A 19‑miRNA Support Vector Machine classifier and a 6‑miRNA risk score system designed for ovarian cancer patients Corrigendum in /10.3892/or.2019.7385

Dong,Jingwei; Xu,Mingjun

doi:10.3892/or.2019.7108

A 19‑miRNA Support Vector Machine classifier and a 6‑miRNA risk score system designed for ovarian cancer patients

Corrigendum in: /10.3892/or.2019.7385

Authors:
- Jingwei Dong
- Mingjun Xu
View Affiliations

Affiliations: Department of Anesthesiology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing 100001, P.R. China
Published online on: April 10, 2019 https://doi.org/10.3892/or.2019.7108
Pages: 3233-3243
Copyright: © Dong et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Ovarian cancer (OC) is the most common gynecologic malignancy with high incidence and mortality. The present study aimed to develop approaches for determining the recurrence type and identify potential miRNA markers for OC prognosis. The miRNA expression profile of OC (the training set, including 390 samples with recurrence information) was downloaded from The Cancer Genome Atlas database. The validation sets GSE25204 and GSE27290 were obtained from the Gene Expression Omnibus database. Prescreening of clinical factors was conducted using the survival package, and the differentially expressed miRNAs (DE‑miRNAs) were identified using the limma package. Using the Caret package, the optimal miRNA set was selected to build a Support Vector Machine (SVM) classifier. The miRNAs and clinical factors independently related to prognosis were analyzed using the survival package, and the risk score system was constructed. Finally, the miRNA‑target regulatory network was built by Cytoscape software, and enrichment analysis was performed. There were 46 DE‑miRNAs between the recurrent and non‑recurrent samples. After the optimal 19‑miRNA set was selected for constructing the SVM classifier, 6 DE‑miRNAs (miR‑193b, miR‑211, miR‑218, miR‑505, miR‑508 and miR‑514) independently related to prognosis were further extracted to build the risk score system. The neoplasm cancer status was independently correlated with the prognosis and conducted with stratified analysis. Additionally, the target genes in the regulatory network were enriched in the regulation of actin cytoskeleton and the TGF‑β signaling pathway. The 6‑miRNA signature may serve as a potential biomarker for OC prognosis, particularlyfor recurrence.

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1	Moufarrij S, Dandapani M, Arthofer E, Gomez S, Srivastava A, Lopez-Acevedo M, Villagra A and Chiappinelli KB: Epigenetic therapy for ovarian cancer: Promise and progress. Clin Epigenetics. 11:72019. View Article : Google Scholar : PubMed/NCBI
2	Mcguire S: World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for research on cancer, WHO Press, 2015. Adv Nutr. 7:4182016. View Article : Google Scholar : PubMed/NCBI
3	Hanley GE, Mcalpine JN, Kwon JS and Mitchell G: Opportunistic salpingectomy for ovarian cancer prevention. Gynecol Oncol Res Pract. 2:52015. View Article : Google Scholar : PubMed/NCBI
4	Gibson SJ, Fleming GF, Temkin SM and Chase DM: The application and outcome of standard of care treatment in elderly women with ovarian cancer: A literature review over the last 10 years. Front Oncol. 6:632016. View Article : Google Scholar : PubMed/NCBI
5	Gov E, Kori M and Arga KY: Multiomics analysis of tumor microenvironment reveals Gata2 and miRNA-124-3p as potential novel biomarkers in ovarian cancer. OMICS. 21:603–615. 2017. View Article : Google Scholar : PubMed/NCBI
6	Wang Z, Ji G, Wu Q, Feng S, Zhao Y, Cao Z and Tao C: Integrated microarray meta-analysis identifies miRNA-27a as an oncogene in ovarian cancer by inhibiting FOXO1. Life Sci. 201:263–270. 2018. View Article : Google Scholar
7	Lv T, Song K, Zhang L, Li W, Chen Y, Diao Y, Yao Q and Liu P: MiRNA-34a decreases ovarian cancer cell proliferation and chemoresistance by targeting HDAC1. Biochem Cell Biol. 96:663–671. 2018. View Article : Google Scholar : PubMed/NCBI
8	Cheng Y, Ban R, Liu W, Wang H, Li S, Yue Z, Zhu G, Zhuan Y and Wang C: MiRNA-409-3p enhances cisplatin-sensitivity of ovarian cancer cells by blocking the autophagy mediated by Fip200. Oncol Res. Jan 2–2018.(Epub ahead of print). doi: 10.3727/096504017X15138991620238. View Article : Google Scholar
9	Koutsaki M, Libra M, Spandidos DA and Zaravinos A: The miR-200 family in ovarian cancer. Oncotarget. 8:66629–66640. 2017. View Article : Google Scholar : PubMed/NCBI
10	Hu X, Macdonald DM, Huettner PC, Feng Z, El Naqa IM, Schwarz JK, Mutch DG, Grigsby PW, Powell SN and Wang X: A miR-200 microRNA cluster as prognostic marker in advanced ovarian cancer. Gynecol Oncol. 114:457–464. 2009. View Article : Google Scholar : PubMed/NCBI
11	Hong F, Li Y, Xu Y and Zhu L: Prognostic significance of serum microRNA-221 expression in human epithelial ovarian cancer. J Int Med Res. 41:64–71. 2013. View Article : Google Scholar : PubMed/NCBI
12	Gao YC and Wu J: MicroRNA-200c and microRNA-141 as potential diagnostic and prognostic biomarkers for ovarian cancer. Tumor Biol. 36:4843–4850. 2015. View Article : Google Scholar
13	Wang S, Zhao X, Wang J, Wen Y, Zhang L, Wang D, Chen H, Chen Q and Xiang W: Upregulation of microRNA-203 is associated with advanced tumor progression and poor prognosis in epithelial ovarian cancer. Med Oncol. 30:6812013. View Article : Google Scholar : PubMed/NCBI
14	Xiaohong Z, Lichun F, Na X, Kejian Z, Xiaolan X and Shaosheng W: MiR-203 promotes the growth and migration of ovarian cancer cells by enhancing glycolytic pathway. Tumor Biol. 37:14989–14997. 2016. View Article : Google Scholar
15	Xu YZ, Xi QH, Ge WL and Zhang XQ: Identification of serum microRNA-21 as a biomarker for early detection and prognosis in human epithelial ovarian cancer. Asian Pac J Cancer Prev. 14:1057–1060. 2013. View Article : Google Scholar : PubMed/NCBI
16	Wilczyński M, Żytko E, Danielska J, Szymańska B, Dzieniecka M, Nowak M, Malinowski J, Owczarek D and Wilczyński JR: Clinical significance of miRNA-21, −103, −129, −150 in serous ovarian cancer. Arch Gynecol Obstet. 297:741–748. 2018. View Article : Google Scholar : PubMed/NCBI
17	Bagnoli M, De Cecco L, Granata A, Nicoletti R, Marchesi E, Alberti P, Valeri B, Libra M, Barbareschi M, Raspagliesi F, et al: Identification of a chrXq27.3 microRNA cluster associated with early relapse in advanced stage ovarian cancer patients. Oncotarget. 6:96432015. View Article : Google Scholar : PubMed/NCBI
18	Shih KK, Qin LX, Tanner EJ, Zhou Q, Bisogna M, Dao F, Olvera N, Viale A, Barakat RR and Levine DA: A microRNA survival signature (MiSS) for advanced ovarian cancer. Gynecol Oncol. 121:444–450. 2011. View Article : Google Scholar : PubMed/NCBI
19	Therneau TM: Survival analysis [R package survival version 2.41–3]. Technometrics. 46:111–112. 2015.
20	Stadler L, Welc A, Humer C and Jordan M: Optimizing R language execution via aggressive speculation. Symposium on Dynamic Languages. 84–95. 2016.
21	Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI
22	Wang L, Cao C, Ma Q, Zeng Q, Wang H, Cheng Z, Zhu G, Qi J, Ma H, Nian H, et al: RNA-seq analyses of multiple meristems of soybean: Novel and alternative transcripts, evolutionary and functional implications. BMC Plant Biol. 14:1692014. View Article : Google Scholar : PubMed/NCBI
23	Huang X, Zhang L, Wang B, Li F and Zhang Z: Feature clustering based support vector machine recursive feature elimination for gene selection. Appl Intelligence. 48:594–607. 2018. View Article : Google Scholar
24	Kumar A: Pre-processing and modelling using caret package in R. Int J Comput Appl. 181:39–42. 2018.
25	Daliri MR: Feature selection using binary particle swarm optimization and support vector machines for medical diagnosis. Biomed Tech. 57:395–402. 2012. View Article : Google Scholar
26	Meyer D: Support vector machines the interface to libsvm in package e1071. R News. 1:1–3. 2013.
27	Schröder MS, Culhane AC, Quackenbush J and Haibe-Kains B: survcomp: An R/BioconductoR package for performance assessment and comparison of survival models. Bioinformatics. 27:3206–3208. 2011. View Article : Google Scholar : PubMed/NCBI
28	Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez JC and Müller M: pROC: An open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 12:772011. View Article : Google Scholar : PubMed/NCBI
29	Li JH, Liu S, Zhou H, Qu LH and Yang JH: starBase v2.0: Decoding miRNA-ceRNA, miRNA-ncRNA and protein-RNA interaction networks from large-scale CLIP-Seq data. Nucleic Acids Res. 42:D92–D97. 2014. View Article : Google Scholar : PubMed/NCBI
30	Kohl M, Wiese S and Warscheid B: Cytoscape: Software for visualization and analysis of biological networks. Methods Mol Biol. 696:291–303. 2011. View Article : Google Scholar : PubMed/NCBI
31	Huang DW, Sherman BT, Tan Q, Kir J, Liu D, Bryant D, Guo Y, Stephens R, Baseler MW, Lane HC, et al: DAVID Bioinformatics Resources: Expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35:W169–W175. 2007. View Article : Google Scholar : PubMed/NCBI
32	Chen K, Liu MX, Mak SL, Yung MM, Leung TH, Xu D, Ngu SF, Chan KK, Yang H, Ngan HY, et al: Methylation-associated silencing of miR-193a-3p promotes ovarian cancer aggressiveness by targeting GRB7 and MAPK/ERK pathways. Theranostics. 8:423–436. 2018. View Article : Google Scholar : PubMed/NCBI
33	Zhang J, Qin J and Su Y: miR-193b-3p possesses anti-tumor activity in ovarian carcinoma cells by targeting p21-activated kinase 3. Biomed Pharmacother. 96:1275–1282. 2017. View Article : Google Scholar : PubMed/NCBI
34	Mitra AK, Chiang CY, Tiwari P, Tomar S, Watters KM, Peter ME and Lengyel E: Microenvironment-induced downregulation of miR-193b drives ovarian cancer metastasis. Oncogene. 34:5923–5932. 2015. View Article : Google Scholar : PubMed/NCBI
35	Li H, Xu Y, Qiu W, Zhao D and Zhang Y: Tissue miR-193b as a novel biomarker for patients with ovarian cancer. Med Sci Monit. 21:3929–3934. 2015. View Article : Google Scholar : PubMed/NCBI
36	Li N, Wang L, Tan G, Guo Z, Liu L, Yang M and He J: MicroRNA-218 inhibits proliferation and invasion in ovarian cancer by targeting Runx2. Oncotarget. 8:91530–91541. 2017.PubMed/NCBI
37	Ye Y, Gu B, Wang Y, Shen S and Huang W: E2F1-mediated MNX1-AS1-miR-218-5p-SEC61A1 feedback loop contributes to the progression of colon adenocarcinoma. J Cell Biochem. 120:6145–6153. 2019. View Article : Google Scholar : PubMed/NCBI
38	Xia B, Yang S, Liu T and Lou G: miR-211 suppresses epithelial ovarian cancer proliferation and cell-cycle progression by targeting Cyclin D1 and CDK6. Mol Cancer. 14:572015. View Article : Google Scholar : PubMed/NCBI
39	Tao F, Tian X, Ruan S, Shen M and Zhang Z: miR-211 sponges lncRNA MALAT1 to suppress tumor growth and progression through inhibiting PHF19 in ovarian carcinoma. FASEB J. fj201800495RR2018.PubMed/NCBI
40	Zhao H, Ding Y, Tie B, Sun ZF, Jiang JY, Zhao J, Lin X and Cui S: miRNA expression pattern associated with prognosis in elderly patients with advanced OPSC and OCC. Int J Oncol. 43:839–849. 2013. View Article : Google Scholar : PubMed/NCBI
41	Chan CK, Pan Y, Nyberg K, Marra MA, Lim EL, Jones SJ, Maar D, Gibb EA, Gunaratne PH, Robertson AG, et al: Tumour-suppressor microRNAs regulate ovarian cancer cell physical properties and invasive behaviour. Open Biol. 6:1602752016. View Article : Google Scholar : PubMed/NCBI
42	Hong L, Wang Y, Chen W and Yang S: MicroRNA-508 suppresses epithelial-mesenchymal transition, migration, and invasion of ovarian cancer cells through the MAPK1/ERK signaling pathway. J Cell Biochem. 119:7431–7440. 2011. View Article : Google Scholar
43	Xiao S, Zhang M, Liu C and Wang D: MiR-514 attenuates proliferation and increases chemoresistance by targeting ATP binding cassette subfamily in ovarian cancer. Mol Genet Genomics. May 11–2018.(Epub ahead of print). doi: 10.1007/s00438-018-1447-0. View Article : Google Scholar
44	Yin J, Lu K, Lin J, Wu L, Hildebrandt MA, Chang DW, Meyer L, Wu X and Liang D: Genetic variants in TGF-β pathway are associated with ovarian cancer Risk. PLoS One. 6:e255592011. View Article : Google Scholar : PubMed/NCBI
45	Matsumura N, Huang Z, Mori S, Baba T, Fujii S, Konishi I, Iversen ES, Berchuck A and Murphy SK: Epigenetic suppression of the TGF-beta pathway revealed by transcriptome profiling in ovarian cancer. Genome Res. 21:74–82. 2011. View Article : Google Scholar : PubMed/NCBI
46	Lee SH and Dominguez R: Regulation of actin cytoskeleton dynamics in cells. Mol Cells. 29:311–325. 2010. View Article : Google Scholar : PubMed/NCBI