancers including hepatocellular cancer. In this study, we aim to identify prognostic model that based on iron metabolic-related genes that could effectively predict the prognosis for HCC patients. Methods The RNA microarray and clinical data of HCC patients that obtained from The Cancer Genome Atlas (TCGA) database. We identify the clusters of HCC patients with different clinical outcome performed by consensus clustering analysis. Four iron metabolic-related genes (FLVCR1, FTL, HIF1A, HMOX1) were screen for prognostic model by performed the Cox regression analysis. The efficacy of prognostic model was validated by the International Cancer Genome Consortium (ICGC) database. Meantime, the expressions value of FLVCR1, FTL, HIF1A, HMOX1 was performed using Oncomine database, the Human Protein Atlas and Kaplan Meier-plotter. Result The patients with low-risk score have better prognosis than high risk score both in TCGA cohort and ICGC cohort. The prognostic model showed well performance for predicting the prognosis of HCC patients than other clinicopathological parameters by OS-related ROC curves. Conclusion Our survival models that based on Iron metabolic can be independent risk factors for hepatocellular carcinoma patients.

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Exploration and validation of a novel prognostic signature based on comprehensive bioinformatics analysis in hepatocellular carcinoma

Bioscience Reports ◽

10.1042/bsr20203263 ◽

2020 ◽

Vol 40 (11) ◽

Author(s):

Xiaofei Wang ◽

Jie Qiao ◽

Rongqi Wang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Overall Survival ◽

Risk Score ◽

Expression Profiles ◽

Univariate Analysis ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Good Prediction ◽

Expression Levels

Abstract The present study aimed to construct a novel signature for indicating the prognostic outcomes of hepatocellular carcinoma (HCC). Gene expression profiles were downloaded from Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) databases. The prognosis-related genes with differential expression were identified with weighted gene co-expression network analysis (WGCNA), univariate analysis, the least absolute shrinkage and selection operator (LASSO). With the stepwise regression analysis, a risk score was constructed based on the expression levels of five genes: Risk score = (−0.7736* CCNB2) + (1.0083* DYNC1LI1) + (−0.6755* KIF11) + (0.9588* SPC25) + (1.5237* KIF18A), which can be applied as a signature for predicting the prognosis of HCC patients. The prediction capacity of the risk score for overall survival was validated with both TCGA and ICGC cohorts. The 1-, 3- and 5-year ROC curves were plotted, in which the AUC was 0.842, 0.726 and 0.699 in TCGA cohort and 0.734, 0.691 and 0.700 in ICGC cohort, respectively. Moreover, the expression levels of the five genes were determined in clinical tumor and normal specimens with immunohistochemistry. The novel signature has exhibited good prediction efficacy for the overall survival of HCC patients.

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Eight-Gene Metabolic Signature Related with Tumor-Associated Macrophages Predicting Overall Survival for Hepatocellular Carcinoma

10.21203/rs.3.rs-36554/v1 ◽

2020 ◽

Author(s):

Junyu Huo ◽

Yunjin Zang ◽

Hongjing Dong ◽

Xiaoqiang Liu ◽

Fu He ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Survival Analysis ◽

Risk Score ◽

Risk Group ◽

Cancer Genome ◽

Metabolic Reprogramming ◽

Tumor Associated Macrophages ◽

Kaplan Meier ◽

The Relationship

Abstract Background: In recent years, the relationship between tumor associated macrophages (TAMs) and solid tumors has become a research hotspot. The study aims at exploring the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma(HCC), in order to provide a new way of treatment for HCC.Materials and methods: The study selected 343 HCC patients with complete survival information(survival time >= 1month) in the Cancer Genome Atlas (TCGA) as the study objects. Kaplan-Meier survival analysis assisted in figuring out the relationship between macrophage infiltration level and overall survival (OS), and Pearson correlation test to identify metabolic reprogramming genes(MRGs) related to tumor macrophage abundance. Lasso regression algorithm were conducted on prognosis related MRGs screened by Univariate Cox regression analysis and Kaplan-Meier survival analysis to construct the riskscore, another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) were used for external validation regarding the prognostic signature.Results: A risk score composed of 8 metabolic genes can accurately predict the OS of training cohort(TCGA) and testing cohort(ICGC). It is important that the risk score could widely used for people with different clinical characteristics, and is an independent predictor independent of other clinical factors affecting prognosis. As expected, high-risk group exhibited an obviously higher macrophage abundance relative to low-risk group, and the risk score presented a positive relation to the expression level of three commonly used immune checkpoints(PD1,PDL1,CTLA4).Conclusion: Our study constructed and validated a novel eight‑gene signature for predicting HCC patients’ OS, which possibly contributed to making clinical treatment decisions.

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Hub gene identification and prognostic model establishment for patients with HBV-related hepatocellular carcinoma

10.21203/rs.3.rs-269454/v2 ◽

2021 ◽

Author(s):

Lianmei Wang ◽

Jing Meng ◽

Shasha Qin ◽

Aihua Liang

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Expression Profiles ◽

Cyclin B1 ◽

Mitotic Checkpoint ◽

Cancer Genome ◽

Hub Genes ◽

Cox Regression Analysis ◽

Model Based

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~50% of HCC cases. Identification of biomarkers is pivotal for the therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. Cyclin-dependent kinase 1 (CDK1), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Aurora kinase A (AURKA), Cyclin B2 (CCNB2), Enhancer of zeste homolog 2 (EZH2), Cell division cycle 20 (CDC20), DNA topoisomerase II alpha (TOP2A), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), and ZW10 interactor (ZWINT), were identified as the top-ten hub genes. The expression of hub-genes was verified in the liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP), the cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. A four-gene prognostic related model based on the expression of ZWINT, EZH2, FOXM1 and CDK1 were established through Cox regression analysis in ICGC-LIRI-JP project, and verified in TCGA-HCC cohort. Furthermore, a nomogram model based on pathology stage, gender and four-genes prognostic model was built to predict the prognosis for HBV–HCC patients. In conclusion, ZWINT, EZH2, FOXM1 and CDK1 play a pivotal role in HBV-HCC, and are potential therapeutic targets of HBV HCC.

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Hub Gene Identification and Prognostic Model Establishment for Patients with HBV-related Hepatocellular Carcinoma

10.21203/rs.3.rs-269454/v1 ◽

2021 ◽

Author(s):

Lianmei Wang ◽

Jing Liu ◽

Zhong Xian ◽

Jingzhuo Tian ◽

Chunying Li ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Prognostic Model ◽

Expression Profiles ◽

Expression Patterns ◽

Cyclin B1 ◽

Mitotic Checkpoint ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Hub Genes

Abstract Hepatocellular carcinoma (HCC) is associated with poor 5-year survival. Chronic infection with hepatitis B virus (HBV) contributes to ~ 50% of HCC cases. Establishment of a prognostic model is pivotal for clinical therapy of HBV-related HCC (HBV–HCC). We downloaded gene-expression profiles from Gene expression omnibus (GEO) datasets with HBV-HCC patients and the corresponding controls. Integration of these differentially expressed genes (DEGs) was achieved with the Robustrankaggreg (RRA) method. DEGs functional analyses and pathway analyses was performed using the Gene ontology (GO) database, and the Kyoto encyclopedia of genes and genomes (KEGG) database respectively. DNA topoisomerase II alpha (TOP2A), Disks large-associated protein 5 (DLGAP5), RAD51 associated protein 1 (RAD51AP1), ZW10 interactor (ZWINT), BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B), Cyclin B1 (CCNB1), Forkhead box M1 (FOXM1), Cyclin B2 (CCNB2), Aurora kinase A (AURKA), and Cyclin-dependent kinase 1 (CDK1) were identified as the top-ten hub genes. These hub-genes were verified by the Liver cancer-riken, JP project from international cancer genome consortium (ICGC-LIRI-JP) project, The Cancer genome atlas (TCGA) HCC cohort, and Human protein profiles dataset. FOXM1 and CDK1 were found to be prognostic-related molecules for HBV-HCC patients. The expression patterns of FOXM1 and CDK1were consistently in human and mouse. Furthermore, a nomogram model based on histology grade, pathology stage, sex and, expression of FOXM1 and CDK1 was built to predict the prognosis for HBV–HCC patients. The nomogram model could be used to predict the prognosis of HBV-HCC cases.

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Identification and Validation of a Prognostic Model Based on Three Autophagy-Related Genes in Hepatocellular Carcinoma

BioMed Research International ◽

10.1155/2021/5564040 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Fanbo Qin ◽

Junyong Zhang ◽

Jianping Gong ◽

Wenfeng Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Risk Score ◽

Prognostic Value ◽

Prognostic Model ◽

Cox Regression ◽

Cancer Genome ◽

Clinical Parameters ◽

Cox Regression Analysis ◽

Model Based

Background. Accumulating studies have demonstrated that autophagy plays an important role in hepatocellular carcinoma (HCC). We aimed to construct a prognostic model based on autophagy-related genes (ARGs) to predict the survival of HCC patients. Methods. Differentially expressed ARGs were identified based on the expression data from The Cancer Genome Atlas and ARGs of the Human Autophagy Database. Univariate Cox regression analysis was used to identify the prognosis-related ARGs. Multivariate Cox regression analysis was performed to construct the prognostic model. Receiver operating characteristic (ROC), Kaplan-Meier curve, and multivariate Cox regression analyses were performed to test the prognostic value of the model. The prognostic value of the model was further confirmed by an independent data cohort obtained from the International Cancer Genome Consortium (ICGC) database. Results. A total of 34 prognosis-related ARGs were selected from 62 differentially expressed ARGs identified in HCC compared with noncancer tissues. After analysis, a novel prognostic model based on ARGs (PRKCD, BIRC5, and ATIC) was constructed. The risk score divided patients into high- or low-risk groups, which had significantly different survival rates. Multivariate Cox analysis indicated that the risk score was an independent risk factor for survival of HCC after adjusting for other conventional clinical parameters. ROC analysis showed that the predictive value of this model was better than that of other conventional clinical parameters. Moreover, the prognostic value of the model was further confirmed in an independent cohort from ICGC patients. Conclusion. The prognosis-related ARGs could provide new perspectives on HCC, and the model should be helpful for predicting the prognosis of HCC patients.

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Immune Implication of ASF1B Gene in Hepatocellular Carcinoma

10.21203/rs.3.rs-455248/v1 ◽

2021 ◽

Author(s):

Tao Meng ◽

Zhong Tong ◽

Ming-Ya Yang ◽

Jing-Jing Wang ◽

Li-Xin Zhu ◽

...

Keyword(s):

Gene Expression ◽

Hepatocellular Carcinoma ◽

Risk Score ◽

Immune Cell ◽

Risk Group ◽

External Validation ◽

Cell Infiltration ◽

Immune Cell Infiltration ◽

Test Set

Abstract Background: Anti-silencing function 1B (ASF1B) has been demonstrated to contribute to tumorigenesis. However, its carcinogenic and immune effects in hepatocellular carcinoma (HCC) have not been reported. This study aimed to identify immune role of ASF1B in HCC.Methods: HCC datasets obtained from The Cancer Genome Atlas (TCGA) database were used to investigate the role of ASF1B gene in HCC, followed by validation using Gene Expression Omnibus (GEO) datasets and Gene Expression Profiling Interactive Analysis (GEPIA) website. CIBERSORT analysis was performed to evaluate immune cell infiltration levels. The TISIDB and cBioPortal network tool were used to seek ASF1B-associated immunomodulators and its co-expressed genes. TCGA cohort was divided into train set and test set according to the ratio of 7:3. Cox regression was used to identify ASF1B-associated prognostic immunomodulators in train set, followed by internal validation using the test set. Based on the median risk-score, HCC patients were divided into high- and low-risk group for the further survival curves and receiver operating characteristic (ROC) analysis, as well as nomogram and calibration curves analysis. Finally, the dataset collected from the GEO was adopted for external validation.Results: ASF1B was over-expressed in TCGA HCC cohort and contributed poor prognosis, which was verified in two GEO datasets (GSE14520 and GSE6764) and GEPIA, as well as Kaplan Meier Plotter network tool. The immune cell infiltration levels were found to be associated with the ASF1B copy numbers and mRNA expression. A total of 78 ASF1B-associated genes were screened out, including 7 immunoinhibitors, 21 immunostimulators and 50 tightly co-expressed genes. Finally, 5 ASF1B-associated genes (TNFSF4, TNFRSF4, KDR, MICB and CST7) were identified to be strongly related to HCC survival. Survival analysis demonstrated that the prognosis of patients in high-risk group was poor. The prognosis predict model, which was established by nomogram based on risk-score, and was validated in both TCGA test set and GEO validated datasets, exerted excellent predictive power in this study.Conclusion: Our findings showed that the ASF1B was associated with HCC immunity. The selected ASF1B-asociated immune markers could be promising biomarkers for the prognosis of HCC.

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Eight-gene metabolic signature related with tumor-associated macrophages predicting overall survival for hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-020-07734-z ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Junyu Huo ◽

Liqun Wu ◽

Yunjin Zang ◽

Hongjing Dong ◽

Xiaoqiang Liu ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Overall Survival ◽

Survival Analysis ◽

Risk Score ◽

Risk Group ◽

Cancer Genome ◽

Metabolic Reprogramming ◽

Tumor Associated Macrophages ◽

Kaplan Meier ◽

The Relationship

Abstract Background In recent years, the relationship between tumor-associated macrophages (TAMs) and solid tumors has become a research hotspot. This study aims to explore the close relationship of TAMs with metabolic reprogramming genes in hepatocellular carcinoma (HCC) to provide new methods of treatment for HCC. Methods The study selected 343 HCC patients with complete survival information (survival time > = 1 month) in the Cancer Genome Atlas (TCGA) as study subjects. Kaplan-Meier survival analysis assisted in determining the relationship between macrophage infiltration and overall survival (OS), and Pearson correlation tests were used to identify metabolic reprogramming genes (MRGs) associated with tumor macrophage abundance. Lasso regression algorithms were used on prognosis-related MRGs identified by Kaplan-Meier survival analysis and univariate Cox regression analysis to construct a risk score; another independent cohort (including 228 HCC patients) from the International Cancer Genome Consortium (ICGC) was used to verify prognostic signature externally. Results A risk score composed of 8 metabolic genes could accurately predict the OS of a training cohort (TCGA) and a testing cohort (ICGC). The risk score could be widely used for people with different clinical characteristics, and it is a predictor that is independent of other clinical factors that affect prognosis. As expected, compared with the low-risk group, the high-risk group exhibited an obviously higher macrophage abundance, together with a positive correlation between the risk score and the expression levels of three commonly used immune checkpoints (PD1, PDL1, and CTLA4). Conclusion Our study constructed and validated a novel eight-gene signature for predicting HCC patient OS, which may contribute to clinical treatment decisions.

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Comprehensive Analysis to Identify the Epithelial–Mesenchymal Transition-Related Immune Signatures as a Prognostic and Therapeutic Biomarkers in Hepatocellular Carcinoma

Frontiers in Surgery ◽

10.3389/fsurg.2021.742443 ◽

2021 ◽

Vol 8 ◽

Author(s):

Guozhi Wu ◽

Yuan Yang ◽

Yu Zhu ◽

Yemao Li ◽

Zipeng Zhai ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Risk Model ◽

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Cancer Genome ◽

Mesenchymal Transition ◽

Immune Genes

Background: Hepatocellular carcinoma (HCC) is a highly heterogeneous disease with the high rates of the morbidity and mortality due to the lack of the effective prognostic model for prediction.Aim: To construct a risk model composed of the epithelial–mesenchymal transition (EMT)-related immune genes for the assessment of the prognosis, immune infiltration status, and chemosensitivity.Methods: We obtained the transcriptome and clinical data of the HCC samples from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC) databases. The Pearson correlation analysis was applied to identify the differentially expressed EMT-related immune genes (DE-EMTri-genes). Subsequently, the univariate Cox regression was introduced to screen out the prognostic gene sets and a risk model was constructed based on the least absolute shrinkage and selection operator-penalized Cox regression. Additionally, the receiver operating characteristic (ROC) curves were plotted to compare the prognostic value of the newly established model compared with the previous model. Furthermore, the correlation between the risk model and survival probability, immune characteristic, and efficacy of the chemotherapeutics were analyzed by the bioinformatics methods.Results: Six DE-EMTri-genes were ultimately selected to construct the prognostic model. The area under the curve (AUC) values for 1-, 2-, and 3- year were 0.773, 0.721, and 0.673, respectively. Stratified survival analysis suggested that the prognosis of the low-score group was superior to the high-score group. Moreover, the univariate and multivariate analysis indicated that risk score [hazard ratio (HR) 5.071, 95% CI 3.050, 8.432; HR 4.396, 95% CI 2.624, 7.366; p < 0.001] and stage (HR 2.500, 95% CI 1.721, 3.632; HR 2.111, 95% CI 1.443, 3.089; p < 0.001) served as an independent predictive factors in HCC. In addition, the macrophages, natural killer (NK) cells, and regulatory T (Treg) cells were significantly enriched in the high-risk group. Finally, the patients with the high-risk score might be more sensitive to cisplatin, doxorubicin, etoposide, gemcitabine, and mitomycin C.Conclusion: We established a reliable EMTri-genes-based prognostic signature, which may hold promise for the clinical prediction.

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Construction prognosis model based on autophagy-related gene signatures in hepatocellular carcinoma

Biomarkers in Medicine ◽

10.2217/bmm-2020-0170 ◽

2020 ◽

Vol 14 (13) ◽

pp. 1229-1242

Author(s):

Jiangtao Wang ◽

Yandong Miao ◽

Juntao Ran ◽

Yuan Yang ◽

Quanlin Guan ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Score ◽

Prognostic Model ◽

Clinical Stage ◽

Enrichment Analysis ◽

Functional Enrichment Analysis ◽

Functional Enrichment ◽

Prognosis Model ◽

Clinicopathological Significance ◽

Autophagy Pathway

Aim: To develop robust and accurate prognostic biomarkers to help clinicians optimize therapeutic strategies. Materials & methods: Differentially prognosis-related autophagy genes were identified by bioinformatics analysis method. Results: Seven prognosis-related autophagy genes were more significantly related to the prognosis of hepatocellular carcinoma (HCC). Functional enrichment analysis demonstrated that these genes were mainly enriched in the autophagy pathway. BIRC5, HSPB8 and TMEM74 exhibited significant prognostic value for HCC. Besides, the risk score and BIRC5 have significant significance with clinicopathological significance of HCC. Conclusion: The research has identified a number of prognosis-related autophagy genes that associated with the survival and clinical stage of HCC. In addition, the prognostic model can be used to calculate the patient’s risk score and these prognosis-related autophagy genes might serve as therapeutic targets.

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