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Molecular Diagnosis in Ovarian Carcinoma

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Molecular Diagnostics in Cancer Patients

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Abstract

Ovarian cancer is a heterogeneous disease that influences women worldwide, is diagnosed at an advanced stage in most patients, and has no effective screening tests for initial detection. The incidence of this cancer is 225,500 diagnoses per year worldwide, and it is the leading cause of death in women with gynecological cancer. Most patients are diagnosed at an advanced stage and have a poor prognosis. Therefore, better management strategies are needed to improve outcomes for women with advanced ovarian cancer.

Human genome draft has open vistas to cultivate very precise, specific and sensitive treatment plan that could not only help in treating the disease effectively but also block the recurrence. Scientific determinations with large-scale, genomic studies of ovarian tumors and cancers have offered a better understanding of the alterations of pathways involved in the development of these cancers. Recent advancement in ovarian cancer fields has presented various molecular targeted agents which have shown spectacular potential in treatment and diagnosis of this dire disease. The targets of these agents include angiogenesis, the human epidermal growth factor receptor family, ubiquitin-proteasome pathway, epigenetic modulators, poly (ADP-ribose) polymerase (PARP), and mammalian target of rapamycin (mTOR) signaling pathway, which are aberrant in tumor tissue.

Molecular investigations, primarily based on next-generation sequencing, else known as high-throughput sequencing, are approving for further refinement of ovarian cancer classification, enabling the revelation of the site(s) of precursor lesions of high-grade serous ovarian cancer, and providing insight into the processes of clonal selection and evolution that may be associated with development of chemo-resistance. Many evolving examples are showing the ability of molecular signatures to classify tumors of the same organ according to their behavior, rather than by morphology and thereby are gradually bringing this revolutionary concept into reality. Understanding the tumor molecular biology and identification of predictive biomarkers are essential steps for selection of the best treatment plans. Thus, current chapter will explore the progress in the field of molecular diagnosis and also provide current update of various studies which are successfully knocking to shift the treatment paradigm from traditional treatment to novel therapeutics plan of precision medicine.

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References

  1. Sankaranarayanan R, Ferlay J. Worldwide burden of gynaecological cancer: the size of the problem. Best Pract Res Clin Obstet Gynaecol. 2006;20(2):207–25. https://doi.org/10.1016/j.bpobgyn.2005.10.007.

    CAS  PubMed  Google Scholar 

  2. Saiki RK, Walsh PS, Levenson CH, Erlich HA. Genetic analysis of amplified DNA with immobilized sequence-specific oligonucleotide probes. Proc Natl Acad Sci U S A. 1989;86(16):6230–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Miller MB, Tang YW. Basic concepts of microarrays and potential applications in clinical microbiology. Clin Microbiol Rev. 2009;22(4):611–33.

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Fan J-B, Gunderson KL, Bibikova M, Yeakley JM, Chen J, Wickham Garcia E, et al. Illumina universal bead arrays. Methods Enzymol. 2006;410:57–73.

    CAS  PubMed  Google Scholar 

  5. Oliphant A, Barker DL, Stuelpnagel JR, Chee MS. BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques. 2002;32:S56.

    Google Scholar 

  6. International Human Genome Sequencing Consortium. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921.

    Google Scholar 

  7. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ, Sutton GG, et al. The sequence of the human genome. Science. 2001;291:1304–51.

    CAS  PubMed  Google Scholar 

  8. Roukos DH. Personalized cancer diagnostics and therapeutics. Expert Rev Mol Diagn. 2009;9(3):227–9.

    PubMed  Google Scholar 

  9. Church GM. The personal genome project. Mol Syst Biol. 2005;1:2005.0030.

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Mardis ER. Next-generation DNA sequencing methods. Annu Rev Genomics Hum Genet. 2008;9:387–402. https://doi.org/10.1146/annurev.genom.9.081307.164359.

    CAS  PubMed  Google Scholar 

  11. Erlich H, Valdes AM, Noble J, Carlson JA, Varney M, Concannon P, et al. HLA DR-DQ haplotypes and genotypes and type 1 diabetes risk: analysis of the type 1 diabetes genetics consortium families. Diabetes. 2008;57(4):1084–92. https://doi.org/10.2337/db07-1331.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Margulies M, Egholm M, Altman WE, Attiya S, Bader JS, Bemben LA, et al. Genome sequencing in microfabricated high-density picolitre reactors. Nature. 2005;437(7057):376–80.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Tawfik DS, Griffiths AD. Man-made cell-like compartments for molecular evolution. Nat Biotechnol. 1998;16:652–6.

    CAS  PubMed  Google Scholar 

  14. Braslavsky I, Hebert B, Kartalov E, Quake SR. Sequence information can be obtained from single DNA molecules. Proc Natl Acad Sci U S A. 2003;100:3960–4.

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Liede A, Tonin PN, Sun CC, Serruya C, Daly MB, Narod SA, Foulkes WD. Is hereditary site-specific ovarian cancer a distinct genetic condition? Am J Med Genet. 1998;75(1):55–8.

    CAS  PubMed  Google Scholar 

  16. Stratton JF, Thompson D, Bobrow L, Dalal N, Gore M, Bishop DT, Scott I, et al. The genetic epidemiology of early-onset epithelial ovarian cancer: a population-based study. Am J Hum Genet. 1999;65(6):1725–32. https://doi.org/10.1086/302671.

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Cesari R, Martin ES, Calin GA, Pentimalli F, Bichi R, McAdams H, Trapasso F, et al. Parkin, a gene implicated in autosomal recessive juvenile parkinsonism, is a candidate tumor suppressor gene on chromosome 6q25-q27. Proc Natl Acad Sci U S A. 2003;100(10):5956–61. https://doi.org/10.1073/pnas.0931262100.

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Sellar GC, Watt KP, Rabiasz GJ, Stronach EA, Li L, Miller EP, Massie CE, et al. OPCML at 11q25 is epigenetically inactivated and has tumor-suppressor function in epithelial ovarian cancer. Nature Genetics. 2003;34(3):337–43. https://doi.org/10.1038/ng1183.

    CAS  PubMed  Google Scholar 

  19. Cancer Genome Atlas Research Network. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609–15. https://doi.org/10.1038/nature10166.

    CAS  Google Scholar 

  20. Wiegand KC, Shah SP, Al-Agha OM, Zhao Y, Tse K, et al. ARID1A mutations in endometriosis-associated ovarian carcinomas. N Engl J Med. 2010;363:1532–43.

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Bell D, Berchuck A, Birrer M, Chien J, Cramer DW, Dao F, Dhir R, et al. Integrated genomic analyses of ovarian carcinoma. Nature. 2011;474(7353):609–15. https://doi.org/10.1038/nature10166.

    CAS  Google Scholar 

  22. Kanchi KL, Johnson KJ, Lu C, McLellan MD, Leiserson MD, Wendl MC, Zhang Q, et al. Integrated analysis of germline and somatic variants in ovarian cancer. Nature Communications. 2014;5:3156. https://doi.org/10.1038/ncomms4156.

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Nick AM, Coleman RL, Ramirez PT, Sood AK. A framework for a personalized surgical approach to ovarian cancer. Nat Rev Clin Oncol. 2015;12(4):239–45. https://doi.org/10.1038/nrclinonc.2015.26.

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Couch FJ, Wang X, McGuffog L, Lee A, Olswold C, Kuchenbaecker KB, Soucy P, et al. Genome-wide association study in BRCA1 mutation carriers identifies novel loci associated with breast and ovarian cancer risk. PLoS Genet. 2013;9(3):e1003212.

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Sangha N, Wu R, Kuick R, Powers S, Mu D, Fiander D, Yuen K, et al. Neurofibromin 1 (NF1) defects are common in human ovarian serous carcinomas and co-occur with TP53 mutations. Neoplasia (New York, NY). 2008;10(12):1362–72, following 1372.

    CAS  Google Scholar 

  26. Dong A, Lu Y, Lu B. Genomic/epigenomic alterations in ovarian carcinoma: translational insight into clinical practice. J Cancer. 2016;7(11):1441–51. https://doi.org/10.7150/jca.15556.

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Kurian AW, Hughes E, Handorf EA, Gutin A, Allen B, Hartman A-R, Hall MJ. Breast and ovarian cancer penetrance estimates derived from germline multiple-gene sequencing results in women. JCO Precis Oncol. 2017;1:1–12.

    Google Scholar 

  28. Kurman RJ, Shih I-M. Molecular pathogenesis and extraovarian origin of epithelial ovarian cancer—shifting the paradigm. Hum Pathol. 2011;42(7):918–31. https://doi.org/10.1016/j.humpath.2011.03.003.

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Nakayama K, Nakayama N, Kurman RJ, Cope L, Pohl G, Samuels Y. Cancer Biol Ther. 2006;5(7):779–85.

    CAS  PubMed  Google Scholar 

  30. Singer G, Oldt R, Cohen Y, Wang BG, Sidransky D, Kurman RJ, Shih I-M. Mutations in BRAF and KRAS characterize the development of low-grade ovarian serous carcinoma. J Natl Cancer Inst. 2003;95(6):484–6.

    CAS  PubMed  Google Scholar 

  31. Zannoni GF, Improta G, Pettinato A, Brunelli C, Troncone G, Scambia G, Fraggetta F. Molecular status of PI3KCA, KRAS and BRAF in ovarian clear cell carcinoma: an analysis of 63 patients. J Clin Pathol. 2016;69(12):1088–92. https://doi.org/10.1136/jclinpath-2016-203776.

    CAS  PubMed  Google Scholar 

  32. Kuo K-T, Mao T-L, Jones S, Veras E, Ayhan A, Wang T-L, Glas R, et al. Frequent activating mutations of PIK3CA in ovarian clear cell carcinoma. Am J Pathol. 2009;174(5):1597–601. https://doi.org/10.2353/ajpath.2009.081000.

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Gemignani F, Perra C, Landi S, Canzian F, Kurg A, Tonisson N, Galanello R, Cao A, Metspalu A, Romeo G. Reliable detection of beta-thalassemia and G6PD mutations by a DNA microarray. Clin Chem. 2002;48:2051–4.

    CAS  PubMed  Google Scholar 

  34. Levine DA, Bogomolniy F, Yee CJ, Lash A, Barakat RR, Borgen PI, Boyd J. Frequent mutation of the PIK3CA gene in ovarian and breast cancers. Clin Cancer Res. 2005;11(8):2875–8.

    CAS  PubMed  Google Scholar 

  35. Campbell IG, Russell SE, Phillips WA. PIK3CA mutations in ovarian cancer. Clin Cancer Res. 2005;11(19 Pt 1):7042. https://doi.org/10.1158/1078-0432.CCR-05-1024; author reply 7042–7043.

    CAS  PubMed  Google Scholar 

  36. Wu R, Baker SJ, Hu TC, Norman KM, Fearon ER, Cho KR. Type I to Type II ovarian carcinoma progression: mutant Trp53 or Pik3ca confers a more aggressive tumor phenotype in a mouse model of ovarian cancer. Am J Pathol. 2013;182(4):1391–9. https://doi.org/10.1016/j.ajpath.2012.12.031.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Cho KR, Shih I-M. Ovarian cancer. Annu Rev Pathol. 2009;4:287–313. https://doi.org/10.1146/annurev.pathol.4.110807.092246.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Samuels Y, Waldman T. Oncogenic mutations of PIK3CA in human cancers. Curr Top Microbiol Immunol. 2010;347:21–41. https://doi.org/10.1007/82_2010_68.

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Landen CN, Birrer MJ, Sood AK. Early events in the pathogenesis of epithelial ovarian cancer. J Clin Oncol. 2008;26(6):995–1005. https://doi.org/10.1200/JCO.2006.07.9970.

    CAS  PubMed  Google Scholar 

  40. Willner J, Wurz K, Allison KH, Galic V, Garcia RL, Goff BA, Swisher EM. Alternate molecular genetic pathways in ovarian carcinomas of common histological types. Hum Pathol. 2007;38(4):607–13. https://doi.org/10.1016/j.humpath.2006.10.007.

    CAS  PubMed  Google Scholar 

  41. McConechy MK, Ding J, Senz J, Yang W, Melnyk N, Tone AA, Prentice LM, et al. Ovarian and endometrial endometrioid carcinomas have distinct CTNNB1 and PTEN mutation profiles. Mod Pathol. 2014;27(1):128–34. https://doi.org/10.1038/modpathol.2013.107.

    CAS  PubMed  Google Scholar 

  42. Obata K, Morland SJ, Watson RH, Hitchcock A, Chenevix-Trench G, Thomas EJ, Campbell IG. Cancer Res. 1998;58(10):2095–7.

    CAS  PubMed  Google Scholar 

  43. Dwivedi S, Goel A, Khattri S, Mandhani A, Sharma P, Misra S, Pant KK. Genetic variability at promoters of IL-18 (pro-) and IL-10 (anti-) inflammatory gene affects susceptibility and their circulating serum levels: an explorative study of prostate cancer patients in North Indian populations. Cytokine. 2015;74(1):117–22. https://doi.org/10.1016/j.cyto.2015.04.001.

    CAS  PubMed  Google Scholar 

  44. Dwivedi S, Goel A, Mandhani A, Khattri S, Sharma P, Misra S, Pant KK. Functional genetic variability at promoters of pro-(IL-18) and anti-(IL-10) inflammatory affects their mRNA expression and survival in prostate carcinoma patients: five year follow-up study. Prostate. 2015;75(15):1737–46. https://doi.org/10.1002/pros.23055.

    CAS  PubMed  Google Scholar 

  45. Dwivedi S, Singh S, Goel A, Khattri S, Mandhani A, Sharma P, Misra S, Pant KK. Pro-(IL-18) and anti-(IL-10) inflammatory promoter genetic variants (intrinsic factors) with tobacco exposure (extrinsic factors) may influence susceptibility and severity of prostate carcinoma: a prospective study. Asian Pac J Cancer Prev. 2015;16(8):3173–81.

    PubMed  Google Scholar 

  46. Dwivedi S, Goel A, Khattri S, Sharma P, Pant KK. Aggravation of inflammation by smokeless tobacco in comparison of smoked tobacco. Indian J Clin Biochem. 2015;30(1):117–9. PMID: 25646053.

    PubMed  Google Scholar 

  47. Dwivedi S, Goel A, Mandhani A, Khattri S, Pant KK. Tobacco exposure may enhance inflammation in prostate carcinoma patients: an explorative study in north Indian population. Toxicol Int. 2012;19(3):310–8. PMID: 2329347.

    PubMed  PubMed Central  Google Scholar 

  48. Dwivedi S, Shukla KK, Gupta G, Sharma P. Non-invasive biomarker in prostate carcinoma: a novel approach. Indian J Clin Biochem. 2013;28(2):107–9. https://doi.org/10.1007/s12291-013-0312-5.

    PubMed  PubMed Central  Google Scholar 

  49. Zhang T, Xu J, Deng S, Zhou F, Li J, Zhang L, et al. Core signaling pathways in ovarian cancer stem cell revealed by integrative analysis of multi-marker genomics data. PLoS One. 2018;13(5):e0196351. https://doi.org/10.1371/journal.pone.0196351.

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Carrarelli P, Funghi L, Ciarmela P, Centini G, Reis FM, Dela Cruz C, Mattei A, Vannuccini S, Petraglia F. Deep infiltrating endometriosis and endometrial adenocarcinoma express high levels of myostatin and its receptors messenger RNAs. Reprod Sci (Thousand Oaks, CA). 2017;24(12):1577–82. https://doi.org/10.1177/1933719117698579.

    CAS  Google Scholar 

  51. Wei L, Yin F, Zhang W, Li L. STROBE-compliant integrin through focal adhesion involve in cancer stem cell and multidrug resistance of ovarian cancer. Medicine. 2017;96(12):e6345. https://doi.org/10.1097/MD.0000000000006345.

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Yang L, Zhang X, Ma Y, Zhao X, Li B, Wang H. Ascites promotes cell migration through the repression of miR-125b in ovarian cancer. Oncotarget. 2017;8(31):51008–15. https://doi.org/10.18632/oncotarget.16846.

    PubMed  PubMed Central  Google Scholar 

  53. Rahimi F, Karimi J, Goodarzi MT, Saidijam M, Khodadadi I, Razavi AN, Nankali M. Overexpression of receptor for advanced glycation end products (RAGE) in ovarian cancer. Cancer Biomark. 2017;18(1):61–8. https://doi.org/10.3233/CBM-160674.

    CAS  PubMed  Google Scholar 

  54. Romeo M, Karachaliou N, Chaid I, Queralt C, De Aguirre I, Del Carmen Gómez M, Sanchez-Ronco M, Radua J, Ramírez JL, Rosell R. Low RAP80 mRNA expression correlates with shorter survival in sporadic high-grade serous ovarian carcinoma. Int J Biol Marker. 2017;32(1):90–5. https://doi.org/10.5301/jbm.5000223.

    CAS  Google Scholar 

  55. Lee J, An S, Choi YM, Lee J, Ahn KJ, Lee JH, et al. Musashi-2 is a novel regulator of paclitaxel sensitivity in ovarian cancer cells. Int J Oncol. 2016;49(5):1945–52.

    CAS  PubMed  Google Scholar 

  56. Shin K, Kim KH, Yoon MS, Suh DS, Lee JY, Kim A, Eo W. Expression of interactive genes associated with apoptosis and their prognostic value for ovarian serous adenocarcinoma. Adv Clin Exp Med. 2016;25(3):513–21. https://doi.org/10.17219/acem/62540.

    PubMed  Google Scholar 

  57. Tang S, Yang F, Du X, Lu Y, Zhang L, Zhou X. Aberrant expression of anaplastic lymphoma kinase in ovarian carcinoma independent of gene rearrangement. Int J Gynecol Pathol. 2016;35(4):337–47.

    CAS  PubMed  Google Scholar 

  58. Xiao L, Shi X-Y, Zhang Y, Zhu Y, Zhu L, Tian W, Zhu B-K, Wei Z-L. YAP induces cisplatin resistance through activation of autophagy in human ovarian carcinoma cells. Onco Targets Ther. 2016;9:1105–14. https://doi.org/10.2147/OTT.S102837.

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Zhang L, Cao X, Zhang L, Zhang X, Sheng H, Tao K. UCA1 overexpression predicts clinical outcome of patients with ovarian cancer receiving adjuvant chemotherapy. Cancer Chemother Pharmacol. 2016;77(3):629–34.

    CAS  PubMed  Google Scholar 

  60. Liu H, Shi H, Fan Q, Sun X. Cyclin Y regulates the proliferation, migration, and invasion of ovarian cancer cells via Wnt signaling pathway. Tumour Biol. 2016;37(8):10161–75. https://doi.org/10.1007/s13277-016-4818-3.

    CAS  PubMed  Google Scholar 

  61. Yang M, Xie X, Ding Y. SALL4 is a marker of poor prognosis in serous ovarian carcinoma promoting invasion and metastasis. Oncol Rep. 2016;35(3):1796–806.

    CAS  PubMed  Google Scholar 

  62. Shao H, Mohamed EM, Xu GG, Waters M, Jing K, Ma Y. Carnitine palmitoyltransferase 1A functions to repress FoxO transcription factors to allow cell cycle progression in ovarian cancer. Oncotarget. 2016;7(4):3832–46.

    PubMed  Google Scholar 

  63. Schulz H, Kuhn C, Hofmann S, Mayr D, Mahner S, Jeschke U, Schmoeckel E. Overall survival of ovarian cancer patients is determined by expression of galectins-8 and -9. Int J Mol Sci. 2018;19(1). pii: E323. https://doi.org/10.3390/ijms19010323.

  64. Nguyen HT, Tian G, Murph MM. Molecular epigenetics in the management of ovarian cancer: are we investigating a rational clinical promise? Front Oncol. 2014;4:71. https://doi.org/10.3389/fonc.2014.00071.

    PubMed  PubMed Central  Google Scholar 

  65. Sapiezynski J, Taratula O, Rodriguez-Rodriguez L, Minko T. Precision targeted therapy of ovarian cancer. J Control Release. 2016;243:250–68. https://doi.org/10.1016/j.jconrel.2016.10.014.

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Bubancova I, Kovarikova H, Laco J, Ruszova E, Dvorak O, Palicka V, Chmelarova M. Next-generation sequencing approach in methylation analysis of HNF1B and GATA4 genes: searching for biomarkers in ovarian cancer. Int J Mol Sci. 2017;18(2). https://doi.org/10.3390/ijms18020474.

  67. Jang K, Kim M, Gilbert CA, Simpkins F, Ince TA, Slingerland JM. VEGFA activates an epigenetic pathway upregulating ovarian cancer-initiating cells. EMBO Mol Med. 2017;9(3):304–18. https://doi.org/10.15252/emmm.201606840.

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Cacan E. Epigenetic regulation of RGS2 (Regulator of G-protein signaling 2) in chemoresistant ovarian cancer cells. J Chemother. 2017;29(3):173–8.

    CAS  PubMed  Google Scholar 

  69. Sung HY, Yang SD, Park AK, Ju W, Ahn JH. Aberrant hypomethylation of solute carrier family 6 member 12 promoter induces metastasis of ovarian cancer. Yonsei Med J. 2017;58(1):27–34. https://doi.org/10.3349/ymj.2017.58.1.27.

    CAS  PubMed  Google Scholar 

  70. Ross-Adams H, Ball S, Lawrenson K, Halim S, Russell R, Wells C. HNF1B variants associate with promoter methylation and regulate gene networks activated in prostate and ovarian cancer. Oncotarget. 2016;7(46):74734–46.

    PubMed  PubMed Central  Google Scholar 

  71. Gozzi G, Chelbi ST, Manni P, Alberti L, Fonda S, Saponaro S, Fabbiani L, Rivasi F, Benhattar J, Losi L. Promoter methylation and downregulated expression of the TBX15 gene in ovarian carcinoma. Oncol Lett. 2016;12(4):2811–9. https://doi.org/10.3892/ol.2016.5019.

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Kaur M, Singh A, Singh K, Gupta S, Sachan M. Development of a multiplex MethyLight assay for the detection of DAPK1 and SOX1 methylation in epithelial ovarian cancer in a north Indian population. Genes Genet Syst. 2016;91(3):175–81. https://doi.org/10.1266/ggs.15-00051.

    CAS  PubMed  Google Scholar 

  73. Xu Y, Li X, Wang H, Xie P, Yan X, Bai Y, Zhang T. Hypermethylation of CDH13, DKK3 and FOXL2 promoters and the expression of EZH2 in ovary granulosa cell tumors. Mol Med Rep. 2016;14(3):2739–45. https://doi.org/10.3892/mmr.2016.5521.

    CAS  PubMed  Google Scholar 

  74. Losi L, Fonda S, Saponaro S, et al. Distinct DNA methylation profiles in ovarian tumors: opportunities for novel biomarkers. Int J Mol Sci. 2018;19(6):1559. https://doi.org/10.3390/ijms19061559.

    CAS  PubMed Central  Google Scholar 

  75. Lehrbach NJ, Miska EA. Functional genomic, computational and proteomic analysis of C. elegans microRNAs. Brief Funct Genomic Proteomic. 2008;7(3):228–35. https://doi.org/10.1093/bfgp/eln024.

    CAS  PubMed  Google Scholar 

  76. Calin GA, Garzon R, Cimmino A, Fabbri M, Croce CM. MicroRNAs and leukemias: how strong is the connection? Leuk Res. 2006;30(6):653–5. https://doi.org/10.1016/j.leukres.2005.10.017.

    CAS  PubMed  Google Scholar 

  77. Calin GA, Ferracin M, Cimmino A, Di Leva G, Shimizu M, Wojcik SE, Iorio MV, et al. A microRNA signature associated with prognosis and progression in chronic lymphocytic leukemia. N Engl J Med. 2005;353(17):1793–801. https://doi.org/10.1056/NEJMoa050995.

    CAS  PubMed  Google Scholar 

  78. Agostini A, Brunetti M, Davidson B, Tropé CG, Heim S, Panagopoulos I, et al. Genomic imbalances are involved in miR-30c and let-7a deregulation in ovarian tumors: implications for HMGA2 expression. Oncotarget. 2017;8(13):21554–60.

    PubMed  PubMed Central  Google Scholar 

  79. Zou J, Liu L, Wang Q, Yin F, Yang Z, Zhang W, Li L. Downregulation of miR-429 contributes to the development of drug resistance in epithelial ovarian cancer by targeting ZEB1. Am J Transl Res. 2017;9(3):1357–68.

    CAS  PubMed  PubMed Central  Google Scholar 

  80. Liu W, Lv C, Zhang B, Zhou Q, Cao Z. MicroRNA-27b functions as a new inhibitor of ovarian cancer-mediated vasculogenic mimicry through suppression of VE-cadherin expression. RNA (New York, NY). 2017;23(7):1019–27. https://doi.org/10.1261/rna.059592.116.

    CAS  Google Scholar 

  81. Chong GO, Jeon H-S, Han HS, Son JW, Lee YH, Hong DG, Park HJ, Lee YS, Cho YL. Overexpression of microRNA-196b accelerates invasiveness of cancer cells in recurrent epithelial ovarian cancer through regulation of homeobox A9. Cancer Genomics Proteomics. 2017;14(2):137–41. https://doi.org/10.21873/cgp.20026.

    CAS  PubMed  PubMed Central  Google Scholar 

  82. Tian J, Xu Y-Y, Li L, Hao Q. MiR-490-3p sensitizes ovarian cancer cells to cisplatin by directly targeting ABCC2. Am J Transl Res. 2017;9(3):1127–38.

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Xiao M, Cai J, Cai L, Jia J, Xie L, Zhu Y, Huang B, Jin D, Wang Z. Let-7e sensitizes epithelial ovarian cancer to cisplatin through repressing DNA double strand break repair. J Ovarian Res. 2017;10(1):24. https://doi.org/10.1186/s13048-017-0321-8.

    CAS  PubMed  PubMed Central  Google Scholar 

  84. Li J, Li Q, Huang H, Li Y, Li L, Hou W, You Z. Overexpression of miRNA-221 promotes cell proliferation by targeting the apoptotic protease activating factor-1 and indicates a poor prognosis in ovarian cancer. Int J Oncol. 2017;50(4):1087–96. https://doi.org/10.3892/ijo.2017.3898.

    CAS  PubMed Central  Google Scholar 

  85. Xu J, Jiang N, Shi H, Zhao S, Yao S, Shen H. miR-28-5p promotes the development and progression of ovarian cancer through inhibition of N4BP1. Int J Oncol. 2017;50(4):1383–91. https://doi.org/10.3892/ijo.2017.3915. [Epub ahead of print].

    CAS  Google Scholar 

  86. Sun X, Cui M, Tong L, Zhang A, Wang K. Upregulation of microRNA-3129 suppresses epithelial ovarian cancer through CD44. Cancer Gene Ther. 2018;25(11–12):317–25. https://doi.org/10.1038/s41417-018-0026-1.

    CAS  PubMed  Google Scholar 

  87. Elzek MA, Rodland KD. Proteomics of ovarian cancer: functional insights and clinical applications. Cancer Metastasis Rev. 2015;34(1):83–96. https://doi.org/10.1007/s10555-014-9547-8.

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Zhu Y, Wu R, Sangha N, Yoo C, Cho KR, Shedden KA, Katabuchi H, Lubman DM. Classifications of ovarian cancer tissues by proteomic patterns. Proteomics. 2006;6(21):5846–56. https://doi.org/10.1002/pmic.200600165.

    CAS  PubMed  Google Scholar 

  89. Tian Y, Yao Z, Roden RBS, Zhang H. Identification of glycoproteins associated with different histological subtypes of ovarian tumors using quantitative glycoproteomics. Proteomics. 2011;11(24):4677–87. https://doi.org/10.1002/pmic.201000811.

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Katchman BA, Chowell D, Wallstrom G, Vitonis AF, LaBaer J, Cramer DW, Anderson KS. Autoantibody biomarkers for the detection of serous ovarian cancer. Gynecol Oncol. 2017;146(1):129–36. https://doi.org/10.1016/j.ygyno.2017.04.005.

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Sereni MI, Baldelli E, Gambara G, Deng J, Zanotti L, Bandiera E, Bignotti E, et al. Functional characterization of epithelial ovarian cancer histotypes by drug target based protein signaling activation mapping: implications for personalized cancer therapy. Proteomics. 2015;15(2–3):365–73. https://doi.org/10.1002/pmic.201400214.

    CAS  PubMed  Google Scholar 

  92. Toyama A, Suzuki A, Shimada T, Aoki C, Aoki Y, Umino Y, et al. Proteomic characterization of ovarian cancers identifying annexin-A4, phosphoserine aminotransferase, cellular retinoic acid-binding protein 2, and serpin B5 as histology-specific biomarkers. Cancer Sci. 2012;103(4):747–55.

    CAS  PubMed  Google Scholar 

  93. Alshenawy HA, Radi DA. Napsin-A, a possible diagnostic marker for differentiating clear cell ovarian carcinoma from other high-grade ovarian carcinomas. Appl Immunohistochem Mol Morphol. 2018;26(8):605–10. https://doi.org/10.1097/PAI.0000000000000510.

    CAS  PubMed  Google Scholar 

  94. Skirnisdottir I, Bjersand K, Akerud H, Seidal T. Napsin A as a marker of clear cell ovarian carcinoma. BMC Cancer. 2013;13:524. https://doi.org/10.1186/1471-2407-13-524.

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Karpathiou G, Venet M, Mobarki M, Forest F, Chauleur C, Peoc’h M. FOXA1 is expressed in ovarian mucinous neoplasms. Pathology. 2017;49(3):271–6. https://doi.org/10.1016/j.pathol.2016.11.009.

    CAS  PubMed  Google Scholar 

  96. Lehtinen L, Vesterkvist P, Roering P, Korpela T, Hattara L, Kaipio K, Mpindi J-P, et al. REG4 is highly expressed in mucinous ovarian cancer: a potential novel serum biomarker. PLoS One. 2016;11(3):e0151590. https://doi.org/10.1371/journal.pone.0151590.

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Zheng X, Chen S, Li L, Liu X, Liu X, Dai S, Zhang P, Lu H, Lin Z, Yu Y, Li G. Evaluation of HE4 and TTR for diagnosis of ovarian cancer: comparison with CA-125. J Gynecol Obstet Hum Reprod. 2018;47(6):227–30.

    PubMed  Google Scholar 

  98. Sharma P, Dwivedi S. Prospects of molecular biotechnology in diagnostics: step towards precision medicine. Indian J Clin Biochem. 2017;32(2):121–3. PMID: 28428685.

    PubMed  PubMed Central  Google Scholar 

  99. Sharma P, Dwivedi S. Nutrigenomics and nutrigenetics: new insight in disease prevention and cure. Indian J Clin Biochem. 2017;32(4):371–3. https://doi.org/10.1007/s12291-017-0699-5. PMID: 29062169.

    PubMed  PubMed Central  Google Scholar 

  100. Hijaz M, Das S, Mert I, Gupta A, Al-Wahab Z, Tebbe C, Dar S, et al. Folic acid tagged nanoceria as a novel therapeutic agent in ovarian cancer. BMC Cancer. 2016;16:220. https://doi.org/10.1186/s12885-016-2206-4.

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Dwivedi S, Purohit P, Misra R , Pareek P, Vishnoi JR, Sharma P, Misra S. Methods for isolation of high quality and quantity of RNA and single cell suspension for flow-cytometry from cancer tissue: a comparative analysis. Indian J Clin Biochem. 2017. https://doi.org/10.1007/s12291-017-0719-5. (Online available).

  102. Gupta G, Dwivedi S, Shukla KK, Sharma P. Tissue-resident memory cells: new marked shield to fight cancers. Indian J Clin Biochem. 2018;33(2):119–20. https://doi.org/10.1007/s12291-018-0745-y.

    PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India

    Shailendra Dwivedi, Purvi Purohit & Praveen Sharma

  2. Era’s Lucknow Medical College and Hospital, Lucknow, India

    Radhieka Misra

  3. Department of Onco-surgery, All India Institute of Medical Sciences, Jodhpur, India

    Jeewan Ram Vishnoi & Sanjeev Misra

  4. Department of Radio-Therapy, All India Institute of Medical Sciences, Jodhpur, India

    Puneet Pareek

  5. Department of Urology, King George Medical University, Lucknow, India

    Apul Goel

  6. Department of Pharmacology and Therapeutics, King George Medical University, Lucknow, India

    Sanjay Khattri & Kamlesh Kumar Pant

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  1. Shailendra Dwivedi
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  2. Radhieka Misra
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  3. Purvi Purohit
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  4. Jeewan Ram Vishnoi
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  5. Puneet Pareek
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  6. Apul Goel
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  7. Sanjay Khattri
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  8. Praveen Sharma
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  9. Kamlesh Kumar Pant
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  10. Sanjeev Misra
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Editors and Affiliations

  1. Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India

    Kamla Kant Shukla

  2. Department of Biochemistry, All India Institute of Medical Sciences, Jodhpur, India

    Praveen Sharma

  3. Department of Surgical Oncology, All India Institute of Medical Sciences, Jodhpur, India

    Sanjeev Misra

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Dwivedi, S. et al. (2019). Molecular Diagnosis in Ovarian Carcinoma. In: Shukla, K., Sharma, P., Misra, S. (eds) Molecular Diagnostics in Cancer Patients. Springer, Singapore. https://doi.org/10.1007/978-981-13-5877-7_19

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  • DOI: https://doi.org/10.1007/978-981-13-5877-7_19

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