Gene-gene Interactions Research Articles

Intestinal mRNA expression analysis of polarity-related genes identified the discriminatory ability of CRB3 as a diagnostic marker for celiac disease.

Celiac disease (CD) is a chronic autoimmune disorder characterized by an abnormal immune response to gluten, a protein found in wheat, barley, and rye. It is well established that the integrity of epithelial tight junctions (TJs) and adherens junctions (AJs) plays a crucial role in the pathogenesis of CD. These junctional complexes contribute to the apical-basal polarity of the intestinal epithelial cells, which is crucial for their proper functioning. Sixty CD subjects, and 50 controls were enrolled in the current study. Mucosal samples were obtained from the distal duodenum, total RNA was extracted and complementary DNA was synthesized. The relative expression levels of the desired genes were evaluated by quantitative real-time polymerase chain reaction based on ΔΔCt method. The gene-gene interaction network was also constructed using GeneMANIA. CRB3 (p = .0005), LKB1 (p < .0001), and SCRIB (p = .0005) had lower expression in CD patients compared to controls, while PRKCZ expression did not differ between groups (p > .05). CRB3 represented a significant diagnostic value for differentiating CD patients from the control group (p = .02). The aim of the current study was to evaluate the changes in the mRNA expression levels of SCRIB, PRKCZ, LKB1, and CRB3 genes in the small intestinal biopsy samples of CD patients in comparison to the healthy control subjects. Our data uncover the importance of polarity-related genes (especially CRB3) in CD pahtomechanism, that may facilitate the planning of the future studies looking for finding innovative diagnostic and therapeutic strategies for CD.

Genetic architecture of alcohol consumption identified by a genotype-stratified GWAS and impact on esophageal cancer risk in Japanese people.

An East Asian-specific variant on aldehyde dehydrogenase 2 (ALDH2 rs671, G>A) is the major genetic determinant of alcohol consumption. We performed an rs671 genotype-stratified genome-wide association study meta-analysis of alcohol consumption in 175,672 Japanese individuals to explore gene-gene interactions with rs671 behind drinking behavior. The analysis identified three genome-wide significant loci (GCKR, KLB, and ADH1B) in wild-type homozygotes and six (GCKR, ADH1B, ALDH1B1, ALDH1A1, ALDH2, and GOT2) in heterozygotes, with five showing genome-wide significant interaction with rs671. Genetic correlation analyses revealed ancestry-specific genetic architecture in heterozygotes. Of the discovered loci, four (GCKR, ADH1B, ALDH1A1, and ALDH2) were suggested to interact with rs671 in the risk of esophageal cancer, a representative alcohol-related disease. Our results identify the genotype-specific genetic architecture of alcohol consumption and reveal its potential impact on alcohol-related disease risk.

ATR1 A1166C (rs5186), FII G20210A (rs1799963), FV G1691A (rs6025), FXIII 97G > T (rs11466016) and MTHFR A1298C (rs1801131) polymorphisms and the risk of ST-elevation myocardial infarction in young Mexican individuals.

Previous studies had identified genetic variants associated with Myocardial Infarction, but results are inconclusive. We examined the association between FII G20210A (rs1799963), FV G1691A (rs6025), FXIII 97G > T (rs11466016), ATR1 A1166C (rs5186) and MTHFR A1298C (rs1801131) polymorphisms and ST elevation Myocardial Infarction in young Mexican individuals. We included a total of 350 patients with Myocardial Infarction <45 years old and 350 controls matched by age and gender. The polymorphisms were analyzed by PCR-RFLP using specific restriction enzymes. DNA fragments were separated by electrophoresis in 2% gel of agarose and visualized using SYBR green. The A1166C (p = 0.004) but not FXIII 97G > T (p = 0.19), G20210A (p = 0.32), G1691A (p = No significant) and A1298C (p = 0.21) polymorphisms were associated with increased risk for ST elevation Myocardial Infarction. Moreover, dyslipidemia, hypertension, smoking and family history of atherothrombotic disease were associated. We found that A1166C represented increased risk for ST elevation Myocardial Infarction. However, G20210A, G1691A, 97G > T, and A1298C were not associated. In addition, we had determined that Glu298Asp, PLA1/A2, TAFI Thr325Ile, ACE I/D, AGT M235T and PAI-1 4G/5G polymorphisms represented increased risk in the same group of patients. However, MTHFR C677T, AGT T174M, FV G1691A, TSP-1 N700S, MTHFR C677T and TAFI 174M polymorphisms were no associated. Our results suggest that in young patients with ST Myocardial Infarction, those polymorphisms could contribute to premature endothelial dysfunction, atherothrombosis, vasoconstriction, increased platelet aggregation, muscle cell migration and proliferation. Further studies are required to try to better assess gene-gene and gene-modifiable factors interaction.

Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, leading to inaccurate gene counts. Existing methods, including advanced deep learning techniques, struggle to reliably impute gene expressions due to a lack of mechanisms that explicitly consider the underlying biological knowledge of the system. In reality, it has long been recognized that gene-gene interactions may serve as reflective indicators of underlying biology processes, presenting discriminative signatures of the cells. A genomic data analysis framework that is capable of leveraging the underlying gene-gene interactions is thus highly desirable and could allow for more reliable identification of distinctive patterns of the genomic data through extraction and integration of intricate biological characteristics of the genomic data. Here we tackle the problem in two steps to exploit the gene-gene interactions of the system. We first reposition the genes into a 2D grid such that their spatial configuration reflects their interactive relationships. To alleviate the need for labeled ground truth gene expression datasets, a self-supervised 2D convolutional neural network is employed to extract the contextual features of the interactions from the spatially configured genes and impute the omitted values. Extensive experiments with both simulated and experimental scRNA-seq datasets are carried out to demonstrate the superior performance of the proposed strategy against the existing imputation methods.

ScHybridBERT: integrating gene regulation and cell graph for spatiotemporal dynamics in single-cell clustering.

Graph learning models have received increasing attention in the computational analysis of single-cell RNA sequencing (scRNA-seq) data. Compared with conventional deep neural networks, graph neural networks and language models have exhibited superior performance by extracting graph-structured data from raw gene count matrices. Established deep neural network-based clustering approaches generally focus on temporal expression patterns while ignoring inherent interactions at gene-level as well as cell-level, which could be regarded as spatial dynamics in single-cell data. Both gene-gene and cell-cell interactions are able to boost the performance of cell type detection, under the framework of multi-view modeling. In this study, spatiotemporal embedding and cell graphs are extracted to capture spatial dynamics at the molecular level. In order to enhance the accuracy of cell type detection, this study proposes the scHybridBERT architecture to conduct multi-view modeling of scRNA-seq data using extracted spatiotemporal patterns. In this scHybridBERT method, graph learning models are employed to deal with cell graphs and the Performer model employs spatiotemporal embeddings. Experimental outcomes about benchmark scRNA-seq datasets indicate that the proposed scHybridBERT method is able to enhance the accuracy of single-cell clustering tasks by integrating spatiotemporal embeddings and cell graphs.

Rapamycin as a Potential Alternative Drug for Squamous Cell Gingiva Carcinoma (Ca9-22): A Focus on Cell Cycle, Apoptosis and Autophagy Genetic Profile.

Oral cancer is considered as one of the most common malignancies worldwide. Its conventional treatment primarily involves surgery with or without postoperative adjuvant therapy. The targeting of signaling pathways implicated in tumorigenesis is becoming increasingly prevalent in the development of new anticancer drug candidates. Based on our recently published data, Rapamycin, an inhibitor of the mTOR pathway, exhibits selective antitumor activity in oral cancer by inhibiting cell proliferation and inducing cancer cell apoptosis, autophagy, and cellular stress. In the present study, our focus is on elucidating the genetic determinants of Rapamycin's action and the interaction networks accountable for tumorigenesis suppression. To achieve this, gingival carcinoma cell lines (Ca9-22) were exposed to Rapamycin at IC50 (10 µM) for 24 h. Subsequently, we investigated the genetic profiles related to the cell cycle, apoptosis, and autophagy, as well as gene-gene interactions, using QPCR arrays and the Gene MANIA website. Overall, our results showed that Rapamycin at 10 µM significantly inhibits the growth of Ca9-22 cells after 24 h of treatment by around 50% by suppression of key modulators in the G2/M transition, namely, Survivin and CDK5RAP1. The combination of Rapamycin with Cisplatin potentializes the inhibition of Ca9-22 cell proliferation. A P1/Annexin-V assay was performed to evaluate the effect of Rapamycin on cell apoptosis. The results obtained confirm our previous findings in which Rapamycin at 10 μM induces a strong apoptosis of Ca9-22 cells. The live cells decreased, and the late apoptotic cells increased when the cells were treated by Rapamycin. To identify the genes responsible for cell apoptosis induced by Rapamycin, we performed the RT2 Profiler PCR Arrays for 84 apoptotic genes. The blocked cells were believed to be directed towards cell death, confirmed by the downregulation of apoptosis inhibitors involved in both the extrinsic and intrinsic pathways, including BIRC5, BNIP3, CD40LG, DAPK1, LTA, TNFRSF21 and TP73. The observed effects of Rapamycin on tumor suppression are likely to involve the autophagy process, evidenced by the inhibition of autophagy modulators (TGFβ1, RGS19 and AKT1), autophagosome biogenesis components (AMBRA1, ATG9B and TMEM74) and autophagy byproducts (APP). Identifying gene-gene interaction (GGI) networks provided a comprehensive view of the drug's mechanism and connected the studied tumorigenesis processes to potential functional interactions of various kinds (physical interaction, co-expression, genetic interactions etc.). In conclusion, Rapamycin shows promise as a clinical agent for managing Ca9-22 gingiva carcinoma cells.

Characterization of the Heat Shock Transcription Factor Family in Lycoris radiata and Its Potential Roles in Response to Abiotic Stresses.

Heat shock transcription factors (HSFs) are an essential plant-specific transcription factor family that regulates the developmental and growth stages of plants, their signal transduction, and their response to different abiotic and biotic stresses. The HSF gene family has been characterized and systematically observed in various species; however, research on its association with Lycoris radiata is limited. This study identified 22 HSF genes (LrHSFs) in the transcriptome-sequencing data of L. radiata and categorized them into three classes including HSFA, HSFB, and HSFC, comprising 10, 8, and 4 genes, respectively. This research comprises basic bioinformatics analyses, such as protein sequence length, molecular weight, and the identification of its conserved motifs. According to the subcellular localization assessment, most LrHSFs were present in the nucleus. Furthermore, the LrHSF gene expression in various tissues, flower developmental stages, two hormones stress, and under four different abiotic stresses were characterized. The data indicated that LrHSF genes, especially LrHSF5, were essentially involved in L. radiata development and its response to different abiotic and hormone stresses. The gene-gene interaction network analysis revealed the presence of synergistic effects between various LrHSF genes' responses against abiotic stresses. In conclusion, these results provided crucial data for further functional analyses of LrHSF genes, which could help successful molecular breeding in L. radiata.

Leveraging GWAS: Path to Prevention?

Developing novel cancer prevention medication strategies is important for reducing mortality. Identification of common genetic variants associated with cancer risk suggests the potential to leverage these discoveries to define causal targets for cancer interception. Although each risk variant confers small increases in risk, researchers propose that blocking those that produce causal carcinogenic effects might have large impacts on cancer prevention. While a promising concept, we describe potential hurdles that may need to be scaled to reach this goal, including: (i) understanding the complexity of risk; (ii) achieving statistical power in studies with binary outcomes (cancer development: yes or no); (iii) characterization of cancer precursors; (iv) heterogeneity of cancer subtypes and the populations in which these diseases occur; (v) impact of static genetic markers across complex events of the life course; (vi) defining gene-gene and gene-environment interactions and (vii) demonstrating functional effects of markers in human populations. We assess short-term prospects for this research against the backdrop of these challenges and the potential to prevent cancer through other means. See related commentary by Peters and Tomlinson, p. 7.

Aspirin and Cancer Survival: An Analysis of Molecular Mechanisms.

The benefit of aspirin on cancer survival is debated. Data from randomized clinical trials and cohort studies are discordant, although a meta-analysis shows a clear survival advantage when aspirin is added to the standard of care. However, the mechanism by which aspirin improves cancer survival is not clear. A PubMed search was carried out to identify articles reporting genes and pathways that are associated with aspirin and cancer survival. Gene ontology and pathway enrichment analysis was carried out using web-based tools. Gene-gene and protein-protein interactions were evaluated. Crosstalk between pathways was identified and plotted. Forty-one genes were identified and classified into primary genes (PTGS2 and PTGES2), genes regulating cellular proliferation, interleukin and cytokine genes, and DNA repair genes. The network analysis showed a rich gene-gene and protein-protein interaction between these genes and proteins. Pathway enrichment showed the interleukin and cellular transduction pathways as the main pathways involved in aspirin-related survival, in addition to DNA repair, autophagy, extracellular matrix, and apoptosis pathways. Crosstalk of PTGS2 with EGFR, JAK/AKT, TP53, interleukin/TNFα/NFκB, GSK3B/BRCA/PARP, CXCR/MUC1, and WNT/CTNNB pathways was identified. The results of the present study demonstrate that aspirin improves cancer survival by the interplay of 41 genes through a complex mechanism. PTGS2 is the primary target of aspirin and impacts cancer survival through six primary pathways: the interleukin pathway, extracellular matrix pathway, signal transduction pathway, apoptosis pathway, autophagy pathway, and DNA repair pathway.

Multicategory Survival Outcomes Classification via Overlapping Group Screening Process Based on Multinomial Logistic Regression Model With Application to TCGA Transcriptomic Data.

Under the classification of multicategory survival outcomes of cancer patients, it is crucial to identify biomarkers that affect specific outcome categories. The classification of multicategory survival outcomes from transcriptomic data has been thoroughly investigated in computational biology. Nevertheless, several challenges must be addressed, including the ultra-high-dimensional feature space, feature contamination, and data imbalance, all of which contribute to the instability of the diagnostic model. Furthermore, although most methods achieve accurate predicted performance for binary classification with high-dimensional transcriptomic data, their extension to multi-class classification is not straightforward. We employ the One-versus-One strategy to transform multi-class classification into multiple binary classification, and utilize the overlapping group screening procedure with binary logistic regression to include pathway information for identifying important genes and gene-gene interactions for multicategory survival outcomes. A series of simulation studies are conducted to compare the classification accuracy of our proposed approach with some existing machine learning methods. In practical data applications, we utilize the random oversampling procedure to tackle class imbalance issues. We then apply the proposed method to analyze transcriptomic data from various cancers in The Cancer Genome Atlas, such as kidney renal papillary cell carcinoma, lung adenocarcinoma, and head and neck squamous cell carcinoma. Our aim is to establish an accurate microarray-based multicategory cancer diagnosis model. The numerical results illustrate that the new proposal effectively enhances cancer diagnosis compared to approaches that neglect pathway information. We showcase the effectiveness of the proposed method in terms of class prediction accuracy through evaluations on simulated synthetic datasets as well as real dataset applications. We also identified the cancer-related gene-gene interaction biomarkers and reported the corresponding network structure. According to the identified major genes and gene-gene interactions, we can predict for each patient the probabilities that he/she belongs to each of the survival outcome classes.

GWAS-significant loci and severe COVID-19: analysis of associations, link with thromboinflammation syndrome, gene-gene, and gene-environmental interactions.

The aim of this study was to replicate associations of GWAS-significant loci with severe COVID-19 in the population of Central Russia, to investigate associations of the SNPs with thromboinflammation parameters, to analyze gene-gene and gene-environmental interactions. DNA samples from 798 unrelated Caucasian subjects from Central Russia (199 hospitalized COVID-19 patients and 599 controls with a mild or asymptomatic course of COVID-19) were genotyped using probe-based polymerase chain reaction for 10 GWAS-significant SNPs: rs143334143 CCHCR1, rs111837807 CCHCR1, rs17078346 SLC6A20-LLZTFL1, rs17713054 SLC6A20-LLZTFL1, rs7949972 ELF5, rs61882275 ELF5, rs12585036 ATP11A, rs67579710 THBS3, THBS3-AS1, rs12610495 DPP9, rs9636867 IFNAR2. SNP rs17713054 SLC6A20-LZTFL1 was associated with increased risk of severe COVID-19 in the entire group (risk allele A, OR = 1.78, 95% CI = 1.22-2.6, p = 0.003), obese individuals (OR = 2.31, 95% CI = 1.52-3.5, p = 0.0002, (p bonf = 0.0004)), patients with low fruit and vegetable intake (OR = 1.72, 95% CI = 1.15-2.58, p = 0.01, (p bonf = 0.02)), low physical activity (OR = 1.93, 95% CI = 1.26-2.94, p = 0.0035, (p bonf = 0.007)), and nonsmokers (OR = 1.65, 95% CI = 1.11-2.46, p = 0.02). This SNP correlated with increased BMI (p = 0.006) and worsened thrombodynamic parameters (maximum optical density of the formed clot, D (p = 0.02), delayed appearance of spontaneous clots, Tsp (p = 0.02), clot size 30min after coagulation activation, CS (p = 0.036)). SNP rs17078346 SLC6A20-LZTFL1 was linked with increased BMI (p = 0.01) and severe COVID-19 in obese individuals (risk allele C, OR = 1.72, 95% CI = 1.15-2.58, p = 0.01, (p bonf = 0.02)). SNP rs12610495 DPP9 was associated with increased BMI (p = 0.01), severe COVID-19 in obese patients (risk allele G, OR = 1.48, 95% CI = 1.09-2.01, p = 0.01, (p bonf = 0.02)), and worsened thrombodynamic parameters (time to the start of clot growth, Tlag (p = 0.01)). For rs7949972 ELF5, a protective effect against severe COVID-19 was observed in non-obese patients (effect allele T, OR = 0.67, 95% CI = 0.47-0.95, p = 0.02, (p bonf = 0.04)), improving thrombodynamic parameters (CS (p = 0.02), stationary spatial clot growth rates, Vst (p = 0.02)). Finally, rs12585036 ATP11A exhibited a protective effect against severe COVID-19 in males (protective allele A, OR = 0.51, 95% CI = 0.32-0.83, p = 0.004). SNPs rs67579710 THBS3, THBS3-AS1, rs17713054 SLC6A20-LZTFL1, rs7949972 ELF5, rs9636867 IFNAR2-were involved in two or more of the most significant G×G interactions (p perm ≤ 0.01). The pairwise combination rs67579710 THBS3, THBS3-AS1 × rs17713054 SLC6A20-LZTFL1 was a priority in determining susceptibility to severe COVID-19 (it was included in four of the top five most significant SNP-SNP interaction models). Overall, this study represents a comprehensive molecular-genetic and bioinformatics analysis of the involvement of GWAS-significant loci in the molecular mechanisms of severe COVID-19, gene-gene and gene-environmental interactions, and provides evidence of their relationship with thromboinflammation parameters in patients hospitalized in intensive care units.

Involvement of Single Nucleotide Variants in the Klotho Gene Among Obesity Individuals with and without Type 2 Diabetes Mellitus in the Saudi Population.

Aging is characterized by the gradual physiological changes and alterations that accumulate over time in the human body. The combination of obesity and ageing can lead to an increased risk of serious health issues or death. Single nucleotide variants (SNVs) in the Klotho gene were commonly studied, including that in type 2 diabetes mellitus (T2DM). The aim of this study is to examine the possible effect of SNVs in Klotho on the obese population in Saudi Arabia using middle-aged participants with and without T2DM. This study consists of 100 controls and 100 obesity patients, in which 50had T2DM and the remaining 50 were obese without T2DM. Genotyping was performed with PCR, and Sanger sequencing analysis was used to validate the molecular association. In this study, rs1207568 (p = 0.001-0.003) and rs9527025 (p = 0.001-0.00004) SNVs were associated with obesity cases. However, none of the genotypes or allele frequencies showed a positive association with the rs564481 SNV (p = 0.344-0.881). The multiple linear regression model showed that waist and hip were associated (p = 0.01-0.02). ANOVA analysis showed age (p = 0.04), hip (p = 0.002), SBP, and TC (p = 0.02) were associated. Finally, SNV (rs1207568 and rs95270250) and obesity (p < 0.001) associations were confirmed through gene multifactor dimensionality reduction analysis with gene-gene interaction, dendrogram, and graphical depletion method. This study concludes that rs1207568 and rs9527025 SNVs are associated with obesity in the Saudi population. Additional genetical statistics showed significant association between dependent and independent variables. SNVs in Klotho play a role in the Saudi population's susceptibility to obesity.

Evaluation of the association between glutathione S-transferase polymorphisms and susceptibility to cutaneous melanoma: a systematic review and meta-analysis.

Glutathione S-transferase (GST) enzymes play a crucial role in detoxification by catalysing the conjugation of many hydrophobic and electrophilic compounds with reduced glutathione. Polymorphisms in GST genes may influence the susceptibility to various cancers, including melanoma. We reported a systematic review and meta-analysis to evaluate the association between GST polymorphisms and susceptibility to cutaneous melanoma. A comprehensive search of four databases, namely PubMed, Scopus, Cochrane Library, and Web of Science, was conducted to gather pertinent studies up until 24 August 2023. No restrictions were imposed during the search. The analysis included 32 studies and was broken down into subgroups based on ethnicity, control source, control matching, quality score, and sample size. The forest plot analyses on GSTM1, GSTT1, combined GSTM1/GSTT1, and GSTP1 polymorphisms in relation to melanoma risk showed no statistically significant differences between the case and control groups, except for the recessive model of GSTP1 polymorphism. The analysis revealed significant associations between GSTM1 polymorphisms and melanoma risk in Asians and in studies with a sample size of less than 200. For the combined GSTM1/GSTT1 polymorphisms, a significant association was found in hospital-based controls. While this study enhances our understanding of the genetic factors influencing melanoma risk, it also highlights the need for further research. The current evidence is not sufficient to confirm or reject the intervention effect. Future research should consider gene-gene and gene-environment interactions, which could offer a more comprehensive understanding of the complex biology of melanoma.

Evaluation of gene-gene interaction between the interleukin (IL)-2 and IL-2RA gene polymorphisms in schizophrenia patients in the Turkish Population.

To evaluate the genetic polymorphisms in IL-2 and IL-2RA genes in schizophrenia (SCZ) patients by comparing them with healthy controls. A sample of 127 patients with SCZ and 100 healthy volunteers were included in the case-control study. These individuals were consecutively selected from the Malazgirt State Hospital Psychiatry Outpatient Clinic in Mus, Turkey, over the three months from October 2020 to December 2020. The Structured Clinical Interview for DSM-5 Disorders, Clinician Version (SCID-5-CV) was used to confirm the diagnosis according to the DSM-5 criteria. In addition, polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) was used to determine gene polymorphisms from DNA material. Our findings indicated significant differences in the IL-2 genotype and allele frequencies between SCZ patients and the healthy control group. Specifically, the frequency of the homozygous GG genotype was notably higher in SCZ patients compared to the control group. Conversely, when comparing the IL-2RA genotype and allele frequencies of SCZ patients with the control group, no statistically significant differences were observed between the 2 groups. When compared to individuals with other genotypes, interaction analysis indicated that carriers of the GG/AG (IL-2/IL-2RA) genotype demonstrated a significantly increased risk of SCZ. In light of the analyses, our study indicates that while the IL-2 genotype polymorphism may be considered a risk factor for developing SCZ, the IL-2RA variant was not associated with SCZ among Turkish patients.

Recognition of altered gene-gene interaction using BiLSTM in different stages of lung adenocarcinoma

Cancer which is caused by abnormal growth or tumour snatches away millions of lives each year. Depending on the organ affected, we find various cancer types like blood, skin, lung, kidney, breast, prostrate, liver etc. Some of these are curable if diagnosis of the disease can be made early. Others like pancreatic cancer are very difficult to cure. Hence early detection is one of most important aspects of treatment. Cancer affected patient goes through several stages of the disease. There are usually four stages of cancer. The Stage 1 of cancer being the preliminary stage and stage 4 being the last stage of the disease. It has been observed that the deadliness of the disease aggravates with each stage. There is no fixed or well defined time limit for the change in the stages. It may depend on the type of cancer, the physical attributes of the patient and on external factors and life-style habits. It is usually observed that symptoms change with change in stages of the disease. We intend to find out whether there is any change in the overall relationship between the genes amongst the various stages of lung cancer patients. Long Short Term Memory (LSTM), Bidirectional Long Short Term Memory (BiLSTM) and Feed Forward Neural Network (FNN) are used for analyzing the gene dependencies. The comparative analysis of the results obtained is done using various statistical methods of correlation and regression. Analysis of the results reveal significant variance in correlation among the various stages of lung cancer, showing Stage 2 and Stage 3 to be the most strongly correlated and Stage 1 and Stage 2 being the most weakly correlated indicating the inconsistency of genetic relationship between the various stages of lung cancer.

Dysregulated microRNAs in prostate cancer: In silico prediction and in vitro validation.

MicroRNAs, which are micro-coordinators of gene expression, have been recently investigated as a potential treatment for cancer. The study used computational techniques to identify microRNAs that could target a set of genes simultaneously. Due to their multi-target-directed nature, microRNAs have the potential to impact multiple key pathways and their pathogenic cross-talk. We identified microRNAs that target a prostate cancer-associated gene set using integrated bioinformatics analyses and experimental validation. The candidate gene set included genes targeted by clinically approved prostate cancer medications. We used STRING, GO, and KEGG web tools to confirm gene-gene interactions and their clinical significance. Then, we employed integrated predicted and validated bioinformatics approaches to retrieve hsa-miR-124-3p, 16-5p, and 27a-3p as the top three relevant microRNAs. KEGG and DIANA-miRPath showed the related pathways for the candidate genes and microRNAs. The Real-time PCR results showed that miR-16-5p simultaneously down-regulated all genes significantly except for PIK3CA/CB in LNCaP; miR-27a-3p simultaneously down-regulated all genes significantly, excluding MET in LNCaP and PIK3CA in PC-3; and miR-124-3p could not down-regulate significantly PIK3CB, MET, and FGFR4 in LNCaP and FGFR4 in PC-3. Finally, we used a cell cycle assay to show significant G0/G1 arrest by transfecting miR-124-3p in LNCaP and miR-16-5p in both cell lines. Our findings suggest that this novel approach may have therapeutic benefits and these predicted microRNAs could effectively target the candidate genes.

Pan-cancer analysis suggests that LY6H is a potential biomarker of diagnosis, immunoinfiltration, and prognosis.

LY6H, a member of the lymphocyte antigen-6(LY6) gene family, is located on human chromosomes 6, 8, 11 and 19. This superfamily is characterized by the presence of LU domains. It has demonstrated its emerging significance in various cancers including adenocarcinoma, bladder cancer, ovarian cancer and skin cancer. However, comprehensive pan-cancer analyses have not been conducted to investigate its role in diagnosis, prognosis and immunological prediction. By conducting comprehensive analysis of patient data obtained from publicly available databases, including The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), University of Alabama at Birmingham (UALCAN), The Comparative Toxicological Genomics Database (CTD), cBioportal, cancerSEA, and UCSC, we systematically investigated the differential expression of LY6H in 33 different types of human tumors. Additionally, we thoroughly analyzed the diagnostic, prognostic, and immunoinfiltration value of LY6H. Simultaneously, we examined the correlation between LY6H and tumor stemness, methylation patterns, drug sensitivity, gene alterations as well as single cell functions. Furthermore, protein-protein interaction networks and gene-gene interaction networks for LY6H were constructed. Moreover, we also explored the network relationship between LY6H and chemical compounds or genes. The results revealed that LY6H exhibited high expression levels in most cancers which were further validated through Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Immunohistochemistry (IHC) analysis using Hepatocellular carcinoma (HCC) samples. Moreover, LY6H displayed early diagnostic potential in 12 tumors while also showing positive or negative correlations with prognosis across different tumor types. Additionally, it was found that LY6H played a pivotal role in regulating immune-infiltrating cells across multiple cancers whereas the correlation between LY6H expression and immune-related genes varied depending on their specific types. Furthermore, the expression of LY6H was significantly associated with DNA methylation patterns in 21 cancers. Therefore, LY6H could serve as an adjunctive biomarker for early tumor detection as well as a prognostic indicator for diverse malignancies.

Molecular role of non-exonic variants in CALPAIN 10 gene in polycystic ovarian syndrome in Saudi women.

Non-diabetic women with polycystic ovarian syndrome (PCOS) often have abnormal insulin regulation. Calpain 10 (CALP10) is a biomarker of type 2 diabetes mellitus, with some of its single-nucleotide polymorphisms (SNPs) influencing PCOS development. In this case-control study on 90 women each with and without PCOS, we explored the molecular role of five CALP10 SNPs using biochemical parameters and Sanger sequencing analyses. Different genetic models, genotypes, and allele frequencies were significantly associated with UCSNP-19 (rs3842570; p=0.01), UCSNP-44 (rs2975760; p=0.009), UCSNP-56 (rs2975762; p<0.0001), and UCSNP-63 (rs5030952; p=0.0003) in women with PCOS. The multiple logistic regression model showed a strong association of CALP10 SNPs with fasting blood glucose (p<0.001). ANOVA showed significant associations with various biochemical parameters such as FSH (p=0.0001) in UCSNP-19 (rs3842570), FI (p=0.002), TG (p=0.01) in UCSNP-56 (rs2975762) and FBG (p=0.001), FI (p=0.004), FSH (p=0.02) & LDLc (p=0.04) in UCSNP-63 (rs5030952) SNPs. Haplotype analysis also revealed significant associations between different combinations of alleles in the studied 5 SNPs in women with PCOS (p<0.05). Generalized multifactor dimensionality reduction analysis showed the best gene-gene interactions among the five SNPs in CALP10I (p<0.05). However, dendrogram and graphical depletion models found no strong association in women with PCOS. In conclusion, this study confirms rs3842570, rs2975760, rs2975767, and rs5030952 SNPs in CALP10 gene is associated in diagnosed PCOS women in the Saudi Arabia.

EPISTASIS ASSOCIATION STUDY TO IDENTIFY NETWORK HUB GENES IN ALZHEIMER’S DISEASE AND RELATED DEMENTIAS

Abstract Despite recent advances in understanding ADRD genetic risk, the vast majority of the genetic variance of AD/ADRD risk remains unexplained by identified loci such as APOE or the polygenic signal. One unexplored reservoir of genetic effects is interactions among genes (epistasis), which could identify key and novel molecular pathways, networks, and specific cell types in which the ADRD genetic risk is manifest. We tested all pairwise gene-gene interactions using our recently-developed transcriptome-wide interaction study method on ADRD in the UKBiobank. While no individual epistatic association was genome-wide significant (p&amp;lt; 2.56e-10), several approached significance (p&amp;lt; 3e-8). More importantly, we identified three core genes with &amp;gt;10 suggestive epistatic interactions. These include genes with some limited, prior evidence of a role in ADRD etiology, but none that have been identified in single-locus GWAS. In follow-up enrichment analyses of neuronal annotations underlying the interaction associations, we found genes intolerant to protein truncating mutations (PI), and those expressed specifically in endothelial cells, excitatory neurons in the prefrontal cortex , neuronal stem cells, and PI excitatory dentate gyrus cells to be nominally enriched (p&amp;lt; 0.05) in interaction associations with ADRD using prefrontal cortex imputed expression. Together, these suggest that we can uncover novel aspects of AD biology through these interaction associations and identify genes that may play a key role in integrating signals of AD risk. We anticipate identifying additional core genes and pathways as we integrate other larger and more diverse datasets.

A comprehensive analysis of mRNA expression profiles of Esophageal Squamous Cell Carcinoma reveals downregulation of Desmoglein 1and crucial genomic targets.

Esophageal Squamous Cell Carcinoma (ESCC) is a histological subtype of esophageal cancer that begins in the squamous cells in the esophagus. In only 19% of the ESCC-diagnosed patients, a five-year survival rate has been seen. This necessitates the identification of high-confidence biomarkers for early diagnosis, prognosis, and potential therapeutic targets for the mitigation of ESCC. We performed a meta-analysis of 10 mRNA datasets and identified consistently perturbed genes across the studies. Then, integrated with ESCC ATLAS to segregate 'core' genes to identify consequences of primary gene perturbation events leading to gene-gene interactions and dysregulated molecular signaling pathways. Further, by integrating with toxicogenomics data, inferences were drawn for gene interaction with environmental exposures, trace elements, chemical carcinogens, and drug chemicals. We also deduce the clinical outcomes of candidate genes based on survival analysis using the ESCC related dataset in The Cancer Genome Atlas. We identified 237 known and 18 novel perturbed candidate genes. Desmoglein 1 (DSG1) is one such gene that we found significantly downregulated (Fold Change =-1.89, p-value = 8.2e-06) in ESCC across six different datasets. Further, we identified 31 'core' genes (that either harbor genetic variants or are regulated by epigenetic modifications) and found regulating key biological pathways via adjoining genes in gene-gene interaction networks. Functional enrichment analysis showed dysregulated biological processes and pathways including "Extracellular matrix", "Collagen trimmer" and "HPV infection" are significantly overrepresented in our candidate genes. Based on the toxicogenomic inferences from Comparative Toxicogenomics Database we report the key genes that interacted with risk factors such as tobacco smoking, zinc, nitroso benzylmethylamine, and drug chemicals such as cisplatin, Fluorouracil, and Mitomycin in relation to ESCC. We also point to the STC2 gene that shows a high risk for mortality in ESCC patients. We identified novel perturbed genes in relation to ESCC and explored their interaction network. DSG1 is one such gene, its association with microbiota and a clinical presentation seen commonly with ESCC hints that it is a good candidate for early diagnostic marker. Besides, in this study we highlight candidate genes and their molecular connections to risk factors, biological pathways, drug chemicals, and the survival probability of ESCC patients.