子宫体子宫内膜癌炎症相关LncRNA预后与免疫特征表
子宫体子宫内膜癌炎症相关LncRNA预后与免疫反应评估的表格数据集,包含基因表达、免疫浸润及药敏数据。

基本信息
资源简介
子宫体子宫内膜癌炎症相关LncRNA预后与免疫特征表
【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 19.43 KB,主题涵盖 UCEC、inflammation、tumor microenvironment、prognostic signature、immunotherapy,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_4_Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma.docx》。配套论文《Long noncoding RNA NEAT1 drives aggressive endometrial cancer progression via miR-361-regulated networks involving STAT3 and tumor microenvironment-related genes》发表于 Journal of Experimental & Clinical Cancer Research(2019),DOI:10.1186/s13046-019-1306-9。源站首次发布:2022-06-02。
【数据内容】
源站原始描述:BackgroundsUterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy. MethodsConsensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prog…
【数据结构与技术规格】
文件数 1 个,合计 19.43 KB。Table_4_Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma.docx:application/vnd.openxmlformats-officedocument.wordprocessingml.document,19.43 KB。数据集 DOI:10.3389/fonc.2022.923641.s010。
【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:UCEC、inflammation、tumor microenvironment、prognostic signature、immunotherapy、TCGA。
【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_4_Inflammation-Related_LncRNAs_Signature_for_Prognosis_and_Immune_Response_Evaluation_in_Uterine_Corpus_Endometrial_Carcinoma_docx/19956563
使用许可:CC BY 4.0(https://creativecommons.org/licenses/by/4.0/)。
规范引用:Gu, Hongmei; Song, Jiahang; Chen, Yizhang; Wang, Yichun; Tan, Xiaofang; Zhao, Hongyu (2022). Table_4_Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma.docx. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-06-03,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。
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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。
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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_4_Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma.docx
- DOI:10.3389/fonc.2022.923641.s010
- 发布日期:2022-06-02
- 文件数:1 个
- 文件总体积:19.43 KB
- 源站页面:https://frontiersin.figshare.com/articles/dataset/Table_4_Inflammation-Related_LncRNAs_Signature_for_Prognosis_and_Immune_Response_Evaluation_in_Uterine_Corpus_Endometrial_Carcinoma_docx/19956563
命令行下载
curl -L -o "Table_4_Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma.docx" "https://ndownloader.figshare.com/files/35512520"
文件清单
Table_4_Inflammation-Related LncRNAs Signature for Prognosis and Immune Response Evaluation in Uterine Corpus Endometrial Carcinoma.docx(19.43 KB)
文件总体积 19.43 KB(源站 API 实测)。
数据说明
BackgroundsUterine corpus endometrial carcinoma (UCEC) is one of the greatest threats on the female reproductive system. The aim of this study is to explore the inflammation-related LncRNA (IRLs) signature predicting the clinical outcomes and response of UCEC patients to immunotherapy and chemotherapy.
MethodsConsensus clustering analysis was employed to determine inflammation-related subtype. Cox regression methods were used to unearth potential prognostic IRLs and set up a risk model. The prognostic value of the prognostic model was calculated by the Kaplan-Meier method, receiver operating characteristic (ROC) curves, and univariate and multivariate analyses. Differential abundance of immune cell infiltration, expression levels of immunomodulators, the status of tumor mutation burden (TMB), the response to immune checkpoint inhibitors (ICIs), drug sensitivity, and functional enrichment in different risk groups were also explored. Finally, we used quantitative real-time PCR (qRT-PCR) to confirm the expression patterns of model IRLs in clinical specimens.
ResultsAll UCEC cases were divided into two clusters (C1 = 454) and (C2 = 57) which had significant differences in prognosis and immune status. Five hub IRLs were selected to develop an IRL prognostic signature (IRLPS) which had value in forecasting the clinical outcome of UCEC patients. Biological processes related to tumor and immune response were screened. Function enrichment algorithm showed tumor signaling pathways (ERBB signaling, TGF-β signaling, and Wnt signaling) were remarkably activated in high-risk group scores. In addition, the high-risk group had a higher infiltration level of M2 macrophages and lower TMB value, suggesting patients with high risk were prone to a immunosuppressive status. Furthermore, we determined several potential molecular drugs for UCEC.
ConclusionWe successfully identified a novel molecular subtype and inflammation-related prognostic model for UCEC. Our constructed risk signature can be employed to assess the survival of UCEC patients and offer a valuable reference for clinical treatment regimens.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fonc.2022.923641.s010(source: https://frontiersin.figshare.com/articles/dataset/Table_4_Inflammation-Related_LncRNAs_Signature_for_Prognosis_and_Immune_Response_Evaluation_in_Uterine_Corpus_Endometrial_Carcinoma_docx/19956563)。
数据缺失?
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