Systematic Identification of Characteristic Genes of Ovarian Clear Cell Carcinoma Compared with High-Grade Serous Carcinoma Based on RNA-Sequencing

Nagasawa, Saya; Ikeda, Kazuhiro; Horie‐Inoue, Kuniko; Sato, Shigeo; Itakura, Atsuo; Takeda, Satoru; Inoue, Satoshi

doi:10.3390/ijms20184330

Cited by 22 publications

(25 citation statements)

References 54 publications

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“…Traditional prognostic criteria are not sufficient in accurately predicting the survival of an individual patient. Multiple large cancer databases, such as TCGA and GEO, offer researchers the opportunity to analyze gene expression data and the corresponding clinical information on a large scale [30,31]. Until now, previous studies on gene expression have seen modifications in the molecular signature between benign and malignant tumors or low and advanced tumor stages [32][33][34].…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile

Zheng

Mullikin

Hester

et al. 2020

IJMS

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(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile

Zheng

Mullikin

Hester

et al. 2020

IJMS

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“…To address this, we exposed clear cell (SKOV3) and serous (OVCAR3) ovarian cancer cells, two major histological subtypes of OC, to the FASN selective inhibitor G28UCM 6,7 . We compared the metabolomes of SKOV3 and OVCAR3 cells in the presence or absence of G28UCM with the corresponding proteomes and kinomes.…”

mentioning

confidence: 99%

Membrane disruption, but not metabolic rewiring, is the key mechanism of anticancer-action of FASN-inhibitors: a multi-omics analysis in ovarian cancer

Grunt

Slany

Semkova

et al. 2020

Sci Rep

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Fatty-acid(FA)-synthase(FASN) is a druggable lipogenic oncoprotein whose blockade causes metabolic disruption. Whether drug-induced metabolic perturbation is essential for anticancer drug-action, or is just a secondary—maybe even a defence response—is still unclear. To address this, SKOV3 and OVCAR3 ovarian cancer(OC) cell lines with clear cell and serous histology, two main OC subtypes, were exposed to FASN-inhibitor G28UCM. Growth-inhibition was compared with treatment-induced cell-metabolomes, lipidomes, proteomes and kinomes. SKOV3 and OVCAR3 were equally sensitive to low-dose G28UCM, but SKOV3 was more resistant than OVCAR3 to higher concentrations. Metabolite levels generally decreased upon treatment, but individual acylcarnitines, glycerophospholipids, sphingolipids, amino-acids, biogenic amines, and monosaccharides reacted differently. Drug-induced effects on central-carbon-metabolism and oxidative-phosphorylation (OXPHOS) were essentially different in the two cell lines, since drug-naïve SKOV3 are known to prefer glycolysis, while OVCAR3 favour OXPHOS. Moreover, drug-dependent increase of desaturases and polyunsaturated-fatty-acids (PUFAs) were more pronounced in SKOV3 and appear to correlate with G28UCM-tolerance. In contrast, expression and phosphorylation of proteins that control apoptosis, FA synthesis and membrane-related processes (beta-oxidation, membrane-maintenance, transport, translation, signalling and stress-response) were concordantly affected. Overall, membrane-disruption and second-messenger-silencing were crucial for anticancer drug-action, while metabolic-rewiring was only secondary and may support high-dose-FASN-inhibitor-tolerance. These findings may guide future anti-metabolic cancer intervention.

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“…et al, 2018 ; Song et al, 2018 ; Teng et al, 2018 ; Liu et al, 2019 ; Pan et al, 2019 ; Wang et al, 2019 ). Others, such as CPNE8, MAGED1, RNF144A, SOX14, ACOT13 (cg15486740), EID1 (cg00481239), RPS4X (cg08859156), and TTRAP (cg15486740), have been identified in various tumors other than GC ( Kamio et al, 2010 ; Zeng et al, 2012 ; Zhou et al, 2013 ; Kuang et al, 2017 ; Stanisavljevic et al, 2017 ; Liu X. et al, 2018 ; Tosic et al, 2018 ; Nagasawa et al, 2019 ; Yang et al, 2019 ). The remaining biomarkers, including hsa-mir-653, hsa-mir-6808, LOC91450, ZNF22, C1orf144 (cg14791193), GOLGA3 (cg26856948), HLA-DPB1 (cg20100408), LRFN4 (cg22740006), MDH2 (cg22813794), MIR365-2 (cg24361571), NUFIP2 (cg25161386), PREP (cg12485556), STYXL1 (cg22813794), TMEM117 (cg07020967), ZC3H10 (ZC3H10), IL1RAPL1 (cg20350671), PC (cg22740006), SHC4 (cg00481239), and ATXN10 (cg22395807), have not been previously reported.…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, SOX14 showed opposite prognostic values in cervical cancer and leukemia, with anti-tumor and carcinogenic effects, respectively ( Stanisavljevic et al, 2017 ; Tosic et al, 2018 ). Studies have also implicated CPNE8, MAGED1, and RNF144A in ovarian and breast cancers ( Zeng et al, 2012 ; Nagasawa et al, 2019 ; Yang et al, 2019 ). However, LOC91450 and ZNF22 have not been reported in cancer.…”

Section: Discussionmentioning

confidence: 99%

Genomics Score Based on Genome-Wide Network Analysis for Prediction of Survival in Gastric Cancer: A Novel Prognostic Signature

et al. 2020

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Purpose Gastric cancer (GC) is a product of multiple genetic abnormalities, including genetic and epigenetic modifications. This study aimed to integrate various biomolecules, such as miRNAs, mRNA, and DNA methylation, into a genome-wide network and develop a nomogram for predicting the overall survival (OS) of GC. Materials and Methods A total of 329 GC cases, as a training cohort with a random of 150 examples included as a validation cohort, were screened from The Cancer Genome Atlas database. A genome-wide network was constructed based on a combination of univariate Cox regression and least absolute shrinkage and selection operator analyses, and a nomogram was established to predict 1-, 3-, and 5-year OS in the training cohort. The nomogram was then assessed in terms of calibration, discrimination, and clinical usefulness in the validation cohort. Afterward, in order to confirm the superiority of the whole gene network model and further reduce the biomarkers for the improvement of clinical usefulness, we also constructed eight other models according to the different combinations of miRNAs, mRNA, and DNA methylation sites and made corresponding comparisons. Finally, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were also performed to describe the function of this genome-wide network. Results A multivariate analysis revealed a novel prognostic factor, a genomics score (GS) comprising seven miRNAs, eight mRNA, and 19 DNA methylation sites. In the validation cohort, comparing to patients with low GS, high-GS patients (HR, 12.886; P < 0.001) were significantly associated with increased all-cause mortality. Furthermore, after stratification of the TNM stage (I, II, III, and IV), there were significant differences revealed in the survival rates between the high-GS and low-GS groups as well ( P < 0.001). The 1-, 3-, and 5-year C-index of whole genomics-based nomogram were 0.868, 0.895, and 0.928, respectively. The other models have comparable or relatively poor comprehensive performance, while they had fewer biomarkers. Besides that, DAVID 6.8 further revealed multiple molecules and pathways related to the genome-wide network, such as cytomembranes, cell cycle, and adipocytokine signaling. Conclusion We successfully developed a GS based on genome-wide network, which may represent a novel prognostic factor for GC. A combination of GS and TNM staging provides additional precision in stratifying patients with different OS prognoses, constituting a more comprehensive sub-typing system. This could potentially play an important role in future clinical practice.

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Systematic Identification of Characteristic Genes of Ovarian Clear Cell Carcinoma Compared with High-Grade Serous Carcinoma Based on RNA-Sequencing

Cited by 22 publications

References 54 publications

Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile

Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile

Membrane disruption, but not metabolic rewiring, is the key mechanism of anticancer-action of FASN-inhibitors: a multi-omics analysis in ovarian cancer

Genomics Score Based on Genome-Wide Network Analysis for Prediction of Survival in Gastric Cancer: A Novel Prognostic Signature

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