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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.

SUBMITTER: Zheng M

PROVIDER: S-EPMC7731240 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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

Zheng Mingjun M Mullikin Heather H Hester Anna A Czogalla Bastian B Heidegger Helene H Vilsmaier Theresa T Vattai Aurelia A Chelariu-Raicu Anca A Jeschke Udo U Trillsch Fabian F Mahner Sven S Kaltofen Till T

International journal of molecular sciences 20201201 23

(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 ...[more]

PMID: 33271935

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Project description:BACKGROUND: It is known that all tumors studied in sufficient number to draw conclusions show characteristic/specific chromosomal rearrangements, and the identification of these chromosomes and the genes rearranged behind the aberrations may ultimately lead to a tailor-made therapy for each cancer patient. Knowledge about the acquired genomic aberrations of ovarian carcinomas is still unsatisfactory. METHODS: We cytogenetically analyzed 110 new cases of ovarian carcinoma of different histological subtypes using karyotyping of G-banded chromosomes and high-resolution comparative genomic hybridization. We first compared the aberration patterns identified by the two genomic screening techniques using the so-called "classical" pathological classification in which the carcinomas are grouped as tumors of types I and II. We also broke down our findings according to the more "modern" classification which groups the carcinomas in five different categories. RESULTS: The chromosomal breakpoints identified by karyotyping tended to cluster to 19p/q and to 11q, but no unquestionably recurrent rearrangement could be seen. Common imbalances were scored as gains from 1q, 3q, 7q, and 8q and losses from 17p, 19q, and 22q. Gain of material from 8q23 and losses from 19q and 22q have previously been described at high frequencies in bilateral and borderline ovarian carcinomas. The fact that they were present both in "precursor" lesions, i.e., borderline tumors, as well as in tumors of more advanced stages, i.e., carcinomas, highlights the possibility of an adenoma-carcinoma sequence in ovarian carcinogenesis. CONCLUSION: Based on the relatively simple genomic changes we identified in the low-grade serous carcinomas examined (n = 7) and which largely corresponded to the aberration pattern formerly identified in borderline tumors, one can interpret the cytogenetic data as supporting the view that the low-grade carcinomas represent a phenotypically more advanced stage of borderline tumors. Whether transition from low-grade to high-grade carcinoma also occurs, is a question about which the genomic data is still inconclusive.

Project description:The current standard of care for epithelial ovarian cancer does not discriminate between different histologic subtypes (serous, clear cell, endometrioid and mucinous) despite the knowledge that ovarian carcinoma subtypes do not respond uniformly to conventional platinum/taxane-based chemotherapy. Exploiting addictions and vulnerabilities in cancers with distinguishable molecular features presents an opportunity to develop individualized therapies that may be more effective than the current 'one size fits all' approach. One such opportunity is arginine depletion therapy with pegylated arginine deiminase, which has shown promise in several cancer types that exhibit low levels of argininosuccinate synthetase including hepatocellular and prostate carcinoma and melanoma. Based on the high levels of argininosuccinate synthetase previously observed in ovarian cancers, these tumours have been considered unlikely candidates for arginine depletion therapy. However, argininosuccinate synthetase levels have not been evaluated in the individual histologic subtypes of ovarian carcinoma. The current study is the first to examine the expression of argininosuccinate synthetase at the mRNA and protein levels in large cohorts of primary and recurrent ovarian carcinomas and ovarian cancer cell lines. We show that the normal fallopian tube fimbria and the majority of primary high-grade and low-grade serous ovarian carcinomas express high levels of argininosuccinate synthetase, which tend to further increase in recurrent tumours. In contrast to the serous subtype, non-serous ovarian carcinoma subtypes (clear cell, endometrioid and mucinous) frequently lack detectable argininosuccinate synthetase expression. The in vitro sensitivity of ovarian cancer cell lines to arginine depletion with pegylated arginine deiminase was inversely correlated with argininosuccinate synthetase expression. Our data suggest that the majority of serous ovarian carcinomas are not susceptible to therapeutic intervention with arginine deiminase while a subset of non-serous ovarian carcinoma subtypes are auxotrophic for arginine and should be considered for clinical trials with pegylated arginine deiminase.

Project description:Purpose: Considerable evidence suggests that autophagy plays a crucial role in the biological processes of ovarian cancer. The aim of this study was to develop a novel autophagy-related prognostic signature for serous ovarian cancer. Methods: A univariate Cox proportional regression model was used to analyze mRNA microarray and clinical data in The Cancer Genome Atlas (TCGA) for the purpose of selecting autophagy-related prognostic genes. A multivariate Cox proportional regression model and the survival analysis were used to develop an eight-gene prognostic signature. The multivariate Cox and stratification analysis suggested that this signature was an independent prognostic factor for serous ovarian cancer patients. Bioinformatics functions were investigated by a principal components analysis and gene set enrichment analysis (GSEA). Finally, the correlation between the prognostic signature and gene mutation status was further analyzed in serous ovarian cancer, and especially with regard to the mutation status of BRCA1 and BRCA2 (BRCA1/2) genes. Results: Distinctly different autophagy-related gene expression profiles were identified in normal ovarian tissues and serous ovarian cancer tissues. We profiled an autophagy-related gene set and identified eight genes with significant prognostic values for serous ovarian cancer. Subsequently, an autophagy-related ovarian cancer risk signature was constructed, and patients at a high-risk or low-risk for poor prognosis were identified based on their signature. High-risk patients had significantly shorter overall survival (OS) and disease-free survival (DFS) times than low-risk patients. GSEA results suggested an enhanced intensity of autophagy regulation in high-risk patients when compared with low-risk patients. When studied as an independent prognostic factor for serous ovarian cancer, the significant prognostic value of this signature could be seen in the stratified cohorts. For clinical use, we developed a nomogram that included the prognostic classifier and seven clinical risk factors. Additionally, we identified the 10 most frequently mutated genes found in serous ovarian cancer patients, and analyzed them for their differences in high-risk and low-risk patients. Among 293 patients, 62 had BRCA1/2 gene mutations, and this result was significantly correlated with the autophagy-related prognostic signature. Conclusions: Our findings suggest that the eight-gene autophagy-related signature could serve as an independent prognostic indicator for cases of serous ovarian cancer.

Dataset Information

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

Publications

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

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