Dataset Information

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma.

ABSTRACT:

Introduction

Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC).

Methods

Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related Scoring System (TRSSys). GSE140520, ICGC-LIRI and CHCC cohorts were used for the validation of TRSSys. Kaplan-Meier, ROC, and Cox regression were utilised for the evaluation of TRSSys. The ESTIMATE, TIMER 2.0, and ssGSEA algorithm were utilised to determine the value of TRSSys in predicting the TIME. GSVA, GO, KEGG, and TMB analyses were used for mechanistic exploration. Finally, the value of TRSSys in predicting drug sensitivity was evaluated based on the oncoPredict algorithm.

Results

Comprehensive validation showed that TRSSys had good prognostic predictive efficacy and applicability. Additionally, ssGSEA, TIMER and ESTIMATE algorithm suggested that TRSSys could help to distinguish different TIME subtypes and determine the beneficiary population of immunotherapy. Finally, the oncoPredict algorithm suggests that TRSSys provides a basis for individualised treatment.

Conclusions

TRSSys constructed in the current study is a novel HCC prognostic prediction biomarker with good predictive efficacy and stability. Additionally, risk stratification based on TRSSys can help to identify the TIME landscape subtypes and provide a basis for individualized treatment options.

SUBMITTER: Jiang B

PROVIDER: S-EPMC11006487 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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Publications

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma.

Jiang Bitao B Ye Xiaojuan X Wang Wenjuan W He Jiajia J Zhang Shuyan S Zhang Song S Bao Lingling L Xu Xin X

Aging 20240308 6

<h4>Introduction</h4>Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC).<h4>Methods</h4>Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related ...[more]

PMID: 38461439

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Project description:Background Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with high morbidity and mortality. Identifying an effective marker for predicting the prognosis and therapeutic response is extremely meaningful. Angiogenesis-related genes (ARGs) play important roles in the tumor progression and immune-suppressive microenvironment formation. Methods The differential expressed ARGs associated with the prognosis of HCC were identified in the TCGA dataset. Univariate Cox and least absolute shrinkage selection operator (LASSO) regression were applied to construct a ARGs Scoring model. The prognostic value of the ARGs Scoring model was assessed by Cox regression, Kaplan-Meier (KM) and ROC curve analyses. Then the model was further validated in an external dataset, ICGC dataset. The patients were split into two groups based on the ARGs Score and the clinical features were compared. TIMER, CIBERSORT and xCell algorithms were utilized to analyze the correlation between the ARGs Score and tumor immune microenvironment (TIME). Furthermore, we analyzed the efficacy of the model in predicting the therapeutic response for immunotherapy, targeted therapy and TACE treatment in different cohorts. Results A total of 97 differential expressed ARGs were identified relating to the prognosis of HCC patients from the TCGA dataset. Then the ARGs Scoring model based on a 9-gene signature was constructed using the Cox and LASSO regression analyses. Higher ARGs Score had a poor clinical outcome and was considered to be an independent prognostic predictor for HCC in the multivariate Cox analysis. The ARGs Score was related to the enrichment of various immune cells, such as CD4+ T cells, Treg, macrophage, neutrophil and dendritic cells, exhibiting a more immunosuppressive phenotype. Higher ARGs Score was correlated with higher expression of immune checkpoint genes and poor response to immunotherapy. Furthermore, higher ARGs Score indicated poor therapeutic response in the sorafenib and TACE treatment cohorts, individually. Conclusions The ARGs Scoring model exhibited robust predictive value for the prognosis and TIME for HCC patients. Higher ARGs Score indicated poor therapeutic response of the immunotherapy, sorafenib and TACE treatment. The ARGs Scoring model could be used as a biomarker to help physicians to develop more individualized treatment for HCC patients.

Project description:BackgroundFerroptosis is a type of regulatory cell death (RCD) mode that depends on iron-mediated oxidative damage. It has the potential to improve the efficacy of tumor immunotherapy by modulating the tumor microenvironment (TME). Currently, immunotherapy has significantly improved the overall treatment strategy for advanced hepatocellular carcinoma (HCC), but the distinct immune microenvironment and high tolerance to the immune make massive differences in the immunotherapy effect of HCC patients. As a result, it is imperative to classify HCC patients who may benefit from immune checkpoint therapy. Simultaneously, the predictive value of ferroptosis in HCC and its potential role in TME immune cell infiltration also need to be further clarified.MethodsThree ferroptosis molecular models were built on the basis of mRNA expression profiles of ferroptosis-related genes (FRGs), with notable variations in immunocyte infiltration, biological function, and survival prediction. In order to further investigate the predictive impact of immunotherapy response in HCC patients, the ferroptosis score was constructed using the principal component analysis (PCA) algorithm to quantify the ferroptosis molecular models of individual tumors.ResultsIn HCC, there were three totally different ferroptosis molecular models. The ferroptosis score can be used to assess genetic variation, immunotherapy response, TME characteristics, and prognosis. Notably, tumors with low ferroptosis scores have extensive tumor mutations and immune exhaustion, which are associated with a poor prognosis and enhanced immunotherapy response.ConclusionsOur study indicates that ferroptosis plays an indispensable role in the regulation of the tumor immune microenvironment. For HCC, the ferroptosis score is an independent prognostic indicator. Assessing the molecular model of ferroptosis in individual tumors will assist us in better understanding the characteristics of TME, predicting the effect of immunotherapy in HCC patients, and thus guiding a more reasonable immunotherapy program.

Project description:BackgroundN6-Methyladenosine (m6A) RNA modification is the most prevalent internal modification pattern in eukaryotic mRNAs and plays critical roles in diverse physiological and pathological processes. However, the expression of m6A regulator YTHDF2, its prognostic value, its biological function, its correlation with tumor microenvironment (TME) immune infiltrates, and related regulatory networks in hepatocellular carcinoma (HCC) remain determined.MethodsTCGA, GTEx, and GEO databases were used to investigate the expression profile of YTHDF2 in HCC. We performed differentially expressed genes (DEGs) analysis and constructed a PPI network to explore the biological processes of YTHDF2 in HCC. Kaplan-Meier curves and Cox regression analysis were used to assess the prognostic value of YTHDF2 and then a clinical prognostic nomogram was constructed. Additionally, ssGSEA was performed to assess the correlation between YTHDF2 and immune infiltration levels. The TISIDB database was applied to explore the expression of YTHDF2 in immune and molecular subtypes of HCC. GSEA identifies the YTHDF2-related signaling pathways. Finally, we utilized miRNet and starBase database to construct regulatory networks for HCC based on lncRNA-miRNA and miRNA-YTHDF2 interactions.ResultsYTHDF2 was significantly upregulated in HCC tumor tissues compared with the adjacent normal tissues. HCC patients in the high YTHDF2 expression group had poorer survival. Multivariate Cox analysis suggested that YTHDF2 may be a new independent prognostic indicator for HCC patients, with the prognostic nomogram exhibiting satisfactory results. YTHDF2 expression was significantly correlated with TME immune cell-infiltrating characteristics. Strong correlations were also shown in immune subtypes, molecular subtypes and immune checkpoints. Further analysis revealed that the combination of YTHDF2 expression and immune cell score was considerably associated with survival outcome in HCC patients. GESA analysis demonstrated that high YTHDF2 expression is associated with multiple biological processes and oncogenic pathways. Moreover, 14 possible regulatory networks were constructed, which are associated with HCC progression.ConclusionOur findings revealed that YTHDF2 may serve as a promising prognostic biomarker for HCC and may regulate the tumor immune microenvironment to provide effective therapeutic strategies.

Dataset Information

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma.

Introduction

Methods

Results

Conclusions

Publications

Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma.

Similar Datasets

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