Identification of unique transcriptomic signatures and key genes through RNA sequencing and integrated WGCNA and PPI network analysis in HIV infected lung cancer.
Liwei WuYongfang ChenLaiyi WanZilu WenRong LiuLeilei LiYanzheng SongLin WangPublished in: Cancer medicine (2022)
With the widespread use of highly active antiretroviral therapy (HARRT), the survival time of AIDS patients has been greatly extended. However, the incidence of lung cancer in HIV-infected patients is increasing and has become a major problem threatening the survival of AIDS patients. The aim of this study is to use Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene analysis to find possible key genes involved in HIV-infected lung cancer. In this study, using lung tissue samples from five pairs of HIV-infected lung cancer patients, second-generation sequencing was performed and transcriptomic data were obtained. A total of 132 HIV-infected lung cancer-related genes were screened out by WGCNA and differential gene expression analysis methods. Based on gene annotation analysis, these genes were mainly enriched in mitosis-related functions and pathways. In addition, in protein-protein interaction (PPI) analysis, a total of 39 hub genes were identified. Among them, five genes (ASPM, CDCA8, CENPF, CEP55, and PLK1) were present in both three hub gene lists (intersection gene, DEGs, and WCGNA module) suggesting that these five genes may become key genes involved in HIV-infected lung cancer.
Keyphrases
- hiv infected
- antiretroviral therapy
- genome wide identification
- hiv infected patients
- genome wide
- network analysis
- human immunodeficiency virus
- hiv aids
- hiv positive
- bioinformatics analysis
- protein protein
- transcription factor
- copy number
- genome wide analysis
- end stage renal disease
- single cell
- ejection fraction
- dna methylation
- chronic kidney disease
- newly diagnosed
- small molecule
- poor prognosis
- risk factors
- prognostic factors
- magnetic resonance imaging
- gene expression
- peritoneal dialysis
- men who have sex with men
- machine learning