International Journal of General Medicine (Dec 2022)
Elucidating the Gene Signatures and Immune Cell Types in HIV-Infected Immunological Non-Responders by Bioinformatics Analyses
Abstract
Qing Xiao,1,2 Junyan Han,3– 6 Fengting Yu,1– 3 Liting Yan,1,2 Qun Li,1,2 Xiaojie Lao,1,2 Hongxin Zhao,1,2 Fujie Zhang1,2 1Department of Infectious Diseases, Capital Medical University Affiliated Beijing Ditan Hospital, Beijing, People’s Republic of China; 2Clinical Center for HIV/AIDS, Capital Medical University, Beijing, People’s Republic of China; 3Beijing Key Laboratory of Emerging Infectious Diseases, Institute of Infectious Diseases, Beijing Ditan Hospital, Beijing, People’s Republic of China; 4National Center for Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China; 5Beijing Institute of Infectious Diseases, Beijing Ditan Hospital, Capital Medical University, Beijing, People’s Republic of China; 6Biomedical Innovation Center, Beijing Shijitan Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Fujie Zhang, Beijing, Ditan Hospital, Capital Medical University, Beijing, 100015, People’s Republic of China, Tel +86 10 84322581, Email [email protected]: Numerous studies have reported on the pathogenesis of poor immune reconstitution (PIR) after antiretroviral treatment in human immunodeficiency virus (HIV) patients. However, fewer studies focused on both immune-related genes (IRGs) and immune cells, and the correlation between IRGs and immune cells was evaluated via bioinformatics analyses.Methods: Gene expression profiling of GSE143742 from the Gene Expression Omnibus (GEO) database was analyzed to get differentially expressed immune-related genes (DEIRGs). The enrichment analysis and protein-protein interaction (PPI) networks of DEIRGs were established. The relative fractions of 22 immune cell types were detected using the “CIBERSORT”. The correlation analysis between DEIRGs and immune cells was constructed to discover the potential IRGs associated with immune cells. A logistic regression diagnostic model was built, and a receiver operating characteristic (ROC) curve was performed to evaluate the model’s diagnostic efficacy. The CMap database was used to find molecules with therapeutic potential. RT-qPCR was used to verify the expression of the hub DEIRGs.Results: We identified eight types of significantly changed immune cells and five hub IRGs in INRs. The DEIRGs were mainly enriched in lymphocyte activation, receptor-ligand activity, and T cell receptor signaling pathway. The correlation analysis showed that the expression of TNF, CXCR4 and TFRC correlate with CD8 cells, resting mast cells, activated NK cells, and naïve CD4 cells in INRs. Meanwhile, TFRC and IL7R relate to activated NK cells and resting memory CD4 cells respectively in IRs. A diagnostic model was constructed using multiple logistic regression and nine small molecules were identified as possible drugs.Conclusion: In this study, we suggested that the process of PIR might be related to TNF, CXCR4, TFRC, CD48, and IL7R. And these IRGs play roles in regulating immune-competent cells. And our constructed diagnostic model has excellent effectiveness. Moreover, some small-molecule drugs are screened to alleviate PIR.Keywords: HIV, immunological non-responders, immune-related genes, immune cells, bioinformatics
