An Integrated Genomic Strategy to Identify CHRNB4 as a Diagnostic/Prognostic Biomarker for Targeted Therapy in Head and Neck Cancer.
Yi-Hsuan ChuangChia-Hwa LeeChun-Yu LinChia-Lin LiuSing-Han HuangJung-Yu LeeYi-Yuan ChiuJih-Chin LeeJinn-Moon YangPublished in: Cancers (2020)
Although many studies have shown the association between smoking and the increased incidence and adverse prognosis of head and neck squamous cell carcinoma (HNSCC), the mechanisms and pharmaceutical targets involved remain unclear. Here, we integrated gene expression signatures, genetic alterations, and survival analyses to identify prognostic indicators and therapeutic targets for smoking HNSCC patients, and we discovered that the FDA-approved drug varenicline inhibits the target for cancer cell migration/invasion. We first identified 18 smoking-related and prognostic genes for HNSCC by using RNA-Seq and clinical follow-up data. One of these genes, CHRNB4 (neuronal acetylcholine receptor subunit beta-4), increased the risk of death by approximately threefold in CHRNB4-high expression smokers compared to CHRNB4-low expression smokers (log rank, p = 0.00042; hazard ratio, 2.82; 95% CI, 1.55-5.14), former smokers, and non-smokers. Furthermore, we examined the functional enrichment of co-regulated genes of CHRNB4 and its 246 frequently occurring copy number alterations (CNAs). We found that these genes were involved in promoting angiogenesis, resisting cell death, and sustaining proliferation, and contributed to much worse outcomes for CHRNB4-high patients. Finally, we performed CHRNB4 gene editing and drug inhibition assays, and the results validate these observations. In summary, our study suggests that CHRNB4 is a prognostic indicator for smoking HNSCC patients and provides a potential new therapeutic drug to prevent recurrence or distant metastasis.
Keyphrases
- smoking cessation
- copy number
- genome wide
- gene expression
- cell death
- newly diagnosed
- cell migration
- rna seq
- dna methylation
- prognostic factors
- mitochondrial dna
- single cell
- type diabetes
- high throughput
- patient reported outcomes
- transcription factor
- replacement therapy
- machine learning
- bioinformatics analysis
- drug induced
- risk assessment
- vascular endothelial growth factor
- signaling pathway
- adverse drug