Referanslar

• Harding C, Pompei F, Burmistrov D, Welch HG, Abebe R, Wilson R. Breast cancer screening, incidence, and mortality across US counties. JAMA Intern Med. 2015;175(9):1483-1489. google scholar
• Sung H, Ferlay J, Siegel RL et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. google scholar
• Kim M, Park J, Bouhaddou M, et al. A protein interaction land-scape of breast cancer. Science. 2021;374(6563):eabf3066. doi: 10.1126/science.abf3066 google scholar
• Bianchini G, Balko JM, Mayer IA, Sanders ME, Gianni L. Triple-negative breast cancer: Challenges and opportunities of a hetero-geneous disease. Nat Rev Clin Oncol. 2016;13(11):674-690. google scholar
• Bianchini G, De Angelis C, Licata L, Gianni L. Treatment land-scape of triple-negative breast cancer-expanded options, evolving needs. Nat Rev Clin Oncol. 2022;19(2):91-113. google scholar
• Fallahpour S, Navaneelan T, De P, Borgo A. Breast cancer sur-vival by molecular subtype: A population-based analysis of cancer registry data. CMAJ Open. 2017;5:E734-E739. google scholar
• Dent R, Trudeau M, Pritchard KI, et al. Triple-negative breast cancer: Clinical features and patterns of recurrence. Clin Cancer Res. 2007;13:4429-4434. google scholar
• Tan AR, Swain SM. Therapeutic strategies for triple-negative breast cancer. CancerJ. 2008;14:343-351. google scholar
• Kaplan HG, Malmgren JA, Atwood M. T1N0 triple negative breast cancer: Risk of recurrence and adjuvant chemotherapy. Breast J. 2009;15:454-460. google scholar
• Won KA, Spruck C. Triple negative breast cancer ther-apy: Current and future perspectives (Review). Int J Oncol. 2020;57:1245-1261. google scholar
• Almansour NM. Triple-negative breast cancer: A brief review about epidemiology, risk factors, signaling pathways, treat-ment and role of artificial intelligence. Front Mol Biosci. 2022;9:836417. doi:10.3389/fmolb.2022.836417. google scholar
• Berkel C, Kucuk B, Usta M, Yılmaz E, Cacan E. The effect of olaparib and bortezomib combination treatment on ovarian cancer cell lines. Eur J Biol. 2020;79(2):115-123. google scholar
• Ciriello G, Gatza ML, Beck AH, et al. Comprehensive molecular portraits of ınvasive lobular breast cancer. Cell. 2015;163(2):506-519. google scholar
• Jensen MA, Ferretti V, Grossman RL, Staudt LM. The NCI Ge-nomic Data Commons as an engine for precision medicine. Blood. 2017;130(4):453-459. google scholar
• Zhang Z, Hernandez K, Savage J, et al. Uniform genomic data analysis in the NCI Genomic Data Commons. Nat Commun. 2021;12(1):1226. doi: 10.1038/s41467-021-21254-9. google scholar
• Berger AC, Korkut A, Kanchi RS, et al. A Comprehensive pan-cancer molecular study of gynecologic and breast cancers. Cancer Cell. 20189;33(4):690-705.e9. doi: 10.1016/j.ccell.2018.03.014. google scholar
• Cancer Genome Atlas Network. Comprehensive molecular por-traits of human breast tumours. Nature. 2012;490(7418):61-70. google scholar
• Rahman M, Jackson LK, Johnson WE, Li DY, Bild AH, Pic-colo SR. Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results. Bioinformatics. 2015;31(22):3666-3672. google scholar
• Weinstein JN, Collisson EA, Mills GB, et al. Cancer Genome Atlas Research Network; The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45(10):1113-1120. google scholar
• Wilks C, Cline MS, Weiler E, et al. The Cancer Genomics Hub (CGHub): Overcoming cancer through the power of torrential data. Database. 2014:1-10. google scholar
• Arora S. GSE62944: GEO accession data GSE62944 as a SummarizedExperiment. R package version 1.28.1;2023. http://bioconductor.org/packages/release/bioc/html/GSE62944.html google scholar
• Huber W, Carey VJ, Gentleman R, et al. Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods. 2015;12(2):115-121. google scholar
• Gentleman RC, Carey VJ, Bates DM, et al. Bioconductor: Open software development for computational biology and bioinfor-matics. Genome Biol. 2004;5(10):R80. doi:10.1186/gb-2004-5-10-r80 google scholar
• R Core Team. R: A language and environment for statistical com-puting. R Foundation for Statistical Computing; 2022. Vienna, Austria. URL https://www.R-project.org/ google scholar
• Wickham H, Averick M, Bryan J, et al. Welcome to the tidyverse. J Open Source Softw. 2019;4(43):1686. doi:10.21105/joss.01686 google scholar
• Wickham H. stringr: Simple, consistent wrappers for common string operations. R package version 1.5.0;2022. https://CRAN.R-project.org/package=stringr google scholar
• Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016. google scholar
• Wickham H, Bryan J. readxl: Read Excel Files. R package version 1.4.2;2023. https://CRAN.R-project.org/package=readxl google scholar
• Morgan M, Shepherd L. ExperimentHub: Client to access Exper-imentHub resources. R package version 2.4.0;2022. google scholar
• Morgan M, Obenchain V, Hester J, Pages H. SummarizedExperiment: SummarizedExperi- ment container. R package version 1.26.1;2022. https://bioconductor.org/packages/SummarizedExperiment google scholar
• Kassambara A. ggpubr: ’ggplot2’ Based Publication Ready Plots. R package version 0.6.0;2023. https://CRAN.R-project.org/package=ggpubr google scholar
• Allaire J, Xie Y, Dervieux C, et al. rmarkdown: Dy-namic Documents for R. R package version 2.21;2023. https://github.com/rstudio/rmarkdown google scholar
• Xie Y. knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version 1.42;2023. google scholar
• Berkel C, Cacan E. In silico analysis of DYNLL1 expression in ovarian cancer chemoresistance. Cell Biol Int. 2020;44(8):1598-1605. google scholar
• Berkel C, Cacan E. Transcriptomic analysis reveals tumor stage-or grade-dependent expression of miRNAs in serous ovarian can-cer. Hum Cell. 2021;34(3):862-877. google scholar
• Li X, Yang J, Peng L, et al. Triple-negative breast cancer has worse overall survival and cause-specific survival than non-triple-negative breast cancer. Breast Cancer Res Treat. 2017;161(2):279-287. google scholar
• Bai X, Ni J, Beretov J, Graham P, Li Y. Triple-negative breast cancer therapeutic resistance: Where is the Achilles’ heel? Cancer Lett. 2021;497:100-111. google scholar
• Bai F, Zhang LH, Liu X, et al. GATA3 functions downstream of BRCA1 to suppress EMT in breast cancer. Theranostics. 2021;11(17):8218-8233. google scholar
• Yu W, Huang W, Yang Y, et al. GATA3 recruits UTX for gene transcriptional activation to suppress metastasis of breast cancer. Cell Death Dis. 2019;10(11):832. doi:10.1038/s41419-019-2062-7 google scholar
• Li S, Shen Y, Wang M, et al. Loss of PTEN expression in breast cancer: Association with clinicopathological characteristics and prognosis. Oncotarget. 2017;8(19):32043-32054. google scholar
• Hong D, Fritz AJ, Gordon JA, et al. RUNX1-dependent mech-anisms in biological control and dysregulation in cancer. J Cell Physiol. 2019;234(6):8597-8609. google scholar
• Chaudhary S, Appadurai MI, Maurya SK, et al. MUC16 pro-motes triple-negative breast cancer lung metastasis by modu-lating RNA-binding protein ELAVL1/HUR. Breast Cancer Res. 2023;25(1):25. doi:10.1186/s13058-023-01630-7 google scholar
• Ryu TY, Kim K, Kim SK, et al. SETDB1 regulates SMAD7 ex-pression for breast cancer metastasis. BMB Rep. 2019;52(2):139-144. google scholar
• Wu M, Fan B, Guo Q, et al. Knockdown of SETDB1 inhibits breast cancer progression by miR-381-3p-related regulation. Biol Res. 2018;51(1):39. doi: 10.1186/s40659-018-0189-0 google scholar
• Liu Z, Liu J, Ebrahimi B, et al. SETDB1 interactions with PELP1 contributes to breast cancer endocrine therapy resistance. Breast Cancer Res. 2022;24(1):26. doi: 10.1186/s13058-022-01520-4 google scholar
• Swetzig WM, Wang J, Das GM. Estrogen receptor alpha (ERa/ESR1) mediates the p53-independent overexpression of MDM4/MDMX and MDM2 in human breast cancer. Oncotarget. 2016;7(13):16049-16069. google scholar
• Zhang Z, Zhang J, Li J, et al. miR-320/ELF3 axis inhibits the progression of breast cancer via the PI3K/AKT pathway. Oncol Lett. 2020;19(4):3239-3248. google scholar
• Kochetkova M, McKenzie OL, Bais AJ, et al. CBFA2T3 (MTG16) is a putative breast tumor suppressor gene from the breast cancer loss of heterozygosity region at 16q24.3. Cancer Res. 2002;62(16):4599-4604. google scholar
• Dong G, MaG,WuR, et al. ZFHX3 promotes the proliferation and tumor growth of ER-positive breast cancer cells likely by enhanc-ing stem-like features and MYC and TBX3 transcription. Cancers (Basel). 2020;12(11):3415. doi: 10.3390/cancers12113415 google scholar
• Chang JW, Kuo WH, Lin CM, et al. Wild-type p53 upregu-lates an early onset breast cancer-associated gene GAS7 to sup-press metastasis via GAS7-CYFIP1-mediated signaling pathway. Oncogene. 2018;37(30):4137-4150. google scholar
• Hsu CH, Ma HP, Ong JR, et al. Cancer-associated exosomal CBFB facilitates the aggressive phenotype, evasion of oxidative stress, and preferential predisposition to bone prometastatic factor of breast cancer progression. Dis Markers. 2022;2022:8446629. doi: 10.1155/2022/8446629 google scholar
• Zhang Z, Yamashita H, Toyama T, et al. NCOR1 mRNA is an independent prognostic factor for breast cancer. Cancer Lett. 2006;237(1):123-129. google scholar
• Mavaddat N, Barrowdale D, Andrulis IL, et al. Consortium of Investigators of Modifiers of BRCA1/2. Pathology of breast and ovarian cancers among BRCA1 and BRCA2 mutation car-riers: results from the Consortium of Investigators of Modi-fiers of BRCA1/2 (CIMBA). Cancer Epidemiol Biomarkers Prev. 2012;21(1):134-147. google scholar
• De Talhouet S, Peron J, Vuilleumier A, et al. Clini-cal outcome of breast cancer in carriers of BRCA1 and BRCA2 mutations according to molecular subtypes. Sci Rep. 202027;10(1):7073. doi:10.1038/s41598-020-63759-1 Erratum in: Sci Rep. 2020;10(1):19248. google scholar
• Lakhani SR, Reis-Filho JS, Fulford L, et al. Prediction of BRCA1 status in patients with breast cancer using estrogen receptor and basal phenotype. Clin Cancer Res. 2005;11:5175-5180. google scholar
• Armes JE, Trute L, White D, et al. Distinct molecular pathogeneses of early-onset breast cancers in BRCA1 and BRCA2 mutation carriers: a population-based study. Cancer Res. 1999;59:2011-2017. google scholar
• Palacios J, Honrado E, Osorio A, et al. Phenotypic characteriza-tion of BRCA1 and BRCA2 tumors based in a tissue microarray study with 37 immunohistochemical markers. Breast Cancer Res Treat. 2005;90:5-14. google scholar
• Lakhani SR, Van De Vijver MJ, Jacquemier J, et al. The pathology of familial breast cancer: predictive value of immunohistochemi-cal markers estrogen receptor, progesterone receptor, HER-2, and p53 in patients with mutations in BRCA1 and BRCA2. J Clin Oncol. 2002;20:2310-2318. google scholar
• Lehmann BD, Bauer JA, Chen X, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011;121(7):2750-2767. google scholar
• Sporikova Z, Koudelakova V, Trojanec R, Hajduch M. Genetic markers in triple-negative breast cancer. Clin Breast Cancer. 2018;18(5):e841-e850. doi: 10.1016/j.clbc.2018.07.023 google scholar

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