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2000
Volume 32, Issue 24
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Aims

This study aimed to determine the molecular markers related to gefitinib sensitivity for guiding the prognosis of lung adenocarcinoma (LUAD) and providing new evidence for promoting the precise treatment of LUAD.

Background

Lung adenocarcinoma (LUAD) is a prevalent lung cancer subtype with inferior survival outcomes. However, gefitinib is the first molecular targeted drug approved by Food and Drug Administration (FDA) to treat advanced LUAD. Gefitinib sensitivity-related gene targets for LUAD are rarely studied.

Objective

This study was designed to probe the potential molecular markers related to the sensitivity of gefitinib in LUAD.

Methods

The gene expression profiles of LUAD cells in the Genomics of Drug Sensitivity in Cancer (GDSC) database were used for Weighted Gene Co-expression Network Analysis (WGCNA) to select the modules most related to gefitinib sensitivity. The Cancer Genome Atlas (TCGA) database was used to compare the expression of LUAD and para-cancerous tissues. Differentially expressed genes (DEGs) were then filtered and intersected with the highly linked genes in the module relevant to gefitinib sensitivity. Univariate Cox regression analysis was conducted to identify prognostically related genes to LUAD. The correlation between genes and drug IC was calculated by Spearman correlation analysis. Quantitative RT-PCR and immunofluorescence detection validated hub genes FAM13B and PFKP expressions.

Results

Among the 10 modules divided by WGCNA, the module with the most significant positive correlation and the most significant negative correlation with gefitinib sensitivity were found. FAM13B, PFKP, FGD3, RNASE1, MUC16, GJB5, and GJB3 were hub genes related to gefitinib sensitivity in LUAD. Significantly, the low expressed FAM13 in LUAD tissues positively correlated with immune response. At the same time, overexpressed PFKP in the LUAD cohort was related to an unfavorable prognosis, cell proliferation, and cell cycle. We also found that FAM13B and PFKP expressions were enhanced in LUAD cell lines.

Conclusions

This study identified 7 critical genes related to gefitinib sensitivity in LUAD. Functionally, genes positively correlated with gefitinib sensitivity might regulate the progression of LUAD through the immune, cell cycle, and metabolic pathways and showed potential effects in predicting sensitivity to different drugs. These findings help offer a theoretical direction for personalized treatment of LUAD.

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References

  1. SiegelR.L. MillerK.D. WagleN.S. JemalA. Cancer statistics, 2023.CA Cancer J. Clin.2023731174810.3322/caac.2176336633525
     [Google Scholar]
  2. BartaJ.A. PowellC.A. WisniveskyJ.P. Global epidemiology of lung cancer.Ann. Glob. Health2019851810.5334/aogh.241930741509
     [Google Scholar]
  3. OrtegaM.A. PekarekL. NavarroF. Fraile-MartínezO. García-MonteroC. Álvarez-MonM.Á. Diez-PedreroR. Boyano-AdánezM.C. GuijarroL.G. Barrena-BlázquezS. Gómez-LahozA.M. HaroS. ArroyoM. MonserratJ. SaezM.A. Alvarez-MonM. Updated views in targeted therapy in the patient with non-small cell lung cancer.J. Pers. Med.202313216710.3390/jpm1302016736836402
     [Google Scholar]
  4. SiegelR.L. MillerK.D. JemalA. Cancer statistics, 2019.CA Cancer J. Clin.201969173410.3322/caac.2155130620402
     [Google Scholar]
  5. NicholsonA.G. TsaoM.S. BeasleyM.B. BorczukA.C. BrambillaE. CooperW.A. DacicS. JainD. KerrK.M. LantuejoulS. NoguchiM. PapottiM. RekhtmanN. ScagliottiG. van SchilP. ShollL. YatabeY. YoshidaA. TravisW.D. The 2021 WHO classification of lung tumors: Impact of advances since 2015.J. Thorac. Oncol.202217336238710.1016/j.jtho.2021.11.00334808341
     [Google Scholar]
  6. ThaiA.A. SolomonB.J. SequistL.V. GainorJ.F. HeistR.S. Lung cancer.Lancet20213981029953555410.1016/S0140‑6736(21)00312‑334273294
     [Google Scholar]
  7. ShiJ. ChenY. PengC. KuangL. ZhangZ. LiY. HuangK. Advances in targeted therapy against driver mutations and epigenetic alterations in non-small cell lung cancer.Oncologie202224461364810.32604/oncologie.2022.027545
     [Google Scholar]
  8. ZhangH. WangX. YuY. YangZ. Progression of exosome-mediated chemotherapy resistance in cancer.Oncologie202224224725910.32604/oncologie.2022.020993
     [Google Scholar]
  9. ChenJ.W. DhahbiJ. Lung adenocarcinoma and lung squamous cell carcinoma cancer classification, biomarker identification, and gene expression analysis using overlapping feature selection methods.Sci. Rep.20211111332310.1038/s41598‑021‑92725‑834172784
     [Google Scholar]
  10. ZhengQ. MinS. ZhouQ. Identification of potential diagnostic and prognostic biomarkers for LUAD based on TCGA and GEO databases.Biosci. Rep.2021416BSR2020437010.1042/BSR2020437034017995
     [Google Scholar]
  11. WeiG. ZhangH. ZhaoH. WangJ. WuN. LiL. WuJ. ZhangD. Emerging immune checkpoints in the tumor microenvironment: Implications for cancer immunotherapy.Cancer Lett.2021511687610.1016/j.canlet.2021.04.02133957184
     [Google Scholar]
  12. BudcziesJ. AllgäuerM. LitchfieldK. RempelE. ChristopoulosP. KazdalD. EndrisV. ThomasM. FröhlingS. PetersS. SwantonC. SchirmacherP. StenzingerA. Optimizing panel-based tumor mutational burden (TMB) measurement.Ann. Oncol.20193091496150610.1093/annonc/mdz20531268125
     [Google Scholar]
  13. SongP. LiW. GuoL. YingJ. GaoS. HeJ. Identification and validation of a novel signature based on NK cell marker genes to predict prognosis and immunotherapy response in lung adenocarcinoma by integrated analysis of single-cell and bulk RNA-sequencing.Front. Immunol.20221385074510.3389/fimmu.2022.85074535757748
     [Google Scholar]
  14. ZuoS. WeiM. WangS. DongJ. WeiJ. Pan-cancer analysis of immune cell infiltration identifies a prognostic Immune-Cell Characteristic Score (ICCS) in lung adenocarcinoma.Front. Immunol.202011121810.3389/fimmu.2020.0121832714316
     [Google Scholar]
  15. WangH. FangJ. WangY. LiS. WangZ. HeW. WangN. LuoS. ZouH. ZhangF. Gene editing in non-small cell lung cancer: Current application and future perspective.Oncologie2022241658310.32604/oncologie.2022.021863
     [Google Scholar]
  16. NormannoN. BarberisM. De MarinisF. GridelliC. Molecular and genomic profiling of lung cancer in the era of precision medicine: A position paper from the italian Association of Thoracic Oncology (AIOT).Cancers2020126162710.3390/cancers1206162732575424
     [Google Scholar]
  17. KrisM.G. NataleR.B. HerbstR.S. LynchT.J.Jr PragerD. BelaniC.P. SchillerJ.H. KellyK. SpiridonidisH. SandlerA. AlbainK.S. CellaD. WolfM.K. AverbuchS.D. OchsJ.J. KayA.C. Efficacy of gefitinib, an inhibitor of the epidermal growth factor receptor tyrosine kinase, in symptomatic patients with non-small cell lung cancer: A randomized trial.JAMA2003290162149215810.1001/jama.290.16.214914570950
     [Google Scholar]
  18. HanJ.Y. YoonK.A. ParkJ.H. LeeY.J. LeeG.K. HanJ.H. YoonS.J. YunT. KimH.T. LeeJ.S. DNA repair gene polymorphisms and benefit from gefitinib in never-smokers with lung adenocarcinoma.Cancer2011117143201320810.1002/cncr.2586321264830
     [Google Scholar]
  19. DenisenkoT.V. BudkevichI.N. ZhivotovskyB. Cell death-based treatment of lung adenocarcinoma.Cell Death Dis.20189211710.1038/s41419‑017‑0063‑y29371589
     [Google Scholar]
  20. XuY. LiuH. ChenJ. ZhouQ. Acquired resistance of lung adenocarcinoma to EGFR-tyrosine kinase inhibitors gefitinib and erlotinib.Cancer Biol. Ther.20109857258210.4161/cbt.9.8.1188120404520
     [Google Scholar]
  21. ZhangX. MaityT. KashyapM.K. BansalM. VenugopalanA. SinghS. AwasthiS. MarimuthuA. Charles JacobH.K. BelkinaN. PittsS. CultraroC.M. GaoS. KirkaliG. BiswasR. ChaerkadyR. CalifanoA. PandeyA. GuhaU. Quantitative tyrosine phosphoproteomics of Epidermal Growth Factor Receptor (EGFR) tyrosine kinase inhibitor-treated lung adenocarcinoma cells reveals potential novel biomarkers of therapeutic response.Mol. Cell. Proteomics201716589191010.1074/mcp.M117.06743928331001
     [Google Scholar]
  22. ChengZ. LiM. DeyR. ChenY. Nanomaterials for cancer therapy: Current progress and perspectives.J. Hematol. Oncol.20211418510.1186/s13045‑021‑01096‑034059100
     [Google Scholar]
  23. EftekhariA. KryschiC. PamiesD. GulecS. AhmadianE. JanasD. DavaranS. KhalilovR. Natural and synthetic nanovectors for cancer therapy.Nanotheranostics20237323625710.7150/ntno.7756437064613
     [Google Scholar]
  24. BaranA. Fırat BaranM. KeskinC. HatipoğluA. YavuzÖ. İrtegün KandemirS. AdicanM.T. KhalilovR. MammadovaA. AhmadianE. RosićG. SelakovicD. EftekhariA. Investigation of antimicrobial and cytotoxic properties and specification of silver nanoparticles (AgNPs) Derived From Cicer arietinum L. Green Leaf Extract.Front. Bioeng. Biotechnol.20221085513610.3389/fbioe.2022.85513635330628
     [Google Scholar]
  25. NasibovaA. Generation of nanoparticles in biological systems and their application prospects.Adv. Biol. Earth Sci.20238140146
     [Google Scholar]
  26. LangfelderP. HorvathS. WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics20089155910.1186/1471‑2105‑9‑55919114008
     [Google Scholar]
  27. 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]
  28. HänzelmannS. CasteloR. GuinneyJ. GSVA: gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics2013141710.1186/1471‑2105‑14‑723323831
     [Google Scholar]
  29. SubramanianA. TamayoP. MoothaV.K. MukherjeeS. EbertB.L. GilletteM.A. PaulovichA. PomeroyS.L. GolubT.R. LanderE.S. MesirovJ.P. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles.Proc. Natl. Acad. Sci.200510243155451555010.1073/pnas.050658010216199517
     [Google Scholar]
  30. YoshiharaK. ShahmoradgoliM. MartínezE. VegesnaR. KimH. Torres-GarciaW. TreviñoV. ShenH. LairdP.W. LevineD.A. CarterS.L. GetzG. Stemke-HaleK. MillsG.B. VerhaakR.G.W. Inferring tumour purity and stromal and immune cell admixture from expression data.Nat. Commun.201341261210.1038/ncomms361224113773
     [Google Scholar]
  31. NewmanA.M. LiuC.L. GreenM.R. GentlesA.J. FengW. XuY. HoangC.D. DiehnM. AlizadehA.A. Robust enumeration of cell subsets from tissue expression profiles.Nat. Methods201512545345710.1038/nmeth.333725822800
     [Google Scholar]
  32. LiuJ. LichtenbergT. HoadleyK.A. PoissonL.M. LazarA.J. CherniackA.D. KovatichA.J. BenzC.C. LevineD.A. LeeA.V. OmbergL. WolfD.M. ShriverC.D. ThorssonV. HuH. Caesar-JohnsonS.J. DemchokJ.A. FelauI. KasapiM. FergusonM.L. HutterC.M. SofiaH.J. TarnuzzerR. WangZ. YangL. ZenklusenJ.C. ZhangJ.J. ChudamaniS. LiuJ. LollaL. NareshR. PihlT. SunQ. WanY. WuY. ChoJ. DeFreitasT. FrazerS. GehlenborgN. GetzG. HeimanD.I. KimJ. LawrenceM.S. LinP. MeierS. NobleM.S. SaksenaG. VoetD. ZhangH. BernardB. ChambweN. DhankaniV. KnijnenburgT. KramerR. LeinonenK. LiuY. MillerM. ReynoldsS. ShmulevichI. ThorssonV. ZhangW. AkbaniR. BroomB.M. HegdeA.M. JuZ. KanchiR.S. KorkutA. LiJ. LiangH. LingS. LiuW. LuY. MillsG.B. NgK-S. RaoA. RyanM. WangJ. WeinsteinJ.N. ZhangJ. AbeshouseA. ArmeniaJ. ChakravartyD. ChatilaW.K. de BruijnI. GaoJ. GrossB.E. HeinsZ.J. KundraR. LaK. LadanyiM. LunaA. NissanM.G. OchoaA. PhillipsS.M. ReznikE. Sanchez-VegaF. SanderC. SchultzN. SheridanR. SumerS.O. SunY. TaylorB.S. WangJ. ZhangH. AnurP. PetoM. SpellmanP. BenzC. StuartJ.M. WongC.K. YauC. HayesD.N. ParkerJ.S. WilkersonM.D. AllyA. BalasundaramM. BowlbyR. BrooksD. CarlsenR. ChuahE. DhallaN. HoltR. JonesS.J.M. KasaianK. LeeD. MaY. MarraM.A. MayoM. MooreR.A. MungallA.J. MungallK. RobertsonA.G. SadeghiS. ScheinJ.E. SipahimalaniP. TamA. ThiessenN. TseK. WongT. BergerA.C. BeroukhimR. CherniackA.D. CibulskisC. GabrielS.B. GaoG.F. HaG. MeyersonM. SchumacherS.E. ShihJ. KucherlapatiM.H. KucherlapatiR.S. BaylinS. CopeL. DanilovaL. BootwallaM.S. LaiP.H. MaglinteD.T. Van Den BergD.J. WeisenbergerD.J. AumanJ.T. BaluS. BodenheimerT. FanC. HoadleyK.A. HoyleA.P. JefferysS.R. JonesC.D. MengS. MieczkowskiP.A. MoseL.E. PerouA.H. PerouC.M. RoachJ. ShiY. SimonsJ.V. SkellyT. SolowayM.G. TanD. VeluvoluU. FanH. HinoueT. LairdP.W. ShenH. ZhouW. BellairM. ChangK. CovingtonK. CreightonC.J. DinhH. DoddapaneniH.V. DonehowerL.A. DrummondJ. GibbsR.A. GlennR. HaleW. HanY. HuJ. KorchinaV. LeeS. LewisL. LiW. LiuX. MorganM. MortonD. MuznyD. SantibanezJ. ShethM. ShinbroE. WangL. WangM. WheelerD.A. XiL. ZhaoF. HessJ. AppelbaumE.L. BaileyM. CordesM.G. DingL. FronickC.C. FultonL.A. FultonR.S. KandothC. MardisE.R. McLellanM.D. MillerC.A. SchmidtH.K. WilsonR.K. CrainD. CurleyE. GardnerJ. LauK. MalleryD. MorrisS. PaulauskisJ. PennyR. SheltonC. SheltonT. ShermanM. ThompsonE. YenaP. BowenJ. Gastier-FosterJ.M. GerkenM. LeraasK.M. LichtenbergT.M. RamirezN.C. WiseL. ZmudaE. CorcoranN. CostelloT. HovensC. CarvalhoA.L. de CarvalhoA.C. FregnaniJ.H. Longatto-FilhoA. ReisR.M. Scapulatempo-NetoC. SilveiraH.C.S. VidalD.O. BurnetteA. EschbacherJ. HermesB. NossA. SinghR. AndersonM.L. CastroP.D. IttmannM. HuntsmanD. KohlB. LeX. ThorpR. AndryC. DuffyE.R. LyadovV. PaklinaO. SetdikovaG. ShabuninA. TavobilovM. McPhersonC. WarnickR. BerkowitzR. CramerD. FeltmateC. HorowitzN. KibelA. MutoM. RautC.P. MalykhA. Barnholtz-SloanJ.S. BarrettW. DevineK. FulopJ. OstromQ.T. ShimmelK. WolinskyY. SloanA.E. De RoseA. GiulianteF. GoodmanM. KarlanB.Y. HagedornC.H. EckmanJ. HarrJ. MyersJ. TuckerK. ZachL.A. DeyarminB. HuH. KvecherL. LarsonC. MuralR.J. SomiariS. VichaA. ZelinkaT. BennettJ. IacoccaM. RabenoB. SwansonP. LatourM. LacombeL. TêtuB. BergeronA. McGrawM. StaugaitisS.M. ChabotJ. HibshooshH. SepulvedaA. SuT. WangT. PotapovaO. VoroninaO. DesjardinsL. MarianiO. Roman-RomanS. SastreX. SternM-H. ChengF. SignorettiS. BerchuckA. BignerD. LippE. MarksJ. McCallS. McLendonR. SecordA. SharpA. BeheraM. BratD.J. ChenA. DelmanK. ForceS. KhuriF. MaglioccaK. MaithelS. OlsonJ.J. OwonikokoT. PickensA. RamalingamS. ShinD.M. SicaG. Van MeirE.G. ZhangH. EijckenboomW. GillisA. KorpershoekE. LooijengaL. OosterhuisW. StoopH. van KesselK.E. ZwarthoffE.C. CalatozzoloC. CuppiniL. CuzzubboS. DiMecoF. FinocchiaroG. MatteiL. PerinA. PolloB. ChenC. HouckJ. LohavanichbutrP. HartmannA. StoehrC. StoehrR. TaubertH. WachS. WullichB. KyclerW. MurawaD. WiznerowiczM. ChungK. EdenfieldW.J. MartinJ. BaudinE. BubleyG. BuenoR. De RienzoA. RichardsW.G. KalkanisS. MikkelsenT. NoushmehrH. ScarpaceL. GirardN. AymerichM. CampoE. GinéE. GuillermoA.L. Van BangN. HanhP.T. PhuB.D. TangY. ColmanH. EvasonK. DottinoP.R. MartignettiJ.A. GabraH. JuhlH. AkeredoluT. StepaS. HoonD. AhnK. KangK.J. BeuschleinF. BreggiaA. BirrerM. BellD. BoradM. BryceA.H. CastleE. ChandanV. ChevilleJ. CoplandJ.A. FarnellM. FlotteT. GiamaN. HoT. KendrickM. KocherJ-P. KoppK. MoserC. NagorneyD. O’BrienD. O’NeillB.P. PatelT. PetersenG. QueF. RiveraM. RobertsL. SmallridgeR. SmyrkT. StantonM. ThompsonR.H. TorbensonM. YangJ.D. ZhangL. BrimoF. AjaniJ.A. Angulo GonzalezA.M. BehrensC. BondarukJ. BroaddusR. CzerniakB. EsmaeliB. FujimotoJ. GershenwaldJ. GuoC. LazarA.J. LogothetisC. Meric-BernstamF. MoranC. RamondettaL. RiceD. SoodA. TamboliP. ThompsonT. TroncosoP. TsaoA. WistubaI. CarterC. HayduL. HerseyP. JakrotV. KakavandH. KeffordR. LeeK. LongG. MannG. QuinnM. SawR. ScolyerR. ShannonK. SpillaneA. StretchJ. SynottM. ThompsonJ. WilmottJ. Al-AhmadieH. ChanT.A. GhosseinR. GopalanA. LevineD.A. ReuterV. SingerS. SinghB. TienN.V. BroudyT. MirsaidiC. NairP. DrwiegaP. MillerJ. SmithJ. ZarenH. ParkJ-W. HungN.P. KebebewE. LinehanW.M. MetwalliA.R. PacakK. PintoP.A. SchiffmanM. SchmidtL.S. VockeC.D. WentzensenN. WorrellR. YangH. MoncrieffM. GoparajuC. MelamedJ. PassH. BotnariucN. CaramanI. CernatM. ChemencedjiI. ClipcaA. DorucS. GorincioiG. MuraS. PirtacM. StanculI. TcaciucD. AlbertM. AlexopoulouI. ArnaoutA. BartlettJ. EngelJ. GilbertS. ParfittJ. SekhonH. ThomasG. RasslD.M. RintoulR.C. BifulcoC. TamakawaR. UrbaW. HaywardN. TimmersH. AntenucciA. FaccioloF. GraziG. MarinoM. MerolaR. de KrijgerR. Gimenez-RoqueploA-P. PichéA. ChevalierS. McKercherG. BirsoyK. BarnettG. BrewerC. FarverC. NaskaT. PennellN.A. RaymondD. SchileroC. SmolenskiK. WilliamsF. MorrisonC. BorgiaJ.A. LiptayM.J. PoolM. SederC.W. JunkerK. OmbergL. DinkinM. ManikhasG. AlvaroD. BragazziM.C. CardinaleV. CarpinoG. GaudioE. CheslaD. CottinghamS. DubinaM. MoiseenkoF. DhanasekaranR. BeckerK-F. JanssenK-P. Slotta-HuspeninaJ. Abdel-RahmanM.H. AzizD. BellS. CebullaC.M. DavisA. DuellR. ElderJ.B. HiltyJ. KumarB. LangJ. LehmanN.L. MandtR. NguyenP. PilarskiR. RaiK. SchoenfieldL. SenecalK. WakelyP. HansenP. LechanR. PowersJ. TischlerA. GrizzleW.E. SextonK.C. KastlA. HendersonJ. PortenS. WaldmannJ. FassnachtM. AsaS.L. SchadendorfD. CouceM. GraefenM. HulandH. SauterG. SchlommT. SimonR. TennstedtP. OlabodeO. NelsonM. BatheO. CarrollP.R. ChanJ.M. DisaiaP. GlennP. KelleyR.K. LandenC.N. PhillipsJ. PradosM. SimkoJ. Smith-McCuneK. VandenBergS. RogginK. FehrenbachA. KendlerA. SifriS. SteeleR. JimenoA. CareyF. ForgieI. MannelliM. CarneyM. HernandezB. CamposB. Herold-MendeC. JungkC. UnterbergA. von DeimlingA. BosslerA. GalbraithJ. JacobusL. KnudsonM. KnutsonT. MaD. MilhemM. SigmundR. GodwinA.K. MadanR. RosenthalH.G. AdebamowoC. AdebamowoS.N. BoussioutasA. BeerD. GiordanoT. Mes-MassonA-M. SaadF. BocklageT. LandrumL. MannelR. MooreK. MoxleyK. PostierR. WalkerJ. ZunaR. FeldmanM. ValdiviesoF. DhirR. LuketichJ. Mora PineroE.M. Quintero-AguiloM. CarlottiC.G.Jr Dos SantosJ.S. KempR. SankarankutyA. TirapelliD. CattoJ. AgnewK. SwisherE. CreaneyJ. RobinsonB. ShelleyC.S. GodwinE.M. KendallS. ShipmanC. BradfordC. CareyT. HaddadA. MoyerJ. PetersonL. PrinceM. RozekL. WolfG. BowmanR. FongK.M. YangI. KorstR. RathmellW.K. Fantacone-CampbellJ.L. HookeJ.A. KovatichA.J. ShriverC.D. DiPersioJ. DrakeB. GovindanR. HeathS. LeyT. Van TineB. WesterveltP. RubinM.A. LeeJ.I. AredesN.D. MariamidzeA. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics.Cell20181732400416.e1110.1016/j.cell.2018.02.05229625055
     [Google Scholar]
  33. GeeleherP. CoxN. HuangR.S. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels.PLoS One201499e10746810.1371/journal.pone.010746825229481
     [Google Scholar]
  34. MotieGhaderH. Tabrizi-NezhadiP. Deldar Abad PaskehM. BaradaranB. MokhtarzadehA. HashemiM. LanjanianH. JazayeriS.M. MalekiM. KhodadadiE. NematzadehS. KianiF. MaghsoudlooM. Masoudi-NejadA. Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks analysis.Sci. Rep.2022121941710.1038/s41598‑022‑13719‑835676421
     [Google Scholar]
  35. CuzickJ. SwansonG.P. FisherG. BrothmanA.R. BerneyD.M. ReidJ.E. MesherD. SpeightsV.O. StankiewiczE. FosterC.S. MøllerH. ScardinoP. WarrenJ.D. ParkJ. YounusA. FlakeD.D.II WagnerS. GutinA. LanchburyJ.S. StoneS. Prognostic value of an RNA expression signature derived from cell cycle proliferation genes in patients with prostate cancer: A retrospective study.Lancet Oncol.201112324525510.1016/S1470‑2045(10)70295‑321310658
     [Google Scholar]
  36. GarneloM. TanA. HerZ. YeongJ. LimC.J. ChenJ. LimK.H. WeberA. ChowP. ChungA. OoiL.L.P.J. TohH.C. HeikenwalderM. NgI.O.L. NardinA. ChenQ. AbastadoJ.P. ChewV. Interaction between tumour-infiltrating B cells and T cells controls the progression of hepatocellular carcinoma.Gut201766234235110.1136/gutjnl‑2015‑31081426669617
     [Google Scholar]
  37. HungR.J. Khodayari MoezE. KimS.J. BudhathokiS. BrooksJ.D. Considerations of biomarker application for cancer continuum in the era of precision medicine.Curr. Epidemiol. Rep.20229320021110.1007/s40471‑022‑00295‑836090700
     [Google Scholar]
  38. TripathiS.K. BiswalB.K. SOX9 promotes epidermal growth factor receptor-tyrosine kinase inhibitor resistance via targeting β-catenin and epithelial to mesenchymal transition in lung cancer.Life Sci.202127711960810.1016/j.lfs.2021.11960833989664
     [Google Scholar]
  39. SusiniT. SaccardinG. RendaI. GianiM. TartarottiE. NoriJ. VanziE. PasqualiniE. BianchiS. Immunohistochemical evaluation of FGD3 expression: A new strong prognostic factor in invasive breast cancer.Cancers20211315382410.3390/cancers1315382434359725
     [Google Scholar]
  40. RendaI. BianchiS. VezzosiV. NoriJ. VanziE. TavellaK. SusiniT. Expression of FGD3 gene as prognostic factor in young breast cancer patients.Sci. Rep.2019911520410.1038/s41598‑019‑51766‑w31645624
     [Google Scholar]
  41. WillisS. SunY. AbramovitzM. FeiT. YoungB. LinX. NiM. AchuaJ. ReganM.M. GrayK.P. GrayR. WangV. LongB. KammlerR. SparanoJ.A. WilliamsC. GoldsteinL.J. SalgadoR. LoiS. PruneriG. VialeG. BrownM. Leyland-JonesB. High expression of FGD3, a putative regulator of cell morphology and motility, is prognostic of favorable outcome in multiple cancers.JCO Precis. Oncol.20171111310.1200/PO.17.0000932913979
     [Google Scholar]
  42. GuoF. ChengX. JingB. WuH. JinX. FGD3 binds with HSF4 to suppress p65 expression and inhibit pancreatic cancer progression.Oncogene202241683885110.1038/s41388‑021‑02140‑634975151
     [Google Scholar]
  43. WangY.N. LeeH.H. ChouC.K. YangW.H. WeiY. ChenC.T. YaoJ. HsuJ.L. ZhuC. YingH. YeY. WangW.J. LimS.O. XiaW. KoH.W. LiuX. LiuC.G. WuX. WangH. LiD. PrakashL.R. KatzM.H. KangY. KimM. FlemingJ.B. FogelmanD. JavleM. MaitraA. HungM.C. Angiogenin/ribonuclease 5 Is an EGFR ligand and a serum biomarker for erlotinib sensitivity in pancreatic cancer.Cancer Cell2018334752769.e810.1016/j.ccell.2018.02.01229606349
     [Google Scholar]
  44. FischerS. GesierichS. GriemertB. SchänzerA. AckerT. AugustinH.G. OlssonA.K. PreissnerK.T. Extracellular RNA liberates tumor necrosis factor-α to promote tumor cell trafficking and progression.Cancer Res.201373165080508910.1158/0008‑5472.CAN‑12‑465723774209
     [Google Scholar]
  45. AithalA. RauthS. KshirsagarP. ShahA. LakshmananI. JunkerW.M. JainM. PonnusamyM.P. BatraS.K. MUC16 as a novel target for cancer therapy.Expert Opin. Ther. Targets201822867568610.1080/14728222.2018.149884529999426
     [Google Scholar]
  46. LakshmananI. SalfityS. SeshacharyuluP. RachaganiS. ThomasA. DasS. MajhiP.D. NimmakayalaR.K. VengojiR. LeleS.M. PonnusamyM.P. BatraS.K. GantiA.K. MUC16 regulates TSPYL5 for lung cancer cell growth and chemoresistance by suppressing p53.Clin. Cancer Res.201723143906391710.1158/1078‑0432.CCR‑16‑253028196872
     [Google Scholar]
  47. HuoY. ZhouY. ZhengJ. JinG. TaoL. YaoH. ZhangJ. SunY. LiuY. HuL.P. GJB3 promotes pancreatic cancer liver metastasis by enhancing the polarization and survival of neutrophil.Front. Immunol.20221398311610.3389/fimmu.2022.98311636341459
     [Google Scholar]
  48. ScatoliniM. PatelA. GrossoE. Mello-GrandM. OstanoP. CoppoR. VitielloM. VenesioT. ZaccagnaA. PisacaneA. SarottoI. TavernaD. PolisenoL. BergamaschiD. ChiorinoG. GJB5 association with BRAF mutation and survival in cutaneous malignant melanoma.Br. J. Dermatol.2022186111712810.1111/bjd.2062934240406
     [Google Scholar]
  49. DaiS. VenturiniE. YadavS. LinX. ClappD. SteckiewiczM. Gocher-DemskeA.M. HardieD.G. EdelmanA.M. Calcium/calmodulin-dependent protein kinase kinase 2 mediates pleiotropic effects of epidermal growth factor in cancer cells.Biochim. Biophys. Acta Mol. Cell Res.20221869711925210.1016/j.bbamcr.2022.11925235271909
     [Google Scholar]
  50. ShenJ. JinZ. LvH. JinK. JonasK. ZhuC. ChenB. PFKP is highly expressed in lung cancer and regulates glucose metabolism.Cell Oncol.202043461762910.1007/s13402‑020‑00508‑632219704
     [Google Scholar]
  51. KaurR. BhardwajA. GuptaS. Cancer treatment therapies: traditional to modern approaches to combat cancers.Mol. Biol. Rep.202350119663967610.1007/s11033‑023‑08809‑337828275
     [Google Scholar]
  52. KhalilovR. A comprehensive review of advanced nano-biomaterials in regenerative medicine and drug delivery.Adv. Biol. Earth Sci.202381
     [Google Scholar]
/content/journals/cmc/10.2174/0109298673276881240111172756
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Identification and Functional Characterization of Essential Genes Related to Gefitinib Sensitivity in Lung Adenocarcinoma
Curr. Med. Chem. 32, 5024 (2025); https://doi.org/10.2174/0109298673276881240111172756
/content/journals/cmc/10.2174/0109298673276881240111172756
/content/journals/cmc/10.2174/0109298673276881240111172756
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  • Article Type:     Research Article
Keyword(s): cell cycle; gefitinib sensitivity; hub gene; immunity; Lung adenocarcinoma; omnibus

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