Generic placeholder image

Recent Patents on Anti-Cancer Drug Discovery

Editor-in-Chief

ISSN (Print): 1574-8928
ISSN (Online): 2212-3970

Research Article

Fatty Acid Metabolism Signature Contributes to the Molecular Diagnosis of a Malignant Gastric Cancer Subtype with Poor Prognosis and Lower Mutation Burden

Author(s): Zhengwei Chen and Guoxiong Cheng*

Volume 19, Issue 5, 2024

Published on: 19 September, 2023

Page: [666 - 680] Pages: 15

DOI: 10.2174/1574892819666230907145036

Price: $65

Abstract

Background: Gastric cancer (GC) is a common gastrointestinal tumor with high morbidity and mortality. Fatty acid metabolism (FAM) contributes to GC development. Patents have been issued for the use of compositions comprising fatty acid analogues for the treatment of many clinical conditions. However, its clinical significance and its relationship with tumor-related mutations have not been thoroughly discovered. This study was conducted to analyze and explore FAM-related genes’ molecular characteristics, prognostic significance, and association with tumor- related mutations.

Methods: The gastric adenocarcinoma’s transcriptome, clinical data, and tumor mutation load (TMB) data were downloaded from TCGA and GEO databases. The differentially expressed FAM genes (FAM DEGs) between cancer and control samples were screened, and their correlation with TMB and survival was analyzed. A PPI network of FAM DEGs was constructed, and a downscaling clustering analysis was performed based on the expression of the FAM DEGs. Further immuno- infiltration and GO/KEGG enrichment analyses of the identified FAM clusters were performed to explore their heterogeneity in biological functions. The effects of FAM score and gastric cancer (STAD) on TMB, MSI, survival prognosis, and drug sensitivity were jointly analyzed, and finally, a single-gene analysis of the obtained core targets was performed.

Results: Through differential analysis, 68 FAM DEGs were obtained, and they were highly associated with STAD tumor mutation load. In addition, a high FAM DEGs CNV rate was observed. The PPI network showed a complex mutual correlation between the FAM DEGs. Consensus clustering classified the patients into three clusters based on the FAM DEGs, and the clusters presented different survival rates. The GSVA and immune infiltration analysis revealed that metabolism, apoptosis, and immune infiltration-related pathways were variated. In addition, FAM genes, STAD prognostic risk genes, and PCA scores were closely associated with the survival status of STAD patients. FAM score was closely correlated with STAD TMB, MSI, and immunotherapy, and the TMB values in the low FAM score group were significantly higher than those in the high FAM score group. Finally, combining the above results, it was found that the core gene PTGS1 performed best in predicting STAD survival prognosis and TMB/MSI/immunotherapy.

Conclusion: Fatty acid metabolism genes affect the development of gastric adenocarcinoma and can predict the survival prognosis, tumor mutational load characteristics, and drug therapy sensitivity of STAD patients, which can help explore more effective immunotherapy targets for GC.

Keywords: Gastric cancer, fatty acid metabolism, consensus clustering, tumor mutational load, immunotherapy, single-gene analysis.

[1]
Zhang Z, Zhao J, Pang Q, Wang A, Chen M, Wei X. An in vitro study on the effects of the combination of salinomycin with cisplatin on human gastric cancer cells. Mol Med Rep 2017; 16(2): 1031-8.
[http://dx.doi.org/10.3892/mmr.2017.6731] [PMID: 28627601]
[2]
Feng RM, Zong YN, Cao SM, Xu RH. Current cancer situation in China: Good or bad news from the 2018 global cancer statistics? Cancer Commun 2019; 39(1): 22.
[http://dx.doi.org/10.1186/s40880-019-0368-6] [PMID: 31030667]
[3]
Hanahan D, Weinberg RA. Hallmarks of cancer: The next generation. Cell 2011; 144(5): 646-74.
[http://dx.doi.org/10.1016/j.cell.2011.02.013] [PMID: 21376230]
[4]
Rosario SR, Long MD, Affronti HC, Rowsam AM, Eng KH, Smiraglia DJ. Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas. Nat Commun 2018; 9(1): 5330.
[http://dx.doi.org/10.1038/s41467-018-07232-8] [PMID: 30552315]
[5]
Crunkhorn S. Targeting cancer cell metabolism in glioblastoma. Nat Rev Cancer 2019; 19(5): 250.
[http://dx.doi.org/10.1038/s41568-019-0139-3] [PMID: 30944412]
[6]
Bennett MW, O’Connell J, O’Sullivan GC, et al. The fas counterattack in vivo: Apoptotic depletion of tumor-infiltrating lymphocytes associated with Fas ligand expression by human esophageal carcinoma. J Immunol 1998; 160(11): 5669-75.
[http://dx.doi.org/10.4049/jimmunol.160.11.5669] [PMID: 9605174]
[7]
Currie E, Schulze A, Zechner R, Walther TC, Farese RV Jr. Cellular fatty acid metabolism and cancer. Cell Metab 2013; 18(2): 153-61.
[http://dx.doi.org/10.1016/j.cmet.2013.05.017] [PMID: 23791484]
[8]
Cheng C, Geng F, Cheng X, Guo D. Lipid metabolism reprogramming and its potential targets in cancer. Cancer Commun 2018; 38(1): 27.
[http://dx.doi.org/10.1186/s40880-018-0301-4] [PMID: 29784041]
[9]
Chaudhry S, Thomas SN, Simmons GE Jr. Targeting lipid metabolism in the treatment of ovarian cancer. Oncotarget 2022; 13(1): 768-83.
[http://dx.doi.org/10.18632/oncotarget.28241] [PMID: 35634242]
[10]
Ben-Sahra I, Manning BD. mTORC1 signaling and the metabolic control of cell growth. Curr Opin Cell Biol 2017; 45: 72-82.
[http://dx.doi.org/10.1016/j.ceb.2017.02.012] [PMID: 28411448]
[11]
Butler LM, Perone Y, Dehairs J, et al. Lipids and cancer: Emerging roles in pathogenesis, diagnosis and therapeutic intervention. Adv Drug Deliv Rev 2020; 159: 245-93.
[http://dx.doi.org/10.1016/j.addr.2020.07.013] [PMID: 32711004]
[12]
Hoy AJ, Nagarajan SR, Butler LM. Tumour fatty acid metabolism in the context of therapy resistance and obesity. Nat Rev Cancer 2021; 21(12): 753-66.
[http://dx.doi.org/10.1038/s41568-021-00388-4] [PMID: 34417571]
[13]
Kopecka J, Trouillas P, Gašparović AČ, Gazzano E, Assaraf YG, Riganti C. Phospholipids and cholesterol: Inducers of cancer multidrug resistance and therapeutic targets. Drug Resist Updat 2020; 49: 100670.
[http://dx.doi.org/10.1016/j.drup.2019.100670] [PMID: 31846838]
[14]
Hajjaji N, Bougnoux P. Selective sensitization of tumors to chemotherapy by marine-derived lipids: A review. Cancer Treat Rev 2013; 39(5): 473-88.
[http://dx.doi.org/10.1016/j.ctrv.2012.07.001] [PMID: 22850619]
[15]
Fendt SM, Frezza C, Erez A. Targeting metabolic plasticity and flexibility dynamics for cancer therapy. Cancer Discov 2020; 10(12): 1797-807.
[http://dx.doi.org/10.1158/2159-8290.CD-20-0844] [PMID: 33139243]
[16]
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: Efficient and comprehensive analysis of somatic variants in cancer. Genome Res 2018; 28(11): 1747-56.
[http://dx.doi.org/10.1101/gr.239244.118] [PMID: 30341162]
[17]
Seiler M, Huang CC, Szalma S, Bhanot G. ConsensusCluster: A software tool for unsupervised cluster discovery in numerical data. OMICS 2010; 14(1): 109-13.
[http://dx.doi.org/10.1089/omi.2009.0083] [PMID: 20141333]
[18]
Rich JT, Neely JG, Paniello RC, Voelker CCJ, Nussenbaum B, Wang EW. A practical guide to understanding Kaplan‐Meier curves. Otolaryngol Head Neck Surg 2010; 143(3): 331-6.
[http://dx.doi.org/10.1016/j.otohns.2010.05.007] [PMID: 20723767]
[19]
Hänzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 2013; 14(1): 7.
[http://dx.doi.org/10.1186/1471-2105-14-7] [PMID: 23323831]
[20]
Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015; 12(5): 453-7.
[http://dx.doi.org/10.1038/nmeth.3337] [PMID: 25822800]
[21]
Gaudet P, Škunca N, Hu JC, Dessimoz C. Primer on the gene ontology. Methods Mol Biol 2017; 1446: 25-37.
[http://dx.doi.org/10.1007/978-1-4939-3743-1_3] [PMID: 27812933]
[22]
Yu G, Wang LG, Han Y, He QY. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 2012; 16(5): 284-7.
[http://dx.doi.org/10.1089/omi.2011.0118] [PMID: 22455463]
[23]
Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 2017; 45(D1): D353-61.
[http://dx.doi.org/10.1093/nar/gkw1092] [PMID: 27899662]
[24]
Wang Y, Zeng Z, Lu J, et al. CPT1A-mediated fatty acid oxidation promotes colorectal cancer cell metastasis by inhibiting anoikis. Oncogene 2018; 37(46): 6025-40.
[http://dx.doi.org/10.1038/s41388-018-0384-z] [PMID: 29995871]
[25]
Madak-Erdogan Z, Band S, Zhao YC, et al. Free fatty acids rewire cancer metabolism in obesity-associated breast cancer via estrogen receptor and mTOR signaling. Cancer Res 2019; 79(10): 2494-510.
[http://dx.doi.org/10.1158/0008-5472.CAN-18-2849] [PMID: 30862719]
[26]
Wu Y, Fabritius M, Ip C. Chemotherapeutic sensitization by endoplasmic reticulum stress: Increasing the efficacy of taxane against prostate cancer. Cancer Biol Ther 2009; 8(2): 146-52.
[http://dx.doi.org/10.4161/cbt.8.2.7087] [PMID: 19182512]
[27]
Han S, Wei R, Zhang X, et al. CPT1A/2-mediated FAO enhancement-a metabolic target in radioresistant breast cancer. Front Oncol 2019; 9: 1201.
[http://dx.doi.org/10.3389/fonc.2019.01201] [PMID: 31803610]
[28]
Corn KC, Windham MA, Rafat M. Lipids in the tumor microenvironment: From cancer progression to treatment. Prog Lipid Res 2020; 80: 101055.
[http://dx.doi.org/10.1016/j.plipres.2020.101055] [PMID: 32791170]
[29]
Veglia F, Tyurin VA, Blasi M, et al. Fatty acid transport protein 2 reprograms neutrophils in cancer. Nature 2019; 569(7754): 73-8.
[http://dx.doi.org/10.1038/s41586-019-1118-2] [PMID: 30996346]
[30]
Foster DW. Malonyl-CoA: The regulator of fatty acid synthesis and oxidation. J Clin Invest 2012; 122(6): 1958-9.
[http://dx.doi.org/10.1172/JCI63967] [PMID: 22833869]
[31]
Jeon SM, Chandel NS, Hay N. AMPK regulates NADPH homeostasis to promote tumour cell survival during energy stress. Nature 2012; 485(7400): 661-5.
[http://dx.doi.org/10.1038/nature11066] [PMID: 22660331]
[32]
Qu Q, Zeng F, Liu X, Wang QJ, Deng F. Fatty acid oxidation and carnitine palmitoyltransferase I: Emerging therapeutic targets in cancer. Cell Death Dis 2016; 7(5): e2226.
[http://dx.doi.org/10.1038/cddis.2016.132] [PMID: 27195673]
[33]
Aktipis CA, Boddy AM, Gatenby RA, Brown JS, Maley CC. Life history trade-offs in cancer evolution. Nat Rev Cancer 2013; 13(12): 883-92.
[http://dx.doi.org/10.1038/nrc3606] [PMID: 24213474]
[34]
Pan Y, Abdureyim M, Yao Q, Li X. Analysis of differentially expressed genes in endothelial cells following tumor cell adhesion, and the role of PRKAA2 and miR-124-3p. Front Cell Dev Biol 2021; 9: 604038.
[http://dx.doi.org/10.3389/fcell.2021.604038] [PMID: 33681194]
[35]
Wang S, Zhang M, Liang B, et al. AMPKalpha2 deletion causes aberrant expression and activation of NAD(P)H oxidase and consequent endothelial dysfunction in vivo: Role of 26S proteasomes. Circ Res 2010; 106(6): 1117-28.
[http://dx.doi.org/10.1161/CIRCRESAHA.109.212530] [PMID: 20167927]
[36]
Kohlstedt K, Trouvain C, Boettger T, Shi L, Fisslthaler B, Fleming I. AMP-activated protein kinase regulates endothelial cell angiotensin-converting enzyme expression via p53 and the post-transcriptional regulation of microRNA-143/145. Circ Res 2013; 112(8): 1150-8.
[http://dx.doi.org/10.1161/CIRCRESAHA.113.301282] [PMID: 23476055]
[37]
Habermann N, Ulrich CM, Lundgreen A, et al. PTGS1, PTGS2, ALOX5, ALOX12, ALOX15, and FLAP SNPs: Interaction with fatty acids in colon cancer and rectal cancer. Genes Nutr 2013; 8(1): 115-26.
[http://dx.doi.org/10.1007/s12263-012-0302-x] [PMID: 22678777]
[38]
Wu H, Han Y, Rodriguez Sillke Y, et al. Lipid droplet‐dependent fatty acid metabolism controls the immune suppressive phenotype of tumor‐associated macrophages. EMBO Mol Med 2019; 11(11): e10698.
[http://dx.doi.org/10.15252/emmm.201910698] [PMID: 31602788]
[39]
He D, Cai L, Huang W, et al. Prognostic value of fatty acid metabolism-related genes in patients with hepatocellular carcinoma. Aging 2021; 13(13): 17847-63.
[http://dx.doi.org/10.18632/aging.203288] [PMID: 34257161]
[40]
Galuppini F, Dal Pozzo CA, Deckert J, Loupakis F, Fassan M, Baffa R. Tumor mutation burden: From comprehensive mutational screening to the clinic. Cancer Cell Int 2019; 19(1): 209.
[http://dx.doi.org/10.1186/s12935-019-0929-4] [PMID: 31406485]
[41]
Venn-watson S. Fatty acid analogs and their use in the treatment of conditions related to metabolic syndrome. US10792266B2, 2020.

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy