Skip to content
2000
image of Telomere Maintenance Characteristics Predict Prognosis and Therapeutic Response in Colorectal Cancer

Abstract

Introduction

The link between telomere length and Colorectal Cancer (CRC) risk and survival has been established. This study aims to investigate Telomere Maintenance-related Genes (TMGs) for predicting immunotherapy response and prognosis in CRC patients.

Methods

In this study, gene expression data and clinical information of CRC patients were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and TMG-related scores were calculated for the samples. Subsequently, Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify gene modules that were highly correlated with the TMG score and intersected with differentially expressed genes to screen for potential functionally relevant candidate genes. The key genes significantly associated with prognosis were further analyzed using Cox regression analysis, from which the key genes were identified, and a risk score model was constructed. Finally, the survival prediction ability of the model was evaluated across multiple cohorts, and differences in immune cell infiltration characteristics and drug sensitivity were analyzed within different risk groups.

Results

A higher TMG score was noticed in CRC, and the TMG score was negatively correlated with the StromalScore, ImmuneScore, and ESTIMATEScore. Gene modules significantly associated with the TMG score were identified using WGCNA. Two key genes, CDC25C and USP39, which were closely associated with prognosis, were screened through differential expression analysis, and a risk score model was constructed. The model showed good survival prediction in both TCGA and GSE17537 independent cohorts. The scores of activated CD4 T cells, Type 17 T helper cells, Type 2 T helper cells, and neutrophils in high-risk patients were lower, while that of macrophages was higher in high-risk patients. Additionally, a negative correlation was observed between the risk score and the IC values of most drugs, as well as the enriched pathways of patients at high risk, which included epithelial-mesenchymal transition, angiogenesis, and myogenesis.

Discussion

This study unveiled a TMG-related signature that predicts prognosis and immunotherapy in CRC. Based on the 2 prognostically relevant genes CDC25C and USP39, a reliable risk score model was established for the prognostic prediction, and the correlation between the drug sensitivity and the risk score was also explored.

Conclusion

This study reveals the significant value of TMGs in CRC prognostic assessment and immunotherapy response prediction, providing a new molecular basis for the development of individualized treatment strategies.

© 2025 Bentham Science Publishers
Loading

Article metrics loading...

/content/journals/ctmc/10.2174/0115680266397024250710105241
2025-07-15
2025-09-14

  • From This Site

    /content/journals/ctmc/10.2174/0115680266397024250710105241
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Klimeck L. Heisser T. Hoffmeister M. Brenner H. Colorectal cancer: A health and economic problem. Best Pract. Res. Clin. Gastroenterol. 2023 66 101839 10.1016/j.bpg.2023.101839 37852707
    [Google Scholar]
  2. Biller L.H. Schrag D. Diagnosis and treatment of metastatic colorectal cancer. JAMA 2021 325 7 669 685 10.1001/jama.2021.0106 33591350
    [Google Scholar]
  3. Zhou P. Li W. Ye M. Chen C. Wang Y. BMPR1A promotes the proliferation of colorectal cancer cells through the activation of Smad1. Oncologie 2025 27 2 261 275 10.1515/oncologie‑2024‑0534
    [Google Scholar]
  4. Xu Y. Li H. You G. CMTM6 deletion affects chemoresistance and macrophage M2 polarization in colorectal cancer cells. Biocell 2024 48 2 229 237 10.32604/biocell.2023.045030
    [Google Scholar]
  5. Li M. Fan J. Hu M. Xu J. He Z. Zeng J. Quercetin enhances 5-fluorouracil sensitivity by regulating the autophagic flux and inducing Drp-1 mediated mitochondrial fragmentation in colorectal cancer cells. Curr. Mol. Pharmacol. 2024 17 18761429283717 10.2174/0118761429283717231222104730 38485683
    [Google Scholar]
  6. Ganesh K. Optimizing immunotherapy for colorectal cancer. Nat. Rev. Gastroenterol. Hepatol. 2022 19 2 93 94 10.1038/s41575‑021‑00569‑4 34907331
    [Google Scholar]
  7. Le D.T. Uram J.N. Wang H. Bartlett B.R. Kemberling H. Eyring A.D. Skora A.D. Luber B.S. Azad N.S. Laheru D. Biedrzycki B. Donehower R.C. Zaheer A. Fisher G.A. Crocenzi T.S. Lee J.J. Duffy S.M. Goldberg R.M. de la Chapelle A. Koshiji M. Bhaijee F. Huebner T. Hruban R.H. Wood L.D. Cuka N. Pardoll D.M. Papadopoulos N. Kinzler K.W. Zhou S. Cornish T.C. Taube J.M. Anders R.A. Eshleman J.R. Vogelstein B. Diaz L.A. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 2015 372 26 2509 2520 10.1056/NEJMoa1500596 26028255
    [Google Scholar]
  8. Overman M.J. McDermott R. Leach J.L. Lonardi S. Lenz H.J. Morse M.A. Desai J. Hill A. Axelson M. Moss R.A. Goldberg M.V. Cao Z.A. Ledeine J.M. Maglinte G.A. Kopetz S. André T. Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): An open-label, multicentre, phase 2 study. Lancet Oncol. 2017 18 9 1182 1191 10.1016/S1470‑2045(17)30422‑9 28734759
    [Google Scholar]
  9. Dekker E. Tanis P.J. Vleugels J.L.A. Kasi P.M. Wallace M.B. Colorectal cancer. Lancet 2019 394 10207 1467 1480 10.1016/S0140‑6736(19)32319‑0 31631858
    [Google Scholar]
  10. Malki A. ElRuz R.A. Gupta I. Allouch A. Vranic S. Al Moustafa A.E. Molecular mechanisms of colon cancer progression and metastasis: Recent insights and advancements. Int. J. Mol. Sci. 2020 22 1 130 10.3390/ijms22010130 33374459
    [Google Scholar]
  11. Wang Y. Zhang Z. Sun W. Zhang J. Xu Q. Zhou X. Ferroptosis in colorectal cancer: Potential mechanisms and effective therapeutic targets. Biomed. Pharmacother. 2022 153 113524 10.1016/j.biopha.2022.113524 36076606
    [Google Scholar]
  12. Duan L. Yang W. Wang X. Zhou W. Zhang Y. Liu J. Zhang H. Zhao Q. Hong L. Fan D. Advances in prognostic markers for colorectal cancer. Expert Rev. Mol. Diagn. 2019 19 4 313 324 10.1080/14737159.2019.1592679 30907673
    [Google Scholar]
  13. Cherri S. Oneda E. Zanotti L. Zaniboni A. Optimizing the first-line treatment for metastatic colorectal cancer. Front. Oncol. 2023 13 1246716 10.3389/fonc.2023.1246716 37909027
    [Google Scholar]
  14. Pauleck S. Sinnott J.A. Zheng Y.L. Gadalla S.M. Viskochil R. Haaland B. Cawthon R.M. Hoffmeister A. Hardikar S. Association of telomere length with colorectal cancer risk and prognosis: A systematic review and meta-analysis. Cancers 2023 15 4 1159 10.3390/cancers15041159 36831502
    [Google Scholar]
  15. Gao J. Pickett H.A. Targeting telomeres: Advances in telomere maintenance mechanism-specific cancer therapies. Nat. Rev. Cancer 2022 22 9 515 532 10.1038/s41568‑022‑00490‑1 35790854
    [Google Scholar]
  16. Valeeva L.R. Abdulkina L.R. Agabekian I.A. Shakirov E.V. Telomere biology and ribosome biogenesis: Structural and functional interconnections. Biochem. Cell Biol. 2023 101 5 394 409 10.1139/bcb‑2022‑0383 36989538
    [Google Scholar]
  17. Holesova Z. Krasnicanova L. Saade R. Pös O. Budis J. Gazdarica J. Repiska V. Szemes T. Telomere length changes in cancer: Insights on carcinogenesis and potential for non-invasive diagnostic strategies. Genes 2023 14 3 715 10.3390/genes14030715 36980987
    [Google Scholar]
  18. Xiao Y. Xu D. Jiang C. Huili Y. Nie S. Zhu H. Fan G. Guan X. Telomere maintenance-related genes are important for survival prediction and subtype identification in bladder cancer. Front. Genet. 2023 13 1087246 10.3389/fgene.2022.1087246 36685927
    [Google Scholar]
  19. Günes C. Wezel F. Southgate J. Bolenz C. Implications of TERT promoter mutations and telomerase activity in urothelial carcinogenesis. Nat. Rev. Urol. 2018 15 6 386 393 10.1038/s41585‑018‑0001‑5 29599449
    [Google Scholar]
  20. Baichoo E. Boardman L.A. Toward a molecular classification of colorectal cancer: The role of telomere length. Front. Oncol. 2014 4 158 10.3389/fonc.2014.00158 24995160
    [Google Scholar]
  21. Tomasova K. Kroupa M. Forsti A. Vodicka P. Vodickova L. Telomere maintenance in interplay with DNA repair in pathogenesis and treatment of colorectal cancer. Mutagenesis 2020 35 3 261 271 10.1093/mutage/geaa005 32083302
    [Google Scholar]
  22. Martel-Martel A. Corchete L.A. Martí M. Vidal-Tocino R. Hurtado E. Álvaro E. Jiménez F. Jiménez-Toscano M. Balaguer F. Sanz G. López I. Hernández-Villafranca S. Ballestero A. Vivas A. Melone S. Pastor C. Brandáriz L. Gómez-Marcos M.A. Cruz-Hernández J.J. Perea J. González-Sarmiento R. Telomere length as a new risk marker of early-onset colorectal cancer. Int. J. Mol. Sci. 2023 24 4 3526 10.3390/ijms24043526 36834938
    [Google Scholar]
  23. Huang W. Wang W. Dong T. Telomere maintenance-related genes are essential for prognosis in breast cancer. Breast Cancer 2025 17 225 239 10.2147/BCTT.S506783 40028272
    [Google Scholar]
  24. Yan M. Zhang Z. Wang L. Huang H. Wang J. Zhu C. Li Z. Xu Z. Cross-talk of three molecular subtypes of telomere maintenance defines clinical characteristics and tumor microenvironment in gastric cancer. J. Cancer 2024 15 10 3227 3241 10.7150/jca.92207 38706908
    [Google Scholar]
  25. Meng L. Zheng Y. Liu H. Fan D. The tumor microenvironment: A key player in multidrug resistance in cancer. Oncologie 2024 26 1 41 58 10.1515/oncologie‑2023‑0459
    [Google Scholar]
  26. Xu J. Huang L. Sha Y. Mo C. Gong W. Tian X. High-throughput sequencing reveals crebanine inhibits colorectal cancer by modulating Tregs immune prognostic target genes. Oncologie 2024 26 4 643 656 10.1515/oncologie‑2024‑0073
    [Google Scholar]
  27. Toubiana D. Puzis R. Sadka A. Blumwald E. A genetic algorithm to optimize weighted gene co-expression network analysis. J. Comput. Biol. 2019 26 12 1349 1366 10.1089/cmb.2019.0221 31356119
    [Google Scholar]
  28. Zhang Y. Luo J. Liu Z. Liu X. Ma Y. Zhang B. Chen Y. Li X. Feng Z. Yang N. Feng D. Wang L. Song X. Identification of hub genes in colorectal cancer based on weighted gene co-expression network analysis and clinical data from The Cancer Genome Atlas. Biosci. Rep. 2021 41 7 BSR20211280 10.1042/BSR20211280 34308980
    [Google Scholar]
  29. Fu C. Tian X. Wu S. Chu X. Cheng Y. Wu X. Yang W. Role of telomere dysfunction and immune infiltration in idiopathic pulmonary fibrosis: New insights from bioinformatics analysis. Front. Genet. 2024 15 1447296 10.3389/fgene.2024.1447296 39346776
    [Google Scholar]
  30. Chen M.S. Lo Y.H. Chen X. Williams C.S. Donnelly J.M. Criss Z.K. Patel S. Butkus J.M. Dubrulle J. Finegold M.J. Shroyer N.F. Growth factor–independent 1 is a tumor suppressor gene in colorectal cancer. Mol. Cancer Res. 2019 17 3 697 708 10.1158/1541‑7786.MCR‑18‑0666 30606770
    [Google Scholar]
  31. Zou J. Chu S. Bao Q. Zhang Y. Telomere maintenance genes-derived prognosis signature characterizes immune landscape and predicts prognosis of head and neck squamous cell carcinoma. Medicine 2023 102 31 34586 10.1097/MD.0000000000034586 37543795
    [Google Scholar]
  32. Hänzelmann S. Castelo R. Guinney J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 2013 14 1 7 10.1186/1471‑2105‑14‑7 23323831
    [Google Scholar]
  33. Charoentong P. Finotello F. Angelova M. Mayer C. Efremova M. Rieder D. Hackl H. Trajanoski Z. Pan-cancer immunogenomic analyses reveal genotype-immunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 2017 18 1 248 262 10.1016/j.celrep.2016.12.019 28052254
    [Google Scholar]
  34. Wei Q. Jiang X. Miao X. Zhang Y. Chen F. Zhang P. Molecular subtypes of lung adenocarcinoma patients for prognosis and therapeutic response prediction with machine learning on 13 programmed cell death patterns. J. Cancer Res. Clin. Oncol. 2023 149 13 11351 11368 10.1007/s00432‑023‑05000‑w 37378675
    [Google Scholar]
  35. Langfelder P. Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008 9 1 559 10.1186/1471‑2105‑9‑559 19114008
    [Google Scholar]
  36. Fang F. Tai R. Yang F. Dong R. Zhang Y. Bioinformatic methods uncover 5 diagnostic biomarkers associated with drug resistance and metastasis for gastrointestinal stromal tumor. Curr. Pharm. Anal. 2025 21 2 67 76 10.1016/j.cpan.2025.01.003
    [Google Scholar]
  37. Yu G. Wang L.G. Han Y. He Q.Y. clusterProfiler: An R package for comparing biological themes among gene clusters. OMICS 2012 16 5 284 287 10.1089/omi.2011.0118 22455463
    [Google Scholar]
  38. Xia S. Tao H. Su S. Chen X. Ma L. Li J. Gao B. Liu X. Pi L. Feng J. Li F. Li J. Zhang Z. DNA methylation variation is identified in monozygotic twins discordant for congenital heart diseases. Congenit. Heart Dis. 2024 19 2 247 256 10.32604/chd.2024.052583
    [Google Scholar]
  39. Shi Y. Wang Y. Dong H. Niu K. Zhang W. Feng K. Crosstalk of ferroptosis regulators and tumor immunity in pancreatic adenocarcinoma: Novel perspective to mRNA vaccines and personalized immunotherapy. Apoptosis 2023 28 9-10 1423 1435 10.1007/s10495‑023‑01868‑8 37369808
    [Google Scholar]
  40. Geeleher P. Cox N. Huang R.S. pRRophetic: An R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One 2014 9 9 107468 10.1371/journal.pone.0107468 25229481
    [Google Scholar]
  41. Liberzon A. Birger C. Thorvaldsdóttir H. Ghandi M. Mesirov J.P. Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015 1 6 417 425 10.1016/j.cels.2015.12.004 26771021
    [Google Scholar]
  42. Poudel B.H. Koks S. The whole transcriptome analysis using FFPE and fresh tissue samples identifies the molecular fingerprint of osteosarcoma. Exp. Biol. Med. 2024 249 10161 10.3389/ebm.2024.10161 38966281
    [Google Scholar]
  43. Reimann E. Kõks S. Ho X.D. Maasalu K. Märtson A. Whole exome sequencing of a single osteosarcoma case--integrative analysis with whole transcriptome RNA-seq data. Hum. Genomics 2014 8 1 20 10.1186/s40246‑014‑0020‑0 25496518
    [Google Scholar]
  44. Zheng H. Liu H. Li H. Dou W. Wang J. Zhang J. Liu T. Wu Y. Liu Y. Wang X. Characterization of stem cell landscape and identification of stemness-relevant prognostic gene signature to aid immunotherapy in colorectal cancer. Stem Cell Res. Ther. 2022 13 1 244 10.1186/s13287‑022‑02913‑0 35681225
    [Google Scholar]
  45. Di Z. Zhou S. Xu G. Ren L. Li C. Ding Z. Huang K. Liang L. Yuan Y. Single-cell and WGCNA uncover a prognostic model and potential oncogenes in colorectal cancer. Biol. Proced. Online 2022 24 1 13 10.1186/s12575‑022‑00175‑x 36117173
    [Google Scholar]
  46. Yuan X. Dai M. Xu D. Telomere-related markers for cancer. Curr. Top. Med. Chem. 2020 20 6 410 432 10.2174/1568026620666200106145340 31903880
    [Google Scholar]
  47. Tardat M. Déjardin J. Telomere chromatin establishment and its maintenance during mammalian development. Chromosoma 2018 127 1 3 18 10.1007/s00412‑017‑0656‑3 29250704
    [Google Scholar]
  48. Luo Z. Wang W. Li F. Songyang Z. Feng X. Xin C. Dai Z. Xiong Y. Pan-cancer analysis identifies telomerase-associated signatures and cancer subtypes. Mol. Cancer 2019 18 1 106 10.1186/s12943‑019‑1035‑x 31179925
    [Google Scholar]
  49. Pfeiffer V. Lingner J. Replication of telomeres and the regulation of telomerase. Cold Spring Harb. Perspect. Biol. 2013 5 5 a010405 10.1101/cshperspect.a010405 23543032
    [Google Scholar]
  50. Hou H. Cooper J.P. Stretching, scrambling, piercing and entangling: Challenges for telomeres in mitotic and meiotic chromosome segregation. Differentiation 2018 100 12 20 10.1016/j.diff.2018.01.002 29413748
    [Google Scholar]
  51. Artandi S.E. Attardi L.D. Pathways connecting telomeres and p53 in senescence, apoptosis, and cancer. Biochem. Biophys. Res. Commun. 2005 331 3 881 890 10.1016/j.bbrc.2005.03.211 15865944
    [Google Scholar]
  52. Liu K. Zheng M. Lu R. Du J. Zhao Q. Li Z. Li Y. Zhang S. The role of CDC25C in cell cycle regulation and clinical cancer therapy: A systematic review. Cancer Cell Int. 2020 20 1 213 10.1186/s12935‑020‑01304‑w 32518522
    [Google Scholar]
  53. Song D. Zhang D. Chen S. Wu J. Hao Q. Zhao L. Ren H. Du N. Identification and validation of prognosis-associated DNA repair gene signatures in colorectal cancer. Sci. Rep. 2022 12 1 6946 10.1038/s41598‑022‑10561‑w 35484177
    [Google Scholar]
  54. Sowa M.E. Bennett E.J. Gygi S.P. Harper J.W. Defining the human deubiquitinating enzyme interaction landscape. Cell 2009 138 2 389 403 10.1016/j.cell.2009.04.042 19615732
    [Google Scholar]
  55. Makarova O.V. Makarov E.M. Lührmann R. The 65 and 110 kDa SR-related proteins of the U4/U6U5 tri-snRNP are essential for the assembly of mature spliceosomes. EMBO J. 2001 20 10 2553 2563 10.1093/emboj/20.10.2553 11350945
    [Google Scholar]
  56. Hadjivassiliou H. Rosenberg O.S. Guthrie C. The crystal structure of S. cerevisiae Sad1, a catalytically inactive deubiquitinase that is broadly required for pre-mRNA splicing. RNA 2014 20 5 656 669 10.1261/rna.042838.113 24681967
    [Google Scholar]
  57. Yuan X. Sun X. Shi X. Wang H. Wu G. Jiang C. Yu D. Zhang W. Xue B. Ding Y. USP39 promotes colorectal cancer growth and metastasis through the Wnt/β-catenin pathway. Oncol. Rep. 2017 37 4 2398 2404 10.3892/or.2017.5454 28259917
    [Google Scholar]
  58. Yuan J. Xu B. Su Y. Zhang P. Zhang X. Gong P. Identification of USP39 as a prognostic and predictive biomarker for determining the response to immunotherapy in pancreatic cancer. BMC Cancer 2025 25 1 758 10.1186/s12885‑025‑14096‑x 40264098
    [Google Scholar]
  59. Lv Y. Hu J. Zheng W. Shan L. Bai B. Zhu H. Dai S. A WGCNA-based cancer-associated fibroblast risk signature in colorectal cancer for prognosis and immunotherapy response. Transl. Cancer Res. 2023 12 9 2256 2275 10.21037/tcr‑23‑261 37859738
    [Google Scholar]
  60. Gong Z. Huang X. Cao Q. Wu Y. Zhang Q.A. CLRN3-based CD8+ T-related gene signature predicts prognosis and immunotherapy response in colorectal cancer. Biomolecules 2024 14 8 891 10.3390/biom14080891 39199281
    [Google Scholar]
  61. Ho X.D. Nguyen H.G. Trinh L.H. Reimann E. Prans E. Kõks G. Maasalu K. Le V.Q. Nguyen V.H. Le N.T.N. Phung P. Märtson A. Lattekivi F. Kõks S. Analysis of the expression of repetitive DNA elements in osteosarcoma. Front. Genet. 2017 8 193 10.3389/fgene.2017.00193 29250102
    [Google Scholar]
  62. Almeida L. Dhillon-LaBrooy A. Carriche G. Berod L. Sparwasser T. CD4+ T-cell differentiation and function: Unifying glycolysis, fatty acid oxidation, polyamines NAD mitochondria. J. Allergy Clin. Immunol. 2021 148 1 16 32 10.1016/j.jaci.2021.03.033 33966898
    [Google Scholar]
  63. Anvar M.T. Rashidan K. Arsam N. Rasouli-Saravani A. Yadegari H. Ahmadi A. Asgari Z. Vanan A.G. Ghorbaninezhad F. Tahmasebi S. Th17 cell function in cancers: Immunosuppressive agents or anti-tumor allies? Cancer Cell Int. 2024 24 1 355 10.1186/s12935‑024‑03525‑9 39465401
    [Google Scholar]
  64. Walker J.A. McKenzie A.N.J. TH2 cell development and function. Nat. Rev. Immunol. 2018 18 2 121 133 10.1038/nri.2017.118 29082915
    [Google Scholar]
  65. Hedrick C.C. Malanchi I. Neutrophils in cancer: Heterogeneous and multifaceted. Nat. Rev. Immunol. 2022 22 3 173 187 10.1038/s41577‑021‑00571‑6 34230649
    [Google Scholar]
  66. Li L. Sun F. Kong F. Feng Y. Song Y. Du Y. Liu F. Kong X. Characterization of a cuproptosis-related signature to evaluate immune features and predict prognosis in colorectal cancer. Front. Oncol. 2023 13 1083956 10.3389/fonc.2023.1083956 37384293
    [Google Scholar]
  67. Zhu H. Yang Y. Zhou Z. Construction and clinical application of a risk model based on N6-methyladenosine regulators for colorectal cancer. PeerJ 2024 12 18719 10.7717/peerj.18719 39717046
    [Google Scholar]
/content/journals/ctmc/10.2174/0115680266397024250710105241
Loading
Telomere Maintenance Characteristics Predict Prognosis and Therapeutic Response in Colorectal Cancer
Bentham Science Publishers ; https://doi.org/10.2174/0115680266397024250710105241
/content/journals/ctmc/10.2174/0115680266397024250710105241
/content/journals/ctmc/10.2174/0115680266397024250710105241
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
Keywords: immunotherapy response ; risk model ; telomere maintenance-related genes ; Colorectal cancer

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