整合基因组分析对子宫内膜癌分子分类和预后风险分层的影响：中国经验表格1

数据集获取（Figshare）

• 源站标题：Table 1_The impact of integrated genomic analysis on molecular classifications and prognostic risk stratification in endometrial cancer: a Chinese experience.docx
• DOI：10.3389/fonc.2025.1541562.s001
• 发布日期：2025-02-06
• 文件数：1 个
• 文件总体积：710.28 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_The_impact_of_integrated_genomic_analysis_on_molecular_classifications_and_prognostic_risk_stratification_in_endometrial_cancer_a_Chinese_experience_docx/28357151

命令行下载

curl -L -o "Table 1_The impact of integrated genomic analysis on molecular classifications and prognostic risk stratification in endometrial cancer: a Chinese exp.docx" "https://ndownloader.figshare.com/files/52165445"

文件清单

• Table 1_The impact of integrated genomic analysis on molecular classifications and prognostic risk stratification in endometrial cancer: a Chinese exp.docx（710.28 KB）

文件总体积 710.28 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

BackgroundThe molecular classification of endometrial cancer (EC), as proposed by The Cancer Genome Atlas (TCGA), has transformed tumor classification, but there is a lack of extensive research on the molecular profiles and subtyping of endometrial cancer patients in China.
Methods200 EC patients were classified into the following four molecular types: (i) POLEmut; (ii) MSI-H; (iii) TP53mut; (iv) NSMP. This study aimed to investigate the molecular characteristics of EC patients at a single center by large-scale next generation sequencing(NGS), including clinicopathological features and gene mutations in patients with distinct molecular types, and to assess the relevance of molecular subtyping for postoperative adjuvant therapy.
ResultsNSMP group was the most prevalent, comprising 46.0% (92/200) of cases, followed by the TP53mut group at 17.5% (35/200), the MSI-H group at 23.5% (47/200), and the POLEmut group at 13.0% (26/200). CTNNB1 mutations were common in the POLEmut group but rare in the TP53mut group. With the application of the new European Society for Medical Oncology (ESMO) 2022 classification, 27 patients (14.1%) were reclassified. Concordance between the two classifications regarding postoperative risk was observed in 85.9% (165/192) of cases. Seven patients (3.6%) were downstaged, and twenty patients (10.4%) were upgraded. Additionally, the analysis revealed that eleven genes were significantly mutated in patients with lymphovascular space invasion (LVSI) compared to those without LVSI. Notably, NSD3 and POLD1 were highly mutated in patients with lymphatic metastasis compared to those without lymphatic metastasis. Conclusively, large-scale NGS has revolutionized EC management by facilitating rapid molecular subtype identification, guiding tailored adjuvant therapies, targeted treatments, and immunotherapies, and efficiently screening for Lynch syndrome, thereby significantly improving patient outcomes.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2025.1541562.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_The_impact_of_integrated_genomic_analysis_on_molecular_classifications_and_prognostic_risk_stratification_in_endometrial_cancer_a_Chinese_experience_docx/28357151）。