Complex effects of sequence variants on lipid levels and coronary artery disease.

Many sequence variants have additive effects on blood lipid levels and, through that, on the risk of coronary artery disease (CAD). We show that variants also have non-additive effects and interact to affect lipid levels as well as affecting variance and correlations. Variance and correlation effects are often signatures of epistasis or gene-environmental interactions. These complex effects can translate into CAD risk. For example, Trp154Ter in FUT2 protects against CAD among subjects with the A1 blood group, whereas it associates with greater risk of CAD in others. His48Arg in ADH1B interacts with alcohol consumption to affect lipid levels and CAD. The effect of variants in TM6SF2 on blood lipids is greatest among those who never eat oily fish but absent from those who often do. This work demonstrates that variants that affect variance of quantitative traits can allow for the discovery of epistasis and interactions of variants with the environment.

Global-local aware Heterogeneous Graph Contrastive Learning for multifaceted association prediction in miRNA-gene-disease networks.

Unraveling the intricate network of associations among microRNAs (miRNAs), genes, and diseases is pivotal for deciphering molecular mechanisms, refining disease diagnosis, and crafting targeted therapies. Computational strategies, leveraging link prediction within biological graphs, present a cost-efficient alternative to high-cost empirical assays. However, while plenty of methods excel at predicting specific associations, such as miRNA-disease associations (MDAs), miRNA-target interactions (MTIs), and disease-gene associations (DGAs), a holistic approach harnessing diverse data sources for multifaceted association prediction remains largely unexplored. The limited availability of high-quality data, as vitro experiments to comprehensively confirm associations are often expensive and time-consuming, results in a sparse and noisy heterogeneous graph, hindering an accurate prediction of these complex associations. To address this challenge, we propose a novel framework called Global-local aware Heterogeneous Graph Contrastive Learning (GlaHGCL). GlaHGCL combines global and local contrastive learning to improve node embeddings in the heterogeneous graph. In particular, global contrastive learning enhances the robustness of node embeddings against noise by aligning global representations of the original graph and its augmented counterpart. Local contrastive learning enforces representation consistency between functionally similar or connected nodes across diverse data sources, effectively leveraging data heterogeneity and mitigating the issue of data scarcity. The refined node representations are applied to downstream tasks, such as MDA, MTI, and DGA prediction. Experiments show GlaHGCL outperforming state-of-the-art methods, and case studies further demonstrate its ability to accurately uncover new associations among miRNAs, genes, and diseases. We have made the datasets and source code publicly available at https://github.com/Sue-syx/GlaHGCL.

Prediction of multi-relational drug-gene interaction via Dynamic hyperGraph Contrastive Learning.

Drug-gene interaction prediction occupies a crucial position in various areas of drug discovery, such as drug repurposing, lead discovery and off-target detection. Previous studies show good performance, but they are limited to exploring the binding interactions and ignoring the other interaction relationships. Graph neural networks have emerged as promising approaches owing to their powerful capability of modeling correlations under drug-gene bipartite graphs. Despite the widespread adoption of graph neural network-based methods, many of them experience performance degradation in situations where high-quality and sufficient training data are unavailable. Unfortunately, in practical drug discovery scenarios, interaction data are often sparse and noisy, which may lead to unsatisfactory results. To undertake the above challenges, we propose a novel Dynamic hyperGraph Contrastive Learning (DGCL) framework that exploits local and global relationships between drugs and genes. Specifically, graph convolutions are adopted to extract explicit local relations among drugs and genes. Meanwhile, the cooperation of dynamic hypergraph structure learning and hypergraph message passing enables the model to aggregate information in a global region. With flexible global-level messages, a self-augmented contrastive learning component is designed to constrain hypergraph structure learning and enhance the discrimination of drug/gene representations. Experiments conducted on three datasets show that DGCL is superior to eight state-of-the-art methods and notably gains a 7.6% performance improvement on the DGIdb dataset. Further analyses verify the robustness of DGCL for alleviating data sparsity and over-smoothing issues.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation.

BACKGROUND: Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. RESULTS: Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. CONCLUSIONS: LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver .

Ant colony optimization for the identification of dysregulated gene subnetworks from expression data.

BACKGROUND: High-throughput experimental technologies can provide deeper insights into pathway perturbations in biomedical studies. Accordingly, their usage is central to the identification of molecular targets and the subsequent development of suitable treatments for various diseases. Classical interpretations of generated data, such as differential gene expression and pathway analyses, disregard interconnections between studied genes when looking for gene-disease associations. Given that these interconnections are central to cellular processes, there has been a recent interest in incorporating them in such studies. The latter allows the detection of gene modules that underlie complex phenotypes in gene interaction networks. Existing methods either impose radius-based restrictions or freely grow modules at the expense of a statistical bias towards large modules. We propose a heuristic method, inspired by Ant Colony Optimization, to apply gene-level scoring and module identification with distance-based search constraints and penalties, rather than radius-based constraints. RESULTS: We test and compare our results to other approaches using three datasets of different neurodegenerative diseases, namely Alzheimer's, Parkinson's, and Huntington's, over three independent experiments. We report the outcomes of enrichment analyses and concordance of gene-level scores for each disease. Results indicate that the proposed approach generally shows superior stability in comparison to existing methods. It produces stable and meaningful enrichment results in all three datasets which have different case to control proportions and sample sizes. CONCLUSION: The presented network-based gene expression analysis approach successfully identifies dysregulated gene modules associated with a certain disease. Using a heuristic based on Ant Colony Optimization, we perform a distance-based search with no radius constraints. Experimental results support the effectiveness and stability of our method in prioritizing modules of high relevance. Our tool is publicly available at github.com/GhadiElHasbani/ACOxGS.git.

The Zymoseptoria tritici Avirulence Factor AvrStb6 Accumulates in Hyphae Close to Stomata and Triggers a Wheat Defense Response Hindering Fungal Penetration.

Zymoseptoria tritici, the causal agent of Septoria tritici blotch, is one of Europe's most damaging wheat pathogens, causing significant economic losses. Genetic resistance is a common strategy to control the disease, Stb6 being a resistance gene used for more than 100 years in Europe. This study investigates the molecular mechanisms underlying Stb6-mediated resistance. Utilizing confocal microscopy imaging, we determined that Z. tritici epiphytic hyphae mainly accumulate the corresponding avirulence factor AvrStb6 in close proximity to stomata. Consequently, the progression of AvrStb6-expressing avirulent strains is hampered during penetration. The fungal growth inhibition co-occurs with a transcriptional reprogramming in wheat characterized by an induction of immune responses, genes involved in stomatal regulation, and cell wall-related genes. Overall, we shed light on the gene-for-gene resistance mechanisms in the wheat-Z. tritici pathosystem at the cytological and transcriptomic level, and our results highlight that stomatal penetration is a critical process for pathogenicity and resistance. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Gene-gene interaction network analysis indicates CNTN2 is a candidate gene for idiopathic generalized epilepsy.

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.

Identification of candidate genes controlling cold tolerance at the early seedling stage from Dongxiang wild rice by QTL mapping, BSA-Seq and RNA-Seq.

BACKGROUND: The cold tolerance of rice is closely related to its production and geographic distribution. The identification of cold tolerance-related genes is of important significance for developing cold-tolerant rice. Dongxiang wild rice (Oryza rufipogon Griff.) (DXWR) is well-adapted to the cold climate of northernmost-latitude habitats ever found in the world, and is one of the most valuable rice germplasms for cold tolerance improvement. RESULTS: Transcriptome analysis revealed genes differentially expressed between Xieqingzao B (XB; a cold sensitive variety) and 19H19 (derived from an interspecific cross between DXWR and XB) in the room temperature (RT), low temperature (LT), and recovery treatments. The results demonstrated that chloroplast genes might be involved in the regulation of cold tolerance in rice. A high-resolution SNP genetic map was constructed using 120 BC5F2 lines derived from a cross between 19H19 and XB based on the genotyping-by-sequencing (GBS) technique. Two quantitative trait loci (QTLs) for cold tolerance at the early seedling stage (CTS), qCTS12 and qCTS8, were detected. Moreover, a total of 112 candidate genes associated with cold tolerance were identified based on bulked segregant analysis sequencing (BSA-seq). These candidate genes were divided into eight functional categories, and the expression trend of candidate genes related to 'oxidation-reduction process' and 'response to stress' differed between XB and 19H19 in the RT, LT and recovery treatments. Among these candidate genes, the expression level of LOC_Os12g18729 in 19H19 (related to 'response to stress') decreased in the LT treatment but restored and enhanced during the recovery treatment whereas the expression level of LOC_Os12g18729 in XB declined during recovery treatment. Additionally, XB contained a 42-bp deletion in the third exon of LOC_Os12g18729, and the genotype of BC5F2 individuals with a survival percentage (SP) lower than 15% was consistent with that of XB. Weighted gene coexpression network analysis (WGCNA) and modular regulatory network learning with per gene information (MERLIN) algorithm revealed a gene interaction/coexpression network regulating cold tolerance in rice. In the network, differentially expressed genes (DEGs) related to 'oxidation-reduction process', 'response to stress' and 'protein phosphorylation' interacted with LOC_Os12g18729. Moreover, the knockout mutant of LOC_Os12g18729 decreased cold tolerance in early rice seedling stage signifcantly compared with that of wild type. CONCLUSIONS: In general, study of the genetic basis of cold tolerance of rice is important for the development of cold-tolerant rice varieties. In the present study, QTL mapping, BSA-seq and RNA-seq were integrated to identify two CTS QTLs qCTS8 and qCTS12. Furthermore, qRT-PCR, genotype sequencing and knockout analysis indicated that LOC_Os12g18729 could be the candidate gene of qCTS12. These results are expected to further exploration of the genetic mechanism of CTS in rice and improve cold tolerance of cultivated rice by introducing the cold tolerant genes from DXWR through marker-assisted selection.

A network-based method for brain disease gene prediction by integrating brain connectome and molecular network.

Brain disease gene identification is critical for revealing the biological mechanism and developing drugs for brain diseases. To enhance the identification of brain disease genes, similarity-based computational methods, especially network-based methods, have been adopted for narrowing down the searching space. However, these network-based methods only use molecular networks, ignoring brain connectome data, which have been widely used in many brain-related studies. In our study, we propose a novel framework, named brainMI, for integrating brain connectome data and molecular-based gene association networks to predict brain disease genes. For the consistent representation of molecular-based network data and brain connectome data, brainMI first constructs a novel gene network, called brain functional connectivity (BFC)-based gene network, based on resting-state functional magnetic resonance imaging data and brain region-specific gene expression data. Then, a multiple network integration method is proposed to learn low-dimensional features of genes by integrating the BFC-based gene network and existing protein-protein interaction networks. Finally, these features are utilized to predict brain disease genes based on a support vector machine-based model. We evaluate brainMI on four brain diseases, including Alzheimer's disease, Parkinson's disease, major depressive disorder and autism. brainMI achieves of 0.761, 0.729, 0.728 and 0.744 using the BFC-based gene network alone and enhances the molecular network-based performance by 6.3% on average. In addition, the results show that brainMI achieves higher performance in predicting brain disease genes compared to the existing three state-of-the-art methods.

BioNet: a large-scale and heterogeneous biological network model for interaction prediction with graph convolution.

MOTIVATION: Understanding chemical-gene interactions (CGIs) is crucial for screening drugs. Wet experiments are usually costly and laborious, which limits relevant studies to a small scale. On the contrary, computational studies enable efficient in-silico exploration. For the CGI prediction problem, a common method is to perform systematic analyses on a heterogeneous network involving various biomedical entities. Recently, graph neural networks become popular in the field of relation prediction. However, the inherent heterogeneous complexity of biological interaction networks and the massive amount of data pose enormous challenges. This paper aims to develop a data-driven model that is capable of learning latent information from the interaction network and making correct predictions. RESULTS: We developed BioNet, a deep biological networkmodel with a graph encoder-decoder architecture. The graph encoder utilizes graph convolution to learn latent information embedded in complex interactions among chemicals, genes, diseases and biological pathways. The learning process is featured by two consecutive steps. Then, embedded information learnt by the encoder is then employed to make multi-type interaction predictions between chemicals and genes with a tensor decomposition decoder based on the RESCAL algorithm. BioNet includes 79 325 entities as nodes, and 34 005 501 relations as edges. To train such a massive deep graph model, BioNet introduces a parallel training algorithm utilizing multiple Graphics Processing Unit (GPUs). The evaluation experiments indicated that BioNet exhibits outstanding prediction performance with a best area under Receiver Operating Characteristic (ROC) curve of 0.952, which significantly surpasses state-of-theart methods. For further validation, top predicted CGIs of cancer and COVID-19 by BioNet were verified by external curated data and published literature.

Two-stage-vote ensemble framework based on integration of mutation data and gene interaction network for uncovering driver genes.

Identifying driver genes, exactly from massive genes with mutations, promotes accurate diagnosis and treatment of cancer. In recent years, a lot of works about uncovering driver genes based on integration of mutation data and gene interaction networks is gaining more attention. However, it is in suspense if it is more effective for prioritizing driver genes when integrating various types of mutation information (frequency and functional impact) and gene networks. Hence, we build a two-stage-vote ensemble framework based on somatic mutations and mutual interactions. Specifically, we first represent and combine various kinds of mutation information, which are propagated through networks by an improved iterative framework. The first vote is conducted on iteration results by voting methods, and the second vote is performed to get ensemble results of the first poll for the final driver gene list. Compared with four excellent previous approaches, our method has better performance in identifying driver genes on $33$ types of cancer from The Cancer Genome Atlas. Meanwhile, we also conduct a comparative analysis about two kinds of mutation information, five gene interaction networks and four voting strategies. Our framework offers a new view for data integration and promotes more latent cancer genes to be admitted.

Identification of biomarkers and potential therapeutic targets of kidney stone disease using bioinformatics.

PURPOSE: Kidney stone disease (KSD) is a common urological disease, but its pathogenesis remains unclear. In this study, we screened KSD-related hub genes using bioinformatic methods and predicted the related pathways and potential drug targets. METHODS: The GSE75542 and GSE18160 datasets in the Gene Expression Omnibus (GEO) were selected to identify common differentially expressed genes (DEGs). We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses to identify enriched pathways. Finally, we constructed a hub gene-miRNA network and drug-DEG interaction network. RESULTS: In total, 44 upregulated DEGs and 1 downregulated DEG were selected from the GEO datasets. Signaling pathways, such as leukocyte migration, chemokine activity, NF-κB, TNF, and IL-17, were identified in GO and KEGG. We identified 10 hub genes using Cytohubba. In addition, 21 miRNAs were predicted to regulate 4 or more hub genes, and 10 drugs targeted 2 or more DEGs. LCN2 expression was significantly different between the GEO datasets. Quantitative real-time polymerase chain reaction (qRT-PCR) analyses showed that seven hub gene expressions in HK-2 cells with CaOx treatment were significantly higher than those in the control group. CONCLUSION: The 10 hub genes identified, especially LCN2, may be involved in kidney stone occurrence and development, and may provide new research targets for KSD diagnosis. Furthermore, KSD-related miRNAs may be targeted for the development of novel drugs for KSD treatment.

Genetic factors and symptom dimensions associated with antidepressant treatment outcomes: clues for new potential therapeutic targets?

Treatment response and resistance in major depressive disorder (MDD) show a significant genetic component, but previous studies had limited power also due to MDD heterogeneity. This literature review focuses on the genetic factors associated with treatment outcomes in MDD, exploring their overlap with those associated with clinically relevant symptom dimensions. We searched PubMed for: (1) genome-wide association studies (GWASs) or whole exome sequencing studies (WESs) that investigated efficacy outcomes in MDD; (2) studies examining the association between MDD treatment outcomes and specific depressive symptom dimensions; and (3) GWASs of the identified symptom dimensions. We identified 13 GWASs and one WES of treatment outcomes in MDD, reporting several significant loci, genes, and gene sets involved in gene expression, immune system regulation, synaptic transmission and plasticity, neurogenesis and differentiation. Nine symptom dimensions were associated with poor treatment outcomes and studied by previous GWASs (anxiety, neuroticism, anhedonia, cognitive functioning, melancholia, suicide attempt, psychosis, sleep, sociability). Four genes were associated with both treatment outcomes and these symptom dimensions: CGREF1 (anxiety); MCHR1 (neuroticism); FTO and NRXN3 (sleep). Other overlapping signals were found when considering genes suggestively associated with treatment outcomes. Genetic studies of treatment outcomes showed convergence at the level of biological processes, despite no replication at gene or variant level. The genetic signals overlapping with symptom dimensions of interest may point to shared biological mechanisms and potential targets for new treatments tailored to the individual patient's clinical profile.

ANK3 rs10994336 and ZNF804A rs7597593 polymorphisms: genetic interaction for emotional and behavioral symptoms of alcohol withdrawal syndrome.

OBJECTIVE: Alcohol withdrawal syndrome (AWS) is a complex condition associated with alcohol use disorder (AUD), characterized by significant variations in symptom severity among patients. The psychological and emotional symptoms accompanying AWS significantly contribute to withdrawal distress and relapse risk. Despite the importance of neural adaptation processes in AWS, limited genetic investigations have been conducted. This study primarily focuses on exploring the single and interaction effects of single-nucleotide polymorphisms in the ANK3 and ZNF804A genes on anxiety and aggression severity manifested in AWS. By examining genetic associations with withdrawal-related psychopathology, we ultimately aim to advance understanding the genetic underpinnings that modulate AWS severity. METHODS: The study involved 449 male patients diagnosed with alcohol use disorder. The Self-Rating Anxiety Scale (SAS) and Buss-Perry Aggression Questionnaire (BPAQ) were used to assess emotional and behavioral symptoms related to AWS. Genomic DNA was extracted from peripheral blood, and genotyping was performed using PCR. RESULTS: Single-gene analysis revealed that naturally occurring allelic variants in ANK3 rs10994336 (CC homozygous vs. T allele carriers) were associated with mood and behavioral symptoms related to AWS. Furthermore, the interaction between ANK3 and ZNF804A was significantly associated with the severity of psychiatric symptoms related to AWS, as indicated by MANOVA. Two-way ANOVA further demonstrated a significant interaction effect between ANK3 rs10994336 and ZNF804A rs7597593 on anxiety, physical aggression, verbal aggression, anger, and hostility. Hierarchical regression analyses confirmed these findings. Additionally, simple effects analysis and multiple comparisons revealed that carriers of the ANK3 rs10994336 T allele experienced more severe AWS, while the ZNF804A rs7597593 T allele appeared to provide protection against the risk associated with the ANK3 rs10994336 mutation. CONCLUSION: This study highlights the gene-gene interaction between ANK3 and ZNF804A, which plays a crucial role in modulating emotional and behavioral symptoms related to AWS. The ANK3 rs10994336 T allele is identified as a risk allele, while the ZNF804A rs7597593 T allele offers protection against the risk associated with the ANK3 rs10994336 mutation. These findings provide initial support for gene-gene interactions as an explanation for psychiatric risk, offering valuable insights into the pathophysiological mechanisms involved in AWS.

Smart Biosensor for Breast Cancer Survival Prediction Based on Multi-View Multi-Way Graph Learning.

Biosensors play a crucial role in detecting cancer signals by orchestrating a series of intricate biological and physical transduction processes. Among various cancers, breast cancer stands out due to its genetic underpinnings, which trigger uncontrolled cell proliferation, predominantly impacting women, and resulting in significant mortality rates. The utilization of biosensors in predicting survival time becomes paramount in formulating an optimal treatment strategy. However, conventional biosensors employing traditional machine learning methods encounter challenges in preprocessing features for the learning task. Despite the potential of deep learning techniques to automatically extract useful features, they often struggle to effectively leverage the intricate relationships between features and instances. To address this challenge, our study proposes a novel smart biosensor architecture that integrates a multi-view multi-way graph learning (MVMWGL) approach for predicting breast cancer survival time. This innovative approach enables the assimilation of insights from gene interactions and biosensor similarities. By leveraging real-world data, we conducted comprehensive evaluations, and our experimental results unequivocally demonstrate the superiority of the MVMWGL approach over existing methods.

MiR-378 Inhibits Angiotensin II-Induced Cardiomyocyte Hypertrophy by Targeting AKT2.

Cardiomyocyte hypertrophy plays a crucial role in heart failure development, potentially leading to sudden cardiac arrest and death. Previous studies suggest that micro-ribonucleic acids (miRNAs) show promise for the early diagnosis and treatment of cardiomyocyte hypertrophy.To investigate the miR-378 expression in the cardiomyocyte hypertrophy model, reverse transcription-polymerase chain reaction (RT-qPCR), Western blot, and immunofluorescence tests were conducted in angiotensin II (Ang II)-induced H9c2 cells and Ang II-induced mouse model of cardiomyocyte hypertrophy. The functional interaction between miR-378 and AKT2 was studied by dual-luciferase reporter, RNA pull-down, Western blot, and RT-qPCR assays.The results of RT-qPCR analysis showed the downregulated expression of miR-378 in both the cell and animal models of cardiomyocyte hypertrophy. It was observed that the introduction of the miR-378 mimic inhibited the hypertrophy of cardiomyocytes induced by Ang II. Furthermore, the co-transfection of AKT2 expression vector partially mitigated the negative impact of miR-378 overexpression on Ang II-induced cardiomyocytes. Molecular investigations provided evidence that miR-378 negatively regulated AKT2 expression by interacting with the 3' untranslated region (UTR) of AKT2 mRNA.Decreased miR-378 expression and AKT2 activation are linked to Ang II-induced cardiomyocyte hypertrophy. Targeting miR-378/AKT2 axis offers therapeutic opportunity to alleviate cardiomyocyte hypertrophy.

Detecting Gene-Gene Interaction among DNA Repair Genes in Chinese non-Syndromic Cleft lip with or Without Palate Trios.

OBJECTIVE: The objective of this study is to investigate the gene-gene interactions associated with NSCL/P among DNA repair genes. DESIGN: This study included 806 NSCL/P case-parent trios from China. Quality control process was conducted for genotyped single nucleotide polymorphisms (SNPs) located in six DNA repair genes (ATR, ERCC4, RFC1, TYMS, XRCC1 and XRCC3). We tested gene-gene interactions with Cordell's method using statistical package TRIO in R software. Bonferroni corrected significance level was set as P = 4.24 × 10-4. We also test the robustness of the interactions by permutation tests. SETTING: Not applicable. PATIENTS/PARTICIPANTS: A total of 806 NSCL/P case-parent trios (complete trios: 682, incomplete trios: 124) with Chinese ancestry. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE(S): Not applicable. RESULTS: A total of 118 SNPs were extracted for the interaction tests. Fourteen pairs of significant interactions were identified after Bonferroni correction, which were confirmed in permutation tests. Twelve pairs were between ATR and ERCC4 or XRCC3. The most significant interaction occurred between rs2244500 in TYMS and rs3213403 in XRCC1(P = 8.16 × 10-15). CONCLUSIONS: The current study identified gene-gene interactions among DNA repair genes in 806 Chinese NSCL/P trios, providing additional evidence for the complicated genetic structure underlying NSCL/P. ATR, ERCC4, XRCC3, TYMS and RFC1 were suggested to be possible candidate genes for NSCL/P.

Network embedding framework for driver gene discovery by combining functional and structural information.

Comprehensive analysis of multiple data sets can identify potential driver genes for various cancers. In recent years, driver gene discovery based on massive mutation data and gene interaction networks has attracted increasing attention, but there is still a need to explore combining functional and structural information of genes in protein interaction networks to identify driver genes. Therefore, we propose a network embedding framework combining functional and structural information to identify driver genes. Firstly, we combine the mutation data and gene interaction networks to construct mutation integration network using network propagation algorithm. Secondly, the struc2vec model is used for extracting gene features from the mutation integration network, which contains both gene's functional and structural information. Finally, machine learning algorithms are utilized to identify the driver genes. Compared with the previous four excellent methods, our method can find gene pairs that are distant from each other through structural similarities and has better performance in identifying driver genes for 12 cancers in the cancer genome atlas. At the same time, we also conduct a comparative analysis of three gene interaction networks, three gene standard sets, and five machine learning algorithms. Our framework provides a new perspective for feature selection to identify novel driver genes.

Identification of multidrug chemoresistant genes in head and neck squamous cell carcinoma cells.

Multidrug resistance renders treatment failure in a large proportion of head and neck squamous cell carcinoma (HNSCC) patients that require multimodal therapy involving chemotherapy in conjunction with surgery and/or radiotherapy. Molecular events conferring chemoresistance remain unclear. Through transcriptome datamining, 28 genes were subjected to pharmacological and siRNA rescue functional assays on 12 strains of chemoresistant cell lines each against cisplatin, 5-fluorouracil (5FU), paclitaxel (PTX) and docetaxel (DTX). Ten multidrug chemoresistance genes (TOP2A, DNMT1, INHBA, CXCL8, NEK2, FOXO6, VIM, FOXM1B, NR3C1 and BIRC5) were identified. Of these, four genes (TOP2A, DNMT1, INHBA and NEK2) were upregulated in an HNSCC patient cohort (n = 221). Silencing NEK2 abrogated chemoresistance in all drug-resistant cell strains. INHBA and TOP2A were found to confer chemoresistance in majority of the drug-resistant cell strains whereas DNMT1 showed heterogeneous results. Pan-cancer Kaplan-Meier survival analysis on 21 human cancer types revealed significant prognostic values for INHBA and NEK2 in at least 16 cancer types. Drug library screens identified two compounds (Sirodesmin A and Carfilzomib) targeting both INHBA and NEK2 and re-sensitised cisplatin-resistant cells. We have provided the first evidence for NEK2 and INHBA in conferring chemoresistance in HNSCC cells and siRNA gene silencing of either gene abrogated multidrug chemoresistance. The two existing compounds could be repurposed to counteract cisplatin chemoresistance in HNSCC. This finding may lead to novel personalised biomarker-linked therapeutics that can prevent and/or abrogate chemoresistance in HNSCC and other tumour types with elevated NEK2 and INHBA expression. Further investigation is necessary to delineate their signalling mechanisms in tumour chemoresistance.

Identifying Network Biomarkers in Early Diagnosis of Hepatocellular Carcinoma via miRNA-Gene Interaction Network Analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) is a highly heterogeneous cancer at the histological level. Despite the emergence of new biological technology, advanced-stage HCC remains largely incurable. The prediction of a cancer biomarker is a key problem for targeted therapy in the disease. METHODS: We performed a miRNA-gene integrated analysis to identify differentially expressed miRNAs (DEMs) and genes (DEGs) of HCC. The DEM-DEG interaction network was constructed and analyzed. Gene ontology enrichment and survival analyses were also performed in this study. RESULTS: By the analysis of healthy and tumor samples, we found that 94 DEGs and 25 DEMs were significantly differentially expressed in different datasets. Gene ontology enrichment analysis showed that these 94 DEGs were significantly enriched in the term "Liver" with a statistical p-value of 1.71 × 10-26. Function enrichment analysis indicated that these genes were significantly overrepresented in the term "monocarboxylic acid metabolic process" with a p-value = 2.94 × 10-18. Two sets (fourteen genes and five miRNAs) were screened by a miRNA-gene integrated analysis of their interaction network. The statistical analysis of these molecules showed that five genes (CLEC4G, GLS2, H2AFZ, STMN1, TUBA1B) and two miRNAs (hsa-miR-326 and has-miR-331-5p) have significant effects on the survival prognosis of patients. CONCLUSION: We believe that our study could provide critical clinical biomarkers for the targeted therapy of HCC.