AUTHOR=Xu Jiasheng , Nie Han , He Jiarui , Wang Xinlu , Liao Kaili , Tu Luxia , Xiong Zhenfang 

TITLE=Using Machine Learning Modeling to Explore New Immune-Related Prognostic Markers in Non-Small Cell Lung Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.550002

DOI=10.3389/fonc.2020.550002

ISSN=2234-943X

ABSTRACT=OBJECTIVE: To find new immune-related prognostic markers for non-small cell lung cancer (NSCLC)
METHODS: We found related NSCLC data chip (GSE14814) as the non-small cell lung cancer observation (NSCLC-OBS) group to evaluated for immunity, then divided into high and low groups according to the immune score.Using single factor COX regression to select the prognosis genes. A prognostic model was constructed by machine learning, and the Receiver Operating Characteristic (ROC) model was used to test patient's prognosis. A chip-in-chip non-small cell lung cancer chemotherapy (NSCLC-ACT) sample was used to validation the model. Analysed the relative infiltration scores of 24 immune cells in NSCLC-ACT patients . Obtained the coexpression genes of hub genes by pearson analysis and gene enrichment, function enrichment analysis were carried out and the correlation between prognostic genes and immune checkpoints was further analyzed. The tumor samples of patients with different clinical stages were detected by immunohistochemistry and the expression difference of prognostic genes in tumor tissues of patients with different stages was compared.
RESULTS: LYN、C3、COPG2IT1、HLA.DQA1、TNFRSF17 are closely related to prognosis and the immune prognosis model constructed from these 5 genes .the AUC values were greater than 0.9 in ROC analysis; the total survival period of the low-risk group was significantly better than the high-risk group.Tthe increase of COPG2IT1、HLA.DQA1 expression and the decrease of LYN、C3、TNFRSF17 expression were significantly related to the survival time.The results of prognosis analysis and ROC analysis in ACT samples were consistent with those of OBS groups. Hubgene was no significant difference in immune infiltration in the high and low risk groups.The coexpression genes are mainly involved B cell receptor signaling pathway and mainly enriched in biological processes such as apoptotic cell clearance. Prognostic key genes are highly correlated with PDCD1、PDCD1LG2、LAG3、CTLA4 immune checkpoints (p<0.05). The immunohistochemical results showed that the expression of COPG2IT1 and HLA.DQA1 in stage III increased significantly and the expression of LYN、C3 and TNFRSF17 in stage III decreased significantly compared with that of stage I. The experimental results are consistent with the previous analysis.
CONCLUSION: LYN、C3、COPG2IT1、HLA.DQA1、TNFRSF17 may be a new immune marker to judge the prognosis of patients with non-small cell lung cancer.