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

2020 ◽  
Vol 21 (23) ◽  
pp. 9169
Author(s):  
Mingjun Zheng ◽  
Heather Mullikin ◽  
Anna Hester ◽  
Bastian Czogalla ◽  
Helene Heidegger ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Lipid Metabolism ◽  
Prognostic Model ◽  
Molecular Subtypes ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Serous Ovarian Cancer ◽  
Ovarian Carcinomas ◽  
Comparable Amount ◽  
Lipid Metabolism Genes

(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.

MOLECULAR AND GENETIC SUBTYPES OF OVARIAN CANCER: PROSPECTS FOR FURTHER STUDIES

2019 ◽  
Vol 65 (1) ◽  
pp. 56-62
Author(s):  
Alisa Villert ◽  
Larisa Kolomiets ◽  
Natalya Yunusova ◽  
Yevgeniya Fesik
Keyword(s):  
Ovarian Cancer ◽  
Ovarian Carcinoma ◽  
Molecular Subtypes ◽  
Survival Rates ◽  
Consensus Algorithm ◽  
Histopathological Diagnosis ◽  
Low Grade ◽  
High Grade ◽  
Ovarian Carcinomas ◽  
Genetic Subtypes

High-grade ovarian carcinoma is a histopathological diagnosis, however, at the molecular level, ovarian cancer represents a heterogeneous group of diseases. Studies aimed at identifying molecular genetic subtypes of ovarian cancer are conducted in order to find the answer to the question: can different molecular subgroups influence the choice of treatment? One of the achievements in this trend is the recognition of the dualistic model that categorizes various types of ovarian cancer into two groups designated high-grade (HG) and low-grade (LG) tumors. However, the tumor genome sequencing data suggest the existence of 6 ovarian carcinoma subtypes, including two LG and four HG subtypes. Subtype C1 exhibits a high stromal response and the lowest survival. Subtypes C2 and C4 demonstrate higher number of intratumoral CD3 + cells, lower stromal gene expression and better survival than sybtype C1. Subtype C5 (mesenchymal) is characterized by mesenchymal cells, over-expression of N-cadherin and P-cadherin, low expression of differentiation markers, and lower survival rates than C2 and C4. The use of a consensus algorithm to determine the subtype allows identification of only a minority of ovarian carcinomas (approximately 25%) therefore, the practical importance of this classification requires additional research. There is evidence that it makes sense to randomize tumors into groups with altered expression of angiogenic genes and groups with overexpression of the immune response genes, as in the angiogenic group there is a comparative superiority in terms of survival. The administration of bevacizumab in the angiogenic group improves survival, while the administration of bevacizumab in the immune group even worsens the outcome. Molecular subtypes with worse survival rates (proliferative and mesenchymal) also benefit most from bevacizumab treatment. This review focuses on some of the advances in understanding molecular, cellular, and genetic changes in ovarian carcinomas with the results achieved so far regarding the formulation of molecular subtypes of ovarian cancer, however further studies are needed.

scholarly journals Dysregulated liver lipid metabolism and innate immunity associated with hepatic steatosis in neonatal BBdp rats and NOD mice

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
D. Serrano ◽  
J. A. Crookshank ◽  
B. S. Morgan ◽  
R. W. Mueller ◽  
M.-F. Paré ◽  
...  
Keyword(s):  
Innate Immunity ◽  
Lipid Metabolism ◽  
Hepatic Steatosis ◽  
Liver Lipid ◽  
Nod Mice ◽  
Gene Signature ◽  
Enhanced Expression ◽  
Liver Lipid Metabolism ◽  
Hepatic Innate Immunity ◽  
Lipid Metabolism Genes

Abstract In a previous study we reported that prediabetic rats have a unique gene signature that was apparent even in neonates. Several of the changes we observed, including enhanced expression of pro-inflammatory genes and dysregulated UPR and metabolism genes were first observed in the liver followed by the pancreas. In the present study we investigated further early changes in hepatic innate immunity and metabolism in two models of type 1 diabetes (T1D), the BBdp rat and NOD mouse. There was a striking increase in lipid deposits in liver, particularly in neonatal BBdp rats, with a less striking but significant increase in neonatal NOD mice in association with dysregulated expression of lipid metabolism genes. This was associated with a decreased number of extramedullary hematopoietic clusters as well as CD68+ macrophages in the liver of both models. In addition, PPARɣ and phosphorylated AMPKα protein were decreased in neonatal BBdp rats. BBdp rats displayed decreased expression of antimicrobial genes in neonates and decreased M2 genes at 30 days. This suggests hepatic steatosis could be a common early feature in development of T1D that impacts metabolic homeostasis and tolerogenic phenotype in the prediabetic liver.

scholarly journals Evaluating Targeted Therapies in Ovarian Cancer Metabolism: Novel Role for PCSK9 and Second Generation mTOR Inhibitors

Cancers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 3727
Author(s):  
Dafne Jacome Sanz ◽  
Juuli Raivola ◽  
Hanna Karvonen ◽  
Mariliina Arjama ◽  
Harlan Barker ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Lipid Metabolism ◽  
Cell Survival ◽  
Drug Testing ◽  
Cancer Metabolism ◽  
Ex Vivo ◽  
Specific Inhibitor ◽  
Low Grade ◽  
Serous Ovarian Cancer

Background: Dysregulated lipid metabolism is emerging as a hallmark in several malignancies, including ovarian cancer (OC). Specifically, metastatic OC is highly dependent on lipid-rich omentum. We aimed to investigate the therapeutic value of targeting lipid metabolism in OC. For this purpose, we studied the role of PCSK9, a cholesterol-regulating enzyme, in OC cell survival and its downstream signaling. We also investigated the cytotoxic efficacy of a small library of metabolic (n = 11) and mTOR (n = 10) inhibitors using OC cell lines (n = 8) and ex vivo patient-derived cell cultures (PDCs, n = 5) to identify clinically suitable drug vulnerabilities. Targeting PCSK9 expression with siRNA or PCSK9 specific inhibitor (PF-06446846) impaired OC cell survival. In addition, overexpression of PCSK9 induced robust AKT phosphorylation along with increased expression of ERK1/2 and MEK1/2, suggesting a pro-survival role of PCSK9 in OC cells. Moreover, our drug testing revealed marked differences in cytotoxic responses to drugs targeting metabolic pathways of high-grade serous ovarian cancer (HGSOC) and low-grade serous ovarian cancer (LGSOC) PDCs. Our results show that targeting PCSK9 expression could impair OC cell survival, which warrants further investigation to address the dependency of this cancer on lipogenesis and omental metastasis. Moreover, the differences in metabolic gene expression and drug responses of OC PDCs indicate the existence of a metabolic heterogeneity within OC subtypes, which should be further explored for therapeutic improvements.

Identification of a 15 DNA Damage Repair–Related Gene Signature as a Prognostic Predictor for Lung Adenocarcinoma

2021 ◽  
Vol 24 ◽  
Author(s):  
Linping Gu ◽  
Yuanyuan Xu ◽  
Hong Jian
Keyword(s):  
Prognostic Model ◽  
Molecular Subtypes ◽  
Prognostic Biomarker ◽  
Gene Signature ◽  
Supplementary Information ◽  
Related Gene ◽  
Accurate Evaluation ◽  
Damage Repair ◽  
Related Feature ◽  
Cluster 2

Background: Lung Adenocarcinoma (LUAD) is a common malignancy with a poor prognosis due to the lack of predictive markers. DNA Damage Repair (DDR)-related genes are closely related to cancer progression and treatment. Introduction: To identify a reliable DDR-related gene signature as an independent predictor of LUAD. Methods: DDR-related genes were obtained using combined analysis of TCGA-LUAD data and literature information, followed by the identification of DDR-related prognostic genes. The DDR-related molecular subtypes were then screened, followed by Kaplan–Meier analysis, feature gene identification, and pathway enrichment analysis of each subtype. Moreover, Cox and LASSO regression analyses were performed for the feature genes of each subtype to construct a prognostic model. The clinical utility of the prognostic model was confirmed using the validation dataset GSE72094 and nomogram analysis. Results: Eight DDR-related prognostic genes were identified from 31 DDR-related genes. Using consensus cluster analysis, three molecular subtypes were screened. Cluster 2 had the best prognosis, while cluster 3 had the worst. Compared to cluster 2, clusters 1 and 3 consisted of more stage 3 – 4, T2–T4, male, and older samples. The feature genes of clusters 1, 2, and 3 were mainly enriched in the cell cycle, arachidonic acid metabolism, and ribosomes. Furthermore, a 15-feature gene signature was identified for improving the prognosis of LUAD patients. Conclusion: The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment. Conclusion: The 15 DDR-related feature gene signature is an independent and powerful prognostic biomarker for LUAD that may improve risk classification and provide supplementary information for a more accurate evaluation and personalized treatment.

Dynamic of molecular subtypes of high‐grade serous ovarian cancer in paired primary and relapsed biopsies.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e17091-e17091
Author(s):  
Elena Ioana Braicu ◽  
Hagen Kulbe ◽  
Felix Dreher ◽  
Eliane T Taube ◽  
Frauke Ringel ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Clinical Outcome ◽  
Molecular Subtypes ◽  
Molecular Subtype ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Microarray Gene Expression ◽  
Tissue Samples ◽  
Paired Samples ◽  
Nanostring Technology

e17091 Background: Previously four molecular subtypes of high grade serous ovarian cancer (HGSOC) with distinct biological features and clinical outcome have been described: C1 (mesenchymal), C2 (immunoreactive), C4 (differentiated), and C5 (proliferative). Using Nanostring technique and a minimal signature of 39 classifier genes could reproduce the subtypes identified by microarray gene expression profiling (Leong HS et al. Australian Ovarian Cancer Study. J Pathol. 2015). Methods: We characterized paraffin embedded tissue samples from 279 HGSOC patients for molecular subtypes, utilizing the 39 classifier signature and 9 control genes by Nanostring nCounter Analysis System. From 16 patients paired primary and relapsed samples were available. Only chemonaive primary HGSOC patients were included in the study. FFPEs and clinical data were provided by TOC ( www.toc-network.de ). For each sample, probability scores for the four molecular subtypes (C1, C2, C4, and C5) were calculated. The highest calculated score determined the most likely subtype of the tumor. Results: Of all analyzed primary tumor samples, 88 (31.5%) were classified as C1, 83 (29.8%), 53 (19.0%) and 55 (19.7%) as subtypes C2, C4 and C5, respectively. Our results confirmed data by the AOCS study, which described the distribution of HGSOC with 40.2% (C1), 22.5% (C2), 20.1% (C4) and 17.2% (C5), respectively. Within the paired samples, for 12 of the 16 patients dynamic changes in the molecular subtypes between primary and relapse occurred, while in the remaining 4 patients the phenotype was stable. Conclusions: Molecular subtypes of HGSOC using Nanostring technology with a small panel of classifier genes can be confirmed. Furthermore, the data showed that a change of the established molecular subtype might occur during the evolution of the disease, and therefore translate in a different clinical outcome.

scholarly journals A Collagen-Remodeling Gene Signature Regulated by TGF-β Signaling Is Associated with Metastasis and Poor Survival in Serous Ovarian Cancer

2013 ◽  
Vol 20 (3) ◽  
pp. 711-723 ◽  
Author(s):  
Dong-Joo Cheon ◽  
Yunguang Tong ◽  
Myung-Shin Sim ◽  
Judy Dering ◽  
Dror Berel ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Gene Signature ◽  
Serous Ovarian Cancer ◽  
Poor Survival ◽  
Collagen Remodeling

scholarly journals Classification of molecular subtypes of high-grade serous ovarian cancer by MALDI-Imaging.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e17544-e17544
Author(s):  
Wanja Nikolai Kassuhn ◽  
Oliver Klein ◽  
Silvia Darb-Esfahani ◽  
Hedwig Lammert ◽  
Sylwia Handzik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ovarian Cancer ◽  
Molecular Subtypes ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Clear Correlation ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Tissue Samples ◽  
Maldi Ims

e17544 Background: High-grade serous ovarian cancer (HGSOC) can be separated by gene expression profiling into four molecular subtypes with clear correlation of the clinical outcome. However, these gene signatures have not been implemented in clinical practice to stratify patients for targeted therapy. This is mainly due to a lack of easy, cost-effective and reproducible methods, as well as the high heterogeneity of HGSOC. Hence, we aimed to examine the potential of unsupervised matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS) to stratify patients, which might benefit from targeted therapeutic strategies. Methods: Molecular subtyping of paraffin-embedded tissue samples from 279 HGSOC patients was performed by NanoString analysis (ground truth labeling). Next, we applied MALDI-IMS, a novel technology to identify distinct mass profiles on the same paraffin-embedded tissue sections paired with machine learning algorithms to identify HGSOC subtypes by proteomic signature. Finally, we devised a novel strategy to annotate spectra of stromal origin. Results: We elucidated a MALDI-derived proteomic signature (135 peptides) able to classify HGSOC subtypes. Random forest classifiers achieved an area under the curve (AUC) of 0.983. Furthermore, we demonstrated that the exclusion of stroma associated spectra provides tangible improvements to classification quality (AUC = 0.988). False discovery rates (FDR) were reduced from 10.2% to 8.0%. Finally, novel MALDI-based stroma annotation achieved near-perfect classifications (AUC = 0.999, FDR < 1.0%). Conclusions: Here, we present a concept integrating MALDI-IMS with machine learning algorithms to classify patients according to distinct molecular subtypes of HGSOC. This has great potential to assign patients for targeted therapies.

scholarly journals Identification of an immune gene signature for predicting the prognosis of patients with uterine corpus endometrial carcinoma

2020 ◽  
Author(s):  
Cankun Zhou ◽  
Chaomei Li ◽  
Fangli Yan ◽  
Yuhua Zheng
Keyword(s):  
Endometrial Carcinoma ◽  
Risk Score ◽  
Prognostic Model ◽  
Immune Cell ◽  
Gene Signature ◽  
Cancer Genome ◽  
Functional Enrichment ◽  
Immune Gene ◽  
Uterine Corpus ◽  
Immune Genes

Abstract Background: Uterine corpus endometrial carcinoma (UCEC) is a frequent gynecological malignancy with a poor prognosis especially when at an advanced stage. In the present study, we explored the potential of an immune-related gene signature to predict overall survival in UCEC patients.Methods: We analyzed expression data of 616 UCEC patients from The Cancer Genome Atlas database and the International Cancer Genome Consortium as well as immune genes from the ImmPort database and identified the signature. We constructed a transcription factor regulatory network based on Cistrome databases and performed functional enrichment and pathway analyses for the differentially expressed immune genes. Moreover, the prognostic value of 410 immune genes was determined using Cox regression analysis then constructed a prognostic model. Finally, we performed immune infiltration analysis using TIMER-generating immune cell content.Results: Results indicated that the immune cell microenvironment as well as the PI3K-Akt, and MARK signaling pathways were involved in UCEC development. The established prognostic model revealed a ten-gene prognosis signature , comprising PDIA3, LTA, PSMC4, TNF, SBDS, HDGF, HTR3E, NR3C1, PGR, and CBLC . This can be used as an independent tool to predict the prognosis of UCEC owing to the observed risk-score. In addition, levels of B cells and neutrophils were significantly correlated with the patient's risk score, and the expression of ten genes is associated with immune cell infiltrates.Conclusions: In summary, we present a 10-gene signature with the potential to predict the prognosis of UCEC. This is expected to guide future development of individualized treatment approaches.

scholarly journals Identification of two molecular subtypes of dysregulated immune lncRNAs in ovarian cancer

2020 ◽  
pp. 153537022097202
Author(s):  
Xiaojun Liu ◽  
Jinghai Gao ◽  
Jing Wang ◽  
Jing Chu ◽  
Jiahao You ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Differentially Expressed Genes ◽  
Immune Cell ◽  
Molecular Subtypes ◽  
Good Prognosis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Cancer Multiple

Long non-coding RNA (lncRNA) has increasingly been identified as a key regulator in pathologies such as cancer. Multiple platforms were used for comprehensive analysis of ovarian cancer to identify molecular subgroups. However, lncRNA and its role in mapping the ovarian cancer subpopulation are still largely unknown. RNA-sequencing and clinical characteristics of ovarian cancer were acquired from The Cancer Genome Atlas database (TCGA). A total of 52 lncRNAs were identified as aberrant immune lncRNAs specific to ovarian cancer. We redefined two different molecular subtypes, C1(188) and C2(184 samples), in “iClusterPlus” R package, among which C2 grouped ovarian cancer samples have higher survival probability and longer median survival time ( P <0.05) with activated IFN-gamma response, Wound Healing and Cytotoxic lymphocytes signal; 456 differentially expressed genes were acquired in C1 and C2 subtypes using limma (3.40.6) package, among which 419 were up-regulated and 37 were down-regulated, in TCGA dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis revealed that these genes were actively involved in ECM-receptor interaction, PI3K-Akt signaling pathway interaction KEGG pathway. Compared with the existing immune subtype, the Cluster2 sample showed a substantial increase in the proportion of the existing C2 immune subtype, accounting for 81.37%, which was associated with good prognosis. Our C1 subtype contains only 56.49% of the existing immune C1 and C4, which also explains the poor prognosis of C1. Furthermore, 52 immune-related lncRNAs were used to divide the TCGA-endometrial cancer and cervical cancer samples into two categories, and C2 had a good prognosis. The differentially expressed genes were highly correlated with immune-cell-related pathways. Based on lncRNA, two molecular subtypes of ovarian cancer were identified and had significant prognostic differences and immunological characteristics.

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