DataSheet1_ESRRG、ATP4A和ATP4B作为胃癌诊断生物标志物的数据表：基于机器学习的生物信息学分析

数据集获取（Figshare）

• 源站标题：DataSheet1_ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning.ZIP
• DOI：10.3389/fphys.2022.905523.s001
• 发布日期：2022-06-23
• 文件数：1 个
• 文件总体积：27.68 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_ESRRG_ATP4A_and_ATP4B_as_Diagnostic_Biomarkers_for_Gastric_Cancer_A_Bioinformatic_Analysis_Based_on_Machine_Learning_ZIP/20129315

命令行下载

curl -L -o "DataSheet1_ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning.ZIP" "https://ndownloader.figshare.com/files/36002297"

文件清单

• DataSheet1_ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning.ZIP（27.68 KB）

文件总体积 27.68 KB（源站 API 实测）。

数据说明

Based on multiple bioinformatics methods and machine learning techniques, this study was designed to explore potential hub genes of gastric cancer with a diagnostic value. The novel biomarkers were detected through multiple databases of gastric cancer–related genes. The NCBI Gene Expression Omnibus (GEO) database was used to obtain gene expression files. Three hub genes (ESRRG, ATP4A, and ATP4B) were detected through a combination of weighted gene co-expression network analysis (WGCNA), gene–gene interaction network analysis, and supervised feature selection method. GEPIA2 was used to verify the differences in the expression levels of the hub genes in normal and cancer tissues in the RNA-seq levels of Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. The objectivity of potential hub genes was also verified by immunohistochemistry in the Human Protein Atlas (HPA) database and transcription factor–hub gene regulatory network. Machine learning (ML) methods including data pre-processing, model selection and cross-validation, and performance evaluation were examined on the hub-gene expression profiles in five Gene Expression Omnibus datasets and verified on a GEO external validation (EV) dataset. Six supervised learning models (support vector machine, random forest, k-nearest neighbors, neural network, decision tree, and eXtreme Gradient Boosting) and one semi-supervised learning model (label spreading) were established to evaluate the diagnostic value of biomarkers. Among the six supervised models, the support vector machine (SVM) algorithm was the most effective one according to calculated performance metrics, including 0.93 and 0.99 area under the curve (AUC) scores on the test and external validation datasets, respectively. Furthermore, the semi-supervised model could also successfully learn and predict sample types, achieving a 0.986 AUC score on the EV dataset, even when 10% samples in the five GEO datasets were labeled. In conclusion, three hub genes (ATP4A, ATP4B, and ESRRG) closely related to gastric cancer were mined, based on which the ML diagnostic model of gastric cancer was conducted.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fphys.2022.905523.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet1_ESRRG_ATP4A_and_ATP4B_as_Diagnostic_Biomarkers_for_Gastric_Cancer_A_Bioinformatic_Analysis_Based_on_Machine_Learning_ZIP/20129315）。