透明细胞肾细胞癌缺氧相关分子亚型多组学数据集

数据集获取（Figshare）

• 源站标题：Table1_Characterization of Hypoxia-Related Molecular Subtypes in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy and Targeted Therapy via Multi-Omics Analysis.XLSX
• DOI：10.3389/fmolb.2021.684050.s003
• 发布日期：2021-06-25
• 文件数：1 个
• 文件总体积：11.85 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_Characterization_of_Hypoxia-Related_Molecular_Subtypes_in_Clear_Cell_Renal_Cell_Carcinoma_to_Aid_Immunotherapy_and_Targeted_Therapy_via_Multi-Omics_Analysis_XLSX/14843580

命令行下载

curl -L -o "Table1_Characterization of Hypoxia-Related Molecular Subtypes in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy and Targeted Therapy via Multi-O.XLSX" "https://ndownloader.figshare.com/files/28573308"

文件清单

• Table1_Characterization of Hypoxia-Related Molecular Subtypes in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy and Targeted Therapy via Multi-O.XLSX（11.85 KB）

文件总体积 11.85 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

Objective: Tumor hypoxia is a key factor in resistance to anti-cancer treatment. Herein, this study aimed to characterize hypoxia-related molecular subtypes and assess their correlations with immunotherapy and targeted therapy in clear cell renal cell carcinoma (ccRCC).
Materials: We comprehensively analyzed copy number variation (CNV), somatic mutation, transcriptome expression profile and clinical information for ccRCC from TCGA and ICGC databases. Based on 98 prognosis-related hypoxia genes, samples were clustered using unsupervized non-negative matrix factorization (NMF) analysis. We characterized the differences between subtypes concerning prognosis, CNV, somatic mutations, pathways, immune cell infiltrations, stromal/immune scores, tumor purity, immune checkpoint inhibitors (ICI), response to immunotherapy and targeted therapy and CXC chemokines. Based on differentially expressed genes (DEGs) between subtypes, a prognostic signature was built by LASSO Cox regression analysis, followed by construction of a nomogram incorporating the signature and clinical features.
Results: Two hypoxia-related molecular subtypes (C1 and C2) were constructed for ccRCC. Differential CNV, somatic mutations and pathways were found between subtypes. C2 exhibited poorer prognosis, higher immune/stromal scores, and lower tumor purity than C1. Furthermore, C2 had more sensitivity to immunotherapy and targeted therapy than C1. The levels of CXCL1/2/3/5/6/8 chemokines in C2 were distinctly higher than in C1. Consistently, DEGs between subtypes were significantly enriched in cytokine-cytokine receptor interaction and immune responses. This subtype-specific signature can independently predict patients’ prognosis. Following verification, the nomogram could be utilized for personalized prediction of the survival probability.
Conclusion: Our findings characterized two hypoxia-related molecular subtypes for ccRCC, which can assist in identifying high-risk patients with poor clinical outcomes and patients who can benefit from immunotherapy or targeted therapy.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmolb.2021.684050.s003（source: https://frontiersin.figshare.com/articles/dataset/Table1_Characterization_of_Hypoxia-Related_Molecular_Subtypes_in_Clear_Cell_Renal_Cell_Carcinoma_to_Aid_Immunotherapy_and_Targeted_Therapy_via_Multi-Omics_Analysis_XLSX/14843580）。