Comprehensive assessment of regulatory T-cells-related scoring system for predicting the prognosis, immune microenvironment and therapeutic response in hepatocellular carcinoma

TL;DR

This study develops a new scoring system to predict outcomes and treatment response in liver cancer by analyzing regulatory T cells and the tumor immune environment.

Contribution

A novel Tregs-related scoring system (TRSSys) is developed for hepatocellular carcinoma prognosis and treatment prediction.

Findings

TRSSys showed good prognostic predictive efficacy and applicability in multiple cohorts.

TRSSys can distinguish different tumor immune microenvironment subtypes.

TRSSys provides a basis for identifying patients who may benefit from immunotherapy.

Abstract

Introduction: Regulatory T cells (Tregs) play important roles in tumor immunosuppression and immune escape. The aim of the present study was to construct a novel Tregs-associated biomarker for the prediction of tumour immune microenvironment (TIME), clinical outcomes, and individualised treatment in hepatocellular carcinoma (HCC).

Methods: Single-cell sequencing data were obtained from the three independent cohorts. Cox and LASSO regression were utilised to develop the Tregs Related Scoring System (TRSSys). GSE140520, ICGC-LIRI and CHCC cohorts were used for the validation of TRSSys. Kaplan-Meier, ROC, and Cox regression were utilised for the evaluation of TRSSys. The ESTIMATE, TIMER 2.0, and ssGSEA algorithm were utilised to determine the value of TRSSys in predicting the TIME. GSVA, GO, KEGG, and TMB analyses were used for mechanistic exploration. Finally, the value of TRSSys in predicting drug sensitivity was evaluated based on the oncoPredict algorithm.

Results: Comprehensive validation showed that TRSSys had good prognostic predictive efficacy and applicability. Additionally, ssGSEA, TIMER and ESTIMATE algorithm suggested that TRSSys could help to distinguish different TIME subtypes and determine the beneficiary population of immunotherapy. Finally, the oncoPredict algorithm suggests that TRSSys provides a basis for individualised treatment.

Conclusions: TRSSys constructed in the current study is a novel HCC prognostic prediction biomarker with good predictive efficacy and stability. Additionally, risk stratification based on TRSSys can help to identify the TIME landscape subtypes and provide a basis for individualized treatment options.

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