An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases - Wang, Rong; Zhang, Xing; He, Changshou; Guo, Wei (Frontiers in Genetics, 2023)
Background: Brain metastasis, with an incidence of more than 30%, is a common complication of non-small cell lung cancer (NSCLC). Therefore, there is an urgent need for an assessment method that can effectively predict brain metastases in NSCLC and to deeply understand its mechanism. Material and methods: GSE30219, GSE31210, GSE37745 and GSE50081 datasets from GSE database were integrated into a dataset (GSE), which divided into train dataset and test dataset. TCGA-NSCLC dataset was acted as an independent verification dataset. Here, the limma R package was used to pick up the differential expression genes (DEGs). Importantly, RiskScore model was constructed using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. Moreover, we explored in detail the tumor mutational signature, immune signature, and sensitivity to treatment of brain metastases in NSCLC. Finally, nomogram was build using rms package. Results: First, 472 DEGs associated with brain metastases in NSCLC were obtained, which closely associated to cancer-associated pathways. Interestingly, a RiskScore model was constructed using 11 genes form 472 DEGs and the robustness was confirmed in GSE test dataset, entire GSE dataset, TCGA dataset. . Samples in low RiskScore group had a higher gene mutation score, a lower immunoinfiltration status.. Moreover, we found that the patients in low RiskScore group were more sensitive to four chemotherapy drugs. In addition, the predictive nomogram model was able to effectively predict the outcome of patients through appropriate RiskScore stratification. Conclusions: The prognostic RiskScore model we established has high prediction accuracy and survival prediction ability for brain metastases in NSCLC.