High Gene Expression Groups Research Articles

Genetically predicted gene expression of prefrontal DRD4 gene and the differential susceptibility to childhood emotional eating in response to positive environment

Genetic differential susceptibility states that individuals may vary both by exhibiting poor responses when exposed to adverse environments, and disproportionally benefiting from positive settings. The dopamine D4 receptor gene (DRD4) may be particularly implicated in these effects, including disturbed eating behaviors that might lead to obesity. Here, we explore differential susceptibility to positive environments according to the predicted genetically regulated gene expression of prefrontal cortex DRD4 gene. Using MAVAN as the discovery cohort (Maternal Adversity, Vulnerability and Neurodevelopment) and GUSTO as the replication cohort (Growing Up in Singapore Towards Healthy Outcomes), we analyzed the interaction between a) a Positive postnatal environmental score, that accounts for positive outcomes in the postnatal period and b) the genetically regulated gene expression of prefrontal DRD4, computed using a machine learning prediction method (PrediXcan). The outcome measures were the pro-intake domains (Emotional over-eating, Food Responsiveness, Food Enjoyment and Desire to Drink) from the Child Eating Behavior Questionnaire at 48 months of age (MAVAN) and 60 months of age (GUSTO). The interaction between the positive environment and the predicted prefrontal DRD4 gene expression was significant for emotional over-eating in MAVAN (β = −0.403, p < 0.02), in which the high gene expression group had more or less emotional eating according to the exposure to lower or higher positive environment respectively, showing evidence of differential susceptibility criteria. In the replication cohort, a similar result was found with the pro-intake domain Desire to drink (β = −0.583, p < 0.05). These results provide further evidence for the genetic differential susceptibility, accounting for the benefit of positive environments.

Development and Internal Validation of a Novel Model and Markers to Identify the Candidates for Lymph Node Metastasis in Patients with Prostate Cancer

Background: The predictive nomogram has recently been recognized as a significant tool to predict LNI in patients with prostate cancer. However, it remains unclear whether incorporation of the genes expression in the nomogram of European Urology (2018) can further improve the LNI prediction. Methods: We analysed clinical data from 320 PCa patients from the Cancer Genome Atlas database. Weighted gene coexpression network analysis was used to identify the genes that were significantly associated with LNI in PCa(n=390). Analyses using the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were performed to identify the activated signalling pathways. Univariate and multivariate logistic regression analyses were performed to identify the independent risk factors for the presence of LNI. Regression coefficients were used to develop a nomogram to predict the probability of LNI. Findings: We found that patients with actual LNI and predicted LNI had the worst survival outcomes. A total of 2294 genes and 22 modules were associated with LNI in PCa. The 5 most significant modules and the 7 most significant genes (CTNNAL1, ENSA, MAP6D1, MBD4, PRCC, SF3B2, TREML1) were selected for further analysis. Pathways in the cell cycle, DNA replication, oocyte meiosis and 9 other pathways were dramatically activated during LNI in PCa. We used the 7 genes to generate a signature, and we found that PCa patients in the high-gene expression group had significantly shorter disease-free survival and overall survival than those in the low-gene expression group (all p<0.05). Multivariate analyses identified that the risk score (OR=1.05 for 1% increase, 95% CI: 1.04-1.07, p<0.001), serum PSA level, clinical stage, primary biopsy Gleason grade (OR=2.52 for a grade increase, 95% CI: 1.27-5.22, p=0.096) and secondary biopsy Gleason grade were independent predictors of LNI. A nomogram built using these predictive variables showed good calibration and a net clinical benefit, with an AUC value of 90.2%. Interpretation: Incorporation of genes risk scoring system can improve prediction of LNI help clinical decision-making. In clinical practice, the application of our nomogram might contribute significantly to the selection of patients who are good candidates for surgery with extended pelvic lymph node dissection. Funding Statement: This work was supported by the Natural Science Fund of Bengbu Medical College (BYKY17115). Declaration of Interests: The authors declare no competing interests.

Abstract 4024: Ten degrees of separation: novel genes affecting outcome in ovarian cancer.

Abstract Objective: To identify genomic predictors of overall survival of women with high grade serous ovarian cancer based on best separation overall survival Kaplan-Meier curves Methods: Comprehensive data analysis of the TCGA dataset of high grade serous epithelial ovarian cancer was carried out with a “training” (n= 375) and “validation” (n= 188) data sets. The R-code was written specifically to identify all genes with high expression that gave the greatest predictive potential for patient survival. The top ten cases with lowest sums of the log rank test p-values obtained for training and validation sets were chosen to be included with Kaplan-Meier survival curves for analysis. Results: The ten genes with high expression that had the smallest p-values in their respective order were: SLC6A1, LIPK, EHBP1, SUSD5, PEX3, SLC22A3, RABGEF1, PPM2C, KIAA1219, and GALNT10. The range of p-value sum ranged from 1.77E-3 to 2.09E-3. The lowest p-value was for SLC6A1, which encodes for GABA Transporter-1 that removes GABA from extracellular to intracellular space. Interestingly, four of the 10 genes predictive for poor outcome are directly involved in cell metabolism: LIPK, PPM2C, PEX3, and GALNT10. The median OS of the high gene expression group ranged from 38 to 48 months. The median OS of the low gene expression group ranged from 55-70 months. The median OS difference of high gene vs. low gene expression groups ranged from 15-25 months. EHBP1 gene, involved in endocytic trafficking of transferrin into endosomes and GLUT4 into adipocytes, had the greatest difference in median OS between high and low expressions at 25 months (p= 2.16E-3). Finally, low expression of PPM2C compared to its high expression was significantly associated with &amp;gt; 5 year survival (10.1% vs. 1.1%, p=0.001) and no recurrence in both training and validation sets (12.5% vs. 2.2%, p=0.04). PPM2C encodes for pyruvate dehydrogenase-phosphatase 1 that catalyzes reactivation of the alpha subunit of the E1 component of the pyruvate dehydrogenase complex. Pyruvate dehydrogenase complex in the active form transforms pyruvate from glycolysis to acetyl-CoA in the mitochondria. To evaluate whether similar findings are noted in other cancers, we also examined the TCGA data for breast, lung, colon cancer, and glioblastoma. PPM2C is amplified by 7% in invasive breast cancer, and leads to an overall worse survival (68.9 months vs. 129.5 months, p = 0.037). Conclusion: Genes involved in cell metabolism, neurotransmitter functioning, and endocytic trafficking are highly predictive of outcome in high grade serous ovarian cancer. High PPM2C expression and gene amplification leads to worsening overall survival in ovarian and invasive breast cancer, respectively. These pathways likely reflect novel and important therapeutic targets. Citation Format: Behrouz Zand, Cristina Ivan, Chad V. Pecot, Rajesha Rupaimoole, Heather J. Dalton, Justin Bottsford-Miller, Wei Hu, Alpa M. Nick, Anil K. Sood. Ten degrees of separation: novel genes affecting outcome in ovarian cancer. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4024. doi:10.1158/1538-7445.AM2013-4024