Interaction of hypoxia and nicotine acetylcholine receptor signaling network reveals a novel mechanism for lung adenocarcinoma progression in never-smokers
Srivastava, T.; Pandey, N.; Chongtham, J.; Pal, S.; Mohan, A.
Show abstract
High incident of lung cancer among never smokers and their disease pathogenesis is an unexplained phenomenon. We have analyzed 1727 lung cancer patient data to understand the impact of smoking on overall survival of lung cancer patients and have observed a difference of only 47 days between smokers and never smokers in adenocarcinoma patients suggesting that the disease is equally fatal in never-smokers irrespective of gender. In this study, we have investigated the possible collaboration between the nAChR and hypoxia signaling pathway to elucidate a mechanism of disease progression in never-smokers. We report a previously unidentified increase in both acetylcholine and nAChR-7 levels in non small cell lung cancer cells in hypoxia. Similar increase in ubiquitously expressed nAChR-7 transcripts was also observed in other cancer lines. A direct binding of HIF-1 with the hypoxia response element (HRE) present at -48 position preceding the transcriptional start site in nAChR-7 promoter region was established. Significantly, the increased acetylcholine levels in hypoxia drove a feedback loop via modulation of PI3K/AKT pathway to stabilize HIF-1 in hypoxia. Further, Bungarotoxin, an antagonist of nAChR-7 significantly reversed hypoxia mediated metastasis and induction of HIF-1 in these cells. Our study gives a plausible explanation for the equally worse prognosis of lung adenocarcinoma in never-smokers wherein the nAChR signaling is enhanced in hypoxia by acetylcholine, in the absence of nicotine.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Systems biology analysis of publicly available transcriptomic data reveals a critical link between AKR1B10 gene expression, smoking and occurrence of lung cancer 95%
- Exploring Hypoxia-Related Genes as Prognostic Indicators in Lung Adenocarcinoma 94%
- Epac activation reduces trans-endothelial migration of undifferentiated neuroblastoma cells and cellular differentiation with a CDK inhibitor further enhances Epac effect. 94%
Similar papers in this journal
- Myogenetic oligodeoxynucleotides as anti-nucleolin aptamers inhibit the growth of embryonal rhabdomyosarcoma cells 93%
- Comparison of Oxidative and Hypoxic Stress Responsive Genes from Meta-Analysis of Public Transcriptomes 93%
- MicroRNAs and mRNA Regulatory Network of Parenchymal Hematoma after Endovascular Mechanical Reperfusion for Acute Ischemic Stroke in Rat 93%
Similar papers in this journal
- Ursolic acid inhibits cell migration and promotes JNK-dependent lysosomal associated cell death in Glioblastoma multiforme cells 92%
- Correlating basal gene expression across chemical sensitivity data to screen for novel synergistic interactors of HDAC inhibitors in pancreatic carcinoma 92%
- Effects of cannabidiol on activated immune-inflammatory pathways in major depressive patients and healthy controls 91%
Similar papers in this journal
- In silico analysis of SNPs in human phosphofructokinase, Muscle (PFKM) gene: An apparent therapeutic target of aerobic glycolysis and cancer 93%
- Interleukin- 10 (IL-10) gene polymorphisms and prostate cancer susceptibility: evidence from a meta-analysis 93%
- Identification of Dysregulated Pathways and key genes in Human Retinal Angiogenesis using Microarray Metadata 93%
Similar papers in this journal
- Lung biopsy cells transcriptional landscape from COVID-19 patient stratified lung injury in SARS-CoV-2 infection through impaired pulmonary surfactant metabolism 95%
- Comparative transcriptome analyses reveal genes associated with SARS-CoV-2 infection of human lung epithelial cells 94%
- Novel Peptide Inhibitor of Human Tumor Necrosis Factor-α has Antiarthritic Activity 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.