Role of environmental pollutants in breast cancer: Emphasis on Methyl-4-hydroxybenzoate and Triple-negative breast cancer

Research Hypothesis
We hypothesized that Methyl-4-hydroxybenzoate (MEP), a common preservative and emerging environmental contaminant, contributes to triple-negative breast cancer (TNBC) pathogenesis through specific molecular interactions. A network toxicology framework was employed to systematically investigate MEP exposure and TNBC risk.

Data Contents
This dataset contains supplementary tables supporting the main findings:

Table S1: 561 potential MEP targets from ChEMBL, STITCH-5.0, and SwissTargetPrediction, standardized to gene symbols via UniProt.
Table S2: 2,447 TNBC-associated targets from GeneCards, OMIM, and TTD.
Tables S3–S5: Differentially expressed genes (DEGs) from GSE76250 (GEO) and TCGA datasets, including 319 and 1,442 DEGs respectively, and 192 intersecting TNBC-related DEGs.
Tables S6–S8: WGCNA results identifying TNBC-associated modules (blue from GEO, yellow from TCGA) and 60 key TNBC-related DEGs.
Key Findings
Integrative analysis identified three hub genes (EZH2, NEK2, TYMS) significantly enriched in cancer-related pathways with high diagnostic and prognostic value. These data form the foundation for conclusions that MEP may influence TNBC through these targets.

Data Generation & Usage
MEP targets were predicted using ChEMBL, STITCH, and SwissTargetPrediction. TNBC targets were retrieved from GeneCards, OMIM, and TTD. Transcriptomic data from GEO and TCGA underwent differential expression analysis (limma/DESeq2) with thresholds of adjusted P < 0.05 and |log₂FC| > 1 (GEO) or > 2 (TCGA). WGCNA was performed to identify co-expression modules. Researchers can use these files for replication, extended analyses, pathway enrichment, or meta-analyses. Data are provided in tab-delimited format compatible with standard bioinformatics tools.

研究假说
我们提出假说：对羟基苯甲酸甲酯（Methyl-4-hydroxybenzoate, MEP）作为一种常见防腐剂与新兴环境污染物，可通过特定分子互作参与三阴性乳腺癌（triple-negative breast cancer, TNBC）的发病进程。本研究采用网络毒理学框架，系统探究MEP暴露与三阴性乳腺癌发病风险之间的关联。

数据集内容
本数据集包含支撑核心研究发现的补充表格：
表S1：从ChEMBL、STITCH-5.0及SwissTargetPrediction数据库中筛选得到的561个MEP潜在靶点，经UniProt标准化为基因符号。
表S2：从GeneCards、OMIM及TTD数据库中获取的2447个三阴性乳腺癌相关靶点。
表S3~S5：来自GEO数据库GSE76250数据集与TCGA数据集的差异表达基因（differentially expressed genes, DEGs），其中GSE76250数据集含319个差异表达基因，TCGA数据集含1442个差异表达基因，二者交集得到192个三阴性乳腺癌相关差异表达基因。
表S6~S8：加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）结果，鉴定得到三阴性乳腺癌相关共表达模块（GSE76250数据集对应蓝色模块，TCGA数据集对应黄色模块），并筛选出60个关键三阴性乳腺癌相关差异表达基因。

核心发现
整合分析共鉴定出3个核心靶点基因（EZH2、NEK2、TYMS），这些基因显著富集于癌症相关通路，且具备较高的诊断与预后价值。上述数据为“MEP可通过上述靶点影响三阴性乳腺癌发生发展”的结论提供了支撑。

数据生成与使用说明
MEP潜在靶点通过ChEMBL、STITCH及SwissTargetPrediction数据库预测得到；三阴性乳腺癌相关靶点从GeneCards、OMIM及TTD数据库检索获取。GEO与TCGA的转录组数据采用limma/DESeq2工具进行差异表达分析，筛选阈值为校正后P值<0.05，且|log₂FC|>1（GSE76250数据集）或|log₂FC|>2（TCGA数据集）。通过加权基因共表达网络分析（WGCNA）鉴定共表达模块。
研究人员可使用本数据集文件开展实验重复、拓展分析、通路富集或荟萃分析。数据以制表符分隔格式存储，可兼容主流生物信息学工具。