A Novel Cancer-associated Fibroblasts-Related Gene Signature Based on Adjuvant Sorafenib Efficacy for Predicting Recurrence and Immunotherapy Response in Hepatocellular Carcinoma After Hepatectomy

Abstract

AbstractBackground Due to its high recurrence rate, hepatocellular carcinoma (HCC) has a poor prognosis after hepatectomy. An effective model to predict postoperative recurrence and identify high-risk patients is essential. Recent studies have revealed the important role of cancer-associated fibroblasts (CAFs) in predicting HCC prognosis. However, the prognostic value of CAFs-related gene signature in HCC recurrence remains unknown. According to the BIOSTORM study, adjuvant sorafenib efficacy data may help to predict the recurrence in HCC. Therefore, we aimed to create a novel CAFs-related gene signature based on adjuvant sorafenib efficacy to predict HCC recurrence. Methods The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to obtain the transcriptomic gene expression profiles and corresponding clinical data of HCC patients. The CAFs-related genes based on adjuvant sorafenib efficacy were identified using EPIC and weighted gene co-expression network analysis (WGCNA) algorithm. Univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used to establish a novel risk model. Univariate and multivariate COX analyses were used to identify independent prognostic factors for disease-free survival (DFS), and a nomogram was developed. The CIBERSORT and ESTIMATE algorithms were used to assess the tumor microenvironment components. Tumor immune dysfunction and exclusion (TIDE) score was used to predict immunotherapy response. Results A novel risk model was created using ten CAFs-related genes based on adjuvant sorafenib efficacy (DCLRE1C, DDX11, MAP4K2, SHCBP1, ADAM12, PAQR4, BEND3, ADAMTSL2, NUP93 and MPP2). Survival analyses revealed that high-risk patients had worse DFS, and the risk model was found as an independent prognostic factor for DFS in both the training and validation groups. A novel nomogram combined with pathologic stage and risk score status was developed. In the high-risk group, the stromal and immune cell content was found significantly lower while the tumor purity was significantly higher. In addition, immune checkpoints genes were highly expressed in the high-risk group and a higher risk score may predict a better response to immunotherapy. Conclusions The novel risk model comprised of ten CAFs-related genes based on adjuvant sorafenib efficacy may accurately predict recurrence and immunotherapy response in HCC patients after hepatectomy.