Skip to content
2000
Volume 25, Issue 5
  • ISSN: 1566-5240
  • E-ISSN: 1875-5666

Abstract

Background

Gastric Cancer (GC) has become one of the most important causes of cancer-related deaths worldwide due to its intractability. Studying the mechanisms of gastric carcinogenesis, recurrence, and metastasis, and searching for new therapeutic targets have become the main directions of today's gastric cancer research. Lactate is considered a metabolic by-product of tumor aerobic glycolysis, which can regulate tumor development through various mechanisms, including cell cycle regulation, immunosuppression, and energy metabolism. However, the effects of genes related to lactate metabolism on the prognosis and tumor microenvironmental characteristics of GC patients are unknown.

Methods

In this study, we have collected gene expression data of gastric cancer from The Cancer Genome Atlas (TCGA) and identified differentially expressed genes in gastric cancer using the “Limma” software package.

Results

76 differentially expressed lactate metabolism-related genes were screened, and then the Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analysis were employed that identified 8 genes, constructed Lactate Metabolism-related gene signals (LMRs), and verified the reliability of the prognostic risk mapping by using TCGA training set and TCGA internal test set. Finally, the functional enrichment analysis was employed to identify the molecular mechanism.

Conclusion

Eight lactate metabolism-related genes were constructed into a new predictive signal that better predicted the overall survival of gastric cancer patients and can guide clinical decisions for more precise and personalized treatment.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cmm/10.2174/0115665240290237240424054233
2024-05-09
2025-12-09

  • From This Site

    /content/journals/cmm/10.2174/0115665240290237240424054233
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. EtemadiA. SafiriS. SepanlouS.G. The global, regional, and national burden of stomach cancer in 195 countries, 1990–2017: A systematic analysis for the Global Burden of Disease study 2017.Lancet Gastroenterol. Hepatol.202051425410.1016/S2468‑1253(19)30328‑0 31648970
     [Google Scholar]
  2. LinJ. LiuG. ChenL. KwokH.F. LinY. Targeting lactate-related cell cycle activities for cancer therapy.Semin. Cancer Biol.202286Pt 31231124310.1016/j.semcancer.2022.10.009 36328311
     [Google Scholar]
  3. SungH. FerlayJ. SiegelR.L. Global Cancer Statistics 2020: GLOBOCAN Estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.202171320924910.3322/caac.21660 33538338
     [Google Scholar]
  4. NakaneK. FujiyaK. TerashimaM. Detection of secondary upper gastrointestinal tract cancer during follow-up esophago-gastroduodenoscopy after gastrectomy for gastric cancer.Ann. Gastroenterol. Surg.20226448649510.1002/ags3.12546 35847443
     [Google Scholar]
  5. FergusonB.S. RogatzkiM.J. GoodwinM.L. KaneD.A. RightmireZ. GladdenL.B. Lactate metabolism: Historical context, prior misinterpretations, and current understanding.Eur. J. Appl. Physiol.2018118469172810.1007/s00421‑017‑3795‑6 29322250
     [Google Scholar]
  6. LiX. YangY. ZhangB. Lactate metabolism in human health and disease.Signal Transduct. Target. Ther.20227130510.1038/s41392‑022‑01151‑3 36050306
     [Google Scholar]
  7. LibertiM.V. LocasaleJ.W. The Warburg effect: How does it benefit cancer cells?Trends Biochem. Sci.201641321121810.1016/j.tibs.2015.12.001 26778478
     [Google Scholar]
  8. VaupelP. MulthoffG. Revisiting the Warburg effect: Historical dogma versus current understanding.J. Physiol.202159961745175710.1113/JP278810 33347611
     [Google Scholar]
  9. TengR. LiuZ. TangH. HSP60 silencing promotes Warburg-like phenotypes and switches the mitochondrial function from ATP production to biosynthesis in ccRCC cells.Redox Biol.20192410121810.1016/j.redox.2019.101218 31112866
     [Google Scholar]
  10. ChenZ. LiuM. LiL. ChenL. Involvement of the Warburg effect in non-tumor diseases processes.J. Cell. Physiol.201823342839284910.1002/jcp.25998 28488732
     [Google Scholar]
  11. CertoM. TsaiC.H. PucinoV. HoP.C. MauroC. Lactate modulation of immune responses in inflammatory versus tumour microenvironments.Nat. Rev. Immunol.202121315116110.1038/s41577‑020‑0406‑2 32839570
     [Google Scholar]
  12. SunZ. TaoW. GuoX. Construction of a Lactate-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in kidney renal clear cell carcinoma.Front. Immunol.20221381898410.3389/fimmu.2022.818984 35250999
     [Google Scholar]
  13. MiaoH. QinY. Teixeira Da SilvaJ. Cloning and expression analysis of S-RNase homologous gene in Citrus reticulata Blanco cv. Wuzishatangju.Plant Sci.20111802358367
     [Google Scholar]
  14. XiaL. OyangL. LinJ. The cancer metabolic reprogramming and immune response.Mol. Cancer20212012810.1186/s12943‑021‑01316‑8 33546704
     [Google Scholar]
  15. OnoyamaT. IshikawaS. IsomotoH. Gastric cancer and genomics: Review of literature.J. Gastroenterol.202257850551610.1007/s00535‑022‑01879‑3 35751736
     [Google Scholar]
  16. DohertyJ.R. ClevelandJ.L. Targeting lactate metabolism for cancer therapeutics.J. Clin. Invest.201312393685369210.1172/JCI69741 23999443
     [Google Scholar]
  17. ZhangD. TangZ. HuangH. Metabolic regulation of gene expression by histone lactylation.Nature2019574777957558010.1038/s41586‑019‑1678‑1 31645732
     [Google Scholar]
  18. TongY. GuoD. LinS.H. SUCLA2-coupled regulation of GLS succinylation and activity counteracts oxidative stress in tumor cells.Mol. Cell2021811123032316.e810.1016/j.molcel.2021.04.002 33991485
     [Google Scholar]
  19. LvL. LiD. ZhaoD. Acetylation targets the M2 isoform of pyruvate kinase for degradation through chaperone-mediated autophagy and promotes tumor growth.Mol. Cell201142671973010.1016/j.molcel.2011.04.025 21700219
     [Google Scholar]
  20. MillsE.L. O’NeillL.A. Reprogramming mitochondrial metabolism in macrophages as an anti-inflammatory signal.Eur. J. Immunol.2016461132110.1002/eji.201445427 26643360
     [Google Scholar]
  21. ChenP. ZuoH. XiongH. Gpr132 sensing of lactate mediates tumor–macrophage interplay to promote breast cancer metastasis.Proc. Natl. Acad. Sci. USA2017114358058510.1073/pnas.1614035114 28049847
     [Google Scholar]
  22. YangD. YinJ. ShanL. YiX. ZhangW. DingY. Identification of lysine-lactylated substrates in gastric cancer cells.iScience202225710463010.1016/j.isci.2022.104630 35800753
     [Google Scholar]
  23. WangX. JiC. Construction of a prognostic risk model based on apoptosis-related genes to assess tumor immune microenvironment and predict prognosis in hepatocellular carcinoma.BMC Gastroenterol.202222140010.1186/s12876‑022‑02481‑w 36028814
     [Google Scholar]
  24. WangY. ZhangX. DaiX. HeD. Applying immune-related lncRNA pairs to construct a prognostic signature and predict the immune landscape of stomach adenocarcinoma.Expert Rev. Anticancer Ther.202121101161117010.1080/14737140.2021.1962297 34319826
     [Google Scholar]
  25. LiuZ. ZhangY. ShiC. A novel immune classification reveals distinct immune escape mechanism and genomic alterations: Implications for immunotherapy in hepatocellular carcinoma.J. Transl. Med.2021191510.1186/s12967‑020‑02697‑y 33407585
     [Google Scholar]
  26. SunY. XuZ. JiangJ. XuT. XuJ. LiuP. High Expression of succinate dehydrogenase subunit a which is regulated by histone acetylation, acts as a good prognostic factor of multiple myeloma patients.Front. Oncol.20201056366610.3389/fonc.2020.563666 33014881
     [Google Scholar]
  27. CaoL. ZhangS. BaY. Identification of m6A-related lncRNAs as prognostic signature within colon tumor immune microenvironmen.Cancer Rep (Hoboken)202366e1828
     [Google Scholar]
  28. LiN. BaiC. ZhangR. Efficacy and safety of apatinib for the treatment of AFP-producing gastric cancer.Transl. Oncol.202114210100410.1016/j.tranon.2020.101004 33383486
     [Google Scholar]
  29. FeiY. XuJ. GeL. Establishment and validation of individualized clinical prognostic markers for LUAD patients based on autophagy-related genes.Aging (Albany NY)202214187328734710.18632/aging.204097 36178365
     [Google Scholar]
  30. CenK. WuZ. MaiY. DaiY. HongK. GuoY. Identification of a novel reactive oxygen species (ROS)-related genes model combined with RT-qPCR experiments for prognosis and immunotherapy in gastric cancer.Front. Genet.202314107490010.3389/fgene.2023.1074900 37124616
     [Google Scholar]
  31. HwangC.S. HwangD.G. AboulafiaD.M. A clinical triad with fatal implications: Recrudescent diffuse large b-cell non-hodgkin lymphoma presenting in the leukemic phase with an elevated serum lactic acid level and dysregulation of the TP53 tumor suppressor gene - a case report and literature review.Clin. Med. Insights Blood Disord.2021144094 33679144
     [Google Scholar]
  32. ZhaoZ. FeiK. BaiH. WangZ. DuanJ. WangJ. Metagenome association study of the gut microbiome revealed biomarkers linked to chemotherapy outcomes in locally advanced and advanced lung cancer.Thorac. Cancer2021121667810.1111/1759‑7714.13711 33111503
     [Google Scholar]
  33. XiangJ. SuR. WuS. ZhouL. Construction of a prognostic signature for serous ovarian cancer based on lactate metabolism-related genes.Front. Oncol.20221296734210.3389/fonc.2022.967342 36185201
     [Google Scholar]
  34. MarcucciF. RumioC. Glycolysis-induced drug resistance in tumors—A response to danger signals?Neoplasia202123223424510.1016/j.neo.2020.12.009 33418276
     [Google Scholar]
  35. VinascoK. MitchellH.M. KaakoushN.O. Castaño-RodríguezN. Microbial carcinogenesis: Lactic acid bacteria in gastric cancer.Biochim. Biophys. Acta Rev. Cancer20191872218830910.1016/j.bbcan.2019.07.004 31394110
     [Google Scholar]
  36. HuangS. LiD. ZhuangL. ZhangJ. WuJ. Identification of an epithelial-mesenchymal transition-related long non-coding RNA prognostic signature to determine the prognosis and drug treatment of hepatocellular carcinoma patients.Front. Med. (Lausanne)2022985034310.3389/fmed.2022.850343 35685422
     [Google Scholar]
  37. XuP. LiuS. SongS. Identification and validation of a novel angiogenesis-related gene signature for predicting prognosis in gastric adenocarcinoma.Front. Oncol.20231296510210.3389/fonc.2022.965102 36727080
     [Google Scholar]
  38. PolańskiR. HodgkinsonCL. FusiA. Activity of the monocarboxylate transporter 1 inhibitor AZD3965 in small cell lung cancer.Clin Cancer Res201420492693710.1158/1078‑0432.CCR‑13‑2270 24277449
     [Google Scholar]
  39. LaczkoR. CsiszarK. Lysyl oxidase (LOX): Functional contributions to signaling pathways.Biomolecules2020108109310.3390/biom10081093 32708046
     [Google Scholar]
  40. ZhangN. YangA. ZhangW. Crosstalk of lysyl oxidase-like 1 and lysyl oxidase prolongs their half-lives and regulates liver fibrosis through Notch signal.Hepatol. Commun.202484e039110.1097/HC9.0000000000000391 38466882
     [Google Scholar]
  41. LiuY. SunB. LinY. Lysyl oxidase promotes the formation of vasculogenic mimicry in gastric cancer through PDGF-PDGFR pathway.J. Cancer20241571816182510.7150/jca.92192 38434983
     [Google Scholar]
  42. ZhuJ. LuoC. ZhaoJ. Expression of LOX suggests poor prognosis in gastric cancer.Front. Med. (Lausanne)2021871898610.3389/fmed.2021.718986 34595188
     [Google Scholar]
  43. KasashimaH. YashiroM. OkunoT. Significance of the lysyl oxidase members lysyl oxidase like 1, 3, and 4 in gastric cancer.Digestion201898423824810.1159/000489558 30045039
     [Google Scholar]
  44. WangX. CaoY. GuoJ. Association between MTTP genotype (-493G/T) polymorphism and hepatic steatosis in hepatitis C: A systematic review and meta-analysis.Lipids Health Dis.202322115410.1186/s12944‑023‑01916‑x 37726765
     [Google Scholar]
  45. ZhangQ. DengT. ZhangH. Adipocyte-derived exosomal MTTP suppresses ferroptosis and promotes chemoresistance in colorectal cancer.Adv. Sci. (Weinh.)2022928220335710.1002/advs.202203357 35978266
     [Google Scholar]
  46. AbuliziA. VatnerD.F. YeZ. Membrane-bound sn-1,2-diacylglycerols explain the dissociation of hepatic insulin resistance from hepatic steatosis in MTTP knockout mice.J. Lipid Res.202061121565157610.1194/jlr.RA119000586 32907986
     [Google Scholar]
  47. GaoZ. LiuC. YangL. SPARC overexpression promotes liver cancer cell proliferation and tumor growth.Front. Mol. Biosci.2021877574310.3389/fmolb.2021.775743 34912848
     [Google Scholar]
/content/journals/cmm/10.2174/0115665240290237240424054233
Loading
Establishment and Validation of Lactate Metabolism-Related Genes as a Prognostic Model for Gastric Cancer
Current Molecular Medicine 25, 637 (2025); https://doi.org/10.2174/0115665240290237240424054233
/content/journals/cmm/10.2174/0115665240290237240424054233
/content/journals/cmm/10.2174/0115665240290237240424054233
Loading

Data & Media loading...


  • Article Type:     Research Article
Keyword(s): Gastric cancer; lactate; nomogram; prognostic marker; prognostic signature; tumor microenvironment

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test