表8：透明细胞肾细胞癌肿瘤微环境综合基因组特征及相关签名

数据集获取（Figshare）

• 源站标题：Table_8_Comprehensive Genomic Characterization of Tumor Microenvironment and Relevant Signature in Clear Cell Renal Cell Carcinoma.xlsx
• DOI：10.3389/fonc.2022.749119.s016
• 发布日期：2022-05-16
• 文件数：1 个
• 文件总体积：17.89 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_8_Comprehensive_Genomic_Characterization_of_Tumor_Microenvironment_and_Relevant_Signature_in_Clear_Cell_Renal_Cell_Carcinoma_xlsx/19770304

命令行下载

curl -L -o "Table_8_Comprehensive Genomic Characterization of Tumor Microenvironment and Relevant Signature in Clear Cell Renal Cell Carcinoma.xlsx" "https://ndownloader.figshare.com/files/35126272"

文件清单

• Table_8_Comprehensive Genomic Characterization of Tumor Microenvironment and Relevant Signature in Clear Cell Renal Cell Carcinoma.xlsx（17.89 KB）

文件总体积 17.89 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

PurposeTo systematically investigate the characterization of tumor microenvironment (TME) in clear cell renal cell carcinoma (ccRCC), we performed a comprehensive analysis incorporating genomic alterations, cellular interactions, infiltrating immune cells, and risk signature.
Patients and MethodsMulti-omics data including RNA-seq, single-nucleotide variant (SNV) data, copy number variation (CNV) data, miRNA, and corresponding prognostic data were obtained from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) database. The CIBERSORT algorithm was utilized to identify prognostic TME subclusters, and TMEscore was further quantified. Moreover, the mutational landscape of TCGA-KIRC was explored. Lastly, TIDE resource was applied to assess the significance of TMEscore in predicting immunotherapeutic benefits.
ResultsWe analyzed the TME infiltration patterns from 621 ccRCC patients and identified 5 specific TME subclusters associated with clinical outcomes. Then, we found that TMEcluster5 was significantly related to favorable prognosis and enriched memory B-cell infiltration. Accordingly, we depicted the clustering landscape of TMEclusters, TMEscore levels, tumor mutation burden (TMB), tumor grades, purity, and ploidy in all patients. Lastly, TIDE was used to assess the efficiency of immune checkpoint blockers (ICBs) and found that the TMEscore has superior predictive significance to TMB, making it an essential independent prognostic biomarker and drug indicator for clinical use.
ConclusionsOur study depicted the clustering landscape of TMEclusters, TMEscore levels, TMB, tumor grades, purity, and ploidy in total ccRCC patients. The TMEscore was proved to have promising significance for predicting prognosis and ICB responses, in accordance with the goal of developing rationally individualized therapeutic interventions.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fonc.2022.749119.s016（source: https://frontiersin.figshare.com/articles/dataset/Table_8_Comprehensive_Genomic_Characterization_of_Tumor_Microenvironment_and_Relevant_Signature_in_Clear_Cell_Renal_Cell_Carcinoma_xlsx/19770304）。