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Breast cancer is one of the most leading causes of death in females all over the world. Although, the incidence rate of breast cancer is low, the death proportion is relatively high. For breast cancer patients, the recurrence ratio is rare, yet it still happens. The highest risk of recurrence usually happens at the early time of treatment, and its severeness depends on the diagnosed stage and the size of the tumor. However, the recurrence prognostic of breast cancer still limited due to the poor prediction strength of input data. Therefore, in this study we used four independent data sets (n = 793) with the same chipsets (HG-U133A) of breast cancer patients to investigate a 67 gene signature used in further building the prognostic model for breast cancer patients. Our results finally confirmed that a 67 gene set was significantly associated with the recurrence (RFS) (p = 1.66e−17), and overall survival (OS) (p = 9.27e−06). Besides, the gene signature also correlated with survival outcomes, such as RFS (p = 0.00167), and distant metastasis-free survival (DMFS) (p < 0.001). Remarkably, our classification analysis results reveal that the breast cancer patients who were categorized into the low-risk group tend to have significantly associated with the longer recurrence time in both training and validation data sets. In conclusion, the 67 gene signature should be considered as potential candidates to be used in building up models or integrated into the current treatment regime for breast cancer.


  1. Laura J. van ’t Veer, Hongyue Dai, Marc J. van de Vijver, Yudong D. He, Augustinus A. M. Hart, Mao Mao, Hans L. Peterse, Karin van der Kooy, Matthew J. Marton, Anke T. Witteveen,GeorgeJ.Schreiber,RonM.Kerkhoven,ChrisRoberts, Peter S. Linsley, René Bernards, and Stephen H. Friend. Gene expressionprofilingpredictsclinicaloutcomeofbreastcancer. Nature, 415(6871):530–536, Jan 2002.
  2. Carol E. DeSantis, Jiemin Ma, Mia M. Gaudet, Lisa A. Newman, Kimberly D. Miller, Ann Goding Sauer, Ahmedin Jemal, and Rebecca L. Siegel. Breast cancer statistics, 2019. CA: A Cancer Journal for Clinicians, 69(6):438–451, 2019.
  3. Meng Zhou, Lei Zhong, Wanying Xu, Yifan Sun, Zhaoyue Zhang, Hengqiang Zhao, Lei Yang, and Jie Sun. Discovery of potential prognostic long non-coding rna biomarkers for predicting the risk of tumor recurrence of breast cancer patients. Scientific Reports, 6:31038, 08 2016.
  4. Abel Matondo, Yong Hwa Jo, Muhammad Shahid, Tae Choi, Nam Nguyen, Ngoc Nguyen, Salima Akter, Insug Kang, Joohun Ha, Chi Maeng, Si-Young Kim, Ju-Seog Lee, Jayoung Kim, and Sung Soo Kim. The prognostic 97 chemoresponse gene signature in ovarian cancer. Scientific Reports, 7, 12 2017.
  5. Mahsa Marzancola, Abootaleb Sedighi, and Paul Li. DNA Microarray-Based Diagnostics, volume 1368, pages 161–178. 05 2016.
  6. GabrieleSchricker,RudolfNapieralski,AureliaNoske,Elodie Piednoir, Olivia Manner, Elisabeth Schüren, Jürgen Lauber, Jonathan Perkins, Viktor Magdolen, Manfred Schmitt, Kurt Ulm,WilkoWeichert,MarionKiechle,JohnMartens,andOlaf Wilhelm. Clinical performance of an analytically validated assay in comparison to microarray technology to assess pitx2 dna-methylation in breast cancer. Scientific Reports, 8, 12 2018.
  7. Marc Vijver, Yudong He, Laura van ’t Veer, Hongyue Dai, AugustinusHart,DorienVoskuil,GeorgeSchreiber,JohannesPeterse, Chris Roberts, Matthew Marton, Mark Parrish, Douwe Atsma, Anke Witteveen, Annuska Glas, Leonie Delahaye, Tony Velde, Harry Bartelink, Sjoerd Rodenhuis, Emiel Rutgers, and Rene Bernards. A gene-expression signature as a predictorofsurvivalinbreastcancer. TheNewEnglandjournal of medicine, 347:1999–2009, 12 2002.
  8. Renaud Sabatier, Pascal Finetti, Nathalie Cervera, Agnes Tallet, Mohamed Benchalal, Gilles Houvenaeghel, Jocelyne Jacquemier, Daniel Birnbaum, and François Bertucci. Gene expression profiling and its utility in prediction of local relapse after breast-conserving therapy in early breast cancer. Cancer genomics proteomics, 8:199–209, 07 2011.
  9. Rui Liu, Xinhao Wang, Grace Chen, Piero Dalerba, Austin Gurney, Timothy Hoey, Gavin Sherlock, John Lewicki, Kerby Shedden, and Michael Clarke. The prognostic role of a gene signature from tumorigenic breast-cancer cells. The New England journal of medicine, 356:217–26, 02 2007.
  10. R.Irizarry,B.Hobbs,F.Collin,Y.Beazer-Barclay,KristenJAntonellis, U. Scherf, and T. Speed. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4 2:249–64, 2003.
  11. Salima Akter, Tae Gyu Choi, Minh Nam Nguyen, Abel Matondo, Jin Hwan Kim, Yong Hwa Jo, Ara Jo, Muhammad Shahid, Dae Young Jun, Ji Youn Yoo, Yen Ngoc Nguyen Ngo, Seong Wook Seo, Liaquat Ali, Ju Seog Lee, Kyung Sik Yoon, Wonchae Choe, Insug Kang, Joohun Ha, Jayoung Kim, and Sung Soo Kim. Prognostic value of a 92-probe signature in breast cancer. Oncotarget, 6(17):15662–15680, 2015.
  12. Yudi Pawitan, Judith Bjöhle, Lukas Amler, Anna-Lena Borg, Suzanne Egyhazi Brage, Per Hall, Xia Han, Lars Holmberg, FeiHuang,SigridKlaar,EdisonLiu,LanceMiller,HansNordgren, Alexander Ploner, Kerstin Sandelin, Peter Shaw, Johanna Smeds, Lambert Skoog, Sara Wedrén, and Jonas Bergh. Pawitan y, bjöhle j, amler l, borg al, egyhazi s, hall p, han x, holmberg l, huang f, klaar s, liu et, miller l, nordgren h, ploner a, sandelin k, shaw pm, smeds j, skoog l, wedrén s, bergh jgene expression profiling spares early breast cancer patients from adjuvant therapy: derived and validated in two population-based cohorts. breast cancer res 7: R953-r964. Breast cancer research : BCR, 7:R953–64, 02 2005.
  13. Ivyna Bong, Zubaidah Zakaria, Rohaizak Muhammad, Abdullah Norlia, Naqiyah Ibrahim, Emran Aina, Nor Abdullah, and akmal Sharifah. Gene expression patterns distinguish breast carcinomas from normal breast tissues: The malaysian context. Pathology, research and practice, 206:223–8, 04 2010.
  14. Marcus Schmidt, Daniel Böhm, Christian Törne, Eric Steiner, Alexander Puhl, Henryk Pilch, Hans-Anton Lehr, Jan Hengstler,HeinzKölbl,andMathiasGehrmann. Thehumoral immunesystemhasakeyprognosticimpactinnode-negative breast cancer. Cancer research, 68:5405–13, 08 2008.
  15. Christos Sotiriou, Pratyaksha Wirapati, Sherene Loi, Adrian Harris, Steve Fox, Johanna Smeds, Hans Nordgren, Pierre Farmer, Viviane Praz, Benjamin Haibe-Kains, Christine Desmedt, Denis Larsimont, Fatima Cardoso, Hans Peterse, Dimitry Nuyten, Marc Buyse, Marc J. Van de Vijver, Jonas Bergh, Martine Piccart, and Mauro Delorenzi. Gene Expression Profiling in Breast Cancer: Understanding the Molecular BasisofHistologicGradeToImprovePrognosis. JNCI:Journal of the National Cancer Institute, 98(4):262–272, 02 2006.
  16. T.-K. Jenssen, W. Kuo, T. Stokke, and E. Hovig. Associations between gene expressions in breast cancer and patient survival. Human Genetics, 111(4):411–420, Oct 2002.
  17. Eric Bair and Robert Tibshirani. Semi-supervised methods to predict patient survival from gene expression data. PLoS biology, 2:E108, 05 2004.
  18. Richard Simon, Amy Lam, Ming-Chung Li, Michael Ngan, Supriya Menenzes, and Yingdong Zhao. Analysis of gene expressiondatausingbrb-arraytools. Cancerinformatics,3:11– 7, 02 2007.
  19. Michael Radmacher, Lisa Mcshane, and Richard Simon. A paradigm for class prediction using gene expression profiles. Journalofcomputationalbiology: ajournalofcomputationalmolecular cell biology, 9:505–11, 02 2002.
  20. Damian Szklarczyk, Andrea Franceschini, Stefan Wyder, Sofia Forslund, Davide Heller, Jaime Huerta-Cepas, Milan Simonovic, Alexander Roth, Alberto Santos, Kalliopi Tsafou, Michael Kuhn, Peer Bork, Lars Jensen, and Christian von Mering. String v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic acids research, 43, 10 2014.
  21. Aamir Ahmad. Breast Cancer Statistics: Recent Trends, pages 1–7. 08 2019.
  22. R. Edward Hendrick, Jay Baker, and Mark Helvie. Breast cancer deaths averted over 3 decades. Cancer, 125, 02 2019.
  23. Giusy Fiucci, Dana Ravid, Reuven Reich, and Mordechai Liscovitch. Caveolin-1 inhibits anchorage-independent growth, anoikis and invasiveness in mcf-7 human breast cancer cells. Oncogene, 21:2365–75, 05 2002.
  24. Tian Gao, Yong Han, Ling Yu, Sheng Ao, ZiYu li, and Jiafu ji. Ccna2 is a prognostic biomarker for er+ breast cancer and tamoxifen resistance. PloS one, 9:e91771, 03 2014. [25] H Karra, H Repo, Ilmari Ahonen, Eliisa Löyttyniemi, Reino Pitkänen, M Lintunen, Teijo Kuopio, Mirva Söderström, and PauliinaKronqvist. Cdc20andsecurinoverexpressionpredict short-term breast cancer survival. Britishjournalofcancer, 110, 05 2014.
  25. Jia-Yi Qian, Jian Gao, Xi Sun, Meng-Da Cao, Liang Shi, TianSong Xia, Wenbin Zhou, Shui Wang, Qiang Ding, and Ji-Fu Wei. Kiaa1429 acts as an oncogenic factor in breast cancer by regulating cdk1 in an n6-methyladenosine-independent manner. Oncogene, 38:1, 07 2019.
  26. Attila Szász, Zsuzsa Schaff, László Harsányi, István Molnár, Zsolt Baranyai, István Besznyák, Attila Zaránd, Ferenc Salamon,andJaninaKulka.Expressionoftightjunctionmolecules inbreastcarcinomasanalysedbyarraypcrandimmunohistochemistry. Pathology oncology research : POR, 18:593–606, 12 2011.
  27. YY Xiang, Virginia Ladeda, and Jorge Filmus. Glypican-3 expressionissilencedinhumanbreastcancer.Oncogene,20:7408– 12, 12 2001.
  28. TimothyKey,PaulAppleby,GillianReeves,andAndrewRoddam. Insulin-like growth factor 1 (igf1), igf binding protein 3 (igfbp3), and breast cancer risk: Pooled individual data analysis of 17 prospective studies. Thelancetoncology, 11:530–42, 06 2010.
  29. MarijeVleugel,AstridGreijer,ReinhardBos,ElskenWall,and Paul Diest. c-jun activation is associated with proliferation and angiogenesis in invasive breast cancer. Human pathology, 37:668–74, 07 2006.
  30. Mark Pickard, Andrew Green, Ian Ellis, Carlos Caldas, VanessaHedge,MirnaMaarabouni,andGwynWilliams. Dysregulated expression of fau and melk is associated with poor prognosis in breast cancer. Breast cancer research: BCR, 11, 09 2009.
  31. Erin Young, Debra Kelly, Insop Shim, Kyle Baumbauer, Angela Starkweather, and Debra Lyon. Variations in comt and ntrk2 influence symptom burden in women undergoing breast cancer treatment. Biological research for nursing, 19:1099800417692877, 02 2017. [33] Arata Shimo, Toshihiko Nishidate, Tomohiko Ohta, Mamoru Fukuda,YusukeNakamura,andToyomasaKatagiri. Elevated expression of protein regulator of cytokinesis 1, involved in thegrowthofbreastcancercells. CancerScience,98(2):174–181, 2007.
  32. Syed Musthapa Meeran, Shweta N. Patel, and Trygve O. Tollefsbol. Sulforaphane causes epigenetic repression of htert expression in human breast cancer cell lines. PLoS ONE, 5, 2010.
  33. Liangliang Shen, John M. O’Shea, Mohan R. Kaadige, Stéphanie Cunha, Blake R. Wilde, Adam L. Cohen, Alana L. Welm, and Donald E. Ayer. Metabolic reprogramming in triple-negative breast cancer through Myc suppression of TXNIP. Proceedings of the National Academy of Sciences, 112(17):5425–5430, April 2015.
  34. Daniel Stover, Carlos Alcazar, Jane Brock, Hao Guo, Beth Overmoyer, Justin Balko, Qiong Xu, Aditya Bardia, Sara Tolaney, Rebecca Gelman, Maxwell Lloyd, Yu Wang, Yaomin Xu, Franziska Michor, Vivian Wang, Eric Winer, Kornelia Polyak, and Nancy Lin. Phase ii study of ruxolitinib, a selective jak1/2 inhibitor, in patients with metastatic triple negative breast cancer. npj Breast Cancer, 4, 12 2018.
  35. K DeLellis, Sue Ingles, L Kolonel, Roberta McKean-Cowdin, Brian Henderson, Frank Stanczyk, and N Probst-Hensch. Igfi genotype, mean plasma level and breast cancer risk in the hawaii/losangelesmultiethniccohort. Britishjournalofcancer, 88:277–82, 01 2003.
  36. Hongchang Dong, Shuai Zhang, Yu Wei, Chunyan Liu, Na Wang, Pan Zhang, Jingling Zhu, and Jin Huang. Bioinformatic analysis of differential expression and core genes in breast cancer. Internationaljournalofclinicalandexperimental pathology, 11:1146–1156, 03 2018.
  37. Chikako Fukukawa, Koji Ueda, Toshihiko Nishidate, Toyomasa Katagiri, and Yusuke Nakamura. Critical roles of lgn/gpsm2 phosphorylation by pbk/topk in cell division of breast cancer cells. Genes, chromosomes cancer, 49:861–72, 10 2010.
  38. JifuSong,ZhibinGuan,MaojiangLi,ShaSha,ChaoSong,Zhiwei Gao, and Yongli Zhao. Microrna-154 inhibits the growth and invasion of gastric cancer cells by targeting dixdc1/wnt signaling. Oncology Research Featuring Preclinical and Clinical Cancer Therapeutics, 26, 08 2017.
  39. Dimo Dietrich, Manuel Krispin, Jörn Dietrich, Anne Fassbender, Jörn Lewin, Nadia Harbeck, Manfred Schmitt, Serenella Eppenberger-Castori, Vincent Vuaroqueaux, FrederiqueSpyratos,JohnFoekens,RalfLesche,andJohnMartens. Cdo1 promoter methylation is a biomarker for outcome prediction of anthracycline treated, estrogen receptor-positive, lymph node-positive breast cancer patients. BMC cancer, 10:247, 06 2010.
  40. Yaxun Wu, Xingsong Zhang, Rong Shen, Jieyu Huang, XiaoyunLu,GuihuaZheng,andXudongChen. Expressionand significance of etfdh in hepatocellular carcinoma. Pathology Research and Practice, 215:152702, 10 2019.
  41. Maja Krajinovic. Mthfd1 gene: Role in disease susceptibility and pharmacogenetics. Pharmacogenomics, 9:829–32, 08 2008.
  42. Feng Liu, Yang Liu, Chuan He, Li Tao, Xiaoguang He, Hongtao Song, and Guoqiang Zhang. Increased mthfd2 expression isassociatedwithpoorprognosisinbreastcancer. Tumourbiology: thejournaloftheInternationalSocietyforOncodevelopmental Biology and Medicine, 35:8685–90, 05 2014.
  43. Wen-Hui Liang, Na Li, Zhi-Qing Yuan, Xin-Lai Qian, and Zhi-Hui Wang. Dscam-as1 promotes tumor growth of breast cancer by reducing mir-204-5p and up-regulating rrm2: The association of dscam-as1 with mir-204-5p and rrm2 in bc. Molecular Carcinogenesis, 58:461–473, 11 2018.
How to Cite
Tam Vy Le, Quang Van Ta, Dinh-Truong Nguyen, & Minh Nam Nguyen. (2021). A Novel Signature Predicts Recurrence Risk In Breast Cancer Patients. TTU Review, 2(1), 60-68.

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