Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2019. CA Cancer J. Clin. 69, 7–34 (2019).
Larsen, I., Møller, B., Johannesen, T. et al. Cancer registry of Norway. Cancer in Norway 2019—Cancer incidence, mortality, survival and prevalence in Norway. Tech. Rep., Cancer Registry of Norway (2020).
Dafni, U., Tsourti, Z. & Alatsathianos, I. Breast cancer statistics in the European union: Incidence and survival across European countries. Breast Care 14, 344–353 (2019).
Chang, J. T., Wang, F., Chapin, W. & Huang, R. S. Identification of micrornas as breast cancer prognosis markers through the cancer genome atlas. PLoS ONE 11, e0168284 (2016).
Adhami, M., Haghdoost, A. A., Sadeghi, B. & Afshar, R. M. Candidate miRNAs in human breast cancer biomarkers: A systematic review. Breast Cancer 25, 198–205 (2018).
Haakensen, V. D. et al. Subtype-specific micro-rna expression signatures in breast cancer progression. Int. J. Cancer 139, 1117–1128 (2016).
Volinia, S. et al. Breast cancer signatures for invasiveness and prognosis defined by deep sequencing of microrna. Proc. Natl. Acad. Sci. 109, 3024–3029 (2012).
Bavelloni, A. et al. Mirna-210: A current overview. Anticancer Res. 37, 6511–6521 (2017).
Shao, B. et al. Plasma micrornas predict chemoresistance in patients with metastatic breast cancer. Technol. Cancer Res. Treatm. 18, 1533033819828709 (2019).
Stepanenko, A., Vassetzky, Y. & Kavsan, V. Antagonistic functional duality of cancer genes. Gene 529, 199–207 (2013).
Rawlings-Goss, R. A., Campbell, M. C. & Tishkoff, S. A. Global population-specific variation in mirna associated with cancer risk and clinical biomarkers. BMC Med. Genom. 7, 1–14 (2014).
Guo, Y. et al. Statistical strategies for micrornaseq batch effect reduction. Transl. Cancer Res. 3, 260 (2014).
Del Vescovo, V., Meier, T., Inga, A., Denti, M. A. & Borlak, J. A cross-platform comparison of affymetrix and agilent microarrays reveals discordant mirna expression in lung tumors of c-raf transgenic mice. PLoS ONE 8, e78870 (2013).
Molinari, R. et al. Swag: A Wrapper Method for Sparse Learning 20–49 (Swiss Finance Institute Research Paper, 2020).
Nielsen, J. Systems biology of metabolism. Annu. Rev. Biochem. 86, 245–275 (2017).
Alon, U. Biological networks: The tinkerer as an engineer. Science 301, 1866–1867 (2003).
Di Carlo, S., Politano, G., Savino, A. & Benso, A. A systematic analysis of a mi-rna inter-pathway regulatory motif. J. Clin. Bioinform. 3, 1–14 (2013).
Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: A network-based approach to human disease. Nat. Rev. Genet. 12, 56–68 (2011).
Caruana, R., Niculescu-Mizil, A., Crew, G. & Ksikes, A. Ensemble selection from libraries of models. In Proceedings of the Twenty-first International Conference on Machine Learning (2004).
Whittingham, M. J., Stephens, P. A., Bradbury, R. B. & Freckleton, R. P. Why do we still use stepwise modelling in ecology and behaviour?. J. Anim. Ecol. 75, 1182–1189 (2006).
Guerrier, S. et al. A predictive based regression algorithm for gene network selection. Front. Genet. 7, 97 (2016).
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021). https://www.R-project.org/
Fushiki, T. Estimation of prediction error by using k-fold cross-validation. Stat. Comput. 21, 137–146 (2011).
Molinaro, A. M., Simon, R. & Pfeiffer, R. M. Prediction error estimation: A comparison of resampling methods. Bioinformatics 21, 3301–3307 (2005).
Bernau, C. et al. Cross-study validation for the assessment of prediction algorithms. Bioinformatics 30, i105–i112 (2014).
Vittinghoff, E., Glidden, D. V., Shiboski, S. C. & McCulloch, C. E. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models (Springer, 2011).
Cox, D. Conditional and marginal association for binary random variables. Biometrika 90, 982–984 (2003).
Yule, G. U. Notes on the theory of association of attributes in statistics. Biometrika 2, 121–134 (1903).
Boehm, L., Reich, B. J. & Bandyopadhyay, D. Bridging conditional and marginal inference for spatially referenced binary data. Biometrics 69, 545–554 (2013).
Zardavas, D., Irrthum, A., Swanton, C. & Piccart, M. Clinical management of breast cancer heterogeneity. Nat. Rev. Clin. Oncol. 12, 381 (2015).
De Saussure, F. Course in General Linguistics (Columbia University Press, 2011).
Chandler, D. Semiotics: The Basics (Taylor & Francis, 2017).
Wang, H., Lengerich, B. J., Aragam, B. & Xing, E. P. Precision lasso: Accounting for correlations and linear dependencies in high-dimensional genomic data. Bioinformatics 35, 1181–1187 (2019).
Parikh, R., Mathai, A., Parikh, S., Sekhar, G. C. & Thomas, R. Understanding and using sensitivity, specificity and predictive values. Indian J. Ophthalmol. 56, 45 (2008).
Peduzzi, P., Concato, J., Kemper, E., Holford, T. R. & Feinstein, A. R. A simulation study of the number of events per variable in logistic regression analysis. J. Clin. Epidemiol. 49, 1373–1379 (1996).
Vittinghoff, E. & McCulloch, C. E. Relaxing the rule of ten events per variable in logistic and cox regression. Am. J. Epidemiol. 165, 710–718 (2007).
Austin, P. C. & Steyerberg, E. W. Events per variable (epv) and the relative performance of different strategies for estimating the out-of-sample validity of logistic regression models. Stat. Methods Med. Res. 26, 796–808 (2017).
Song, L., Langfelder, P. & Horvath, S. Comparison of co-expression measures: Mutual information, correlation, and model based indices. BMC Bioinform. 13, 1–21 (2012).
Cun, J. & Yang, Q. Bioinformatics-based interaction analysis of mir-92a-3p and key genes in tamoxifen-resistant breast cancer cells. Biomed. Pharmacother. 107, 117–128 (2018).
Jinghua, H. et al. Microrna mir-92a-3p regulates breast cancer cell proliferation and metastasis via regulating b-cell translocation gene 2 (btg2). Bioengineered 12, 2033–2044 (2021).
Nilsson, S. et al. Downregulation of mir-92a is associated with aggressive breast cancer features and increased tumour macrophage infiltration. PLoS ONE 7, e36051 (2012).
Cava, C. et al. Integration of mrna expression profile, copy number alterations, and microrna expression levels in breast cancer to improve grade definition. PLoS ONE 9, e97681 (2014).
Maltseva, D. V. et al. Mirnome of inflammatory breast cancer. BMC Res. Notes 7, 1–10 (2014).
Li, J. et al. Long non-coding rna snhg1 activates hoxa1 expression via sponging mir-193a-5p in breast cancer progression. Aging 12, 10223–10234 (2020).
Yang, S.-J. et al. The mir-30 family: Versatile players in breast cancer. Tumor Biol. 39, 1010428317692204 (2017).
Song, C. et al. mir-200c inhibits breast cancer proliferation by targeting kras. Oncotarget 6, 34968 (2015).
Cochrane, D. R., Howe, E. N., Spoelstra, N. S. & Richer, J. K. Loss of mir-200c: A marker of aggressiveness and chemoresistance in female reproductive cancers. J. Oncol. 2010, 1–10 (2010).
Tfaily, M. A. et al. mirna expression in advanced Algerian breast cancer tissues. PLoS ONE 15, e0227928 (2020).
Hayes, J., Peruzzi, P. P. & Lawler, S. Micrornas in cancer: Biomarkers, functions and therapy. Trends Mol. Med. 20, 460–469 (2014).
Kozomara, A., Birgaoanu, M. & Griffiths-Jones, S. Mirbase: From microrna sequences to function. Nucleic Acids Res. 47, D155–D162 (2019).
Kozomara, A. & Griffiths-Jones, S. Mirbase: Annotating high confidence micrornas using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014).
Kozomara, A. & Griffiths-Jones, S. mirbase: Integrating microrna annotation and deep-sequencing data. Nucleic Acids Res. 39, D152–D157 (2010).
Griffiths-Jones, S., Saini, H. K., Van Dongen, S. & Enright, A. J. Mirbase: Tools for microrna genomics. Nucleic Acids Res. 36, D154–D158 (2007).
Griffiths-Jones, S., Grocock, R. J., Van Dongen, S., Bateman, A. & Enright, A. J. Mirbase: Microrna sequences, targets and gene nomenclature. Nucleic Acids Res. 34, D140–D144 (2006).
Griffiths-Jones, S. The microrna registry. Nucleic Acids Res. 32, D109–D111 (2004).
Agarwal, V., Bell, G. W., Nam, J.-W. & Bartel, D. P. Predicting effective microrna target sites in mammalian mrnas. Elife 4, e05005 (2015).
Chiang, H. R. et al. Mammalian micrornas: Experimental evaluation of novel and previously annotated genes. Genes Dev. 24, 992–1009 (2010).
Chen, Y. & Wang, X. mirdb: An online database for prediction of functional microrna targets. Nucleic Acids Res. 48, D127–D131 (2020).
Luo, K. et al. Usp49 negatively regulates tumorigenesis and chemoresistance through fkbp51-akt signaling. EMBO J. 36, 1434–1446 (2017).
Liu, J. et al. Targeting the brd4/foxo3a/cdk6 axis sensitizes akt inhibition in luminal breast cancer. Nat. Commun. 9, 1–17 (2018).
Lin, S. et al. Comprehensive analysis of the value of rab family genes in prognosis of breast invasive carcinoma. Biosci. Rep. 40, 1–10 (2020).
Martínez-Ramírez, A., Garay, E., García-Carrancá, A. & Vázquez-Cuevas, F. G. The p2ry2 receptor induces carcinoma cell migration and emt through cross-talk with epidermal growth factor receptor. J. Cell. Biochem. 117, 1016–1026 (2016).
Gay-Bellile, M. et al. Ercc1 and telomere status in breast tumours treated with neoadjuvant chemotherapy and their association with patient prognosis. J. Pathol. Clin. Res. 2, 234–246 (2016).
Pan, D. et al. A major chromatin regulator determines resistance of tumor cells to t cell-mediated killing. Science 359, 770–775 (2018).
Smart, S. K., Vasileiadi, E., Wang, X., DeRyckere, D. & Graham, D. K. The emerging role of tyro3 as a therapeutic target in cancer. Cancers 10, 474 (2018).
Liu, Y. et al. Eif5a2 is a novel chemoresistance gene in breast cancer. Breast Cancer 22, 602–607 (2015).
Aure, M. R. et al. Integrated analysis reveals microrna networks coordinately expressed with key proteins in breast cancer. Genome Med. 7, 1–17 (2015).
Wallden, B. et al. Development and verification of the pam50-based prosigna breast cancer gene signature assay. BMC Med. Genom. 8, 1–14 (2015).
Wong, A. K., Sealfon, R. S., Theesfeld, C. L. & Troyanskaya, O. G. Decoding disease: From genomes to networks to phenotypes. Nat. Rev. Genet. 22, 774–790 (2021).
Claeskens, G. et al. Model Selection and Model Averaging (Cambridge Books, 2008).
Sagi, O. & Rokach, L. Ensemble learning: A survey. Wiley Interdiscipl. Rev. 8, e1249 (2018).
Nakagawa, H. & Fujita, M. Whole genome sequencing analysis for cancer genomics and precision medicine. Cancer Sci. 109, 513–522 (2018).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Kantarjian, H. & Yu, P. P. Artificial intelligence, big data, and cancer. JAMA Oncol. 1, 573–574 (2015).
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