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THE EXPRESSION ANALYSIS OF SPECIFIC GENES IN OVARIAN CANCER

Year 2023, , 136 - 143, 31.10.2023
https://doi.org/10.59312/ebshealth.1367196

Abstract

Aim: Ovarian cancer (OC) is the most lethal gynecologic malignancy and frequently diagnosed at an advanced stage because of the inadequate number of biomarkers. Therefore, identification of OC specific biological markers is a vital step for diagnosis and treatment response. Our goal is to examine functional gene sets which are possibly markers for ovarian cancer and their expression profiles in OC patients. We also aim to determine the potential genes for therapeutic targets for OC patients.
Method: The expression levels of seven genes (FOS, FOSL2, JUN, MMP-2, MMP-9, TIMP-2, and VEGFA) were identified by qRT-PCR. The tumor-free control group consisted of total abdominal hysterectomy (n=1) and bilateral salpingo-oophorectomy (n=9) patients who underwent gynecologic procedures. High-grade serous OC epithelial samples (n=10) were used for the experiment group.
Results and Conclusions: According to the qRT-PCR data, there is an increased expression of FOS (p=0.0089), MMP-9 (p=0.0029), VEGFA (p=0.0434) and decreased expression of FOSL2 (p=0.0271), JUN (p=0.0041), TIMP-2 (p=0.0062). In conclusion, the results can indicate the new perspective for OC pathogenesis and treatment. For future studies, these genes can be used in personalized diagnosis and therapy of OC.

Ethical Statement

Whole sample collection and analysis processes are ethically approved by the Istanbul University Faculty of Medicine Clinical Researches Ethics Committee (Permission No: 2014/1175) on 08.08.2014

References

  • Alshamrani A. A. (2020). Roles of microRNAs in Ovarian Cancer Tumorigenesis: Two Decades Later, What Have We Learned?. Frontiers in oncology, 10, 1084.
  • Apostolou, P., Iliopoulos, A. C., Parsonidis, P., & Papasotiriou, I. (2019). Gene expression profiling as a potential predictor between normal and cancer samples in gastrointestinal carcinoma. Oncotarget, 10(36), 3328–3338.
  • Bejjani, Fabienne, et al. "The AP-1 transcriptional complex: Local switch or remote command?." Biochimica et Biophysica Acta (BBA)-Reviews on Cancer 1872.1 (2019): 11-23.
  • Escalona, R.M., Bilandzic, M., Western, P. et al. TIMP-2 regulates proliferation, invasion and STAT3-mediated cancer stem cell-dependent chemoresistance in ovarian cancer cells. BMC Cancer 20, 960 (2020). https://doi.org/10.1186/s12885-020-07274-6.
  • Gumusoglu-Acar, E., Gunel, T., Hosseını, M. K., Dogan, B., Tekarslan, E. E., Gurdamar, B., Cevık, N., Sezerman, U., Topuz, S., Aydınlı, K. (2023). Metabolic pathways of potential miRNA biomarkers derived from liquid biopsy in epithelial ovarian cancer. Oncology Letters, 25(4), 142.
  • Gunel, T. G., Dogan, B., Gumusoglu, E., Hosseini, M. K., Topuz, S., & Aydinli, K. (2019). Regulation of HMGA2 and KRAS genes in epithelial ovarian cancer by miRNA hsa-let-7d-3p. Journal of Cancer Research and Therapeutics, 15(6), 1321–1327.
  • Jang K, Kim M, Gilbert CA, et al. VEGFA activates an epigenetic pathway upregulating ovarian cancer-initiating cells. EMBO Mol Med. 2017;9(3):304–318.
  • Kicman, A., Niczyporuk, M., Kulesza, M., Motyka, J., & Ławicki, S. (2022). Utility of Matrix Metalloproteinases in the Diagnosis, Monitoring and Prognosis of Ovarian Cancer Patients. Cancer management and research, 14, 3359–3382. https://doi.org/10.2147/CMAR.S385658
  • Li, X., Hu, Z., Shi, H., Wang, C., Lei, J., & Cheng, Y. (2020). Inhibition of VEGFA Increases the Sensitivity of Ovarian Cancer Cells to Chemotherapy by Suppressing VEGFA-Mediated Autophagy. OncoTargets and therapy, 13, 8161–8171.
  • Li, J., Zhou, L., Jiang, H., Lin, L., & Li, Y. (2022). Inhibition of FOSL2 aggravates the apoptosis of ovarian cancer cells by promoting the formation of inflammasomes. Genes Genom, 44, 29–38.
  • Liu, Q., Yang, X., Yin, Y., Zhang, H., Yin, F., Guo, P., Zhang, X., Sun, C., Li, S., Han, Y., & Yang, Z. (2022). Identifying the Role of Oxidative Stress-Related Genes as Prognostic Biomarkers and Predicting the Response of Immunotherapy and Chemotherapy in Ovarian Cancer. Oxidative medicine and cellular longevity, 2022, 6575534.
  • Narrandes, S., & Xu, W. (2018). Gene Expression Detection Assay for Cancer Clinical Use. Journal of Cancer, 9(13), 2249–2265.
  • Olbromski PJ, Pawlik P, Bogacz A, Sajdak S. Identification of New Molecular Biomarkers in Ovarian Cancer Using the Gene Expression Profile. Journal of Clinical Medicine. 2022; 11(13):3888.
  • Ono, K., Tanaka, T., Tsunoda, T., Kitahara, O., Kihara, C., Okamoto, A., Ochiai, K., Takagi, T., & Nakamura, Y. (2000). Identification by cDNA microarray of genes involved in ovarian carcinogenesis. Cancer research, 60(18), 5007–5011.
  • Poon SL, Klausen C, Hammond GL, Leung PC (2011) 37-kDa laminin receptor precursor mediates GnRH-II-induced MMP-2 expression and invasiveness in ovarian cancer cells. Mol Endocrinol 25(2):327–338. https://doi.org/10.1210/me.2010-0334
  • Rutter, L., Moran Lauter, A. N., Graham, M. A., & Cook, D. (2019). Visualization methods for differential expression analysis. BMC bioinformatics, 20(1), 458.
  • Saharinen P, Eklund L, Pulkki K, Bono P, Alitalo K. VEGF and angiopoietin signaling in tumor angiogenesis and metastasis. Trends Mol Med. 2011;17(7):347–62.
  • Singh, R., & Som, A. (2021). Common miRNAs, candidate genes and their interaction network across four subtypes of epithelial ovarian cancer. Bioinformation, 17(8), 748–759.
  • Sopo, M., Anttila, M., Hämäläinen, K. et al. Expression profiles of VEGF-A, VEGF-D and VEGFR1 are higher in distant metastases than in matched primary high grade epithelial ovarian cancer. BMC Cancer 19, 584 (2019).
  • Torre, L. A., Trabert, B., DeSantis, C. E., Miller, K. D., Samimi, G., Runowicz, C. D., Gaudet, M. M., Jemal, A., & Siegel, R. L. (2018). Ovarian cancer statistics, 2018. CA: a cancer journal for clinicians, 68(4), 284–296.
  • Yang, D., He, Y., Wu, B., Deng, Y., Wang, N., Li, M., & Liu, Y. (2020). Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer. Journal of ovarian research, 13(1), 10.
  • Zeng L, Qian J, Zhu F, Wu F, Zhao H, Zhu H. The prognostic values of matrix metalloproteinases in ovarian cancer. Journal of International Medical Research. 2020;48(1).
  • Zhang Y, Chen Q (2017) Relationship between matrix metalloproteinases and the occurrence and development of ovarian cancer. Braz J Med Biol Res 50(6):e6104. https://doi.org/10.1590/1414-431X20176104
  • Zheng MJ, Li X, Hu YX, Dong H, Gou R, Nie X, Liu Q, Ying-Ying H, Liu JJ, Lin B. Identification of molecular marker associated with ovarian cancer prognosis using bioinformatics analysis and experiments. J Cell Physiol. 2019 Jul;234(7):11023-11036.

Over Kanserinde Belirli Genlerin Anlatım Analizi

Year 2023, , 136 - 143, 31.10.2023
https://doi.org/10.59312/ebshealth.1367196

Abstract

Amaç: En ölümcül jinekolojik malignite olan over kanseri (OK), tanısal ve prognostik biyobelirteçlerin eksikliği nedeniyle genellikle ileri evrede teşhis edilir. Bu nedenle, OK'ye özgü biyolojik belirteçlerin tanımlanması, teşhis ve tedavi yanıtı için çok önemli bir adımdır. Amacımız, OK hastalarında over kanseri için potansiyel belirteç olan fonksiyonel gen setlerini ve ekspresyon profillerini incelemektir. Ayrıca OK hastaları için olası terapötik hedefler olabilecek genlerin potansiyelini belirlemeyi de amaçlarımızdandır.
Yöntem: qRT-PCR kullanılarak yedi genin (FOS, FOSL2, JUN, MMP-2, MMP-9, TIMP-2 ve VEGFA) ekspresyon profilleri belirlenmiştir. Kontrol grubu, tümör oluşumu gözlenmeyen, jinekolojik prosedür uygulanan total abdominal histerektomi (n=1) ve bilateral salpingo-ooferektomi (n=9) hastalarından oluşturulmuştur. Deney grubu için yüksek dereceli seröz OC epitel örnekleri (n=10) kullanılmıştır.
Bulgular ve Sonuçlar: qRT-PCR verilerine göre FOS (p=0,0089), MMP-9 (p=0,0029), VEGFA (p=0,0434) ekspresyonunda artış ve FOSL2 (p=0.0271), JUN (p=0.0041) ve TIMP-2 (p=0.0062) ekspresyonunda azalma tespit edilmiştir. Sonuç olarak, veriler OK patogenezi ve tedavisi ile ilgili yeni yaklaşımlar geliştirilmesini sağlayacaktır. Aday genler, gelecekte OK için kişiselleştirilmiş tanı ve tedaviyi geliştirebilecektir.

References

  • Alshamrani A. A. (2020). Roles of microRNAs in Ovarian Cancer Tumorigenesis: Two Decades Later, What Have We Learned?. Frontiers in oncology, 10, 1084.
  • Apostolou, P., Iliopoulos, A. C., Parsonidis, P., & Papasotiriou, I. (2019). Gene expression profiling as a potential predictor between normal and cancer samples in gastrointestinal carcinoma. Oncotarget, 10(36), 3328–3338.
  • Bejjani, Fabienne, et al. "The AP-1 transcriptional complex: Local switch or remote command?." Biochimica et Biophysica Acta (BBA)-Reviews on Cancer 1872.1 (2019): 11-23.
  • Escalona, R.M., Bilandzic, M., Western, P. et al. TIMP-2 regulates proliferation, invasion and STAT3-mediated cancer stem cell-dependent chemoresistance in ovarian cancer cells. BMC Cancer 20, 960 (2020). https://doi.org/10.1186/s12885-020-07274-6.
  • Gumusoglu-Acar, E., Gunel, T., Hosseını, M. K., Dogan, B., Tekarslan, E. E., Gurdamar, B., Cevık, N., Sezerman, U., Topuz, S., Aydınlı, K. (2023). Metabolic pathways of potential miRNA biomarkers derived from liquid biopsy in epithelial ovarian cancer. Oncology Letters, 25(4), 142.
  • Gunel, T. G., Dogan, B., Gumusoglu, E., Hosseini, M. K., Topuz, S., & Aydinli, K. (2019). Regulation of HMGA2 and KRAS genes in epithelial ovarian cancer by miRNA hsa-let-7d-3p. Journal of Cancer Research and Therapeutics, 15(6), 1321–1327.
  • Jang K, Kim M, Gilbert CA, et al. VEGFA activates an epigenetic pathway upregulating ovarian cancer-initiating cells. EMBO Mol Med. 2017;9(3):304–318.
  • Kicman, A., Niczyporuk, M., Kulesza, M., Motyka, J., & Ławicki, S. (2022). Utility of Matrix Metalloproteinases in the Diagnosis, Monitoring and Prognosis of Ovarian Cancer Patients. Cancer management and research, 14, 3359–3382. https://doi.org/10.2147/CMAR.S385658
  • Li, X., Hu, Z., Shi, H., Wang, C., Lei, J., & Cheng, Y. (2020). Inhibition of VEGFA Increases the Sensitivity of Ovarian Cancer Cells to Chemotherapy by Suppressing VEGFA-Mediated Autophagy. OncoTargets and therapy, 13, 8161–8171.
  • Li, J., Zhou, L., Jiang, H., Lin, L., & Li, Y. (2022). Inhibition of FOSL2 aggravates the apoptosis of ovarian cancer cells by promoting the formation of inflammasomes. Genes Genom, 44, 29–38.
  • Liu, Q., Yang, X., Yin, Y., Zhang, H., Yin, F., Guo, P., Zhang, X., Sun, C., Li, S., Han, Y., & Yang, Z. (2022). Identifying the Role of Oxidative Stress-Related Genes as Prognostic Biomarkers and Predicting the Response of Immunotherapy and Chemotherapy in Ovarian Cancer. Oxidative medicine and cellular longevity, 2022, 6575534.
  • Narrandes, S., & Xu, W. (2018). Gene Expression Detection Assay for Cancer Clinical Use. Journal of Cancer, 9(13), 2249–2265.
  • Olbromski PJ, Pawlik P, Bogacz A, Sajdak S. Identification of New Molecular Biomarkers in Ovarian Cancer Using the Gene Expression Profile. Journal of Clinical Medicine. 2022; 11(13):3888.
  • Ono, K., Tanaka, T., Tsunoda, T., Kitahara, O., Kihara, C., Okamoto, A., Ochiai, K., Takagi, T., & Nakamura, Y. (2000). Identification by cDNA microarray of genes involved in ovarian carcinogenesis. Cancer research, 60(18), 5007–5011.
  • Poon SL, Klausen C, Hammond GL, Leung PC (2011) 37-kDa laminin receptor precursor mediates GnRH-II-induced MMP-2 expression and invasiveness in ovarian cancer cells. Mol Endocrinol 25(2):327–338. https://doi.org/10.1210/me.2010-0334
  • Rutter, L., Moran Lauter, A. N., Graham, M. A., & Cook, D. (2019). Visualization methods for differential expression analysis. BMC bioinformatics, 20(1), 458.
  • Saharinen P, Eklund L, Pulkki K, Bono P, Alitalo K. VEGF and angiopoietin signaling in tumor angiogenesis and metastasis. Trends Mol Med. 2011;17(7):347–62.
  • Singh, R., & Som, A. (2021). Common miRNAs, candidate genes and their interaction network across four subtypes of epithelial ovarian cancer. Bioinformation, 17(8), 748–759.
  • Sopo, M., Anttila, M., Hämäläinen, K. et al. Expression profiles of VEGF-A, VEGF-D and VEGFR1 are higher in distant metastases than in matched primary high grade epithelial ovarian cancer. BMC Cancer 19, 584 (2019).
  • Torre, L. A., Trabert, B., DeSantis, C. E., Miller, K. D., Samimi, G., Runowicz, C. D., Gaudet, M. M., Jemal, A., & Siegel, R. L. (2018). Ovarian cancer statistics, 2018. CA: a cancer journal for clinicians, 68(4), 284–296.
  • Yang, D., He, Y., Wu, B., Deng, Y., Wang, N., Li, M., & Liu, Y. (2020). Integrated bioinformatics analysis for the screening of hub genes and therapeutic drugs in ovarian cancer. Journal of ovarian research, 13(1), 10.
  • Zeng L, Qian J, Zhu F, Wu F, Zhao H, Zhu H. The prognostic values of matrix metalloproteinases in ovarian cancer. Journal of International Medical Research. 2020;48(1).
  • Zhang Y, Chen Q (2017) Relationship between matrix metalloproteinases and the occurrence and development of ovarian cancer. Braz J Med Biol Res 50(6):e6104. https://doi.org/10.1590/1414-431X20176104
  • Zheng MJ, Li X, Hu YX, Dong H, Gou R, Nie X, Liu Q, Ying-Ying H, Liu JJ, Lin B. Identification of molecular marker associated with ovarian cancer prognosis using bioinformatics analysis and experiments. J Cell Physiol. 2019 Jul;234(7):11023-11036.
There are 24 citations in total.

Details

Primary Language English
Subjects Cancer Genetics
Journal Section Research Articles
Authors

Ece Gümüşoğlu-acar 0000-0003-3807-0330

Berkcan Doğan 0000-0001-8061-8131

Mehmet Ulaş Bilir 0000-0001-8469-705X

Tugce Senturk-kirmizitas 0000-0001-6235-251X

Samet Topuz 0000-0002-9069-0185

Tuba Gunel 0000-0003-3514-5210

Publication Date October 31, 2023
Published in Issue Year 2023

Cite

APA Gümüşoğlu-acar, E., Doğan, B., Bilir, M. U., Senturk-kirmizitas, T., et al. (2023). THE EXPRESSION ANALYSIS OF SPECIFIC GENES IN OVARIAN CANCER. Doğu Karadeniz Sağlık Bilimleri Dergisi, 2(Kongre Özel Sayısı), 136-143. https://doi.org/10.59312/ebshealth.1367196

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