表1_基于增强子RNA的预测特征与免疫浸润相关并辅助透明细胞肾细胞癌治疗决策.docx
透明细胞肾细胞癌中基于增强子RNA预测特征的补充表格数据，用于预后分层、免疫浸润及治疗决策研究。
基本信息
资源简介
表1_基于增强子RNA的预测特征与免疫浸润相关并辅助透明细胞肾细胞癌治疗决策.docx
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 16.70 KB，主题涵盖 clear cell renal cell carcinoma、drug sensitivity、enhancer RNA、immunotherapy、treatment decision，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Table_1_A predictive signature based on enhancer RNA associates with immune infiltration and aids treatment decision in clear cell renal cell carcinoma.docx》。配套论文《A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients》发表于 Cancers（2022），DOI：10.3390/cancers14092111。源站首次发布：2022-10-12。
【数据内容】
源站原始描述：Clear cell renal cell carcinoma (ccRCC) is a prevalent urinary malignancy. Despite the recent development of better diagnostic tools and therapy, the five-year survival rate for individuals with advanced and metastatic ccRCC remains dismal. Unfortunately, ccRCC is less susceptible to radiation and chemotherapy. Consequently, targeted therapy and immunotherapy play a crucial role in the treatment of ccRCC. Enhancer RNAs (eRNAs) are noncoding RNAs transcribed by enhancers. Extensive research has s…
中文译文（机器翻译，仅供快速理解，请以原文为准）：肾透明细胞癌（ccRCC）是一种常见的泌尿系统恶性肿瘤。尽管近年来诊断工具和治疗手段不断改进，但晚期和转移性ccRCC患者的五年生存率仍然很低。遗憾的是，ccRCC对放疗和化疗不太敏感。因此，靶向治疗和免疫治疗在ccRCC治疗中起着关键作用。增强子RNA（eRNAs）是由增强子转录的非编码RNA。大量研究表明，eRNAs参与多种癌症信号通路。然而，eRNAs在ccRCC中的生物学功能尚未得到系统研究。在本研究中，我们对eRNAs在ccRCC发生和治疗管理中的作用进行了全面探究。选择影响患者预后的eRNAs和靶基因来构建预测特征。根据风险评分中位数，将ccRCC患者分为高风险和低风险亚组。在多个队列中评估了预测效能，并开展多组学分析，以探究高、低风险组之间的差异及其潜在机制。此外，我们还探讨了其在促进临床治疗选择方面的潜力。该风险
【数据结构与技术规格】
文件数 1 个，合计 16.70 KB。Table_1_A predictive signature based on enhancer RNA associates with immune infiltration and aids treatment decision in clear cell renal cell carcinom.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，16.70 KB。数据集 DOI：10.3389/fonc.2022.964838.s002。
【主题与分类】
源站学科分类：Cancer、Haematological Tumours、Molecular Targets、Radiation Therapy、Oncology and Carcinogenesis not elsewhere classified、Solid Tumours。主题标签：clear cell renal cell carcinoma、drug sensitivity、enhancer RNA、immunotherapy、treatment decision。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_A_predictive_signature_based_on_enhancer_RNA_associates_with_immune_infiltration_and_aids_treatment_decision_in_clear_cell_renal_cell_carcinoma_docx/21315351
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Li, Qinyu; Xiao, Xueyan; Chen, Bingliang; Song, Guoda; Zeng, Kai; Li, Beining; et al. (2022). Table_1_A predictive signature based on enhancer RNA associates with immune infiltration and aids treatment decision in clear cell renal cell carcinoma.docx. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-06-13，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
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公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Table_1_A predictive signature based on enhancer RNA associates with immune infiltration and aids treatment decision in clear cell renal cell carcinoma.docx
- DOI：10.3389/fonc.2022.964838.s002
- 发布日期：2022-10-12
- 文件数：1 个
- 文件总体积：16.70 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_A_predictive_signature_based_on_enhancer_RNA_associates_with_immune_infiltration_and_aids_treatment_decision_in_clear_cell_renal_cell_carcinoma_docx/21315351
命令行下载
curl -L -o "Table_1_A predictive signature based on enhancer RNA associates with immune infiltration and aids treatment decision in clear cell renal cell carcinom.docx" "https://ndownloader.figshare.com/files/37830066"
文件清单
Table_1_A predictive signature based on enhancer RNA associates with immune infiltration and aids treatment decision in clear cell renal cell carcinom.docx（16.70 KB）
文件总体积 16.70 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
Clear cell renal cell carcinoma (ccRCC) is a prevalent urinary malignancy. Despite the recent development of better diagnostic tools and therapy, the five-year survival rate for individuals with advanced and metastatic ccRCC remains dismal. Unfortunately, ccRCC is less susceptible to radiation and chemotherapy. Consequently, targeted therapy and immunotherapy play a crucial role in the treatment of ccRCC. Enhancer RNAs (eRNAs) are noncoding RNAs transcribed by enhancers. Extensive research has shown that eRNAs are implicated in a variety of cancer signaling pathways. However, the biological functions of eRNAs have not been systematically investigated in ccRCC. In this study, we conducted a comprehensive investigation of the role of eRNAs in the onset and management of ccRCC. Patient prognosis-influencing eRNAs and target genes were chosen to construct a predictive signature. On the basis of the median riskscore, ccRCC patients were split into high- and low-risk subgroups. The prediction efficiency was assessed in several cohorts, and multi-omics analysis was carried out to investigate the differences and underlying mechanisms between the high- and low-risk groups. In addition, we investigated its potential to facilitate clinical treatment choices. The riskscore might be used to forecast a patient’s response to immunotherapy and targeted therapy, giving a revolutionary method for selecting treatment regimens with pinpoint accuracy.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fonc.2022.964838.s002（source: https://frontiersin.figshare.com/articles/dataset/Table_1_A_predictive_signature_based_on_enhancer_RNA_associates_with_immune_infiltration_and_aids_treatment_decision_in_clear_cell_renal_cell_carcinoma_docx/21315351）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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