The impact on high‐grade serous ovarian cancer of obesity and lipid metabolism‐related gene expression patterns: the underestimated driving force affecting prognosis

Abstract

To investigate whether specific obesity/metabolism‐related gene expression patterns affect the survival of patients with ovarian cancer. Clinical and genomic data of 590 samples from the high‐grade ovarian serous carcinoma (HGOSC) study of The Cancer Genome Atlas (TCGA) and 91 samples from the Australian Ovarian Cancer Study were downloaded from the International Cancer Genome Consortium (ICGC) portal. Clustering of mRNA microarray and reverse‐phase protein array (RPPA) data was performed with 83 consensus driver genes and 144 obesity and lipid metabolism‐related genes. Association between different clusters and survival was analyzed with the Kaplan–Meier method and a Cox regression. Mutually exclusive, co‐occurrence and network analyses were also carried out. Using RNA and RPPA data, it was possible to identify two subsets of HGOSCs with similar clinical characteristics and cancer driver mutation profiles (e.g. TP53), but with different outcome. These differences depend more on up‐regulation of specific obesity and lipid metabolism‐related genes than on the number of gene mutations or copy number alterations. It was also found that CD36 and TGF‐ß are highly up‐regulated at the protein levels in the cluster with the poorer outcome. In contrast, BSCL2 is highly up‐regulated in the cluster with better progression‐free and overall survival. Different obesity/metabolism‐related gene expression patterns constitute a risk factor for prognosis independent of the therapy results in the Cox regression. Prognoses were conditioned by the differential expression of obesity and lipid metabolism‐related genes in HGOSCs with similar cancer driver mutation profiles, independent of the initial therapeutic response.

Identification of Ovarian Cancer Gene Expression Patterns Associated with Disease Progression and Mortality

“…From KEGG pathway analysis, this gene is found actively involved in Adipocytokine signaling pathway, PPAR signalling pathway, and AMPK signaling pathways. In other studies it found to be a significant gene affecting survival of patient of ovarian cancer (Ladanyi et al (2018); Cuello et al (2018)). SCGB2A1 (Secretoglobin Family 2A Member 1) is a top differentially expressed gene in all grades of OC (Bellone et al (2013);Fischer et al (2014)).…”

Simvastatin interferes with cancer ‘stem-cell’ plasticity reducing metastasis in ovarian cancer

“…TP53 mutation is involved in controlling EMT, stemness, and metabolic adaptations of cancer cells destined to form spheroids (Vousden & Ryan 2009, Freed-Pastor et al 2012. Interestingly, TP53 mutations correlate with the acquisition of a mevalonate gene signature in HGS-ovC (Greenaway et al 2016, Cuello et al 2018. Thus, we postulated that the mevalonate pathway might be critical for stemness, EMT and spheroid formation.…”

“…We recently showed that high leptin levels, observed among obese patients, contribute to a more favorable microenvironment for cancer cells to easily transit and metastasize (Kato et al 2015). Even more recently, we have shown that HGS-ovCs exhibiting gene signatures related to abnormal lipid metabolism and obesity resulted in poorer outcomes, independently of the expression profile of well-known gene drivers for this disease (e.g., WT or mutant TP53) (Cuello et al 2018). Under these conditions, cancer cells can easily aggregate, adhere to the mesothelium and remain dormant and protected within the metastatic foci (Liu et al 2015).…”

Leptin contributes to the taxol chemoresistance in epithelial ovarian cancer

“…Cuello et al (26) analyzed 83 consensus driver genes and 143 lipid metabolism-associated genes in 681 patients with high-grade serous ovarian cancer, obtained from the ICGC database, using gene chip and reverse protein chip clustering analyses. This study demonstrated that certain genes associated with obesity and lipid metabolism disorder may have significant effects on the prognosis of patients with ovarian cancer.…”