Survival-Critical Genes Associated with Copy Number Alterations in Lung Adenocarcinoma.
Chinthalapally V RaoChao XuMudassir FarooquiYuting ZhangAdam S AschHiroshi Y YamadaPublished in: Cancers (2021)
Chromosome Instability (CIN) in tumors affects carcinogenesis, drug resistance, and recurrence/prognosis. Thus, it has a high impact on outcomes in clinic. However, how CIN occurs in human tumors remains elusive. Although cells with CIN (i.e., pre/early cancer cells) are proposed to be removed by apoptosis and/or a surveillance mechanism, this surveillance mechanism is poorly understood. Here we employed a novel data-mining strategy (Gene Expression to Copy Number Alterations [CNA]; "GE-CNA") to comprehensively identify 1578 genes that associate with CIN, indicated by genomic CNA as its surrogate marker, in human lung adenocarcinoma. We found that (a) amplification/insertion CNA is facilitated by over-expressions of DNA replication stressor and suppressed by a broad range of immune cells (T-, B-, NK-cells, leukocytes), and (b) deletion CNA is facilitated by over-expressions of mitotic regulator genes and suppressed predominantly by leukocytes guided by leukocyte extravasation signaling. Among the 39 CNA- and survival-associated genes, the purine metabolism (PPAT, PAICS), immune-regulating CD4-LCK-MEC2C and CCL14-CCR1 axes, and ALOX5 emerged as survival-critical pathways. These findings revealed a broad role of the immune system in suppressing CIN/CNA and cancer development in lung, and identified components representing potential targets for future chemotherapy, chemoprevention, and immunomodulation approaches for lung adenocarcinoma.
Keyphrases
- copy number
- genome wide
- mitochondrial dna
- dna methylation
- gene expression
- endothelial cells
- nk cells
- free survival
- cell cycle arrest
- public health
- peripheral blood
- bioinformatics analysis
- primary care
- induced pluripotent stem cells
- oxidative stress
- papillary thyroid
- squamous cell carcinoma
- transcription factor
- cell death
- pluripotent stem cells
- type diabetes
- machine learning
- risk assessment
- current status
- climate change
- squamous cell
- endoplasmic reticulum stress
- genome wide analysis
- weight loss
- young adults
- cell proliferation
- glycemic control
- deep learning