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A transcriptome based molecular classification scheme for cholangiocarcinoma and subtype-derived prognostic biomarker.

Zhongqi FanXinchen ZouGuangyi WangYahui LiuYanfang JiangHaoyan WangPing ZhangFeng WeiXiaohong DuMeng WangXiaodong SunBai JiXintong HuLiguo ChenPeiwen ZhouDuo WangJing BaiXiao XiaoLijiao ZuoXuefeng XiaXin YiGuo-Yue Lv
Published in: Nature communications (2024)
Previous studies on the molecular classification of cholangiocarcinoma (CCA) focused on certain anatomical sites, and disregarded tissue contamination biases in transcriptomic profiles. We aim to provide universal molecular classification scheme and prognostic biomarker of CCAs across anatomical locations. Comprehensive bioinformatics analysis is performed on transcriptomic data from 438 CCA cases across various anatomical locations. After excluding CCA tumors showing normal tissue expression patterns, we identify two universal molecular subtypes across anatomical subtypes, explore the molecular, clinical, and microenvironmental features of each class. Subsequently, a 30-gene classifier and a biomarker (called "CORE-37") are developed to predict the molecular subtype of CCA and prognosis, respectively. Two subtypes display distinct molecular characteristics and survival outcomes. Key findings are validated in external cohorts regardless of the stage and anatomical location. Our study provides a CCA classification scheme that complements the conventional anatomy-based classification and presents a promising prognostic biomarker for clinical application.
Keyphrases
  • machine learning
  • deep learning
  • single molecule
  • gene expression
  • rna seq
  • dna methylation
  • electronic health record
  • artificial intelligence
  • visible light