Molecular Diagnostics

FXYD5 (Dysadherin) upregulation predicts shorter survival and reveals platinum resistance in high-grade serous ovarian cancer patients

Article metrics



High-grade serous ovarian carcinoma (HGSOC) is generally associated with a very dismal prognosis. Nevertheless, patients with similar clinicopathological characteristics can have markedly different clinical outcomes. Our aim was the identification of novel molecular determinants influencing survival.


Gene expression profiles of extreme HGSOC survivors (training set) were obtained by microarray. Differentially expressed genes (DEGs) and enriched signalling pathways were determined. A prognostic signature was generated and validated on curatedOvarianData database through a meta-analysis approach. The best prognostic biomarker from the signature was confirmed by RT-qPCR and by immunohistochemistry on an independent validation set. Cox regression model was chosen for survival analysis.


Eighty DEGs and the extracellular matrix-receptor (ECM-receptor) interaction pathway were associated to extreme survival. A 10-gene prognostic signature able to correctly classify patients with 98% of accuracy was identified. By an ‘in-silico’ meta-analysis, overexpression of FXYD domain-containing ion transport regulator 5 (FXYD5), also known as dysadherin, was confirmed in HGSOC short-term survivors compared to long-term ones. Its prognostic and predictive power was then successfully validated, both at mRNA and protein level, first on training than on validation sample set.


We demonstrated the possible involvement of FXYD5 and ECM-receptor interaction signal pathway in HCSOC survival and prognosis.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1
Fig. 2


  1. 1.

    Siegel, R. L., Miller, K. D. & Jemal, A. Cancer statistics, 2018. CA Cancer J. Clin. 68, 7–30 (2018).

  2. 2.

    Prat, J. New insights into ovarian cancer pathology. Ann Oncol. 23, x111–x117 (2012).

  3. 3.

    Matz, M., Coleman, M. P., Carreira, H., Salmerón, D., Chirlaque, M. D. & Allemani, C. CONCORD Working Group. Worldwide comparison of ovarian cancer survival: histological group and stage at diagnosis (CONCORD-2). Gynecol. Oncol. 144, 396–404 (2017).

  4. 4.

    Gockley, A., Melamed, A., Bregar, A. J., Clemmer, J. T., Birrer, M., Schorge, J. O. et al. Outcomes of women with high-grade and low-grade advanced-stage serous epithelial ovarian cancer. Obstet. Gynecol. 129, 439–447 (2017).

  5. 5.

    DiSaia P. J., Creasman W. T. Epithelial Ovarian Cancer. In: Clinical gynecologic oncology, 6th edn, 185–206 (Mosby Year Book, Inc: St. Louis, 2002)

  6. 6.

    Sweadner, K. J. & Rael, E. The FXYD gene family of small ion transport regulators or channels: cDNA sequence, protein signature sequence, and expression. Genomics 68, 41–56 (2000).

  7. 7.

    Nam, J. S., Hirohashi, S. & Wakefield, L. M. Dysadherin: a new player in cancer progression. Cancer Lett. 255, 161–169 (2007).

  8. 8.

    Lubarski Gotliv, I. FXYD5: Na(+)/K(+)-ATPase regulator in health and disease. Front. Cell Dev. Biol. 4, 26 (2016).

  9. 9.

    Colombo, N. Optimizing treatment of the partially platinum-sensitive ovarian cancer patient. Future Oncol. 9, 19–23 (2013).

  10. 10.

    Bignotti, E., Tassi, R. A., Calza, S., Ravaggi, A., Romani, C., Rossi, E. et al. Differential gene expression profiles between tumor biopsies and short-term primary cultures of ovarian serous carcinomas: identification of novel molecular biomarkers for early diagnosis and therapy. Gynecol. Oncol. 103, 405–416 (2006).

  11. 11.

    Tassi, R. A., Todeschini, P., Siegel, E. R., Calza, S., Cappella, P., Ardighieri, L. et al. FOXM1 expression is significantly associated with chemotherapy resistance and adverse prognosis in non-serous epithelial ovarian cancer patients. J. Exp. Clin. Cancer Res. 36, 63 (2017).

  12. 12.

    Edgar, R., Domrachev, M. & Lash, A. E. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30, 207–210 (2002).

  13. 13.

    Livak, K. J. & Schmittgen, T. D. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 25, 402–408 (2001).

  14. 14.

    Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

  15. 15.

    Ferrari, F., Bortoluzzi, S., Coppe, A., Sirota, A., Safran, M., Shmoish, M. et al. Novel definition files for human GeneChips based on GeneAnnot. BMC Bioinformatics 8, 446 (2007).

  16. 16.

    Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

  17. 17.

    Smyth G. K. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 2004, 3, Article 3. PMID: 16646809.

  18. 18.

    Huang, D. W., Sherman, B. T., Tan, Q., Kir, J., Liu, D., Bryant, D. 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).

  19. 19.

    Sales, G., Calura, E., Martini, P. & Romualdi, C. Graphite web: web tool for gene set analysis exploiting pathway topology. Nucleic Acids Res 41 W89–W97 (2013).

  20. 20.

    Goeman, J. J., Van de Geer, S. A., de Kort, F. & Van Houwelingen, H. C. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics 20, 93–99 (2004).

  21. 21.

    Ganzfried, B. F., Riester, M., Haibe-Kains, B., Risch, T., Tyekucheva, S., Jazic, I. et al. CuratedOvarianData: clinically annotated data for the ovarian cancer transcriptome. Database (Oxford) 2013, bat013 (2013).

  22. 22.

    Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature 474, 609–615 (2011).

  23. 23.

    Cox, D. R. Regression models and life tables. J. Royal Stat. Soc. Ser. B 34, 187–220 (1972).

  24. 24.

    Kaplan, E. L. & Meier, P. Nonparametric estimation for incomplete observations. J. Am. Stat. Assoc. 53, 457–481 (1958).

  25. 25.

    Tarca, A. L., Draghici, S., Khatri, P., Hassan, S. S., Mittal, P., Kim, J. S. et al. A novel signaling pathway impact analysis. Bioinformatics 25, 75–82 (2009).

  26. 26.

    Cotto, K. C., Wagner, A. H., Feng, Y. Y., Kiwala, S., Coffman, A. C., Spies, G. et al. DGIdb 3.0: a redesign and expansion of the drug-gene interaction database. Nucleic Acids Res. 46, D1068–D1073 (2018).

  27. 27.

    Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S. et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

  28. 28.

    Berchuck, A., Iversen, E. S., Lancaster, J. M., Pittman, J., Luo, J., Lee, P. et al. Patterns of gene expression that characterize long-term survival in advanced stage serous ovarian cancers. Clin. Cancer Res. 11, 3686–3696 (2005).

  29. 29.

    Hoppenot, C., Eckert, M. A., Tienda, S. M. & Lengyel, E. Who are the long-term survivors of high grade serous ovarian cancer? Gynecol. Oncol. 148, 204–212 (2018).

  30. 30.

    Spentzos, D., Levine, D. A., Ramoni, M. F., Joseph, M., Gu, X., Boyd, J. et al. Gene expression signature with independent prognostic significance in epithelial ovarian cancer. J. Clin. Oncol. 22, 4700–4710 (2004).

  31. 31.

    Partheen, K., Levan, K., Osterberg, L. & Horvath, G. Expression analysis of stage III serous ovarian adenocarcinoma distinguishes a sub-group of survivors. Eur. J. Cancer 42, 2846–2854 (2006).

  32. 32.

    Jochumsen, K. M., Tan, Q., Høgdall, E. V., Høgdall, C., Kjaer, S. K., Blaakaer, J. et al. Gene expression profiles as prognostic markers in women with ovarian cancer. Intl. J. Gynecol. Cancer 19, 1205–1213 (2009).

  33. 33.

    Nikas, J. B., Boylan, K. L., Skubitz, A. P. & Low, W. C. Mathematical prognostic biomarker models for treatment response and survival in epithelial ovarian cancer. Cancer Inform. 10, 233–247 (2011).

  34. 34.

    Barlin, J. N., Jelinic, P., Olvera, N., Bogomolniy, F., Bisogna, M., Dao, F. et al. Validated gene targets associated with curatively treated advanced serous ovarian carcinoma. Gynecol. Oncol. 128, 512–517 (2013).

  35. 35.

    Pickup, M. W., Mouw, J. K. & Weaver, V. M. The extracellular matrix modulates the hallmarks of cancer. EMBO Rep. 12, 1243–1253 (2014).

  36. 36.

    Durlacher, C. T., Chow, K., Chen, X. W., He, Z. X., Zhang, X., Yang, T. & Zhou, S. F. Targeting Na+/K+ -translocating adenosine triphosphatase in cancer treatment. Clin. Exp. Pharmacol. Physiol. 42, 427–443 (2015).

  37. 37.

    Jiang, N., Chen, W., Zhang, J. W., Li, Y., Zeng, X. C., Zhang, T. et al. Aberrantly regulated dysadherin and B-cell lymphoma 2/B-cell lymphoma 2-associated X enhances tumorigenesis and DNA targeting drug resistance of liver cancer stem cells. Mol. Med. Rep. 12, 7239–7246 (2015).

  38. 38.

    Lee, Y. K., Lee, S. Y., Park, J. R., Kim, R. J., Kim, S. R., Roh, K. J. & Nam, J. S. Dysadherin expression promotes the motility and survival of human breast cancer cells by AKT activation. Cancer Sci. 103, 1280–1289 (2012).

  39. 39.

    Raman, P., Purwin, T., Pestell, R. & Tozeren, A. FXYD5 is a marker for poor prognosis and a potential driver for metastasis in ovarian carcinomas. Cancer Inform. 14, 113–119 (2015).

  40. 40.

    Sung, C. O., Song, I. H. & Sohn, I. A distinctive ovarian cancer molecular subgroup characterized by poor prognosis and somatic focal copy number amplifications at chromosome 19. Gynecol. Oncol. 132, 343–350 (2014).

  41. 41.

    Alkema, N. G., Wisman, G. B., van der Zee, A. G., van Vugt, M. A. & de Jong, S. Studying platinum sensitivity and resistance in high-grade serous ovarian cancer: different models for different questions. Drug Resist. Updat. 24, 55–69 (2016).

  42. 42.

    Mijatovic, T. & Kiss, R. Cardiotonic steroids-mediated Na+/K+ -ATPase targeting could circumvent various chemoresistance pathways. Planta. Med. 79, 189–198 (2013).

  43. 43.

    Tummala, R., Wolle, D., Barwe, S. P., Sampson, V. B., Rajasekaran, A. K. & Pendyala, L. Expression of Na,K-ATPase-beta(1) subunit increases uptake and sensitizes carcinoma cells to oxaliplatin. Cancer Chemother. Pharmacol. 64, 1187–1194 (2009).

  44. 44.

    Wu, D., Qiao, Y., Kristensen, G. B., Li, S., Troen, G., Holm, R. et al. Prognostic significance of dysadherin expression in cervical squamous cell carcinoma. Pathol. Oncol. Res. 10, 212–218 (2004).

  45. 45.

    Batistatou, A., Peschos, D., Tsanou, H., Charalabopoulos, A., Nakanishi, Y., Hirohashi, S. et al. Agnantis NJ and charalabopoulos K. In breast carcinoma dysadherin expression is correlated with invasiveness but not with E-cadherin. Br. J. Cancer 96, 1404–1408 (2007).

  46. 46.

    Muramatsu, H., Akimoto, T., Maebayashi, K., Kita, M. & Mitsuhashi, N. Prognostic significance of dysadherin and E-cadherin expression in patients with head and neck cancer treated by radiation therapy. Anticancer Res. 28, 3859–3864 (2008).

  47. 47.

    Tamura, M., Ohta, Y., Tsunezuka, Y., Matsumoto, I., Kawakami, K., Oda, M. & Watanabe, G. Prognostic significance of dysadherin expression in patients with non-small cell lung cancer. J. Thorac. Cardiovasc. Surg. 130, 740–745 (2005).

  48. 48.

    Ino, Y., Gotoh, M., Sakamoto, M., Tsukagoshi, K. & Hirohashi, S. Dysadherin, a cancer-associated cell membrane glycoprotein, down-regulates E-cadherin and promotes metastasis. Proc. Natl Acad. Sci. USA 99, 365–370 (2002).

  49. 49.

    Nam, J. S., Kang, M. J., Suchar, A. M., Shimamura, T., Kohn, E. A., Michalowska, A. M. et al. Chemokine (C-C motif) ligand 2 mediates the prometastatic effect of dysadherin in human breast cancer cells. Cancer Res. 66, 7176–7184 (2006).

  50. 50.

    Lubarski-Gotliv, I., Dey, K., Kuznetsov, Y., Kalchenco, V., Asher, C. & Garty, H. FXYD5 (dysadherin) may mediate metastatic progression through regulation of the β-Na+-K+-ATPase subunit in the 4T1 mouse breast cancer model. Am. J. Physiol. Cell Physiol. 313, C108–C117 (2017).

  51. 51.

    Jang, S., Yu, X. M., Montemayor-Garcia, C., Ahmed, K., Weinlander, E., Lloyd, R. V., Dammalapati, A., Marshall, D., Prudent, J. R. & Chen, H. Dysadherin specific drug conjugates for the treatment of thyroid cancers with aggressive phenotypes. Oncotarget 8, 24457–24468 (2017).

Download references


We would like to thank Dr Francesco Gebbia for the support in collecting clinical data and Mrs Adele Bellandi for her excellent support to the project. We are also grateful to Dr Laura Tassone for technical help in biopsy validation. Finally, we wish to thank all the physicians and the nurses working in the Department of Obstetrics and Gynecology, ASST Spedali Civili of Brescia, University of Brescia.

Author information

R.A.T. participated in the study design, performed the experiments, interpreted the data, drafted and wrote the report. C.R. and L.Z. helped in collecting patients’ tissue samples, P.T. helped in the creation of patients’ database, E.B. performed microarray experiments. G.T. helped in the collection of data from patients’ medical records and in the selection of patients’ training set. L.A. and M.B. performed immunohistochemical experiments and their evaluation. A.G., E.S. and F.O. participated in the study design. D.K. and F.B. selected and provided tissue samples of the validation set. C.R. preformed all the statistical analyses, helped in data interpretation and critically reviewed the paper. A.R. coordinated the study, interpreted the data and critically reviewed the paper. All of the authors read and approved the final paper.

Correspondence to Antonella Ravaggi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards, and approved by the Research Review Board—the Ethic Committee—of the ASST Spedali Civili, Brescia, Italy (study reference number: NP1676). Informed consent was obtained from all individual participants included in the study.


The study was supported in part by grants from: EULO Foundation and Donazione Pizzini Maria Luisa to F. Odicino, Italian Association for Cancer Research (IG17185) to C. Romualdi, and Fondazione Umberto Veronesi post-doctoral fellowship to C. Romani and P. Todeschini.

Consent to publish

Not applicable.

Data availability

All data not included in this published article are available upon reasonable request.


This work is published under the standard license to publish agreement. After 12 months the work will become freely available and the license terms will switch to a Creative Commons Attribution 4.0 International (CC BY 4.0).

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark