Development and Validation of a Gene Mutation-Associated Nomogram for Hepatocellular Carcinoma Patients From Four Countries

Background: Genomic alteration is the basis of occurrence and development of carcinoma. Specific gene mutation may be associated with the prognosis of hepatocellular carcinoma (HCC) patients without distant or lymphatic metastases. Hence, we developed a nomogram based on prognostic gene mutations that could predict the overall survival of HCC patients at early stage and provide reference for immunotherapy.Methods: HCC cohorts were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The total patient was randomly assigned to training and validation sets. Univariate and multivariate cox analysis were used to select significant variables for construction of nomogram. The support vector machine (SVM) and principal component analysis (PCA) were used to assess the distinguished effect of significant genes. Besides, the nomogram model was evaluated by concordance index, time-dependent receiver operating characteristics (ROC) curve, calibration curve and decision curve analysis (DCA). Gene Set Enrichment Analysis (GSEA), CIBERSORT, Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS) were utilized to explore the potential mechanism of immune-related process and immunotherapy.Results: A total of 695 HCC patients were selected in the process including 495 training patients and 200 validation patients. Nomogram was constructed based on T stage, age, country, mutation status of DOCK2, EYS, MACF1 and TP53. The assessment showed the nomogram has good discrimination and high consistence between predicted and actual data. Furthermore, we found T cell exclusion was the potential mechanism of malignant progression in high-risk group. Meanwhile, low-risk group might be sensitive to immunotherapy and benefit from CTLA-4 blocker treatment.Conclusion: Our research established a nomogram based on mutant genes and clinical parameters, and revealed the underlying association between these risk factors and immune-related process.

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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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Hypoxic Characteristic in the Immunosuppressive Microenvironment of Hepatocellular Carcinoma

Frontiers in Immunology ◽

10.3389/fimmu.2021.611058 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhuomao Mo ◽

Daiyuan Liu ◽

Dade Rong ◽

Shijun Zhang

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Cell ◽

Cox Regression ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Cell Infiltration ◽

Immune Checkpoints ◽

Immune Cell Infiltration ◽

Immunosuppressive Microenvironment

Background: Generally, hepatocellular carcinoma (HCC) exists in an immunosuppressive microenvironment that promotes tumor evasion. Hypoxia can impact intercellular crosstalk in the tumor microenvironment. This study aimed to explore and elucidate the underlying relationship between hypoxia and immunotherapy in patients with HCC.Methods: HCC genomic and clinicopathological datasets were obtained from The Cancer Genome Atlas (TCGA-LIHC), Gene Expression Omnibus databases (GSE14520) and International Cancer Genome Consortium (ICGC-LIRI). The TCGA-LIHC cases were divided into clusters based on single sample gene set enrichment analysis and hierarchical clustering. After identifying patients with immunosuppressive microenvironment with different hypoxic conditions, correlations between immunological characteristics and hypoxia clusters were investigated. Subsequently, a hypoxia-associated score was established by differential expression, univariable Cox regression, and lasso regression analyses. The score was verified by survival and receiver operating characteristic curve analyses. The GSE14520 cohort was used to validate the findings of immune cell infiltration and immune checkpoints expression, while the ICGC-LIRI cohort was employed to verify the hypoxia-associated score.Results: We identified hypoxic patients with immunosuppressive HCC. This cluster exhibited higher immune cell infiltration and immune checkpoint expression in the TCGA cohort, while similar significant differences were observed in the GEO cohort. The hypoxia-associated score was composed of five genes (ephrin A3, dihydropyrimidinase like 4, solute carrier family 2 member 5, stanniocalcin 2, and lysyl oxidase). In both two cohorts, survival analysis revealed significant differences between the high-risk and low-risk groups. In addition, compared to other clinical parameters, the established score had the highest predictive performance at both 3 and 5 years in two cohorts.Conclusion: This study provides further evidence of the link between hypoxic signals in patients and immunosuppression in HCC. Defining hypoxia-associated HCC subtypes may help reveal potential regulatory mechanisms between hypoxia and the immunosuppressive microenvironment, and our hypoxia-associated score could exhibit potential implications for future predictive models.

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Identification and Validation of an 6-Metabolism-Related Gene Signature and Its Correlation With Immune Checkpoint in Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.783934 ◽

2021 ◽

Vol 11 ◽

Author(s):

He Ren ◽

Wanjing Li ◽

Xin Liu ◽

Shuliang Li ◽

Hao Guo ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Immune Checkpoint ◽

Immune Cell ◽

Enrichment Analysis ◽

Gene Signature ◽

Clinicopathological Characteristics ◽

Gene Set Enrichment Analysis ◽

Operating Characteristics ◽

Kaplan Meier ◽

Characteristics Analysis

Hepatocellular carcinoma (HCC) is a common malignant tumor with relatively high malignancy and rapid disease progression. Metabolism-related genes (MRGs) are involved in the pathogenesis of HCC. This study explored potential key MRGs and their effect on T-cell immune function in the tumor immune microenvironment to provide new insight for the treatment of HCC. Of 456 differentially expressed MRGs identified from TCGA database, 21 were screened by MCODE and cytoHubba algorithms. From the key module, GAD1, SPP1, WFS1, GOT2, EHHADH, and APOA1 were selected for validation. The six MRGs were closely correlated with survival outcomes and clinicopathological characteristics in HCC. Receiver operating characteristics analysis and Kaplan-Meier plots showed that these genes had good prognostic value for HCC. Gene set enrichment analysis of the six MRGs indicated that they were associated with HCC development. TIMER and GEPIA databases revealed that WFS1 was significantly positively correlated and EHHADH was negatively correlated with tumor immune cell infiltration and immune checkpoint expression. Finally, quantificational real-time polymerase chain reaction (qRT-PCR) confirmed the expression of WFS1 and EHHADH mRNA in our own patients’ cohort samples and four HCC cell lines. Collectively, the present study identified six potential MRG biomarkers associated with the prognosis and tumor immune infiltration of HCC, thus providing new insight into the pathogenesis and treatment of HCC.

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A Starvation-Based 9-mRNA Signature Correlates With Prognosis in Patients With Hepatocellular Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.716757 ◽

2021 ◽

Vol 11 ◽

Author(s):

Dengliang Lei ◽

Yue Chen ◽

Yang Zhou ◽

Gangli Hu ◽

Fang Luo

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Liver Cancer ◽

Cancer Patients ◽

Enrichment Analysis ◽

Clinical Information ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

Biological Processes ◽

Gene Set Enrichment

BackgroundHepatocellular carcinoma (HCC) is one of the world’s most prevalent and lethal cancers. Notably, the microenvironment of tumor starvation is closely related to cancer malignancy. Our study constructed a signature of starvation-related genes to predict the prognosis of liver cancer patients.MethodsThe mRNA expression matrix and corresponding clinical information of HCC patients were obtained from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). Gene set enrichment analysis (GSEA) was used to distinguish different genes in the hunger metabolism gene in liver cancer and adjacent tissues. Gene Set Enrichment Analysis (GSEA) was used to identify biological differences between high- and low-risk samples. Univariate and multivariate analyses were used to construct prognostic models for hunger-related genes. Kaplan-Meier (KM) and receiver-operating characteristic (ROC) were used to assess the model accuracy. The model and relevant clinical information were used to construct a nomogram, protein expression was detected by western blot (WB), and transwell assay was used to evaluate the invasive and metastatic ability of cells.ResultsFirst, we used univariate analysis to identify 35 prognostic genes, which were further demonstrated to be associated with starvation metabolism through Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). We then used multivariate analysis to build a model with nine genes. Finally, we divided the sample into low- and high-risk groups according to the median of the risk score. KM can be used to conclude that the prognosis of high- and low-risk samples is significantly different, and the prognosis of high-risk samples is worse. The prognostic accuracy of the 9-mRNA signature was also tested in the validation data set. GSEA was used to identify typical pathways and biological processes related to 9-mRNA, cell cycle, hypoxia, p53 pathway, and PI3K/AKT/mTOR pathway, as well as biological processes related to the model. As evidenced by WB, EIF2S1 expression was increased after starvation. Overall, EIF2S1 plays an important role in the invasion and metastasis of liver cancer.ConclusionsThe 9-mRNA model can serve as an accurate signature to predict the prognosis of liver cancer patients. However, its mechanism of action warrants further investigation.

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Identification and Validation of the N6-Methyladenosine RNA Methylation Regulator YTHDF1 as a Novel Prognostic Marker and Potential Target for Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.604766 ◽

2020 ◽

Vol 7 ◽

Author(s):

Saiyan Bian ◽

Wenkai Ni ◽

Mengqi Zhu ◽

Qianqian Song ◽

Jianping Zhang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Biological Activities ◽

Enrichment Analysis ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

Cell Counting ◽

Migration And Invasion ◽

Mesenchymal Transition ◽

Rna Methylation ◽

Hcc Cells

Purpose: N6-methyladenosine (m6A) RNA methylation has been implicated in various malignancies. This study aimed to identify the m6A methylation regulator-based prognostic signature for hepatocellular carcinoma (HCC) as well as provide candidate targets for HCC treatment.Methods: The least absolute shrinkage and selection operator (LASSO) analyses were performed to identify a risk signature in The Cancer Genome Atlas (TCGA) datasets. The risk signature was further validated in International Cancer Genome Consortium (ICGC) and Pan-Cancer Analysis of Whole Genomes (PCAWG) datasets. Following transfection of short hairpin RNA (shRNA) targeting YTHDF1, the biological activities of HCC cells were evaluated by Cell Counting Kit-8 (CCK-8), wound-healing, Transwell, flow cytometry, and xenograft tumor assays, respectively. The potential mechanisms mediated by YTHDF1 were predicted by overrepresentation enrichment analysis (ORA)/gene set enrichment analysis (GSEA) and validated by Western blotting.Results: Overexpression of m6A RNA methylation regulators was correlated with malignant clinicopathological characteristics of HCC patients. The Cox regression and LASSO analyses identified a risk signature with five m6A methylation regulators (KIAA1429, ZC3H13, YTHDF1, YTHDF2, and METTL3). In accordance with HCC cases in TCGA, the prognostic value of risk signature was also determined in ICGC and PCAWG datasets. Following analyzing the expression and clinical implications in TCGA and Gene Expression Omnibus (GEO), YTHDF1 was chosen for further experimental validation. Knockdown of YTHDF1 significantly inhibited the proliferation, migration, and invasion of HCC cells, as well as enhanced the apoptosis in vitro. Moreover, silencing YTHDF1 repressed the growth of xenograft tumors in vivo. Mechanism investigation indicated that YTHDF1 might promote the aggressive phenotypes by facilitating epithelial–mesenchymal transition (EMT) and activating AKT/glycogen synthase kinase (GSK)-3β/β-catenin signaling.Conclusion: The current study identified a robust risk signature consisting of m6A RNA methylation regulators for HCC prognosis. In addition, YTHDF1 was a potential molecular target for HCC treatment.

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Novel Hypoxia-Related Gene Signature for Risk Stratification and Prognosis in Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.613890 ◽

2021 ◽

Vol 12 ◽

Author(s):

Quanxiao Li ◽

Limin Jin ◽

Meng Jin

Keyword(s):

Hepatocellular Carcinoma ◽

Risk Stratification ◽

Risk Group ◽

External Validation ◽

Gene Signature ◽

Cancer Genome ◽

Low Risk ◽

The Cancer Genome Atlas ◽

Individual Risk ◽

Tumor Node Metastasis Stage

Hepatocellular carcinoma (HCC) is the most common form of liver cancer with limited therapeutic options and low survival rate. The hypoxic microenvironment plays a vital role in progression, metabolism, and prognosis of malignancies. Therefore, this study aims to develop and validate a hypoxia gene signature for risk stratification and prognosis prediction of HCC patients. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases were used as a training cohort, and one Gene Expression Omnibus database (GSE14520) was served as an external validation cohort. Our results showed that eight hypoxia-related genes (HRGs) were identified by the least absolute shrinkage and selection operator analysis to develop the hypoxia gene signature and demarcated HCC patients into the high- and low-risk groups. In TCGA, ICGC, and GSE14520 datasets, patients in the high-risk group had worse overall survival outcomes than those in the low-risk group (all log-rank P < 0.001). Besides, the risk score derived from the hypoxia gene signature could serve as an independent prognostic factor for HCC patients in the three independent datasets. Finally, a nomogram including the gene signature and tumor-node-metastasis stage was constructed to serve clinical practice. In the present study, a novel hypoxia signature risk model could reflect individual risk classification and provide therapeutic targets for patients with HCC. The prognostic nomogram may help predict individualized survival.

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Identification of the HMMR Gene as a Diagnostic and Prognostic Biomarker in Hepatocellular Carcinoma Based on Integrated Bioinformatics Analysis

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/5970085 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Honglan Guo ◽

Qinqiao Fan

Keyword(s):

Hepatocellular Carcinoma ◽

Roc Curve ◽

Cox Regression ◽

Enrichment Analysis ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Roc Curve Analysis ◽

Cox Regression Analysis ◽

Normal Tissues

Background. We aimed to investigate the expression of the hyaluronan-mediated motility receptor (HMMR) gene in hepatocellular carcinoma (HCC) and nonneoplastic tissues and to investigate the diagnostic and prognostic value of HMMR. Method. With the reuse of the publicly available The Cancer Genome Atlas (TCGA) data, 374 HCC patients and 50 nonneoplastic tissues were used to investigate the diagnostic and prognostic values of HMMR genes by receiver operating characteristic (ROC) curve analysis and survival analysis. All patients were divided into low- and high-expression groups based on the median value of HMMR expression level. Univariate and multivariate Cox regression analysis were used to identify prognostic factors. Gene set enrichment analysis (GSEA) was performed to explore the potential mechanism of the HMMR genes involved in HCC. The diagnostic and prognostic values were further validated in an external cohort from the International Cancer Genome Consortium (ICGC). Results. HMMR mRNA expression was significantly elevated in HCC tissues compared with that in normal tissues from both TCGA and the ICGC cohorts (all P values <0.001). Increased HMMR expression was significantly associated with histologic grade, pathological stage, and survival status (all P values <0.05). The area under the ROC curve for HMMR expression in HCC and normal tissues was 0.969 (95% CI: 0.948–0.983) in the TCGA cohort and 0.956 (95% CI: 0.932–0.973) in the ICGC cohort. Patients with high HMMR expression had a poor prognosis than patients with low expression group in both cohorts (all P < 0.001 ). Univariate and multivariate analysis also showed that HMMR is an independent predictor factor associated with overall survival in both cohorts (all P values <0.001). GSEA showed that genes upregulated in the high-HMMR HCC subgroup were mainly significantly enriched in the cell cycle pathway, pathways in cancer, and P53 signaling pathway. Conclusion. HMMR is expressed at high levels in HCC. HMMR overexpression may be an unfavorable prognostic factor for HCC.

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Identification of Aging-Related Genes Associated With Clinical and Prognostic Features of Hepatocellular Carcinoma

Frontiers in Genetics ◽

10.3389/fgene.2021.661988 ◽

2021 ◽

Vol 12 ◽

Author(s):

Xingte Chen ◽

Lei Wang ◽

Liang Hong ◽

Zhixiong Su ◽

Xiaohong Zhong ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

High Risk ◽

Risk Score ◽

Cancer Genome ◽

Gene Set Enrichment Analysis ◽

Differentially Expressed ◽

Survival Prediction ◽

Lasso Regression ◽

Prognostic Features

Background: Aging is a well-studied concept, but no studies have comprehensively analyzed the association between aging-related genes (AGs) and hepatocellular carcinoma (HCC) prognosis.Methods: Gene candidates were selected from differentially expressed genes and prognostic genes in The Cancer Genome Atlas (TCGA) database. A gene risk score for overall survival prediction was established using the least absolute shrinkage and selection operator (LASSO) regression analysis, and this was validated using data from the International Cancer Genome Consortium (ICGC) database. Functional analysis was conducted using gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis, and immune microenvironment and tumor stemness analyses.Results: Initially, 72 AGs from the TCGA database were screened as differentially expressed between normal and tumor tissues and as genes associated with HCC prognosis. Then, seven AGs (POLA1, CDK1, SOCS2, HDAC1, MAPT, RAE1, and EEF1E1) were identified using the LASSO regression analysis. The seven AGs were used to develop a risk score in the training set, and the risk was validated to have a significant prognostic value in the ICGC set (p < 0.05). Patients with high risk scores had lower tumor differentiation, higher stage, and worse prognosis (all p < 0.05). Multivariate Cox regression analyses also confirmed that the risk score was an independent prognostic factor for HCC in both the TCGA and ICGC sets (all p < 0.05). Further analysis showed that a high risk score was correlated with the downregulation of metabolism and tumor immunity.Conclusion: The risk score predicts HCC prognosis and could thus be used as a biomarker not only for predicting HCC prognosis but also for deciding on treatment.

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Identification of a Five-Autophagy-Related-lncRNA Signature as a Novel Prognostic Biomarker for Hepatocellular Carcinoma

Frontiers in Molecular Biosciences ◽

10.3389/fmolb.2020.611626 ◽

2021 ◽

Vol 7 ◽

Author(s):

Xiaoyu Deng ◽

Qinghua Bi ◽

Shihan Chen ◽

Xianhua Chen ◽

Shuhui Li ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

High Risk ◽

Cox Regression ◽

Risk Group ◽

High Risk Group ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Signature ◽

Prediction Ability ◽

Cox Regression Analysis

Although great progresses have been made in the diagnosis and treatment of hepatocellular carcinoma (HCC), its prognostic marker remains controversial. In this current study, weighted correlation network analysis and Cox regression analysis showed significant prognostic value of five autophagy-related long non-coding RNAs (AR-lncRNAs) (including TMCC1-AS1, PLBD1-AS1, MKLN1-AS, LINC01063, and CYTOR) for HCC patients from data in The Cancer Genome Atlas. By using them, we constructed a five-AR-lncRNA prognostic signature, which accurately distinguished the high- and low-risk groups of HCC patients. All of the five AR lncRNAs were highly expressed in the high-risk group of HCC patients. This five-AR-lncRNA prognostic signature showed good area under the curve (AUC) value (AUC = 0.751) for the overall survival (OS) prediction in either all HCC patients or HCC patients stratified according to several clinical traits. A prognostic nomogram with this five-AR-lncRNA signature predicted the 3- and 5-year OS outcomes of HCC patients intuitively and accurately (concordance index = 0.745). By parallel comparison, this five-AR-lncRNA signature has better prognosis accuracy than the other three recently published signatures. Furthermore, we discovered the prediction ability of the signature on therapeutic outcomes of HCC patients, including chemotherapy and immunotherapeutic responses. Gene set enrichment analysis and gene mutation analysis revealed that dysregulated cell cycle pathway, purine metabolism, and TP53 mutation may play an important role in determining the OS outcomes of HCC patients in the high-risk group. Collectively, our study suggests a new five-AR-lncRNA prognostic signature for HCC patients.

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Exploration and Verification of a Six-RNA Binding Proteins-based Prognosis Evaluating Model for Hepatocellular Carcinoma

10.21203/rs.3.rs-117829/v1 ◽

2020 ◽

Author(s):

Miao Chen ◽

Shujie Li ◽

Jiakang Zhang ◽

Jianbo Han ◽

Lili Wang ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Regression Analysis ◽

Survival Analysis ◽

Binding Proteins ◽

Cancer Genome ◽

Functional Enrichment ◽

Operating Characteristics ◽

Training Cohort ◽

Cox Regression Analysis ◽

Receiver Operating

Abstract BackgroundRNA binding protein (RBP) plays a crucial role in tumorigenesis at post-transcriptional level in various cancer types. Nevertheless, the role of RBPs in liver hepatocellular carcinoma (LIHC) remains obscure. We attempted to uncover the association between RBPs and the prognosis of LIHC patients. MethodsWe analyzed the transcriptome and corresponding clinical data of LIHC patients from the cancer genome atlas (TCGA) (training cohort) and international cancer genome consortium (ICGC) (validating cohort) database with a series of bioinformatics methods. Differently expressed RNA-binding proteins (DERBPs) were screened and subjected to functional enrichment analysis and co-expression network establishment. Overall survival (OS) related DERBPs and our prognosis risk model were confirmed by univariate, LASSO and multivariate regression analysis in training cohort. Survival analysis, Receiver operating characteristic curve (ROC) and nomogram were conducted in both training and validating groups to confirm the performance of our model. Human protein atlas (HPA) database and Kaplan-Meier plotter were used to verify the expression and prognostic significance of the hub RBPs respectively.Results There were 330 RBPs were found significantly different in TCGA. Functional analysis indicated most of the DERBPs were majored in RNA processing, alternative splicing and metabolism, etc. 6 RBPs (UPF3B, MRPL54, ZC3H13, DHX58, PPARGC1A, EIF2AK4) were recognized as OS related and enrolled into our prognostic model. Survival analysis showed the risk signature was negatively correlated with the OS of LIHC patients in both training (p = 5.808e-06) and validating (p = 3.38e-03) groups. The area under curves (AUC) of the receiver operating characteristics (ROC) curve in training and validating cohorts was 0.756, 0.781 respectively which indicating the good performance of our model. The risk signature was an independent hazardous factor in multivariate COX regression analysis either in TCGA (HR = 1.626;95% CI 1.394 -1.897, p < 0.001) or ICGC (HR = 1.939;95% CI 1.324 -2.838, p < 0.001). Nomogram and calibration curve indicated our model had best performance in predicting 3-year survival rate.ConclusionsWe constructed a six-RBPs based risk signature model which had moderate efficiency in LIHC patients’ prognosis forecasting which may assist practitioners to make better decision in the management of LIHC.

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