Skip to content
2000
Volume 20, Issue 7
  • ISSN: 1574-8936
  • E-ISSN: 2212-392X

Abstract

Background

TTN mutations are the most common genetic mutations found in cervical squamous cell carcinoma and endocervical adenocarcinoma. They have been shown to affect the progression and prognosis of Cervical Endometrial glandular carcinoma (CESC). TTN mutations may also regulate the immune phenotype of CESC, which could impact its prognosis. Previous studies have demonstrated that CESC patients with TTN mutations had a significantly higher overall survival rate than those with wild-type TTN. However, the impact of TTN mutations on the immune microenvironment of CESC has not been thoroughly investigated.

Methods

This paper aims to examine the TTN mutation status and RNA expression in the CESC dataset from TCGA. Two gene features were identified to predict the prognosis of CESC. Consequently, a CESC Immune Prognosis Model (CIPM) based on a LASSO-Cox regression analysis was developed for the differential expression of immune-related genes between TTN-WT and TTN-MUT CESC samples.

Results

The results showed that TTN mutations weaken the immune response in CESCs. Out of the 152 genes associated with the immune response, 21 displayed varying expression levels depending on the presence or absence of TTN mutations.

Conclusion

The study suggests that TTN mutations have an impact on the immune response in CESCs. The CIPM was introduced and validated in 232 CESC patients to distinguish between high- and low-risk patients with an unsatisfactory prognosis, regardless of various clinical features.

Loading

Article metrics loading...

/content/journals/cbio/10.2174/0115748936143065240826061114
2024-09-26
2025-09-06
Loading full text...

Full text loading...

References

  1. ArbynM. WeiderpassE. BruniL. Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis.Lancet Glob. Health202082e191e20310.1016/S2214‑109X(19)30482‑6 31812369
    [Google Scholar]
  2. BrayF. FerlayJ. SoerjomataramI. SiegelR.L. TorreL.A. JemalA. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.201868639442410.3322/caac.21492 30207593
    [Google Scholar]
  3. SinghD. VignatJ. LorenzoniV. Global estimates of incidence and mortality of cervical cancer in 2020: A baseline analysis of the WHO global cervical cancer elimination initiative.Lancet Glob. Health2023112e197e20610.1016/S2214‑109X(22)00501‑0 36528031
    [Google Scholar]
  4. SenekjianE.K. YoungJ.M. WeiserP.A. SpencerC.E. MagicS.E. HerbstA.L. An evaluation of squamous cell carcinoma antigen in patients with cervical squamous cell carcinoma.Am. J. Obstet. Gynecol.1987157243343910.1016/S0002‑9378(87)80187‑4 3475982
    [Google Scholar]
  5. LiuC. ZhangM. YanX. Single-cell dissection of cellular and molecular features underlying human cervical squamous cell carcinoma initiation and progression.Sci. Adv.202394eadd897710.1126/sciadv.add8977 36706185
    [Google Scholar]
  6. FidlerM.M. GuptaS. SoerjomataramI. FerlayJ. Steliarova-FoucherE. BrayF. Cancer incidence and mortality among young adults aged 20–39 years worldwide in 2012: A population-based study.Lancet Oncol.201718121579158910.1016/S1470‑2045(17)30677‑0 29111259
    [Google Scholar]
  7. NilenduP. SarodeS.C. JahagirdarD. Mutual concessions and compromises between stromal cells and cancer cells: Driving tumor development and drug resistance.Cell Oncol. (Dordr.)201841435336710.1007/s13402‑018‑0388‑2 30027403
    [Google Scholar]
  8. QinY. EkmekciogluS. ForgetM.A. Cervical cancer neoantigen landscape and immune activity is associated with human papillomavirus master regulators.Front. Immunol.2017868910.3389/fimmu.2017.00689 28670312
    [Google Scholar]
  9. MekalaV.R. Hui-ShanC. Jan-GowthC. NgK-L. Identification of key prognosis-related microRNAs in early- and late- stage gynecological cancers based on TCGA data.Curr. Bioinform.202217986087210.2174/1574893617666220802154148
    [Google Scholar]
  10. FrancoP.I.R. RodriguesA.P. de MenezesL.B. MiguelM.P. Tumor microenvironment components: Allies of cancer progression.Pathol. Res. Pract.2020216115272910.1016/j.prp.2019.152729 31735322
    [Google Scholar]
  11. PiersmaS.J. JordanovaE.S. van PoelgeestM.I.E. High number of intraepithelial CD8+ tumor-infiltrating lymphocytes is associated with the absence of lymph node metastases in patients with large early-stage cervical cancer.Cancer Res.200767135436110.1158/0008‑5472.CAN‑06‑3388 17210718
    [Google Scholar]
  12. CarusA. LadekarlM. HagerH. NedergaardB.S. DonskovF. Tumour-associated CD66b+ neutrophil count is an independent prognostic factor for recurrence in localised cervical cancer.Br. J. Cancer2013108102116212210.1038/bjc.2013.167 23591202
    [Google Scholar]
  13. DenkoN. SchindlerC. KoongA. LaderouteK. GreenC. GiacciaA. Epigenetic regulation of gene expression in cervical cancer cells by the tumor microenvironment.Clin. Cancer Res.200062480487 10690527
    [Google Scholar]
  14. ChengH.S. LeeJ.X.T. WahliW. TanN.S. Exploiting vulnerabilities of cancer by targeting nuclear receptors of stromal cells in tumor microenvironment.Mol. Cancer20191815110.1186/s12943‑019‑0971‑9 30925918
    [Google Scholar]
  15. GuoS. DengC.X. Effect of stromal cells in tumor microenvironment on metastasis initiation.Int. J. Biol. Sci.201814142083209310.7150/ijbs.25720 30585271
    [Google Scholar]
  16. VentrigliaJ. PaciollaI. PisanoC. Immunotherapy in ovarian, endometrial and cervical cancer: State of the art and future perspectives.Cancer Treat. Rev.20175910911610.1016/j.ctrv.2017.07.008 28800469
    [Google Scholar]
  17. TewariK.S. MonkB.J. New strategies in advanced cervical cancer: From angiogenesis blockade to immunotherapy.Clin. Cancer Res.201420215349535810.1158/1078‑0432.CCR‑14‑1099 25104084
    [Google Scholar]
  18. LiuJ. WuZ. WangY. A prognostic signature based on immune-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma.Int. Immunopharmacol.20208810688410.1016/j.intimp.2020.106884 32795900
    [Google Scholar]
  19. MarabelleA. FakihM. LopezJ. Association of tumour mutational burden with outcomes in patients with advanced solid tumours treated with pembrolizumab: Prospective biomarker analysis of the multicohort, open-label, phase 2 KEYNOTE-158 study.Lancet Oncol.202021101353136510.1016/S1470‑2045(20)30445‑9 32919526
    [Google Scholar]
  20. MauricioD. ZeybekB. Tymon-RosarioJ. HaroldJ. SantinA.D. Immunotherapy in cervical cancer.Curr. Oncol. Rep.20212366110.1007/s11912‑021‑01052‑8 33852056
    [Google Scholar]
  21. AshrafzadehS. AsgariM.M. GellerA.C. The need for critical examination of disparities in immunotherapy and targeted therapy use among patients with cancer.JAMA Oncol.2021781115111610.1001/jamaoncol.2021.1322 34042941
    [Google Scholar]
  22. ChauveauC. RowellJ. FerreiroA. A rising titan: TTN review and mutation update.Hum. Mutat.20143591046105910.1002/humu.22611 24980681
    [Google Scholar]
  23. ChanJ.Y. A clinical overview of centrosome amplification in human cancers.Int. J. Biol. Sci.2011781122114410.7150/ijbs.7.1122 22043171
    [Google Scholar]
  24. HanX. ChenJ. WangJ. XuJ. LiuY. TTN mutations predict a poor prognosis in patients with thyroid cancer.Biosci. Rep.2022427BSR2022116810.1042/BSR20221168 35766333
    [Google Scholar]
  25. HänzelmannS. CasteloR. GuinneyJ. GSVA: Gene set variation analysis for microarray and RNA-Seq data.BMC Bioinformatics2013141710.1186/1471‑2105‑14‑7 23323831
    [Google Scholar]
  26. ShiJ. WalkerM. Gene set enrichment analysis (GSEA) for interpreting gene expression profiles.Curr. Bioinform.20072213313710.2174/157489307780618231
    [Google Scholar]
  27. ReimandJ. IsserlinR. VoisinV. Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap.Nat. Protoc.201914248251710.1038/s41596‑018‑0103‑9 30664679
    [Google Scholar]
  28. RapaportF. KhaninR. LiangY. Comprehensive evaluation of differential gene expression analysis methods for RNA-seq data.Genome Biol.2013149R9510.1186/gb‑2013‑14‑9‑r95 24020486
    [Google Scholar]
  29. TibshiraniR. The lasso method for variable selection in the Cox model.Stat. Med.199716438539510.1002/(SICI)1097‑0258(19970228)16:4<385::AID‑SIM380>3.0.CO;2‑3 9044528
    [Google Scholar]
  30. IasonosA. SchragD. RajG.V. PanageasK.S. How to build and interpret a nomogram for cancer prognosis.J. Clin. Oncol.20082681364137010.1200/JCO.2007.12.9791 18323559
    [Google Scholar]
  31. ShenW. SongZ. ZhongX. Sangerbox: A comprehensive, interaction‐friendly clinical bioinformatics analysis platform.iMeta202213e3610.1002/imt2.36 38868713
    [Google Scholar]
  32. OhJ.H. JangS.J. KimJ. Spontaneous mutations in the single TTN gene represent high tumor mutation burden.NPJ Genom. Med.2020513310.1038/s41525‑019‑0107‑6 32821429
    [Google Scholar]
  33. JiaQ. WangJ. HeN. HeJ. ZhuB. Titin mutation associated with responsiveness to checkpoint blockades in solid tumors.JCI Insight2019410e12790110.1172/jci.insight.127901 31092729
    [Google Scholar]
  34. BoleslawskiE. DecanterG. TruantS. Right hepatectomy with extra-hepatic vascular division prior to transection: Intention-to-treat analysis of a standardized policy.HPB (Oxford)2012141068869910.1111/j.1477‑2574.2012.00519.x 22954006
    [Google Scholar]
  35. LiY. LuZ. CheY. Immune signature profiling identified predictive and prognostic factors for esophageal squamous cell carcinoma.OncoImmunology2017611e135614710.1080/2162402X.2017.1356147 29147607
    [Google Scholar]
  36. HeC. RenL. YuanM. Identification of cervical squamous cell carcinoma feature genes and construction of a prognostic model based on immune-related features.BMC Womens Health202222136510.1186/s12905‑022‑01942‑4 36057587
    [Google Scholar]
  37. TianQ. GaoH. ZhaoW. ZhouY. YangJ. Development and validation of an immune gene set-based prognostic signature in cutaneous melanoma.Future Oncol.202117314115412910.2217/fon‑2021‑0104 34291650
    [Google Scholar]
  38. YangY. LuQ. ShaoX. Development of a three-gene prognostic signature for hepatitis B virus associated hepatocellular carcinoma based on integrated transcriptomic analysis.J. Cancer20189111989200210.7150/jca.23762 29896284
    [Google Scholar]
  39. ZhangJ. EndresS. KoboldS. Enhancing tumor T cell infiltration to enable cancer immunotherapy.Immunotherapy201911320121310.2217/imt‑2018‑0111 30730277
    [Google Scholar]
  40. LiC. JiangP. WeiS. XuX. WangJ. Regulatory T cells in tumor microenvironment: New mechanisms, potential therapeutic strategies and future prospects.Mol. Cancer202019111610.1186/s12943‑020‑01234‑1 32680511
    [Google Scholar]
/content/journals/cbio/10.2174/0115748936143065240826061114
Loading
/content/journals/cbio/10.2174/0115748936143065240826061114
Loading

Data & Media loading...

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