| [1] |
Giaquinto AN, Sung H, Miller KD, Kramer JL, Newman LA, et al. 2022. Breast cancer statistics, 2022. CA: A Cancer Journal for Clinicians 72:524−41 doi: 10.3322/caac.21754 |
| [2] |
Van De Voorde L, Speeckaert R, Van Gestel D, Bracke M, De Neve W, et al. 2012. DNA methylation-based biomarkers in serum of patients with breast cancer. Mutation Research 751:304−25 doi: 10.1016/j.mrrev.2012.06.001 |
| [3] |
Akulenko R, Helms V. 2013. DNA co-methylation analysis suggests novel functional associations between gene pairs in breast cancer samples. Human Molecular Genetics 22:3016−22 doi: 10.1093/hmg/DDT158 |
| [4] |
Zhang J, Huang K. 2017. Pan-cancer analysis of frequent DNA co-methylation patterns reveals consistent epigenetic landscape changes in multiple cancers. BMC Genomics 18:1045 doi: 10.1186/s12864-016-3259-0 |
| [5] |
Sun W, Yang J. 2010. Functional mechanisms for human tumor suppressors. Journal of Cancer 1:136−40 doi: 10.7150/jca.1.136 |
| [6] |
Yang X, Yan L, Davidson NE. 2001. DNA methylation in breast cancer. Endocrine-Related Cancer 8:115−27 doi: 10.1677/erc.0.0080115 |
| [7] |
Wang LH, Wu CF, Rajasekaran N, Shin YK. 2018. Loss of tumor suppressor gene function in human cancer: an overview. Cellular Physiology and Biochemistry 51:2647−93 doi: 10.1159/000495956 |
| [8] |
Arslan S, Dogan T, Koksal B, Yildirim ME, Gumus C, et al. 2008. Tumoral tissue specific promoter hypermethylation of distinct tumor suppressor genes in a case with nonsmall cell lung carcinoma: a case report. Lung India 25:148−51 doi: 10.4103/0970-2113.45279 |
| [9] |
Cul'bová M, Lasabová Z, Stanclová A, Tilandyová P, Zúbor P, et al. 2011. Methylation of selected tumor-supressor genes in benign and malignant ovarian tumors. Ceska Gynekologie 76:274−79 |
| [10] |
Tawe L, Grover S, Zetola N, Robertson ES, Gaseitsiwe S, et al. 2021. Promoter hypermethylation analysis of host genes in cervical cancer patients with and without human immunodeficiency virus in Botswana. Frontiers in Oncology 11:560296 doi: 10.3389/fonc.2021.560296 |
| [11] |
Wang LQ, Chim CS. 2015. DNA methylation of tumor-suppressor miRNA genes in chronic lymphocytic leukemia. Epigenomics 7:461−73 doi: 10.2217/epi.15.6 |
| [12] |
TCGA GDC Data Portal. n.d. https://portal.gdc.cancer.gov |
| [13] |
Tian S, Bertelsmann K, Yu L, Sun S. 2016. DNA methylation heterogeneity patterns in breast cancer cell lines. Cancer Informatics 15:1−9 doi: 10.4137/CIN.S40300 |
| [14] |
Zhao M, Kim P, Mitra R, Zhao J, Zhao Z. 2016. TSGene 2.0: an updated literature-based knowledgebase for tumor suppressor genes. Nucleic Acids Research 44:D1023−D1031 doi: 10.1093/nar/gkv1268 |
| [15] |
Eisenberg E, Levanon EY. 2013. Human housekeeping genes, revisited. Trends in Genetics 29:569−74 doi: 10.1016/j.tig.2013.05.010 |
| [16] |
Du P, Zhang X, Huang CC, Jafari N, Kibbe WA, et al. 2010. Comparison of Beta-value and M-value methods for quantifying methylation levels by microarray analysis. BMC Bioinformatics 11:587 doi: 10.1186/1471-2105-11-587 |
| [17] |
Sun S, Dammann J, Lai P, Tian C. 2022. Thorough statistical analyses of breast cancer co-methylation patterns. BMC Genomic Data 23:29 doi: 10.1186/s12863-022-01046-w |
| [18] |
Tang G, Pan H, Xu L, Feng R, Jiang Y, et al. 2019. A comparison of co-methylation relationships between rheumatoid arthritis and Parkinson's disease. Frontiers in Neuroscience 12:1001 doi: 10.3389/fnins.2018.01001 |
| [19] |
Kamburov A, Pentchev K, Galicka H, Wierling C, Lehrach H, et al. 2011. ConsensusPathDB: toward a more complete picture of cell biology. Nucleic Acids Research 39:D712−D717 doi: 10.1093/nar/gkq1156 |
| [20] |
Kamburov A, Stelzl U, Lehrach H, Herwig R. 2013. The ConsensusPathDB interaction database: 2013 update. Nucleic Acids Research 41:D793−D800 doi: 10.1093/nar/gks1055 |
| [21] |
Kamburov A, Wierling C, Lehrach H, Herwig R. 2009. ConsensusPathDB — a database for integrating human functional interaction networks. Nucleic Acids Research 37:D623−D628 doi: 10.1093/nar/gkn698 |
| [22] |
Dustin D, Gu G, Fuqua SAW. 2019. ESR1 mutations in breast cancer. Cancer 125:3714−28 doi: 10.1002/cncr.32345 |
| [23] |
Xia X, Yin W, Zhang X, Yu X, Wang C, et al. 2015. PAX6 overexpression is associated with the poor prognosis of invasive ductal breast cancer. Oncology Letters 10:1501−6 doi: 10.3892/ol.2015.3434 |
| [24] |
Han W, Cao F, Gao XJ, Wang HB, Chen F, et al. 2018. ZIC1 acts a tumor suppressor in breast cancer by targeting survivin. International Journal of Oncology 53:937−48 doi: 10.3892/ijo.2018.4450 |
| [25] |
Yao J, Xu F, Zhang D, Yi W, Chen X, et al. 2018. TP73-AS1 promotes breast cancer cell proliferation through miR-200a-mediated TFAM inhibition. Journal of Cellular Biochemistry 119:680−90 doi: 10.1002/jcb.26231 |
| [26] |
Xie W, Sun Y, Zeng Y, Hu L, Zhi J, et al. 2022. Comprehensive analysis of PPPCs family reveals the clinical significance of PPP1CA and PPP4C in breast cancer. Bioengineered 13:190−205 doi: 10.1080/21655979.2021.2012316 |
| [27] |
Schwartz S, Mumbach MR, Jovanovic M, Wang T, Maciag K, et al. 2014. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5' sites. Cell Reports 8:284−96 doi: 10.1016/j.celrep.2014.05.048 |
| [28] |
Yue Y, Liu J, Cui X, Cao J, Luo G, et al. 2018. VIRMA mediates preferential m6A mRNA methylation in 3'UTR and near stop codon and associates with alternative polyadenylation. Cell Discovery 4:10 doi: 10.1038/s41421-018-0019-0 |
| [29] |
Gu J, Chen Z, Chen X, Wang Z. 2020. Heterogeneous nuclear ribonucleoprotein (hnRNPL) in cancer. Clinica Chimica Acta 507:286−94 doi: 10.1016/j.cca.2020.04.040 |
| [30] |
Tang Q, Holland-Letz T, Slynko A, Cuk K, Marme F, et al. 2016. DNA methylation array analysis identifies breast cancer associated RPTOR, MGRN1 and RAPSN hypomethylation in peripheral blood DNA. Oncotarget 7:64191−202 doi: 10.18632/oncotarget.11640 |
| [31] |
Hsieh TH, Hsu CY, Tsai CF, Long CY, Chai CY, et al. 2015. miR-125a-5p is a prognostic biomarker that targets HDAC4 to suppress breast tumorigenesis. Oncotarget 6:494−509 doi: 10.18632/oncotarget.2674 |
| [32] |
Alday-Parejo B, Richard F, Wörthmüller J, Rau T, Galván JA, et al. 2020. MAGI1, a new potential tumor suppressor gene in estrogen receptor positive breast cancer. Cancers 12:223 doi: 10.3390/cancers12010223 |
| [33] |
Rousselet GA, Pernet CR. 2012. Improving standards in brain-behavior correlation analyses. Frontiers in Human Neuroscience 6:119 doi: 10.3389/fnhum.2012.00119 |
| [34] |
de Winter JCF, Gosling SD, Potter J. 2016. Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: a tutorial using simulations and empirical data. Psychological Methods 21:273−90 doi: 10.1037/met0000079 |
| [35] |
Puth MT, Neuhäuser M, Ruxton GD. 2014. Effective use of Pearson's product-moment correlation coefficient. Animal Behaviour 93:183−89 doi: 10.1016/j.anbehav.2014.05.003 |
| [36] |
Puth MT, Neuhäuser M, Ruxton GD. 2015. Effective use of Spearman's and Kendall's correlation coefficients for association between two measured traits. Animal Behaviour 102:77−84 doi: 10.1016/j.anbehav.2015.01.010 |
| [37] |
Cao YN, Li QZ, Liu YX, Jin W, Hou R. 2022. Discovering the key genes and important DNA methylation regions in breast cancer. Hereditas 159:7 doi: 10.1186/s41065-022-00220-5 |
| [38] |
Dennis G, Jr., Sherman BT, Hosack DA, Yang J, Gao W, et al. 2003. DAVID: database for annotation, visualization, and integrated discovery. Genome Biology 4:P3 |
| [39] |
Huang DW, Sherman BT, Stephens R, Baseler MW, Lane HC, Lempicki RA. 2008. DAVID gene ID conversion tool. Bioinformation 2:428−30 doi: 10.6026/97320630002428 |
| [40] |
Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, et al. 2007. The DAVID gene functional classification tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biology 8:R183 doi: 10.1186/gb-2007-8-9-r183 |
| [41] |
Huang DW, Sherman BT, Tan Q, Kir J, Liu D, et al. 2007. DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Research 35:W169−W175 doi: 10.1093/nar/gkm415 |
| [42] |
Jiao X, Sherman BT, Huang DW, Stephens R, Baseler MW, et al. 2012. DAVID-WS: a stateful web service to facilitate gene/protein list analysis. Bioinformatics 28:1805−6 doi: 10.1093/bioinformatics/bts251 |
| [43] |
Sherman BT, Hao M, Qiu J, Jiao X, Baseler MW, et al. 2022. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Research 50:W216−W221 doi: 10.1093/nar/gkac194 |
| [44] |
Sherman BT, Huang DW, Tan Q, Guo Y, Bour S, et al. 2007. DAVID Knowledgebase: a gene-centered database integrating heterogeneous gene annotation resources to facilitate high-throughput gene functional analysis. BMC Bioinformatics 8:426 doi: 10.1186/1471-2105-8-426 |
| [45] |
Haney S, Kam M, Hrebien L. 2008. Benefits of Using Paired Controls for Analyzing Gene Expression of Prostate Cancer. 2008 8 th IEEE International Conference on BioInformatics and BioEngineering, Athens, Greece, 8−10 October 2008. USA: IEEE. doi: 10.1109/BIBE.2008.4696742 |
| [46] |
Stevens JR, Herrick JS, Wolff RK, Slattery ML. 2018. Power in pairs: assessing the statistical value of paired samples in tests for differential expression. BMC Genomics 19:953 doi: 10.1186/s12864-018-5236-2 |
| [47] |
Teer JK, Zhang Y, Chen L, Welsh EA, Douglas Cress W, et al. 2017. Evaluating somatic tumor mutation detection without matched normal samples. Human Genomics 11:22 doi: 10.1186/s40246-017-0118-2 |
| [48] |
Sun S, Yu X. 2016. HMM-Fisher: identifying differential methylation using a hidden Markov model and Fisher's exact test. Statistical Applications in Genetics and Molecular Biology 15:55−67 doi: 10.1515/sagmb-2015-0076 |
| [49] |
Yu X, Sun S. 2016. HMM-DM: identifying differentially methylated regions using a hidden Markov model. Statistical Applications in Genetics and Molecular Biology 15:69−81 doi: 10.1515/sagmb-2015-0077 |