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2000
Volume 32, Issue 21
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Introduction

Gastric cancer (GC) is the fifth most common cancer globally, and the relationship between type 2 diabetes mellitus (T2DM) and cancer risk remains controversial.

Methods

We performed Mendelian randomization (MR) analysis using publicly available GWAS data to assess the causal relationship between T2DM and GC, validated by heterogeneity and pleiotropy analyses. Transcriptomic data from TCGA and GEO were analyzed to identify common differentially expressed genes (DEGs). Weighted gene co- expression network analysis (WGCNA) was used to construct a prognostic risk model. Drug sensitivity and immune infiltration were evaluated using GDSC and ImmuCellAI, respectively. Additionally, gene mutation analysis was conducted using TCGA data.

Results

The Mendelian randomization analysis revealed a causal relationship between T2DM and GC at the genetic level. Specifically, the causal effect of T2DM on GC was estimated with an odds ratio (OR) of 1.32 (95% CI: 1.12-1.56), while the reverse causal effect of GC on T2DM was estimated at an OR of 0.78 (95% CI: 0.67-0.91). Sensitivity analyses, including Cochran's Q test and the leave-one-out test, confirmed the robustness of these findings. We constructed a prognostic risk score consisting of three T2DM-related genes (CST2, PSAPL1, and C4orf48) based on transcriptome data analysis. Patients with high-risk scores exhibited significantly worse overall survival (OS) ( < 0.05). Cox regression analysis further confirmed the independent predictive value of the risk score for GC prognosis. Our predictive model demonstrated good performance, with an AUC of 0.786 in the training set and 0.757 in the validation set. Gene enrichment analysis indicated that the genes shared between T2DM and GC were associated with inflammatory response, immune response, and metabolic pathways. Tumor immune microenvironment analysis suggested that immune evasion mechanisms may play a key role in developing GC in patients with coexisting T2DM.

Conclusion

T2DM is associated with reduced GC risk. The risk score and model may help guide GC prognosis and management.

Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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References

  1. SungH. FerlayJ. SiegelR.L. LaversanneM. SoerjomataramI. JemalA. BrayF. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.202171320924910.3322/caac.2166033538338
    [Google Scholar]
  2. GuanW.L. HeY. XuR.H. Gastric cancer treatment: Recent progress and future perspectives.J. Hematol. Oncol.20231615710.1186/s13045‑023‑01451‑337245017
    [Google Scholar]
  3. HuangB. LiuJ. DingF. LiY. Epidemiology, risk areas and macro determinants of gastric cancer: A study based on geospatial analysis.Int. J. Health Geogr.20232213210.1186/s12942‑023‑00356‑138007458
    [Google Scholar]
  4. ThriftA.P. WenkerT.N. El-SeragH.B. Global burden of gastric cancer: Epidemiological trends, risk factors, screening and prevention.Nat. Rev. Clin. Oncol.202320533834910.1038/s41571‑023‑00747‑036959359
    [Google Scholar]
  5. LordickF. RhaS.Y. MuroK. YongW.P. Lordick ObermannováR. Systemic therapy of gastric cancer-state of the art and future perspectives.Cancers20241619333710.3390/cancers1619333739409957
    [Google Scholar]
  6. LordickF. CarneiroF. CascinuS. FleitasT. HaustermansK. PiessenG. VogelA. SmythE.C. Gastric cancer: ESMO clinical practice guideline for diagnosis, treatment and follow-up.Ann. Oncol.202233101005102010.1016/j.annonc.2022.07.00435914639
    [Google Scholar]
  7. JanjigianY.Y. KawazoeA. BaiY. XuJ. LonardiS. MetgesJ.P. YanezP. WyrwiczL.S. ShenL. OstapenkoY. BiliciM. ChungH.C. ShitaraK. QinS.K. Van CutsemE. TaberneroJ. LiK. ShihC.S. BhagiaP. RhaS.Y. Pembrolizumab plus trastuzumab and chemotherapy for HER2-positive gastric or gastro-oesophageal junction adenocarcinoma: Interim analyses from the phase 3 KEYNOTE-811 randomised placebo-controlled trial.Lancet2023402104182197220810.1016/S0140‑6736(23)02033‑037871604
    [Google Scholar]
  8. YangY. WangZ. XinD. GuanL. YueB. ZhangQ. WangF. Analysis of the treatment efficacy and prognostic factors of PD-1/PD-L1 inhibitors for advanced gastric or gastroesophageal junction cancer: A multicenter, retrospective clinical study.Front. Immunol.202415146834210.3389/fimmu.2024.146834239512347
    [Google Scholar]
  9. MatsuokaT. YashiroM. Molecular mechanism for malignant progression of gastric cancer within the tumor microenvironment.Int. J. Mol. Sci.202425211173510.3390/ijms25211173539519285
    [Google Scholar]
  10. MachlowskaJ. BajJ. SitarzM. MaciejewskiR. SitarzR. Gastric cancer: epidemiology, risk factors, classification, genomic characteristics and treatment strategies.Int. J. Mol. Sci.20202111401210.3390/ijms2111401232512697
    [Google Scholar]
  11. ThriftA.P. El-SeragH.B. Burden of gastric cancer.Clin. Gastroenterol. Hepatol.202018353454210.1016/j.cgh.2019.07.04531362118
    [Google Scholar]
  12. SunH. SaeediP. KarurangaS. PinkepankM. OgurtsovaK. DuncanB.B. SteinC. BasitA. ChanJ.C.N. MbanyaJ.C. PavkovM.E. RamachandaranA. WildS.H. JamesS. HermanW.H. ZhangP. BommerC. KuoS. BoykoE.J. MaglianoD.J. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045.Diabetes Res. Clin. Pract.202218310911910.1016/j.diabres.2021.10911934879977
    [Google Scholar]
  13. EisenbergD. ShikoraS.A. AartsE. AminianA. AngrisaniL. CohenR.V. de LucaM. FariaS.L. GoodpasterK.P.S. HaddadA. HimpensJ.M. KowL. KurianM. LoiK. MahawarK. NimeriA. O’KaneM. PapasavasP.K. PonceJ. PrattJ.S.A. RogersA.M. SteeleK.E. SuterM. KothariS.N. 2022 american society of metabolic and bariatric surgery (ASMBS) and international federation for the surgery of obesity and metabolic disorders (IFSO) indications for metabolic and bariatric surgery.Obes. Surg.202333131410.1007/s11695‑022‑06332‑136336720
    [Google Scholar]
  14. SaarelaK. TuomilehtoJ. SundR. KeskimäkiI. HartikainenS. PukkalaE. Cancer incidence among finnish people with type 2 diabetes during 1989–2014.Eur. J. Epidemiol.201934325926510.1007/s10654‑018‑0438‑030182324
    [Google Scholar]
  15. ZhanZ.Q. ChenY.Z. HuangZ.M. LuoY.H. ZengJ.J. WangY. TanJ. ChenY.X. FangJ.Y. Metabolic syndrome, its components, and gastrointestinal cancer risk: A meta-analysis of 31 prospective cohorts and mendelian randomization study.J. Gastroenterol. Hepatol.202439463064110.1111/jgh.1647738230882
    [Google Scholar]
  16. VincentE.E. YaghootkarH. Using genetics to decipher the link between type 2 diabetes and cancer: Shared aetiology or downstream consequence?Diabetologia20206391706171710.1007/s00125‑020‑05228‑y32705315
    [Google Scholar]
  17. Cosmin StanM. PaulD. Diabetes and cancer: A twisted bond.Oncol. Rev.202418135454910.3389/or.2024.135454938835644
    [Google Scholar]
  18. ScherüblH. Type-2-diabetes and gastrointestinal cancer screening.Z. Gastroenterol.202361668368910.1055/a‑1821‑910835697066
    [Google Scholar]
  19. GuoJ. LiuC. PanJ. YangJ. Relationship between diabetes and risk of gastric cancer: A systematic review and meta-analysis of cohort studies.Diabetes Res. Clin. Pract.202218710986610.1016/j.diabres.2022.10986635398143
    [Google Scholar]
  20. DaboB. PelucchiC. RotaM. JainH. BertuccioP. BonziR. PalliD. FerraroniM. ZhangZ.F. Sanchez-AnguianoA. Thi-Hai PhamY. Thi-Du TranC. Gia PhamA. YuG.P. NguyenT.C. MuscatJ. TsuganeS. HidakaA. HamadaG.S. ZaridzeD. MaximovitchD. KogevinasM. Fernàndez de LarreaN. BocciaS. PastorinoR. KurtzR.C. LagiouA. LagiouP. VioqueJ. CamargoM.C. Paula CuradoM. LunetN. BoffettaP. NegriE. La VecchiaC. LuuH.N. The association between diabetes and gastric cancer: Results from the stomach cancer pooling project consortium.Eur. J. Cancer Prev.202231326026910.1097/CEJ.000000000000070334183534
    [Google Scholar]
  21. XuH.L. TanY.T. EppleinM. LiH.L. GaoJ. GaoY.T. ZhengW. ShuX.O. XiangY.B. Population-based cohort studies of type 2 diabetes and stomach cancer risk in chinese men and women.Cancer Sci.2015106329429810.1111/cas.1259725557005
    [Google Scholar]
  22. QiH. WenF.Y. XieY.Y. LiuX.H. LiB.X. PengW.J. CaoH. ZhangL. Associations between depressive, anxiety, stress symptoms and elevated blood pressure: Findings from the CHCN-BTH cohort study and a two-sample Mendelian randomization analysis.J. Affect. Disord.202334117618410.1016/j.jad.2023.08.08637598715
    [Google Scholar]
  23. YuZ. CoreshJ. QiG. GramsM. BoerwinkleE. SniederH. TeumerA. PattaroC. KöttgenA. ChatterjeeN. TinA. A bidirectional mendelian randomization study supports causal effects of kidney function on blood pressure.Kidney Int.202098370871610.1016/j.kint.2020.04.04432454124
    [Google Scholar]
  24. XuR. ZhengT. OuyangC. DingX. GeC. Causal associations between site-specific cancer and diabetes risk: A two-sample Mendelian randomization study.Front. Endocrinol.202314111052310.3389/fendo.2023.111052336860363
    [Google Scholar]
  25. DaviesN.M. HolmesM.V. Davey SmithG. Reading Mendelian randomisation studies: A guide, glossary, and checklist for clinicians.BMJ2018362k60110.1136/bmj.k60130002074
    [Google Scholar]
  26. SuzukiK. AkiyamaM. IshigakiK. KanaiM. HosoeJ. ShojimaN. HozawaA. KadotaA. KurikiK. NaitoM. TannoK. IshigakiY. HirataM. MatsudaK. IwataN. IkedaM. SawadaN. YamajiT. IwasakiM. IkegawaS. MaedaS. MurakamiY. WakaiK. TsuganeS. SasakiM. YamamotoM. OkadaY. KuboM. KamataniY. HorikoshiM. YamauchiT. KadowakiT. Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population.Nat. Genet.201951337938610.1038/s41588‑018‑0332‑430718926
    [Google Scholar]
  27. SakaueS. KanaiM. TanigawaY. KarjalainenJ. KurkiM. KoshibaS. NaritaA. KonumaT. YamamotoK. AkiyamaM. IshigakiK. SuzukiA. SuzukiK. ObaraW. YamajiK. TakahashiK. AsaiS. TakahashiY. SuzukiT. ShinozakiN. YamaguchiH. MinamiS. MurayamaS. YoshimoriK. NagayamaS. ObataD. HigashiyamaM. MasumotoA. KoretsuneY. ItoK. TeraoC. YamauchiT. KomuroI. KadowakiT. TamiyaG. YamamotoM. NakamuraY. KuboM. MurakamiY. YamamotoK. KamataniY. PalotieA. RivasM.A. DalyM.J. MatsudaK. OkadaY. FinnGen A cross-population atlas of genetic associations for 220 human phenotypes.Nat. Genet.202153101415142410.1038/s41588‑021‑00931‑x34594039
    [Google Scholar]
  28. VanderWeeleT.J. Tchetgen TchetgenE.J. CornelisM. KraftP. Methodological challenges in mendelian randomization.Epidemiology201425342743510.1097/EDE.000000000000008124681576
    [Google Scholar]
  29. Gagliano TaliunS.A. EvansD.M. Ten simple rules for conducting a mendelian randomization study.PLOS Comput. Biol.2021178e100923810.1371/journal.pcbi.100923834383747
    [Google Scholar]
  30. EmdinC.A. KheraA.V. KathiresanS. Mendelian randomization.JAMA2017318191925192610.1001/jama.2017.1721929164242
    [Google Scholar]
  31. BowdenJ. Del Greco MF. MinelliC. ZhaoQ. LawlorD.A. SheehanN.A. ThompsonJ. Davey SmithG. Improving the accuracy of two-sample summary-data Mendelian randomization: Moving beyond the nome assumption.Int. J. Epidemiol.201948372874210.1093/ije/dyy25830561657
    [Google Scholar]
  32. CoddV. NelsonC. P. AlbrechtE. ManginoM. DeelenJ. BuxtonJ. L. HottengaJ. J. FischerK. EskoT. SurakkaI. Identification of seven loci affecting mean telomere length and their association with disease.Nat. Genet.201345442242710.1038/ng.252823535734
    [Google Scholar]
  33. SlobE.A.W. BurgessS. A comparison of robust Mendelian randomization methods using summary data.Genet. Epidemiol.202044431332910.1002/gepi.2229532249995
    [Google Scholar]
  34. BowdenJ. Davey SmithG. BurgessS. Mendelian randomization with invalid instruments: Effect estimation and bias detection through Egger regression.Int. J. Epidemiol.201544251252510.1093/ije/dyv08026050253
    [Google Scholar]
  35. BurgessS. ThompsonS.G. Interpreting findings from Mendelian randomization using the MR-Egger method.Eur. J. Epidemiol.201732537738910.1007/s10654‑017‑0255‑x28527048
    [Google Scholar]
  36. BowdenJ. Del Greco MF. MinelliC. Davey SmithG. SheehanN. ThompsonJ. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization.Stat. Med.201736111783180210.1002/sim.722128114746
    [Google Scholar]
  37. VerbanckM. ChenC.Y. NealeB. DoR. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases.Nat. Genet.201850569369810.1038/s41588‑018‑0099‑729686387
    [Google Scholar]
  38. Greco MF.D. MinelliC. SheehanN.A. ThompsonJ.R. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome.Stat. Med.201534212926294010.1002/sim.652225950993
    [Google Scholar]
  39. BowdenJ. Del Greco MF. MinelliC. Davey SmithG. SheehanN.A. ThompsonJ.R. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: The role of the I2 statistic.Int. J. Epidemiol.2016456dyw22010.1093/ije/dyw22027616674
    [Google Scholar]
  40. BowdenJ. SpillerW. Del Greco MF. SheehanN. ThompsonJ. MinelliC. Davey SmithG. Improving the visualization, interpretation and analysis of two-sample summary data Mendelian randomization via the radial plot and radial regression.Int. J. Epidemiol.20184741264127810.1093/ije/dyy10129961852
    [Google Scholar]
  41. BurgessS. BowdenJ. FallT. IngelssonE. ThompsonS.G. Sensitivity analyses for robust causal inference from Mendelian randomization analyses with multiple genetic variants.Epidemiology2017281304210.1097/EDE.000000000000055927749700
    [Google Scholar]
  42. GaoY. ZhouM. XuX. MaJ.Y. QinM.F. Body composition and risk of gestational diabetes mellitus: A univariable and multivariable Mendelian randomization study.J. Diabetes Investig.202315334635410.1111/jdi.1411538013660
    [Google Scholar]
  43. ChengJ. DekkersJ.C.M. FernandoR.L. Cross-validation of best linear unbiased predictions of breeding values using an efficient leave-one-out strategy.J. Anim. Breed. Genet.2021138551952710.1111/jbg.1254533729622
    [Google Scholar]
  44. WangZ. JensenM.A. ZenklusenJ.C. A practical guide to the cancer genome atlas (TCGA).Methods Mol. Biol.2016141811114110.1007/978‑1‑4939‑3578‑9_627008012
    [Google Scholar]
  45. MarselliL. ThorneJ. DahiyaS. SgroiD.C. SharmaA. Bonner-WeirS. MarchettiP. WeirG.C. Gene expression profiles of beta-cell enriched tissue obtained by laser capture microdissection from subjects with type 2 diabetes.PLoS One201057e1149910.1371/journal.pone.001149920644627
    [Google Scholar]
  46. RitchieM.E. PhipsonB. WuD. HuY. LawC.W. ShiW. SmythG.K. limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res.2015437e4710.1093/nar/gkv00725605792
    [Google Scholar]
  47. LangfelderP. HorvathS. WGCNA: An R package for weighted correlation network analysis.BMC Bioinformatics20089155910.1186/1471‑2105‑9‑55919114008
    [Google Scholar]
  48. KleinbaumD.G. KleinM. Chapter 4 - Survival analysis a self-learning text.3rd Ed.New York, NYSpringer1996XV, 70010.1007/978‑1‑4419‑6646‑9
    [Google Scholar]
  49. TibshiraniR. The lasso method for variable selection in the Cox model.Stat. Med.199716438539510.1002/(SICI)1097‑0258(19970228)16:4<385::AID‑SIM380>3.0.CO;2‑39044528
    [Google Scholar]
  50. ChenF. GongX. XiaM. YuF. WuJ. YuC. LiJ. The aging-related prognostic signature reveals the landscape of the tumor immune microenvironment in head and neck squamous cell carcinoma.Front. Oncol.20221285799410.3389/fonc.2022.85799435619896
    [Google Scholar]
  51. HänzelmannS. CasteloR. GuinneyJ. GSVA: Gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics2013141710.1186/1471‑2105‑14‑723323831
    [Google Scholar]
  52. KanehisaM. FurumichiM. SatoY. KawashimaM. Ishiguro-WatanabeM. KEGG for taxonomy-based analysis of pathways and genomes.Nucleic Acids Res.202351D1D587D59210.1093/nar/gkac96336300620
    [Google Scholar]
  53. YangW. SoaresJ. GreningerP. EdelmanE.J. LightfootH. ForbesS. BindalN. BeareD. SmithJ.A. ThompsonI.R. RamaswamyS. FutrealP.A. HaberD.A. StrattonM.R. BenesC. McDermottU. GarnettM.J. Genomics of drug sensitivity in cancer (GDSC): A resource for therapeutic biomarker discovery in cancer cells.Nucleic Acids Res.201241D1D955D96110.1093/nar/gks111123180760
    [Google Scholar]
  54. GarnettM.J. EdelmanE.J. HeidornS.J. GreenmanC.D. DasturA. LauK.W. GreningerP. ThompsonI.R. LuoX. SoaresJ. LiuQ. IorioF. SurdezD. ChenL. MilanoR.J. BignellG.R. TamA.T. DaviesH. StevensonJ.A. BarthorpeS. LutzS.R. KogeraF. LawrenceK. McLaren-DouglasA. MitropoulosX. MironenkoT. ThiH. RichardsonL. ZhouW. JewittF. ZhangT. O’BrienP. BoisvertJ.L. PriceS. HurW. YangW. DengX. ButlerA. ChoiH.G. ChangJ.W. BaselgaJ. StamenkovicI. EngelmanJ.A. SharmaS.V. DelattreO. Saez-RodriguezJ. GrayN.S. SettlemanJ. FutrealP.A. HaberD.A. StrattonM.R. RamaswamyS. McDermottU. BenesC.H. Systematic identification of genomic markers of drug sensitivity in cancer cells.Nature2012483739157057510.1038/nature1100522460902
    [Google Scholar]
  55. XieJ. ChenL. TangQ. WeiW. CaoY. WuC. HangJ. ZhangK. ShiJ. WangM. A necroptosis-related prognostic model of uveal melanoma was constructed by single-cell sequencing analysis and weighted co-expression network analysis based on public databases.Front. Immunol.20221384762410.3389/fimmu.2022.84762435242144
    [Google Scholar]
  56. AhmadE. LimS. LampteyR. WebbD.R. DaviesM.J. Type 2 diabetes.Lancet2022400103651803182010.1016/S0140‑6736(22)01655‑536332637
    [Google Scholar]
  57. SuhS. KimK.W. Diabetes and cancer: cancer should be screened in routine diabetes assessment.Diabetes Metab. J.201943673374310.4093/dmj.2019.017731902143
    [Google Scholar]
  58. ZhangF. de Haan-DuJ. SidorenkovG. LandmanG.W.D. JalvingM. ZhangQ. de BockG.H. Type 2 diabetes mellitus and clinicopathological tumor characteristics in women diagnosed with breast cancer: A systematic review and meta-analysis.Cancers20211319499210.3390/cancers1319499234638475
    [Google Scholar]
  59. HuoQ. WangS. HouY. GorczynskiR.M. ShenY. WangB. GeH. LiT. Editorial: The relationship between diabetes and cancers and its underlying mechanisms, volume II.Front. Endocrinol.202414135757710.3389/fendo.2023.135757738292767
    [Google Scholar]
  60. JenkinsD.J.A. WillettW.C. YusufS. HuF.B. GlennA.J. LiuS. MenteA. MillerV. BangdiwalaS.I. GersteinH.C. SieriS. FerrariP. PatelA.V. McCulloughM.L. Le MarchandL. FreedmanN.D. LoftfieldE. SinhaR. ShuX.O. TouvierM. SawadaN. TsuganeS. van den BrandtP.A. ShuvalK. KhanT.A. PaquetteM. Sahye-PudaruthS. PatelD. SiuT.F.Y. SrichaikulK. KendallC.W.C. SievenpiperJ.L. BalachandranB. ZurbauA. WangX. LiangF. YangW. Clinical nutrition & risk factor modification centre collaborators association of glycaemic index and glycaemic load with type 2 diabetes, cardiovascular disease, cancer, and all-cause mortality: A meta-analysis of mega cohorts of more than 100 000 participants.Lancet Diabetes Endocrinol.202412210711810.1016/S2213‑8587(23)00344‑338272606
    [Google Scholar]
  61. AnN. ZhangY. ShaZ. XuZ. LiuX. T2DM may exert a protective effect against digestive system tumors in East Asian populations: A Mendelian randomization analysis.Front. Oncol.202414132715410.3389/fonc.2024.132715438947888
    [Google Scholar]
  62. SmithG.D. EbrahimS. What can mendelian randomisation tell us about modifiable behavioural and environmental exposures?BMJ200533074991076107910.1136/bmj.330.7499.107615879400
    [Google Scholar]
  63. BastaracheL. DennyJ.C. RodenD.M. Phenome-Wide association studies.JAMA20223271757610.1001/jama.2021.2035634982132
    [Google Scholar]
  64. ZhengJ. BairdD. BorgesM.C. BowdenJ. HemaniG. HaycockP. EvansD.M. SmithG.D. Recent developments in mendelian randomization studies.Curr. Epidemiol. Rep.20174433034510.1007/s40471‑017‑0128‑629226067
    [Google Scholar]
  65. HuoJ. XieW. FanX. SunP. Pyroptosis, apoptosis, and necroptosis molecular subtype derived prognostic signature universal applicable for gastric cancer-A large sample and multicenter retrospective analysis.Comput. Biol. Med.202214910603710.1016/j.compbiomed.2022.10603736044785
    [Google Scholar]
  66. XingY. ZhangZ. GaoW. SongW. LiT. Immune infiltration and prognosis in gastric cancer: Role of NAD+ metabolism-related markers.PeerJ202412e1783310.7717/peerj.1783339099656
    [Google Scholar]
  67. LiJ. HanT. WangX. WangY. ChenX. ChenW. YangQ. H19 may regulate the immune cell infiltration in carcinogenesis of gastric cancer through miR-378a-5p/SERPINH1 signaling.World J. Surg. Oncol.202220129510.1186/s12957‑022‑02760‑636104825
    [Google Scholar]
  68. FuC. KouR. MengJ. JiangD. ZhongR. DongM. m6A genotypes and prognostic signature for assessing the prognosis of patients with acute myeloid leukemia.BMC Med. Genomics202316119110.1186/s12920‑023‑01629‑137596597
    [Google Scholar]
  69. YangJ. ZhuangH. LiJ. Nunez-NescolardeA.B. LuoN. ChenH. LiA. QuX. WangQ. FanJ. BaiX. YeZ. GuB. MengY. ZhangX. WuD. SiaY. JiangX. ChenW. CombesA.N. Nikolic-PatersonD.J. YuX. The secreted micropeptide C4orf48 enhances renal fibrosis via an RNA-binding mechanism.J. Clin. Invest.202413410e17839210.1172/JCI17839238625739
    [Google Scholar]
  70. ZhangJ. HuC. ZhangR. XuJ. ZhangY. YuanL. ZhangS. PanS. CaoM. QinJ. ChengX. XuZ. The role of macrophages in gastric cancer.Front. Immunol.202314128217610.3389/fimmu.2023.128217638143746
    [Google Scholar]
  71. MansorunovD. ApanovichN. KipkeevaF. NikulinM. MalikhovaO. StilidiI. KarpukhinA. The correlation of ten immune checkpoint gene expressions and their association with gastric cancer development.Int. J. Mol. Sci.202223221384610.3390/ijms23221384636430322
    [Google Scholar]
  72. GaoQ. CuiL. HuangC. ChenZ. WangX. WenS. ZhaoY. WangM. ShenB. ZhuW. Gastric cancer-derived mesenchymal stem cells promote gastric cancer cell lines migration by modulating CD276 expression.Exp. Cell Res.2023422111341410.1016/j.yexcr.2022.11341436368567
    [Google Scholar]
  73. LuX. XieQ. PanX. ZhangR. ZhangX. PengG. ZhangY. ShenS. TongN. Type 2 diabetes mellitus in adults: Pathogenesis, prevention and therapy.Signal Transduct. Target. Ther.20249126210.1038/s41392‑024‑01951‑939353925
    [Google Scholar]
  74. WernerH. LeRoithD. Hallmarks of cancer: The insulin- like growth factors perspective.Front. Oncol.202212105558910.3389/fonc.2022.105558936479090
    [Google Scholar]
  75. LuC. WolfsD. El ghormliL. LevitskyL.L. Levitt KatzL.E. LaffelL.M. PattiM.E. IsganaitisE. Growth hormone mediators and glycemic control in youths with type 2 diabetes.JAMA Netw. Open202472e24044710.1001/jamanetworkopen.2024.044738421647
    [Google Scholar]
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  • Article Type:
    Research Article
Keyword(s): CST2; Gastric cancer; mendelian randomization; PSAPL1; transcriptomic data; Type 2 diabetes
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