[1]Deb B, Uddin A, Chakraborty S. miRNAs and ovarian cancer: An overview. J Cell Physiol. 2018; 233: 3846-3854. doi: 10.1002/jcp.26095.
[2]Elzek MA, Rodland KD. Proteomics of ovarian cancer: functional insights and clinical applications. Cancer Metastasis Rev. 2015; 34: 83-96. doi: 10.1007/s10555-014-9547-8.
[3]Emmings E, Mullany S, Chang Z, Landen CN, Jr Linder S, Bazzaro M. Targeting Mitochondria for Treatment of Chemoresistant Ovarian Cancer. Int J Mol Sci. 2019; 20: 229. doi: 10.3390/ijms20010229.
[4]Tassi RA, Gambino A, Ardighieri L, Bignotti E, Todeschini P, Romani C, et al. FXYD5 (Dysadherin) upregulation predicts shorter survival and reveals platinum resistance in high-grade serous ovarian cancer patients. Br J Cancer. 2019; 121: 584-592. doi: 10.1038/s41416-019-0553-z.
[5]Mou T, Zhu D, Wei X, Li T, Zheng D, Pu J, et al. Identification and interaction analysis of key genes and microRNAs in hepatocellular carcinoma by bioinformatics analysis. World J Surg Oncol. 2017;15: 63. doi: 10.1186/s12957-017-1127-2.
[6]Karnezis AN, Cho KR, Gilks CB, Pearce CL, Huntsman DG. The disparate origins of ovarian cancers: pathogenesis and prevention strategies. Nat Rev Cancer. 2017; 17: 65-74. doi: 10.1038/nrc.2016.113.
[7]Hennigs JK, Minner S, Tennstedt P, Löser R, Huland H, Klose H, et al. Subcellular Compartmentalization of Survivin is Associated with Biological Aggressiveness and Prognosis in Prostate Cancer. Sci Rep. 2020; 10: 3250. doi: 10.1038/s41598-020-60064-9.
[8]Hussain M, Adah D, Tariq M, Lu Y, Zhang J, Liu J. CXCL13/CXCR5 signaling axis in cancer. Life Sci. 2019; 227: 175-186. doi: 10.1016/j.lfs.2019.04.053.
[9]Wei Y, Lin C, Li H, Xu Z, Wang J, Li R, et al. CXCL13 expression is prognostic and predictive for postoperative adjuvant chemotherapy benefit in patients with gastric cancer. Cancer Immunol Immunother. 2018; 67: 261-269. doi: 10.1007/s00262-017-2083-y.
[10]Su TC, Chen CY, Tsai WC, Hsu HT, Yen HH, Sung WW, et al. Cytoplasmic, nuclear, and total PBK/TOPK expression is associated with prognosis in colorectal cancer patients: A retrospective analysis based on immunohistochemistry stain of tissue microarrays. PLoS One. 2018; 13: e0204866. doi: 10.1371/journal.pone.0204866.
[11]Chang CF, Chen SL, Sung WW, Hsieh MJ, Hsu HT, Chen LH, et al. PBK/TOPK Expression Predicts Prognosis in Oral Cancer. Int J Mol Sci. 2016; 17: 1007. doi: 10.3390/ijms17071007.
[12]Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002; 30: 207-10. doi: 10.1093/nar/30.1.207.
[13]Barrett T, Troup DB, Wilhite SE, Ledoux P, Rudnev D, Evangelista C, et al. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009; 37: D885-890. doi: 10.1093/nar/gkn764.
[14]Smyth GK, Speed T. Normalization of cDNA microarray data. Methods. 2003; 31: 265-73. doi: 10.1016/s1046-2023(03)00155-5.
[15]Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004; 3: Article3. doi: 10.2202/1544-6115.1027.
[16]Dessau RB, Pipper CB. [''R"--project for statistical computing]. Ugeskr Laeger. 2008;170:328-30.
[17]Leek JT, Johnson WE, Parker HS, Jaffe AE, Storey JD. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics. 2012; 28: 882-3. doi: 10.1093/bioinformatics/bts034.
[18]Hochberg Y, Benjamini Y. More powerful procedures for multiple significance testing. Stat Med. 1990; 9: 811-8. doi: 10.1002/sim.4780090710.
[19]Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25: 25-29. doi: 10.1038/75556.
[20]Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013; 41: D808-15. doi: 10.1093/nar/gks1094.
[21]Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol. 2011; 696:291-303. doi: 10.1007/978-1-60761-987-1_18.
[22]Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011; 27: 431-2. doi: 10.1093/bioinformatics/btq675.
[23]Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017; 45: W98-W102. doi: 10.1093/nar/gkx247.
[24]Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, et al. ONCOMINE: a cancer microarray database and integrated data-mining platform. Neoplasia. 2004; 6: 1-6. doi: 10.1016/s1476-5586(04)80047-2.
[25]Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012; 2: 401-404. doi: 10.1158/2159-8290.CD-12-0095.
[26]Hanzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013; 14: 7. doi: 10.1186/1471-2105-14-7.
[27]Liu W, Wang X. Prediction of functional microRNA targets by integrative modeling of microRNA binding and target expression data. Genome Biol. 2019; 20: 18. doi: 10.1186/s13059-019-1629-z.
[28]Chen Y, Wang X. miRDB: an online database for prediction of functional microRNA targets. Nucleic Acids Res. 2020; 48: D127-D131. doi: 10.1093/nar/gkz757.