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統合的なトランスクリプトミクスと機械学習の枠組みは,乳がんにおけるアフラトキシンB1曝露の候補遺伝子と潜在的なメカニズムを明らかにしています
Wenjing Wang1, Mengting Liu1, Xiang Li2
1Department of Breast Disease Center, General Surgery, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, China.
Scientific reports
|February 13, 2026
まとめ
アフラトキシンB1 (AFB1) 曝露は乳がんと関連しています. この研究では,マルチオミクスと機械学習を使用して,乳がんリスクを予測し,AFB1を理解するための7つの主要なバイオマーカー (EGFR,MIF,MET,PPARG,MME,NQO2,NR3C2) を特定しました.
科学分野:
- 環境性がん発生は,環境性がん発生である.
- ゲノミクスとコンピューティング生物学
- がん生物学 がん生物学
背景:
- アフラトキシンB1 (AFB1) は,乳がんと関連している環境性菌毒素です.
- 乳がんにおけるAFB1の腫瘍性可能性の基礎となる正確な生物学的メカニズムは,完全に理解されていません.
研究 の 目的:
- AFB1が乳がんの生物学に影響を与える分子経路を包括的に調査する.
- マルチオミックと機械学習を使用してAFB1関連乳がんの予測バイオマーカーを特定する.
主な方法:
- トランスクリプトミクス,WGCNA,免疫プロファイリング,空間/単細胞トランスクリプトミクスを含む,統合されたマルチオミクス分析.
- バイオマーカー発見と分類者開発のための機械学習パイプライン.
- バイオマーカーの解釈可能性のためのSHAP分析.
主要な成果:
- 7つの主要なバイオマーカー (EGFR,MIF,MET,PPARG,MME,NQO2,NR3C2) のパネルを特定し,高い差別的精度 (AUC=0.996) を示しました.
- バイオマーカーの異なる役割が発見され,PPARGは潜在的に保護的であり,MIFはリスクを促進する.
- 細胞集団と空間領域における表現の異質性を明らかにした.
結論:
- 統合された分析フレームワークは,乳がんの発症におけるAFB1の役割に関する新しい洞察を提供します.
- 特定された遺伝子セットは,AFB1誘発の乳がんのメカニズム的メディエーターおよび診断マーカーとして機能する可能性があります.
- 環境発がん研究におけるマルチオミクスと機械学習の有用性を強調する.
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