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 signature high-risk patients were optimal candidates for immunotherapy. 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.

Systematic Pan-Cancer Analysis of KLRB1 with Prognostic Value and Immunological Activity across Human Tumors

“…It is undeniable that there are some limitations in our research. Although we have used two different methods to evaluate TME, both were achieved through bioinformatics methods and as mentioned in the previous study [46] may have been influence by noise. We do not have specific drug efficacy data; thus, the evaluation of drug sensitivity was also based on the R package, so there were also absolute deviations.…”

Signature based on metabolic‐related gene pairs can predict overall survival of osteosarcoma patients

“…Nowadays, many signatures based on certain characteristics have been developed to predict the prognosis of tumour patients 53,54 . Recent studies have constructed several signatures that can predict the overall survival of osteosarcoma 55–57 .…”

“…Nowadays, many signatures based on certain characteristics have been developed to predict the prognosis of tumour patients. 53 , 54 Recent studies have constructed several signatures that can predict the overall survival of osteosarcoma. 55 , 56 , 57 Compared with previous studies, our study incorporates the most comprehensive data set and achieved more consistent results, enhancing the persuasiveness of our study.…”

Identification of an inflammatory response signature associated with prognostic stratification and drug sensitivity in lung adenocarcinoma

“…Probability of survival was estimated by the Kaplan-Meier method, with differences between two groups tested using the log-rank test. The IRG signature constructed in this study was compared to these signatures constructed in other publications [28][29][30] . The receiver operating characteristic (ROC) curve, a combination of sensitivity (true positive rate) and specificity (true negative rate), was used to assess prediction performance of the IRGS, and the corresponding area under the curve (AUC) values were also calculated.…”