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Telomere length-related signature as a novel biomarker of prognosis and immune response in non-small cell lung cancer.

OBJECTIVE
Telomere length-related genes (TLRGs) play an important role in multiple tumors; however, there is a lack of systematic reporting about their relevance in non-small cell lung cancer (NSCLC). This study investigated the relation between TLRG gene expression and the immunotherapeutic response of patients with NSCLC.


MATERIALS AND METHODS
Differentially expressed TLRGs in tumor tissues and normal tissues were screened using Gene Expression Omnibus (GEO) datasets. A univariate Cox regression analysis was performed to identify the optimal prognosis-related genes. A prognostic risk model was constructed by using least absolute shrinkage, selection operator, and multivariate Cox regression analysis results. The model was then evaluated by a Kaplan-Meier analysis, functional enrichment annotation, and a receiver operating characteristic curve analysis; after which, it was validated in the TCGA dataset. The model was used to predict immunotherapeutic response and drug sensitivity.


RESULTS
An 18-gene prognostic signature was developed and used to stratify NSCLC patients into a low- or high-risk group in GEO cohorts. Patients in the low-risk group had better survival possibilities than those in the high-risk group, and showed significantly higher overall survival times in the TCGA cohort. The risk score was identified as an independent prognostic factor, when compared with other clinical factors. ssGSEA scores showed that the risk model was mainly linked to cancer- and immune-related pathways. Importantly, the candidate risk model was linked to tumor immunity and predicted a patient's response to PDL-1 blockade immune therapy. Several potential drugs that might target this model were identified.


CONCLUSIONS
This study provides broad molecular signatures that can be used in further functional and therapeutic studies of the telomere system, and also represents an integrated approach for characterizing key protein complexes when creating a prognosis and identifying new targets for cancer immunotherapy.