Zhujiang Dai1,2, Xiang Peng1,2, Yuegui Guo1,2, Xia Shen1,2, Wenjun Ding1,2, Jihong Fu1,2, Zhonglin Liang3,4, Jinglue Song5,6. 1. Department of Colorectal Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China. 2. Shanghai Colorectal Cancer Research Center, Shanghai, 200092, China. 3. Department of Colorectal Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China. liangzhonglin@xinhuamed.com.cn. 4. Shanghai Colorectal Cancer Research Center, Shanghai, 200092, China. liangzhonglin@xinhuamed.com.cn. 5. Department of Colorectal Surgery, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, 200092, China. jl_song@outlook.com. 6. Shanghai Colorectal Cancer Research Center, Shanghai, 200092, China. jl_song@outlook.com.
Abstract
PURPOSE: Colon cancer presents challenges to clinical diagnosis and management due to its high heterogeneity. For more efficient and convenient diagnosis and treatment of colon cancer, we are committed to characterizing the molecular features of colon cancer by pioneering a classification system based on metabolic pathways. METHODS: Based on the 113 metabolic pathways and genes collected in the previous stage, we scored and filtered the metabolic pathways of each sample in the training set by ssGSEA, and obtained 16 metabolic pathways related to colon cancer recurrence. In consistent clustering of training set samples with recurrence-related metabolic pathway scores, we identified two robust molecular subtypes of colon cancer (MC1 and MC2). Furthermore, we performed multi-angle analysis on the survival differences of subtypes, metabolic characteristics, clinical characteristics, functional enrichment, immune infiltration, differences with other subtypes, stemness indices, TIDE prediction, and drug sensitivity, and finally constructed colon cancer prognostic model. RESULTS: The results showed that the MC1 subtype had a poor prognosis based on higher immune activity and immune checkpoint gene expression. The MC2 subtype is associated with high metabolic activity and low expression of immune checkpoint genes and a better prognosis. The MC2 subtype was more responsive to PD-L1 immunotherapy than the MC1 subclass. However, we did not observe significant differences in tumor mutational burden between the two. CONCLUSION: Two molecular subtypes of colon cancer based on metabolic pathways have distinct immune signatures. Constructing prognostic models based on subtype differential genes provides valuable reference for personalized therapy targeting unique tumor metabolic signatures.
PURPOSE: Colon cancer presents challenges to clinical diagnosis and management due to its high heterogeneity. For more efficient and convenient diagnosis and treatment of colon cancer, we are committed to characterizing the molecular features of colon cancer by pioneering a classification system based on metabolic pathways. METHODS: Based on the 113 metabolic pathways and genes collected in the previous stage, we scored and filtered the metabolic pathways of each sample in the training set by ssGSEA, and obtained 16 metabolic pathways related to colon cancer recurrence. In consistent clustering of training set samples with recurrence-related metabolic pathway scores, we identified two robust molecular subtypes of colon cancer (MC1 and MC2). Furthermore, we performed multi-angle analysis on the survival differences of subtypes, metabolic characteristics, clinical characteristics, functional enrichment, immune infiltration, differences with other subtypes, stemness indices, TIDE prediction, and drug sensitivity, and finally constructed colon cancer prognostic model. RESULTS: The results showed that the MC1 subtype had a poor prognosis based on higher immune activity and immune checkpoint gene expression. The MC2 subtype is associated with high metabolic activity and low expression of immune checkpoint genes and a better prognosis. The MC2 subtype was more responsive to PD-L1 immunotherapy than the MC1 subclass. However, we did not observe significant differences in tumor mutational burden between the two. CONCLUSION: Two molecular subtypes of colon cancer based on metabolic pathways have distinct immune signatures. Constructing prognostic models based on subtype differential genes provides valuable reference for personalized therapy targeting unique tumor metabolic signatures.
Authors: K Chang; J A Willis; J Reumers; M W Taggart; F A San Lucas; S Thirumurthi; P Kanth; D A Delker; C H Hagedorn; P M Lynch; L M Ellis; E T Hawk; P A Scheet; S Kopetz; J Arts; J Guinney; R Dienstmann; E Vilar Journal: Ann Oncol Date: 2018-10-01 Impact factor: 32.976
Authors: Nehal M El-Deeb; Omar M Ibrahim; Mahmoud A Mohamed; Mohamed M S Farag; Ayman A Farrag; M R El-Aassar Journal: Int J Biol Macromol Date: 2022-02-17 Impact factor: 6.953
Authors: Michele Ceccarelli; Floris P Barthel; Tathiane M Malta; Thais S Sabedot; Sofie R Salama; Bradley A Murray; Olena Morozova; Yulia Newton; Amie Radenbaugh; Stefano M Pagnotta; Samreen Anjum; Jiguang Wang; Ganiraju Manyam; Pietro Zoppoli; Shiyun Ling; Arjun A Rao; Mia Grifford; Andrew D Cherniack; Hailei Zhang; Laila Poisson; Carlos Gilberto Carlotti; Daniela Pretti da Cunha Tirapelli; Arvind Rao; Tom Mikkelsen; Ching C Lau; W K Alfred Yung; Raul Rabadan; Jason Huse; Daniel J Brat; Norman L Lehman; Jill S Barnholtz-Sloan; Siyuan Zheng; Kenneth Hess; Ganesh Rao; Matthew Meyerson; Rameen Beroukhim; Lee Cooper; Rehan Akbani; Margaret Wrensch; David Haussler; Kenneth D Aldape; Peter W Laird; David H Gutmann; Houtan Noushmehr; Antonio Iavarone; Roel G W Verhaak Journal: Cell Date: 2016-01-28 Impact factor: 41.582