Low expression of KCNN3 may affect drug resistance in ovarian cancer

Liu,Xia; Wei,Luwei; Zhao,Bingbing; Cai,Xiangxue; Dong,Caihua; Yin,Fuqiang

doi:10.3892/mmr.2018.9107

Low expression of KCNN3 may affect drug resistance in ovarian cancer

Authors:
- Xia Liu
- Luwei Wei
- Bingbing Zhao
- Xiangxue Cai
- Caihua Dong
- Fuqiang Yin
View Affiliations

Affiliations: Key Laboratory of Longevity and Ageing‑Related Disease of Chinese Ministry of Education, Centre for Translational Medicine and School of Preclinical Medicine, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China, Department of Gynecologic Oncology, The Affiliated Tumor Hospital, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China, Life Sciences Institute, Guangxi Medical University, Nanning, Guangxi 530021, P.R. China
Published online on: May 31, 2018 https://doi.org/10.3892/mmr.2018.9107
Pages: 1377-1386
Copyright: © Liu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Drug resistance is a principal contributor to the poor prognosis of ovarian cancer (OC). Therefore, identifying factors that affect drug resistance in OC is critical. In the present study, 51 OC specimens from lab collections were immunohistochemically tested, public data for 489 samples from The Cancer Genome Atlas cohort and 1,656 samples from the Kaplan‑Meier Plotter were downloaded, and data were retrieved from Oncomine. It was identified that the mRNA and protein expression of the potassium calcium‑activated channel subfamily N member 3 (KCNN3) was markedly lower in OC tissues compared with normal tissues, and in drug‑resistant OC tissues compared with sensitive OC tissues. Low KCNN3 expression consistently predicted shorter disease‑free and overall survival (OS). Specifically, low KCNN3 expression predicted shorter OS in 395 patients with low expression levels of mucin‑16. There was additional evidence that KCNN3 expression is mediated by microRNA‑892b. Furthermore, text mining and analyses of protein and gene interactions indicated that KCNN3 affects drug resistance. To the best of the authors' knowledge, this is the first report to associate KCNN3 with poor prognosis and drug resistance in OC. The present findings indicated that KCNN3 is a potential prognostic marker and therapeutic target for OC.

View Figures

View References

1	Siegel RL, Miller KD and Jemal A: Cancer statistics, 2017. CA Cancer J Clin. 67:7–30. 2017. View Article : Google Scholar : PubMed/NCBI
2	Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China, 2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI
3	Cooke SL and Brenton JD: Evolution of platinum resistance in high-grade serous ovarian cancer. Lancet Oncol. 12:1169–1174. 2011. View Article : Google Scholar : PubMed/NCBI
4	Miller DS, Blessing JA, Krasner CN, Mannel RS, Hanjani P, Pearl ML, Waggoner SE and Boardman CH: Phase II evaluation of pemetrexed in the treatment of recurrent or persistent platinum-resistant ovarian or primary peritoneal carcinoma: A study of the Gynecologic Oncology Group. J Clin Oncol. 27:2686–2691. 2009. View Article : Google Scholar : PubMed/NCBI
5	Pfisterer J and Ledermann JA: Management of platinum-sensitive recurrent ovarian cancer. Semin Oncol. 33 2 Suppl:S12–S16. 2006. View Article : Google Scholar : PubMed/NCBI
6	Siegel R, Ma J, Zou Z and Jemal A: Cancer statistics, 2014. CA Cancer J Clin. 64:9–29. 2014. View Article : Google Scholar : PubMed/NCBI
7	Holohan C, Van Schaeybroeck S, Longley DB and Johnston PG: Cancer drug resistance: An evolving paradigm. Nat Rev Cancer. 13:714–726. 2013. View Article : Google Scholar : PubMed/NCBI
8	Arcangeli A, Crociani O, Lastraioli E, Masi A, Pillozzi S and Becchetti A: Targeting ion channels in cancer: A novel frontier in antineoplastic therapy. Curr Med Chem. 16:66–93. 2009. View Article : Google Scholar : PubMed/NCBI
9	Cuddapah VA and Sontheimer H: Ion channels and transporters [corrected] in cancer. 2. Ion channels and the control of cancer cell migration. Am J Physiol Cell Physiol. 301:C541–C549. 2011. View Article : Google Scholar : PubMed/NCBI
10	Prevarskaya N, Skryma R and Shuba Y: Ion channels and the hallmarks of cancer. Trends Mol Med. 16:107–121. 2010. View Article : Google Scholar : PubMed/NCBI
11	D'Amico M, Gasparoli L and Arcangeli A: Potassium channels: Novel emerging biomarkers and targets for therapy in cancer. Recent Pat Anticancer Drug Discov. 8:53–65. 2013. View Article : Google Scholar : PubMed/NCBI
12	Pardo LA and Stühmer W: The roles of K(+) channels in cancer. Nat Rev Cancer. 14:39–48. 2014. View Article : Google Scholar : PubMed/NCBI
13	Innamaa A, Jackson L, Asher V, Van Shalkwyk G, Warren A, Hay D, Bali A, Sowter H and Khan R: Expression and prognostic significance of the oncogenic K2P potassium channel KCNK9 (TASK-3) in ovarian carcinoma. Anticancer Res. 33:1401–1408. 2013.PubMed/NCBI
14	Asher V, Khan R, Warren A, Shaw R, Schalkwyk GV, Bali A and Sowter HM: The Eag potassium channel as a new prognostic marker in ovarian cancer. Diagn Pathol. 5:782010. View Article : Google Scholar : PubMed/NCBI
15	Cicek MS, Koestler DC, Fridley BL, Kalli KR, Armasu SM, Larson MC, Wang C, Winham SJ, Vierkant RA, Rider DN, et al: Epigenome-wide ovarian cancer analysis identifies a methylation profile differentiating clear-cell histology with epigenetic silencing of the HERG K⁺ channel. Hum Mol Genet. 22:3038–3047. 2013. View Article : Google Scholar : PubMed/NCBI
16	Pillozzi S, Masselli M, De Lorenzo E, Accordi B, Cilia E, Crociani O, Amedei A, Veltroni M, D'Amico M, Basso G, et al: Chemotherapy resistance in acute lymphoblastic leukemia requires hERG1 channels and is overcome by hERG1 blockers. Blood. 117:902–914. 2011. View Article : Google Scholar : PubMed/NCBI
17	Köhler M, Hirschberg B, Bond CT, Kinzie JM, Marrion NV, Maylie J and Adelman JP: Small-conductance, calcium-activated potassium channels from mammalian brain. Science. 273:1709–1714. 1996. View Article : Google Scholar : PubMed/NCBI
18	Chantome A, Girault A, Potier M, Collin C, Vaudin P, Pagès JC, Vandier C and Joulin V: KCa2.3 channel-dependent hyperpolarization increases melanoma cell motility. Exp Cell Res. 315:3620–3630. 2009. View Article : Google Scholar : PubMed/NCBI
19	Potier M, Joulin V, Roger S, Besson P, Jourdan ML, Leguennec JY, Bougnoux P and Vandier C: Identification of SK3 channel as a new mediator of breast cancer cell migration. Mol Cancer Ther. 5:2946–2953. 2006. View Article : Google Scholar : PubMed/NCBI
20	Jelassi B, Chantome A, Alcaraz-Pérez F, Baroja-Mazo A, Cayuela ML, Pelegrin P, Surprenant A and Roger S: P2X(7) receptor activation enhances SK3 channels- and cystein cathepsin-dependent cancer cells invasiveness. Oncogene. 30:2108–2122. 2011. View Article : Google Scholar : PubMed/NCBI
21	Gueguinou M, Harnois T, Crottes D, Uguen A, Deliot N, Gambade A, Chantôme A, Haelters JP, Jaffrès PA, Jourdan ML, et al: SK3/TRPC1/Orai1 complex regulates SOCE-dependent colon cancer cell migration: A novel opportunity to modulate anti-EGFR mAb action by the alkyl-lipid Ohmline. Oncotarget. 7:36168–36184. 2016. View Article : Google Scholar : PubMed/NCBI
22	Li X, Pennisi A, Zhan F, Sawyer JR, Shaughnessy JD and Yaccoby S: Establishment and exploitation of hyperdiploid and non-hyperdiploid human myeloma cell lines. Br J Haematol. 138:802–811. 2007. View Article : Google Scholar : PubMed/NCBI
23	Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P, et al: Oncomine 3.0: Genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia. 9:166–180. 2007. View Article : Google Scholar : PubMed/NCBI
24	Rhodes DR, Yu J, Shanker K, Deshpande N, Varambally R, Ghosh D, Barrette T, Pandey A and Chinnaiyan AM: ONCOMINE: A cancer microarray database and integrated data-mining platform. Neoplasia. 6:1–6. 2004. View Article : Google Scholar : PubMed/NCBI
25	Cancer Genome Atlas Research Network: Integrated genomic analyses of ovarian carcinoma. Nature. 474:609–615. 2011. View Article : Google Scholar : PubMed/NCBI
26	Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, Jacobsen A, Byrne CJ, Heuer ML, Larsson E, et al: The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2:401–404. 2012. View Article : Google Scholar : PubMed/NCBI
27	Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E, et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci Signal. 6:pl12013. View Article : Google Scholar : PubMed/NCBI
28	Gyorffy B, Lánczky A and Szállási Z: Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients. Endocr Relat Cancer. 19:197–208. 2012. View Article : Google Scholar : PubMed/NCBI
29	Remmele W and Stegner HE: Recommendation for uniform definition of an immunoreactive score (IRS) for immunohistochemical estrogen receptor detection (ER-ICA) in breast cancer tissue. Pathologe. 8:138–140. 1987.PubMed/NCBI
30	Jenssen TK, Laegreid A, Komorowski J and Hovig E: A literature network of human genes for high-throughput analysis of gene expression. Nat Genet. 28:21–28. 2001. View Article : Google Scholar : PubMed/NCBI
31	Mostafavi S, Ray D, Warde-Farley D, Grouios C and Morris Q: GeneMANIA: A real-time multiple association network integration algorithm for predicting gene function. Genome Biol. 9 Suppl 1:S42008. View Article : Google Scholar : PubMed/NCBI
32	Warde-Farley D, Donaldson SL, Comes O, Zuberi K, Badrawi R, Chao P, Franz M, Grouios C, Kazi F, Lopes CT, et al: The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 38:W214–W220. 2010. View Article : Google Scholar : PubMed/NCBI
33	Lu TP, Lee CY, Tsai MH, Chiu YC, Hsiao CK, Lai LC and Chuang EY: miRSystem: An integrated system for characterizing enriched functions and pathways of microRNA targets. PLoS One. 7:e423902012. View Article : Google Scholar : PubMed/NCBI
34	Hedditch EL, Gao B, Russell AJ, Lu Y, Emmanuel C, Beesley J, Johnatty SE, Chen X, Harnett P, George J, et al: ABCA transporter gene expression and poor outcome in epithelial ovarian cancer. J Natl Cancer Inst. 106:pii: dju149. 2014. View Article : Google Scholar : PubMed/NCBI
35	Liu X, Gao Y, Lu Y, Zhang J, Li L and Yin F: Oncogenes associated with drug resistance in ovarian cancer. J Cancer Res Clin Oncol. 141:381–395. 2015. View Article : Google Scholar : PubMed/NCBI
36	Yin F, Liu X, Li D, Wang Q, Zhang W and Li L: Tumor suppressor genes associated with drug resistance in ovarian cancer (review). Oncol Rep. 30:3–10. 2013. View Article : Google Scholar : PubMed/NCBI
37	Yin F, Liu X, Li D, Wang Q, Zhang W and Li L: Bioinformatic analysis of chemokine (C-C motif) ligand 21 and SPARC-like protein 1 revealing their associations with drug resistance in ovarian cancer. Int J Oncol. 42:1305–1316. 2013. View Article : Google Scholar : PubMed/NCBI
38	Yin F, Liu L, Liu X, Li G, Zheng L, Li D, Wang Q, Zhang W and Li L: Downregulation of tumor suppressor gene ribonuclease T2 and gametogenetin binding protein 2 is associated with drug resistance in ovarian cancer. Oncol Rep. 32:362–372. 2014. View Article : Google Scholar : PubMed/NCBI
39	Liu X, Gao Y, Lu Y, Zhang J, Li L and Yin F: Upregulation of NEK2 is associated with drug resistance in ovarian cancer. Oncol Rep. 31:745–754. 2014. View Article : Google Scholar : PubMed/NCBI
40	Liu X, Gao Y, Lu Y, Zhang J, Li L and Yin F: Downregulation of NEK11 is associated with drug resistance in ovarian cancer. Int J Oncol. 45:1266–1274. 2014. View Article : Google Scholar : PubMed/NCBI
41	Liu X, Gao Y, Zhao B, Li X, Lu Y, Zhang J, Li D, Li L and Yin F: Discovery of microarray-identified genes associated with ovarian cancer progression. Int J Oncol. 46:2467–2478. 2015. View Article : Google Scholar : PubMed/NCBI
42	Liu X, Zou J, Su J, Lu Y, Zhang J, Li L and Yin F: Downregulation of transient receptor potential cation channel, subfamily C, member 1 contributes to drug resistance and high histological grade in ovarian cancer. Int J Oncol. 48:243–252. 2016. View Article : Google Scholar : PubMed/NCBI
43	Sparks R, Lau WW and Tsang JS: Expanding the immunology toolbox: Embracing public-data reuse and crowdsourcing. Immunity. 45:1191–1204. 2016. View Article : Google Scholar : PubMed/NCBI
44	Grechkin M, Poon H and Howe B: Wide-Open: Accelerating public data release by automating detection of overdue datasets. PLoS Biol. 15:e20024772017. View Article : Google Scholar : PubMed/NCBI
45	Rung J and Brazma A: Reuse of public genome-wide gene expression data. Nat Rev Genet. 14:89–99. 2013. View Article : Google Scholar : PubMed/NCBI
46	Kannan L, Ramos M, Re A, El-Hachem N, Safikhani Z, Gendoo DM, Davis S, Gomez-Cabrero D, Castelo R, Hansen KD, et al: Public data and open source tools for multi-assay genomic investigation of disease. Brief Bioinform. 17:603–615. 2016. View Article : Google Scholar : PubMed/NCBI
47	Sharan R, Ulitsky I and Shamir R: Network-based prediction of protein function. Mol Syst Biol. 3:882007. View Article : Google Scholar : PubMed/NCBI
48	Shin SS, Park SS, Hwang B, Moon B, Kim WT, Kim WJ and Moon SK: MicroRNA-892b influences proliferation, migration and invasion of bladder cancer cells by mediating the p19ARF/cyclin D1/CDK6 and Sp-1/MMP-9 pathways. Oncol Rep. 36:2313–2320. 2016. View Article : Google Scholar : PubMed/NCBI
49	Jiang L, Yu L, Zhang X, Lei F, Wang L, Liu X, Wu S, Zhu J, Wu G, Cao L, et al: miR-892b silencing activates NF-κB and promotes aggressiveness in breast cancer. Cancer Res. 76:1101–1111. 2016. View Article : Google Scholar : PubMed/NCBI
50	Lloyd KL, Cree IA and Savage RS: Prediction of resistance to chemotherapy in ovarian cancer: A systematic review. BMC Cancer. 15:1172015. View Article : Google Scholar : PubMed/NCBI
51	Emmons MF, Anreddy N, Cuevas J, Steinberger K, Yang S, McLaughlin M, Silva A and Hazlehurst LA: MTI-101 treatment inducing activation of Stim1 and TRPC1 expression is a determinant of response in multiple myeloma. Sci Rep. 2:72017.