Aim of the Study: The purpose of this study was to identify specific circulating microRNAs (miRNAs) and investigate expression level of their target genes for evaluation of pathogenesis of epithelial ovarian cancer (EOC). Materials and Methods: In this study, we have studied on EOC patients' serum and whole blood, healthy control (HC) serum, and whole blood samples. Sixteen serum samples were collected to compare miRNA expression analysis through microarray. According to microarray results, one of the dysregulated miRNA in serum, hsa-let-7d-3p, was validated by RT-qPCR for discriminate two groups. The hsa-let-7d-3p is one of the tumor suppressive let-7d family members. Let-7d is downregulated in numerous types of cancer, including ovarian cancer and directly targets various oncogenes. We analyzed the let-7d targets, which are High Mobility Group A2 (HMGA2) and (Kirsten Rat Sarcoma Viral Oncogene Homolog), as the oncogenes that are associated with EOC. The relation between target genes of hsa-let-7d-3p and EOC has been examined by Pathway Studio. Twenty serum and whole blood samples collected to analyze expression level of target genes were analyzed by real-time PCR. Results: 31 significantly dysregulated miRNAs were identified by microarray in serum. Hsa-let-7d-3p has been selected for the validation, according to P-value and dysregulated level. RT-qPCR results showed that hsa-let-7d-3p could discriminate EOC patients from HC (P = 0.0484, AUC = 0.7). Furthermore, we identified hsa-let-7d-3p's target genes (HMGA2, KRAS) by bioinformatic analysis. The expression level of genes could discriminate patients with EOC from HC, with a power area under the ROC curves (AUC) of 62 and 64.2, respectively. Conclusion: HMGA2 and KRAS could be translationally downregulated by the hsa-let-7d-3p, and the loss of hsa-let-7d-3p expression led to the progression of EOC related to the tumorigenesis, invasion, and metastasis.

Recent studies have focused on the early detection of ovarian cancer (OC) using tumor materials by liquid biopsy. The mechanisms of microRNAs (miRNAs) to impact OC and signaling pathways are still unknown. This study aims to reliably perform functional analysis of previously validated circulating miRNAs' target genes by using pathfindR. Also, overall survival and pathological stage analyses were evaluated with miRNAs' target genes which are common in the The Cancer Genome Atlas and GTEx datasets. Our previous studies have validated three downregulated miRNAs (hsa-miR-885-5p, hsa-miR-1909-5p, and hsa-let7d-3p) having a diagnostic value in OC patients' sera, with high-throughput techniques. The predicted target genes of these miRNAs were retrieved from the miRDB database (v6.0). Active-subnetwork-oriented Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted by pathfindR using the target genes. Enrichment of KEGG pathways assessed by the analysis of pathfindR indicated that 24 pathways were related to the target genes. Ubiquitin-mediated proteolysis, spliceosome and Notch signaling pathway were the top three pathways with the lowest p-values (p < 0.001). Ninety-three common genes were found to be differentially expressed (p < 0.05) in the datasets. No significant genes were found to be significant in the analysis of overall survival analyses, but 24 genes were found to be significant with pathological stages analysis (p < 0.05). The findings of our study provide in-silico evidence that validated circulating miRNAs' target genes and enriched pathways are related to OC and have potential roles in theranostics applications. Further experimental investigations are required to validate our results which will ultimately provide a new perspective for translational applications in OC management.

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.

BRCA1/2 mutations play a significant role in cancer pathogenesis and predisposition particularly in breast, ovarian and prostate cancers. Thus, germline analysis of BRCA1 and BRCA2 is essential for clinical management strategies aiming at the identification of recurrent and novel mutations that could be used as a first screening approach. We analyzed germline variants of BRCA1/2 genes for 2168 individuals who had cancer diagnosis or high risk assessment due to BRCAs related cancers, referred to 10 health care centers distributed across 7 regions covering the Turkish landscape. Overall, 68 and 157 distinct mutations were identified in BRCA1 and BRCA2, respectively. Twenty-two novel variants were reported from both genes while BRCA2 showed higher mutational heterogeneity. We herein report the collective data as BRCA Turkish consortium that confirm the molecular heterogeneity in BRCAs among Turkish population, and also as the first study presenting the both geographical, demographical and gene based landscape of all recurrent and novel mutations which some might be a founder effect in comparison to global databases. This wider perspective leads to the most accurate variant interpretations which pave the way for the more precise and efficient management affecting the clinical and molecular aspects.