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.
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Data availability
The datasets used during the current study are available from the corresponding author on reasonable request.
Abbreviations
- BCR:
-
B-cell receptor
- CGP:
-
Cancer genome project
- CMS:
-
Consensus molecular subtype
- DEGs:
-
Differentially expressed genes
- GSVA:
-
Gene set variation analysis
- LDHA:
-
Lactate depletion hydrogenase A1
- TCR:
-
T-cell receptor
- TMB:
-
Tumor mutational burden
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This work was sponsored by the National Natural Science Foundation of China (82002507) and Shanghai Sailing Program (20YF1430100).
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DZ and PX completed the construction of the model and the writing of the article. GY and SX completed the visualization of model features. DW, FJ, and LZ completed critical review and guidance of the manuscript. SJ completed model guidance, critical review, and funding support. All authors read and approved the final manuscript.
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Dai, Z., Peng, X., Guo, Y. et al. Metabolic pathway-based molecular subtyping of colon cancer reveals clinical immunotherapy potential and prognosis. J Cancer Res Clin Oncol 149, 2393–2416 (2023). https://doi.org/10.1007/s00432-022-04070-6
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DOI: https://doi.org/10.1007/s00432-022-04070-6