Dataset Information

Microenvironment characterization and multi-omics signatures related to prognosis and immunotherapy response of hepatocellular carcinoma.

ABSTRACT:

Background

Immune cell infiltration in the tumor microenvironment (TME) affects tumor initiation, patients' prognosis and immunotherapy strategies. However, their roles and interactions with genomics and molecular processes in hepatocellular carcinoma (HCC) still have not been systematically evaluated.

Methods

We performed unsupervised clustering of total 1000 HCC samples including discovery and validation group from available public datasets. Immune heterogeneity of each subtype was explored by multi-dimension analysis. And a support vector machine (SVM) model based on multi-omics signatures was trained and tested. Finally, we performed immunohistochemistry to verify the immune role of signatures.

Results

We defined three immune subtypes in HCC, with diverse clinical, molecular, and genomic characteristics. Cluster1 had worse prognosis, better anti-tumor characteristics and highest immune scores, but also accompanied by immunosuppression and T cell dysfunction. Meanwhile, a better anti-PD1/CTLA4 immunotherapeutic response was predicted in cluster1. Cluster2 was enriched in TAM-M2 and stromal cells, indicating immunosuppression. Cluster3, with better prognosis, had lowest CD8 T cell but highest immune resting cells. Further, based on genomic signatures, we developed an SVM classifier to identify the patient's immunological status, which was divided into Type A and Type B, in which Type A had poorer prognosis, higher T cell dysfunction despite higher T cell infiltration, and had better immunotherapeutic response. At the same time, MMP9 may be a potential predictor of the immune characteristics and immunotherapeutic response in HCC.

Conclusions

Our work demonstrated 3 immune clusters with different features. More importantly, multi-omics signatures, such as MMP9 was identified based on three clusters to help us recognize patients with different prognosis and responses to immunotherapy in HCC. This study could further reveal the immune status of HCC and provide potential predictors for immune checkpoint treatment response.

SUBMITTER: Liu F

PROVIDER: S-EPMC7249423 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Microenvironment characterization and multi-omics signatures related to prognosis and immunotherapy response of hepatocellular carcinoma.

Liu Furong F Qin Lu L Liao Zhibin Z Song Jia J Yuan Chaoyi C Liu Yachong Y Wang Yu Y Xu Heze H Zhang Qiaofeng Q Pei Youliang Y Zhang Hongwei H Pan Yonglong Y Chen Xiaoping X Zhang Zhanguo Z Zhang Wanguang W Zhang Bixiang B

Experimental hematology & oncology 20200525

<h4>Background</h4>Immune cell infiltration in the tumor microenvironment (TME) affects tumor initiation, patients' prognosis and immunotherapy strategies. However, their roles and interactions with genomics and molecular processes in hepatocellular carcinoma (HCC) still have not been systematically evaluated.<h4>Methods</h4>We performed unsupervised clustering of total 1000 HCC samples including discovery and validation group from available public datasets. Immune heterogeneity of each subtype ...[more]

PMID: 32509418

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Project description:Background: Long noncoding RNAs (lncRNAs) are significantly implicated in tumor proliferation. Nevertheless, proliferation-derived lncRNAs and their latent clinical significance remain largely unrevealed in hepatocellular carcinoma (HCC). Methods: This research enrolled 658 HCC patients from five independent cohorts. We retrieved 50 Hallmark gene sets from the MSigDB portal. Consensus clustering was applied to identify heterogeneous proliferative subtypes, and the nearest template prediction (NTP) was utilized to validate the subtypes. We introduced an integrative framework (termed "ProLnc") to identify proliferation-derived lncRNAs. Moreover, a proliferation-related signature was developed and verified in four independent cohorts. Results: In 50 Hallmarks, seven proliferation pathways were significantly upregulated and correlated with a worse prognosis. Subsequently, we deciphered two heterogeneous proliferative subtypes in TCGA-LIHC. Subtype 2 displayed enhanced proliferative activities and a worse prognosis, whereas subtype 1 was associated with hyperproliferative HCC and a favorable prognosis. The NTP further verified the robustness and reproducibility of two subtypes in four cohorts derived from different platforms. Combining the differentially expressed lncRNAs from two subtypes with proliferative lncRNA modulators from our ProLnc pipeline, we determined 230 proliferation-associated lncRNAs. Based on the bootstrapping channel and the verification of multiple cohorts, we further identified ten lncRNAs that stably correlated with prognosis. Subsequently, we developed and validated a proliferative lncRNA signature (ProLncS) that could independently and accurately assess the overall survival (OS) and relapse-free survival (RFS) of HCC patients in the four cohorts. Patients with high ProLncS score displayed significantly genomic alterations (e.g., TP53 mutation, 8p23-8p24 copy number variation) and higher abundances of immune cells and immune checkpoint molecules, which suggested immunotherapy was more suitable for patients with high ProLncS score. Conclusion: Our work provided new insights into the heterogeneity of tumor proliferation, and ProLncS could be a prospective tool for tailoring the clinical decision and management of HCC.

Dataset Information

Microenvironment characterization and multi-omics signatures related to prognosis and immunotherapy response of hepatocellular carcinoma.

Background

Methods

Results

Conclusions

Publications

Microenvironment characterization and multi-omics signatures related to prognosis and immunotherapy response of hepatocellular carcinoma.

Similar Datasets

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