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2000
Volume 25, Issue 11
  • ISSN: 1871-5303
  • E-ISSN: 2212-3873
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Abstract

Background

Osteoporosis (OP) is a skeletal condition characterized by increased susceptibility to fractures. Programmed cell death (PCD) is the orderly process of cells ending their own life that has not been thoroughly explored in relation to OP.

Objective

This study is to investigate PCD-related genes in OP, shedding light on potential mechanisms underlying the disease.

Methods

Public datasets (GSE56814 and GSE56815) were analyzed to identify differentially expressed genes (DEGs). We employed the least absolute shrinkage and selection operator (LASSO), Boruta, and random forest (RF) algorithms to pinpoint hub PCD-related genes in OP and construct a predictive nomogram model. The performance of the model was validated through ROC curve analysis, calibration curves, and decision curve analysis. Additionally, transcription factor (TF) interaction analysis and functional enrichment analysis were conducted to explore the regulatory networks and biological pathways involved.

Results

We identified 161 DEGs, with 30 prominently associated with PCD. Five hub genes, PDPK1, MAP1LC3B, ZFP36, DRAM1, and MPO, were highlighted as particularly significant. A predictive nomogram integrating these genes demonstrated high accuracy (AUC) in forecasting OP risk, with an AUC of 0.911 in the GSE56815 dataset. The validation confirmed the gene model efficacy in differentiating OP risk and clinical applicability. The subsequent TF-gene interaction analyses revealed that these hub genes are regulated by multiple TFs, indicating their central role in OP pathology. Functional enrichment analysis of the hub genes indicated significant involvement in apoptosis, autophagy, and immune response pathways.

Conclusion

This study identified PDPK1, MAP1LC3B, ZFP36, DRAM1, and MPO as potential biomarkers and proposes a nomogram based on hub genes for predicting osteoporosis risk.

© 2025 The Author(s). Published by Bentham Science Publishers.
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. MullinB.H. TicknerJ. ZhuK. KennyJ. MullinS. BrownS.J. DudbridgeF. PavlosN.J. MocarskiE.S. WalshJ.P. XuJ. WilsonS.G. Characterisation of genetic regulatory effects for osteoporosis risk variants in human osteoclasts.Genome Biol.20202118010.1186/s13059‑020‑01997‑232216834
     [Google Scholar]
  2. CummingsS.R. MeltonL.J. Epidemiology and outcomes of osteoporotic fractures.Lancet200235993191761176710.1016/S0140‑6736(02)08657‑912049882
     [Google Scholar]
  3. JingH. SuX. GaoB. ShuaiY. ChenJ. DengZ. LiaoL. JinY. Epigenetic inhibition of Wnt pathway suppresses osteogenic differentiation of BMSCs during osteoporosis.Cell Death Dis.20189217610.1038/s41419‑017‑0231‑029416009
     [Google Scholar]
  4. KanisJ.A. MeltonL.J.III ChristiansenC. JohnstonC.C. KhaltaevN. The diagnosis of osteoporosis.J. Bone Miner. Res.1994981137114110.1002/jbmr.56500908027976495
     [Google Scholar]
  5. AnamA.K. InsognaK. Update on osteoporosis screening and management.Med. Clin. North Am.202110561117113410.1016/j.mcna.2021.05.01634688418
     [Google Scholar]
  6. JohnstonC.B. DagarM. Osteoporosis in older adults.Med. Clin. North Am.2020104587388410.1016/j.mcna.2020.06.00432773051
     [Google Scholar]
  7. Martínez-AguilàD. Gómez-VaqueroC. RozadillaA. RomeraM. NarváezJ. NollaJ.M. Decision rules for selecting women for bone mineral density testing: Application in postmenopausal women referred to a bone densitometry unit.J. Rheumatol.20073461307131217552058
     [Google Scholar]
  8. BurgeR. Dawson-HughesB. SolomonD.H. WongJ.B. KingA. TostesonA. Incidence and economic burden of osteoporosis-related fractures in the United States, 2005-2025.J. Bone Miner. Res.200722346547510.1359/jbmr.06111317144789
     [Google Scholar]
  9. ZouY. XieJ. ZhengS. LiuW. TangY. TianW. DengX. WuL. ZhangY. WongC.W. TanD. LiuQ. XieX. Leveraging diverse cell-death patterns to predict the prognosis and drug sensitivity of triple-negative breast cancer patients after surgery.Int. J. Surg.202210710693610.1016/j.ijsu.2022.10693636341760
     [Google Scholar]
  10. TangD. KangR. BergheT.V. VandenabeeleP. KroemerG. The molecular machinery of regulated cell death.Cell Res.201929534736410.1038/s41422‑019‑0164‑530948788
     [Google Scholar]
  11. NössingC. RyanK.M. 50 years on and still very much alive: ‘Apoptosis: A basic biological phenomenon with wide-ranging implications in tissue kinetics’.Br. J. Cancer2023128342643110.1038/s41416‑022‑02020‑036369364
     [Google Scholar]
  12. BergsbakenT. FinkS.L. CooksonB.T. Pyroptosis: Host cell death and inflammation.Nat. Rev. Microbiol.2009729910910.1038/nrmicro207019148178
     [Google Scholar]
  13. GalluzziL. KroemerG. Necroptosis: A specialized pathway of programmed necrosis.Cell200813571161116310.1016/j.cell.2008.12.00419109884
     [Google Scholar]
  14. TsvetkovP. CoyS. PetrovaB. DreishpoonM. VermaA. AbdusamadM. RossenJ. Joesch-CohenL. HumeidiR. SpanglerR.D. EatonJ.K. FrenkelE. KocakM. CorselloS.M. LutsenkoS. KanarekN. SantagataS. GolubT.R. Copper induces cell death by targeting lipoylated TCA cycle proteins.Science202237565861254126110.1126/science.abf052935298263
     [Google Scholar]
  15. StockwellB.R. Friedmann AngeliJ.P. BayirH. BushA.I. ConradM. DixonS.J. FuldaS. GascónS. HatziosS.K. KaganV.E. NoelK. JiangX. LinkermannA. MurphyM.E. OverholtzerM. OyagiA. PagnussatG.C. ParkJ. RanQ. RosenfeldC.S. SalnikowK. TangD. TortiF.M. TortiS.V. ToyokuniS. WoerpelK.A. ZhangD.D. Ferroptosis: A regulated cell death nexus linking metabolism, redox biology, and disease.Cell2017171227328510.1016/j.cell.2017.09.02128985560
     [Google Scholar]
  16. DentonD. KumarS. Autophagy-dependent cell death.Cell Death Differ.201926460561610.1038/s41418‑018‑0252‑y30568239
     [Google Scholar]
  17. OverholtzerM. MailleuxA.A. MouneimneG. NormandG. SchnittS.J. KingR.W. CibasE.S. BruggeJ.S. A nonapoptotic cell death process, entosis, that occurs by cell-in-cell invasion.Cell2007131596697910.1016/j.cell.2007.10.04018045538
     [Google Scholar]
  18. FatokunA.A. DawsonV.L. DawsonT.M. Parthanatos: Mitochondrial-linked mechanisms and therapeutic opportunities.Br. J. Pharmacol.201417182000201610.1111/bph.1241624684389
     [Google Scholar]
  19. FuchsT.A. AbedU. GoosmannC. HurwitzR. SchulzeI. WahnV. WeinrauchY. BrinkmannV. ZychlinskyA. Novel cell death program leads to neutrophil extracellular traps.J. Cell Biol.2007176223124110.1083/jcb.20060602717210947
     [Google Scholar]
  20. AitsS. JäätteläM. Lysosomal cell death at a glance.J. Cell Sci.201312691905191210.1242/jcs.09118123720375
     [Google Scholar]
  21. HolzeC. MichaudelC. MackowiakC. HaasD.A. BendaC. HubelP. PennemannF.L. SchnepfD. WettmarshausenJ. BraunM. LeungD.W. AmarasingheG.K. PerocchiF. StaeheliP. RyffelB. PichlmairA. Oxeiptosis, a ROS-induced caspase-independent apoptosis-like cell-death pathway.Nat. Immunol.201819213014010.1038/s41590‑017‑0013‑y29255269
     [Google Scholar]
  22. LiuJ. KuangF. KangR. TangD. Alkaliptosis: A new weapon for cancer therapy.Cancer Gene Ther.202027526726910.1038/s41417‑019‑0134‑631467365
     [Google Scholar]
  23. Föger-SamwaldU. Kerschan-SchindlK. ButylinaM. PietschmannP. Age related osteoporosis: Targeting cellular senescence.Int. J. Mol. Sci.2022235270110.3390/ijms2305270135269841
     [Google Scholar]
  24. ZhouY. GaoY. XuC. ShenH. TianQ. DengH.W. A novel approach for correction of crosstalk effects in pathway analysis and its application in osteoporosis research.Sci. Rep.20188166810.1038/s41598‑018‑19196‑229330445
     [Google Scholar]
  25. DavisS. MeltzerP.S. GEOquery: A bridge between the Gene Expression Omnibus (GEO) and BioConductor.Bioinformatics2007231418467
     [Google Scholar]
  26. ChakrabortyS. DattaS. DattaS. Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies.Bioinformatics201228679980610.1093/bioinformatics/bts02222238271
     [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. TibshiraniR. Regression shrinkage and selection via the Lasso.J. R. Stat. Soc. Series B Stat. Methodol.199658126728810.1111/j.2517‑6161.1996.tb02080.x
     [Google Scholar]
  29. KursaM.B. RudnickiW.R. Feature selection with the boruta package.J. Stat. Softw.2010361111310.18637/jss.v036.i11
     [Google Scholar]
  30. PaulA. MukherjeeD.P. DasP. GangopadhyayA. ChinthaA.R. KunduS. Improved random forest for classification.IEEE Trans. Image Process.20182784012402410.1109/TIP.2018.283483029993742
     [Google Scholar]
  31. AnJ. LaiJ. SajjanharA. BatraJ. WangC. NelsonC.C. J- Circos: An interactive circos plotter.Bioinformatics20153191463146510.1093/bioinformatics/btu84225540184
     [Google Scholar]
  32. AroraS. RanaR. ChhabraA. JaiswalA. RaniV. miRNA–transcription factor interactions: A combinatorial regulation of gene expression.Mol. Genet. Genomics20132883-4778710.1007/s00438‑013‑0734‑z23334784
     [Google Scholar]
  33. ZhouG. SoufanO. EwaldJ. HancockR.E.W. BasuN. XiaJ. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis.Nucleic Acids Res.201947W1W234W24110.1093/nar/gkz24030931480
     [Google Scholar]
  34. ShannonP. MarkielA. OzierO. BaligaN.S. WangJ.T. RamageD. AminN. SchwikowskiB. IdekerT. Cytoscape: A software environment for integrated models of biomolecular interaction networks.Genome Res.200313112498250410.1101/gr.123930314597658
     [Google Scholar]
  35. RobinX. TurckN. HainardA. TibertiN. LisacekF. SanchezJ.C. MüllerM. pROC: An open-source package for R and S+ to analyze and compare ROC curves.BMC Bioinformatics20111217710.1186/1471‑2105‑12‑7721414208
     [Google Scholar]
  36. VickersA.J. ElkinE.B. Decision curve analysis: A novel method for evaluating prediction models.Med. Decis. Making200626656557410.1177/0272989X0629536117099194
     [Google Scholar]
  37. WilkersonM.D. HayesD.N. ConsensusClusterPlus: A class discovery tool with confidence assessments and item tracking.Bioinformatics201026121572157310.1093/bioinformatics/btq17020427518
     [Google Scholar]
  38. HänzelmannS. CasteloR. GuinneyJ. GSVA: Gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics2013141710.1186/1471‑2105‑14‑723323831
     [Google Scholar]
  39. BauerS. RobinsonP.N. GagneurJ. Model-based gene set analysis for bioconductor.Bioinformatics201127131882188310.1093/bioinformatics/btr29621561920
     [Google Scholar]
  40. AshburnerM. BallC.A. BlakeJ.A. BotsteinD. ButlerH. CherryJ.M. DavisA.P. DolinskiK. DwightS.S. EppigJ.T. HarrisM.A. HillD.P. Issel-TarverL. KasarskisA. LewisS. MateseJ.C. RichardsonJ.E. RingwaldM. RubinG.M. SherlockG. Gene ontology: Tool for the unification of biology.Nat. Genet.2000251252910.1038/7555610802651
     [Google Scholar]
  41. KanehisaM. GotoS. KEGG: Kyoto encyclopedia of genes and genomes.Nucleic Acids Res.2000281273010.1093/nar/28.1.2710592173
     [Google Scholar]
  42. SaxenaV. OrgillD. KohaneI. Absolute enrichment: Gene set enrichment analysis for homeostatic systems.Nucleic Acids Res.20063422e15110.1093/nar/gkl76617130162
     [Google Scholar]
  43. LiberzonA. BirgerC. ThorvaldsdóttirH. GhandiM. MesirovJ.P. TamayoP. The molecular signatures database (MSigDB) hallmark gene set collection.Cell Syst.20151641742510.1016/j.cels.2015.12.00426771021
     [Google Scholar]
  44. LiZ. LiD. ChenR. GaoS. XuZ. LiN. Cell death regulation: A new way for natural products to treat osteoporosis.Pharmacol. Res.202318710663510.1016/j.phrs.2022.10663536581167
     [Google Scholar]
  45. ZhangL. KwackK.H. ThiyagarajanR. MullaneyK.K. LambN.A. BardJ.E. SohnJ. SeldeenK.L. AraoY. BlackshearP.J. AbramsS.I. TroenB.R. KirkwoodK.L. Tristetraprolin regulates the skeletal phenotype and osteoclastogenic potential through monocytic myeloid-derived suppressor cells.FASEB J.2024381e2333810.1096/fj.202301703R38038723
     [Google Scholar]
  46. XuF. TeitelbaumS.L. Osteoclasts: New insights.Bone Res.201311112610.4248/BR20130100326273491
     [Google Scholar]
  47. GaoM. MonianP. PanQ. ZhangW. XiangJ. JiangX. Ferroptosis is an autophagic cell death process.Cell Res.20162691021103210.1038/cr.2016.9527514700
     [Google Scholar]
  48. ManolagasS.C. Birth and death of bone cells: Basic regulatory mechanisms and implications for the pathogenesis and treatment of osteoporosis.Endocr. Rev.200021211513710.1210/edrv.21.2.039510782361
     [Google Scholar]
  49. MizushimaN. LevineB. CuervoA.M. KlionskyD.J. Autophagy fights disease through cellular self-digestion.Nature200845171821069107510.1038/nature0663918305538
     [Google Scholar]
  50. LinN.Y. BeyerC. GießlA. KirevaT. ScholtysekC. UderhardtS. MunozL.E. DeesC. DistlerA. WirtzS. KrönkeG. SpencerB. DistlerO. SchettG. DistlerJ.H.W. Autophagy regulates TNFα-mediated joint destruction in experimental arthritis.Ann. Rheum. Dis.201372576176810.1136/annrheumdis‑2012‑20167122975756
     [Google Scholar]
  51. Pierrefite-CarleV. Santucci-DarmaninS. BreuilV. CamuzardO. CarleG.F. Autophagy in bone: Self-eating to stay in balance.Ageing Res. Rev.201524Pt B20621710.1016/j.arr.2015.08.00426318060
     [Google Scholar]
  52. WeinsteinR.S. ManolagasS.C. Apoptosis and osteoporosis.Am. J. Med.2000108215316410.1016/S0002‑9343(99)00420‑911126309
     [Google Scholar]
  53. SchneiderA.H. TairaT.M. PúblioG.A. da Silva PradoD. Donate YabutaP.B. dos SantosJ.C. MachadoC.C. de SouzaF.F.L. Rodrigues VenturiniL.G. de OliveiraR.D.R. CunhaT.M. Alves-FilhoJ.C. Louzada-JúniorP. Aparecida da SilvaT. FukadaS.Y. CunhaF.Q. Neutrophil extracellular traps mediate bone erosion in rheumatoid arthritis by enhancing RANKL-induced osteoclastogenesis.Br. J. Pharmacol.2024181342944610.1111/bph.1622737625900
     [Google Scholar]
  54. PyoJ.O. NahJ. JungY.K. Molecules and their functions in autophagy.Exp. Mol. Med.2012442738010.3858/emm.2012.44.2.02922257882
     [Google Scholar]
  55. SchilleS. CrauwelsP. BohnR. BagolaK. WaltherP. van ZandbergenG. LC3-associated phagocytosis in microbial pathogenesis.Int. J. Med. Microbiol.2018308122823610.1016/j.ijmm.2017.10.01429169848
     [Google Scholar]
  56. DeSelmC.J. MillerB.C. ZouW. BeattyW.L. van MeelE. TakahataY. KlumpermanJ. ToozeS.A. TeitelbaumS.L. VirginH.W. Autophagy proteins regulate the secretory component of osteoclastic bone resorption.Dev. Cell201121596697410.1016/j.devcel.2011.08.01622055344
     [Google Scholar]
  57. CrightonD. WilkinsonS. O’PreyJ. SyedN. SmithP. HarrisonP.R. GascoM. GarroneO. CrookT. RyanK.M. DRAM, a p53-induced modulator of autophagy, is critical for apoptosis.Cell2006126112113410.1016/j.cell.2006.05.03416839881
     [Google Scholar]
  58. CrightonD. WilkinsonS. RyanK.M. DRAM links autophagy to p53 and programmed cell death.Autophagy200731727410.4161/auto.343817102582
     [Google Scholar]
  59. van der VaartM. KorbeeC.J. LamersG.E.M. TengelerA.C. HosseiniR. HaksM.C. OttenhoffT.H.M. SpainkH.P. MeijerA.H. The DNA damage-regulated autophagy modulator DRAM1 links mycobacterial recognition via TLR-MYD88 to autophagic defense [corrected].Cell Host Microbe201415675376710.1016/j.chom.2014.05.00524922577
     [Google Scholar]
  60. TangN. ZhaoH. ZhangH. DongY. Effect of autophagy gene DRAM on proliferation, cell cycle, apoptosis, and autophagy of osteoblast in osteoporosis rats.J. Cell. Physiol.201923445023503210.1002/jcp.2730430203495
     [Google Scholar]
  61. ZhangZ. GuoM. LiY. ShenM. KongD. ShaoJ. DingH. TanS. ChenA. ZhangF. ZhengS. RNA-binding protein ZFP36/TTP protects against ferroptosis by regulating autophagy signaling pathway in hepatic stellate cells.Autophagy20201681482150510.1080/15548627.2019.168798531679460
     [Google Scholar]
  62. LeeS.E. WooK.M. KimS.Y. KimH.M. KwackK. LeeZ.H. KimH.H. The phosphatidylinositol 3-Kinase, p38, and extracellular signal-regulated kinase pathways are involved in osteoclast differentiation.Bone2002301717710.1016/S8756‑3282(01)00657‑311792567
     [Google Scholar]
  63. XiaoD. ZhouQ. GaoY. CaoB. ZhangQ. ZengG. ZongS. PDK1 is important lipid kinase for RANKL-induced osteoclast formation and function via the regulation of the Akt-GSK3β-NFATc1 signaling cascade.J. Cell. Biochem.2020121114542455710.1002/jcb.2967732048762
     [Google Scholar]
  64. BaiY. ZhangQ. ChenQ. ZhouQ. ZhangY. ShiZ. NongH. LiuM. ZengG. ZongS. Conditional knockout of the PDK-1 gene in osteoblasts affects osteoblast differentiation and bone formation.J. Cell. Physiol.202123675432544510.1002/jcp.3024933377210
     [Google Scholar]
  65. ZhangY. NongH. BaiY. ZhouQ. ZhangQ. LiuM. LiuP. ZengG. ZongS. Conditional knockout of PDK1 in osteoclasts suppressed osteoclastogenesis and ameliorated prostate cancer-induced osteolysis in murine model.Eur. J. Med. Res.202328143310.1186/s40001‑023‑01425‑837828580
     [Google Scholar]
  66. van der VeenB.S. de WintherM.P.J. HeeringaP. Myeloperoxidase: Molecular mechanisms of action and their relevance to human health and disease.Antioxid. Redox Signal.200911112899293710.1089/ars.2009.253819622015
     [Google Scholar]
  67. DeNichiloM.O. ShoubridgeA.J. PanagopoulosV. LiapisV. ZyskA. ZinonosI. HayS. AtkinsG.J. FindlayD.M. EvdokiouA. Peroxidase enzymes regulate collagen biosynthesis and matrix mineralization by cultured human osteoblasts.Calcif. Tissue Int.201698329430510.1007/s00223‑015‑0090‑626643175
     [Google Scholar]
  68. ZhaoX. LinS. LiH. SiS. WangZ. Myeloperoxidase controls bone turnover by suppressing osteoclast differentiation through modulating reactive oxygen species level.J. Bone Miner. Res.202036359160310.1002/jbmr.421533289180
     [Google Scholar]
  69. DuanX. LiuJ. ZhengX. WangZ. ZhangY. HaoY. YangT. DengH. Deficiency of ATP6V1H causes bone loss by inhibiting bone resorption and bone formation through the TGF-β1 pathway.Theranostics20166122183219510.7150/thno.1714027924156
     [Google Scholar]
  70. AhmadzadehK. PereiraM. VanoppenM. BernaertsE. KoJ.H. MiteraT. MaksoudianC. ManshianB.B. SoenenS. RoseC.D. WilliamsG.R. BassettJ.H.D. MatthysP. WoutersC. BehmoarasJ. Multinucleation resets human macrophages for specialized functions at the expense of their identity.EMBO Rep.2023243e5631010.15252/embr.20225631036597777
     [Google Scholar]
  71. WalshM.C. ChoiY. Biology of the RANKL-RANK-OPG system in immunity, bone, and beyond.Front. Immunol.2014551110.3389/fimmu.2014.0051125368616
     [Google Scholar]
  72. BonnetN. BourgoinL. BiverE. DouniE. FerrariS. RANKL inhibition improves muscle strength and insulin sensitivity and restores bone mass.J. Clin. Invest.201912983214322310.1172/JCI12591531120440
     [Google Scholar]
  73. FengY. DingL. LiL. LPS-inducible circAtp9b is highly expressed in osteoporosis and promotes the apoptosis of osteoblasts by reducing the formation of mature miR-17-92a.J. Orthop. Surg. Res.202217119310.1186/s13018‑022‑03072‑x35346278
     [Google Scholar]
  74. ZhuX. ZhangK. LuK. ShiT. ShenS. ChenX. DongJ. GongW. BaoZ. ShiY. MaY. TengH. JiangQ. Inhibition of pyroptosis attenuates Staphylococcus aureus-induced bone injury in traumatic osteomyelitis.Ann. Transl. Med.20197817017010.21037/atm.2019.03.4031168451
     [Google Scholar]
  75. TaoH. LiW. ZhangW. YangC. ZhangC. LiangX. YinJ. BaiJ. GeG. ZhangH. YangX. LiH. XuY. HaoY. LiuY. GengD. Urolithin A suppresses RANKL-induced osteoclastogenesis and postmenopausal osteoporosis by, suppresses inflammation and downstream NF-κB activated pyroptosis pathways.Pharmacol. Res.202117410596710.1016/j.phrs.2021.10596734740817
     [Google Scholar]
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Identification of Programmed Cell Death-related Biomarkers for the Potential Diagnosis and Treatment of Osteoporosis
Endocr Metab Immune Disord Drug Targets 25, 864 (2025); https://doi.org/10.2174/0118715303326112241021061805
/content/journals/emiddt/10.2174/0118715303326112241021061805
/content/journals/emiddt/10.2174/0118715303326112241021061805
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  • Article Type:     Research Article
Keyword(s): bone homeostasis; diagnosis; machine learning; nomogram; osteoblast; osteoclast; Osteoporosis (OP); programmed cell death (PCD)

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