Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.
Junzhi LiuHuimin LiNannan ZhangQiuping DongZheng LiangPublished in: Current medicinal chemistry (2024)
Our research introduces an innovative prognostic risk model predicated upon disulfidptosis genes for patients afflicted with Lung Adenocarcinoma (LUAD). This model proficiently forecasts the survival rates and therapeutic outcomes for LUAD patients, thereby delineating the high-risk population with distinctive immune cell infiltration and a state of immunosuppression. Furthermore, NAPSA can inhibit the proliferation and invasion capabilities of LUAD cells, thereby identifying new molecules for clinical targeted therapy.
Keyphrases
- end stage renal disease
- machine learning
- ejection fraction
- newly diagnosed
- chronic kidney disease
- prognostic factors
- peritoneal dialysis
- type diabetes
- patient reported outcomes
- induced apoptosis
- genome wide
- gene expression
- metabolic syndrome
- deep learning
- artificial intelligence
- cell proliferation
- transcription factor
- big data
- patient reported
- endoplasmic reticulum stress