GSEA分析
基于TCGA数据构建的6个m7G相关lncRNA的肝细胞癌预后签名，用于预测预后及免疫/化疗反应。
基本信息
资源简介
GSEA分析
【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 14.02 KB，主题涵盖 tide analyses showed、cancer genome atlas、risk group might、independent risk factor、prediction signature based，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《GSEA analysis.》。源站首次发布：2023-08-03。
【数据内容】
源站原始描述：Background N7-methylguanosine (m7G) is one of the most common RNA posttranscriptional modifications; however, its potential role in hepatocellular carcinoma (HCC) remains unknown. We developed a prediction signature based on m7G-related long noncoding RNAs (lncRNAs) to predict HCC prognosis and provide a reference for immunotherapy and chemotherapy. Methods RNA-seq data from The Cancer Genome Atlas (TCGA) database and relevant clinical data were used. Univariate and multivariate Cox regression a…
中文译文（机器翻译，仅供快速理解，请以原文为准）：背景 N7-甲基鸟苷（m7G）是最常见的RNA转录后修饰之一；然而，其在肝细胞癌（HCC）中的潜在作用仍不清楚。我们基于m7G相关长链非
【数据结构与技术规格】
文件数 1 个，合计 14.02 KB。pone.0289552.s004.xlsx：undefined，14.02 KB。数据集 DOI：10.1371/journal.pone.0289552.s004。
【主题与分类】
源站学科分类：Genetics、Biotechnology、Biological Sciences not elsewhere classified、Developmental Biology、Cancer、Infectious Diseases。主题标签：tide analyses showed、cancer genome atlas、risk group might、independent risk factor、prediction signature based、relevant clinical data、related lncrna signature、predict hcc prognosis、chemotherapy treatment response、seq data。
【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://plos.figshare.com/articles/dataset/GSEA_analysis_/23840469
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Peng, Yue-Ling; Dong, Ya-Fang; Guo, Li-Li; Li, Mu-Ye; Liao, Hui; Li, Rong-Shan (2023). GSEA analysis.. PLOS ONE. Dataset
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。
免登录有偿下载
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）
- 源站标题：GSEA analysis.
- DOI：10.1371/journal.pone.0289552.s004
- 发布日期：2023-08-03
- 文件数：1 个
- 文件总体积：14.02 KB
- 源站页面：https://plos.figshare.com/articles/dataset/GSEA_analysis_/23840469
命令行下载
curl -L -o "pone.0289552.s004.xlsx" "https://ndownloader.figshare.com/files/41827395"
文件清单
pone.0289552.s004.xlsx（14.02 KB）
文件总体积 14.02 KB（源站 API 实测）。
数据说明
Background
N7-methylguanosine (m7G) is one of the most common RNA posttranscriptional modifications; however, its potential role in hepatocellular carcinoma (HCC) remains unknown. We developed a prediction signature based on m7G-related long noncoding RNAs (lncRNAs) to predict HCC prognosis and provide a reference for immunotherapy and chemotherapy.
Methods
RNA-seq data from The Cancer Genome Atlas (TCGA) database and relevant clinical data were used. Univariate and multivariate Cox regression analyses were conducted to identify m7G-related lncRNAs with prognostic value to build a predictive signature. We evaluated the prognostic value and clinical relevance of this signature and explored the correlation between the predictive signature and the chemotherapy treatment response of HCC. Moreover, an in vitro study to validate the function of CASC19 was performed.
Results
Six m7G-related lncRNAs were identified to create a signature. This signature was considered an independent risk factor for the prognosis of patients with HCC. TIDE analyses showed that the high-risk group might be more sensitive to immunotherapy. ssGSEA indicated that the predictive signature was strongly related to the immune activities of HCC. HCC in high-risk patients was more sensitive to the common chemotherapy drugs bleomycin, doxorubicin, gemcitabine, and lenalidomide. In vitro knockdown of CASC19 inhibited the proliferation, migration and invasion of HCC cells.
Conclusion
We established a 6 m7G-related lncRNA signature that may assist in predicting the prognosis and response to chemotherapy and immunotherapy of HCC.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0289552.s004（source: https://plos.figshare.com/articles/dataset/GSEA_analysis_/23840469）。
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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