Integrative meta‑analysis of gene expression profiles identifies FEN1 and ENDOU as potential diagnostic biomarkers for cervical squamous cell carcinoma

Basic,Vladimir; Zhang,Boxi; Domert,Jakob; Pellas,Ulrika; Tot,Tibor

doi:10.3892/ol.2021.13101

Integrative meta‑analysis of gene expression profiles identifies FEN1 and ENDOU as potential diagnostic biomarkers for cervical squamous cell carcinoma

Authors:
- Vladimir Basic
- Boxi Zhang
- Jakob Domert
- Ulrika Pellas
- Tibor Tot
View Affiliations

Affiliations: Pathology and Cytology Dalarna, County Hospital Falun, Falun 791 82, Sweden, Department of Physiology and Pharmacology, Karolinska Institute, Stockholm 171 65, Sweden, Clinical Research Center Dalarna, Uppsala University, Falun 791 82, Sweden
Published online on: October 19, 2021 https://doi.org/10.3892/ol.2021.13101
Article Number: 840
Copyright: © Basic et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Cervical carcinoma is a global public health burden. Given that it is usually asymptomatic at potentially curative stages, the development of clinically accurate tests is critical for early detection and individual risk stratification. The present study performed an integrative meta‑analysis of the transcriptomes from 10 cervical carcinoma cohorts, with the aim of identifying biomarkers that are associated with malignant transformation of cervical epithelium, and establish their clinical applicability. From among the top ranked differentially expressed genes, flap structure‑specific endonuclease 1 (FEN1) and poly (U)‑specific endoribonuclease (ENDOU) were selected for further validation, and their clinical applicability was assessed using immunohistochemically stained microarrays comprising 110 tissue cores, using p16 and Ki67 staining as the comparator tests. The results demonstrated that FEN1 expression was significantly upregulated in 65% of tumor specimens (P=0.0001), with no detectable expression in the non‑tumor tissues. Furthermore, its expression was significantly associated with Ki67 staining in tumor samples (P<0.0001), but no association was observed with p16 expression or the presence of human papilloma virus types 16/18, patient age, tumor grade or stage. FEN1 staining demonstrated lower sensitivity than p16 (69.3 vs. 96.8%) and Ki67 (69.3 vs. 76.3%); however, the specificity was identical to p16 and higher than that of Ki67 (100 vs. 71.4%).ENDOU staining was consistent with the microarray results, demonstrating 1% positivity in tumors and 40% positivity in non‑tumor tissues. Gene set enrichment analysis of cervical tumors overexpressing FEN1 revealed its association with enhanced growth factor signaling, immune response inhibition and extracellular matrix remodeling, whereas tumors with low ENDOU expression exhibited inhibition of epithelial development and differentiation processes. Taken together, the results of the present study demonstrate the feasibility of the integrative meta‑analysis approach to identify relevant biomarkers associated with cervical carcinogenesis. Thus, FEN1 and ENDOU may be useful diagnostic biomarkers for squamous cervical carcinoma. However, further studies are required to determine their diagnostic performance in larger patient cohorts and validate the results presented here.

View Figures

View References

1	Arbyn M, Weiderpass E, Bruni L, de Sanjosé S, Saraiya M, Ferlay J and Bray F: Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis. Lancet Glob Health. 8:e191–e203. 2020. View Article : Google Scholar : PubMed/NCBI
2	Maher EJ and Denton A: Survivorship, late effects and cancer of the cervix. Clin Oncol (R Coll Radiol). 20:479–487. 2008. View Article : Google Scholar : PubMed/NCBI
3	Waggoner SE: Cervical cancer. Lancet. 361:2217–2225. 2003. View Article : Google Scholar : PubMed/NCBI
4	Arbyn M, Tommasino M, Depuydt C and Dillner J: Are 20 human papillomavirus types causing cervical cancer? J Pathol. 234:431–435. 2014. View Article : Google Scholar : PubMed/NCBI
5	Walboomers JM, Jacobs MV, Manos MM, Bosch FX, Kummer JA, Shah KV, Snijders PJ, Peto J, Meijer CJ and Muñoz N: Human papillomavirus is a necessary cause of invasive cervical cancer worldwide. J Pathol. 189:12–19. 1999. View Article : Google Scholar : PubMed/NCBI
6	Ibeanu OA: Molecular pathogenesis of cervical cancer. Cancer Biol Ther. 11:295–306. 2011. View Article : Google Scholar : PubMed/NCBI
7	Arvanitis DA and Spandidos DA: Deregulation of the G₁/S phase transition in cancer and squamous intraepithelial lesions of the uterine cervix: A case control study. Oncol Rep. 20:751–760. 2008.PubMed/NCBI
8	von Knebel Doeberitz M, Reuschenbach M, Schmidt D and Bergeron C: Biomarkers for cervical cancer screening: The role of p16(INK4a) to highlight transforming HPV infections. Expert Rev Proteomics. 9:149–163. 2012. View Article : Google Scholar : PubMed/NCBI
9	Malumbres M and Barbacid M: To cycle or not to cycle: A critical decision in cancer. Nat Rev Cancer. 1:222–231. 2001. View Article : Google Scholar : PubMed/NCBI
10	Gaillard H, García-Muse T and Aguilera A: Replication stress and cancer. Nat Rev Cancer. 15:276–289. 2015. View Article : Google Scholar : PubMed/NCBI
11	Crozier L, Foy R, Mouery BL, Whitaker RH, Corno A, Spanos C, Ly T, Cook JG and Saurin AT: CDK4/6 inhibitors induce replication stress to cause long-term cell cycle withdrawal. bioRxiv: 2021. doi.org/10.1101/2021.02.03.428245 (Preprint).
12	Spardy N, Covella K, Cha E, Hoskins EE, Wells SI, Duensing A and Duensing S: Human papillomavirus 16 E7 oncoprotein attenuates DNA damage checkpoint control by increasing the proteolytic turnover of claspin. Cancer Res. 69:7022–7029. 2009. View Article : Google Scholar : PubMed/NCBI
13	Yu LL, Guo HQ, Lei XQ, Qin Y, Wu ZN, Kang LN, Zhang X, Qiao YL and Chen W: p16/Ki-67 co-expression associates high risk human papillomavirus persistence and cervical histopathology: A 3-year cohort study in China. Oncotarget. 7:64810–64819. 2016. View Article : Google Scholar : PubMed/NCBI
14	Reuschenbach M, Wentzensen N, Dijkstra MG, von Knebel Doeberitz M and Arbyn M: p16INK4a immunohistochemistry in cervical biopsy specimens: A systematic review and meta-analysis of the interobserver agreement. Am J Clin Pathol. 142:767–772. 2014. View Article : Google Scholar : PubMed/NCBI
15	Lim S, Lee MJ, Cho I, Hong R and Lim SC: Efficacy of p16 and Ki-67 immunostaining in the detection of squamous intraepithelial lesions in a high-risk HPV group. Oncol Lett. 11:1447–1452. 2016. View Article : Google Scholar : PubMed/NCBI
16	Tsoumpou I, Arbyn M, Kyrgiou M, Wentzensen N, Koliopoulos G, Martin-Hirsch P, Malamou-Mitsi V and Paraskevaidis E: p16(INK4a) immunostaining in cytological and histological specimens from the uterine cervix: A systematic review and meta-analysis. Cancer Treat Rev. 35:210–220. 2009. View Article : Google Scholar : PubMed/NCBI
17	Gonçalves JES, de Andrade CV, Russomano FB, Nuovo G, Amaro-Filho SM, Carvalho MOO and Nicol AF: The role of p16 as putative biomarker for cervical neoplasia: A controversial issue? MedicalExpress. 4:M1706012017.
18	Zhai Y, Kuick R, Nan B, Ota I, Weiss SJ, Trimble CL, Fearon ER and Cho KR: Gene expression analysis of preinvasive and invasive cervical squamous cell carcinomas identifies HOXC10 as a key mediator of invasion. Cancer Res. 67:10163–10172. 2007. View Article : Google Scholar : PubMed/NCBI
19	Scotto L, Narayan G, Nandula SV, Arias-Pulido H, Subramaniyam S, Schneider A, Kaufmann AM, Wright JD, Pothuri B, Mansukhani M, et al: Identification of copy number gain and overexpressed genes on chromosome arm 20q by an integrative genomic approach in cervical cancer: Potential role in progression. Genes Chromosomes Cancer. 47:755–765. 2008. View Article : Google Scholar : PubMed/NCBI
20	Pyeon D, Newton MA, Lambert PF, den Boon JA, Sengupta S, Marsit CJ, Woodworth CD, Connor JP, Haugen TH, Smith EM, et al: Fundamental differences in cell cycle deregulation in human papillomavirus-positive and human papillomavirus-negative head/neck and cervical cancers. Cancer Res. 67:4605–4619. 2007. View Article : Google Scholar : PubMed/NCBI
21	Pappa KI, Polyzos A, Jacob-Hirsch J, Amariglio N, Vlachos GD, Loutradis D and Anagnou NP: Profiling of Discrete Gynecological Cancers Reveals Novel Transcriptional Modules and Common Features Shared by Other Cancer Types and Embryonic Stem Cells. PLoS One. 10:e01422292015. View Article : Google Scholar : PubMed/NCBI
22	Harding NJSH, How C, Saha S, Hui AB, Chong LC, Yan R, Fei-Fei Liu FF and Kasprzyk A: Identification and Correction of Amplification Protocol Bias in Microarray Studies, arrayexpress-repository. 2014. https://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-42764/July 27–2020
23	Winder DM, Chattopadhyay A, Muralidhar B, Bauer J, English WR, Zhang X, Karagavriilidou K, Roberts I, Pett MR, Murphy G, et al: Overexpression of the oncostatin M receptor in cervical squamous cell carcinoma cells is associated with a pro-angiogenic phenotype and increased cell motility and invasiveness. J Pathol. 225:448–462. 2011. View Article : Google Scholar : PubMed/NCBI
24	den Boon JA, Pyeon D, Wang SS, Horswill M, Schiffman M, Sherman M, Zuna RE, Wang Z, Hewitt SM, Pearson R, et al: Molecular transitions from papillomavirus infection to cervical precancer and cancer: Role of stromal estrogen receptor signaling. Proc Natl Acad Sci USA. 112:E3255–E3264. 2015. View Article : Google Scholar : PubMed/NCBI
25	Medina-Martinez I, Barrón V, Roman-Bassaure E, Juárez-Torres E, Guardado-Estrada M, Espinosa AM, Bermudez M, Fernández F, Venegas-Vega C, Orozco L, et al: Impact of gene dosage on gene expression, biological processes and survival in cervical cancer: A genome-wide follow-up study. PLoS One. 9:e978422014. View Article : Google Scholar : PubMed/NCBI
26	Biewenga P, Buist MR, Moerland PD, Ver Loren van Themaat E, van Kampen AH, ten Kate FJ and Baas F: Gene expression in early stage cervical cancer. Gynecol Oncol. 108:520–526. 2008. View Article : Google Scholar : PubMed/NCBI
27	Sharma S, Mandal P, Sadhukhan T, Roy Chowdhury R, Ranjan Mondal N, Chakravarty B, Chatterjee T, Roy S and Sengupta S: Bridging Links between Long Noncoding RNA HOTAIR and HPV Oncoprotein E7 in Cervical Cancer Pathogenesis. Sci Rep. 5:117242015. View Article : Google Scholar : PubMed/NCBI
28	Warbrick E, Coates PJ and Hall PA: Fen1 expression: A novel marker for cell proliferation. J Pathol. 186:319–324. 1998. View Article : Google Scholar : PubMed/NCBI
29	Sharma S, Sommers JA, Gary RK, Friedrich-Heineken E, Hübscher U and Brosh RM Jr: The interaction site of Flap Endonuclease-1 with WRN helicase suggests a coordination of WRN and PCNA. Nucleic Acids Res. 33:6769–6781. 2005. View Article : Google Scholar : PubMed/NCBI
30	Becker JR, Gallo D, Leung W, Croissant T, Thu YM, Nguyen HD, Starr TK, Brown GW and Bielinsky AK: Flap endonuclease overexpression drives genome instability and DNA damage hypersensitivity in a PCNA-dependent manner. Nucleic Acids Res. 46:5634–5650. 2018. View Article : Google Scholar : PubMed/NCBI
31	Singh P, Yang M, Dai H, Yu D, Huang Q, Tan W, Kernstine KH, Lin D and Shen B: Overexpression and hypomethylation of flap endonuclease 1 gene in breast and other cancers. Mol Cancer Res. 6:1710–1717. 2008. View Article : Google Scholar : PubMed/NCBI
32	Jiao X, Wu Y, Zhou L, He J, Yang C, Zhang P, Hu R, Luo C, Du J, Fu J, et al: Variants and haplotypes in Flap endonuclease 1 and risk of gallbladder cancer and gallstones: A population-based study in China. Sci Rep. 5:181602015. View Article : Google Scholar : PubMed/NCBI
33	Abdel-Fatah TMA, Russell R, Albarakati N, Maloney DJ, Dorjsuren D, Rueda OM, Moseley P, Mohan V, Sun H, Abbotts R, et al: Genomic and protein expression analysis reveals flap endonuclease 1 (FEN1) as a key biomarker in breast and ovarian cancer. Mol Oncol. 8:1326–1338. 2014. View Article : Google Scholar : PubMed/NCBI
34	He L, Luo L, Zhu H, Yang H, Zhang Y, Wu H, Sun H, Jiang F, Kathera CS, Liu L, et al: FEN1 promotes tumor progression and confers cisplatin resistance in non-small-cell lung cancer. Mol Oncol. 11:640–654. 2017. View Article : Google Scholar : PubMed/NCBI
35	Nikolova T, Christmann M and Kaina B: FEN1 is overexpressed in testis, lung and brain tumors. Anticancer Res. 29:2453–2459. 2009.PubMed/NCBI
36	Urbanucci A, Sahu B, Seppälä J, Larjo A, Latonen LM, Waltering KK, Tammela TL, Vessella RL, Lähdesmäki H, Jänne OA, et al: Overexpression of androgen receptor enhances the binding of the receptor to the chromatin in prostate cancer. Oncogene. 31:2153–2163. 2012. View Article : Google Scholar : PubMed/NCBI
37	Liu L, Zhou C, Zhou L, Peng L, Li D, Zhang X, Zhou M, Kuang P, Yuan Q, Song X, et al: Functional FEN1 genetic variants contribute to risk of hepatocellular carcinoma, esophageal cancer, gastric cancer and colorectal cancer. Carcinogenesis. 33:119–123. 2012. View Article : Google Scholar : PubMed/NCBI
38	Chen Y-D, Zhang X, Qiu X-G, Li J, Yuan Q, Jiang T and Yang M: Functional FEN1 genetic variants and haplotypes are associated with glioma risk. J Neurooncol. 111:145–151. 2013. View Article : Google Scholar : PubMed/NCBI
39	Jia F, Chi C and Han M: Regulation of Nucleotide Metabolism and Germline Proliferation in Response to Nucleotide Imbalance and Genotoxic Stresses by EndoU Nuclease. Cell Rep. 30:1848–1861.e1845. 2020. View Article : Google Scholar : PubMed/NCBI
40	Xu C, Zhang Y, Shen Y, Shi Y, Zhang M and Zhou L: Integrated Analysis Reveals ENDOU as a Biomarker in Head and Neck Squamous Cell Carcinoma Progression. Front Oncol. 10:5223322021. View Article : Google Scholar : PubMed/NCBI
41	Inaba N, Renk T, Daume E and Bohn H: Ectopic production of placenta-‘specific’ tissue proteins (PP5 and PP11) by malignant breast tumors. Arch Gynecol. 231:87–90. 1981. View Article : Google Scholar : PubMed/NCBI
42	Inaba N, Ishige H, Ijichi M, Satoh N, Ohkawa R, Sekiya S, Shirotake S, Takamizawa H, Renk T and Bohn H: Immunohistochemical detection of pregnancy-specific protein (SP1) and placenta-specific tissue proteins (PP5, PP10, PP11 and PP12) in ovarian adenocarcinomas. Oncodev Biol Med. 3:379–389. 1982.PubMed/NCBI
43	Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, Bravo HC, Davis S, Gatto L, Girke T, et al: Orchestrating high-throughput genomic analysis with Bioconductor. Nat Methods. 12:115–121. 2015. View Article : Google Scholar : PubMed/NCBI
44	Gautier L, Cope L, Bolstad BM and Irizarry RA: affy - analysis of Affymetrix GeneChip data at the probe level. Bioinformatics. 20:307–315. 2004. View Article : Google Scholar : PubMed/NCBI
45	Carvalho BS and Irizarry RA: A framework for oligonucleotide microarray preprocessing. Bioinformatics. 26:2363–2367. 2010. View Article : Google Scholar : PubMed/NCBI
46	Du P, Kibbe WA and Lin SM: lumi: A pipeline for processing Illumina microarray. Bioinformatics. 24:1547–1548. 2008. View Article : Google Scholar : PubMed/NCBI
47	Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 3:Article32004. View Article : Google Scholar : PubMed/NCBI
48	Prada C LD and Nakaya H: MetaVolcanoR: Gene Expression Meta-analysis Visualization Tool. R package version 1.3.0. 2020.https://www.bioconductor.org/packages/release/bioc/html/MetaVolcanoR.html
49	Viechtbauer W: Conducting Meta-Analyses in R with The metafor Package. Journal of Statistical Software. 36:1–48. 2010. View Article : Google Scholar
50	Harrison PF, Pattison AD, Powell DR and Beilharz TH: Topconfects: A package for confident effect sizes in differential expression analysis provides a more biologically useful ranked gene list. Genome Biol. 20:672019. View Article : Google Scholar : PubMed/NCBI
51	Xia J, Gill EE and Hancock RE: NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nat Protoc. 10:823–844. 2015. View Article : Google Scholar : PubMed/NCBI
52	Leek JT, Johnson WE, Parker HS, Jaffe AE and Storey JD: The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 28:882–883. 2012. View Article : Google Scholar : PubMed/NCBI
53	Huang W, Sherman BT and Lempicki RA: Bioinformatics enrichment tools: Paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37:1–13. 2009. View Article : Google Scholar : PubMed/NCBI
54	Kulinskaya E and Dollinger MB: An accurate test for homogeneity of odds ratios based on Cochran's Q-statistic. BMC Med Res Methodol. 15:492015. View Article : Google Scholar : PubMed/NCBI
55	Riley RD, Higgins JPT and Deeks JJ: Interpretation of random effects meta-analyses. BMJ. 342:d5492011. View Article : Google Scholar : PubMed/NCBI
56	Yu G, Wang L-G, Yan G-R and He Q-Y: DOSE: An R/Bioconductor package for disease ontology semantic and enrichment analysis. Bioinformatics. 31:608–609. 2015. View Article : Google Scholar : PubMed/NCBI
57	G Y: Enrichplot: Visualization of Functional Enrichment Result. R package version 1.8.1. 2020. https://bioconductor.org/packages/release/bioc/html/enrichplot.html
58	Szklarczyk D, Gable AL, Lyon D, Junge A, Wyder S, Huerta-Cepas J, Simonovic M, Doncheva NT, Morris JH, Bork P, et al: STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47D:D607–D613. 2019. View Article : Google Scholar : PubMed/NCBI
59	Feng G, Shaw P, Rosen ST, Lin SM and Kibbe WA: Using the bioconductor GeneAnswers package to interpret gene lists. Methods Mol Biol. 802:101–112. 2012. View Article : Google Scholar : PubMed/NCBI
60	Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 102:15545–15550. 2005. View Article : Google Scholar : PubMed/NCBI
61	Hänzelmann S, Castelo R and Guinney J: GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 14:72013. View Article : Google Scholar : PubMed/NCBI
62	Cancer Genome Atlas Research NetworkAlbert Einstein College of MedicineAnalytical Biological ServicesBarretos Cancer HospitalBaylor College of MedicineBeckman Research Institute of City of HopeBuck Institute for Research on AgingCanada's Michael Smith Genome Sciences CentreHarvard Medical SchoolHelen F. Graham Cancer Center &Research Institute at Christiana Care Health Services, et al, . Integrated genomic and molecular characterization of cervical cancer. Nature. 543:378–384. 2017. View Article : Google Scholar : PubMed/NCBI
63	Colaprico A, Silva TC, Olsen C, Garofano L, Cava C, Garolini D, Sabedot TS, Malta TM, Pagnotta SM, Castiglioni I, et al: TCGAbiolinks: An R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res. 44:e71. 2016. View Article : Google Scholar : PubMed/NCBI
64	Villanueva RAM and Chen ZJ: ggplot2: Elegant Graphics for Data Analysis (2nd ed.). Measurement: Interdisciplinary Res Perspectives. 17:160–167. 2019.
65	Babion I, Miok V, Jaspers A, Huseinovic A, Steenbergen RDM, van Wieringen WN and Wilting SM: Identification of Deregulated Pathways, Key Regulators, and Novel miRNA-mRNA Interactions in HPV-Mediated Transformation. Cancers (Basel). 12:7002020. View Article : Google Scholar : PubMed/NCBI
66	Therneau TM and Terry M: A Package for Survival Analysis in R, 2020. https://CRAN.R-project.org/package=survival
67	Nagy Á, Munkácsy G and Győrffy B: Pancancer survival analysis of cancer hallmark genes. Sci Rep. 11:60472021. View Article : Google Scholar : PubMed/NCBI
68	Altman DG and De Stavola BL: Practical problems in fitting a proportional hazards model to data with updated measurements of the covariates. Stat Med. 13:301–341. 1994. View Article : Google Scholar : PubMed/NCBI
69	Rosty C, Sheffer M, Tsafrir D, Stransky N, Tsafrir I, Peter M, de Crémoux P, de La Rochefordière A, Salmon R, Dorval T, et al: Identification of a proliferation gene cluster associated with HPV E6/E7 expression level and viral DNA load in invasive cervical carcinoma. Oncogene. 24:7094–7104. 2005. View Article : Google Scholar : PubMed/NCBI
70	Kim I-S, Lee M-Y, Lee I-H, Shin S-L and Lee S-Y: Gene expression of flap endonuclease-1 during cell proliferation and differentiation. Biochim Biophys Acta. 1496:333–340. 2000. View Article : Google Scholar : PubMed/NCBI
71	Zhang K, Keymeulen S, Nelson R, Tong TR, Yuan YC, Yun X, Liu Z, Lopez J, Raz DJ and Kim JY: Overexpression of Flap Endonuclease 1 Correlates with Enhanced Proliferation and Poor Prognosis of Non-Small-Cell Lung Cancer. Am J Pathol. 188:242–251. 2018. View Article : Google Scholar : PubMed/NCBI
72	Moody C: Mechanisms by which HPV Induces a Replication Competent Environment in Differentiating Keratinocytes. Viruses. 9:2612017. View Article : Google Scholar : PubMed/NCBI
73	Chen J: Signaling pathways in HPV-associated cancers and therapeutic implications. Rev Med Virol. 25 (Suppl 1):24–53. 2015. View Article : Google Scholar : PubMed/NCBI
74	Egawa N, Wang Q, Griffin HM, Murakami I, Jackson D, Mahmood R and Doorbar J: HPV16 and 18 genome amplification show different E4-dependence, with 16E4 enhancing E1 nuclear accumulation and replicative efficiency via its cell cycle arrest and kinase activation functions. PLoS Pathog. 13:e10062822017. View Article : Google Scholar : PubMed/NCBI
75	Wilhelm T, Olziersky A-M, Harry D, De Sousa F, Vassal H, Eskat A and Meraldi P: Mild replication stress causes chromosome mis-segregation via premature centriole disengagement. Nat Commun. 10:35852019. View Article : Google Scholar : PubMed/NCBI
76	Zhang J, Dai Q, Park D and Deng X: Targeting DNA Replication Stress for Cancer Therapy. Genes (Basel). 7:512016. View Article : Google Scholar : PubMed/NCBI
77	Bordignon V, Di Domenico EG, Trento E, D'Agosto G, Cavallo I, Pontone M, Pimpinelli F, Mariani L and Ensoli F: How Human Papillomavirus Replication and Immune Evasion Strategies Take Advantage of the Host DNA Damage Repair Machinery. Viruses. 9:3902017. View Article : Google Scholar : PubMed/NCBI
78	Oshi M, Newman S, Tokumaru Y, Yan L, Matsuyama R, Endo I, Katz MHG and Takabe K: High G₂M Pathway Score Pancreatic Cancer is Associated with Worse Survival, Particularly after Margin-Positive (R1 or R2) Resection. Cancers (Basel). 12:28712020. View Article : Google Scholar : PubMed/NCBI
79	Oshi M, Takahashi H, Tokumaru Y, Yan L, Rashid OM, Matsuyama R, Endo I and Takabe K: G₂M Cell Cycle Pathway Score as a Prognostic Biomarker of Metastasis in Estrogen Receptor (ER)-Positive Breast Cancer. Int J Mol Sci. 21:29212020. View Article : Google Scholar : PubMed/NCBI
80	Cerciello F, Hofstetter B, Fatah SA, Zaghloul M, Odermatt B, Bodis S, Varga Z, Pruschy M and Ciernik IF: G₂/M cell cycle checkpoint is functional in cervical cancer patients after initiation of external beam radiotherapy. Int J Radiat Oncol Biol Phys. 62:1390–1398. 2005. View Article : Google Scholar : PubMed/NCBI
81	Poe JC, Kountikov EI, Lykken JM, Natarajan A, Marchuk DA and Tedder TF: EndoU is a novel regulator of AICD during peripheral B cell selection. J Exp Med. 211:57–69. 2014. View Article : Google Scholar : PubMed/NCBI
82	Qi W, Gromoff EDV, Xu F, Zhao Q, Yang W, Pfeifer D, Maier W, Long L and Baumeister R: The secreted endoribonuclease ENDU-2 from the soma protects germline immortality in C. elegans. Nat Commun. 12:12622021. View Article : Google Scholar : PubMed/NCBI
83	Perl K, Ushakov K, Pozniak Y, Yizhar-Barnea O, Bhonker Y, Shivatzki S, Geiger T, Avraham KB and Shamir R: Reduced changes in protein compared to mRNA levels across non-proliferating tissues. BMC Genomics. 18:3052017. View Article : Google Scholar : PubMed/NCBI
84	Kotake Y, Naemura M, Murasaki C, Inoue Y and Okamoto H: Transcriptional Regulation of the p16 Tumor Suppressor Gene. Anticancer Res. 35:4397–4401. 2015.PubMed/NCBI
85	Hasan S, Stucki M, Hassa PO, Imhof R, Gehrig P, Hunziker P, Hübscher U and Hottiger MO: Regulation of human flap endonuclease-1 activity by acetylation through the transcriptional coactivator p300. Mol Cell. 7:1221–1231. 2001. View Article : Google Scholar : PubMed/NCBI
86	Uxa S, Castillo-Binder P, Kohler R, Stangner K, Müller GA and Engeland K: Ki-67 gene expression. Cell Death Differ. 1–14. 2021.PubMed/NCBI