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

International Journal of Molecular Sciences

Heather Mullikin¹  Fabian Trillsch¹  Bastian Czogalla¹  Theresa Vilsmaier¹  Helene Heidegger¹  Anca Chelariu-Raicu¹  Aurelia Vattai¹  Mingjun Zheng¹  Anna Hester¹  Udo Jeschke¹  Till Kaltofen¹  Sven Mahner¹ 

[1] Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany;

关键词: ovarian neoplasms;    lipid metabolism;    genes;    The Cancer Genome Atlas (TCGA);    Gene Expression Omnibus (GEO);   

DOI  :  10.3390/ijms21239169

来源: DOAJ

【 摘 要 】

(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.

【 授权许可】

Unknown