The Co-Occurrence of X, Y, and Z SNPs in BRCA1 Gene: an in silico Investigation
Abstract
Breast Cancer (BC) is the most common cancer type seen in women and the third most common one worldwide with an increasing rate of cases. Genomic studies revealed X, Y, and Z SNPs on Breast Cancer Gene 1 (BRCA1) may be co-occurring and affecting the BC formation. If they co-occur, investigating them individually would be misdirecting. In this research, the aim was to investigate whether 1) X, Y, and Z are on the same haplotype and co-occur and 2) co-occurrence of X, Y, and Z is pathogenic. During this study, frequencies and conservation scores of SNPs’, haplotype status, linkage disequilibrium (LD), dual and triple co-occurrence statuses, BRCA1 transcripts, and possible protein changes are investigated through data portals and R. By the comparison of the healthy dataset (2,504), general cancer dataset (296), and BC dataset (98), association between co-occurrence of X, Y, and Z with cancer/BC formation is done. Associations are tested with logistic regression, odds ratio, Fisher’s Exact Test, and Chi-Square test. All results are cross-checked with the variant classification guidelines for pathogenicity. As a result, these SNPs coherent with the same haplotype pattern, co-occurrence experiments supported the co-occurrence of these three SNPs and also strengthen the pathogenicity hypothesis. It was shown that odds to have cancer (Odds Ratio (OR): 34.28, probability value (p-value): 0.0006) and BC (OR: 52.15, p-value: 0.0041) are significantly higher for the individuals with triple co-occurrence. More in vitro research needs to be done to strengthen the pieces of evidence obtained in silico.
Alper Bülbül
Variant pathgenicity prediction tool with 3-D and sequence analyzes of protein-protein interactions
Abstract
Motivation: A large number of patient samples can be analyzed with the developing next-generation sequence and protein interaction technologies. In this way, we see that many genes are involved, especially in autoinflammatory monogenic diseases. At the same time, the number of variants associated with diseases is increasing. We used protein-protein interactions and 3D structure analysis for the classification of large number of variants.
Results: 3D docking analysis of proteins, sequence-based interaction scores and delta delta Gibbs free energy (ddG) were created using stability analysis based on protein binary interactions from STRING and Intact databases. ZDOCK and SPRINT values were weighted according to the HGPEC gene rank scores with a variant in 36 monogenic autoinflammatory diseases. When the relationships between ZDOCK, SPRINT, and ddG values were examined in the benign and pathogenic variant groups, we find that the ZDOCK and SPRINT values were positively correlated with each other. In addition, ddG values are negatively correlated with ZDOCK and SPRINT values. 702 missense disease associated variants are retrieved from infevers database. Since there was an imbalance between the sample number of 130 Bening and 572 pathogenic mutations, we created synthetic data with the SMOTE algorithm. The ROC AUC values of the model, created with the Random Forest algorithm, are 97%.
Ekin Köni
Integrated Analysis of Mutated Genes in Leptomeningeal Metastasis Caused by Breast Cancer
Abstract
Background/aim:
Leptomeningeal carcinomatosis (LMC) is a rare type of cancer that settles through metastasis from a tumor in the body to the meninges and affects the brain, spinal cord, and nerves, causing sudden neurological disorders and death. Most common solid tumors causing LMC include breast, lung, and melanoma. The average life expectancy of LMC patients with the prescribed treatments is an average of 6 months. Due to the unknown molecular mechanism and genetic state of the disease, next-generation sequencing (NGS), Whole-exome sequencing (WES) and RNA sequencing (RNA-seq) are being performed to investigate the transcriptome properties of circulating tumor cells (CTCs) found in cerebrospinal fluid (CSF). Currently, the diversification of cancer treatment and the prolonged patient survival have also led to increased LMC incidence. Therefore, molecular studies investigating the development of LMC are required. The aim of this study is to gather information about the genes that are mutated in Breast-LMC studies to analyze possible molecular interactions.
Results:
According to our results in Breast cancer-LMC there were in total 24 mutated genes. 7 of these were only seen in Breast cancer-LMC, only one mutual gene with melanoma-LMC and 11 common genes with NSCLC-LMC.
The PPI network constructed with STRING showed interactions among these 24 genes. In addition, pathway enrichment analysis which was observed with g:profiler and Cytoscape revealed the enriched pathways. The networks contained 24 nodes and 87 edges. Chromatin organization, modification of cellular content were some of the enriched pathways. Moreover, transcription regulation, immune system development, activation and regulation pathways were some of the most important pathways in which the mutated genes were involved. Finally, drugs that interact with breast cancer genes, have been approved or are under clinical trials, were identified with DrugBank and online tools.
İrem Çongur
Bioinformatic Analysis of Mutated Genes in Leptomeningeal Carcinoma Caused by Non-Small Cell Lung Cancer
Abstract
Background/aim: Leptomeningeal carcinoma (LM) is mostly seen as a result of metastasis caused by melanoma, breast, and non-small cell lung cancer (NSCLC) and is formed by the placement of tumor cells in the meninges of the brain. As a result of this metastasis tumor cells also leak into the cerebrospinal fluid (CSF). The average survival time for LM patients is less than one year. Mutations and gene expression changes in patients are being studied with next-generation sequencing (NGS), whole exome sequencing (WES) and single-cell RNA sequencing (scRNA-seq), but no study has yet been conducted to elucidate the molecular mechanism of this disease. Since LM disease has a narrow patient population, studies on candidate marker genes are limited. Therefore, there is a great lack of information in the literature about its mechanism. The aim of this study is to analyze genes mutated in NSCLC-LM in order to determine which pathways may be involved in the development of LM.
Results: 87 genes were found to be mutated in NSCLC-LM patients after classifying the mutated genes from 11 articles. Among 87 genes, 65 were mutated only in NSCLC-LM patients. There were common mutations: 5 with both breast and melanoma LM, 6 with melanoma-LM, and 11 with breast-LM patients. PPI network of mutated genes in NSCLC-LM was composed of 87 nodes and 1181 edges which was constructed using the String database. EnrichmentMap plug-in of Cytoscape was used to construct a network of enriched pathways to visualize the output of g:Profiler. The network contained 856 nodes and 31411 edges. Using the MCODE plug-in 25 clusters were created. Some of the clusters included the following pathways: regulation of cell cycle, DNA damage and repair, cell adhesion, regulation of cytoskeleton and cellular response to environmental stimulus. Finally, drugs that interact with 8 NSCLC biomarkers were identified with DrugBank and publicly available articles.
