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2000
Volume 27, Issue 1
  • ISSN: 1389-4501
  • E-ISSN: 1873-5592

Abstract

Introduction

Astragalus mongholicus is distributed in Inner Mongolia, China, and has a certain therapeutic effect on silicosis. However, the regulatory mechanisms of Astragalus mongholicus mediated by alternative splicing (AS) sis pathology and treatment remain unclear.

Methods

The pathological examination was performed on the lung tissue from a constructed mouse model of silicosis. Then, rMATS-based AS detection, target prediction, PPI analysis, and molecular docking were conducted to investigate the mechanism of Astragalus mongholicus-mediated treatment of silicosis in mice from the perspective of AS.

Results

A total of 404 differentially alternatively spliced genes (DASGs) were identified between the Astragalus mongholicus treatment and the silicosis model group. Moreover, 194 potential targets were predicted from 33 active components of Astragalus mongholicus, of which the targets, Rps6ka2 and Clk4, underwent differential AS. Network pharmacology analysis indicated that the Isomucronulatol, 7-o-methylisomucronulatol, and Medicarpin in Astragalus mongholicus might participate in the treatment of silicosis through differential splicing of Rps6ka2 or Clk4. Molecular docking confirmed a strong binding affinity between the protein Rps6ka2 and Medicarpin.

Discussion

This study suggests that Isomucronulatol, 7-o-methylisomucronulatol, and Medicarpin, being active components in Astragalus mongholicus, may intervene sis pathogenesis through differential splicing of Rps6ka2 or Clk4, involving biological processes such as protein serine/threonine kinase activity. However, further experimental validation is required to confirm these findings.

Conclusion

A large number of DASEs exist in the development and treatment of silicosis. Astragalus mongholicus may alleviate silicosis through AS-regulated mechanisms involving Rps6ka2 and Clk4. This finding provides novel strategies and potential molecular targets for silicosis treatment.

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References

  1. KrefftS. WolffJ. RoseC. Silicosis: An update and guide for clinicians.Clin. Chest Med.202041470972210.1016/j.ccm.2020.08.01233153689
    [Google Scholar]
  2. JamshidiP. DanaeiB. ArbabiM. MohammadzadehB. KhelghatiF. Akbari AghababaA. NayebzadeA. Shahidi BonjarA.H. CentisR. SotgiuG. NasiriM.J. MiglioriG.B. Silicosis and tuberculosis: A systematic review and meta-analysis.Pulmonology2025311241679110.1016/j.pulmoe.2023.05.00137349198
    [Google Scholar]
  3. TanS. ChenS. Macrophage Autophagy and Silicosis: Current Perspective and Latest Insights.Int. J. Mol. Sci.202122145310.3390/ijms2201045333466366
    [Google Scholar]
  4. Marques Da SilvaV. BenjdirM. MontagneP. PaironJ.C. LanoneS. AndujarP. Pulmonary toxicity of silica linked to its micro- or nanometric particle size and crystal structure: A review.Nanomaterials20221214239210.3390/nano1214239235889616
    [Google Scholar]
  5. FearyJ. DevarajA. BurtonM. ChuaF. CokerR.K. DattaA. HewittR.J. KokosiM. KouranosV. ReynoldsC.J. RossC.L. SmithV. WardK. WickremasingheM. SzramJ. Artificial stone silicosis: A UK case series.Thorax2024791097998110.1136/thorax‑2024‑22171539107113
    [Google Scholar]
  6. YinH. XieY. GuP. LiW. ZhangY. YaoY. ChenW. MaJ. The emerging role of epigenetic regulation in the progression of silicosis.Clin. Epigenetics202214116910.1186/s13148‑022‑01391‑836494831
    [Google Scholar]
  7. BonellaF. SpagnoloP. RyersonC. Current and future treatment landscape for idiopathic pulmonary fibrosis.Drugs202383171581159310.1007/s40265‑023‑01950‑037882943
    [Google Scholar]
  8. Scalia CarneiroA.P. AlgrantiE. Chérot-KornobisN. Silva BezerraF. Tibiriça BonA.M. Felicidade Tomaz BrazN. Soares SouzaD.M. de Paula CostaG. BussacosM.A. de Paula Alves BezerraO.M. TalvaniA. Inflammatory and oxidative stress biomarkers induced by silica exposure in crystal craftsmen.Am. J. Ind. Med.202063433734710.1002/ajim.2308831953962
    [Google Scholar]
  9. ZhaoM. WangL. WangM. ZhouS. LuY. CuiH. RacanelliA.C. ZhangL. YeT. DingB. ZhangB. YangJ. YaoY. Targeting fibrosis: Mechanisms and clinical trials.Signal Transduct. Target. Ther.20227120610.1038/s41392‑022‑01070‑335773269
    [Google Scholar]
  10. KumarS. MalviyaR. UniyalP. Vaccine for targeted therapy of lung cancer: Advances and developments.Curr. Drug Targets202425852652910.2174/011389450130610324042613124938712374
    [Google Scholar]
  11. PengF.D. DaiJ. DingC.G. Research progress of macrophage polarization in silico fibrosis.Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi202442431532010.3760/cma.j.cn121094‑20230306‑0006738678001
    [Google Scholar]
  12. YangX. WangH. LiuA. NiY. WangJ. HanY. XieB. GengJ. RenY. ZhangR. LiuM. DaiH. Evaluation of respiratory muscle dysfunction in patients with idiopathic pulmonary fibrosis: A prospective observational study with magnetic resonance imaging.BMC Pulm. Med.202525111810.1186/s12890‑025‑03572‑640087606
    [Google Scholar]
  13. HasegawaY. FranksJ.M. TanakaY. UeharaY. ReadD.F. WilliamsC. SrivatsanS. PitstickL.B. NikolaidisN.M. ShaverC.M. KropskiJ. WareL.B. TaylorC.J. BanovichN.E. WuH. GardnerJ.C. OsterburgA.R. YuJ.J. KoprasE.J. TeitelbaumS.L. Wikenheiser-BrokampK.A. TrapnellC. McCormackF.X. Pulmonary osteoclast- like cells in silica induced pulmonary fibrosis.Sci. Adv.20241028eadl491310.1126/sciadv.adl491338985878
    [Google Scholar]
  14. TanS. ChenS. The mechanism and effect of autophagy, apoptosis, and pyroptosis on the progression of silicosis.Int. J. Mol. Sci.20212215811010.3390/ijms2215811034360876
    [Google Scholar]
  15. YangB. LiuX. PengC. MengX. JiaQ. Silicosis: From pathogenesis to therapeutics.Front. Pharmacol.202516151620010.3389/fphar.2025.151620039944632
    [Google Scholar]
  16. TernierG. ShahzadK. EdirisingheO. OkotoP. AlraawiZ. SonnailaS. PhanP. AdamsP.D. ThallapuranamS.K. Fibroblast growth factors: Roles and emerging therapeutic applications.Curr. Drug Targets202526855157010.2174/011389450135146125030107244440051360
    [Google Scholar]
  17. ZhangX. ZhaoL. HeM. HuangX. LiuD. Burden of silicosis based on the Global Burden of Disease Study 2021: Trend analysis of incidence, mortality, and disability-adjusted life years, and projections for the next 30 years.J. Thorac. Dis.202517287288610.21037/jtd‑24‑134140083511
    [Google Scholar]
  18. LiuX. JiangQ. WuP. HanL. ZhouP. Global incidence, prevalence and disease burden of silicosis: 30 years’ overview and forecasted trends.BMC Public Health2023231136610.1186/s12889‑023‑16295‑237461046
    [Google Scholar]
  19. ChenY. LiX. YangM. LiuS.B. Research progress on morphology and mechanism of programmed cell death.Cell Death Dis.202415532710.1038/s41419‑024‑06712‑838729953
    [Google Scholar]
  20. CaoZ. LiuY. ZhangZ. YangP. LiZ. SongM. QiX. HanZ. PangJ. LiB. ZhangX. DaiH. WangJ. WangC. Pirfenidone ameliorates silica-induced lung inflammation and fibrosis in mice by inhibiting the secretion of interleukin-17A.Acta Pharmacol. Sin.202243490891810.1038/s41401‑021‑00706‑434316030
    [Google Scholar]
  21. JiaY. WangA. LiuL. WangH. LiG. ZhangF. Chinese medicinal plant Polygonum cuspidatum ameliorates silicosis via suppressing the Wnt/β-catenin pathway.Open Chem.20222011601161110.1515/chem‑2022‑0266
    [Google Scholar]
  22. GaoJ. LiC. WangX. SunX. ZhangR. chenC. YuM. LiuY. ZhuY. ChenJ. Oridonin attenuates lung inflammation and fibrosis in silicosis via covalent targeting iNOS.Biomed. Pharmacother.202215311353210.1016/j.biopha.2022.11353236076611
    [Google Scholar]
  23. ChenS. TangK. HuP. TanS. YangS. YangC. ChenG. LuoY. ZouH. Atractylenolide III alleviates the apoptosis through inhibition of autophagy by the mTOR-dependent pathway in alveolar macrophages of human silicosis.Mol. Cell. Biochem.2021476280981810.1007/s11010‑020‑03946‑w33078341
    [Google Scholar]
  24. LiT. YangX. XuH. LiuH. Early identification, accurate diagnosis, and treatment of silicosis.Can. Respir. J.202220221610.1155/2022/376913435509892
    [Google Scholar]
  25. LiJ. ZhaoH. XieY. LiJ. LiQ. ChenX. ZhangW. Clinical efficacy of comprehensive therapy based on traditional Chinese medicine patterns on patients with pneumoconiosis: A pilot double-blind, randomized, and placebo-controlled study.Front. Med.202216573674410.1007/s11684‑021‑0870‑535451681
    [Google Scholar]
  26. Ferreira de SousaNatália Ribeiro de SousaGabriela Teles Ramos de LimaNatanael Bezerra de AssisEdileuza Costa AragãoMariana Paiva de MouraÉrika GopalsamyRajiv Gandhi ScottiMarcus Tullius ScottiLuciana Multitarget Compounds for Neglected Diseases: A Review.Curr Med Chem202425957760110.2174/011389450129886424062706024738967077
    [Google Scholar]
  27. AdamcakovaJ. MokraD. Herbal compounds in the treatment of pulmonary silicosis.Physiol. Res.202170S3S275S28710.33549/physiolres.93481735099247
    [Google Scholar]
  28. LiN. WuK. FengF. WangL. ZhouX. WangW. Astragaloside IV alleviates silica-induced pulmonary fibrosis via inactivation of the TGF-β1/Smad2/3 signaling pathway.Int. J. Mol. Med.20214731610.3892/ijmm.2021.484933448318
    [Google Scholar]
  29. LiR. KangH. ChenS. From basic research to clinical practice: Considerations for treatment drugs for silicosis.Int. J. Mol. Sci.2023249833310.3390/ijms2409833337176040
    [Google Scholar]
  30. YangC.G. MaoX.L. WuJ.F. AnX. CaoJ.J. ZhangX.Y. LiM. ZhangF.F. Amelioration of lung fibrosis by total flavonoids of astragalus via inflammatory modulation and epithelium regeneration.Am. J. Chin. Med.202351237338910.1142/S0192415X2350019236655684
    [Google Scholar]
  31. LiN. FengF. WuK. ZhangH. ZhangW. WangW. Inhibitory effects of astragaloside IV on silica-induced pulmonary fibrosis via inactivating TGF-β1/Smad3 signaling.Biomed. Pharmacother.201911910938710.1016/j.biopha.2019.10938731487583
    [Google Scholar]
  32. WangW. ZhangY. ZhaiY. YangW. XingY. Alternative splicing dynamics during gastrulation in mouse embryo.Sci. Rep.20251511094810.1038/s41598‑025‑96148‑740159515
    [Google Scholar]
  33. ChoiS. ChoN. KimK.K. The implications of alternative pre-mRNA splicing in cell signal transduction.Exp. Mol. Med.202355475576610.1038/s12276‑023‑00981‑737009804
    [Google Scholar]
  34. BaoN. WangZ. FuJ. DongH. JinY. RNA structure in alternative splicing regulation: From mechanism to therapy.Acta Biochim. Biophys. Sin.202457132110.3724/abbs.202411939034824
    [Google Scholar]
  35. HuangR. LiuX. LiH. NingH. ZhouP.K. PRKCSH alternative splicing involves in silica-induced expression of epithelial-mesenchymal transition markers and cell proliferation.Dose Response2020182155932582092382510.1177/155932582092382532425726
    [Google Scholar]
  36. HeY. YangF. YangL. YuanH. YouY. ChenY. WuX. MinH. ChenJ. LiC. Mechanics-activated fibroblasts promote pulmonary group 2 innate lymphoid cell plasticity propelling silicosis progression.Nat. Commun.2024151977010.1038/s41467‑024‑54174‑539532893
    [Google Scholar]
  37. ZhaoH. JiangZ. LvR. LiX. XingY. GaoY. LvD. SiY. WangJ. LiJ. ZhaoX. CaiL. Transcriptome profile analysis reveals a silica-induced immune response and fibrosis in a silicosis rat model.Toxicol. Lett.2020333424810.1016/j.toxlet.2020.07.02132721576
    [Google Scholar]
  38. JiY. MishraR.K. DavuluriR.V. in silico analysis of alternative splicing on drug-target gene interactions.Sci. Rep.202010113410.1038/s41598‑019‑56894‑x31924844
    [Google Scholar]
  39. TomokuniA. AikohT. MatsukiT. IsozakiY. OtsukiT. KitaS. UekiH. KusakaM. KishimotoT. UekiA. Elevated soluble Fas/APO-1 (CD95) levels in silicosis patients without clinical symptoms of autoimmune diseases or malignant tumours.Clin. Exp. Immunol.2007110230330910.1111/j.1365‑2249.1997.tb08332.x9367417
    [Google Scholar]
  40. Andrade da SilvaL.H. VieiraJ.B. CabralM.R. AntunesM.A. LeeD. CruzF.F. HanesJ. RoccoP.R.M. MoralesM.M. SukJ.S. Development of nintedanib nanosuspension for inhaled treatment of experimental silicosis.Bioeng. Transl. Med.2023821040110.1002/btm2.1040136925690
    [Google Scholar]
  41. Martínez-LópezA. CandelS. TyrkalskaS.D. Animal models of silicosis: Fishing for new therapeutic targets and treatments.Eur. Respir. Rev.20233216923007810.1183/16000617.0078‑202337558264
    [Google Scholar]
  42. SongM. WangJ. SunY. HanZ. ZhouY. LiuY. FanT. LiZ. QiX. LuoY. YangP. LiB. ZhangX. WangJ. WangC. Tetrandrine alleviates silicosis by inhibiting canonical and non-canonical NLRP3 inflammasome activation in lung macrophages.Acta Pharmacol. Sin.20224351274128410.1038/s41401‑021‑00693‑634417574
    [Google Scholar]
  43. SchneiderC.A. RasbandW.S. EliceiriK.W. NIH image to imageJ: 25 years of image analysis.Nat. Methods20129767167510.1038/nmeth.208922930834
    [Google Scholar]
  44. AndrewsS. FastQc a quality control tool for high throughput sequence data.2014
    [Google Scholar]
  45. BolgerA.M. LohseM. UsadelB. Trimmomatic: A flexible trimmer for Illumina sequence data.Bioinformatics201430152114212010.1093/bioinformatics/btu17024695404
    [Google Scholar]
  46. DobinA. DavisC.A. SchlesingerF. DrenkowJ. ZaleskiC. JhaS. BatutP. ChaissonM. GingerasT.R. STAR: Ultrafast universal RNA-seq aligner.Bioinformatics2013291152110.1093/bioinformatics/bts63523104886
    [Google Scholar]
  47. PerteaM. PerteaG.M. AntonescuC.M. ChangT.C. MendellJ.T. SalzbergS.L. StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.Nat. Biotechnol.201533329029510.1038/nbt.312225690850
    [Google Scholar]
  48. ShenS. ParkJ.W. LuZ. LinL. HenryM.D. WuY.N. ZhouQ. XingY. rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data.Proc. Natl. Acad. Sci. USA201411151E5593E560110.1073/pnas.141916111125480548
    [Google Scholar]
  49. KatzY. WangE.T. SilterraJ. SchwartzS. WongB. ThorvaldsdóttirH. RobinsonJ.T. MesirovJ.P. AiroldiE.M. BurgeC.B. Quantitative visualization of alternative exon expression from RNA-seq data.Bioinformatics201531142400240210.1093/bioinformatics/btv03425617416
    [Google Scholar]
  50. XuS. HuE. CaiY. XieZ. LuoX. ZhanL. TangW. WangQ. LiuB. WangR. XieW. WuT. XieL. YuG. Using clusterProfiler to characterize multiomics data.Nat. Protoc.202419113292332010.1038/s41596‑024‑01020‑z39019974
    [Google Scholar]
  51. GuZ. HübschmannD. SimplifyEnrichment : A bioconductor package for clustering and visualizing functional enrichment results.Genomics Proteomics Bioinformatics202321119020210.1016/j.gpb.2022.04.00835680096
    [Google Scholar]
  52. FangS. DongL. LiuL. GuoJ. ZhaoL. ZhangJ. BuD. LiuX. HuoP. CaoW. DongQ. WuJ. ZengX. WuY. ZhaoY. HERB: A high-throughput experiment- and reference-guided database of traditional Chinese medicine.Nucleic Acids Res.202149D1D1197D120610.1093/nar/gkaa106333264402
    [Google Scholar]
  53. KimS. ChenJ. ChengT. GindulyteA. HeJ. HeS. LiQ. ShoemakerB.A. ThiessenP.A. YuB. ZaslavskyL. ZhangJ. BoltonE.E. PubChem in 2021: New data content and improved web interfaces.Nucleic Acids Res.202149D1D1388D139510.1093/nar/gkaa97133151290
    [Google Scholar]
  54. HastingsJ. OwenG. DekkerA. EnnisM. KaleN. MuthukrishnanV. TurnerS. SwainstonN. MendesP. SteinbeckC. ChEBI in 2016: Improved services and an expanding collection of metabolites.Nucleic Acids Res.201644D1D1214D121910.1093/nar/gkv103126467479
    [Google Scholar]
  55. SamigulinaG. SamigulinaZ. Ontological model of multi-agent Smart-system for predicting drug properties based on modified algorithms of artificial immune systems.Theor. Biol. Med. Model.20201711210.1186/s12976‑020‑00130‑x32669115
    [Google Scholar]
  56. DainaA. MichielinO. ZoeteV. SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules.Nucleic Acids Res.201947W1W357W36410.1093/nar/gkz38231106366
    [Google Scholar]
  57. SzklarczykD. KirschR. KoutrouliM. NastouK. MehryaryF. HachilifR. GableA.L. FangT. DonchevaN.T. PyysaloS. BorkP. JensenL.J. von MeringC. The STRING database in 2023: Protein-protein association networks and functional enrichment analyses for any sequenced genome of interest.Nucleic Acids Res.202351D1D638D64610.1093/nar/gkac100036370105
    [Google Scholar]
  58. 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]
  59. KillcoyneS. CarterG.W. SmithJ. BoyleJ. Cytoscape: A community-based framework for network modeling.Methods Mol. Biol.200956321923910.1007/978‑1‑60761‑175‑2_1219597788
    [Google Scholar]
  60. PettersenE.F. GoddardT.D. HuangC.C. CouchG.S. GreenblattD.M. MengE.C. FerrinT.E. UCSF Chimera—A visualization system for exploratory research and analysis.J. Comput. Chem.200425131605161210.1002/jcc.2008415264254
    [Google Scholar]
  61. EberhardtJ. Santos-MartinsD. TillackA.F. ForliS. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.J. Chem. Inf. Model.20216183891389810.1021/acs.jcim.1c0020334278794
    [Google Scholar]
  62. BatemanA. MartinM-J. OrchardS. MagraneM. AhmadS. AlpiE. Bowler-BarnettE.H. BrittoR. Bye-A-JeeH. CukuraA. DennyP. DoganT. EbenezerT.G. FanJ. GarmiriP. da Costa GonzalesL.J. Hatton-EllisE. HusseinA. IgnatchenkoA. InsanaG. IshtiaqR. JoshiV. JyothiD. KandasaamyS. LockA. LucianiA. LugaricM. LuoJ. LussiY. MacDougallA. MadeiraF. MahmoudyM. MishraA. MoulangK. NightingaleA. PundirS. QiG. RajS. RaposoP. RiceD.L. SaidiR. SantosR. SperettaE. StephensonJ. TotooP. TurnerE. TyagiN. VasudevP. WarnerK. WatkinsX. ZaruR. ZellnerH. BridgeA.J. AimoL. Argoud-PuyG. AuchinclossA.H. AxelsenK.B. BansalP. BaratinD. Batista NetoT.M. BlatterM-C. BollemanJ.T. BoutetE. BreuzaL. GilB.C. Casals-CasasC. EchioukhK.C. CoudertE. CucheB. de CastroE. EstreicherA. FamigliettiM.L. FeuermannM. GasteigerE. GaudetP. GehantS. GerritsenV. GosA. GruazN. HuloC. Hyka-NouspikelN. JungoF. KerhornouA. Le MercierP. LieberherrD. MassonP. MorgatA. MuthukrishnanV. PaesanoS. PedruzziI. PilboutS. PourcelL. PouxS. PozzatoM. PruessM. RedaschiN. RivoireC. SigristC.J.A. SonessonK. SundaramS. WuC.H. ArighiC.N. ArminskiL. ChenC. ChenY. HuangH. LaihoK. McGarveyP. NataleD.A. RossK. VinayakaC.R. WangQ. WangY. ZhangJ. UniProt: The universal protein knowledgebase in 2023.Nucleic Acids Res.202351D1D523D53110.1093/nar/gkac105236408920
    [Google Scholar]
  63. DaoF. LebeauB. LingC.C.Y. RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing.2025Available from: https://doi.org/https://doi.org/10.1002/imt2.70052
  64. HuB. ZhangX. FanH. JinX. QiY. LiuR. LiX. DuanM. ZhangC. LiS. YaoW. HaoC. FOXF1 reverses lung fibroblasts transdifferentiation via inhibiting TGF-β/SMAD2/3 pathway in silica-induced pulmonary fibrosis.Int. Immunopharmacol.202413311206710.1016/j.intimp.2024.11206738608444
    [Google Scholar]
  65. LongL. DaiX. YaoT. ZhangX. JiangG. ChengX. JiangM. HeY. PengZ. HuG. TaoL. MengJ. Mefunidone alleviates silica-induced inflammation and fibrosis by inhibiting the TLR4-NF-κB/MAPK pathway and attenuating pyroptosis in murine macrophages.Biomed. Pharmacother.202417811721610.1016/j.biopha.2024.11721639096618
    [Google Scholar]
  66. LiK. LiuX. HouR. ZhaoH. ZhaoP. TianY. LiJ. Uncovering mechanisms of Baojin Chenfei formula treatment for silicosis by inhibiting inflammation and fibrosis based on serum pharmacochemistry and network analysis.Ecotoxicol. Environ. Saf.202326011508210.1016/j.ecoenv.2023.11508237257350
    [Google Scholar]
  67. ThannickalV.J. FanburgB.L. Reactive oxygen species in cell signaling.Am. J. Physiol. Lung Cell. Mol. Physiol.20002796L1005L102810.1152/ajplung.2000.279.6.L100511076791
    [Google Scholar]
  68. RahimiR.A. LeofE.B. TGF-β signaling: A tale of two responses.J. Cell. Biochem.2007102359360810.1002/jcb.2150117729308
    [Google Scholar]
  69. ZouM. ZhangG. ZouJ. LiuY. LiuB. HuX. ChengZ. Inhibition of the ERK1/2-ubiquitous calpains pathway attenuates experimental pulmonary fibrosis in vivo and in vitro.Exp. Cell Res.2020391111188610.1016/j.yexcr.2020.11188632017927
    [Google Scholar]
  70. NakamuraK. ShiraiT. MorishitaS. UchidaS. Saeki-MiuraK. MakishimaF. p38 mitogen-activated protein kinase functionally contributes to chondrogenesis induced by growth/differentiation factor-5 in ATDC5 cells.Exp. Cell Res.1999250235136310.1006/excr.1999.453510413589
    [Google Scholar]
  71. BignoneP.A. LeeK.Y. LiuY. EmilionG. FinchJ. SoosayA.E.R. CharnockF.M.L. BeckS. DunhamI. MungallA.J. GanesanT.S. RPS6KA2, a putative tumour suppressor gene at 6q27 in sporadic epithelial ovarian cancer.Oncogene200726568370010.1038/sj.onc.120982716878154
    [Google Scholar]
  72. MilosevicN. KühnemuthB. MühlbergL. RipkaS. GriesmannH. LölkesC. BuchholzM. AustD. PilarskyC. KrugS. GressT. MichlP. Synthetic lethality screen identifies RPS6KA2 as modifier of epidermal growth factor receptor activity in pancreatic cancer.Neoplasia201315121354136210.1593/neo.13166024403857
    [Google Scholar]
  73. ZhangJ. LiaoJ.Q. WenL.R. PadhiarA.A. LiZ. HeZ.Y. WuH.C. LiJ.F. ZhangS. ZhouY. PanX.H. YangJ.H. ZhouG.Q. Rps6ka2 enhances iMSC chondrogenic differentiation to attenuate knee osteoarthritis through articular cartilage regeneration in mice.Biochem. Biophys. Res. Commun.2023663617010.1016/j.bbrc.2023.04.04937119767
    [Google Scholar]
  74. KangE. KimK. JeonS.Y. JungJ.G. KimH.K. LeeH.B. HanW. Targeting CLK4 inhibits the metastasis and progression of breast cancer by inactivating TGF-β pathway.Cancer Gene Ther.2022298-91168118010.1038/s41417‑021‑00419‑035046528
    [Google Scholar]
  75. YangC.L. WuY.W. TuH.J. YehY.H. LinT.E. SungT.Y. LiM.C. YenS.C. HsiehJ.H. YuM.C. HsiehS.Y. HsiehH.P. PanS.L. HsuK.C. Identification and biological evaluation of a novel clk4 inhibitor targeting alternative splicing in pancreatic cancer using structure-based virtual screening.Adv. Sci.20251219241632310.1002/advs.20241632340126184
    [Google Scholar]
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