Dataset Information

Identification of a costimulatory molecule-based signature for predicting prognosis risk and immunotherapy response in patients with lung adenocarcinoma.

ABSTRACT:

Background

Costimulatory molecules play significant roles in mounting anti-tumor immune responses, and antibodies targeting these molecules are recognized as promising adjunctive cancer immunotherapies. Here, we aim to conduct a first full-scale exploration of costimulatory molecules from the B7-CD28 and TNF families in patients with lung adenocarcinoma (LUAD) and generated a costimulatory molecule-based signature (CMS) to predict survival and response to immunotherapy.

Methods

We enrolled 1549 LUAD cases across 10 different cohorts and included 502 samples from TCGA for discovery. The validation set included 970 cases from eight different Gene Expression Omnibus (GEO) datasets and 77 frozen tumor tissues with qPCR data. The underlying mechanisms and predictive immunotherapy capabilities of the CMS were also explored.

Results

A five gene-based CMS (CD40LG, TNFRSF6B, TNFSF13, TNFRSF13C, and TNFRSF19) was initially constructed using the bioinformatics method from TCGA that classifies cases as high- vs. low-risk groups per OS. Multivariable Cox regression analysis confirmed that the CMS was an independent prognostic factor. As expected, CMS exhibited prognostic significance in the stratified cohorts and different validation cohorts. Additionally, the prognostic meta-analysis revealed that CMS was superior to the previous signature. Samples in high- and low-risk groups exhibited significantly different tumor-infiltrating leukocytes and inflammatory activities. Importantly, we found that the CMS scores were closely related to multiple immunotherapy biomarkers.

Conclusion

We conducted the first and most comprehensive costimulatory molecule landscape analysis of patients with LUAD and built a clinically feasible CMS for prognosis and immunotherapy response prediction, which will be helpful for further optimize immunotherapies for cancer.

SUBMITTER: Zhang C

PROVIDER: S-EPMC7781839 | biostudies-literature |

REPOSITORIES: biostudies-literature

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