Novel Potential Therapeutic Targets of PTPN Families for Lung Cancer.
Chin-Chou WangWan-Jou ShenGangga AnuragaHoang Dang Khoa TaDo Thi Minh XuanSih-Tong ChenChiu-Fan ShenJia-Zhen JiangZhengda SunChih-Yang WangWei-Jan WangPublished in: Journal of personalized medicine (2022)
Despite the treatment of lung adenocarcinoma (LUAD) having partially improved in recent years, LUAD patients still have poor prognosis rates. Therefore, it is especially important to explore effective biomarkers and exploit novel therapeutic developments. High-throughput technologies are widely used as systematic approaches to explore differences in expressions of thousands of genes for both biological and genomic systems. Recently, using big data analyses in biomedicine research by integrating several high-throughput databases and tools, including The Cancer Genome Atlas (TCGA), cBioportal, Oncomine, and Kaplan-Meier plotter, is an important strategy to identify novel biomarkers for cancer therapy. Here, we used two different comprehensive bioinformatics analysis and revealed protein tyrosine phosphatase non-receptor type (PTPN) family genes, especially PTPN1 and PTPN22, were downregulated in lung cancer tissue in comparison with normal samples. The survival curves indicated that LUAD patients with high transcription levels of PTPN5 were significantly associated with a good prognosis. Meanwhile, Gene Ontology (GO) and MetaCore analyses indicated that co-expression of the PTPN1, PTPN5, and PTPN21 genes was significantly enriched in cancer development-related pathways, including GTPase activity, regulation of small GTPase-mediated signal transduction, response to mechanical stimuli, vasculogenesis, organ morphogenesis, regulation of stress fiber assembly, mitogen-activated protein kinase (MAPK) cascade, cell migration, and angiogenesis. Collectively, this study revealed that PTPN family members are both significant prognostic biomarkers for lung cancer progression and promising clinical therapeutic targets, which provide new targets for treating LUAD patients.
Keyphrases
- poor prognosis
- high throughput
- big data
- bioinformatics analysis
- end stage renal disease
- genome wide
- cancer therapy
- ejection fraction
- cell migration
- chronic kidney disease
- long non coding rna
- machine learning
- papillary thyroid
- peritoneal dialysis
- squamous cell carcinoma
- signaling pathway
- gene expression
- oxidative stress
- endothelial cells
- copy number
- cell proliferation
- patient reported outcomes
- binding protein
- squamous cell
- small molecule
- combination therapy
- dna methylation
- deep learning
- pi k akt
- vascular endothelial growth factor
- protein kinase
- lymph node metastasis
- clinical evaluation