PMID- 36059957
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20220907
IS  - 1663-9812 (Print)
IS  - 1663-9812 (Electronic)
IS  - 1663-9812 (Linking)
VI  - 13
DP  - 2022
TI  - Identification of endoplasmic reticulum stress-associated genes and subtypes for 
      prediction of Alzheimer's disease based on interpretable machine learning.
PG  - 975774
LID - 10.3389/fphar.2022.975774 [doi]
LID - 975774
AB  - Introduction: Alzheimer's disease (AD) is a severe dementia with clinical and 
      pathological heterogeneity. Our study was aim to explore the roles of endoplasmic 
      reticulum (ER) stress-related genes in AD patients based on interpretable machine 
      learning. Methods: Microarray datasets were obtained from the Gene Expression 
      Omnibus (GEO) database. We performed nine machine learning algorithms including 
      AdaBoost, Logistic Regression, Light Gradient Boosting (LightGBM), Decision Tree 
      (DT), eXtreme Gradient Boosting (XGBoost), Random Forest, K-nearest neighbors 
      (KNN), Naive Bayes, and support vector machines (SVM) to screen ER stress-related 
      feature genes and estimate their efficiency of these genes for early diagnosis of 
      AD. ROC curves were performed to evaluate model performance. Shapley additive 
      explanation (SHAP) was applied for interpreting the results of these models. AD 
      patients were classified using a consensus clustering algorithm. Immune 
      infiltration and functional enrichment analysis were performed via CIBERSORT and 
      GSVA, respectively. CMap analysis was utilized to identify subtype-specific 
      small-molecule compounds. Results: Higher levels of immune infiltration were 
      found in AD individuals and were markedly linked to deregulated ER stress-related 
      genes. The SVM model exhibited the highest AUC (0.879), accuracy (0.808), recall 
      (0.773), and precision (0.809). Six characteristic genes (RNF5, UBAC2, DNAJC10, 
      RNF103, DDX3X, and NGLY1) were determined, which enable to precisely predict AD 
      progression. The SHAP plots illustrated how a feature gene influence the output 
      of the SVM prediction model. Patients with AD could obtain clinical benefits from 
      the feature gene-based nomogram. Two ER stress-related subtypes were defined in 
      AD, subtype2 exhibited elevated immune infiltration levels and immune score, as 
      well as higher expression of immune checkpoint. We finally identified several 
      subtype-specific small-molecule compounds. Conclusion: Our study provides new 
      insights into the role of ER stress in AD heterogeneity and the development of 
      novel targets for individualized treatment in patients with AD.
CI  - Copyright (c) 2022 Lai, Lin, Lin, Lin, Chen and Zhang.
FAU - Lai, Yongxing
AU  - Lai Y
AD  - Department of Geriatric Medicine, Shengli Clinical Medical College of Fujian 
      Medical University, Fujian Provincial Hospital, Fuzhou, China.
AD  - Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, 
      China.
FAU - Lin, Xueyan
AU  - Lin X
AD  - Department of Gastroenterology, Shengli Clinical Medical College of Fujian 
      Medical University, Fujian Provincial Hospital, Fuzhou, China.
FAU - Lin, Chunjin
AU  - Lin C
AD  - Department of Geriatric Medicine, Shengli Clinical Medical College of Fujian 
      Medical University, Fujian Provincial Hospital, Fuzhou, China.
AD  - Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, 
      China.
FAU - Lin, Xing
AU  - Lin X
AD  - Department of Geriatric Medicine, Shengli Clinical Medical College of Fujian 
      Medical University, Fujian Provincial Hospital, Fuzhou, China.
AD  - Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou, 
      China.
FAU - Chen, Zhihan
AU  - Chen Z
AD  - Department of Rheumatology and Immunology, Shengli Clinical Medical College of 
      Fujian Medical University, Fujian Provincial Hospital, Fuzhou, China.
FAU - Zhang, Li
AU  - Zhang L
AD  - Department of Nephrology, Shengli Clinical Medical College of Fujian Medical 
      University, Fujian Provincial Hospital, Fuzhou, China.
LA  - eng
PT  - Journal Article
DEP - 20220819
PL  - Switzerland
TA  - Front Pharmacol
JT  - Frontiers in pharmacology
JID - 101548923
PMC - PMC9438901
OTO - NOTNLM
OT  - Alzheimer's disease
OT  - ER stress
OT  - machine learning
OT  - molecular subtypes
OT  - prediction model
COIS- The authors declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2022/09/06 06:00
MHDA- 2022/09/06 06:01
PMCR- 2022/08/19
CRDT- 2022/09/05 03:46
PHST- 2022/06/22 00:00 [received]
PHST- 2022/07/19 00:00 [accepted]
PHST- 2022/09/05 03:46 [entrez]
PHST- 2022/09/06 06:00 [pubmed]
PHST- 2022/09/06 06:01 [medline]
PHST- 2022/08/19 00:00 [pmc-release]
AID - 975774 [pii]
AID - 10.3389/fphar.2022.975774 [doi]
PST - epublish
SO  - Front Pharmacol. 2022 Aug 19;13:975774. doi: 10.3389/fphar.2022.975774. 
      eCollection 2022.