食管癌代谢和免疫亚型数据表

食管癌代谢与免疫亚型分析数据集,包含基因表达、通路和免疫数据,用于发现治疗新靶点。

NIAID Data Ecosystem
浏览 29
文本metabolic subtypesimmunological subtypesesophageal cancercancer metabolismbiomarkers
Data_Sheet_1_Metabolic and Immunological Subtypes of Esophageal Cancer Reveal Potential Therapeutic Opportunities.PDF

基本信息

模态
文本
大小
0.004683497 GB
许可
CC BY 4.0
创建/更新时间
2026-10-07
版本
v1

资源简介

食管癌代谢和免疫亚型数据表

【一句话概览】

本数据集是 figshare 公开的文本数据集,收录 1 个文件、合计 4.68 MB,主题涵盖 metabolic subtypes、immunological subtypes、esophageal cancer、cancer metabolism、biomarkers,可用于医学自然语言处理(实体识别、信息抽取等),CC BY 4.0 许可开放获取。

【数据集背景】

本数据集源站标题为《Data_Sheet_1_Metabolic and Immunological Subtypes of Esophageal Cancer Reveal Potential Therapeutic Opportunities.PDF》。配套论文《Metabolic Competition in the Tumor Microenvironment Is a Driver of Cancer Progression》发表于 Cell(2015),DOI:10.1016/j.cell.2015.08.016。源站首次发布:2021-07-08。

【数据内容】

源站原始描述:BackgroundEsophageal cancer has the sixth highest rate of cancer-associated deaths worldwide, with many patients displaying metastases and chemotherapy resistance. We sought to find subtypes to see if precision medicine could play a role in finding new potential targets and predicting responses to therapy. Since metabolism not only drives cancers but also serves as a readout, metabolism was examined as a key reporter for differences. MethodsUnsupervised and supervised classification methods, inc…

【数据结构与技术规格】

文件数 1 个,合计 4.68 MB。Data_Sheet_1_Metabolic and Immunological Subtypes of Esophageal Cancer Reveal Potential Therapeutic Opportunities.PDF:application/pdf,4.68 MB。数据集 DOI:10.3389/fcell.2021.667852.s001。

【主题与分类】

源站学科分类:Cell Biology、Cell Development, Proliferation and Death、Cell Metabolism、Cell Neurochemistry、Cellular Interactions (incl. Adhesion, Matrix, Cell Wall)、Marine Biology。主题标签:metabolic subtypes、immunological subtypes、esophageal cancer、cancer metabolism、biomarkers。

【适用方向】

文本类数据可用于医学自然语言处理,如命名实体识别、信息抽取与文本分类。

【获取与许可】

源站页面:https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Metabolic_and_Immunological_Subtypes_of_Esophageal_Cancer_Reveal_Potential_Therapeutic_Opportunities_PDF/14929110

使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。

规范引用:King, Ryan J.; Qiu, Fang; Yu, Fang; Singh, Pankaj K. (2021). Data_Sheet_1_Metabolic and Immunological Subtypes of Esophageal Cancer Reveal Potential Therapeutic Opportunities.PDF. Frontiers. Dataset

【使用注意】

· 源站最后修改于 2023-06-04,引用时请注意版本时效。

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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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有偿下载

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)

命令行下载

curl -L -o "Data_Sheet_1_Metabolic and Immunological Subtypes of Esophageal Cancer Reveal Potential Therapeutic Opportunities.PDF" "https://ndownloader.figshare.com/files/28743246"

文件清单

  • Data_Sheet_1_Metabolic and Immunological Subtypes of Esophageal Cancer Reveal Potential Therapeutic Opportunities.PDF(4.68 MB)

文件总体积 4.68 MB(源站 API 实测)。

数据说明

BackgroundEsophageal cancer has the sixth highest rate of cancer-associated deaths worldwide, with many patients displaying metastases and chemotherapy resistance. We sought to find subtypes to see if precision medicine could play a role in finding new potential targets and predicting responses to therapy. Since metabolism not only drives cancers but also serves as a readout, metabolism was examined as a key reporter for differences.
MethodsUnsupervised and supervised classification methods, including hierarchical clustering, partial least squares discriminant analysis, k-nearest neighbors, and machine learning techniques, were used to discover and display two major subgroups. Genes, pathways, gene ontologies, survival, and immune differences between the groups were further examined, along with biomarkers between the groups and against normal tissue.
ResultsEsophageal cancer had two major unique metabolic profiles observed between the histological subtypes esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). The metabolic differences suggest that ESCC depends on glycolysis, whereas EAC relies more on oxidative metabolism, catabolism of glycolipids, the tricarboxylic acid (TCA) cycle, and the electron transport chain. We also noted a robust prognostic risk associated with COQ3 expression. In addition to the metabolic alterations, we noted significant alterations in key pathways regulating immunity, including alterations in cytokines and predicted immune infiltration. ESCC appears to have increased signature associated with dendritic cells, Th17, and CD8 T cells, the latter of which correlate with survival in ESCC. We bioinformatically observed that ESCC may be more responsive to checkpoint inhibitor therapy than EAC and postulate targets to enhance therapy further. Lastly, we highlight correlations between differentially expressed enzymes and the potential immune status.
ConclusionOverall, these results highlight the extreme differences observed between the histological subtypes and may lead to novel biomarkers, therapeutic strategies, and differences in therapeutic response for targeting each esophageal cancer subtype.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fcell.2021.667852.s001(source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_1_Metabolic_and_Immunological_Subtypes_of_Esophageal_Cancer_Reveal_Potential_Therapeutic_Opportunities_PDF/14929110)。

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