Biomarkers of coordinate metabolic reprogramming and the construction of a co-expression network in colorectal cancer

Lihong Lin; Xiuxiu Zeng; Shanyan Liang; Yunzhi Wang; Xiaoyu Dai; Yuechao Sun; Zhou Wu

doi:10.21037/atm-22-4767

Biomarkers of coordinate metabolic reprogramming and the construction of a co-expression network in colorectal cancer

Ann Transl Med. 2022 Oct;10(20):1115. doi: 10.21037/atm-22-4767.

Authors

Lihong Lin^#¹, Xiuxiu Zeng^#¹, Shanyan Liang¹, Yunzhi Wang², Xiaoyu Dai¹, Yuechao Sun^{1

3}, Zhou Wu¹

Affiliations

¹ Department of Anorectal Surgery, Hwa Mei Hospital, University of Chinese Academy of Sciences, Ningbo, China.
² School of Health Sciences, University of Sydney, Sydney, Australia.
³ Ningbo Institute of Life and Health Industry, University of Chinese Academy of Sciences, Ningbo, China.

^# Contributed equally.

Abstract

Background: Globally, the incidence and mortality of colorectal cancer (CRC) rank amongst the highest of all malignancies. Thus, research aimed at developing new screening strategies and biomarkers for the early detection of CRC is needed. At present, conventional screening methods have limitations; therefore, new testing strategies have been considered. Using metabolomics to explore the molecular changes in CRC tissue is a mainstream method for identifying potential biomarkers and key cancer factors.

Methods: In the present study, 27 samples from nine CRC patients were used to analyze the metabolite differences between the tumor, paracancerous, and normal tissues. The metabolite differences in the various stages of CRC (stages IIA, IIB, and IIIC) were analyzed as well. Subsequently, principal component analysis (PCA), permutation, and trend analyses were performed. Weighted gene co-expression and metabolite-metabolite interaction networks were also constructed.

Results: A total of 5,834 metabolites were identified among the included samples. Permutation analysis showed a clear separation between the different tissues and different stages. Compared with normal tissues, tumor tissues exhibited 11, 233, and 25 up-regulated metabolites as well as one, 77, and zero down-regulated metabolites in stages IIA, IIB, and IIIC, respectively. Moreover, tumor tissues in stage IIB exhibited more differential metabolites (233 up-regulated and 77 down-regulated). Weighted Gene Correlation Network Analysis (WGCNA) clustered the 5,834 metabolites into 15 different modules, of which four modules were significantly correlated with tissue specificity. Notably, glycerophospholipid metabolism, fatty acid metabolism, and other pathways were enriched in these modules.

Conclusions: Fatty acids and glycerophospholipids were significantly related to the development of CRC. This result is of great significance for future targeted screening of CRC biomarkers and further clarifying the nutrient metabolism of cancer cells.

Keywords: Colorectal cancer (CRC); biomarker; fatty acids; metabolomics.