scholarly journals Characterization of an Aging-Based Diagnostic Gene Signature and Molecular Subtypes With Diverse Immune Infiltrations in Atherosclerosis

2022 ◽  
Vol 8 ◽  
Author(s):  
Lei Zhao ◽  
Fengfeng Lv ◽  
Ye Zheng ◽  
Liqiu Yan ◽  
Xufen Cao
Keyword(s):  
Western Blotting ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Macrophage Polarization ◽  
Differential Expression Analysis ◽  
Gene Signature ◽  
Immune Checkpoints ◽  
Foam Cell Formation ◽  
Diagnostic Model

Objective: Advancing age is a major risk factor of atherosclerosis (AS). Nevertheless, the mechanism underlying this phenomenon remains indistinct. Herein, this study conducted a comprehensive analysis of the biological implications of aging-related genes in AS.Methods: Gene expression profiles of AS and non-AS samples were curated from the GEO project. Differential expression analysis was adopted for screening AS-specific aging-related genes. LASSO regression analysis was presented for constructing a diagnostic model, and the discriminatory capacity was evaluated with ROC curves. Through consensus clustering analysis, aging-based molecular subtypes were conducted. Immune levels were estimated based on the expression of HLAs, immune checkpoints, and immune cell infiltrations. Key genes were then identified via WGCNA. The effects of CEBPB knockdown on macrophage polarization were examined with western blotting and ELISA. Furthermore, macrophages were exposed to 100 mg/L ox-LDL for 48 h to induce macrophage foam cells. After silencing CEBPB, markers of cholesterol uptake, esterification and hydrolysis, and efflux were detected with western blotting.Results: This study identified 28 AS-specific aging-related genes. The aging-related gene signature was developed, which could accurately diagnose AS in both the GSE20129 (AUC = 0.898) and GSE43292 (AUC = 0.685) datasets. Based on the expression profiling of AS-specific aging-related genes, two molecular subtypes were clustered, and with diverse immune infiltration features. The molecular subtype–relevant genes were obtained with WGCNA, which were markedly associated with immune activation. Silencing CEBPB triggered anti-inflammatory M2-like polarization and suppressed foam cell formation.Conclusion: Our findings suggest the critical implications of aging-related genes in diagnosing AS and modulating immune infiltrations.

scholarly journals A Novel Ferroptosis-Related Biomarker Signature to Predict Overall Survival of Esophageal Squamous Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Jiahang Song ◽  
Yanhu Liu ◽  
Xiang Guan ◽  
Xun Zhang ◽  
Wenda Yu ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Squamous Cell ◽  
Risk Model ◽  
Expression Profiles ◽  
Gene Signature ◽  
Immune Checkpoints ◽  
Related Gene

Esophageal squamous cell carcinoma (ESCC) accounts for the main esophageal cancer (ESCA) type, which is also associated with the greatest malignant grade and low survival rates worldwide. Ferroptosis is recently discovered as a kind of programmed cell death, which is indicated in various reports to be involved in the regulation of tumor biological behaviors. This work focused on the comprehensive evaluation of the association between ferroptosis-related gene (FRG) expression profiles and prognosis in ESCC patients based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). ALOX12, ALOX12B, ANGPTL7, DRD4, MAPK9, SLC38A1, and ZNF419 were selected to develop a novel ferroptosis-related gene signature for GEO and TCGA cohorts. The prognostic risk model exactly classified patients who had diverse survival outcomes. In addition, this study identified the ferroptosis-related signature as a factor to independently predict the risk of ESCC. Thereafter, we also constructed the prognosis nomogram by incorporating clinical factors and risk score, and the calibration plots illustrated good prognostic performance. Moreover, the association of the risk score with immune checkpoints was observed. Collectively, the proposed ferroptosis-related gene signature in our study is effective and has a potential clinical application to predict the prognosis of ESCC.

scholarly journals Pan-cancer machine learning predictors of primary site of origin and molecular subtype

2018 ◽  
Author(s):  
William F. Flynn ◽  
Sandeep Namburi ◽  
Carolyn A. Paisie ◽  
Honey V. Reddi ◽  
Sheng Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Metastatic Cancer ◽  
Gene Expression Profiles ◽  
Primary Site ◽  
Cancer Types ◽  
Tissue Of Origin ◽  
Pan Cancer

ABSTRACTBackgroundIt is estimated by the American Cancer Society that approximately 5% of all metastatic tumors have no defined primary site (tissue) of origin and are classified as cancers of unknown primary (CUPs). The current standard of care for CUP patients depends on immunohistochemistry (IHC) based approaches to identify the primary site. The addition of post-mortem evaluation to IHC based tests helps to reveal the identity of the primary site for only 25% of the CUPs, emphasizing the acute need for better methods of determination of the site of origin. CUP patients are therefore given generic chemotherapeutic agents resulting in poor prognosis. When the tissue of origin is known, patients can be given site specific therapy with significant improvement in clinical outcome. Similarly, identifying the primary site of origin of metastatic cancer is of great importance for designing treatment.Identification of the primary site of origin is an import first step but may not be sufficient information for optimal treatment of the patient. Recent studies, primarily from The Cancer Genome Atlas (TCGA) project, and others, have revealed molecular subtypes in several cancer types with distinct clinical outcome. The molecular subtype captures the fundamental mechanisms driving the cancer and provides information that is essential for the optimal treatment of a cancer. Thus, along with primary site of origin, molecular subtype of a tumor is emerging as a criterion for personalized medicine and patient entry into clinical trials.However, there is no comprehensive toolset available for precise identification of tissue of origin or molecular subtype for precision medicine and translational research.Methods and FindingsWe posited that metastatic tumors will harbor the gene expression profiles of the primary site of origin of the cancer. Therefore, we decided to learn the molecular characteristics of the primary tumors using the large number of cancer genome profiles available from the TCGA project. Our predictors were trained for 33 cancer types and for the 11 cancers where there are established molecular subtypes. We estimated the accuracy of several machine learning models using cross-validation methods. The extensive testing using independent test sets revealed that the predictors had a median sensitivity and specificity of 97.2% and 99.9% respectively without losing classification of any tumor. Subtype classifiers achieved median sensitivity of 87.7% and specificity of 94.5% via cross validation and presented median sensitivity of 79.6% and specificity of 94.6% in two external datasets of 1,999 total samples. Importantly, these external data shows that our classifiers can robustly predict the primary site of origin from external microarray data, metastatic cancer data, and patient-derived xenograft (PDX) data.ConclusionWe have demonstrated the utility of gene expression profiles to solve the important clinical challenge of identifying the primary site of origin and the molecular subtype of cancers based on machine learning algorithms. We show, for the first time to our knowledge, that our pan-cancer classifiers can predict multiple cancers’ primary site of origin from metastatic samples. The predictors will be made available as open source software, freely available for academic non-commercial use.

scholarly journals Comprehensive analysis based on DNA methylation and RNA-seq reveals hypermethylation of the up-regulated WT1 gene with potential mechanisms in PAM50 subtypes of breast cancer

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11377
Author(s):  
Chongyang Ren ◽  
Xiaojiang Tang ◽  
Haitao Lan
Keyword(s):  
Breast Cancer ◽  
Dna Methylation ◽  
Molecular Mechanisms ◽  
Molecular Subtypes ◽  
Wilms Tumor ◽  
Expression Profiles ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Body Region ◽  
Rna Seq

Background Breast cancer (BC), one of the most widespread cancers worldwide, caused the deaths of more than 600,000 women in 2018, accounting for about 15% of all cancer-associated deaths in women that year. In this study, we aimed to discover potential prognostic biomarkers and explore their molecular mechanisms in different BC subtypes using DNA methylation and RNA-seq. Methods We downloaded the DNA methylation datasets and the RNA expression profiles of primary tissues of the four BC molecular subtypes (luminal A, luminal B, basal-like, and HER2-enriched), as well as the survival information from The Cancer Genome Atlas (TCGA). The highly expressed and hypermethylated genes across all the four subtypes were screened. We examined the methylation sites and the downstream co-expressed genes of the selected genes and validated their prognostic value using a different dataset (GSE20685). For selected transcription factors, the downstream genes were predicted based on the Gene Transcription Regulation Database (GTRD). The tumor microenvironment was also evaluated based on the TCGA dataset. Results We found that Wilms tumor gene 1 (WT1), a transcription factor, was highly expressed and hypermethylated in all the four BC subtypes. All the WT1 methylation sites exhibited hypermethylation. The methylation levels of the TSS200 and 1stExon regions were negatively correlated with WT1 expression in two BC subtypes, while that of the gene body region was positively associated with WT1 expression in three BC subtypes. Patients with low WT1 expression had better overall survival (OS). Five genes including COL11A1, GFAP, FGF5, CD300LG, and IGFL2 were predicted as the downstream genes of WT1. Those five genes were dysregulated in the four BC subtypes. Patients with a favorable 6-gene signature (low expression of WT1 and its five predicted downstream genes) exhibited better OS than that with an unfavorable 6-gene signature. We also found a correlation between WT1 and tamoxifen using STITCH. Higher infiltration rates of CD8 T cells, plasma cells, and monocytes were found in the lower quartile WT1 group and the favorable 6-gene signature group. In conclusion, we demonstrated that WT1 is hypermethylated and up-regulated in the four BC molecular subtypes and a 6-gene signature may predict BC prognosis.

scholarly journals Molecular and Clinicopathological Characterization of a Prognostic Immune Gene Signature Associated With MGMT Methylation in Glioblastoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Liang Zhao ◽  
Jiayue Zhang ◽  
Shurui Xuan ◽  
Zhiyuan Liu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Dna Methyltransferase ◽  
Macrophage Polarization ◽  
Methylation Status ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Immune Checkpoints ◽  
M2 Macrophage ◽  
Immune Gene ◽  
Genome Atlas

Background: O6-methylguanine-DNA methyltransferase (MGMT) methylation status affects tumor chemo-resistance and the prognosis of glioblastoma (GBM) patients. We aimed to investigate the role of MGMT methylation in the regulation of GBM immunophenotype and discover an effective biomarker to improve prognosis prediction of GBM patients.Methods: A total of 769 GBM patients with clinical information from five independent cohorts were enrolled in the present study. Samples from the Cancer Genome Atlas (TCGA) dataset were used as the training set, whereas transcriptome data from the Chinese Glioma Genome Atlas (CGGA) RNA-seq, CGGA microarray, GSE16011, and the Repository for Molecular Brain Neoplasia (REMBRANDT) cohort were used for validation. A series of bioinformatics approaches were carried out to construct a prognostic signature based on immune-related genes, which were tightly related to the MGMT methylation status. In silico analyses were performed to investigate the influence of the signature on immunosuppression and remodeling of the tumor microenvironment. Then, the utility of this immune gene signature was analyzed by the development and evaluation of a nomogram. In vitro experiments were further used to verify the immunologic function of the genes in the signature.Results: We found that MGMT unmethylation was closely associated with immune-related biological processes in GBM. Sixty-five immune genes were more highly expressed in the MGMT unmethylated than the MGMT-methylated group. An immune gene-based risk model was further established to divide patients into high and low-risk groups, and the prognostic value of this signature was validated in several GBM cohorts. Functional analyses manifested a universal up-regulation of immune-related pathways in the high-risk group. Furthermore, the risk score was highly correlated to the immune cell infiltration, immunosuppression, inflammatory activities, as well as the expression levels of immune checkpoints. A nomogram was developed for clinical application. Knockdown of the five genes in the signature remodeled the immunosuppressive microenvironment by restraining M2 macrophage polarization and suppressing immunosuppressive cytokines production.Conclusions:MGMT methylation is strongly related to the immune responses in GBM. The immune gene-based signature we identified may have potential implications in predicting the prognosis of GBM patients and mechanisms underlying the role of MGMT methylation.

scholarly journals A Novel Assessment Model Based on Molecular Subtypes of Hypoxia-Related LncRNAs for Prognosis of Bladder Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Xianwu Chen ◽  
Yan Zhang ◽  
Feifan Wang ◽  
Xuejian Zhou ◽  
Qinghe Fu ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Tumor Therapy ◽  
Assessment Model ◽  
Immune Checkpoints ◽  
Cox Regression Analysis ◽  
Model Based

Hypoxia is a common feature in various tumors that regulates aggressiveness. Previous studies have demonstrated that some dysregulated long non-coding RNAs (lncRNAs) are correlated with tumor progression, including bladder cancer (BCa). However, the prognostic effect of hypoxia-related lncRNAs (HRLs) and their clinical relevance, as well as their regulatory effect on the tumor immune microenvironment, are largely unknown in BCa. A co-expression analysis between hypoxia genes and lncRNA expression, which was downloaded from the TCGA database, was performed to identify HRLs. Univariate Cox regression analysis was performed to select the most desirable lncRNAs for molecular subtype, and further LASSO analysis was performed to develop a prognostic model. This molecular subtype based on four HRLs (AC104653, AL136084, AL139393, and LINC00892) showed good performance in the tumor microenvironment and tumor mutation burden. The prognostic risk model suggested better performance in predicting BCa patients’ prognosis and obtained a close correlation with clinicopathologic features. Furthermore, four of five first-line clinical chemotherapies showed different sensitivities to this model, and nine immune checkpoints showed different expression in the molecular subtypes or the risk model. In conclusion, this study indicates that this molecular subtype and risk model based on HRLs may be useful in improving the prognostic prediction of BCa patients with different clinical situations and may help to find a useful target for tumor therapy.

scholarly journals Molecular signatures of tumor progression in pancreatic adenocarcinoma identified by energy metabolism characteristics

2021 ◽  
Author(s):  
Xin Wang ◽  
Cong Tan ◽  
Weiwei Weng ◽  
Shujuan Ni ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Energy Metabolism ◽  
Pancreatic Adenocarcinoma ◽  
Clustering Algorithm ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Algorithm Analysis ◽  
Receptor Interaction ◽  
Prognostic Gene

Abstract Background: In this study, we aimed to describe a molecular evaluation of primary pancreatic adenocarcinoma (PAAD) based on comprehensive analysis of energy-metabolism-related gene (EMRG) expression profiles.Methods: Molecular subtypes were identified by non-negative matrix clustering algorithm clustering on 565 EMRGs. The overall survival (OS) predictive gene signature was developed, internally and externally validated based on three online PAAD datasets. Hub genes were identified in molecular subtypes by weighted gene correlation network analysis (WGCNA) co-expression algorithm analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analyses were performed to assess prognostic genes and construct the prognostic gene signature. Time-dependent receiver operating characteristic (ROC) curve, Kaplan-Meier curve and nomogram were used to assess the performance of the gene signature.Results: On the basis of EMRGs expression profile, we propose a molecular classification dividing PAAD into two subtypes: Cluster 1, which display more immune and stromal cell components in tumor microenvironment and higher tumor purity; and Cluster 2, which display worse OS. Moreover, by using a three-phase training, test and validation process, we construct a 4-gene signature that can constantly classify the prognostic risk of patients in all three datasets, and which present higher robustness and clinical usability compared with four previous reported prognostic gene signatures. In addition, a novel nomogram constructed by combining clinical features and the 4-gene signature showed confident clinical utility in PAAD. According to gene set enrichment analysis (GSEA), gene sets related to the high-risk group were participated in the neuroactive ligand receptor interaction pathway. Conclusions: In summary, the EMRG-based molecular subtypes and prognostic gene model provides a roadmap for patient stratification and trials of targeted therapies.

scholarly journals Immune Score-based Molecular Subtypes and Signature Associated with Clinical Outcome in Hepatoblastoma

2021 ◽  
Vol 21 (9) ◽  
Author(s):  
Yongping Huang ◽  
Jinlong Yan ◽  
Ruiqi Liu ◽  
Guang Tang ◽  
Qi Dong ◽  
...  
Keyword(s):  
T Cells ◽  
Scoring System ◽  
Prognostic Value ◽  
Molecular Subtypes ◽  
Risk Group ◽  
Molecular Subtype ◽  
Gene Signature ◽  
Risk Scoring ◽  
Immune Score ◽  
Risk Scoring System

Background: This study aimed to identify genes related to the immune score of hepatoblastoma, examine the characteristics of the immune microenvironment of hepatoblastoma, and construct a risk scoring system for predicting the prognosis of hepatoblastoma. Methods: Through using the gene chip data of patients with hepatoblastoma with survival data in the ArrayExpress and GEO databases, the immune score of hepatoblastoma was calculated by the ESITIMATE algorithm, and the prognostic value of immune score in patients with hepatoblastoma was studied by the survival analysis. Genes related to the immune score were identified by the WGCNA algorithm. According to these genes, patients with hepatoblastoma were clustered unsupervised. Finally, the risk scoring system was constructed according to the immune score-related genes. Results: The immune score calculated by the ESTIMATE algorithm had a good prognostic value in patients with hepatoblastoma. Patients with high immune scores had better OS than those with low immune scores (P < 0.001). A total of 146 immune score-related genes were identified by WGCNA analysis, and univariate COX regression analysis indicated that 59 of the genes had prognostic value. According to the unsupervised clustering results of the 146 immune score-related genes, patients with hepatoblastoma could be divided into two subtypes with different prognoses, namely molecular subtype 1 and subtype 2, with molecular subtype 1 having a better prognosis. The immunocyte infiltration analysis results showed that the difference between the two subtypes was mainly in activated CD4 T cells, activated dendritic cells, CD56 bright natural killer cells, the macrophage, and regulatory T cells. According to the immune score-related genes, a risk scoring system was constructed based on a five-gene signature. After the cut-off value was determined, patients with hepatoblastoma were divided into a high-risk group and a low-risk group. The prognosis of the two groups was different. Conclusions: The immune score has a good prognostic value in patients with hepatoblastoma. Based on the different expression patterns of immune score-related genes, hepatoblastoma can be divided into two different prognostic molecular subtypes, showing different immunocyte infiltration patterns. The established risk scoring system based on a five-gene signature has a good predictive value in patients with hepatoblastoma.

scholarly journals An ANOCEF Genomic and Transcriptomic Microarray Study of the Response to Irinotecan and Bevacizumab in Recurrent Glioblastomas

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Julien Laffaire ◽  
Anna Luisa Di Stefano ◽  
Olivier Chinot ◽  
Ahmed Idbaih ◽  
Jaime Gallego Perez-Larraya ◽  
...  
Keyword(s):  
Response Rate ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Molecular Characteristics ◽  
Egfr Amplification ◽  
Lower Response ◽  
Lower Response Rate ◽  
Cdkn2a Deletion

Background. We performed a retrospective study to assess whether the initial molecular characteristics of glioblastomas (GBMs) were associated with the response to the bevacizumab/irinotecan chemotherapy regimen given at recurrence.Results. Comparison of the genomic and gene expression profiles of the responders (n=12) and nonresponders (n=13) demonstrated only slight differences and could not identify any robust biomarkers associated with the response. In contrast, a significant association was observed between GBMs molecular subtypes and response rates. GBMs assigned to molecular subtype IGS-18 and to classical subtype had a lower response rate than those assigned to other subtypes. In an independent series of 33 patients, neither EGFR amplification nor CDKN2A deletion (which are frequent in IGS-18 and classical GBMs) was significantly associated with the response rate, suggesting that these two alterations are unlikely to explain the lower response rate of these GBMs molecular subtypes.Conclusion. Despite its limited sample size, the present study suggests that comparing the initial molecular profiles of responders and nonresponders might not be an effective strategy to identify biomarkers of the response to bevacizumab given at recurrence. Yet it suggests that the response rate might differ among GBMs molecular subtypes.

scholarly journals Systematic Profiling of Invasion-related Genes Signature Predicts Prognostic Features and Identificates Molecular Subtypes of Lung Adenocarcinoma

2021 ◽  
Author(s):  
Ping Yu ◽  
Linlin Tong ◽  
Yujia Song ◽  
Hui Qu ◽  
Ying Chen
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Molecular Subtypes ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
Normal Tissues ◽  
Tumor Tissues ◽  
Cox Analysis

Abstract Background: Due to the high heterogeneity of lung adenocarcinoma (LUAD), molecular subtype based on gene expression profiles is of great significance for diagnosis, and prognosis prediction in patients with LUAD.Methods: Invasion-related genes were obtained from the CancerSEA database, and LUAD expression profiles were downloaded from The Cancer Genome Atlas. The ConsensusClusterPlus was used to obtain molecular subtypes based on invasion-related genes. The limma software package was used to identify differentially expressed genes (DEGs). A multi-gene risk model was constructed by Lasso-Cox analysis. A nomogram was also constructed based on risk scores and meaningful clinical features.Results: 3 subtypes (C1, C2, C3) based on the expression of invasion-related genes were obtained. C3 had the worst prognosis. A total of 669 DEGs were identified among the subtypes. Pathway enrichment analysis results showed that the DEGs were mainly enriched in the cell cycle, DNA replication, the p53 signaling pathway, and other tumor-related pathways. A 5-gene signature (KRT6A, MELTF, IRX5, MS4A1, CRTAC1) was identified by using Lasso-Cox analysis. The training, validation, and external independent cohorts proved that the model was robust and had better prediction ability than other lung cancer models. The gene expression results showed that the expression levels of MS4A1 and KRT6A in tumor tissues were higher than in normal tissues, while CRTAC1 expression in tumor tissues was lower than in normal tissues. At the same time, the 5 genes were significantly expressed in pan-cancer immune subtypes. Gene set enrichment analysis showed that MS4A1, KRT6A, and CRAT1 genes were both enriched in the HALLMARK_IL2_STAT5_SIGNALING pathway, and IRX5 and MELTF gene were both enriched in the HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION pathway. Conclusion: The 5-gene signature prognostic stratification system based on invasion-related genes could be used to assess prognostic risk in patients with LUAD.

scholarly journals Identifying Methylation-Driven Immune-related Molecular Subtypes Predicts the Prognosis of Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Qian Yan ◽  
Baoqian Ye ◽  
Boqing Wang ◽  
Wenjiang Zheng ◽  
Xiongwen Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Cpg Sites ◽  
Prognosis Model ◽  
Immune Related Genes

Abstract The purpose of this study is to analyze the DNA methylation and gene expression profiles of immune-related CpG sites to identify the molecular subtypes and CpG sites related to the prognosis of HCC. In this study, the DNA methylation and gene expression datasets were downloaded from The Cancer Genome Atlas database, together with immune-related genes downloaded from the immunology database and analysis portal database to explore the prognostic molecular subtypes of HCC. By performing consistent clustering analysis on 830 immune-related CpG sites, we identified seven subgroups with significant differences in overall survival. Finally, 16 classifiers of immune-related CpG sites were constructed and used in the testing set to verify the prognosis of DNA methylation subgroups, and the results were consistent with the training set. Using the TIMER database, we analyzed 16 immune-related CpG sites expression with the abundance of six types of immune infiltrating cells and found that most are positively correlated with the level of infiltration of multiple immune cells in HCC. This study screened potential immune-related prognostic methylation sites and established a new prognosis model of HCC based on DNA methylation molecular subtype, which may help in the early diagnosis of HCC and developing more effective personalized treatments.

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