Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

Keynote webinar | Spotlight on medication adherence

LIVE: Thursday 27th June 2024, 18:00-19:30 (CEST)
Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.
ADVANCED SEARCH
Log in
Top
Cancer Cell International
Published in: Cancer Cell International 1/2019

Open Access 01-12-2019 | Breast Cancer | Primary research

Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer

Authors: Xuemei Lv, Miao He, Yanyun Zhao, Liwen Zhang, Wenjing Zhu, Longyang Jiang, Yuanyuan Yan, Yue Fan, Hongliang Zhao, Shuqi Zhou, Heyao Ma, Yezhi Sun, Xiang Li, Hong Xu, Minjie Wei

Published in: Cancer Cell International | Issue 1/2019

Login to get access

Abstract

Background

Triple negative breast cancer (TNBC) is a specific subtype of breast cancer with a poor prognosis due to its aggressive biological behaviour and lack of therapeutic targets. We aimed to explore some novel genes and pathways related to TNBC prognosis through bioinformatics methods as well as potential initiation and progression mechanisms.

Methods

Breast cancer mRNA data were obtained from The Cancer Genome Atlas database (TCGA). Differential expression analysis of cancer and adjacent cancer, as well as, triple negative breast cancer and non-triple negative breast cancer were performed using R software. The key genes related to the pathogenesis were identified by functional and pathway enrichment analysis and protein–protein interaction network analysis. Based on univariate and multivariate Cox proportional hazards model analyses, a gene signature was established to predict overall survival. Receiver operating characteristic curve was used to evaluate the prognostic performance of our model.

Results

Based on mRNA expression profiling of breast cancer patients from the TCGA database, 755 differentially expressed overlapping mRNAs were detected between TNBC/non-TNBC samples and normal tissue. We found eight hub genes associated with the cell cycle pathway highly expressed in TNBC. Additionally, a novel six-gene (TMEM252, PRB2, SMCO1, IVL, SMR3B and COL9A3) signature from the 755 differentially expressed mRNAs was constructed and significantly associated with prognosis as an independent prognostic signature. TNBC patients with high-risk scores based on the expression of the 6-mRNAs had significantly shorter survival times compared to patients with low-risk scores (P < 0.0001).

Conclusions

The eight hub genes we identified might be tightly correlated with TNBC pathogenesis. The 6-mRNA signature established might act as an independent biomarker with a potentially good performance in predicting overall survival.
Appendix
Available only for authorised users
Literature
1.
Cejalvo JM, Martinez de Duenas E, Galvan P, Garcia Recio S, Burgues Gasion O, Pare L, Antolin S, Martinello R, Blancas I, Adamo B, et al. Intrinsic subtypes and gene expression profiles in primary and metastatic breast cancer. Cancer Res. 2017;77(9):2213–21.PubMedPubMedCentralCrossRef
2.
Foulkes WD, Smith IE, Reis-Filho JS. Triple-negative breast cancer. N Engl J Med. 2010;363(20):1938–48.PubMedCrossRef
3.
Haffty BG, Yang Q, Reiss M, Kearney T, Higgins SA, Weidhaas J, Harris L, Hait W, Toppmeyer D. Locoregional relapse and distant metastasis in conservatively managed triple negative early-stage breast cancer. J Clin Oncol. 2006;24(36):5652–7.PubMedCrossRef
4.
Carey LA, Dees EC, Sawyer L, Gatti L, Moore DT, Collichio F, Ollila DW, Sartor CI, Graham ML, Perou CM. The triple negative paradox: primary tumor chemosensitivity of breast cancer subtypes. Clin Cancer Res. 2007;13(8):2329–34.PubMedCrossRef
5.
Malorni L, Shetty PB, De Angelis C, Hilsenbeck S, Rimawi MF, Elledge R, Osborne CK, De Placido S, Arpino G. Clinical and biologic features of triple-negative breast cancers in a large cohort of patients with long-term follow-up. Breast Cancer Res Treat. 2012;136(3):795–804.PubMedPubMedCentralCrossRef
6.
Burstein MD, Tsimelzon A, Poage GM, Covington KR, Contreras A, Fuqua SA, Savage MI, Osborne CK, Hilsenbeck SG, Chang JC, et al. Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin Cancer Res. 2015;21(7):1688–98.PubMedCrossRef
7.
Lehmann BD, Bauer JA, Chen X, Sanders ME, Chakravarthy AB, Shyr Y, Pietenpol JA. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750–67.PubMedPubMedCentralCrossRef
8.
9.
Craig DW, O’Shaughnessy JA, Kiefer JA, Aldrich J, Sinari S, Moses TM, Wong S, Dinh J, Christoforides A, Blum JL, et al. Genome and transcriptome sequencing in prospective metastatic triple-negative breast cancer uncovers therapeutic vulnerabilities. Mol Cancer Ther. 2013;12(1):104–16.PubMedCrossRef
10.
Bianchini G, Balko JM, Mayer IA, Sanders ME, Gianni L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat Rev Clin Oncol. 2016;13(11):674–90.PubMedPubMedCentralCrossRef
11.
12.
Tian T, Gong Z, Wang M, Hao R, Lin S, Liu K, Guan F, Xu P, Deng Y, Song D, et al. Identification of long non-coding RNA signatures in triple-negative breast cancer. Cancer Cell Int. 2018;18:103.PubMedPubMedCentralCrossRef
13.
Kulasingam V, Diamandis EP. Strategies for discovering novel cancer biomarkers through utilization of emerging technologies. Nat Clin Pract Oncol. 2008;5(10):588–99.PubMedCrossRef
14.
Chen B, Tang H, Chen X, Zhang G, Wang Y, Xie X, Liao N. Transcriptomic analyses identify key differentially expressed genes and clinical outcomes between triple-negative and non-triple-negative breast cancer. Cancer Manag Res. 2019;11:179–90.PubMedCrossRef
15.
Li Z, Zhang Y, Zhang Z, Zhao Z, Lv Q. A four-gene signature predicts the efficacy of paclitaxel-based neoadjuvant therapy in human epidermal growth factor receptor 2-negative breast cancer. J Cell Biochem. 2019;120(4):6046–56.PubMedCrossRef
16.
Sherman BT, da Huang W, Tan Q, Guo Y, Bour S, Liu D, Stephens R, Baseler MW, Lane HC, Lempicki RA. DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC Bioinformatics. 2007;8:426.PubMedPubMedCentralCrossRef
17.
Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, Simonovic M, Roth A, Santos A, Tsafou KP, et al. STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015;43(Database issue):D447–52.PubMedCrossRef
18.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498–504.PubMedPubMedCentralCrossRef
19.
20.
Heagerty PJ, Zheng Y. Survival model predictive accuracy and ROC curves. Biometrics. 2005;61(1):92–105.PubMedCrossRef
21.
Kalimutho M, Parsons K, Mittal D, Lopez JA, Srihari S, Khanna KK. Targeted therapies for triple-negative breast cancer: combating a stubborn disease. Trends Pharmacol Sci. 2015;36(12):822–46.PubMedCrossRef
22.
Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.CrossRef
23.
Arsic N, Bendris N, Peter M, Begon-Pescia C, Rebouissou C, Gadea G, Bouquier N, Bibeau F, Lemmers B, Blanchard JM. A novel function for Cyclin A2: control of cell invasion via RhoA signaling. J Cell Biol. 2012;196(1):147–62.PubMedPubMedCentralCrossRef
24.
25.
Zhao QJ, Zhang J, Xu L, Liu FF. Identification of a five-long non-coding RNA signature to improve the prognosis prediction for patients with hepatocellular carcinoma. World J Gastroenterol. 2018;24(30):3426–39.PubMedPubMedCentralCrossRef
26.
Thu KL, Silvester J, Elliott MJ, Ba-Alawi W, Duncan MH, Elia AC, Mer AS, Smirnov P, Safikhani Z, Haibe-Kains B, et al. Disruption of the anaphase-promoting complex confers resistance to TTK inhibitors in triple-negative breast cancer. Proc Natl Acad Sci USA. 2018;115(7):E1570–7.PubMedCrossRef
27.
Komatsu M, Yoshimaru T, Matsuo T, Kiyotani K, Miyoshi Y, Tanahashi T, Rokutan K, Yamaguchi R, Saito A, Imoto S, et al. Molecular features of triple negative breast cancer cells by genome-wide gene expression profiling analysis. Int J Oncol. 2013;42(2):478–506.PubMedCrossRef
28.
Naorem LD, Muthaiyan M, Venkatesan A. Integrated network analysis and machine learning approach for the identification of key genes of triple-negative breast cancer. J Cell Biochem. 2019;120(4):6154–67.PubMedCrossRef
29.
Merino VF, Cho S, Nguyen N, Sadik H, Narayan A, Talbot C Jr, Cope L, Zhou XC, Zhang Z, Gyorffy B, et al. Induction of cell cycle arrest and inflammatory genes by combined treatment with epigenetic, differentiating, and chemotherapeutic agents in triple-negative breast cancer. Breast Cancer Res. 2018;20(1):145.PubMedPubMedCentralCrossRef
30.
Papadakis ES, Reeves T, Robson NH, Maishman T, Packham G, Cutress RI. BAG-1 as a biomarker in early breast cancer prognosis: a systematic review with meta-analyses. Br J Cancer. 2017;116(12):1585–94.PubMedPubMedCentralCrossRef
31.
Zheng S, Lv P, Su J, Miao K, Xu H, Li M. Overexpression of CBX2 in breast cancer promotes tumor progression through the PI3K/AKT signaling pathway. Am J Transl Res. 2019;11(3):1668–82.PubMedPubMedCentral
32.
Xie X, Wang J, Shi D, Zou Y, Xiong Z, Li X, Zhou J, Tang H, Xie X. Identification of a 4-mRNA metastasis-related prognostic signature for patients with breast cancer. J Cell Mol Med. 2019;23(2):1439–47.PubMedCrossRef
33.
Borge KS, Nord S, Van Loo P, Lingjaerde OC, Gunnes G, Alnaes GI, Solvang HK, Luders T, Kristensen VN, Borresen-Dale AL, et al. Canine mammary tumours are affected by frequent copy number aberrations, including amplification of MYC and loss of PTEN. PLoS ONE. 2015;10(5):e0126371.PubMedPubMedCentralCrossRef
34.
Rubin A, Salzberg AC, Imamura Y, Grivitishvilli A, Tombran-Tink J. Identification of novel targets of diabetic nephropathy and PEDF peptide treatment using RNA-seq. BMC Genomics. 2016;17(1):936.PubMedPubMedCentralCrossRef
35.
Macaluso M, Montanari M, Noto PB, Gregorio V, Surmacz E, Giordano A. Nuclear and cytoplasmic interaction of pRb2/p130 and ER-beta in MCF-7 breast cancer cells. Ann Oncol. 2006;17(Suppl 7):vii27–9.PubMedCrossRef
36.
Macaluso M, Cinti C, Russo G, Russo A, Giordano A. pRb2/p130-E2F4/5-HDAC1-SUV39H1-p300 and pRb2/p130-E2F4/5-HDAC1-SUV39H1-DNMT1 multimolecular complexes mediate the transcription of estrogen receptor-alpha in breast cancer. Oncogene. 2003;22(23):3511–7.PubMedCrossRef
37.
38.
Wu JC, Sun BS, Ren N, Ye QH, Qin LX. Genomic aberrations in hepatocellular carcinoma related to osteopontin expression detected by array-CGH. J Cancer Res Clin Oncol. 2010;136(4):595–601.PubMedCrossRef
Metadata
Title
Identification of potential key genes and pathways predicting pathogenesis and prognosis for triple-negative breast cancer
Authors
Xuemei Lv
Miao He
Yanyun Zhao
Liwen Zhang
Wenjing Zhu
Longyang Jiang
Yuanyuan Yan
Yue Fan
Hongliang Zhao
Shuqi Zhou
Heyao Ma
Yezhi Sun
Xiang Li
Hong Xu
Minjie Wei
Publication date
01-12-2019
Publisher
BioMed Central
Published in
Cancer Cell International / Issue 1/2019
Electronic ISSN: 1475-2867
DOI
https://doi.org/10.1186/s12935-019-0884-0

Other articles of this Issue 1/2019

Cancer Cell International 1/2019 Go to the issue

Primary research

Harmine suppresses hyper-activated Ras–MAPK pathway by selectively targeting oncogenic mutated Ras/Raf in Caenorhabditis elegans

Primary research

The survival analysis and oncogenic effects of CFP1 and 14-3-3 expression on gastric cancer

Primary research

ZEB1-AS1 initiates a miRNA-mediated ceRNA network to facilitate gastric cancer progression

Primary research

Long noncoding RNA DLX6-AS1 promotes neuroblastoma progression by regulating miR-107/BDNF pathway

Primary research

Upregulation of NPL4 promotes bladder cancer cell proliferation by inhibiting DXO destabilization of cyclin D1 mRNA

Primary research

Long non-coding RNA MIAT promotes papillary thyroid cancer progression through upregulating LASP1
Webinar | 19-02-2024 | 17:30 (CET)

Keynote webinar | Spotlight on antibody–drug conjugates in cancer

Watch now

Antibody–drug conjugates (ADCs) are novel agents that have shown promise across multiple tumor types. Explore the current landscape of ADCs in breast and lung cancer with our experts, and gain insights into the mechanism of action, key clinical trials data, existing challenges, and future directions.

Dr. Véronique Diéras
Prof. Fabrice Barlesi
Developed by: Springer Medicine
Image Credits