Dataset Information

Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC.

ABSTRACT:

Background and aims

Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for patients with HCC. The clinical applications of documented molecular subtypes are constrained by several issues.

Approach and results

We integrated 3 single-cell data sets to describe the TME landscape and identified 6 prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate transcriptomic subtypes. The predictive value of these molecular subtypes for prognosis and treatment response was explored in multiple external HCC cohorts and the Xiangya HCC cohort. TME features were estimated using single-cell immune repertoire sequencing, mass cytometry, and multiplex immunofluorescence. The prognosis-related score was constructed based on a machine-learning algorithm. Comprehensive single-cell analysis described TME heterogeneity in HCC. The 5 transcriptomic subtypes possessed different clinical prognoses, stemness characteristics, immune landscapes, and therapeutic responses. Class 1 exhibited an inflamed phenotype with better clinical outcomes, while classes 2 and 4 were characterized by a lack of T-cell infiltration. Classes 5 and 3 indicated an inhibitory tumor immune microenvironment. Analysis of multiple therapeutic cohorts suggested that classes 5 and 3 were sensitive to immune checkpoint blockade and targeted therapy, whereas classes 1 and 2 were more responsive to transcatheter arterial chemoembolization treatment. Class 4 displayed resistance to all conventional HCC therapies. Four potential therapeutic agents and 4 targets were further identified for high prognosis-related score patients with HCC.

Conclusions

Our study generated a clinically valid molecular classification to guide precision medicine in patients with HCC.

SUBMITTER: Chen Y

PROVIDER: S-EPMC11332383 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature

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Publications

Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC.

Chen Yihong Y Deng Xiangying X Li Yin Y Han Ying Y Peng Yinghui Y Wu Wantao W Wang Xinwen X Ma Jiayao J Hu Erya E Zhou Xin X Shen Edward E Zeng Shan S Cai Changjing C Qin Yiming Y Shen Hong H

Hepatology (Baltimore, Md.) 20240327 3

<h4>Background and aims</h4>Tumor microenvironment (TME) heterogeneity leads to a discrepancy in survival prognosis and clinical treatment response for patients with HCC. The clinical applications of documented molecular subtypes are constrained by several issues.<h4>Approach and results</h4>We integrated 3 single-cell data sets to describe the TME landscape and identified 6 prognosis-related cell subclusters. Unsupervised clustering of subcluster-specific markers was performed to generate trans ...[more]

PMID: 38537130

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Project description:AbstractGrowing evidence supports that the tumor microenvironment plays a key role in the development and progression of tumors. But immune microenvironment of hepatocellular carcinoma (HCC) has not yet been fully explored. In the present investigation, the clinical value and prognostic significance of immune-related genes in HCC were investigated.The immune and stromal scores of HCC were calculated through the application of Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data Algorithm based on the Cancer Genome Atlas database. Differentially expressed genes were identified using the "edgeR" package of the R software. Functional annotation and pathway enrichment were performed using "ggplots2" and "clusterProfiler" packages in R software. Protein-protein interaction network was constructed using STRING, and the hub genes were identified through the Cytoscape. Survival analysis was performed using Kaplan-Meier methods. Tumor Immune Estimation Resource algorithm was used to view the immune landscape of the microenvironment in HCC.Firstly, the immune and stromal scores of HCC were calculated and we found that the immune and stromal scores of HCC were closely related to the patients' prognosis. Then the differentially expressed genes were identified respectively stratified by the median value of the immune and stromal scores, and the immune-related genes that related to the prognosis in HCC patients were further identified. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune-related biological process. In addition, dendritic cells were found to be the most abundant in the microenvironment of HCC through Tumor Immune Estimation Resource algorithm and were significantly associated with the patients' prognosis. To robust the results, the immune-related genes were validated in an independent dataset from the Gene Expression Omnibus database.We arrived at a more comprehensive understanding of the microenvironment of HCC and extracted 7 immune-related genes that were significantly associated with the recurrence survival of HCC.

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Dataset Information

Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC.

Background and aims

Approach and results

Conclusions

Publications

Comprehensive molecular classification predicted microenvironment profiles and therapy response for HCC.

Similar Datasets

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