A Comprehensive Analysis of the Effects of Key Mitophagy Genes on the Progression and Prognosis of Lung Adenocarcinoma.
Dongjun DaiLihong LiuYinglu GuoYongjie ShuiQichun WeiPublished in: Cancers (2022)
The aim of our study was to perform a comprehensive analysis of the gene expression, copy number variation (CNV) and mutation of key mitophagy genes in the progression and prognosis of lung adenocarcinoma (LUAD). We obtained the data from The Cancer Genome Atlas (TCGA). Clustering analysis was performed to stratify the mitophagy related groups. The least absolute shrinkage and selection operator (LASSO) based cox model was used to select hub survival genes. An independent validation cohort was retrieved from Gene Expression Omnibus database. We found 24 out of 27 mitophagy genes were aberrantly expressed between tumor and normal samples. CNV gains were associated with higher expression of mitophagy genes in 23 of 27 mitophagy genes. The clustering analysis identified high and low risk mitophagy groups with distinct survival differences. The high risk mitophagy groups had higher tumor mutation burden, stemness phenotype, total CNVs and lower CD4+ T cells infiltration. Drugs targeted to high risk mitophagy groups were identified including the PI3K/AKT/mTOR inhibitor, HDAC inhibitor and chemotherapy agents such as cisplatin and gemcitabine. In addition, the differentially expressed genes (DEGs) were identified between mitophagy groups. Further univariate Cox analysis of each DEG and subsequent LASSO-based Cox model revealed a mitophagy-related prognostic signature. The risk score model of this signature showed a strong ability to predict the overall survival of LUAD patients in training and validation datasets. In conclusion, the mitophagy genes played an important role in the progression and prognosis of LUAD, which might provide useful information for the treatment of LUAD.
Keyphrases
- genome wide
- bioinformatics analysis
- nlrp inflammasome
- gene expression
- copy number
- genome wide identification
- dna methylation
- stem cells
- genome wide analysis
- mitochondrial dna
- emergency department
- squamous cell carcinoma
- radiation therapy
- end stage renal disease
- healthcare
- chronic kidney disease
- young adults
- poor prognosis
- electronic health record
- epithelial mesenchymal transition
- free survival
- prognostic factors
- drug induced
- deep learning
- lymph node metastasis
- health information
- binding protein
- childhood cancer