Clinical Significance of Metabolism-related Genes and FAK Activity in Ovarian High-grade Serous Carcinoma

Overview

Journal BMC Cancer

Publisher Biomed Central

Specialty Oncology

Date 2022 Jan 14

PMID 35027024

Authors

Masakazu Sato

Sho Sato

Daisuke Shintani

Mieko Hanaoka

Aiko Ogasawara

Maiko Miwa

Akira Yabuno

Akira Kurosaki

Hiroyuki Yoshida

Keiichi Fujiwara

Kosei Hasegawa

Affiliations

Soon will be listed here.

Abstract

Background: Administration of poly (ADP-ribose) polymerase (PARP) inhibitors after achieving a response to platinum-containing drugs significantly prolonged relapse-free survival compared to placebo administration. PARP inhibitors have been used in clinical practice. However, patients with platinum-resistant relapsed ovarian cancer still have a poor prognosis and there is an unmet need. The purpose of this study was to examine the clinical significance of metabolic genes and focal adhesion kinase (FAK) activity in advanced ovarian high-grade serous carcinoma (HGSC).

Methods: The RNA sequencing (RNA-seq) data and clinical data of HGSC patients were obtained from the Genomic Data Commons (GDC) Data Portal and analysed ( https://portal.gdc.cancer.gov/ ). In addition, tumour tissue was sampled by laparotomy or screening laparoscopy prior to treatment initiation from patients diagnosed with stage IIIC ovarian cancer (International Federation of Gynecology and Obstetrics (FIGO) classification, 2014) at the Saitama Medical University International Medical Center, and among the patients diagnosed with HGSC, 16 cases of available cryopreserved specimens were included in this study. The present study was reviewed and approved by the Institutional Review Board of Saitama Medical University International Medical Center (Saitama, Japan). Among the 6307 variable genes detected in both The Cancer Genome Atlas-Ovarian (TCGA-OV) data and clinical specimen data, 35 genes related to metabolism and FAK activity were applied. RNA-seq data were analysed using the Subio Platform (Subio Inc, Japan). JMP 15 (SAS, USA) was used for statistical analysis and various types of machine learning. The Kaplan-Meier method was used for survival analysis, and the Wilcoxon test was used to analyse significant differences. P < 0.05 was considered significant.

Results: In the TCGA-OV data, patients with stage IIIC with a residual tumour diameter of 1-10 mm were selected for K means clustering and classified into groups with significant prognostic correlations (p = 0.0444). These groups were significantly associated with platinum sensitivity/resistance in clinical cases (χ test, p = 0.0408) and showed significant relationships with progression-free survival (p = 0.0307).

Conclusion: In the TCGA-OV data, 2 groups classified by clustering focusing on metabolism-related genes and FAK activity were shown to be associated with platinum resistance and a poor prognosis.

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References

He Z, Zhang J, Yuan X, Liu Z, Liu B, Tuo S . Network based stratification of major cancers by integrating somatic mutation and gene expression data. PLoS One. 2017; 12(5):e0177662. PMC: 5433734. DOI: 10.1371/journal.pone.0177662. View

Batlle E, Clevers H . Cancer stem cells revisited. Nat Med. 2017; 23(10):1124-1134. DOI: 10.1038/nm.4409. View

Bao M, Zhang L, Hu Y . Novel gene signatures for prognosis prediction in ovarian cancer. J Cell Mol Med. 2020; 24(17):9972-9984. PMC: 7520318. DOI: 10.1111/jcmm.15601. View

Zhang G, Zhang J, Zhu Y, Liu H, Shi Y, Mi K . Association of somatic mutations in BRCA2 BRC domain with chemotherapy sensitivity and survival in high grade serous ovarian cancer. Exp Cell Res. 2021; 406(1):112742. DOI: 10.1016/j.yexcr.2021.112742. View

Tischler J, Gruhn W, Reid J, Allgeyer E, Buettner F, Marr C . Metabolic regulation of pluripotency and germ cell fate through α-ketoglutarate. EMBO J. 2018; 38(1). PMC: 6315289. DOI: 10.15252/embj.201899518. View

Akbani R, Ng P, Werner H, Shahmoradgoli M, Zhang F, Ju Z . A pan-cancer proteomic perspective on The Cancer Genome Atlas. Nat Commun. 2014; 5:3887. PMC: 4109726. DOI: 10.1038/ncomms4887. View

Lu M, Fan Z, Xu B, Chen L, Zheng X, Li J . Using machine learning to predict ovarian cancer. Int J Med Inform. 2020; 141:104195. DOI: 10.1016/j.ijmedinf.2020.104195. View

Shinagare A, Balthazar P, Ip I, Lacson R, Liu J, Ramaiya N . High-Grade Serous Ovarian Cancer: Use of Machine Learning to Predict Abdominopelvic Recurrence on CT on the Basis of Serial Cancer Antigen 125 Levels. J Am Coll Radiol. 2018; 15(8):1133-1138. DOI: 10.1016/j.jacr.2018.04.008. View

Kawakami E, Tabata J, Yanaihara N, Ishikawa T, Koseki K, Iida Y . Application of Artificial Intelligence for Preoperative Diagnostic and Prognostic Prediction in Epithelial Ovarian Cancer Based on Blood Biomarkers. Clin Cancer Res. 2019; 25(10):3006-3015. DOI: 10.1158/1078-0432.CCR-18-3378. View

10.

McGrail D, Khambhati N, Qi M, Patel K, Ravikumar N, Brandenburg C . Alterations in ovarian cancer cell adhesion drive taxol resistance by increasing microtubule dynamics in a FAK-dependent manner. Sci Rep. 2015; 5:9529. PMC: 4400875. DOI: 10.1038/srep09529. View

11.

Pavlova N, Thompson C . The Emerging Hallmarks of Cancer Metabolism. Cell Metab. 2016; 23(1):27-47. PMC: 4715268. DOI: 10.1016/j.cmet.2015.12.006. View

12.

Niu Y, Sun W, Chen K, Fu Z, Chen Y, Zhu J . A Novel Scoring System for Pivotal Autophagy-Related Genes Predicts Outcomes after Chemotherapy in Advanced Ovarian Cancer Patients. Cancer Epidemiol Biomarkers Prev. 2019; 28(12):2106-2114. DOI: 10.1158/1055-9965.EPI-19-0359. View

13.

Lin H, Wang J, Wen X, Wen Q, Huang S, Mai Z . A prognosis-predictive nomogram of ovarian cancer with two immune-related genes: and . Oncol Lett. 2020; 20(5):204. PMC: 7491088. DOI: 10.3892/ol.2020.12067. View

14.

Bickell N, Egorova N, Prasad-Hayes M, Franco R, Howell E, Wisnivesky J . Secondary Surgery Versus Chemotherapy for Recurrent Ovarian Cancer. Am J Clin Oncol. 2016; 41(5):458-464. PMC: 5665721. DOI: 10.1097/COC.0000000000000310. View

15.

Bristow R, Tomacruz R, Armstrong D, Trimble E, Montz F . Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis. J Clin Oncol. 2002; 20(5):1248-59. DOI: 10.1200/JCO.2002.20.5.1248. View

16.

Saji H, Tsuboi M, Shimada Y, Kato Y, Hamanaka W, Kudo Y . Gene expression profiling and molecular pathway analysis for the identification of early-stage lung adenocarcinoma patients at risk for early recurrence. Oncol Rep. 2013; 29(5):1902-6. DOI: 10.3892/or.2013.2332. View

17.

Moore K, Secord A, Geller M, Miller D, Cloven N, Fleming G . Niraparib monotherapy for late-line treatment of ovarian cancer (QUADRA): a multicentre, open-label, single-arm, phase 2 trial. Lancet Oncol. 2019; 20(5):636-648. DOI: 10.1016/S1470-2045(19)30029-4. View

18.

Jayson G, Kohn E, Kitchener H, Ledermann J . Ovarian cancer. Lancet. 2014; 384(9951):1376-88. DOI: 10.1016/S0140-6736(13)62146-7. View

19.

Mairinger F, Bankfalvi A, Schmid K, Mairinger E, Mach P, Walter R . Digital Immune-Related Gene Expression Signatures In High-Grade Serous Ovarian Carcinoma: Developing Prediction Models For Platinum Response. Cancer Manag Res. 2019; 11:9571-9583. PMC: 6858803. DOI: 10.2147/CMAR.S219872. View

20.

Huang C, Mezencev R, McDonald J, Vannberg F . Open source machine-learning algorithms for the prediction of optimal cancer drug therapies. PLoS One. 2017; 12(10):e0186906. PMC: 5658085. DOI: 10.1371/journal.pone.0186906. View