Biomarkers of coordinate metabolic reprogramming and the construction of a co-expression network in colorectal cancer.
Biomarkers of coordinate metabolic reprogramming and the construction of a co-expression network in colorectal cancer.
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DOI：
10.21037/atm-22-4767
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发表时间：
2022-10
影响因子：
--
通讯作者：
Wu, Zhou
中科院分区：
文献类型：
--
作者：
Lin, Lihong;Zeng, Xiuxiu;Liang, Shanyan;Wang, Yunzhi;Dai, Xiaoyu;Sun, Yuechao;Wu, Zhou
在全球范围内，结直肠癌（CRC）的发病率和死亡率居所有恶性肿瘤之首。因此，需要开展旨在开发新的筛查策略和生物标志物以早期检测CRC的研究。目前，传统的筛查方法有局限性，因此，新的检测策略已被考虑。利用代谢组学探索结直肠癌组织中的分子变化是识别潜在生物标志物和关键癌症因素的主流方法。在本研究中，使用来自9名CRC患者的27个样本来分析肿瘤、癌旁组织和正常组织之间的代谢物差异。还分析了CRC各个阶段（IIA、IIB和IIIC期）的代谢物差异。随后，进行主成分分析（PCA）、排列和趋势分析。加权基因共表达和代谢物-代谢物相互作用网络也被构建。在纳入的样品中共鉴定出5，834种代谢物。排列分析表明，不同组织和不同阶段之间有明显的分离。与正常组织相比，肿瘤组织在IIA、IIB和IIIC期分别表现出11、233和25种上调代谢物以及1、77和0种下调代谢物。此外，IIB期肿瘤组织显示出更多的差异代谢产物（233个上调和77个下调）。加权基因相关网络分析（WGCNA）将5，834种代谢物聚类为15个不同的模块，其中4个模块与组织特异性显著相关。值得注意的是，甘油磷脂代谢，脂肪酸代谢和其他途径在这些模块中富集。脂肪酸和甘油磷脂与结直肠癌的发生密切相关。这一结果对于未来靶向筛选CRC生物标志物，进一步阐明癌细胞的营养代谢具有重要意义。
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. 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. 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. 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.
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影响因子：
5.7
作者：
Couvillion, Sneha P.;Agrawal, Neha;Metz, Thomas O.
通讯作者：
Metz, Thomas O.
影响因子：
2.3
作者：
Passarelli, Michael N.;Newcomb, Polly A.;Makar, Karen W.;Burnett-Hartman, Andrea N.;Potter, John D.;Upton, Melissa P.;Zhu, Lee-Ching;Rosenfeld, Michael E.;Schwartz, Stephen M.;Rutter, Carolyn M.
通讯作者：
Rutter, Carolyn M.
肥胖，代谢产物和大肠癌风险：孟德尔随机研究。
影响因子：
9.3
作者：
Bull CJ;Bell JA;Murphy N;Sanderson E;Davey Smith G;Timpson NJ;Banbury BL;Albanes D;Berndt SI;Bézieau S;Bishop DT;Brenner H;Buchanan DD;Burnett-Hartman A;Casey G;Castellví-Bel S;Chan AT;Chang-Claude J;Cross AJ;de la Chapelle A;Figueiredo JC;Gallinger SJ;Gapstur SM;Giles GG;Gruber SB;Gsur A;Hampe J;Hampel H;Harrison TA;Hoffmeister M;Hsu L;Huang WY;Huyghe JR;Jenkins MA;Joshu CE;Keku TO;Kühn T;Kweon SS;Le Marchand L;Li CI;Li L;Lindblom A;Martín V;May AM;Milne RL;Moreno V;Newcomb PA;Offit K;Ogino S;Phipps AI;Platz EA;Potter JD;Qu C;Quirós JR;Rennert G;Riboli E;Sakoda LC;Schafmayer C;Schoen RE;Slattery ML;Tangen CM;Tsilidis KK;Ulrich CM;van Duijnhoven FJB;van Guelpen B;Visvanathan K;Vodicka P;Vodickova L;Wang H;White E;Wolk A;Woods MO;Wu AH;Campbell PT;Zheng W;Peters U;Vincent EE;Gunter MJ
通讯作者：
Gunter MJ
影响因子：
64.5
作者：
Nomura DK;Long JZ;Niessen S;Hoover HS;Ng SW;Cravatt BF
通讯作者：
Cravatt BF
影响因子：
30.3
作者：
Cai J;Sun L;Gonzalez FJ
通讯作者：
Gonzalez FJ