An Integrated Fibrosis Signature for Predicting Survival and Immunotherapy Efficacy of Patients With Hepatocellular Carcinoma

Abstract

Introduction: Fibrosis, a primary cause of hepatocellular carcinoma (HCC), is intimately associated with inflammation, the tumor microenvironment (TME), and multiple carcinogenic pathways. Currently, due to widespread inter- and intra-tumoral heterogeneity of HCC, the efficacy of immunotherapy is limited. Seeking a stable and novel tool to predict prognosis and immunotherapy response is imperative.Methods: Using stepwise Cox regression, least absolute shrinkage and selection operator (LASSO), and random survival forest algorithms, the fibrosis-associated signature (FAIS) was developed and further validated. Subsequently, comprehensive exploration was conducted to identify distinct genomic alterations, clinical features, biological functions, and immune landscapes of HCC patients.Results: The FAIS was an independent prognostic predictor of overall survival and recurrence-free survival in HCC. In parallel, the FAIS exhibited stable and accurate performance at predicting prognosis based on the evaluation of Kaplan–Meier survival curves, receiver operator characteristic curves, decision curve analysis, and Harrell’s C-index. Further investigation elucidated that the high-risk group presented an inferior prognosis with advanced clinical traits and a high mutation frequency of TP53, whereas the low-risk group was characterized by superior CD8+ T cell infiltration, a higher TIS score, and a lower TIDE score. Additionally, patients in the low-risk group might yield more benefits from immunotherapy.Conclusion: The FAIS was an excellent scoring system that could stratify HCC patients and might serve as a promising tool to guide surveillance, improve prognosis, and facilitate clinical management.

Necroptosis throws novel insights on patient classification and treatment strategies for hepatocellular carcinoma

“…For necroptosis subtypes, the prognosis value was assessed by survival curves of overall survival (OS) and recurrence-free survival (RFS). The molecular characteristics were also deciphered by gene set variation analysis (GSVA), which was broadly applied in pathway activities exploration ( 22 ). With the use of 50 Hallmark gene sets from Molecular Signatures Database (MSigDB), distinct biological characteristics were elucidated in necroptosis subtypes.…”

A Subset of Secreted Proteins in Ascites Can Predict Platinum-Free Interval in Ovarian Cancer

“…Lasso (least absolute shrinkage and selection operator) is a machine learning tool that has been used in a variety of medical studies [ 28 , 29 , 30 , 31 , 32 , 33 ]. Like many machine learning tools, Lasso seeks a linear relationship between the independent variables (i.e., analyte levels in ascites) and a variable that is hypothesized to be dependent (i.e., PFI).…”

Machine learning-based prognostic modeling of lysosome-related genes for predicting prognosis and immune status of patients with hepatocellular carcinoma

“…However, we found from the available reports [21] that the Lasso commonly used in clinical practice may also not be the best way for us to construct the model. Therefore, we chose three common ways of constructing models (RSF, Lasso, stepwise) [20,22,23]for two-by-two combinations or separate algorithms to analyse 46 lysosomalassociated genes with prognostic signi cance and calculate the C-index for the training set (TCGA) and test set (GSE14520) separately (Fig. 3A-B;Additional le 2: Fig.…”