4.7 Article

Employing hypoxia characterization to predict tumour immune microenvironment, treatment sensitivity and prognosis in hepatocellular carcinoma

Journal

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL
Volume 19, Issue -, Pages 2775-2789

Publisher

ELSEVIER
DOI: 10.1016/j.csbj.2021.03.033

Keywords

Hepatocellular carcinoma; Hypoxia; Gene set enrichment analysis; Tumor immune microenvironment; Risk model; Treatment sensitivity; Prognostic

Funding

  1. National Key R&D Program of China [2018YFC1106400, 2018YFA0108200]
  2. Science and Technology Planning Project of Guangdong Province [2015B020229002]
  3. National Natural Science Foundation of China [31972926]
  4. Natural Science Foundation of Guangdong Province [2014A030312013, 2018A030313128]
  5. Guangdong key research and development plan [2019B020234003]
  6. Science and Technology Program of Guangzhou [201803010086]
  7. Guangdong Basic and Applied Basic Research Foundation [2021A1515011040]

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The hypoxic microenvironment plays a crucial role in driving the malignant phenotype in hepatocellular carcinoma and modulating tumor immune microenvironment remodeling. Understanding the relationship between hypoxia and tumor progression can provide insights for improving treatment and prognosis. A hypoxia risk model based on four hypoxia-related genes has been developed to predict treatment sensitivity and outcomes in HCC patients.
The hypoxic microenvironment was recognized as a major driving force of the malignant phenotype in hepatocellular carcinoma (HCC), which contributes to tumour immune microenvironment (TIM) remodeling and tumor progression. Dysregulated hypoxia-related genes (HRGs) result in treatment resistance and poor prognosis by reshaping tumor cellular activities and metabolism. Approaches to identify the relationship between hypoxia and tumor progression provided new sight for improving tumor treatment and prognosis. But, few practical tools, forecasting relationship between hypoxia, TIM, treatment sensitivity and prognosis in HCC were reported. Here, we pooled mRNA transcriptome and clinical pathology data from the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), and later developed a hypoxia risk model including four HRGs (DCN, DDIT4, PRKCA and NDRG1). The high-risk group displayed poor clinical characteristics, a malignant phenotype with carcinogenesis/proliferation pathways activation (MTORC1 and E2F) and immunosuppressive TIM (decreased immune cell infiltrations and upregulated immunosuppressive cytokines). Meanwhile, activated B cells, effector memory CD8 T cells and EZH2 deregulation were associated with patient's survival, which might be the core changes of HCC hypoxia. Finally, we validated the ability of the hypoxia risk model to predict treatment sensitivity and found high hypoxia risk patients had poor responses to HCC treatment, including surgical resection, Sorafenib, Transarterial Chemoembolization (TACE) and immunotherapy. In conclusion, based on 4 HRGs, we developed and validated a hypoxia risk model to reflect pathological features, evaluate TIM landscape, predict treatment sensitivity and compounds specific to hypoxia signatures in HCC patients. (C) 2021 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.

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