Bioinformatics identification of a T‐cell‐related signature for predicting prognosis and drug sensitivity in hepatocellular carcinoma

Abstract

AbstractHepatocellular carcinoma (HCC) is a fatal disease with poor clinical outcomes. T cells play a vital role in the crosstalk between the tumour microenvironment and HCC. Single‐cell RNA sequencing data were downloaded from the GSE149614 dataset. The T‐cell‐related prognostic signature (TRPS) was developed with the integrative procedure including 10 machine learning algorithms. The TRPS was established using 7 T‐cell‐related markers in the Cancer Genome Atlas cohort with 1‐, 2‐ and 3‐year area under curve values of 0.820, 0.725 and 0.678, respectively. TRPS acted as an independent risk factor for HCC patients. HCC patients with a high TRPS‐based risk score had a higher Tumour Immune Dysfunction and Exclusion score, lower PD1 and CTLA4 immunophenoscore and lower level of immunoactivated cells, including CD8+ T cells and NK cells. The response rate was significantly higher in patients with low‐risk scores in immunotherapy cohorts, including IMigor210 and GSE91061. The TRPS‐based nomogram had a relatively good predictive value in evaluating the mortality risk at 1, 3 and 5 years in HCC. Overall, this study develops a TRPS by integrated bioinformatics analysis. This TRPS acted as an independent risk factor for the OS rate of HCC patients. It can screen for HCC patients who might benefit from immunotherapy, chemotherapy and targeted therapy.