基于单细胞亚型特异性基因的食管鳞状细胞癌血清蛋白生物标志物筛选、验证与机器学习评估数据集
本数据集包含344例食管鳞状细胞癌患者、46例HGIN和390例正常对照的血清蛋白水平数据，基于单细胞亚型特异性基因筛选生物标志物，并利用机器学习构建诊断模型。
基本信息
资源简介
基于单细胞亚型特异性基因的食管鳞状细胞癌血清蛋白生物标志物筛选、验证与机器学习评估数据集
【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 67.02 KB，主题涵盖 five selected proteins、construct diagnostic models、build diagnostic models、among 12 algorithms、support vector machine，可用于统计建模、特征工程与队列分析，CC BY-NC 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Screening,
Validation, and Machine Learning-Based
Evaluation of Serum Protein Biomarkers for Esophageal Squamous Cell
Carcinoma Based on Single-Cell Subtype-Specific Genes》。配套论文《Screening, Validation, and Machine Learning-Based Evaluation of Serum Protein Biomarkers for Esophageal Squamous Cell Carcinoma Based on Single-Cell Subtype-Specific Genes》发表于 Journal of Proteome Research（2025），DOI：10.1021/acs.jproteome.5c00475。源站首次发布：2025-08-13。
【数据内容】
源站原始描述：Cellular heterogeneity of epithelial cells and fibroblasts is critical in esophageal squamous cell carcinoma development (ESCC). Identifying dysregulated subtype-specific genes in these cells is essential for early diagnosis and treatment. In this study, our pipeline integrated scRNA-seq, proteomics, and ELISA to screen biomarkers: scRNA-seq defined epithelial and fibroblast subtypes and their markers, while proteomics and secretory profiling identified dysregulated secretory proteins. Serum lev…
【数据结构与技术规格】
文件数 1 个，合计 67.02 KB。pr5c00475_si_002.xlsx：application/vnd.openxmlformats-officedocument.spreadsheetml.sheet，67.02 KB。数据集 DOI：10.1021/acs.jproteome.5c00475.s002。
【主题与分类】
源站学科分类：Biochemistry、Medicine、Cell Biology、Genetics、Biotechnology、Biological Sciences not elsewhere classified。主题标签：five selected proteins、construct diagnostic models、build diagnostic models、among 12 algorithms、support vector machine、identifying dysregulated subtype、based diagnostic tool、serum protein biomarkers、interactive web tool、four fibroblast subtypes。
【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://acs.figshare.com/articles/dataset/Screening_Validation_and_Machine_Learning-Based_Evaluation_of_Serum_Protein_Biomarkers_for_Esophageal_Squamous_Cell_Carcinoma_Based_on_Single-Cell_Subtype-Specific_Genes/29899496
使用许可：CC BY-NC 4.0（https://creativecommons.org/licenses/by-nc/4.0/）。
规范引用：Zhang, Xiuzhi; Xiao, Zhefeng; Chen, Fengqi; Sun, Wenke; Li, Tiandong; Ye, Hua; et al. (2025). Screening, Validation, and Machine Learning-Based Evaluation of Serum Protein Biomarkers for Esophageal Squamous Cell Carcinoma Based on Single-Cell Subtype-Specific Genes. ACS Publications. 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：Screening,
Validation, and Machine Learning-Based
Evaluation of Serum Protein Biomarkers for Esophageal Squamous Cell
Carcinoma Based on Single-Cell Subtype-Specific Genes - DOI：10.1021/acs.jproteome.5c00475.s002
- 发布日期：2025-08-13
- 文件数：1 个
- 文件总体积：67.02 KB
- 源站页面：https://acs.figshare.com/articles/dataset/Screening_Validation_and_Machine_Learning-Based_Evaluation_of_Serum_Protein_Biomarkers_for_Esophageal_Squamous_Cell_Carcinoma_Based_on_Single-Cell_Subtype-Specific_Genes/29899496
命令行下载
curl -L -o "pr5c00475_si_002.xlsx" "https://ndownloader.figshare.com/files/57149915"
文件清单
pr5c00475_si_002.xlsx（67.02 KB）
文件总体积 67.02 KB（源站 API 实测）。
数据说明
Cellular
heterogeneity of epithelial cells and fibroblasts
is critical
in esophageal squamous cell carcinoma development (ESCC). Identifying
dysregulated subtype-specific genes in these cells is essential for
early diagnosis and treatment. In this study, our pipeline integrated
scRNA-seq, proteomics, and ELISA to screen biomarkers: scRNA-seq defined
epithelial and fibroblast subtypes and their markers, while proteomics
and secretory profiling identified dysregulated secretory proteins.
Serum levels of five selected proteins were measured in 344 ESCC patients,
46 HGIN cases, and 390 normal controls. Machine learning was employed
to construct diagnostic models. An interactive web tool was implemented
in R Shiny. Six epithelial and four fibroblast subtypes, proportionally
distinct between ESCC and normal tissues, were identified. Four validated
dysregulated proteins were used to build diagnostic models; among
12 algorithms, the Support Vector Machine (SVM) achieved the best
performance with AUCs of 0.829 and 0.767 in the training and validation
sets, respectively (p > 0.05). The model effectively
distinguished early- and late-stage ESCC and HGIN from normal controls.
The web-based diagnostic tool is publicly available at https://zhangxz.shinyapps.io/P4_Pred/. The identified serum biomarkers may enhance early ESCC detection
and diagnosis. Our pipeline, leveraging heterogeneity-related genes
in fibroblasts and epithelial cells, is readily adaptable to other
tumors.
许可
CC BY-NC 4.0
引用
学术使用请引用 DOI 10.1021/acs.jproteome.5c00475.s002（source: https://acs.figshare.com/articles/dataset/Screening_Validation_and_Machine_Learning-Based_Evaluation_of_Serum_Protein_Biomarkers_for_Esophageal_Squamous_Cell_Carcinoma_Based_on_Single-Cell_Subtype-Specific_Genes/29899496）。
数据缺失？
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