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Liu Y, Liu Q, Zhang Z, Yang Y, Zhou Y, Yan H, Wang X, Li X, Zhao J, Hu J, Yang S, Tian Y, Yao Y, Qiu Z, Song Y, Yang Y. The regulatory role of PI3K in ageing-related diseases. Ageing Res Rev 2023; 88:101963. [PMID: 37245633 DOI: 10.1016/j.arr.2023.101963] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 05/30/2023]
Abstract
Ageing is a physiological/pathological process accompanied by the progressive damage of cell function, triggering various ageing-related disorders. Phosphatidylinositol 3-kinase (PI3K), which serves as one of the central regulators of ageing, is closely associated with cellular characteristics or molecular features, such as genome instability, telomere erosion, epigenetic alterations, and mitochondrial dysfunction. In this review, the PI3K signalling pathway was firstly thoroughly explained. The link between ageing pathogenesis and the PI3K signalling pathway was then summarized. Finally, the key regulatory roles of PI3K in ageing-related illnesses were investigated and stressed. In summary, we revealed that drug development and clinical application targeting PI3K is one of the focal points for delaying ageing and treating ageing-related diseases in the future.
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Affiliation(s)
- Yanqing Liu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Qiong Liu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Zhe Zhang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Yaru Yang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Yazhe Zhou
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Huanle Yan
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Xin Wang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Xiaoru Li
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Jing Zhao
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Jingyan Hu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Shulin Yang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Yifan Tian
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Yu Yao
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Zhenye Qiu
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China
| | - Yanbin Song
- Department of Cardiology, Affiliated Hospital, Yan'an University, 43 North Street, Yan'an 716000, China.
| | - Yang Yang
- Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, Faculty of Life Sciences and Medicine, Northwest University, Xi'an 710069, China.
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Identification of potentially functional modules and diagnostic genes related to amyotrophic lateral sclerosis based on the WGCNA and LASSO algorithms. Sci Rep 2022; 12:20144. [PMID: 36418457 PMCID: PMC9684499 DOI: 10.1038/s41598-022-24306-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Accepted: 11/14/2022] [Indexed: 11/24/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a genetically and phenotypically heterogeneous disease results in the loss of motor neurons. Mounting information points to involvement of other systems including cognitive impairment. However, neither the valid biomarker for diagnosis nor effective therapeutic intervention is available for ALS. The present study is aimed at identifying potentially genetic biomarker that improves the diagnosis and treatment of ALS patients based on the data of the Gene Expression Omnibus. We retrieved datasets and conducted a weighted gene co-expression network analysis (WGCNA) to identify ALS-related co-expression genes. Functional enrichment analysis was performed to determine the features and pathways of the main modules. We then constructed an ALS-related model using the least absolute shrinkage and selection operator (LASSO) regression analysis and verified the model by the receiver operating characteristic (ROC) curve. Besides we screened the non-preserved gene modules in FTD and ALS-mimic disorders to distinct ALS-related genes from disorders with overlapping genes and features. Altogether, 4198 common genes between datasets with the most variation were analyzed and 16 distinct modules were identified through WGCNA. Blue module had the most correlation with ALS and functionally enriched in pathways of neurodegeneration-multiple diseases', 'amyotrophic lateral sclerosis', and 'endocytosis' KEGG terms. Further, some of other modules related to ALS were enriched in 'autophagy' and 'amyotrophic lateral sclerosis'. The 30 top of hub genes were recruited to a LASSO regression model and 5 genes (BCLAF1, GNA13, ARL6IP5, ARGLU1, and YPEL5) were identified as potentially diagnostic ALS biomarkers with validating of the ROC curve and AUC value.
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