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Classification of colorectal cancer into clinically relevant subtypes based on genes and mesenchymal cells

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Abstract

Background

Most studies on subtype identification of colorectal cancer (CRC) were based on expressions of either genes or immune cells. However, few studies have hitherto used the combination of genes with immune and stroma cells for subtype identification.

Methods

Dataset GSE17536 was obtained from the Gene Expression Omnibus (GEO) database. The xCell algorithm was used to estimate the composition and density of 64 cell types, including immune and stroma cell types. Clustering analysis was then conducted on the top 3000 most variable genes from a total of 20,174 genes for CRC subtype identification. We employed the ensemble method of Similarity network fusion and 112 Consensus Clustering (SNF-CC) for cancer subtype identification. Reactome pathway analysis was conducted to identify the impact of the representative genes on prognosis. The results were validated in independent gene expression data from dataset GSE17537.

Results

In this study, we identified 3 clinically relevant subtypes and their representative genes, immune and stroma cells. Moreover, we confirmed the correlation of these subtypes with their clinical characteristics. The representative genes of the subtype with poor prognosis correlated with extracellular matrix structural constituent, while the subtype with good prognosis correlated with Toll-like receptor signaling pathway or chemokine signaling pathway. However, different subtypes were associated with distinct cell subtypes; the subtype with poor prognosis had a high abundance of fibroblasts and endothelial cells; the subtype with median prognosis had a higher abundance of immune cells, such as CD4 + T-cell, Th2 cells and aDC; the subtype with good prognosis had a higher abundance of NKT.

Conclusion

This study highlights the utility of immune and innate cells, especially during gene analysis, to provide the theoretical basis for personalized treatment in colorectal cancer patients.

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References

  1. Xi Y, Xu P. Global colorectal cancer burden in 2020 and projections to 2040. Transl Oncol. 2021;14(10): 101174.

    Article  Google Scholar 

  2. Rawla P, Sunkara T, Barsouk A. Epidemiology of colorectal cancer: incidence, mortality, survival, and risk factors. Prz Gastroenterol. 2019;14(2):89–103.

    CAS  Google Scholar 

  3. Hammond WA, Swaika A, Mody K. Pharmacologic resistance in colorectal cancer: a review. Ther Adv Med Oncol. 2016;8(1):57–84.

    Article  CAS  Google Scholar 

  4. Messersmith WA. NCCN guidelines updates: management of metastatic colorectal cancer. J Natl Compr Canc Netw. 2019;17:599–601.

    Google Scholar 

  5. Hull MA, Rees CJ, Sharp L, Koo S. A risk-stratified approach to colorectal cancer prevention and diagnosis. Nat Rev Gastroenterol Hepatol. 2020;17(12):773–80.

    Article  Google Scholar 

  6. Kamps R, Brandão RD, Bosch BJ, Paulussen AD, Xanthoulea S, Blok MJ, et al. Next-generation sequencing in oncology: genetic diagnosis, risk prediction and cancer classification. Int J Mol Sci. 2017;18:308.

    Article  Google Scholar 

  7. Alwers E, Bläker H, Walter V, Jansen L, Kloor M, Arnold A, et al. External validation of molecular subtype classifications of colorectal cancer based on microsatellite instability, CIMP, BRAF and KRAS. BMC Cancer. 2019;19(1):681.

    Article  Google Scholar 

  8. Karpinski P, Rossowska J, Sasiadek MM. Immunological landscape of consensus clusters in colorectal cancer. Oncotarget. 2017;8(62):105299–311.

    Article  Google Scholar 

  9. Becht E, de Reyniès A, Giraldo NA, Pilati C, Buttard B, Lacroix L, et al. Immune and stromal classification of colorectal cancer is associated with molecular subtypes and relevant for precision immunotherapy. Clin Cancer Res. 2016;22(16):4057–66.

    Article  CAS  Google Scholar 

  10. Li R, Gong J, Xiao C, Zhu S, Hu Z, Liang J, et al. A comprehensive analysis of the MAGE family as prognostic and diagnostic markers for hepatocellular carcinoma. Genomics. 2020;112(6):5101–14.

    Article  CAS  Google Scholar 

  11. Xiao C, Gong J, Jie Y, Cao J, Chen Z, Li R, et al. NCAPG Is a promising therapeutic target across different tumor types. Front Pharmacol. 2020;11:387.

    Article  CAS  Google Scholar 

  12. Xu T, Le TD, Liu L, Wang R, Sun B, Li J. Identifying cancer subtypes from miRNA-TF-mRNA regulatory networks and expression data. PLoS ONE. 2016;11(4): e0152792.

    Article  Google Scholar 

  13. Zhao L, Pan Y. SSCS: a stage supervised subtyping system for colorectal cancer. Biomedicines. 2021;9:1815.

    Article  CAS  Google Scholar 

  14. Gang W, Wang JJ, Guan R, Yan S, Shi F, Zhang JY, et al. Strategy to targeting the immune resistance and novel therapy in colorectal cancer. Cancer Med. 2018;7(5):1578–603.

    Article  Google Scholar 

  15. Koi M, Carethers JM. The colorectal cancer immune microenvironment and approach to immunotherapies. Future Oncol. 2017;13(18):1633–47.

    Article  CAS  Google Scholar 

  16. Cao J, Gong J, Li X, Hu Z, Xu Y, Shi H, et al. Unsupervised hierarchical clustering identifies immune gene subtypes in gastric cancer. Front Pharmacol. 2021;12: 692454.

    Article  CAS  Google Scholar 

  17. Pang X, Cao J, Chen S, Gao Z, Liu G, Chong Y, et al. Unsupervised clustering reveals distinct subtypes of biliary atresia based on immune cell types and gene expression. Front Immunol. 2021;12: 720841.

    Article  CAS  Google Scholar 

  18. Mokhtari Y, Pourbagheri-Sigaroodi A, Zafari P, Bagheri N, Ghaffari SH, Bashash D. Toll-like receptors (TLRs): An old family of immune receptors with a new face in cancer pathogenesis. J Cell Mol Med. 2021;25(2):639–51.

    Article  CAS  Google Scholar 

  19. Wang Y, Su L, Morin MD, Jones BT, Mifune Y, Shi H, et al. Adjuvant effect of the novel TLR1/TLR2 agonist Diprovocim synergizes with anti-PD-L1 to eliminate melanoma in mice. Proc Natl Acad Sci U S A. 2018;115(37):E8698-e8706.

    Article  CAS  Google Scholar 

  20. Feng Z, Duan Z, Shi G, Wang Q, Zhou J, Chen Y. Pharmacological inhibition of IRAK1 attenuates colitis-induced tumorigenesis in mice by inhibiting the inflammatory response and epithelial-mesenchymal transition. J Biochem Mol Toxicol. 2021;35(9): e22838.

    Article  CAS  Google Scholar 

  21. Li Q, Chen Y, Zhang D, Grossman J, Li L, Khurana N, et al. IRAK4 mediates colitis-induced tumorigenesis and chemoresistance in colorectal cancer. JCI Insight. 2019. https://doi.org/10.1172/jci.insight.130867.

    Article  Google Scholar 

  22. Zhong F, Lin Y, Jing X, Ye Y, Wang S, Shen Z. Innate tumor killers in colorectal cancer. Cancer Lett. 2022;527:115–26.

    Article  CAS  Google Scholar 

  23. Yue Q, Zhang Y, Wang F, Cao F, Duan X, Bai J. Classification of colorectal carcinoma subtypes based on ferroptosis-associated molecular markers. World J Surg Oncol. 2022;20(1):117.

    Article  Google Scholar 

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Acknowledgements

We thank Dr. Yihan Zhang and Christ-Jonathan Tsia Hin Fong for their contribution to language polishing.

Funding

This work is funded by the National Natural Science Foundation of China (81974296, 81772127), the Science and Technology Program of Guangzhou, China (201903010039), the International cooperation project of Guangdong science and technology plan (2020A0505100034), and the Natural Science Foundation of Guangdong Province, China (2017A030313492; 2018A030313608).

Author information

Author notes

  1. Cuicui Xiao, Xuegang Zhao, Xiaojie Li, and Shuguang Zhu have contributed equally to this work.

Authors and Affiliations

  1. Department of Laboratory Medicine, The Third Affiliated Hospital of Sun Yat-Sen University, Tianhe Road 600#, Guangzhou, 510630, People’s Republic of China

    Xiaojie Li, Hongping Chen, Jiao Gong & Bo Hu

  2. Department of Anesthesiology, Key Laboratory of Liver Disease of Guangdong Province, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, People’s Republic of China

    Cuicui Xiao, Fei Huang & Ziqing Hei

  3. Department of Hepatic Surgery and Liver Transplantation Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China

    Shuguang Zhu

  4. Department of Infectious Diseases, Key Laboratory of Liver Disease of Guangdong Province, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China

    Jing Cao, Danyang Li & Liuping Sha

  5. Department of Surgical Intensive Care, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, People’s Republic of China

    Xuegang Zhao

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  1. Cuicui Xiao
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Contributions

BH, JG, and QZH: designed the study and take responsibility for the integrity of the data and the accuracy of the data analysis. CCX, XJL, GSZ, JC, FH, and HPC: collected and analyzed the data and wrote the paper. JG, DLY, and LPS: contributed to the statistical analysis. XGZ, XJL, and CCX: contributed to revision of the manuscript. All authors contributed to data interpretation and reviewed and approved the final version.

Corresponding authors

Correspondence to Ziqing Hei, Jiao Gong or Bo Hu.

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The authors have no conflicts of interest to declare.

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We acknowledge GEO database for providing their platforms and contributors for uploading their meaningful datasets.

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Xiao, C., Zhao, X., Li, X. et al. Classification of colorectal cancer into clinically relevant subtypes based on genes and mesenchymal cells. Clin Transl Oncol 25, 491–502 (2023). https://doi.org/10.1007/s12094-022-02964-y

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