Skip to content
2000
Volume 32, Issue 35
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Background

As the most common subtype of colorectal cancer, colorectal adenocarcinoma (COAD) still needs better prognostic stratification methods and new intervention targets. The mitochondrial stress response, linked to mitochondrial homeostasis and cancer metabolism, warrants further investigation.

Methods

We identified mitochondrial oxidative stress-related genes (MOS) associated with COAD prognosis through the TCGA and GEO databases. Molecular subtype characteristics were identified based on MOS gene signatures, and an MOS scoring system was established to comprehensively evaluate its clinical value. Additionally, the effect of one of the screened genes, NDRG1, was investigated through a series of experiments, including Western blot, qRT-PCR, CCK8 assay, clone formation, and Transwell assay, to explore its impact on COAD proliferation and migration ability.

Results

Our analysis revealed that MOS gene signatures effectively distinguished molecular subtypes of COAD, and the MOS scoring system was found to be independent in predicting prognosis. Evaluation of microenvironment infiltration characteristics, mutation characteristics, immunotherapy response, and drug sensitivity analysis further suggested the potential clinical utility of this study. experimental results showed that NDRG1 significantly affected the proliferation and migration of COAD cells, partially verifying the reliability of our bioinformatics analysis.

Conclusion

This study provides a novel perspective on the role of mitochondrial oxidative stress in COAD, proposing innovative prognostic evaluation methods and potential therapeutic targets, thus offering new directions for the clinical treatment of COAD.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/cmc/10.2174/0109298673318692240829053543
2024-09-05
2025-10-31

  • From This Site

    /content/journals/cmc/10.2174/0109298673318692240829053543
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

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. ThrumurthyS.G. ThrumurthyS.S.D. GilbertC.E. RossP. HajiA. Colorectal adenocarcinoma: risks, prevention and diagnosis.BMJ2016354i359010.1136/bmj.i359027418368
     [Google Scholar]
  3. AlmquistD.R. AhnD.H. Bekaii-SaabT.S. The role of immune checkpoint inhibitors in colorectal adenocarcinoma.BioDrugs202034334936210.1007/s40259‑020‑00420‑332246441
     [Google Scholar]
  4. DekkerE. TanisP.J. VleugelsJ.L.A. KasiP.M. WallaceM.B. Colorectal cancer.Lancet2019394102071467148010.1016/S0140‑6736(19)32319‑031631858
     [Google Scholar]
  5. MazzaferroV. DindzansV. MakowkaL. Van ThielD. Approach to hepatic metastases from colorectal adenocarcinoma.Semin. Liver Dis.19888324725310.1055/s‑2008‑10405462466342
     [Google Scholar]
  6. YueT. CaiY. ZhuJ. LiuY. ChenS. WangP. RongL. Autophagy-related IFNG is a prognostic and immunochemotherapeutic biomarker of COAD patients.Front. Immunol.202314106470410.3389/fimmu.2023.106470436756126
     [Google Scholar]
  7. WarburgO. On the origin of cancer cells.Science1956123319130931410.1126/science.123.3191.30913298683
     [Google Scholar]
  8. ZhongX. HeX. WangY. HuZ. HuangH. ZhaoS. WeiP. LiD. Warburg effect in colorectal cancer: the emerging roles in tumor microenvironment and therapeutic implications.J. Hematol. Oncol.202215116010.1186/s13045‑022‑01358‑536319992
     [Google Scholar]
  9. LeeY.G. ParkD.H. ChaeY.C. Role of mitochondrial stress response in cancer progression.Cells202211577110.3390/cells1105077135269393
     [Google Scholar]
  10. ZhengS. WangX. ZhaoD. LiuH. HuY. Calcium homeostasis and cancer: insights from endoplasmic reticulum-centered organelle communications.Trends Cell Biol.202333431232310.1016/j.tcb.2022.07.00435915027
     [Google Scholar]
  11. SrinivasU.S. TanB.W.Q. VellayappanB.A. JeyasekharanA.D. ROS and the DNA damage response in cancer.Redox Biol.20192510108410.1016/j.redox.2018.10108430612957
     [Google Scholar]
  12. MoloneyJ.N. CotterT.G. ROS signalling in the biology of cancer.Semin. Cell Dev. Biol.201880506410.1016/j.semcdb.2017.05.02328587975
     [Google Scholar]
  13. WallaceD.C. Mitochondria and cancer.Nat. Rev. Cancer2012121068569810.1038/nrc336523001348
     [Google Scholar]
  14. MishraA.P. SalehiB. Sharifi-RadM. PezzaniR. KobarfardF. Sharifi-RadJ. NigamM. Programmed cell death, from a cancer perspective: an overview.Mol. Diagn. Ther.201822328129510.1007/s40291‑018‑0329‑929560608
     [Google Scholar]
  15. O’MalleyJ. KumarR. InigoJ. YadavaN. ChandraD. Mitochondrial stress response and cancer.Trends Cancer20206868870110.1016/j.trecan.2020.04.00932451306
     [Google Scholar]
  16. Pakos-ZebruckaK. KorygaI. MnichK. LjujicM. SamaliA. GormanA.M. The integrated stress response.EMBO Rep.201617101374139510.15252/embr.20164219527629041
     [Google Scholar]
  17. FioreseC.J. HaynesC.M. Integrating the UPR mt into the mitochondrial maintenance network.Crit. Rev. Biochem. Mol. Biol.201752330431310.1080/10409238.2017.129157728276702
     [Google Scholar]
  18. MelberA. HaynesC.M. UPRmt regulation and output: a stress response mediated by mitochondrial-nuclear communication.Cell Res.201828328129510.1038/cr.2018.1629424373
     [Google Scholar]
  19. ZinatizadehM.R. SchockB. ChalbataniG.M. ZarandiP.K. JalaliS.A. MiriS.R. The Nuclear Factor Kappa B (NF-kB) signaling in cancer development and immune diseases.Genes Dis.20218328729710.1016/j.gendis.2020.06.00533997176
     [Google Scholar]
  20. KalyanaramanB. ChengG. HardyM. OuariO. LopezM. JosephJ. ZielonkaJ. DwinellM.B. A review of the basics of mitochondrial bioenergetics, metabolism, and related signaling pathways in cancer cells: Therapeutic targeting of tumor mitochondria with lipophilic cationic compounds.Redox Biol.20181431632710.1016/j.redox.2017.09.02029017115
     [Google Scholar]
  21. ChiangJ.H. TsaiF.J. HsuY.M. YinM.C. ChiuH.Y. YangJ.S. Sensitivity of allyl isothiocyanate to induce apoptosis via ER stress and the mitochondrial pathway upon ROS production in colorectal adenocarcinoma cells.Oncol. Rep.20204441415142410.3892/or.2020.770032700751
     [Google Scholar]
  22. SauvageT. PlouviezS. SchmidtW.E. FredericqS. TREE2FASTA: a flexible Perl script for batch extraction of FASTA sequences from exploratory phylogenetic trees.BMC Res. Notes201811116410.1186/s13104‑018‑3268‑y29506565
     [Google Scholar]
  23. BarrettT. WilhiteS.E. LedouxP. EvangelistaC. KimI.F. TomashevskyM. MarshallK.A. PhillippyK.H. ShermanP.M. HolkoM. YefanovA. LeeH. ZhangN. RobertsonC.L. SerovaN. DavisS. SobolevaA. NCBI GEO: archive for functional genomics data sets--update.Nucleic Acids Res.201341Database issueD991D99523193258
     [Google Scholar]
  24. TomczakK. CzerwińskaP. WiznerowiczM. Review The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge.Contemp. Oncol. (Pozn.)20151A1A687710.5114/wo.2014.4713625691825
     [Google Scholar]
  25. 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]
  26. LeekJ.T. JohnsonW.E. ParkerH.S. JaffeA.E. StoreyJ.D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments.Bioinformatics201228688288310.1093/bioinformatics/bts03422257669
     [Google Scholar]
  27. StelzerG. RosenN. PlaschkesI. ZimmermanS. TwikM. FishilevichS. SteinT.I. NudelR. LiederI. MazorY. KaplanS. DaharyD. WarshawskyD. Guan-GolanY. KohnA. RappaportN. SafranM. LancetD. The genecards suite: From gene data mining to disease genome sequence analyses.Curr. Protoc. Bioinform.20165430
     [Google Scholar]
  28. LangfelderP. HorvathS. WGCNA: an R package for weighted correlation network analysis.BMC Bioinformatics20089155910.1186/1471‑2105‑9‑55919114008
     [Google Scholar]
  29. ShuQ. SheH. ChenX. ZhongL. ZhuJ. FangL. Identification and experimental validation of mitochondria-related genes biomarkers associated with immune infiltration for sepsis.Front. Immunol.202314118412610.3389/fimmu.2023.118412637228596
     [Google Scholar]
  30. ZhouY. ChenY. LiJ. FuZ. ChenQ. ZhangW. LuoH. XieM. The development of endoplasmic reticulum-related gene signatures and the immune infiltration analysis of sepsis.Front. Immunol.202314118376910.3389/fimmu.2023.118376937346041
     [Google Scholar]
  31. YuG. WangL.G. HanY. HeQ.Y. clusterProfiler: an R package for comparing biological themes among gene clusters.OMICS201216528428710.1089/omi.2011.011822455463
     [Google Scholar]
  32. WilkersonM.D. HayesD.N. ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking.Bioinformatics201026121572157310.1093/bioinformatics/btq17020427518
     [Google Scholar]
  33. GustavssonE.K. ZhangD. ReynoldsR.H. Garcia-RuizS. RytenM. ggtranscript : an R package for the visualization and interpretation of transcript isoforms using ggplot2.Bioinformatics202238153844384610.1093/bioinformatics/btac40935751589
     [Google Scholar]
  34. RizviA.A. KaraesmenE. MorganM. PreusL. WangJ. SovicM. HahnT. Sucheston-CampbellL.E. gwasurvivr: an R package for genome-wide survival analysis.Bioinformatics201935111968197010.1093/bioinformatics/bty92030395168
     [Google Scholar]
  35. HänzelmannS. CasteloR. GuinneyJ. GSVA: gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics2013141710.1186/1471‑2105‑14‑723323831
     [Google Scholar]
  36. ScireJ. HuismanJ.S. GrosuA. AngstD.C. LisonA. LiJ. MaathuisM.H. BonhoefferS. StadlerT. estimateR: an R package to estimate and monitor the effective reproductive number.BMC Bioinformatics202324131010.1186/s12859‑023‑05428‑437568078
     [Google Scholar]
  37. 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]
  38. BalarA.V. GalskyM.D. RosenbergJ.E. PowlesT. PetrylakD.P. BellmuntJ. LoriotY. NecchiA. Hoffman-CensitsJ. Perez-GraciaJ.L. DawsonN.A. van der HeijdenM.S. DreicerR. SrinivasS. RetzM.M. JosephR.W. DrakakiA. VaishampayanU.N. SridharS.S. QuinnD.I. DuránI. ShafferD.R. EiglB.J. GrivasP.D. YuE.Y. LiS. KadelE.E.III BoydZ. BourgonR. HegdeP.S. MariathasanS. ThåströmA. AbidoyeO.O. FineG.D. BajorinD.F. GroupI.M.S. Atezolizumab as first-line treatment in cisplatin-ineligible patients with locally advanced and metastatic urothelial carcinoma: a single-arm, multicentre, phase 2 trial.Lancet201738910064677610.1016/S0140‑6736(16)32455‑227939400
     [Google Scholar]
  39. NukuiT. OnogiA. An R package for ensemble learning stacking.Bioinform Adv.202331vbad139
     [Google Scholar]
  40. ZhangS. SunL. CaiD. LiuG. JiangD. YinJ. FangY. WangH. ShenY. HouY. ShiH. TanL. Development and validation of PET/CT-based nomogram for preoperative prediction of lymph node status in esophageal squamous cell carcinoma.Ann. Surg. Oncol.202330127452746010.1245/s10434‑023‑13694‑y37355519
     [Google Scholar]
  41. LiuT.T. LiR. HuoC. LiJ.P. YaoJ. JiX. QuY.Q. Identification of CDK2-related immune forecast model and ceRNA in lung adenocarcinoma, a pan-cancer analysis.Front. Cell Dev. Biol.2021968200210.3389/fcell.2021.68200234409029
     [Google Scholar]
  42. ZhangZ. CorteseG. CombescureC. MarshallR. LeeM. LimH. HallerB. Overview of model validation for survival regression model with competing risks using melanoma study data.Ann. Transl. Med.201861632510.21037/atm.2018.07.3830364028
     [Google Scholar]
  43. LiM. WeiX. ZhangS.S. LiS. ChenS.H. ShiS.J. ZhouS.H. SunD.Q. ZhaoQ.Y. XuY. Recognition of refractory Mycoplasma pneumoniae pneumonia among Myocoplasma pneumoniae pneumonia in hospitalized children: development and validation of a predictive nomogram model.BMC Pulm. Med.202323138310.1186/s12890‑023‑02684‑137817172
     [Google Scholar]
  44. MogensenU.B. IshwaranH. GerdsT.A. Evaluating random forests for survival analysis using prediction error curves.J. Stat. Softw.2012501112310.18637/jss.v050.i1125317082
     [Google Scholar]
  45. MayakondaA. LinD.C. AssenovY. PlassC. KoefflerH.P. Maftools: efficient and comprehensive analysis of somatic variants in cancer.Genome Res.201828111747175610.1101/gr.239244.11830341162
     [Google Scholar]
  46. WangJ. HanQ. LiuH. LuoH. LiL. LiuA. JiangY. Identification of radiotherapy-associated genes in lung adenocarcinoma by an integrated bioinformatics analysis approach.Front. Mol. Biosci.2021862457510.3389/fmolb.2021.62457534212001
     [Google Scholar]
  47. 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]
  48. PorporatoP.E. FilighedduN. PedroJ.M.B.S. KroemerG. GalluzziL. Mitochondrial metabolism and cancer.Cell Res.201828326528010.1038/cr.2017.15529219147
     [Google Scholar]
  49. SrinivasanS. GuhaM. KashinaA. AvadhaniN.G. Mitochondrial dysfunction and mitochondrial dynamics. The cancer connection.Biochim. Biophys. Acta Bioenerg.20171858860261410.1016/j.bbabio.2017.01.00428104365
     [Google Scholar]
  50. RodriguesT. FerrazL.S. Therapeutic potential of targeting mitochondrial dynamics in cancer.Biochem. Pharmacol.202018211428210.1016/j.bcp.2020.11428233058754
     [Google Scholar]
  51. ZongW.X. RabinowitzJ.D. WhiteE. Mitochondria and Cancer.Mol. Cell201661566767610.1016/j.molcel.2016.02.01126942671
     [Google Scholar]
  52. YazdaniH.O. RoyE. ComerciA.J. van der WindtD.J. ZhangH. HuangH. LoughranP. ShivaS. GellerD.A. BartlettD.L. TsungA. ShengT. SimmonsR.L. TohmeS. Neutrophil extracellular traps drive mitochondrial homeostasis in tumors to augment growth.Cancer Res.201979215626563910.1158/0008‑5472.CAN‑19‑080031519688
     [Google Scholar]
  53. BorcherdingN. BrestoffJ.R. The power and potential of mitochondria transfer.Nature2023623798628329110.1038/s41586‑023‑06537‑z37938702
     [Google Scholar]
  54. AgarwalS.N. ChatterjeeB. TripathiB.N. ChandraR. Role of lithium carbonate in pancytopenia following cytotoxic therapy.J. Indian Med. Assoc.19898751151162600434
     [Google Scholar]
  55. ZielonkaJ. JosephJ. SikoraA. HardyM. OuariO. Vasquez-VivarJ. ChengG. LopezM. KalyanaramanB. Mitochondria-targeted triphenylphosphonium-based compounds: syntheses, mechanisms of action, and therapeutic and diagnostic applications.Chem. Rev.201711715100431012010.1021/acs.chemrev.7b0004228654243
     [Google Scholar]
  56. BoyleK.A. Van WickleJ. HillR.B. MarcheseA. KalyanaramanB. DwinellM.B. Mitochondria-targeted drugs stimulate mitophagy and abrogate colon cancer cell proliferation.J. Biol. Chem.201829338148911490410.1074/jbc.RA117.00146930087121
     [Google Scholar]
  57. WeinbergF. HamanakaR. WheatonW.W. WeinbergS. JosephJ. LopezM. KalyanaramanB. MutluG.M. BudingerG.R.S. ChandelN.S. Mitochondrial metabolism and ROS generation are essential for Kras-mediated tumorigenicity.Proc. Natl. Acad. Sci. USA2010107198788879310.1073/pnas.100342810720421486
     [Google Scholar]
  58. ZhouY. ZouJ. XuJ. ZhouY. CenX. ZhaoY. Recent advances of mitochondrial complex I inhibitors for cancer therapy: Current status and future perspectives.Eur. J. Med. Chem.202325111521910.1016/j.ejmech.2023.11521936893622
     [Google Scholar]
  59. BasitF. van OppenL.M.P.E. SchöckelL. BossenbroekH.M. van Emst-de VriesS.E. HermelingJ.C.W. GrefteS. KopitzC. HeroultM. HGM WillemsP. KoopmanW.J.H. Mitochondrial complex I inhibition triggers a mitophagy-dependent ROS increase leading to necroptosis and ferroptosis in melanoma cells.Cell Death Dis.201783e271610.1038/cddis.2017.13328358377
     [Google Scholar]
  60. AndrzejewskiS. GravelS.P. PollakM. St-PierreJ. Metformin directly acts on mitochondria to alter cellular bioenergetics.Cancer Metab.2014211210.1186/2049‑3002‑2‑1225184038
     [Google Scholar]
  61. WheatonW.W. WeinbergS.E. HamanakaR.B. SoberanesS. SullivanL.B. AnsoE. GlasauerA. DufourE. MutluG.M. BudignerG.R.S. ChandelN.S. Metformin inhibits mitochondrial complex I of cancer cells to reduce tumorigenesis.eLife20143e0224210.7554/eLife.0224224843020
     [Google Scholar]
  62. KozovskaZ. GabrisovaV. KucerovaL. Colon cancer: Cancer stem cells markers, drug resistance and treatment.Biomed. Pharmacother.201468891191610.1016/j.biopha.2014.10.01925458789
     [Google Scholar]
  63. CeballosM.P. RigalliJ.P. CeréL.I. SemeniukM. CataniaV.A. RuizM.L. TransportersA.B.C. ABC Transporters: Regulation and association with multidrug resistance in hepatocellular carcinoma and colorectal carcinoma.Curr. Med. Chem.20192671224125010.2174/092986732566618010510363729303075
     [Google Scholar]
  64. KoboriT. TameishiM. TanakaC. UrashimaY. ObataT. Subcellular distribution of ezrin/radixin/moesin and their roles in the cell surface localization and transport function of P-glycoprotein in human colon adenocarcinoma LS180 cells.PLoS One2021165e025088910.1371/journal.pone.025088933974673
     [Google Scholar]
  65. SaunaZ.E. SmithM.M. MüllerM. KerrK.M. AmbudkarS.V. The mechanism of action of multidrug-resistance-linked P-glycoprotein.J. Bioenerg. Biomembr.200133648149110.1023/A:101287510500611804190
     [Google Scholar]
  66. BaiF. WangC. LuQ. ZhaoM. BanF.Q. YuD.H. GuanY.Y. LuanX. LiuY.R. ChenH.Z. FangC. Nanoparticle-mediated drug delivery to tumor neovasculature to combat P-gp expressing multidrug resistant cancer.Biomaterials201334266163617410.1016/j.biomaterials.2013.04.06223706689
     [Google Scholar]
  67. BoonyongC. PattamadilokC. SuttisriR. JianmongkolS. Benzophenones and xanthone derivatives from Garcinia schomburgkiana -induced P-glycoprotein overexpression in human colorectal Caco-2 cells via oxidative stress-mediated mechanisms.Phytomedicine20172781410.1016/j.phymed.2017.01.01128314481
     [Google Scholar]
  68. ChengG. ZielonkaJ. DrankaB.P. McAllisterD. MackinnonA.C.Jr JosephJ. KalyanaramanB. Mitochondria-targeted drugs synergize with 2-deoxyglucose to trigger breast cancer cell death.Cancer Res.201272102634264410.1158/0008‑5472.CAN‑11‑392822431711
     [Google Scholar]
  69. FormanH.J. ZhangH. Targeting oxidative stress in disease: promise and limitations of antioxidant therapy.Nat. Rev. Drug Discov.202120968970910.1038/s41573‑021‑00233‑134194012
     [Google Scholar]
  70. SahaS. SasoL. ArmaganG. Cancer prevention and therapy by targeting oxidative stress pathways.Molecules20232811429310.3390/molecules2811429337298769
     [Google Scholar]
  71. GorriniC. HarrisI.S. MakT.W. Modulation of oxidative stress as an anticancer strategy.Nat. Rev. Drug Discov.2013121293194710.1038/nrd400224287781
     [Google Scholar]
  72. LongE.O. Sik KimH. LiuD. PetersonM.E. RajagopalanS. Controlling natural killer cell responses: integration of signals for activation and inhibition.Annu. Rev. Immunol.201331122725810.1146/annurev‑immunol‑020711‑07500523516982
     [Google Scholar]
  73. PlantoneD. MartiA. FrisulloG. IorioR. DamatoV. NocitiV. PatanellaA.K. BiancoA. MirabellaM. BatocchiA.P. Circulating CD56dim NK cells expressing perforin are increased in progressive multiple sclerosis.J. Neuroimmunol.20132651-212412710.1016/j.jneuroim.2013.10.00424157130
     [Google Scholar]
  74. NielsenN. ØdumN. UrsøB. LanierL.L. SpeeP. Cytotoxicity of CD56(bright) NK cells towards autologous activated CD4+ T cells is mediated through NKG2D, LFA-1 and TRAIL and dampened via CD94/NKG2A.PLoS One201272e3195910.1371/journal.pone.003195922384114
     [Google Scholar]
  75. LaroniA. ArmentaniE. Kerlero de RosboN. IvaldiF. MarcenaroE. SivoriS. GandhiR. WeinerH.L. MorettaA. MancardiG.L. UccelliA. Dysregulation of regulatory CD56bright NK cells/T cells interactions in multiple sclerosis.J. Autoimmun.20167281810.1016/j.jaut.2016.04.00327157273
     [Google Scholar]
  76. ThiV.A.D. JeonH.M. ParkS.M. LeeH. KimY.S. Cell-based IL-15:IL-15 Rα secreting vaccine as an effective therapy for CT26 colon cancer in mice.Mol. Cells2019421286988331760731
     [Google Scholar]
  77. KuniyasuH. OueN. NakaeD. TsutsumiM. DendaA. TsujiuchiT. YokozakiH. YasuiW. Interleukin-15 expression is associated with malignant potential in colon cancer cells.Pathobiology2001692869510.1159/00004876111752902
     [Google Scholar]
  78. RomagnaniC. JuelkeK. FalcoM. MorandiB. D’AgostinoA. CostaR. RattoG. ForteG. CarregaP. LuiG. ConteR. StrowigT. MorettaA. MünzC. ThielA. MorettaL. FerlazzoG. CD56brightCD16- killer Ig-like receptor- NK cells display longer telomeres and acquire features of CD56dim NK cells upon activation.J. Immunol.200717884947495510.4049/jimmunol.178.8.494717404276
     [Google Scholar]
  79. WaldmannT.A. DuboisS. MiljkovicM.D. ConlonK.C. IL-15 in the combination immunotherapy of cancer.Front. Immunol.20201186810.3389/fimmu.2020.0086832508818
     [Google Scholar]
  80. NaH.K. LeeJ. Molecular basis of alcohol-related gastric and colon cancer.Int. J. Mol. Sci.2017186111610.3390/ijms1806111628538665
     [Google Scholar]
  81. FloudS. HermonC. SimpsonR.F. ReevesG.K. Alcohol consumption and cancer incidence in women: interaction with smoking, body mass index and menopausal hormone therapy.BMC Cancer202323175810.1186/s12885‑023‑11184‑837587405
     [Google Scholar]
  82. JokelainenK. RoineR.P. VäänänenH. FärkkiläM. SalaspuroM. In vitro acetaldehyde formation by human colonic bacteria.Gut19943591271127410.1136/gut.35.9.12717959236
     [Google Scholar]
  83. SalaspuroM. Bacteriocolonic pathway for ethanol oxidation: characteristics and implications.Ann. Med.199628319520010.3109/078538996090331208811162
     [Google Scholar]
  84. BiswasA. SenthilkumarS.R. SaidH.M. Effect of chronic alcohol exposure on folate uptake by liver mitochondria.Am. J. Physiol. Cell Physiol.20123021C203C20910.1152/ajpcell.00283.201121956163
     [Google Scholar]
  85. TsermpiniE.E. Plemenitaš IlješA. DolžanV. Alcohol-induced oxidative stress and the role of antioxidants in alcohol use disorder: A systematic review.Antioxidants2022117137410.3390/antiox1107137435883865
     [Google Scholar]
  86. BansalS. LiuC.P. SepuriN.B.V. AnandatheerthavaradaH.K. SelvarajV. HoekJ. MilneG.L. GuengerichF.P. AvadhaniN.G. Mitochondria-targeted cytochrome P450 2E1 induces oxidative damage and augments alcohol-mediated oxidative stress.J. Biol. Chem.201028532246092461910.1074/jbc.M110.12182220529841
     [Google Scholar]
  87. SabetC. HammondA. RavidN. TongM.S. StanfordF.C. Harnessing big data for health equity through a comprehensive public database and data collection framework.NPJ Digit. Med.2023619110.1038/s41746‑023‑00844‑537210430
     [Google Scholar]
  88. LalA. LashA.E. AltschulS.F. VelculescuV. ZhangL. McLendonR.E. MarraM.A. PrangeC. MorinP.J. PolyakK. PapadopoulosN. VogelsteinB. KinzlerK.W. StrausbergR.L. RigginsG.J. A public database for gene expression in human cancers.Cancer Res.199959215403540710554005
     [Google Scholar]
/content/journals/cmc/10.2174/0109298673318692240829053543
Loading
Molecular Subtypes based on Mitochondrial Oxidative Stress-related Gene Signature and Tumor Microenvironment Infiltration Characterization of Colon Adenocarcinoma
Curr. Med. Chem. 32, 7859 (2025); https://doi.org/10.2174/0109298673318692240829053543
/content/journals/cmc/10.2174/0109298673318692240829053543
/content/journals/cmc/10.2174/0109298673318692240829053543
Loading

Data & Media loading...

Supplements

Supplementary material is available on the publisher’s website along with the published article.

file format download Download

  • Article Type:     Research Article
Keyword(s): colon adenocarcinoma; Genecards; immune infiltration; Mitochondrial oxidative stress; NDRG1; prognostic value

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