scholarly journals Identification of Tumor Mutation Burden and Immune Infiltrates in Hepatocellular Carcinoma Based on Multi-Omics Analysis

2020 ◽  
Author(s):  
Lu Yin ◽  
Liuzhi Zhou ◽  
Rujun Xu
Keyword(s):  
T Cells ◽  
Prediction Model ◽  
Risk Prediction ◽  
Immune Cell ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Risk Prediction Model ◽  
Activated T Cells ◽  
Immune Infiltration ◽  
Differential Genes

Abstract Background: We aimed to explore the tumor mutational burden (TMB) and immune infiltration in HCC and investigate new biomarkers for immunotherapy.Methods: Transcriptome and gene mutation data were downloaded from the GDC portal, including 374 HCC samples and 50 matched samples. Furthermore, we divided the samples into high and low TMB groups, and analyzed the differential genes between them with GO, KEGG, and GSEA. Cibersort was used to assess the immune cell infiltration in the samples. Finally, univariate and multivariate Cox regression analyses were performed to identify differential genes related to TMB and immune infiltration, and a risk prediction model was constructed.Results: We found 10 frequently mutated genes, including TP53, TTN, CTNNB1, MUC16, ALB, PCLO, MUC, APOB, RYR2, and ABCA. Pathway analysis indicated that these TMB-related differential genes were mainly enriched in PI3K-AKT. Cibersort analysis showed that memory B cells (P=0.02), CD8+ T cells (P=0.09), CD4+ memory activated T cells (P=0.07), and neutrophils (P=0.06) demonstrated a difference in immune infiltration between high and low TMB groups. On multivariate analysis, GABRA3 (P=0.05), CECR7 (P<0.001), TRIM16 (P=0.003), and IL7R (P=0.04) were associated with TMB and immune infiltration. The risk prediction model had an area under the curve (AUC) of 0.69, suggesting that patients with low risk had better survival outcomes.Conclusions: Our study demonstrated for the first time that CECR7, GABRA3, IL7R, and TRIM16L mutations were associated with TMB and promoted antitumor immunity in HCC.

scholarly journals Identification of Tumor Mutation Burden and Immune Infiltrates in Hepatocellular Carcinoma Based on Multi-Omics Analysis

2021 ◽  
Vol 7 ◽  
Author(s):  
Lu Yin ◽  
Liuzhi Zhou ◽  
Rujun Xu
Keyword(s):  
T Cells ◽  
Prediction Model ◽  
Risk Prediction ◽  
Immune Cell ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Risk Prediction Model ◽  
Activated T Cells ◽  
Immune Infiltration ◽  
Differential Genes

We aimed to explore the tumor mutational burden (TMB) and immune infiltration in HCC and investigate new biomarkers for immunotherapy. Transcriptome and gene mutation data were downloaded from the GDC portal, including 374 HCC samples and 50 matched normal samples. Furthermore, we divided the samples into high and low TMB groups, and analyzed the differential genes between them with GO, KEGG, and GSEA. Cibersort was used to assess the immune cell infiltration in the samples. Finally, univariate and multivariate Cox regression analyses were performed to identify differential genes related to TMB and immune infiltration, and a risk prediction model was constructed. We found 10 frequently mutated genes, including TP53, TTN, CTNNB1, MUC16, ALB, PCLO, MUC, APOB, RYR2, and ABCA. Pathway analysis indicated that these TMB-related differential genes were mainly enriched in PI3K-AKT. Cibersort analysis showed that memory B cells (p = 0.02), CD8+ T cells (p = 0.09), CD4+ memory activated T cells (p = 0.07), and neutrophils (p = 0.06) demonstrated a difference in immune infiltration between high and low TMB groups. On multivariate analysis, GABRA3 (p = 0.05), CECR7 (p &lt; 0.001), TRIM16 (p = 0.003), and IL7R (p = 0.04) were associated with TMB and immune infiltration. The risk prediction model had an area under the curve (AUC) of 0.69, suggesting that patients with low risk had better survival outcomes. Our study demonstrated for the first time that CECR7, GABRA3, IL7R, and TRIM16L were associated with TMB and promoted antitumor immunity in HCC.

scholarly journals Establishment of non–small-cell lung cancer risk prediction model based on prognosis-associated ADME genes

2021 ◽  
Author(s):  
Ke Han ◽  
Jukun Wang ◽  
Kun Qian ◽  
Teng Zhao ◽  
Yi Zhang
Keyword(s):  
Lung Cancer ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Score ◽  
Small Cell ◽  
Risk Prediction Model ◽  
Low Risk ◽  
Regression Analyses ◽  
Small Cell Lung ◽  
Immune Infiltration

Purpose: ADME genes are those involved in the absorption, distribution, metabolism, and excretion (ADME) of drugs. In this study, a non–small-cell lung cancer (NSCLC) risk prediction model was established using prognosis-associated ADME genes, and the predictive performance of this model was evaluated and verified. In addition, multifaceted difference analysis was performed on groups with high and low risk scores. Methods: An NSCLC sample transcriptome and clinical data were obtained from public databases. The prognosis-associated ADME genes were obtained by univariate Cox and lasso regression analyses to build a risk model. Tumor samples were divided into high-risk and low-risk score groups according to the risk score. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of the differentially expressed genes and the differences in the immune infiltration, mutation, and medication reactions in the two groups were studied in detail. Results: A risk prediction model was established with seven prognosis-associated ADME genes. Its good predictive ability was confirmed by studies of the model’s effectiveness. Univariate and multivariate Cox regression analyses showed that the model’s risk score was an independent prognostic factor for patients with NSCLC. The study also showed that the risk score closely correlated with immune infiltration, mutations, and medication reactions. Conclusion: The risk prediction model established with seven ADME genes in this study can predict the prognosis of patients with NSCLC. In addition, significant differences in immune infiltration, mutations, and therapeutic efficacy exist between the high- and low-risk score groups.

scholarly journals Identification of a Ferroptosis-Related LncRNA Signature as a Novel Prognosis Model for Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Lu Lu ◽  
Le-Ping Liu ◽  
Qiang-Qiang Zhao ◽  
Rong Gui ◽  
Qin-Yu Zhao
Keyword(s):  
Regression Analysis ◽  
Prediction Model ◽  
Lung Adenocarcinoma ◽  
Tumor Cells ◽  
Risk Prediction ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Prediction Model ◽  
Functional Enrichment ◽  
Cox Regression Analysis

Lung adenocarcinoma (LUAD) is a highly heterogeneous malignancy, which makes prognosis prediction of LUAD very challenging. Ferroptosis is an iron-dependent cell death mechanism that is important in the survival of tumor cells. Long non-coding RNAs (lncRNAs) are considered to be key regulators of LUAD development and are involved in ferroptosis of tumor cells, and ferroptosis-related lncRNAs have gradually emerged as new targets for LUAD treatment and prognosis. It is essential to determine the prognostic value of ferroptosis-related lncRNAs in LUAD. In this study, we obtained RNA sequencing (RNA-seq) data and corresponding clinical information of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database and ferroptosis-related lncRNAs by co-expression analysis. The best predictors associated with LUAD prognosis, including C5orf64, LINC01800, LINC00968, LINC01352, PGM5-AS1, LINC02097, DEPDC1-AS1, WWC2-AS2, SATB2-AS1, LINC00628, LINC01537, LMO7DN, were identified by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis, and the LUAD risk prediction model was successfully constructed. Kaplan-Meier analysis, receiver operating characteristic (ROC) time curve analysis and univariate and multivariate Cox regression analysis and further demonstrated that the model has excellent robustness and predictive ability. Further, based on the risk prediction model, functional enrichment analysis revealed that 12 prognostic indicators involved a variety of cellular functions and signaling pathways, and the immune status was different in the high-risk and low-risk groups. In conclusion, a risk model of 12 ferroptosis related lncRNAs has important prognostic value for LUAD and may be ferroptosis-related therapeutic targets in the clinic.

scholarly journals Prediction of first cardiovascular disease event in 2.9 million individuals using Danish administrative healthcare data: a nationwide, registry-based derivation and validation study

2021 ◽  
Author(s):  
Daniel Mølager Christensen ◽  
Matthew Phelps ◽  
Thomas Gerds ◽  
Morten Malmborg ◽  
Anne-Marie Schjerning ◽  
...  
Keyword(s):  
Cardiovascular Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Sex Education ◽  
Cox Regression ◽  
Population Level ◽  
Risk Prediction Model ◽  
Lipid Lowering ◽  
Chronic Obstructive ◽  
Cvd Risk

Abstract Aims To derive and validate a risk prediction model with nationwide coverage to predict individual and population-level risk of cardiovascular disease (CVD). Methods and Results All 2.98 million Danish residents aged 30-85 years free of CVD were included on January 1, 2014 and followed through December 31, 2018 using nationwide administrative healthcare registries. Model predictors and outcome were pre-specified. Predictors were: Age, sex, education, use of antithrombotic, blood pressure-lowering, glucose-lowering, or lipid-lowering drugs, and a smoking proxy of smoking-cessation drug use or chronic obstructive pulmonary disease. Outcome was 5-year risk of first CVD event, a combination of ischemic heart disease, heart failure, peripheral artery disease, stroke, or cardiovascular death. Predictions were computed using cause-specific Cox regression models. The final model fitted in the full data was internally-externally validated in each Danish Region. The model was well-calibrated in all Regions. Areas under the curve (AUC) and Brier scores ranged from 76.3% to 79.6% and 3.3 to 4.4. The model was superior to an age-sex benchmark model with differences in AUC and Brier scores ranging from 1.2% to 1.5% and -0.02 to -0.03. Average predicted risks in each Danish municipality ranged from 2.8% to 5.9%. Predicted risks for a 66-year-old ranged from 2.6% to 25.3%. Personalized predicted risks across ages 30-85 were presented in an online calculator (https://hjerteforeningen.shinyapps.io/cvd-risk-manuscript/). Conclusion A CVD risk prediction model based solely on nationwide administrative registry data provided accurate prediction of personal and population-level 5-year first CVD event risk in the Danish population. This may inform clinical and public health primary prevention efforts.

scholarly journals EVA1B to Evaluate the Tumor Immune Microenvironment and Clinical Prognosis in Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Shanqiang Qu ◽  
Jin Liu ◽  
Huafu Wang
Keyword(s):  
T Cells ◽  
B Cells ◽  
Immune Cells ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Area Under The Curve ◽  
Biological Databases ◽  
Clinical Prognosis ◽  
Immune Infiltration ◽  
Potential Biomarker

BackgroundPrevious research indicated that the tumor cells and microenvironment interactions are critical for the immunotherapeutic response. However, predicting the clinical response to immunotherapy remains a dilemma for clinicians. Hence, this study aimed to investigate the associations between EVA1B expression and prognosis and tumor-infiltrating immune cells in glioma.MethodsFirstly, we detected the EVA1B expression in glioma tissues through biological databases. The chi-squared test, Kaplan-Meier, and univariate and multivariate Cox regression analyses were used to analyze the clinical significance of EVA1B expression. The correlation between EVA1B expression and levels of tumor-infiltrating immune cells in glioma tissues was investigated. Receiver operating characteristic (ROC) analysis was performed to compare the predictive power between EVA1B and other commonly immune-related markers.ResultsIn the CGGA cohort of 325 glioma patients, we found that EVA1B was upregulated in glioma, and increased with tumor grade. High EVA1B expression was prominently associated with unfavorable clinicopathological features, and poorer survival of patients, which were further confirmed by TCGA (n=609) and GEO (n=74) cohorts. Furthermore, multivariate analysis indicated that EVA1B is an independent prognostic biomarker for glioma. Importantly, EVA1B overexpression was associated with a higher infiltration level of CD4+ T cells, CD8+ T cells, B cells, macrophages, and neutrophils in glioma. ROC curves showed that, compared with PD-L1, CTLA-4, and Siglec15, EVA1B presented a higher area under the curve (AUC) value (AUC=0.824) for predicting high immune infiltration levels in glioma.ConclusionsWe found that EVA1B was upregulated and could act as a poor prognostic biomarker in glioma. Importantly, EVA1B overexpression was associated with the immune infiltration levels of immune cells including B cells, CD4+ T cells, CD8+ T cells, macrophages, and neutrophils, and strongly with the overall immune infiltration levels of glioma. These findings suggested that EVA1B might be a potential biomarker for evaluating prognosis and immune infiltration in glioma.

scholarly journals Recognition of Immune Microenvironment Landscape and Immune-Related Prognostic Genes in Breast Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Huiling Wang ◽  
Shuo You ◽  
Meng Fang ◽  
Qian Fang
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Regression Analysis ◽  
Clustering Algorithm ◽  
Immune Cell ◽  
Cox Regression ◽  
Therapeutic Potential ◽  
Enrichment Analysis ◽  
Cox Regression Analysis ◽  
Immune Infiltration

Background. Breast cancer (BC) is the most common malignant tumor in women. The immunophenotype of tumor microenvironment (TME) has shown great therapeutic potential in tumor. Method. The transcriptome was obtained from TCGA and GEO data. Immune infiltration was analyzed by single-sample gene set enrichment (ssGSEA). The immune feature was constructed by Cox regression analysis. In addition, the coexpression of differential expression genes (DEGs) was identified. Through enrichment analysis, the function and pathway of module genes were identified. The somatic mutations related to immune characteristics were analyzed by Maftools. By using the consistency clustering algorithm, the molecular subtypes were constructed, and the overall survival time (OS) was predicted. Results. Immune landscape can be divided into low immune infiltration and high immune infiltration. Cox regression analysis identified seven immune cells as protective factors of BC. In the coexpression modules for DEGs of high and low immune infiltration, module 1 was related to T cells and high immune infiltration. In particular, the area under the curve (AUC) value of hub gene SASH3 was the highest, and the correlation with T cells was stronger in the high immune infiltration. Enrichment analysis found that oxidative stress, T cell aggregation, and apoptosis were observed in high immune infiltration. In addition, TP53 was identified as the most important somatic gene mutation related to immune characteristics. Importantly, we also constructed seven immune cell-based breast cancer subtypes to predict OS. Conclusion. We evaluated the immune landscape of BC and constructed the gene characteristics related to the immune landscape. The potential of T cells and SASH3 in immunotherapy of BC was revealed, which may guide the development of new clinical treatment strategies.

scholarly journals Immune profile differences between chronic GVHD and late acute GVHD: results of the ABLE/PBMTC 1202 studies

Blood ◽  
2020 ◽  
Vol 135 (15) ◽  
pp. 1287-1298 ◽  
Author(s):  
Kirk R. Schultz ◽  
Amina Kariminia ◽  
Bernard Ng ◽  
Sayeh Abdossamadi ◽  
Madeline Lauener ◽  
...  
Keyword(s):  
T Cells ◽  
Nk Cells ◽  
Acute Gvhd ◽  
Immune Cell ◽  
Cytotoxic T Cells ◽  
Chronic Gvhd ◽  
Area Under The Curve ◽  
Activated T Cells ◽  
Hematopoietic Stem ◽  
Effect Ratio

Abstract Human graft-versus-host disease (GVHD) biology beyond 3 months after hematopoietic stem cell transplantation (HSCT) is complex. The Applied Biomarker in Late Effects of Childhood Cancer study (ABLE/PBMTC1202, NCT02067832) evaluated the immune profiles in chronic GVHD (cGVHD) and late acute GVHD (L-aGVHD). Peripheral blood immune cell and plasma markers were analyzed at day 100 post-HSCT and correlated with GVHD diagnosed according to the National Institutes of Health consensus criteria (NIH-CC) for cGVHD. Of 302 children enrolled, 241 were evaluable as L-aGVHD, cGVHD, active L-aGVHD or cGVHD, and no cGVHD/L-aGVHD. Significant marker differences, adjusted for major clinical factors, were defined as meeting all 3 criteria: receiver-operating characteristic area under the curve ≥0.60, P ≤ .05, and effect ratio ≥1.3 or ≤0.75. Patients with only distinctive features but determined as cGVHD by the adjudication committee (non-NIH-CC) had immune profiles similar to NIH-CC. Both cGVHD and L-aGVHD had decreased transitional B cells and increased cytolytic natural killer (NK) cells. cGVHD had additional abnormalities, with increased activated T cells, naive helper T (Th) and cytotoxic T cells, loss of CD56bright regulatory NK cells, and increased ST2 and soluble CD13. Active L-aGVHD before day 114 had additional abnormalities in naive Th, naive regulatory T (Treg) cell populations, and cytokines, and active cGVHD had an increase in PD-1− and a decrease in PD-1+ memory Treg cells. Unsupervised analysis appeared to show a progression of immune abnormalities from no cGVHD/L-aGVHD to L-aGVHD, with the most complex pattern in cGVHD. Comprehensive immune profiling will allow us to better understand how to minimize L-aGVHD and cGVHD. Further confirmation in adult and pediatric cohorts is needed.

scholarly journals Identification of Prognostic Biomarkers and Correlation With Immune Infiltrates in Hepatocellular Carcinoma Based on a Competing Endogenous RNA Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhangya Pu ◽  
Yuanyuan Zhu ◽  
Xiaofang Wang ◽  
Yun Zhong ◽  
Fang Peng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Survival Analysis ◽  
Immune Cell ◽  
Cox Regression ◽  
Quantitative Polymerase Chain Reaction ◽  
Prognostic Biomarkers ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Cerna Network

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Recently, competing endogenous RNAs (ceRNA) have revealed a significant role in the progression of HCC. Herein, we aimed to construct a ceRNA network to identify potential biomarkers and illustrate its correlation with immune infiltration in HCC.MethodsRNA sequencing data and clinical traits of HCC patients were downloaded from TCGA. The limma R package was used to identify differentially expressed (DE) RNAs. The predicted prognostic model was established using univariate and multivariate Cox regression. A K-M curve, TISIDB and GEPIA website were utilized for survival analysis. Functional annotation was determined using Enrichr and Reactome. Protein-to-protein network analysis was implemented using SRTNG and Cytoscape. Hub gene expression was validated by quantitative polymerase chain reaction, Oncomine and the Hunan Protein Atlas database. Immune infiltration was analyzed by TIMMER, and Drugbank was exploited to identify bioactive compounds.ResultsThe predicted model that was established revealed significant efficacy with 3- and 5-years of the area under ROC at 0.804 and 0.744, respectively. Eleven DEmiRNAs were screened out by a K-M survival analysis. Then, we constructed a ceRNA network, including 56 DElncRNAs, 6 DEmiRNAs, and 28 DEmRNAs. The 28 DEmRNAs were enriched in cancer-related pathways, for example, the TNF signaling pathway. Moreover, six hub genes, CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1, were all overexpressed in HCC tissues and independently correlated with survival rate. Furthermore, expression of hub genes was related to immune cell infiltration in HCC, including B cells, CD8+ T cells, CD4+ T cells, monocytes, macrophages, neutrophils, and dendritic cells.ConclusionThe findings from this study demonstrate that CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1 are closely associated with prognosis and immune infiltration, representing potential therapeutic targets or prognostic biomarkers in HCC.

scholarly journals Prediction of COPD risk accounting for time-varying smoking exposures

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248535
Author(s):  
Joanne T. Chang ◽  
Rafael Meza ◽  
David T. Levy ◽  
Douglas Arenberg ◽  
Jihyoun Jeon
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Cox Regression ◽  
The United States ◽  
Risk Prediction Model ◽  
Health Study ◽  
Time Varying ◽  
Smoking Duration ◽  
Summary Measures ◽  
Time Varying Covariates

Rationale Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death in the United States. Studies have primarily assessed the relationship between smoking on COPD risk focusing on summary measures, like smoking status. Objective Develop a COPD risk prediction model incorporating individual time-varying smoking exposures. Methods The Nurses’ Health Study (N = 86,711) and the Health Professionals Follow-up Study (N = 39,817) data was used to develop a COPD risk prediction model. Data was randomly split in 50–50 samples for model building and validation. Cox regression with time-varying covariates was used to assess the association between smoking duration, intensity and year-since-quit and self-reported COPD diagnosis incidence. We evaluated the model calibration as well as discriminatory accuracy via the Area Under the receiver operating characteristic Curve (AUC). We computed 6-year risk of COPD incidence given various individual smoking scenarios. Results Smoking duration, year-since-quit (if former smokers), sex, and interaction of sex and smoking duration are significantly associated with the incidence of diagnosed COPD. The model that incorporated time-varying smoking variables yielded higher AUCs compared to models using only pack-years. The AUCs for the model were 0.80 (95% CI: 0.74–0.86) and 0.73 (95% CI: 0.70–0.77) for males and females, respectively. Conclusions Utilizing detailed smoking pattern information, the model predicts COPD risk with better accuracy than models based on only smoking summary measures. It might serve as a tool for early detection programs by identifying individuals at high-risk for COPD.

scholarly journals Identification of Prognostic Biomarkers and Correlation with Immune Infiltrates in Hepatocellular Carcinoma Based on a Competing Endogenous RNA Network

2020 ◽  
Author(s):  
Zhangya Pu ◽  
Yuanyuan Zhu ◽  
Xiaofang Wang ◽  
Yun Zhong ◽  
Fang Peng ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
T Cells ◽  
Survival Analysis ◽  
Immune Cell ◽  
Cox Regression ◽  
Malignant Tumors ◽  
Prognostic Biomarkers ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Cerna Network

Abstract Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Recently, competing endogenous RNAs (ceRNA) have revealed a significant role in the progression of HCC. Herein, we aimed to construct a ceRNA network to identify potential biomarkers and illustrate its correlation with immune infiltration in HCC. Methods: RNA sequencing data and clinical traits of HCC patients were downloaded from TCGA. The limma R package was used to identify differentially expressed (DE) RNAs. The predicted prognostic model was established using univariate and multivariate Cox regression. A K-M curve and GEPIA website were utilized for survival analysis. Functional annotation was determined using Enrichr and Reactome. Protein-to-protein network analysis was implemented using SRTNG and Cytoscape. Hub gene expression was validated by Oncomine and the Hunan Protein Atlas database. Immune infiltration was analyzed by TIMMER, and Drugbank was exploited to identify bioactive compounds. Results: The predicted model that was established revealed significant efficacy with 3- and 5-years of the area under ROC at 0.804 and 0.744, respectively. Eleven DEmiRNAs were screened out by a K-M survival analysis. Then, we constructed a ceRNA network, including 56 DElncRNAs, 6 DEmiRNAs, and 28 DEmRNAs. The 28 DEmRNAs were enriched in cancer-related pathways, for example, the TNF signaling pathway. Moreover, six hub genes, CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1, were all overexpressed in HCC tissues and independently correlated with survival rate. Furthermore, expression of hub genes was related to immune cell infiltration in HCC, including B cells, CD8 + T cells, CD4 + T cells, monocytes, macrophages, neutrophils, and dendritic cells. Conclusions: The findings from this study demonstrate that CEP55, DEPDC1, KIF23, CLSPN, MYBL2, and RACGAP1 are closely associated with prognosis and immune infiltration, representing potential therapeutic targets or prognostic biomarkers in HCC.

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