Comprehensive Characterization of Immunological Profiles and Clinical Significance in Hepatocellular Carcinoma.

Abstract

BACKGROUND:

Therapies targeting immune molecules have rapidly been adopted and advanced the treatment of hepatocellular carcinoma (HCC). Nonetheless, no studies have reported a systematic analysis between immunological profiles and clinical significance in HCC.

METHODS:

We comprehensively investigated immune patterns and systematically correlated 22 types of both adaptive and innate immune cells with genomic characteristics and clinical outcomes based on 370 HCC patients from The Cancer Genome Atlas (TCGA) database through a metagene approach (known as CIBERSORT). Based on the Quantitative Pathology Imaging and Analysis System coupled with integrated high-dimensional bioinformatics analysis, we further independently validated six immune subsets (CD4+ T cells, CD8+ T cells, CD20+ B cells, CD14+ monocytes, CD56+ NK cells, and CD68+ macrophages), and shortlisted three (CD4+ T cells, CD8+ T cells, and CD56+ NK cells) of which to investigate their association with clinical outcomes in two independent Zhongshan cohorts of HCC patients (n = 258 and n = 178). Patient prognosis was further evaluated by Kaplan-Meier analysis and univariate and multivariate regression analysis.

RESULTS:

By using the CIBERSORT method, the immunome landscape of HCC was constructed based on integrated transcriptomics analysis and multiplexed sequential immunohistochemistry. Further, the patients were categorized into four immune subgroups featured with distinct clinical outcomes. Strikingly, significant inter-tumoral and intra-tumoral immune heterogeneity was further identified according to the in-depth interrogation of the immune landscape.

CONCLUSION:

This work represents a potential useful resource for the immunoscore establishment for prognostic prediction in HCC patients.

• Authors: Ping Tao, Liang Hong, Wenqing Tang, Qun Lu, Yanrong Zhao, Si Zhang, Lijie Ma, Ruyi Xue
• Journal: Frontiers in oncology (Front Oncol) Vol. 10 Pg. 574778 ( 2020) ISSN: 2234-943X [Print] Switzerland
• PMID: 33552954 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Tao, Hong, Tang, Lu, Zhao, Zhang, Ma and Xue.