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Published on: October 26, 2017
Screening Potential Diagnostic Biomarkers of Lung Adenocarcinoma Related to Immune Infiltration Based on Single-Cell
Ningxin Xu1, Yuwei Zhang1, Qiongke Lian1
1Department of Oncology, Luoyang Central Hospital Affiliated to Zhengzhou University, Luoyang 471000, Henan, China, zzu.edu.cn.
Background:
Macrophages play a crucial role in mediating immune infiltration in lung adenocarcinoma (LUAD). However, the potential of macrophage-related targets in the diagnosis and treatment of LUAD remains largely unexplored.
Methods:
The proteome data of LUAD were obtained from the CPTAC database, and the single-cell sequencing (scRNA-seq) data of non-small-cell lung cancer (NSCLC) were collected from the GEO database. The differentially expressed proteins (DEPs) of LUAD and module proteins closely associated with clinical features were analyzed by the limma tool and WGCNA. The 61 intersection targets of DEPs and WGCNA were obtained for GO and KEGG pathway enrichment analysis. Machine learning (random forest algorithm, LASSO regression analysis, and support vector machine recursive feature elimination algorithm) approaches screened the hub targets related to macrophages in LUAD. The CIBERSORT method and GSEA were used to explore the connection between key targets and immune cells. ROC curve was used to construct genetic diagnosis model, and the potential targeted drugs of four hub genes were screened by DSigDB database.
Results:
A total of 1149 DEPs in LUAD and 65 proteins associated with clinical features were screened. After the intersection, 61 targets were obtained for GO and KEGG analysis. Macrophages were the key cell type in LUAD, which had 5438 differentially expressed genes in LUAD. Moreover, 13 intersection targets were obtained by taking the intersection of proteomes (61 targets) and macrophage-related genes (5438 genes). And seven targets (CRYAB, EHD2, FHL1, TNS1, TNXB, SORBS2, and LTBP4) were obtained through machine learning. Based on CIBERSORT method and GSEA enrichment results, four genes (TNS1, EHD2, CRYAB, and TNXB) related to immune infiltration were screened. ROC analysis combined with the nomogram-related model showed that only four hub genes (EHD2, FHL1, TNS1, and SORBS2) could be used as diagnostic biomarkers, and their potential therapeutic drugs were revealed.
Conclusion:
This study identified four hub targets, which have certain value in the diagnosis and the development of targeted drugs for LUAD.