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

Abstract

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.

P4HA2: A link between tumor-intrinsic hypoxia, partial EMT and collective migration

“…In the ‘late tumor-intrinsic hypoxia’ signature of 600/D6 microtumors, PLIN2 was the most upregulated gene followed by JUN, KLF7, PRKCA and IER3 while SLC25A1, SDC2, NDST1, CASP6 and TPBG were top five downregulated genes (in descending order of expression) ( Figure 1D ). Of these gene signatures, PLIN2, JUN and PRKCA are shown to be induced in response to hypoxia [41–43]. Taken together; gene expression analysis of large microtumor models (600/D1, 600/D6) with tumor size-induced natural hypoxia at day 1 and day 6 compared to non-hypoxic 150/D6 microtumors contributed ‘early’ and ‘late’ tumor-intrinsic hypoxia gene signatures.…”

A 10-Gene Signature Identified by Machine Learning for Predicting the Response to Transarterial Chemoembolization in Patients with Hepatocellular Carcinoma

“…Instead, we found that pathways related to an aberrant cell cycle and proliferation, including G2/M checkpoint, E2F, MYC, and mTORC1 were significantly enriched in TACE nonresponders [ 27 – 29 ]. However, some previous studies have suggested that there is a positive correlation between hypoxia and activated mTORC1 and E2F pathways [ 30 ]. Additionally, an enrichment analysis of DEGs in our study recapitulated the relationship between the augmented IL-17 pathway and TACE nonresponse.…”

Easy or Not—The Advances of EZH2 in Regulating T Cell Development, Differentiation, and Activation in Antitumor Immunity

“…In patients with ovarian cancer, high rate of EZH2+ CD8+ T cells positively affect cancer survival ( 44 ). However, systemic overexpression of EZH2 was significantly associated with impaired effector memory CD8+ T cell infiltration and poor survival in patients with hepatocellular carcinoma ( 102 ). These results suggest that EZH2 exerts opposite effects in tumor cells and T cells on antitumor immunity.…”