scholarly journals Identification of Biomarkers to Construct a Competing Endogenous RNA Network and Establishment of a Genomic-Clinicopathologic Nomogram to Predict Survival for Children with Rhabdoid Tumors of the Kidney

2020 ◽  
Vol 2020 ◽  
pp. 1-27
Author(s):  
Xiaoqing Wang ◽  
Xiangyu Wu ◽  
Tianyou Li ◽  
Mingyu Cui ◽  
Lichao Zhu ◽  
...  
Keyword(s):  
Regulatory Networks ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Differentially Expressed ◽  
Risk Scores ◽  
Messenger Rnas ◽  
Sequencing Data ◽  
Competing Endogenous Rna ◽  
Clinical Value ◽  
Cox Analysis

Rhabdoid tumor of the kidney (RTK) is a rare and severely malignant tumor occurring in infancy and early childhood, with the overall outcomes remain poor. Neither gene regulatory networks nor biomarkers to predict the prognostic outcomes have been elucidated in RTK. In this study, RNA sequencing data were obtained to identify differentially expressed messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and microRNAs (miRNAs) between RTK samples and normal samples. A total of 4217 mRNAs, 284 lncRNAs, and 286 miRNAs were screened out. Of those, 103 mRNAs, 80 lncRNAs, and 45 miRNAs were identified for a competing endogenous RNA (ceRNA) regulatory network, in which three significant modules were identified. A protein-protein interaction (PPI) network was constructed, and the hub-gene cluster consisted of four core genes (EXOSC2, PAK1IP1, WDR43, and POLR1D) was selected. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were also performed to analyze the functional characteristics of differentially expressed mRNAs. Subsequently, among 211 mRNAs, 8 lncRNAs, and 12 miRNAs associated with overall survival (OS) obtained by univariate Cox analysis, 5 mRNAs, 7 lncRNAs, and 7 miRNAs were identified and the risk score formulas were constructed correspondingly using the least absolute shrinkage and selection operator (LASSO) Cox regression model analysis. The log-rank tests and Kaplan-Meier analyses were performed to confirm the predictive value of the risk scores for OS in RTK patients. A genomic-clinicopathologic nomogram integrating the stage and risk scores based on RNAs was established and demonstrated high predictive accuracy and clinical value, which was validated through calibration curves, time-dependent receiver operating characteristic (ROC) curve analyses, and decision curve analysis (DCA). In conclusion, this study not only provided potential insights into the mechanisms underlying RTK, but also presented a practicable tool for predicting the prognosis in children with RTK.

scholarly journals Development of a Prognostic Signature Based on Hypoxia-related Genes for Thyroid Cancer

2021 ◽  
Author(s):  
HongYang Zhang ◽  
Sijia Li ◽  
Wei Li
Keyword(s):  
Overall Survival ◽  
Thyroid Cancer ◽  
Prognostic Value ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Risk Scores ◽  
Prognostic Signature ◽  
Prognosis Model ◽  
Cox Analysis

Abstract Background. We aimed to establish a model to predict the prognosis of patients with thyroid cancer based on differentially expressed hypoxia-related genes.Methods. By comparing the genes in TCGA database and hypoxiaDB database, we obtained differentially expressed genes (DEGs) related to hypoxia in thyroid cancer. Gene function enrichment analysis was performed, and a protein-protein interaction network was constructed using the STRING database. Univariate Cox regression were used to screen hypoxia-related genes with prognostic value. Subsequently, multivariate Cox analysis was used to determine prognostic markers based on thyroid cancer, a prognosis model based on these genes was established. The Kaplan-Meier analysis, Receiver operating characteristic (ROC) analysis and The Harrell’s concordance indexes in the training set and the validation set were used to evaluate the performance of the model. Finally, we conducted univariate analyses of the prognostic value of clinical data (including risk scores) of thyroid cancer patients.Results. 326 hypoxia-related thyroid cancer genes were found. Functional enrichment analysis demonstrated they were mainly involved in regulating biological functions. 23 genes have been proved to be associated with the prognosis of thyroid cancer with univariate Cox regression, among them, 11 marker genes were used to construct a new prognosis model by multivariate Cox analysis. Accordingly, the system of risk scores was constructed, patients with high-risk scores (P <0.005) had shorter overall survival than those with low-risk scores. The ROC curve indicated good performance of the eleven-gene signature at predicting overall survival. The Harrell’s concordance indexes in the internally validated for the 11-gene prognostic signature was 0.881. Moreover, univariate analysis showed that the risk score and age were significantly associated with patient overall survival. The model we created was significantly associated with patient overall survival.Conclusions. The model we established had excellent performance in the prognosis of thyroid cancer.

scholarly journals Identification of the angiogenesis related genes for predicting prognosis of patients with gastric cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Sheng Zheng ◽  
Zizhen Zhang ◽  
Ning Ding ◽  
Jiawei Sun ◽  
Yifeng Lin ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Overall Survival ◽  
High Efficiency ◽  
Cox Regression ◽  
Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Lasso Regression ◽  
Sequencing Data ◽  
Cox Regression Analysis

Abstract Introduction Angiogenesis is a key factor in promoting tumor growth, invasion and metastasis. In this study we aimed to investigate the prognostic value of angiogenesis-related genes (ARGs) in gastric cancer (GC). Methods mRNA sequencing data with clinical information of GC were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. The differentially expressed ARGs between normal and tumor tissues were analyzed by limma package, and then prognosis‑associated genes were screened using Cox regression analysis. Nine angiogenesis genes were identified as crucially related to the overall survival (OS) of patients through least absolute shrinkage and selection operator (LASSO) regression. The prognostic model and corresponding nomograms were establish based on 9 ARGs and verified in in both TCGA and GEO GC cohorts respectively. Results Eighty-five differentially expressed ARGs and their enriched pathways were confirmed. Significant enrichment analysis revealed that ARGs-related signaling pathway genes were highly related to tumor angiogenesis development. Kaplan–Meier analysis revealed that patients in the high-risk group had worse OS rates compared with the low-risk group in training cohort and validation cohort. In addition, RS had a good prognostic effect on GC patients with different clinical features, especially those with advanced GC. Besides, the calibration curves verified fine concordance between the nomogram prediction model and actual observation. Conclusions We developed a nine gene signature related to the angiogenesis that can predict overall survival for GC. It’s assumed to be a valuable prognosis model with high efficiency, providing new perspectives in targeted therapy.

scholarly journals Effect of Different Expression of Immune-Related lncRNA on Colon Adenocarcinoma and Its Relation to Prognosis

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Meiwei Mu ◽  
Yi Tang ◽  
Zheng Yang ◽  
Yuling Qiu ◽  
Xiaohong Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Future Research ◽  
Sequencing Data ◽  
Tissue Samples ◽  
Gene Sets ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Objective. To explore the expression of immune-related lncRNAs in colon adenocarcinoma and find out the effect on how these lncRNAs influence the development and prognosis of colon adenocarcinoma. Method. Transcriptome data of colon adenocarcinoma from The Cancer Genome Atlas (TCGA) were downloaded, and gene sets “IMMUNE RESPONSE” and “IMMUNE SYSTEM PROCESS” were sought from the Molecular Signatures Database (MSigDB). The expression of immune-related genes was extracted that were immune-related mRNAs. Then, the immune-related lncRNAs were sought out by utilizing of the above data. Clinical traits were combined with immune-related lncRNAs, so that prognostic-related lncRNAs were identified by Cox regression. Multivariate Cox regression was built to calculate risk scores. Relationships between clinical traits and immune-related lncRNAs were also calculated. Result. A total of 480 colorectal adenocarcinoma patients and 41 normal control patients’ transcriptome sequencing data of tissue samples were obtained from TCGA database. 918 immune-related lncRNAs were screened. Cox regression showed that 34 immune-related lncRNAs were associated with colon adenocarcinoma prognosis. Seven lncRNAs were independent risk factors. Conclusion. This study revealed that some lncRNAs can affect the development and prognosis of colon adenocarcinoma. It may provide new theory evidence of molecular mechanism for the future research and molecular targeted therapy of colon adenocarcinoma.

scholarly journals Ferroptosis-Related Long Non-Coding RNAs and the Roles of LASTR in Stomach Adenocarcinoma

2021 ◽  
Author(s):  
Gongjun Wang ◽  
Libin Sun ◽  
Shasha Wang ◽  
Jing Guo ◽  
Hui Li ◽  
...  
Keyword(s):  
Cox Regression ◽  
Comprehensive Evaluation ◽  
Progression Free Survival ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Regulatory Relationship ◽  
Data Resource ◽  
Stomach Adenocarcinoma

Abstract Background: Ferroptosis is a form of cell death involved in diverse physiological context. Increasing evidence suggests that there is a closely regulatory relationship between ferroptosis and long noncoding RNAs (lncRNAs).Method: RNA-sequencing data from The Cancer Genome Atlas (TCGA) data resource and ferroptosis-related genes from FerrDb (http://www.zhounan.org/ferrdb/) data resource were employed to select differentially expressed lncRNAs. We performed Univariate Cox regression and multivariate Cox analyses analysis on these differentially expressed lncRNAs to screen independent predictive factors. Subsequently, we established two signatures for predicting overall survival (OS) and progression-free survival (PFS). Finally, experiments were conducted to verify the roles of LASTR in gastric cancer (GC).Results: We identified 12 differentially expressed lncRNAs linked with OS and 13 associated with PFS. Kaplan-Meier(K-M) analyses exhibited that the high-risk group was related to a poor prognosis of stomach adenocarcinoma (STAD). The AUCs of the OS, as well as PFS signatures of lncRNAs were 0.734 and 0.771, respectively, indicating their excellent efficacy in predicting STAD prognosis. Our experimental results illustrated that the inhibition of LASTR inhibited tumor proliferation and migration in GC.Conclusion: This comprehensive evaluation of the ferroptosis-related lncRNA landscape in STAD unearthed novel lncRNAs related to carcinogenesis. In addition, we also experimentally confirmed the effects of LASTR on proliferation, migration and ferroptosis. These results provide potential novel targets for tumor treatment and promote personalized medicine.

scholarly journals Prognostic model of head and neck squamous cell carcinoma based on 12 ferroptosis-related genes

2021 ◽  
Author(s):  
Sijia Li ◽  
Hongyang Zhang ◽  
Wei Li
Keyword(s):  
Regression Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
High Risk Group ◽  
Risk Scores ◽  
Cox Regression Analysis ◽  
Normal Tissues ◽  
Model Based ◽  
Cox Analysis

Abstract Background: The purpose of our study is establishing a model based on ferroptosis-related genes predicting the prognosis of patients with head and neck squamous cell carcinoma (HNSCC).Methods: In our study, transcriptome and clinical data of HNSCC patients were from The Cancer Genome Atlas, ferroptosis-related genes and pathways were from Ferroptosis Signatures Database. Differentially expressed genes (DEGs) were screened by comparing tumor and adjacent normal tissues. Functional enrichment analysis of DEGs, protein-protein interaction network and gene mutation examination were applied. Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression were used to identified DEGs. The model was constructed by multivariate Cox regression analysis and verified by Kaplan-Meier analysis. The relationship between risk scores and other clinical features was also analyzed. Univariate and multivariate Cox analysis was used to verified the independence of our model. The model was evaluated by receiver operating characteristic analysis and calculation of the area under the curve (AUC). A nomogram model based on risk score, age, gender and TNM stages was constructed.Results: We analyzed data including 500 tumor tissues and 44 adjacent normal tissues and 259 ferroptosis-related genes, then obtained 73 DEGs. Univariate Cox regression analysis screened out 16 genes related to overall survival, and LASSO analysis fingered out 12 of them with prognostic value. A risk score model based on these 12 genes was constructed by multivariate Cox regression analysis. According to the median risk score, patients were divided into high-risk group and low-risk group. The survival rate of high-risk group was significantly lower than that of low-risk group in Kaplan-Meier curve. Risk scores were related to T and grade. Univariate and multivariate Cox analysis showed our model was an independent prognostic factor. The AUC was 0.669. The nomogram showed high accuracy predicting the prognosis of HNSCC patients.Conclusion: Our model based on 12 ferroptosis-related genes performed excellently in predicting the prognosis of HNSCC patients. Ferroptosis-related genes may be promising biomarkers for HNSCC treatment and prognosis.

scholarly journals Identification of Non-coding RNA Biomarkers in Stress-induced Depression via Comprehensive Analysis of Competing Endogenous RNA Network

2020 ◽  
Author(s):  
xuanjun liu ◽  
Lan Yan ◽  
Chun Lin ◽  
Yiliang Zhang ◽  
Haofei Miao ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Comprehensive Analysis ◽  
Differentially Expressed ◽  
Psychiatric Disease ◽  
Circular Rnas ◽  
Messenger Rnas ◽  
Competing Endogenous Rna ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Promising Perspective

Abstract BackgroundDepression is one of the most common psychiatric disease worldwide. Although the research about the pathogenesis of depression have achieved progress, the detailed effect of non-coding RNAs (ncRNAs) in depression are still not clearly elucidated. This study was aimed to identify non-coding RNA biomarkers in stress-induced depression via comprehensive analysis of competing endogenous RNA networkMethodsIn this present study, we acquired RNA expression from RNA seq expression profile in three mice with depressive-like behaviors using chronic restraint stress paradigm and three C57BL/6J wild-type mice as control mice. ResultsA total of 41 differentially expressed circular RNAs (circRNAs) and 181 differentially expressed messenger RNAs (mRNAs) were up-regulated, and 65 differentially expressed circRNAs and 289 differentially expressed mRNAs were down-regulated, which were selected by a threshold of fold change ≥2 and a p-value < 0.05. Gene Ontology was performed to analyze the biological functions, and we predicted potential signaling pathways based on Kyoto Encyclopedia of Genes and Genomes pathway database. In addition, we constructed a circRNA-microRNA (miRNA)-mRNA regulatory network to further identify non-coding RNAs biomarkers. ConclusionsOur findings provide a promising perspective for further research into molecular mechanisms of depression, and targeting circRNA -mediated competing endogenous RNA (ceRNA) network is a useful strategy to early recognize the depression.

scholarly journals Identification and validation of a five-lncRNA prognostic signature related to Glioma using bioinformatics analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chunyu Zhang ◽  
Haitao Liu ◽  
Pengfei Xu ◽  
Yinqiu Tan ◽  
Yang Xu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Cox Regression ◽  
Predictive Accuracy ◽  
Characteristic Curve ◽  
Prognostic Indicator ◽  
High Risk Group ◽  
Survival Prediction ◽  
Prognostic Signature ◽  
Who Grade ◽  
Cox Analysis

Abstract Background To accurately predict the prognosis of glioma patients. Methods A total of 541 samples from the TCGA cohort, 181 observations from the CGGA database and 91 samples from our cohort were included in our study. Long non-coding RNAs (LncRNAs) associated with glioma WHO grade were evaluated by weighted gene co-expression network analysis (WGCNA). Five lncRNA features were selected out to construct prognostic signatures based on the Cox regression model. Results By weighted gene co-expression network analysis (WGCNA), 14 lncRNAs related to glioma grade were identified. Using univariate and multivariate Cox analysis, five lncRNAs (CYTOR, MIR155HG, LINC00641, AC120036.4 and PWAR6) were selected to develop the prognostic signature. The Kaplan-Meier curve depicted that the patients in high risk group had poor prognosis in all cohorts. The areas under the receiver operating characteristic curve of the signature in predicting the survival of glioma patients at 1, 3, and 5 years were 0.84, 0.92, 0.90 in the CGGA cohort; 0.8, 0.85 and 0.77 in the TCGA set and 0.72, 0.90 and 0.86 in our own cohort. Multivariate Cox analysis demonstrated that the five-lncRNA signature was an independent prognostic indicator in the three sets (CGGA set: HR = 2.002, p < 0.001; TCGA set: HR = 1.243, p = 0.007; Our cohort: HR = 4.457, p = 0.008, respectively). A nomogram including the lncRNAs signature and clinical covariates was constructed and demonstrated high predictive accuracy in predicting 1-, 3- and 5-year survival probability of glioma patients. Conclusion We established a five-lncRNA signature as a potentially reliable tool for survival prediction of glioma patients.

scholarly journals Identification Sixteen Metabolic Genes as Potential Biomarkers for Colon Adenocarcinoma 

2020 ◽  
Author(s):  
Binbin Cui ◽  
Fuqiang Zhao ◽  
Yanlong Liu ◽  
Xinyue Gu ◽  
Bomiao Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Risk Score ◽  
Cox Regression ◽  
Colon Adenocarcinoma ◽  
Curve Analysis ◽  
Gene Set Enrichment Analysis ◽  
Risk Scores ◽  
Metabolic Genes ◽  
Cox Analysis

Abstract Purpose Colon adenocarcinoma (COAD) is the most common primary malignant tumor of the digestive tract. It is still important to find important markers that affect the prognosis of COAD. This research aims to identify some key prognosis-related metabolic genes (PRMG) and establish a clinical prognosis model for COAD patients. Method We used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) to obtain gene expression profiles of COAD, and then identified differentially expressed prognostic-related metabolic genes through R language and Perl software, Through univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis to obtain target genes, established metabolic genes prognostic models and risk scores. Through COX regression analysis, independent risk factors affecting the prognosis of COAD were analyzed, and Receiver Operating Characteristic (ROC) curve analysis of independent prognostic factors was performed and a nomogram for predicting overall survival was constructed. Perform the consistency index (C-index) test and decision curve analysis (DCA) on the nomogram, and use Gene Set Enrichment Analysis (GSEA) to identify the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of model genes. Result We selected PRMG based on the expression of metabolic genes, and used LASSO Cox regression to construct 16 metabolic gene (SEPHS1, P4HA1, ENPP2, PTGDS, GPX3, CP, ASPA, POLR3A, PKM, POLR2D , XDH, EPHX2, ADH1B, HMGCL, GPD1L and MAOA) models. The risk score generated from our model can well predict the survival prognosis of COAD. A nomogram based on the clinicopathological characteristics and risk scores of COAD can personally predict the overall survival rate of COAD patients. Conclusion We comprehensively identified metabolic genes related to the prognosis of COAD. The risk score based on the expression of 16 metabolic genes can effectively predict the prognosis of patients with COAD.

scholarly journals Identification of serum prognostic marker miRNAs and construction of microRNA-mRNA networks of esophageal cancer

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0255479
Author(s):  
Yue Jiang ◽  
Chengda Zhang ◽  
Wenbin Shen ◽  
Yiming Li ◽  
Yun Wang ◽  
...  
Keyword(s):  
Esophageal Cancer ◽  
Signaling Pathway ◽  
Target Genes ◽  
Pentose Phosphate ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Cox Analysis ◽  
Core Genes

Esophageal cancer is a common tumor of the digestive system with poor prognosis. This study was to gain a better understanding of the mechanisms involved in esophageal cancer and to identify new prognostic markers. We downloaded the esophageal cancer miRNA expression profile microarray data (GSE113740, GSE112264, GSE122497, GSE113486, and GSE106817) from the GEO database, extracted the esophageal cancer miRNA sequencing data from The Cancer Genome Atlas (TCGA) database, and then used a bioinformatics approach to select common differentially expressed miRNAs (DEMs). Differentially expressed genes (DEGs) were selected by predicting DEM target genes using the miRWalk database and intersecting with differential genes obtained from TCGA database for esophageal cancer. The STRING database was used to obtain protein–protein interaction (PPI) relationships to construct the DEM-DEG network. Furthermore, we selected core genes and core miRNAs associated with esophageal cancer prognosis by performing survival and univariate/multivariate COX analysis on DEMs and DEGs in the network and performed GSEA analysis on core genes alone, and finally the expression of the markers was verified by qPCR in esophageal cancer cell lines Eca109, SKGT-4 and normal esophageal epithelial cells HEEC. Nine DEMs were obtained, of which three were upregulated and six were downregulated, and 326 DEGs were obtained, of which 105 were upregulated and 221 were downregulated. Survival univariate/multivariate COX analysis revealed that five genes, ZBTB16, AQP4, ADCYAP1R1, PDGFD, and VIPR2, and two microRNAs, miR-99a-5p, and miR-508-5p, were related to esophageal cancer prognosis. GSEA analysis showed that the following genes may be involved in esophageal cancer prognosis: ZBTB16 may through the MTOR signaling pathway, AQP4 through the GNRH signaling pathway, ADCYAP1R1 through the PPAR signaling pathway, VIPR2 through the P53 signaling pathway and PDGFD through the PENTOSE-PHOSPHATE signaling pathway.

scholarly journals A prognostic model for hepatocellular carcinoma based on apoptosis-related genes

2021 ◽  
Author(s):  
Renjie Liu ◽  
Guifu Wang ◽  
Chi Zhang ◽  
Dousheng Bai
Keyword(s):  
Hepatocellular Carcinoma ◽  
Prognostic Model ◽  
Molecular Mechanisms ◽  
Risk Model ◽  
Cox Regression ◽  
Risk Groups ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Risk Scores

Abstract Background: Dysregulation of the balance between proliferation and apoptosis is the basis for human hepatocarcinogenesis. In many malignant tumors, such as hepatocellular carcinoma (HCC), there is a correlation between apoptotic dysregulation and poor prognosis. However, the prognostic values of apoptosis-related genes (ARGs) in HCC have not been elucidated. Methods: To screen for differentially expressed ARGs, the expression levels of 161 ARGs from The Cancer Genome Atlas (TCGA) database(https://cancergenome.nih.gov/) were analyzed. Gene Ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed to evaluate the underlying molecular mechanisms of differentially expressed ARGs in HCC. The prognostic values of ARGs were established using Cox regression, and subsequently, a prognostic risk model for scoring patients was developed. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves were plotted to determine the prognostic value of the model. Results: Compared to normal tissues, 43 highly up-regulated and 8 down-regulated ARGs in HCC tissues were screened. GO analysis results revealed that these 51 genes are indeed related to the apoptosis function. KEGG analysis revealed that these 51 genes were correlated with MAPK, P53, TNF, and PI3K-AKT signaling pathways, while Cox regression revealed that 5 ARGs (PPP2R5B, SQSTM1, TOP2A, BMF, and LGALS3) were associated with prognosis and were, therefore, obtained to develop the prognostic model. Based on the median risk scores, patients were categorized into high-risk and low-risk groups. Patients in the low-risk groups exhibited significantly elevated two-year or five-year survival probabilities (p < 0.0001). The risk model had a better clinical potency than the other clinical characteristics, with the area under the ROC curve (AUC = 0.741). The prognosis of HCC patients was established from a plotted nomogram. Conclusion: Based on the differential expression of ARGs, we established a novel risk model for predicting HCC prognosis. This model can also be used to inform the individualized treatment of HCC patients.

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