抗PD-1单药治疗黑色素瘤获得的基因面板揭示预后标志物和治疗靶点
包含黑色素瘤抗PD-1治疗中差异基因表达数据，构建多基因面板用于预后和耐药预测，并筛选出五种潜在联合化疗药物。
基本信息
资源简介
抗PD-1单药治疗黑色素瘤获得的基因面板揭示预后标志物和治疗靶点
【数据集背景】
本数据集源站标题为《The gene-panel obtained by anti-PD-1 monotherapy for melanoma reveals prognostic markers and therapeutic targets》。配套论文《The gene-panel obtained by anti-PD-1 monotherapy for melanoma reveals prognostic markers and therapeutic targets》发表于 Journal of Chemotherapy（2025），DOI：10.1080/1120009X.2025.2465013。项目资助方：This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors.。源站首次发布：2026-01-24。
【数据内容】
源站原始描述：To search for key drug resistance biomarkers and potential chemotherapeutic agents for combination therapy in melanoma patients. The common up-regulated differentially expressed genes were acquired from two cohort studies, GSE91061 and PRJEB23709. Univariate and multivariate Cox regression survival analyses were performed to screen for important prognostic biomarkers. A multi-gene panel including APP, ARHGAP42, GSTA4 and UCHL1, not only plays an important role in prognostic indicators, but also…
【数据结构与技术规格】
文件数 2 个，合计 2.61 MB。Supplementary Figures.pdf：application/pdf，2.59 MB。Table S1.docx：application/vnd.openxmlformats-officedocument.wordprocessingml.document，19.71 KB。数据集 DOI：10.6084/m9.figshare.28416041.v2。
【主题与分类】
源站学科分类：Cell Biology、Genetics、Pharmacology、Biotechnology、Ecology、Biological Sciences not elsewhere classified。主题标签：Anti-PD-1、melanoma、prognostic biomarkers、target therapy resistance、molecular docking。
【适用方向】
组学数据适用于生物标志物发现与分子分型相关建模研究。
【获取与许可】
源站页面：https://tandf.figshare.com/articles/dataset/The_gene-panel_obtained_by_anti-PD-1_monotherapy_for_melanoma_reveals_prognostic_markers_and_therapeutic_targets/28416041
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：The gene-panel obtained by anti-PD-1 monotherapy for melanoma reveals prognostic markers and therapeutic targets. Taylor & Francis. Wang, Miaomiao; Zhou, Zhike; Wang, Yingying; Wang, Lixin
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：The gene-panel obtained by anti-PD-1 monotherapy for melanoma reveals prognostic markers and therapeutic targets
- DOI：10.6084/m9.figshare.28416041.v2
- 发布日期：2026-01-24
- 文件数：2 个
- 文件总体积：2.61 MB
- 源站页面：https://tandf.figshare.com/articles/dataset/The_gene-panel_obtained_by_anti-PD-1_monotherapy_for_melanoma_reveals_prognostic_markers_and_therapeutic_targets/28416041
命令行下载
curl -L -o "Supplementary Figures.pdf" "https://ndownloader.figshare.com/files/61309230"
curl -L -o "Table S1.docx" "https://ndownloader.figshare.com/files/61309233"
文件清单
Supplementary Figures.pdf（2.59 MB）Table S1.docx（19.71 KB）
文件总体积 2.61 MB（源站 API 实测）。
数据说明
To search for key drug resistance biomarkers and potential chemotherapeutic agents for combination therapy in melanoma patients. The common up-regulated differentially expressed genes were acquired from two cohort studies, GSE91061 and PRJEB23709. Univariate and multivariate Cox regression survival analyses were performed to screen for important prognostic biomarkers. A multi-gene panel including APP, ARHGAP42, GSTA4 and UCHL1, not only plays an important role in prognostic indicators, but also in predicting the probability of resistance to anti-PD-1 in patients with melanoma. Chemotherapy drug response models found five potential drugs, including all-trans retinoic acid, doxorubicin, etoposide, methotrexate and MG-132, were significantly associated with target genes in gene-panel. Molecular docking confirmed that potential drugs have good binding ability to target genes APP, GSTA4 and UCHL1, suggesting that the multi-gene panel is expected to provide assistance for drug resistance prediction and prognosis assessment and combination therapy in patients with anti-PD-1 resistant melanoma.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.6084/m9.figshare.28416041.v2（source: https://tandf.figshare.com/articles/dataset/The_gene-panel_obtained_by_anti-PD-1_monotherapy_for_melanoma_reveals_prognostic_markers_and_therapeutic_targets/28416041）。
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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