Gene signatures of endoplasmic reticulum stress and mitophagy for prognostic risk prediction in lung adenocarcinoma.
Xiong LinMiaoling YangYuanling HuangXiaoli HuangHuibo ShiBinbin ChenJianle KangSunkui KePublished in: IET systems biology (2024)
Genes associated with endoplasmic reticulum stress (ERS) and mitophagy can be conducive to predicting solid tumour prognosis. The authors aimed to develop a prognosis prediction model for these genes in lung adenocarcinoma (LUAD). Relevant gene expression and clinical information were collected from public databases including Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). A total of 265 differentially expressed genes was finally selected (71 up-regulated and 194 downregulated) in the LUAD dataset. Among these, 15 candidate ERS and mitophagy genes (ATG12, CSNK2A1, MAP1LC3A, MAP1LC3B, MFN2, PGAM5, PINK1, RPS27A, SQSTM1, SRC, UBA52, UBB, UBC, ULK1, and VDAC1) might be critical to LUAD based on the expression analysis after crossing with the ERS and mitochondrial autophagy genes. The prediction model demonstrated the ability to effectively predict the 5-, 3-, and 1-year prognoses of LUAD patients in both GEO and TCGA databases. Moreover, high VDAC1 expression was associated with poor overall survival in LUAD (p < 0.001), suggesting it might be a critical gene for LUAD prognosis prediction. Overall, the prognosis model based on ERS and mitophagy genes in LUAD can be useful for evaluating the prognosis of patients with LUAD, and VDAC1 may serve as a promising biomarker for LUAD prognosis.
Keyphrases
- endoplasmic reticulum stress
- genome wide
- genome wide identification
- gene expression
- dna methylation
- induced apoptosis
- bioinformatics analysis
- transcription factor
- genome wide analysis
- oxidative stress
- copy number
- healthcare
- cell death
- nlrp inflammasome
- squamous cell carcinoma
- poor prognosis
- mass spectrometry
- young adults
- emergency department
- big data
- single cell
- binding protein
- patient reported outcomes
- machine learning
- signaling pathway
- health information
- patient reported