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

Abstract	(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.
Authors	Mingjun Zheng, Heather Mullikin, Anna Hester, Bastian Czogalla, Helene Heidegger, Theresa Vilsmaier, Aurelia Vattai, Anca Chelariu-Raicu, Udo Jeschke, Fabian Trillsch, Sven Mahner, Till Kaltofen
Journal	International journal of molecular sciences (Int J Mol Sci) Vol. 21 Issue 23 (Dec 01 2020) ISSN: 1422-0067 [Electronic] Switzerland
PMID	33271935 (Publication Type: Journal Article)
Chemical References	Biomarkers, Tumor
Topics	Biomarkers, Tumor Computational Biology Cystadenocarcinoma, Serous (etiology, metabolism, mortality, pathology) Databases, Genetic Disease Susceptibility Female Gene Expression Profiling Gene Expression Regulation, Neoplastic Gene Regulatory Networks Humans Kaplan-Meier Estimate Lipid Metabolism Ovarian Neoplasms (etiology, metabolism, mortality, pathology) Prognosis ROC Curve Transcriptome

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