Unsupervised machine learning-based clustering identifies unique molecular signatures of colorectal cancer with distinct clinical outcomes

作     者：Manish Pratap Singh Sandhya Rai Sarvesh K.Gupta Nand K.Singh Sameer Srivastava Manish Pratap Singh;Sandhya Rai;Sarvesh K.Gupta;Nand K.Singh;Sameer Srivastava

作者机构：Motilal Nehru National Institute of Technology AllahabadPrayagraj 211004India Deen Dayal Upadhyay Gorakhpur UniversityGorakhpur 273001India 

出 版 物：《Genes & Diseases》 (基因与疾病（英文）)

年 卷 期：2023年第10卷第6期

页      面：2270-2273页

核心收录：

学科分类：0710[理学-生物学] 1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：funded by the Department of Biotechnology India section order 6242-P103/RGCB/PMD/DBT/SMSV/2015. 

主　　题：cancer colorectal signature 

摘      要：Colorectal cancer(CRC)is known to harbor considerable heterogeneity.1 Consequently,it could be hypothesized that similar-appearing tumors might exhibit substantial genetic differences while diverse-appearing tumors may have a similar genetic landscape.2 Due to these differences at the molecular level,they behave or respond differently to therapies as well.CRC progression is a multistep process and involves the accumulation of substantial genetic and epigenetic events in a stage-dependent manner.