Table4_卵巢癌铁死亡相关生物标志物特征整合分析以改善诊断与预后预测

数据集获取（Figshare）

• 源站标题：Table4_Integrated Analysis of Ferroptosis-Related Biomarker Signatures to Improve the Diagnosis and Prognosis Prediction of Ovarian Cancer.XLS
• DOI：10.3389/fcell.2021.807862.s006
• 发布日期：2022-01-05
• 文件数：1 个
• 文件总体积：241.66 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table4_Integrated_Analysis_of_Ferroptosis-Related_Biomarker_Signatures_to_Improve_the_Diagnosis_and_Prognosis_Prediction_of_Ovarian_Cancer_XLS/17869424

命令行下载

curl -L -o "Table4_Integrated Analysis of Ferroptosis-Related Biomarker Signatures to Improve the Diagnosis and Prognosis Prediction of Ovarian Cancer.XLS" "https://ndownloader.figshare.com/files/32582930"

文件清单

• Table4_Integrated Analysis of Ferroptosis-Related Biomarker Signatures to Improve the Diagnosis and Prognosis Prediction of Ovarian Cancer.XLS（241.66 KB）

文件总体积 241.66 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

Ovarian cancer remains the most lethal gynecological malignancy. Ferroptosis, a specialized form of iron-dependent, nonapoptotic cell death, plays a crucial role in various cancers. However, the contribution of ferroptosis to ovarian cancer is poorly understood. Here, we characterized the diagnostic, prognostic, and therapeutic value of ferroptosis-related genes in ovarian cancer by analyzing transcriptomic data from The Cancer Genome Atlas and Gene Expression Omnibus databases. A reliable 10-gene ferroptosis signature (HIC1, ACSF2, MUC1, etc.) for the diagnosis of ovarian cancer was identified. Notably, we constructed and validated a novel prognostic signature including three FRGs: HIC1, LPCAT3, and DUOX1. We also further developed a risk score model based on these three genes which divided ovarian cancer patients into two risk groups. Functional analysis revealed that immune response and immune-related pathways were enriched in the high-risk group. Meanwhile, the tumor microenvironment was distinct between the two groups, with more M2 Macrophage infiltration and higher expression of key immune checkpoint molecules in the high-risk group than in the other group. Low-risk patients exhibited more favorable immunotherapy and chemotherapy responses. We conclude that crosstalk between ferroptosis and immunity may contribute to the worse prognosis of patients in the high-risk group. In particular, HIC1 showed both diagnostic and prognostic value in ovarian cancer. In vitro experiments demonstrated that inhibition of HIC1 improved drug sensitivity of chemotherapy and immunotherapy agents by inducing ferroptosis. Our findings provide new insights into the potential role of FRGs in the early detection, prognostic prediction, and individualized treatment decision-making for ovarian cancer patients.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcell.2021.807862.s006（source: https://frontiersin.figshare.com/articles/dataset/Table4_Integrated_Analysis_of_Ferroptosis-Related_Biomarker_Signatures_to_Improve_the_Diagnosis_and_Prognosis_Prediction_of_Ovarian_Cancer_XLS/17869424）。