A Prognostic Model Based on Six Metabolism-Related Genes in Colorectal Cancer

Sun, Yuanlin; Zhang, Yang; Guo, Yuchen; Yang, Zihao; Xu, You

doi:10.1155/2020/5974350

Cited by 19 publications

(16 citation statements)

References 51 publications

(48 reference statements)

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“…Decreased expression of CPT2 in HCC have been confirmed by other recent studies [ 50 , 51 ]. In colon cancer, CPT2 expression is negatively correlated with tumor stage and low expression of CPT2 is associated with poor prognosis [ 52 ]. Glioblastoma cells store excess FAs into triglycerides and lipid droplets (LDs) by upregulating diacylglycerol-acyltransferase 1 (DGAT1), and inhibition of DGAT1 promotes glioblastoma cell death through excessive FAO-mediated reactive oxidative species (ROS) production [ 53 ].…”

Section: Cell-intrinsic Effects Of Lipid Metabolic Reprogramming Imentioning

confidence: 99%

Lipid Metabolism in Oncology: Why It Matters, How to Research, and How to Treat

Matsushita

Nakagawa

Koike

2021

Cancers

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Lipids in our body, which are mainly composed of fatty acids, triacylglycerides, sphingolipids, phospholipids, and cholesterol, play important roles at the cellular level. In addition to being energy sources and structural components of biological membranes, several types of lipids serve as signaling molecules or secondary messengers. Metabolic reprogramming has been recognized as a hallmark of cancer, but changes in lipid metabolism in cancer have received less attention compared to glucose or glutamine metabolism. However, recent innovations in mass spectrometry- and chromatography-based lipidomics technologies have increased our understanding of the role of lipids in cancer. Changes in lipid metabolism, so-called “lipid metabolic reprogramming”, can affect cellular functions including the cell cycle, proliferation, growth, and differentiation, leading to carcinogenesis. Moreover, interactions between cancer cells and adjacent immune cells through altered lipid metabolism are known to support tumor growth and progression. Characterization of cancer-specific lipid metabolism can be used to identify novel metabolic targets for cancer treatment, and indeed, several clinical trials are currently underway. Thus, we discuss the latest findings on the roles of lipid metabolism in cancer biology and introduce current advances in lipidomics technologies, focusing on their applications in cancer research.

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Section: Cell-intrinsic Effects Of Lipid Metabolic Reprogramming Imentioning

confidence: 99%

Lipid Metabolism in Oncology: Why It Matters, How to Research, and How to Treat

Matsushita

Nakagawa

Koike

2021

Cancers

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“…Notably, two genes, GPX3 and ENPP2, were also identi ed in the other two studies for MRGs in colorectal cancer 11,12 . GPX3, the only extracellular GPX of the family of oxidoreductases, can catalyze the detoxi cation of lipid hydroperoxides by reduced glutathione.…”

Section: Discussionmentioning

confidence: 84%

“…Recently, two study teams analyzed DEMRGs between colorectal cancer and normal tissues using TCGA database, then used DEMRGs in constructing a prognosis MRG model. One study constructed a model comprising six genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5) for colorectal cancer 11 and another model comprising AOC2, ENPP2, ADA, ACADL, GPD1L, and CPT2 12 . Although they used the same data, they obtained completely different prognostic models.…”

Section: Discussionmentioning

confidence: 99%

“…Metabolic phenotypes evolve at the different stages of tumorigenesis, early tumor growth requires nutrient uptake and biosynthesis, and other subtypes of selective metabolic requirements occur during local in ltration and distant metastasis 10 . Two studies proposed prognostic models of prognosis-related metabolic genes for colorectal cancer prognosis 11,12 . In the present study, we modeled the prognosis genes of colorectal and rectal cancers separately for the individualized detection of patients with colorectal cancer.…”

Section: Introductionmentioning

confidence: 99%

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Survival-associated Metabolic Genes in Colon and Rectal Cancers

Cui

Han²,

He³

et al. 2020

Preprint

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Background Uncontrolled proliferation is the most prominent biological feature of tumors. To rapidly proliferate and maximize the use of available nutrients, tumor cells regulate their metabolic behavior and the expression of metabolism-related genes (MRGs). In this study, we aimed to construct prognosis models for colon and rectal cancers, using MRGs to indicate the prognoses of patients. MethodsWe first acquired the gene expression profiles of colon and rectal cancers from the TCGA and GEO database, and utilized univariate Cox analysis, lasso regression, and multivariable cox analysis to identify MRGs for risk models. Then GSEA and KEGG functional enrichment analysis were utilized to identify the metabolism pathway of MRGs in the risk models and analyzed these genes comprehensively using GSCALite.ResultsEight genes (CPT1C, PLCB2, PLA2G2D, GAMT, ENPP2, PIP4K2B, GPX3, and GSR) in the colon cancer risk model and six genes (TDO2, PKLR, GAMT, EARS2, ACO1, and WAS) in the rectal cancer risk model were identified successfully. Multivariate Cox analysis indicated that the models predicted overall survival accurately and independently for patients with colon or rectal cancer. Furthermore, we identified the metabolism pathway of MRGs in the risk models and analyzed these genes comprehensively. Then, we verified the prognosis model in independent colon cancer cohorts (GSE17538) and detected the correlations of the protein expression levels of GSR and ENPP2 with prognosis for colon or rectal cancer. ConclusionsOur findings demonstrated that 14 MRGs are potential prognostic biomarkers and therapeutic targets of colorectal cancer.

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“…Reviewing the previous literature on the prognosis of CRC, 4 gene signatures [ 28 ], 9 gene signatures [ 7 ], hypoxia-related signature [ 29 ], autophagy score signature [ 30 , 31 ], somatic mutation signatures [ 32 ], metabolism-related signature [ 33 ], chemokine/chemokine receptor signature [ 34 ], and immune-related signature [ 35 ] had been constructed to predict the OS of CRC patients. However, these bioinformatic analyses only predict the prognosis of CRC patients at the RNA level, and there are few studies on the protein prognostic signature.…”

Section: Discussionmentioning

confidence: 99%

Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer

Yue

Liu

Zhu

et al. 2020

BioMed Research International

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Background. Colorectal cancer (CRC) is the second most common cause of cancer death in the United States and the third most common cancer globally. The incidence of CRC tends to be younger, and we urgently need a reliable prognostic assessment strategy. Methods. Protein expression profile and clinical information of 390 CRC patients/samples were downloaded from the TCPA and TCGA database, respectively. The Kaplan-Meier, Cox regression, and Pearson correlation analysis were applied in this study. Results. Based on the TCPA and TCGA database, we screened 6 hub proteins and first constructed protein risk signature, all of which were significantly associated with CRC patients’ overall survival (OS). The risk score was an independent prognostic factor and significantly related with the size of the tumor in situ ( T ). 6 hub proteins were differentially expressed in cancer and normal tissues and in different CRC stages, which were validated at the ONCOMINE database. Next, 40 coexpressed proteins of 6 hub proteins were extracted from the TCPA database. In the protein-protein interaction (PPI) network, HER1, HER2, and CTNNB1 were at the center. Function enrichment analysis illustrated that 46 proteins were mainly involved in the EGFR (HER1) tyrosine kinase inhibitor resistance pathway. Conclusion. Studies indicated that 6 hub proteins might be considered as new targets for CRC therapies, and the protein risk signature can be used to predict the OS of CRC patients.

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A Prognostic Model Based on Six Metabolism-Related Genes in Colorectal Cancer

Cited by 19 publications

References 51 publications

Lipid Metabolism in Oncology: Why It Matters, How to Research, and How to Treat

Lipid Metabolism in Oncology: Why It Matters, How to Research, and How to Treat

Survival-associated Metabolic Genes in Colon and Rectal Cancers

Identification of 6 Hub Proteins and Protein Risk Signature of Colorectal Cancer

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