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05 24, 2022;13 (6):

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases.

Dongli Zhao, Zhe Zhang, Zhonghuang Wang, Zhenglin Du, Meng Wu, Tingting Zhang, Jialu Zhou, Wenming Zhao, Yuanguang Meng
Author Information
  1. Dongli Zhao: Department of Obstetrics & Gynecology, Chinese People's Liberation Army (PLA) Medical School, No. 28, Fuxing Road, Haidian District, Beijing 100853, China.
  2. Zhe Zhang: Department of Obstetrics and Gynecology, Seventh Medical Center of Chinese PLA General Hospital, No. 5, Nanmencang, Dongsishitiao, Dongcheng District, Beijing 100700, China.
  3. Zhonghuang Wang: National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Building 104, Courtyard 1, Beichen West Road, Chaoyang District, Beijing 100101, China.
  4. Zhenglin Du: National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Building 104, Courtyard 1, Beichen West Road, Chaoyang District, Beijing 100101, China. ORCID
  5. Meng Wu: Medical College, Graduate School of Nankai University, No. 94, Weijin Road, Nankai District, Tianjin 300110, China.
  6. Tingting Zhang: Department of Obstetrics & Gynecology, Chinese People's Liberation Army (PLA) Medical School, No. 28, Fuxing Road, Haidian District, Beijing 100853, China.
  7. Jialu Zhou: Department of Obstetrics & Gynecology, Chinese People's Liberation Army (PLA) Medical School, No. 28, Fuxing Road, Haidian District, Beijing 100853, China.
  8. Wenming Zhao: National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Building 104, Courtyard 1, Beichen West Road, Chaoyang District, Beijing 100101, China.
  9. Yuanguang Meng: Department of Obstetrics & Gynecology, Chinese People's Liberation Army (PLA) Medical School, No. 28, Fuxing Road, Haidian District, Beijing 100853, China. ORCID
PMID: 35741697 DOI: 10.3390/genes13060935

Abstract

Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.

Keywords

GEO TCGA endometrial carcinoma random forest receiver operating characteristic curve

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MeSH Term

Ecosystem
Endometrial Neoplasms
Female
Humans
Machine Learning
Neural Networks, Computer
Reproducibility of Results
Tumor Microenvironment
Journal Article

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