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2000
Volume 32, Issue 28
  • ISSN: 0929-8673
  • E-ISSN: 1875-533X

Abstract

Background

Esophageal squamous cell carcinoma (ESCC) is a highly fatal malignancy with increasing incidence, and programmed cell death (PCD) plays an important role in homeostasis.

Aims

This study aimed to explore the ESCC of heterogeneity based on the PCD signatures for the diagnosis and treatment of patients.

Methods

The clinical information and RNA-seq data of patients with ESCC and the PCD-related genes set were used to identify PCD signatures. The “limma” package was used to identify the differentially expressed genes (DEGs). “Clusterprofiler” package was used for function enrichment analysis, and the “ConsensusClusterPlus” package was performed for consensus clustering. Finally, the “GSVA” package and the Cibersort algorithm were used for the immune infiltration analysis.

Results

We performed differential expression analysis between ESCC and normal samples and identified 1659 DEGs, of which 124 DEGs were PCD genes. Then, the patients were divided into cluster1 and cluster2 based on the expression of 124 PCD genes. There was a significant difference in immune infiltration between the two clusters. The patients in cluster 1 had a higher immune score and more CD56dim natural killer cells, monocytes, activated CD4 T cells, eosinophil, and activated B cells infiltration, while cluster2 had a higher stromal score, more immune regulation, and immune checkpoint genes expression.

Conclusion

We identified two clusters based on PCD gene expression and characterized their tumor microenvironment and immune checkpoint difference. Our findings may provide some new insight into the treatment of ESCC.

© 2025 Bentham Science Publishers
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References

  1. AbnetC.C. HuppiK. CarreraA. ArmisteadD. McKenneyK. HuN. TangZ.Z. TaylorP.R. DawseyS.M. Control region mutations and the ‘common deletion’ are frequent in the mitochondrial DNA of patients with esophageal squamous cell carcinoma.BMC Cancer2004413010.1186/1471‑2407‑4‑3015230979
     [Google Scholar]
  2. ZhanJ. WangW. TangY. ZhouN. JiangD. Development of a prognostic signature for esophageal cancer based on a novel 7-DNA damage repair genes signature.Biocell202246122601261310.32604/biocell.2022.021300
     [Google Scholar]
  3. SiegelR.L. MillerK.D. JemalA. Cancer statistics, 2019.CA Cancer J. Clin.201969173410.3322/caac.2155130620402
     [Google Scholar]
  4. SiegelR.L. MillerK.D. WagleN.S. JemalA. Cancer statistics, 2023.CA Cancer J. Clin.2023731174810.3322/caac.2176336633525
     [Google Scholar]
  5. FengR.M. ZongY.N. CaoS.M. XuR.H. Current cancer situation in China: Good or bad news from the 2018 Global Cancer Statistics?Cancer Commun.201939111210.1186/s40880‑019‑0368‑631030667
     [Google Scholar]
  6. TsaiH.W. ChangC.C. SunJ.T. LiouC.B. LinH.C. LinI.H. YuY.C. WengW.L. LeongK.I. YenT.H. WuJ.M. Clinical features of patients with esophageal and second primary cancers.Asian Pac. J. Cancer Prev.201415229831983410.7314/APJCP.2014.15.22.983125520113
     [Google Scholar]
  7. Surdea-BlagaT. NegrutiuD.E. PalageM. DumitrascuD.L. Food and gastroesophageal reflux disease.Curr. Med. Chem.201926193497351110.2174/092986732466617051512380728521699
     [Google Scholar]
  8. McGrattanK.E. McGheeH. DeTomaA. HillE.G. ZyblewskiS.C. Lefton-GreifM. HalsteadL. BradleyS.M. Martin-HarrisB. Dysphagia in infants with single ventricle anatomy following stage 1 palliation: Physiologic correlates and response to treatment.Congenit. Heart Dis.201712338238810.1111/chd.1245628244680
     [Google Scholar]
  9. BujandaD.E. HachemC. Barrett’s Esophagus.Mo. Med.2018115321121330228724
     [Google Scholar]
  10. CookM.B. ThriftA.P. Epidemiology of barrett’s esophagus and esophageal adenocarcinoma.Gastrointest. Endosc. Clin. N. Am.202131112610.1016/j.giec.2020.08.00133213789
     [Google Scholar]
  11. MaoW.M. ZhengW.H. LingZ.Q. Epidemiologic risk factors for esophageal cancer development.Asian Pac. J. Cancer Prev.201112102461246622320939
     [Google Scholar]
  12. PoehlmannA. KuesterD. MalfertheinerP. GuentherT. RoessnerA. Inflammation and Barrett’s carcinogenesis.Pathol. Res. Pract.2012208526928010.1016/j.prp.2012.03.00722541897
     [Google Scholar]
  13. WangG-Q. AbnetC.C. ShenQ. LewinK.J. SunX.D. RothM.J. QiaoY.L. MarkS.D. DongZ.W. TaylorP.R. DawseyS.M. Histological precursors of oesophageal squamous cell carcinoma: Results from a 13 year prospective follow up study in a high risk population.Gut200554218719210.1136/gut.2004.04663115647178
     [Google Scholar]
  14. DinhH.Q. PanF. WangG. HuangQ.F. OlingyC.E. WuZ.Y. WangS.H. XuX. XuX.E. HeJ.Z. YangQ. OrsulicS. HaroM. LiL.Y. HuangG.W. BreunigJ.J. KoefflerH.P. HedrickC.C. XuL.Y. LinD.C. LiE.M. Integrated single-cell transcriptome analysis reveals heterogeneity of esophageal squamous cell carcinoma microenvironment.Nat. Commun.2021121733510.1038/s41467‑021‑27599‑534921160
     [Google Scholar]
  15. Bobillo-PerezS. Sanchez-de-ToledoJ. SeguraS. Girona-AlarconM. MeleM. Sole-RibaltaA. Cañizo VazquezD. JordanI. CambraF.J. Risk stratification models for congenital heart surgery in children: Comparative single-center study.Congenit. Heart Dis.20191461066107710.1111/chd.1284631545015
     [Google Scholar]
  16. LuoH. LuJ. BaiY. MaoT. WangJ. FanQ. ZhangY. ZhaoK. ChenZ. GaoS. LiJ. FuZ. GuK. LiuZ. WuL. ZhangX. FengJ. NiuZ. BaY. ZhangH. LiuY. ZhangL. MinX. HuangJ. ChengY. WangD. ShenY. YangQ. ZouJ. XuR.H. YuanX. MaD. LiuL. YeF. LiuT. WangX. LiuL. XiaB. RanF. SunS. MiaoZ. BieJ. GaoY. YuJ. ChenL. HeY. RenW. LuoS. ZhaoG. LinY. ChenL. GuoZ. HuC. WangY. XuN. ChenB. ZhanX. ChenY. LuH. QinS. WangG. ChenL. BaiL. ZhangJ. ESCORT-1st Investigators Effect of camrelizumab vs. placebo added to chemotherapy on survival and progression-free survival in patients with advanced or metastatic esophageal squamous cell carcinoma.JAMA20213261091692510.1001/jama.2021.1283634519801
     [Google Scholar]
  17. WatersJ.K. ReznikS.I. Update on management of squamous cell esophageal cancer.Curr. Oncol. Rep.202224337538510.1007/s11912‑021‑01153‑435142974
     [Google Scholar]
  18. ZhongL. LiH. ChangW. AoY. WenZ. ChenY. TP53 mutations in esophageal squamous cell carcinoma.Frontiers in Bioscience-Landmark202328921910.31083/j.fbl280921937796679
     [Google Scholar]
  19. KoK.P. HuangY. ZhangS. ZouG. KimB. ZhangJ. JunS. MartinC. DunbarK.J. EfeG. RustgiA.K. NakagawaH. ParkJ.I. Key genetic determinants driving esophageal squamous cell carcinoma initiation and immune evasion.Gastroenterology20231653613628.e2010.1053/j.gastro.2023.05.03037257519
     [Google Scholar]
  20. SongY. LiL. OuY. GaoZ. LiE. LiX. ZhangW. WangJ. XuL. ZhouY. MaX. LiuL. ZhaoZ. HuangX. FanJ. DongL. ChenG. MaL. YangJ. ChenL. HeM. LiM. ZhuangX. HuangK. QiuK. YinG. GuoG. FengQ. ChenP. WuZ. WuJ. MaL. ZhaoJ. LuoL. FuM. XuB. ChenB. LiY. TongT. WangM. LiuZ. LinD. ZhangX. YangH. WangJ. ZhanQ. Identification of genomic alterations in oesophageal squamous cell cancer.Nature20145097498919510.1038/nature1317624670651
     [Google Scholar]
  21. BedouiS. HeroldM.J. StrasserA. Emerging connectivity of programmed cell death pathways and its physiological implications.Nat. Rev. Mol. Cell Biol.2020211167869510.1038/s41580‑020‑0270‑832873928
     [Google Scholar]
  22. HänggiK. RuffellB. Cell death, therapeutics, and the immune response in cancer.Trends Cancer20239538139610.1016/j.trecan.2023.02.00136841748
     [Google Scholar]
  23. ElmoreS. Apoptosis: A review of programmed cell death.Toxicol. Pathol.200735449551610.1080/0192623070132033717562483
     [Google Scholar]
  24. de VasconcelosN.M. Van OpdenboschN. Van GorpH. Martín-PérezR. ZecchinA. VandenabeeleP. LamkanfiM. An apoptotic caspase network safeguards cell death induction in pyroptotic macrophages.Cell Rep.202032410795910.1016/j.celrep.2020.10795932726624
     [Google Scholar]
  25. ChristgenS. ZhengM. KesavardhanaS. KarkiR. MalireddiR.K.S. BanothB. PlaceD.E. BriardB. SharmaB.R. TuladharS. SamirP. BurtonA. KannegantiT.D. Identification of the PANoptosome: A molecular platform triggering pyroptosis, apoptosis, and necroptosis (PANoptosis).Front. Cell. Infect. Microbiol.20201023710.3389/fcimb.2020.0023732547960
     [Google Scholar]
  26. AgliettiR.A. EstevezA. GuptaA. RamirezM.G. LiuP.S. KayagakiN. CiferriC. DixitV.M. DueberE.C. GsdmD p30 elicited by caspase-11 during pyroptosis forms pores in membranes.Proc. Natl. Acad. Sci. USA2016113287858786310.1073/pnas.160776911327339137
     [Google Scholar]
  27. DegterevA. HitomiJ. GermscheidM. Ch’enI.L. KorkinaO. TengX. AbbottD. CunyG.D. YuanC. WagnerG. HedrickS.M. GerberS.A. LugovskoyA. YuanJ. Identification of RIP1 kinase as a specific cellular target of necrostatins.Nat. Chem. Biol.20084531332110.1038/nchembio.8318408713
     [Google Scholar]
  28. SunL. WangH. WangZ. HeS. ChenS. LiaoD. WangL. YanJ. LiuW. LeiX. WangX. Mixed lineage kinase domain-like protein mediates necrosis signaling downstream of RIP3 kinase.Cell20121481-221322710.1016/j.cell.2011.11.03122265413
     [Google Scholar]
  29. KesavardhanaS. MalireddiR.K.S. KannegantiT.D. Caspases in cell death, inflammation, and pyroptosis.Annu. Rev. Immunol.202038156759510.1146/annurev‑immunol‑073119‑09543932017655
     [Google Scholar]
  30. MalireddiR.K.S. GurungP. MavuluriJ. DasariT.K. KlcoJ.M. ChiH. KannegantiT.D. TAK1 restricts spontaneous NLRP3 activation and cell death to control myeloid proliferation.J. Exp. Med.201821541023103410.1084/jem.2017192229500178
     [Google Scholar]
  31. MalireddiR.K.S. KarkiR. SundaramB. KancharanaB. LeeS. SamirP. KannegantiT.D. Inflammatory cell death, PANoptosis, mediated by cytokines in diverse cancer lineages inhibits tumor growth.Immunohorizons20215756858010.4049/immunohorizons.210005934290111
     [Google Scholar]
  32. KoppulaP. ZhuangL. GanB. Cystine transporter SLC7A11/xCT in cancer: Ferroptosis, nutrient dependency, and cancer therapy.Protein Cell202112859962010.1007/s13238‑020‑00789‑533000412
     [Google Scholar]
  33. AnandhanA. DodsonM. SchmidlinC.J. LiuP. ZhangD.D. Breakdown of an ironclad defense system: The critical role of NRF2 in mediating ferroptosis.Cell Chem. Biol.202027443644710.1016/j.chembiol.2020.03.01132275864
     [Google Scholar]
  34. JiangL. KonN. LiT. WangS.J. SuT. HibshooshH. BaerR. GuW. Ferroptosis as a p53-mediated activity during tumour suppression.Nature20155207545576210.1038/nature1434425799988
     [Google Scholar]
  35. KarkiR. KannegantiT.D. Diverging inflammasome signals in tumorigenesis and potential targeting.Nat. Rev. Cancer201919419721410.1038/s41568‑019‑0123‑y30842595
     [Google Scholar]
  36. IgneyF.H. KrammerP.H. Death and anti-death: Tumour resistance to apoptosis.Nat. Rev. Cancer20022427728810.1038/nrc77612001989
     [Google Scholar]
  37. PistrittoG. TrisciuoglioD. CeciC. GarufiA. D’OraziG. Apoptosis as anticancer mechanism: Function and dysfunction of its modulators and targeted therapeutic strategies.Aging20168460361910.18632/aging.10093427019364
     [Google Scholar]
  38. CoyleR. O’SullivanM.J. ZistererD.M. Targeting inhibitor of apoptosis proteins (IAPs) with IAP inhibitors sensitises malignant rhabdoid tumour cells to cisplatin.Cancer Treat. Res. Commun.20223210057910.1016/j.ctarc.2022.10057935613525
     [Google Scholar]
  39. SeymourJ.F. KippsT.J. EichhorstB. HillmenP. D’RozarioJ. AssoulineS. OwenC. GerecitanoJ. RobakT. De la SernaJ. JaegerU. CartronG. MontilloM. HumerickhouseR. PunnooseE.A. LiY. BoyerM. HumphreyK. MobasherM. KaterA.P. Venetoclax-rituximab in relapsed or refractory chronic lymphocytic leukemia.N. Engl. J. Med.2018378121107112010.1056/NEJMoa171397629562156
     [Google Scholar]
  40. LinX. TianC. PanF. WangR. A novel immune-associated prognostic signature based on the immune cell infiltration analysis for hepatocellular carcinoma.Oncologie20242619110310.1515/oncologie‑2023‑0360
     [Google Scholar]
  41. XiaoY. JiangJ. ChenY. HuangY. WeiC. PD-1 relevant genes predict the prognosis of breast cancer and their prediction effect in tumor response to immunotherapy.Oncologie202224472974210.32604/oncologie.2022.026118
     [Google Scholar]
  42. LiM. LiuM. ZhuL. LuH. MaoA. LiuH. YuK. DongL. Establishing a predictive model to predict survival of de novo metastatic HER2-low breast cancer: A national cancer database analysis.Discov. Med.20233517628329210.24976/Discov.Med.202335176.2937272095
     [Google Scholar]
  43. TomczakK. CzerwińskaP. WiznerowiczM. Review the cancer genome atlas (TCGA): An immeasurable source of knowledge.Contemp. Oncol.20151A1A687710.5114/wo.2014.4713625691825
     [Google Scholar]
  44. ColapricoA. SilvaT.C. OlsenC. GarofanoL. CavaC. GaroliniD. SabedotT.S. MaltaT.M. PagnottaS.M. CastiglioniI. CeccarelliM. BontempiG. NoushmehrH. TCGAbiolinks: An R/Bioconductor package for integrative analysis of TCGA data.Nucleic Acids Res.2016448e7110.1093/nar/gkv150726704973
     [Google Scholar]
  45. 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]
  46. GuoS. WuJ. ZhouW. LiuX. LiuY. ZhangJ. JiaS. LiJ. WangH. Identification and analysis of key genes associated with acute myocardial infarction by integrated bioinformatics methods.Medicine202110015e2555310.1097/MD.000000000002555333847684
     [Google Scholar]
  47. 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]
  48. YuG. WangL.G. HanY. HeQ.Y. ClusterProfiler: An R package for comparing biological themes among gene clusters.OMICS201216528428710.1089/omi.2011.011822455463
     [Google Scholar]
  49. ChenH. LiuX. YaoF. YinM. ChengB. YangS. Identification of metabolic signatures related to metastasis and immunotherapy resistance in oral squamous cell carcinoma.Am. J. Transl. Res.202315137339136777871
     [Google Scholar]
  50. HänzelmannS. CasteloR. GuinneyJ. GSVA: gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics2013141710.1186/1471‑2105‑14‑723323831
     [Google Scholar]
  51. ChenB. KhodadoustM.S. LiuC.L. NewmanA.M. AlizadehA.A. Profiling tumor infiltrating immune cells with CIBERSORT.Methods Mol. Biol.2018171124325910.1007/978‑1‑4939‑7493‑1_1229344893
     [Google Scholar]
  52. WangX. TanX. ZhangJ. WuJ. ShiH. The emerging roles of MAPK-AMPK in ferroptosis regulatory network.Cell Commun. Signal.202321120010.1186/s12964‑023‑01170‑937580745
     [Google Scholar]
  53. MantiaC.M. McDermottD.F. Vascular endothelial growth factor and programmed death-1 pathway inhibitors in renal cell carcinoma.Cancer2019125234148415710.1002/cncr.3236131532565
     [Google Scholar]
  54. SakanashiF. ShintaniM. TsuneyoshiM. OhsakiH. KamoshidaS. Apoptosis, necroptosis and autophagy in colorectal cancer: Associations with tumor aggressiveness and p53 status.Pathol. Res. Pract.2019215715242510.1016/j.prp.2019.04.01731097354
     [Google Scholar]
  55. XuQ.T. QiangJ.K. HuangZ.Y. JiangW.J. CuiX.M. HuR.H. WangT. YiX.L. LiJ.Y. YuZ. ZhangS. DuT. LiuJ. JiangX.H. Integration of machine learning for developing a prognostic signature related to programmed cell death in colorectal cancer.Environ. Toxicol.20243952908292610.1002/tox.2415738299230
     [Google Scholar]
  56. KreugerI.Z.M. SliekerR.C. van GroningenT. van DoornR. Therapeutic strategies for targeting CDKN2A loss in melanoma.J. Invest. Dermatol.202314311825.e110.1016/j.jid.2022.07.01636123181
     [Google Scholar]
  57. GołąbekK. RączkaG. GaździckaJ. Miśkiewicz-OrczykK. ZiębaN. KrakowczykŁ. MisiołekM. StrzelczykJ.K. Expression profiles of CDKN2A, MDM2, E2F2 and LTF genes in oral squamous cell carcinoma.Biomedicines20221012301110.3390/biomedicines1012301136551770
     [Google Scholar]
  58. KanellouP. ZaravinosA. ZiogaM. SpandidosD.A. Deregulation of the tumour suppressor genes p14 ARF, p15 INK4b, p16 INK4a and p53 in basal cell carcinoma.Br. J. Dermatol.200916061215122110.1111/j.1365‑2133.2009.09079.x19298278
     [Google Scholar]
  59. TateJ.G. BamfordS. JubbH.C. SondkaZ. BeareD.M. BindalN. BoutselakisH. ColeC.G. CreatoreC. DawsonE. FishP. HarshaB. HathawayC. JupeS.C. KokC.Y. NobleK. PontingL. RamshawC.C. RyeC.E. SpeedyH.E. StefancsikR. ThompsonS.L. WangS. WardS. CampbellP.J. ForbesS.A. COSMIC: The catalogue of somatic mutations in cancer.Nucleic Acids Res.201947D1D941D94710.1093/nar/gky101530371878
     [Google Scholar]
  60. JiangB. HuL. DongD. GuoZ. WeiW. WangC. ShaoW. MaT. ChenY. LiQ. HuW. TP53 or CDKN2A/B covariation in ALK/RET/ROS1-rearranged NSCLC is associated with a high TMB, tumor immunosuppressive microenvironment and poor prognosis.J. Cancer Res. Clin. Oncol.202314912100411005210.1007/s00432‑023‑04924‑737261522
     [Google Scholar]
  61. YinY. HeQ. LiY. LongJ. LeiX. LiZ. ZhuW. Emerging functions of PRKDC in the initiation and progression of cancer.Tumori2021107648348810.1177/030089162095047232867618
     [Google Scholar]
  62. TanK.T. YehC.N. ChangY.C. ChengJ.H. FangW.L. YehY.C. WangY.C. HsuD.S.S. WuC.E. LaiJ.I. ChangP.M.H. ChenM.H. LuM.L. ChenS.J. ChaoY. HsiaoM. ChenM.H. PRKDC : New biomarker and drug target for checkpoint blockade immunotherapy.J. Immunother. Cancer202081e00048510.1136/jitc‑2019‑00048532238472
     [Google Scholar]
  63. WangC. NieH. LiY. LiuG. WangX. XingS. ZhangL. ChenX. ChenY. LiY. The study of the relation of DNA repair pathway genes SNPs and the sensitivity to radiotherapy and chemotherapy of NSCLC.Sci. Rep.2016612652610.1038/srep2652627246533
     [Google Scholar]
  64. EngblomC. PfirschkeC. PittetM.J. The role of myeloid cells in cancer therapies.Nat. Rev. Cancer201616744746210.1038/nrc.2016.5427339708
     [Google Scholar]
  65. JahchanN.S. MujalA.M. PollackJ.L. BinnewiesM. SriramV. ReynoL. KrummelM.F. Tuning the tumor myeloid microenvironment to fight cancer.Front. Immunol.201910161110.3389/fimmu.2019.0161131402908
     [Google Scholar]
  66. YangL. LiuY. ZhangB. YuM. HuangF. WenY. ZengJ. LuY. YangC. Prognostic tumor microenvironment gene and the relationship with immune infiltration characteristics in metastatic breast cancer.Biocell20224651215124310.32604/biocell.2022.018221
     [Google Scholar]
  67. CoussensL.M. ZitvogelL. PaluckaA.K. Neutralizing tumor-promoting chronic inflammation: A magic bullet?Science2013339611728629110.1126/science.123222723329041
     [Google Scholar]
  68. EvrenE. RingqvistE. TripathiK.P. SleiersN. RivesI.C. AlisjahbanaA. GaoY. SarhanD. HalleT. SoriniC. LepzienR. MarquardtN. MichaëlssonJ. Smed-SörensenA. BotlingJ. KarlssonM.C.I. VillablancaE.J. WillingerT. Distinct developmental pathways from blood monocytes generate human lung macrophage diversity.Immunity2021542259275.e710.1016/j.immuni.2020.12.00333382972
     [Google Scholar]
  69. MantovaniA. AllavenaP. SozzaniS. VecchiA. LocatiM. SicaA. Chemokines in the recruitment and shaping of the leukocyte infiltrate of tumors.Semin. Cancer Biol.200414315516010.1016/j.semcancer.2003.10.00115246050
     [Google Scholar]
  70. MantovaniA. MarchesiF. MalesciA. LaghiL. AllavenaP. Tumour-associated macrophages as treatment targets in oncology.Nat. Rev. Clin. Oncol.201714739941610.1038/nrclinonc.2016.21728117416
     [Google Scholar]
  71. YunnaC. MengruH. LeiW. WeidongC. Macrophage M1/M2 polarization.Eur. J. Pharmacol.202087717309010.1016/j.ejphar.2020.17309032234529
     [Google Scholar]
  72. CassettaL. PollardJ.W. Targeting macrophages: Therapeutic approaches in cancer.Nat. Rev. Drug Discov.2018171288790410.1038/nrd.2018.16930361552
     [Google Scholar]
  73. WangH. SunY. ZhouX. ChenC. JiaoL. LiW. GouS. LiY. DuJ. ChenG. ZhaiW. WuY. QiY. GaoY. CD47/SIRPα blocking peptide identification and synergistic effect with irradiation for cancer immunotherapy.J. Immunother. Cancer202082e00090510.1136/jitc‑2020‑000905
     [Google Scholar]
  74. LvY. MaX. MaY. DuY. FengJ. A new emerging target in cancer immunotherapy: Galectin-9 (LGALS9).Genes Dis.20231062366238210.1016/j.gendis.2022.05.02037554219
     [Google Scholar]
  75. ChamL.B. AdomatiT. LiF. AliM. LangK.S. CD47 as a potential target to therapy for infectious diseases.Antibodies2020934410.3390/antib903004432882841
     [Google Scholar]
  76. LinF. XiongM. HaoW. SongY. LiuR. YangY. YuanX. FanD. ZhangY. HaoM. YeZ. LuY. ZhangY. WangJ. XiongD. A novel blockade CD47 antibody with therapeutic potential for cancer.Front. Oncol.20211061553410.3389/fonc.2020.61553433469516
     [Google Scholar]
  77. ZhuC. AndersonA.C. SchubartA. XiongH. ImitolaJ. KhouryS.J. ZhengX.X. StromT.B. KuchrooV.K. The Tim-3 ligand galectin-9 negatively regulates T helper type 1 immunity.Nat. Immunol.20056121245125210.1038/ni127116286920
     [Google Scholar]
  78. SekiM. OomizuS. SakataK. SakataA. ArikawaT. WatanabeK. ItoK. TakeshitaK. NikiT. SaitaN. NishiN. YamauchiA. KatohS. MatsukawaA. KuchrooV. HirashimaM. Galectin-9 suppresses the generation of Th17, promotes the induction of regulatory T cells, and regulates experimental autoimmune arthritis.Clin. Immunol.20081271788810.1016/j.clim.2008.01.00618282810
     [Google Scholar]
  79. LabrieM. De AraujoL.O.F. CommunalL. Mes-MassonA.M. St-PierreY. Tissue and plasma levels of galectins in patients with high grade serous ovarian carcinoma as new predictive biomarkers.Sci. Rep.2017711324410.1038/s41598‑017‑13802‑529038585
     [Google Scholar]
  80. PoureauP.G. MetgesJ.P. Fundamentals of digestive cancers immunology, especially gastric and hepatocellular carcinomas.Oncologie2021231475910.32604/Oncologie.2021.15525
     [Google Scholar]
  81. LiuJ. GuM. XueY. WangQ. RenY. HuangW. Clinical significance of PD-L1 expression and CD8-positive tumor-infiltrating lymphocytes in patients with cavitary lung adenocarcinoma.Oncologie202123343945210.32604/oncologie.2021.017220
     [Google Scholar]
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Identification of Molecular Subtypes Associated with PCD in Esophageal Squamous Cell Carcinoma and Analysis of Immune Microenvironment
Curr. Med. Chem. 32, 6077 (2025); https://doi.org/10.2174/0109298673305351240507060832
/content/journals/cmc/10.2174/0109298673305351240507060832
/content/journals/cmc/10.2174/0109298673305351240507060832
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  • Article Type:     Research Article
Keyword(s): consensus clustering; enrichment analysis; Esophageal squamous cell carcinoma (ESCC); immune infiltration; programmed cell death (PCD)-related signature; variation landscape

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