子宫内膜癌关键基因识别与预后模型数据表

数据集获取（Figshare）

• 源站标题：Table_2_Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer.XLSX
• DOI：10.3389/fgene.2019.00373.s002
• 发布日期：2019-04-26
• 文件数：1 个
• 文件总体积：272.78 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_2_Identification_of_Potential_Crucial_Genes_Associated_With_the_Pathogenesis_and_Prognosis_of_Endometrial_Cancer_XLSX/8047511

命令行下载

curl -L -o "Table_2_Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer.XLSX" "https://ndownloader.figshare.com/files/14994662"

文件清单

• Table_2_Identification of Potential Crucial Genes Associated With the Pathogenesis and Prognosis of Endometrial Cancer.XLSX（272.78 KB）

文件总体积 272.78 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Background and ObjectiveEndometrial cancer (EC) is a common gynecological malignancy worldwide. Despite advances in the development of strategies for treating EC, prognosis of the disease remains unsatisfactory, especially for advanced EC. The aim of this study was to identify novel genes that can be used as potential biomarkers for identifying the prognosis of EC and to construct a novel risk stratification using these genes.
Methods and ResultsAn mRNA sequencing dataset, corresponding survival data and expression profiling of an array of EC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus, respectively. Common differentially expressed genes (DEGs) were identified based on sequencing and expression as given in the profiling dataset. Pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization, and Integrated Discovery. The protein–protein interaction network was established using the string online database in order to identify hub genes. Univariate and multivariable Cox regression analyses were used to screen prognostic DEGs and to construct a prognostic signature. Survival analysis based on the prognostic signature was performed on TCGA EC dataset. A total of 255 common DEGs were found and 11 hub genes (TOP2A, CDK1, CCNB1, CCNB2, AURKA, PCNA, CCNA2, BIRC5, NDC80, CDC20, and BUB1BA) that may be closely related to the pathogenesis of EC were identified. A panel of 7 DEG signatures consisting of PHLDA2, GGH, ESPL1, FAM184A, KIAA1644, ESPL1, and TRPM4 were constructed. The signature performed well for prognosis prediction (p This study identified potential genes that may be involved in the pathophysiology of EC and constructed a novel gene expression signature for EC risk stratification and prognosis prediction.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fgene.2019.00373.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_2_Identification_of_Potential_Crucial_Genes_Associated_With_the_Pathogenesis_and_Prognosis_of_Endometrial_Cancer_XLSX/8047511）。