A prognostic signature based on insulin-signaling-pathway genes for hepatocellular carcinoma.

Abstract

Background: Hepatocellular carcinoma (HCC) is the most common subtype of liver cancer. HCC occurrence, metastasis and therapeutic effect are closely related to tumor metabolic microenvironment. However, the role of insulin-related glucose metabolism in HCC has also not been extensively studied.

Method: Transcriptional profiles and clinical data of HCC samples were retrieved from The Cancer Genome Atlas (TCGA). The insulin signaling pathway related genes were derived from GeneCards and only the protein-coding genes with the top 100 relevance score were retained. Univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox analysis were conducted to develop the prognosis model. The predictive performance of our prognosis model was assessed by using receiver operating characteristic (ROC) curve, calibration curve and nomogram. Further studies, such as enrichment analysis, drug sensitivity and immunotherapy response were performed to assess the tumor microenvironment and treatment response. The clinical significance of SLC2A1 in HCC was validated with an independent cohort.

Results: We constructed a prognostic signature based on 4 insulin pathway related genes: RHEB, PRKAA2, SLC2A1 and FOXO1. HCC patients divided into high-risk and low-risk group according to the median risk score. We evaluated the signature in training set, validation set and entire set. The Kaplan-Meier (K-M) survival curve revealed that patients in low-risk group had longer survival. Even in different clinicopathological subgroups, the prognostic signature had good prognostic performance. We also identified that commonly used chemotherapy agents such as 5-fluorouracil, gemcitabine, paclitaxel, sorafenib and sunitinib showed significant sensitivity in the high-risk group. The TIDE algorithm suggested that patients in high-risk group may be more sensitive to immunotherapy. SLC2A1 was selected as the core gene, and the Kaplan-Meier survival curve showed that SLC2A1 positive was significantly associated with prognosis in a HCC independent cohort. Univariate and multivariate Cox analysis demonstrated that SLC2A1 was an independent risk variable for poor prognosis.

Conclusions: In summary, we constructed a prognostic signature based on insulin signaling pathway genes. The excellent performance and applicability of our model underscores its advantages and reliability as a clinical tool. Moreover, we validated SLC2A1 was an independent prognostic factor in a HCC independent cohort.