A T-cell exhaustion-related signature for predicting response to immunotherapy among patients with lung adenocarcinoma.

BACKGROUND

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality, and although immunotherapy has improved outcomes for a proportion of patients, reliable predictive biomarkers of response are lacking. T-cell exhaustion-associated transcriptional states are increasingly recognized as relevant to antitumor immunity, yet their prognostic and predictive associations in LUAD remain incompletely defined. The aim of this study was to evaluate transcription factors previously linked to T-cell exhaustion and to construct a prognostic gene signature for stratifying patients with LUAD and estimating potential benefit from immunotherapy.

METHODS

Ten transcription factors previously linked to T-cell exhaustion were identified from the literature. Using LUAD transcriptomic data, we evaluated their prognostic relevance and identified four factors associated with overall survival (OS). Based on these, patients were divided into high- and low-risk exhaustion groups, and differentially expressed genes (DEGs) were derived. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox analysis were applied to screen prognosis-related DEGs, through which a six-gene T-cell exhaustion-related prognostic score (TEXRGPS) was constructed. The expression of these six genes was validated via reverse-transcription quantitative polymerase chain reaction (RT-qPCR) in 20 pairs of LUAD and adjacent normal tissues from our hospital. Associations of TEXRGPS with clinical characteristics, immune cell infiltration, and predicted response to immune checkpoint inhibitors (ICIs) were also assessed.

RESULTS

Four of the ten transcription factors were associated with prognosis and were used to stratify patients. Six key DEGs-CCL19, CD79A, CHRDL1, MS4A1, S100P, and SFTPC-were selected to construct the TEXRGPS. RT-qPCR confirmed differential expression between tumor and adjacent non-tumor tissues. A high TEXRGPS was associated with adverse clinical features and poor prognosis, whereas a low TEXRGPS was associated with prolonged survival. The TEXRGPS was negatively correlated with the estimated infiltration of multiple immune cell types, including CD8+ T cells and dendritic cells. Low-risk patients had higher computationally predicted sensitivity to anti-PD-1/PD-L1 and anti-CTLA-4 therapies than high-risk patients. These retrospective associations require prospective validation before clinical application.

CONCLUSIONS

The TEXRGPS was associated with prognosis, computational estimates of immune infiltration, and predicted immunotherapy response in retrospective LUAD cohorts. The signature may support future risk-stratification research, but it does not establish a direct functional relationship between the selected genes and exhausted T cells. Prospective multicenter validation, together with single-cell, protein-level, and functional studies, is required before clinical use.