Abstract
Background Hepatocellular carcinoma (HCC) is one of the most heterogeneous malignant tumors that have been discovered so far, which makes the prognostic prediction difficult. The hypoxia, angiogenesis, and immunity-related genes (HAIRGs) are closely related to the development of liver cancer. However, the prognostic and treatment effect of hypoxia, angiogenesis, and immunity-related genes in HCC continues to be further clarified. Methods The gene expression quantification data and clinical information in patients with liver cancer were downloaded from the TCGA database, and HAIRG signature was built by using the least absolute shrinkage and selection operator (LASSO) technique. Patient from the ICGC database validated the model. Then, tumor immune dysfunction and exclusion (TIDE) algorithm was applied to estimate the clinical response to immunotherapy and the sensitivity of drugs was evaluated by the half-maximal inhibitory concentration (IC50). Result The HAIRGs were identified between the HCC patients and normal patients in the TCGA database. In univariate Cox regression analysis, seventeen differentially expressed genes (DEGs) were associated with overall survival (OS). An eight HAIRG signature model was constructed and was used to divide the patients into two groups according to the median value of the risk score base on the TCGA dataset. Patients in the high-risk group had a significant reduction in OS compared to those in the low-risk group (P < 0.001 in the TCGA, P < 0.001 in the ICGC). For TCGA and ICGC databases of univariate Cox regression analyses, the risk score was used as an independent predictor of OS (HR > 1, P < 0.001). Functional analysis showed that the relevant immune pathways and immune responses were enriched, cellular component analysis showed that the immunoglobulin complex and other related substances were enriched, and immune status existed a difference in the high- and low-risk groups. Then, the tumor immune dysfunction and exclusion (TIDE) algorithm presented differences in immune response in the high- and low-risk groups (P < 0.05), and based on drug sensitivity prediction, patients in the high-risk group were more sensitive to cisplatin compared to those in the low-risk group in both the TCGA and ICGC cohorts (P < 0.05). Conclusions HAIRG signature can be utilized for prognostic prediction in HCC, while it can be considered a prediction model for clinical evaluation of immunotherapy response and chemotherapy sensitivity in HCC.
Highlights
Liver cancer is the sixth most commonly diagnosed cancer in terms of morbidity and the fourth leading cause of cancer related to death [1]
Figure 3: 8-gene characteristic model prognostic analyzed in ICGC cohort. (a) The distribution and median value of the risk score in the ICGC cohort. (b) The distributions of overall survival (OS) condition, OS, and risk score in the ICGC queue. (e) Kaplan-Meier curves of OS in the high- and low-risk TCGA cohorts for Hepatocellular carcinoma (HCC) patients in the ICGC cohort. (c) PC plot of the ICGC cohort. (d) t-SNE analysis of the ICGC cohort. (f) The area under the curve of time-dependent receiver operating characteristic (ROC) curves validated the prognostic manifestation of the risk score in the ICGC queue
We picked out 8 HAIRGs (VEGFA, CTNNB1, PPARG, HSP90AA1, HMOX1, LGALS3, SPP1, and RAC1) from the 17 HAIRG model through the least absolute shrinkage and selection operator (LASSO) Cox regression, upregulated genes accounted for 7/8 in tumor tissue, which was shown by a heat map (Figure 1(c))
Summary
Liver cancer is the sixth most commonly diagnosed cancer in terms of morbidity and the fourth leading cause of cancer related to death [1]. Hypoxia, angiogenesis, and immune response act as critical roles in the progression of HCC. The hypoxia, angiogenesis, and immunity-related genes (HAIRGs) are closely related to the development of liver cancer. The gene expression quantification data and clinical information in patients with liver cancer were downloaded from the TCGA database, and HAIRG signature was built by using the least absolute shrinkage and selection operator (LASSO) technique. The tumor immune dysfunction and exclusion (TIDE) algorithm presented differences in immune response in the highand low-risk groups (P < 0:05), and based on drug sensitivity prediction, patients in the high-risk group were more sensitive to cisplatin compared to those in the low-risk group in both the TCGA and ICGC cohorts (P < 0:05). HAIRG signature can be utilized for prognostic prediction in HCC, while it can be considered a prediction model for clinical evaluation of immunotherapy response and chemotherapy sensitivity in HCC
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