Immune Characteristics Analysis and Transcriptional Regulation Prediction Based on Gene Signatures of Chronic Obstructive Pulmonary Disease
Hui Yu,1 Weikang Guo,2 Yunduo Liu,2 Yaoxian Wang2 1Cardiopulmonary Function Department, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150081, People’s Republic of China; 2Gynecological Department, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150081, People’s Re...
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2021
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| Acceso en línea: | https://doaj.org/article/31e93c05a38d45e3a4241ca829cebbec |
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| Sumario: | Hui Yu,1 Weikang Guo,2 Yunduo Liu,2 Yaoxian Wang2 1Cardiopulmonary Function Department, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150081, People’s Republic of China; 2Gynecological Department, Harbin Medical University Cancer Hospital, Harbin, Heilongjiang, 150081, People’s Republic of ChinaCorrespondence: Yaoxian WangGynecological Department, Harbin Medical University Cancer Hospital, No. 150 Haping Road, Nangang District, Harbin, Heilongjiang, 150081, People’s Republic of ChinaTel +86-0451-86298578Email wyxxs012@126.comPurpose: The variation in inflammation in chronic obstructive pulmonary disease (COPD) between individuals is genetically determined. This study aimed to identify gene signatures of COPD through bioinformatics analysis based on multiple gene sets and explore their immune characteristics and transcriptional regulation mechanisms.Methods: Data from four microarrays were downloaded from the Gene Expression Omnibus database to screen differentially expressed genes (DEGs) between COPD patients and controls. Weighted gene co-expression network analysis was applied to identify trait-related modules and then select key module-related DEGs. The optimized gene set of signatures was obtained using the least absolute shrinkage and selection operator (LASSO) regression analysis. The CIBERSORT algorithm and Pearson correlation test were used to analyze the relationship between gene signatures and immune cells. Finally, public databases were used to predict the transcription factors (TFs) and upstream miRNAs.Results: A total of 127 DEGs in COPD were identified from the combined dataset. By considering the intersection of DEGs and genes in two trait-related modules, 83 key module-related DEGs were identified, which were mainly enriched in interleukin-related pathways. Seven-gene signatures, including MTHFD2, KANK3, GFPT2, PHLDA1, HS3ST2, FGG, and RPS4Y1, were further selected using the LASSO algorithm. These gene signatures showed the predictive potential for COPD risks and were significantly correlated with 18 types of immune cells. Finally, nine miRNAs and three TFs were predicted to target MTHFD2, GFPT2, PHLDA1, and FGG.Conclusion: We proposed the seven-gene-signature to predict COPD risk and explored its potential immune characteristics and regulatory mechanisms.Keywords: COPD, gene signatures, immune infiltration, transcriptional regulation |
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