Association between lactate metabolism‑related molecules and venous thromboembolism: A study based on bioinformatics and an <em>in vitro</em> model

Qin,Zhong; Chen,Jing; Zhang,Jianfeng; Lu,Hailin; Chen,Quanzhi

doi:10.3892/etm.2023.12359

Association between lactate metabolism‑related molecules and venous thromboembolism: A study based on bioinformatics and an in vitro model

Authors:
- Zhong Qin
- Jing Chen
- Jianfeng Zhang
- Hailin Lu
- Quanzhi Chen
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Affiliations: Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China, School of Basic Medical Sciences, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
Published online on: December 19, 2023 https://doi.org/10.3892/etm.2023.12359
Article Number: 70
Copyright: © Qin et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Venous thromboembolism (VTE) is characterized by a high recurrence rate and adverse consequences, including high mortality. Damage to vascular endothelial cells (VECs) serves a key role in VTE and lactate (LA) metabolism is associated with VEC damage. However, the pathogenesis of VTE and the role of lactate metabolism‑related molecules (LMRMs) remain unclear. Based on the GSE48000 dataset, the present study identified differentially expressed (DE‑)LMRMs between healthy individuals and those with VTE. Thereafter, LMRMs were used to establish four machine learning models, namely, the random forest, support vector machine and generalized linear model (GLM) and eXtreme gradient boosting. To verify disease prediction efficiency of the models, nomograms, calibration curves, decision curve analyses and external datasets were used. The optimal machine learning model was used to predict genes involved in disease and an in vitro oxygen‑glucose deprivation (OGD) model was used to detect the survival rate, LA levels and LMRM expression levels of VECs. A total of four DE‑LMRMs, solute carrier family 16 member 1 (SLC16A1), SLC16A7, SLC16A8 and SLC5A12 were obtained and GLM was identified as the best performing model based on its ability to predict differential expression of the embigin, lactate dehydrogenase B, SLC16A1, SLC5A12 and SLC16A8 genes. Additionally, SLC16A1, SLC16A7 and SLC16A8 served key roles in VTE and the OGD model demonstrated a significant decrease in VEC survival rate as well as a significant increase and decrease in intracellular LA and SLC16A1 expression levels in VECs, respectively. Thus, LMRMs may be involved in VTE pathogenesis and be used to build accurate VTE prediction models. Further, it was hypothesized that the observed increase in intracellular LA levels in VECS was associated with the decrease in SLC16A1 expression. Therefore, SLC16A1 expression may be an essential target for VTE treatment.

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1	Battinelli EM, Murphy DL and Connors JM: Venous thromboembolism overview. Hematol Oncol Clin North Am. 26:345–367, ix. 2012.PubMed/NCBI View Article : Google Scholar
2	Bartholomew JR: Update on the management of venous thromboembolism. Cleve Clin J Med. 84 (Suppl 3):39–46. 2017.PubMed/NCBI View Article : Google Scholar
3	Klok FA, van der Hulle T, den Exter PL, Lankeit M, Huisman MV and Konstantinides S: The post-PE syndrome: A new concept for chronic complications of pulmonary embolism. Blood Rev. 28:221–226. 2014.PubMed/NCBI View Article : Google Scholar
4	Palta S, Saroa R and Palta A: Overview of the coagulation system. Indian J Anaesth. 58:515–523. 2014.PubMed/NCBI View Article : Google Scholar
5	Torres C, Matos R, Morais S, Campos M and Lima M: Soluble endothelial cell molecules and circulating endothelial cells in patients with venous thromboembolism. Blood Coagul Fibrinolysis. 28:589–595. 2017.PubMed/NCBI View Article : Google Scholar
6	Pilard M, Ollivier EL, Gourdou-Latyszenok V, Couturaud F and Lemarié CA: Endothelial cell phenotype, a major determinant of venous thrombo-inflammation. Front Cardiovasc Med. 9(864735)2022.PubMed/NCBI View Article : Google Scholar
7	Poredos P and Jezovnik MK: The role of inflammation in venous thromboembolism and the link between arterial and venous thrombosis. Int Angiol. 26:306–311. 2007.PubMed/NCBI
8	Barsh GS, Molina-Ortiz P, Orban T, Martin M, Habets A, Dequiedt F and Schurmans S: Rasa3 controls turnover of endothelial cell adhesion and vascular lumen integrity by a Rap1-dependent mechanism. PLoS Genet. 14(e1007195)2018.PubMed/NCBI View Article : Google Scholar
9	Yang K, Fan M, Wang X, Xu J, Wang Y, Gill PS, Ha T, Liu L, Hall JV, Williams DL and Li C: Lactate induces vascular permeability via disruption of VE-cadherin in endothelial cells during sepsis. Sci Adv. 8(eabm8965)2022.PubMed/NCBI View Article : Google Scholar
10	Yang K, Holt M, Fan M, Lam V, Yang Y, Ha T, Williams DL, Li C and Wang X: Cardiovascular dysfunction in covid-19: Association between endothelial cell injury and lactate. Front Immunol. 13(868679)2022.PubMed/NCBI View Article : Google Scholar
11	Mamun AA, Hayashi H, Yamamura A, Nayeem MJ and Sato M: Hypoxia induces the translocation of glucose transporter 1 to the plasma membrane in vascular endothelial cells. J Physiol Sci. 70(44)2020.PubMed/NCBI View Article : Google Scholar
12	Shao H and Li S: A new perspective on HIV: Effects of HIV on brain-heart axis. Front Cardiovasc Med. 10(1226782)2023.PubMed/NCBI View Article : Google Scholar
13	Edwards YH, Povey S, LeVan KM, Driscoll CE, Millan JL and Goldberg E: Locus determining the human sperm-specific lactate dehydrogenase, LDHC, is syntenic with LDHA. Dev Genet. 8:219–232. 1987.PubMed/NCBI View Article : Google Scholar
14	Mdluli K, Booth MP, Brady RL and Rumsby G: A preliminary account of the properties of recombinant human glyoxylate reductase (GRHPR), LDHA and LDHB with glyoxylate, and their potential roles in its metabolism. Biochim Biophys Acta. 1753:209–216. 2005.PubMed/NCBI View Article : Google Scholar
15	Koukourakis MI, Giatromanolaki A, Sivridis E, Bougioukas G, Didilis V, Gatter KC and Harris AL: Tumour and Angiogenesis Research Group. Lactate dehydrogenase-5 (LDH-5) overexpression in non-small-cell lung cancer tissues is linked to tumour hypoxia, angiogenic factor production and poor prognosis. Br J Cancer. 89:877–885. 2003.PubMed/NCBI View Article : Google Scholar
16	Stabenow LK, Zibrova D, Ender C, Helbing DL, Spengler K, Marx C, Wang ZQ and Heller R: Oxidative glucose metabolism promotes senescence in vascular endothelial cells. Cells. 11(2213)2022.PubMed/NCBI View Article : Google Scholar
17	Franczyk B, Gluba-Brzózka A, Ławiński J, Rysz-Górzyńska M and Rysz J: Metabolomic profile in venous thromboembolism (VTE). Metabolites. 11(495)2021.PubMed/NCBI View Article : Google Scholar
18	Lim CS, Kiriakidis S, Sandison A, Paleolog EM and Davies AH: Hypoxia-inducible factor pathway and diseases of the vascular wall. J Vasc Surg. 58:219–230. 2013.PubMed/NCBI View Article : Google Scholar
19	Li WQ, Qin ZS, Chen S, Cheng D, Yang SC, Choi YMM, Chu B, Zhou WH and Zhang ZJ: Hirudin alleviates acute ischemic stroke by inhibiting NLRP3 inflammasome-mediated neuroinflammation: In vivo and in vitro approaches. Int Immunopharmacol. 110(108967)2022.PubMed/NCBI View Article : Google Scholar
20	Wang Z, Liu P, Hu M, Lu S, Lyu Z, Kou Y, Sun Y, Zhao X, Liu F and Tian J: Naoxintong restores ischemia injury and inhibits thrombosis via COX2-VEGF/ NFκB signaling. J Ethnopharmacol. 270(113809)2021.PubMed/NCBI View Article : Google Scholar
21	Li S, Long Q, Nong L, Zheng Y, Meng X and Zhu Q: Identification of immune infiltration and cuproptosis-related molecular clusters in tuberculosis. Front Immunol. 14(1205741)2023.PubMed/NCBI View Article : Google Scholar
22	Li S, Zheng Y, Long Q, Nong J, Shao H, Liang G and Wu F: Drug-drug interactions between propofol and ART drugs: Inhibiting neuronal activity by affecting glucose metabolism. CNS Neurosci Ther: Aug 31, 2023 (Epub ahead of print).
23	Xie X, Hu Y, Ye T, Chen Y, Zhou L, Li F, Xi X, Wang S, He Y, Gao X, et al: Therapeutic vaccination against leukaemia via the sustained release of co-encapsulated anti-PD-1 and a leukaemia-associated antigen. Nat Biomed Eng. 5:414–428. 2020.PubMed/NCBI View Article : Google Scholar
24	Wang S, Zhou L, Ji N, Sun C, Sun L, Sun J, Du Y, Zhang N, Li Y, Liu W, et al: Targeting acyp1-mediated glycolysis reverses lenvatinib resistance and restricts hepatocellular carcinoma progression. Drug Resist Updat,. 69(100976)2023.PubMed/NCBI View Article : Google Scholar
25	Kumar P, Nagarajan A and Uchil PD: Analysis of cell viability by the MTT assay. Cold Spring Harb Protoc. 2018:2018.PubMed/NCBI View Article : Google Scholar
26	Sivaprakasam S, Bhutia YD, Yang S and Ganapathy V: Short-chain fatty acid transporters: Role in colonic homeostasis. Compr Physiol. 8:299–314. 2017.PubMed/NCBI View Article : Google Scholar
27	Zhao X, Lu Y, Li S, Guo F, Xue H, Jiang L, Wang Z, Zhang C, Xie W and Zhu F: Predicting renal function recovery and short-term reversibility among acute kidney injury patients in the ICU: Comparison of machine learning methods and conventional regression. Ren Fail. 44:1327–1338. 2022.PubMed/NCBI View Article : Google Scholar
28	Xu B, Zhang M, Zhang B, Chi W, Ma X, Zhang W, Dong M, Sheng L, Zhang Y, Jiao W, et al: Embigin facilitates monocarboxylate transporter 1 localization to the plasma membrane and transition to a decoupling state. Cell Rep. 40(111343)2022.PubMed/NCBI View Article : Google Scholar
29	Li SS, Luedemann M, Sharief FS, Takano T and Deaven LL: Mapping of human lactate dehydrogenase-A -B and -C genes and their related sequences: the gene for LDHC is located with that for LDHA on chromosome 11. Cytogenet Cell Genet. 48:16–18. 1988.PubMed/NCBI View Article : Google Scholar
30	Markert CL, Shakle JB and Whitt GS: Evolution of a gene. Multiple genes for LDH isozymes provide a model of the evolution of gene structure, function and regulation. Science. 189:102–114. 1975.PubMed/NCBI View Article : Google Scholar
31	Swiderek K and Paneth P: Differences and similarities in binding of pyruvate and L-lactate in the active site of M4 and H4 isoforms of human lactate dehydrogenase. Arch Biochem Biophys. 505:33–41. 2011.PubMed/NCBI View Article : Google Scholar
32	Srivastava AK, Kalita J, Haris M, Gupta RK and Misra UK: Radiological and histological changes following cerebral venous sinus thrombosis in a rat model. Neurosci Res. 65:343–346. 2009.PubMed/NCBI View Article : Google Scholar
33	Song W, Ci H, Tian G, Zhang Y and Ge X: Edoxaban improves venous thrombosis via increasing hydrogen sulfide and homocysteine in rat model. Mol Med Rep. 16:7706–7714. 2017.PubMed/NCBI View Article : Google Scholar
34	Zhao Z, Wu C, He X, Zhao E, Hu S, Han Y, Wang T, Chen Y, Liu T and Huang S: Microrna let-7f alleviates vascular endothelial cell dysfunction via targeting HMGA2 under oxygen-glucose deprivation and reoxygenation. Brain Res. 1772(147662)2021.PubMed/NCBI View Article : Google Scholar
35	Benjamin D, Robay D, Hindupur SK, Pohlmann J, Colombi M, El-Shemerly MY, Maira SM, Moroni C, Lane HA and Hall MN: Dual inhibition of the lactate transporters MCT1 and MCT4 is synthetic lethal with metformin due to NAD+ depletion in cancer cells. Cell Rep. 25:3047–3058.e4. 2018.PubMed/NCBI View Article : Google Scholar
36	Yu X, Zhang R, Wei C, Gao Y, Yu Y, Wang L, Jiang J, Zhang X, Li J and Chen X: MCT2 overexpression promotes recovery of cognitive function by increasing mitochondrial biogenesis in a rat model of stroke. Anim Cells Syst (Seoul). 25:93–101. 2021.PubMed/NCBI View Article : Google Scholar
37	Srinivas SR, Gopal E, Zhuang L, Itagaki S, Martin PM, Fei YJ, Ganapathy V and Prasad PD: Cloning and functional identification of slc5a12 as a sodium-coupled low-affinity transporter for monocarboxylates (SMCT2). Biochem J. 392:655–664. 2005.PubMed/NCBI View Article : Google Scholar
38	Daniele LL, Sauer B, Gallagher SM, Pugh EN Jr and Philp NJ: Altered visual function in monocarboxylate transporter 3 (slc16a8) knockout mice. Am J Physiol Cell Physiol. 295:C451–C457. 2008.PubMed/NCBI View Article : Google Scholar