PMID- 37330465 OWN - NLM STAT- MEDLINE DCOM- 20230619 LR - 20231124 IS - 1755-8794 (Electronic) IS - 1755-8794 (Linking) VI - 16 IP - 1 DP - 2023 Jun 17 TI - Construction and verification of atopic dermatitis diagnostic model based on pyroptosis related biological markers using machine learning methods. PG - 138 LID - 10.1186/s12920-023-01552-5 [doi] LID - 138 AB - OBJECTIVE: The aim of this study was to construct a model used for the accurate diagnosis of Atopic dermatitis (AD) using pyroptosis related biological markers (PRBMs) through the methods of machine learning. METHOD: The pyroptosis related genes (PRGs) were acquired from molecular signatures database (MSigDB). The chip data of GSE120721, GSE6012, GSE32924, and GSE153007 were downloaded from gene expression omnibus (GEO) database. The data of GSE120721 and GSE6012 were combined as the training group, while the others were served as the testing groups. Subsequently, the expression of PRGs was extracted from the training group and differentially expressed analysis was conducted. CIBERSORT algorithm calculated the immune cells infiltration and differentially expressed analysis was conducted. Consistent cluster analysis divided AD patients into different modules according to the expression levels of PRGs. Then, weighted correlation network analysis (WGCNA) screened the key module. For the key module, we used Random forest (RF), support vector machines (SVM), Extreme Gradient Boosting (XGB), and generalized linear model (GLM) to construct diagnostic models. For the five PRBMs with the highest model importance, we built a nomogram. Finally, the results of the model were validated using GSE32924, and GSE153007 datasets. RESULTS: Nine PRGs were significant differences in normal humans and AD patients. Immune cells infiltration showed that the activated CD4+ memory T cells and Dendritic cells (DCs) were significantly higher in AD patients than normal humans, while the activated natural killer (NK) cells and the resting mast cells were significantly lower in AD patients than normal humans. Consistent cluster analysis divided the expressing matrix into 2 modules. Subsequently, WGCNA analysis showed that the turquoise module had a significant difference and high correlation coefficient. Then, the machine model was constructed and the results showed that the XGB model was the optimal model. The nomogram was constructed by using HDAC1, GPALPP1, LGALS3, SLC29A1, and RWDD3 five PRBMs. Finally, the datasets GSE32924 and GSE153007 verified the reliability of this result. CONCLUSIONS: The XGB model based on five PRBMs can be used for the accurate diagnosis of AD patients. CI - (c) 2023. The Author(s). FAU - Wu, Wenfeng AU - Wu W AD - The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Chen, Gaofei AU - Chen G AD - The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China. AD - Zhongshan Hospital of Traditional Chinese Medicine Affiliated to Guangzhou University of Traditional Chinese Medicine, Zhongshan, China. FAU - Zhang, Zexin AU - Zhang Z AD - The First Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - He, Meixing AU - He M AD - The Second Clinical College, Guangzhou University of Chinese Medicine, Guangzhou, China. FAU - Li, Hongyi AU - Li H AD - Department of Dermatology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China. lihongyich@126.com. AD - State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. lihongyich@126.com. FAU - Yan, Fenggen AU - Yan F AD - Department of Dermatology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China. fenggen_yan@gzucm.edu.cn. AD - Guangdong Provincial Key Laboratory of Chinese Medicine for Prevention and Treatment of Refractory Chronic Diseases, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China. fenggen_yan@gzucm.edu.cn. AD - Guangdong-Hong Kong-Macau Joint Lab on Chinese Medicine and Immune Disease Research, Guangzhou, China. fenggen_yan@gzucm.edu.cn. AD - State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China. fenggen_yan@gzucm.edu.cn. LA - eng PT - Journal Article PT - Research Support, Non-U.S. Gov't DEP - 20230617 PL - England TA - BMC Med Genomics JT - BMC medical genomics JID - 101319628 RN - 0 (Biomarkers) SB - IM MH - Humans MH - *Pyroptosis MH - *Dermatitis, Atopic/diagnosis/genetics MH - Reproducibility of Results MH - Genes, Regulator MH - Biomarkers PMC - PMC10276470 OTO - NOTNLM OT - Atopic dermatitis OT - Disease diagnosis OT - Immune cells infiltration OT - Machine learning OT - Pyroptosis COIS- The authors declared that they have no competing interests. EDAT- 2023/06/18 01:07 MHDA- 2023/06/19 16:16 PMCR- 2023/06/17 CRDT- 2023/06/17 23:13 PHST- 2023/01/24 00:00 [received] PHST- 2023/05/17 00:00 [accepted] PHST- 2023/06/19 16:16 [medline] PHST- 2023/06/18 01:07 [pubmed] PHST- 2023/06/17 23:13 [entrez] PHST- 2023/06/17 00:00 [pmc-release] AID - 10.1186/s12920-023-01552-5 [pii] AID - 1552 [pii] AID - 10.1186/s12920-023-01552-5 [doi] PST - epublish SO - BMC Med Genomics. 2023 Jun 17;16(1):138. doi: 10.1186/s12920-023-01552-5.