The largest genome-wide association study for breast cancer in Taiwanese Han population.

PURPOSE: Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap. METHODS: A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. RESULTS: The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79-7.66) and 3.55 (2.13-6.14), respectively. CONCLUSION: In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women.

Twnbiome: a public database of the healthy Taiwanese gut microbiome.

With new advances in next generation sequencing (NGS) technology at reduced costs, research on bacterial genomes in the environment has become affordable. Compared to traditional methods, NGS provides high-throughput sequencing reads and the ability to identify many species in the microbiome that were previously unknown. Numerous bioinformatics tools and algorithms have been developed to conduct such analyses. However, in order to obtain biologically meaningful results, the researcher must select the proper tools and combine them to construct an efficient pipeline. This complex procedure may include tens of tools, each of which require correct parameter settings. Furthermore, an NGS data analysis involves multiple series of command-line tools and requires extensive computational resources, which imposes a high barrier for biologists and clinicians to conduct NGS analysis and even interpret their own data. Therefore, we established a public gut microbiome database, which we call Twnbiome, created using healthy subjects from Taiwan, with the goal of enabling microbiota research for the Taiwanese population. Twnbiome provides users with a baseline gut microbiome panel from a healthy Taiwanese cohort, which can be utilized as a reference for conducting case-control studies for a variety of diseases. It is an interactive, informative, and user-friendly database. Twnbiome additionally offers an analysis pipeline, where users can upload their data and download analyzed results. Twnbiome offers an online database which non-bioinformatics users such as clinicians and doctors can not only utilize to access a control set of data, but also analyze raw data with a few easy clicks. All results are customizable with ready-made plots and easily downloadable tables. Database URL: http://twnbiome.cgm.ntu.edu.tw/ .

High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data.

In this study, we proposed a deep learning (DL) model for classifying individuals from mixtures of DNA samples using 27 short tandem repeats and 94 single nucleotide polymorphisms obtained through massively parallel sequencing protocol. The model was trained/tested/validated with sequenced data from 6 individuals and then evaluated using mixtures from forensic DNA samples. The model successfully identified both the major and the minor contributors with 100% accuracy for 90 DNA mixtures, that were manually prepared by mixing sequence reads of 3 individuals at different ratios. Furthermore, the model identified 100% of the major contributors and 50-80% of the minor contributors in 20 two-sample external-mixed-samples at ratios of 1:39 and 1:9, respectively. To further demonstrate the versatility and applicability of the pipeline, we tested it on whole exome sequence data to classify subtypes of 20 breast cancer patients and achieved an area under curve of 0.85. Overall, we present, for the first time, a complete pipeline, including sequencing data processing steps and DL steps, that is applicable across different NGS platforms. We also introduced a sliding window approach, to overcome the sequence length variation problem of sequencing data, and demonstrate that it improves the model performance dramatically.

Copy number variation of gasdermin D gene is associated with atrial fibrillation-related thromboembolic stroke.

AIMS: Atrial fibrillation (AF) is one of the major causes of ischaemic stroke. In addition to clinical risk evaluated by the CHA2DS2-VASC score, the impact of genetic factors on the risk of AF-related thromboembolic stroke has been largely unknown. We found several copy number variations (CNVs) in novel genes that were associated with thromboembolic stroke risk in our AF patients by genome-wide approach. Among them, the gasdermin D (GSDMD) gene was related to inflammation. We aimed to test whether GSDMD deletion was associated with AF-related stroke. METHODS AND RESULTS: A total of 400 patients with documented non-familial AF were selected, of which 100 patients were diagnosed with ischaemic stroke. The baseline characteristics of age, sex, valvular heart disease, coronary artery disease, heart failure, and CHA2DS2-VASc scores were not statistically different between cases and controls. We found that individuals who carried GSDMD homozygous deletion genotype had a higher risk for ischaemic stroke (odds ratio 2.195; 95% confidence interval, 1.24-3.90; P = 0.007), even adjusted by CHA2DS2-VASc scores. We also validated the association of GSDMD with AF stroke in a large Caucasian population (UK Biobank). CONCLUSION: We found a link between the homozygous deletion of the GSDMD gene and an increased risk of stroke in patients with AF. This may implicate the use of therapy targeting GSDMD in the prevention of ischaemic stroke for AF patients.

Multi-ethnic Imputation System (MI-System): A genotype imputation server for high-dimensional data.

OBJECTIVE: Genotype imputation is a commonly used technique that infers un-typed variants into a study's genotype data, allowing better identification of causal variants in disease studies. However, due to overrepresentation of Caucasian studies, there's a lack of understanding of genetic basis of health-outcomes in other ethnic populations. Therefore, facilitating imputation of missing key-predictor-variants that can potentially improve a risk health-outcome prediction model, specifically for Asian ancestry, is of utmost relevance. METHODS: We aimed to construct an imputation and analysis web-platform, that primarily facilitates, but is not limited to genotype imputation on East-Asians. The goal is to provide a collaborative imputation platform for researchers in the public domain towards rapidly and efficiently conducting accurate genotype imputation. RESULTS: We present an online genotype imputation platform, Multi-ethnic Imputation System (MI-System) (https://misystem.cgm.ntu.edu.tw/), that offers users 3 established pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1 for conducting imputation analyses. In addition to 1000 Genomes and Hapmap3, a new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. MI-System further offers functions to create customized reference panels to be used for imputation, conduct quality control, split whole genome data into chromosomes, and convert genome builds. CONCLUSION: Users can upload their genotype data and perform imputation with minimum effort and resources. The utility functions further can be utilized to preprocess user uploaded data with easy clicks. MI-System potentially contributes to Asian-population genetics research, while eliminating the requirement for high performing computational resources and bioinformatics expertise. It will enable an increased pace of research and provide a knowledge-base for genetic carriers of complex diseases, therefore greatly enhancing patient-driven research. STATEMENT OF SIGNIFICANCE: Multi-ethnic Imputation System (MI-System), primarily facilitates, but is not limited to, imputation on East-Asians, through 3 established prephasing-imputation pipelines, SHAPEIT2-IMPUTE2, SHAPEIT4-IMPUTE5, and Beagle5.1, where users can upload their genotype data and perform imputation and other utility functions with minimum effort and resources. A new customized Taiwan Biobank (TWB) reference panel, specifically created for Taiwanese-Chinese ancestry is provided. Utility functions include (a) create customized reference panels, (b) conduct quality control, (c) split whole genome data into chromosomes, and (d) convert genome builds. Users can also combine 2 reference panels using the system and use combined panels as reference to conduct imputation using MI-System.

CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants.

BACKGROUND: Availability of next generation sequencing data, allows low-frequency and rare variants to be studied through strategies other than the commonly used genome-wide association studies (GWAS). Rare variants are important keys towards explaining the heritability for complex diseases that remains to be explained by common variants due to their low effect sizes. However, analysis strategies struggle to keep up with the huge amount of data at disposal therefore creating a bottleneck. This study describes CLIN_SKAT, an R package, that provides users with an easily implemented analysis pipeline with the goal of (i) extracting clinically relevant variants (both rare and common), followed by (ii) gene-based association analysis by grouping the selected variants. RESULTS: CLIN_SKAT offers four simple functions that can be used to obtain clinically relevant variants, map them to genes or gene sets, calculate weights from global healthy populations and conduct weighted case-control analysis. CLIN_SKAT introduces improvements by adding certain pre-analysis steps and customizable features to make the SKAT results clinically more meaningful. Moreover, it offers several plot functions that can be availed towards obtaining visualizations for interpretation of the analyses results. CLIN_SKAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It can be freely downloaded from https://github.com/ShihChingYu/CLIN_SKAT , installed through devtools::install_github("ShihChingYu/CLIN_SKAT", force=T) and executed by loading the package into R using library(CLIN_SKAT). All outputs (tabular and graphical) can be downloaded in simple, publishable formats. CONCLUSIONS: Statistical association analysis is often underpowered due to low sample sizes and high numbers of variants to be tested, limiting detection of causal ones. Therefore, retaining a subset of variants that are biologically meaningful seems to be a more effective strategy for identifying explainable associations while reducing the degrees of freedom. CLIN_SKAT offers users a one-stop R package that identifies disease risk variants with improved power via a series of tailor-made procedures that allows dimension reduction, by retaining functionally relevant variants, and incorporating ethnicity based priors. Furthermore, it also eliminates the requirement for high computational resources and bioinformatics expertise.

The extended concurrent genes signature for disease-free survival in breast cancer.

BACKGROUND/PURPOSE: Previously we had identified concurrent genes, which highlighted the interplay between copy number variation (CNV) and differential gene expression (GE) for Han Chinese breast cancers. The merit of the approach is to discovery biomarkers not identifiable by conventional GE only data, for which phenotype-correlation or gene variability is the criteria of gene selection. MATERIALS AND METHODS: Thirty-one comparative genomic hybridization (CGH) and 83 GE microarrays were performed, with 29 breast cancers assayed from both platforms. Potential targets were revealed by Genomic Identification of Significant Targets in Cancer (GISTIC) from CGH arrays. Concurrent genes and genes with significant GISTIC scores were used to derive the extended concurrent genes signature, which was consensus from leading edge analysis across all studies and a supervised partial least square (PLS) regression predictive model of disease-free survival was constructed. RESULTS: There were 1584 concurrent genes from 29 samples with both CGH and GE microarrays. Enriched concurrent genes sets for disease-free survival were identified independently from 83 GE arrays and another one with Han Chinese origin as well as three studies of Western origin. For five studies with disease-free survival follow up, prognostic discrepancy was observed between predicted high-risk and low-risk group patients. CONCLUSION: We concluded that through parallel analyses of CGH and GE microarrays, the proposed extended concurrent gene expression signature can identify biomarkers with prognostic values.

Prevalence of sudden arrhythmic death syndrome-related genetic mutations in an Asian cohort of whole genome sequence.

AIMS: Recently, the spectrum of background mutation in the genes implicated in sudden arrhythmic death syndrome (SADS), has been elucidated in the Caucasian populations. However, this information is largely unknown in the Asian populations. METHODS AND RESULTS: We assessed the background rare variants (minor allele frequency < 0.01) of major SADS genes in whole genome sequence data of 1514 healthy Taiwanese subjects from the Taiwan Biobank. We found up to 45% of healthy subjects have a rare variant in at least one of the major SADS genes. Around 3.44% of healthy subjects had multiple mutations in one or multiple genes. The background mutation rates in long QT syndrome, catecholaminergic polymorphic ventricular tachycardia, and arrhythmogenic right ventricular cardiomyopathy genes were similar, but those in Brugada syndrome (BrS) (SCN5A) and hypertrophic cardiomyopathy (HCM) genes (MYBPC3, MYH7, and TNNT2) were higher, compared to those reported in the Caucasian populations. Furthermore, the rate of incidental pathogenic variant was highest in MYBPC3 gene. Finally, the number of variant was proportional to the exon length of the gene (R2 = 0.486, P = 0.0056) but not related to its functional or evolutionary importance (degree of evolutionary conservation) (R2 = 0.0008, P = 0.9218), suggesting that the mutation was random. The ratio of variant number over exon nucleotide length was highest in MYBPC3, MYH7, and TNNT2 genes. CONCLUSION: Unique features of background SADS gene mutation in the Asian populations include higher prevalence of incidental variant in HCM, BrS, and long QT 3 (SCN5A) genes. HCM genes have the highest variant number per exon length.

Comparison of DNA stabilizers and storage conditions on preserving fecal microbiota profiles.

BACKGROUND/PURPOSE: Appropriate storage of fecal samples is a critical step for the unbiased analysis of microbial communities in metagenomic studies. Rapid freezing at -80 °C is usually considered to be best practice, but this approach is challenging. DNA stabilizing kits may provide a more convenient method to preserve and store clinical samples. We evaluated the reliability of two collection kits (Stratec stool collection tube with stabilizer, #1038111200 and OMNIgene.GUT OMR-200) on preserving fecal microbiota. METHODS: Samples were collected from two locations of the fecal specimen, in four healthy volunteers. The samples were sub-aliquoted and stored in a -80 °C freezer, in Stratec and OMNIgene.GUT (incubation at ambient temperature for 0, 3, or 7 days). The fecal microbial composition was assessed by 16S rRNA sequencing. RESULTS: We found that alpha diversity was not significantly affected by storage conditions. Samples stored in DNA stabilizers were still representative of the original microbial community after 7 days at ambient temperature. Individual differences were found to have a greater contribution to the differences in microbial community composition than storage conditions or sampling location. Samples subjected to stabilizers displayed microbial community shifts compared with immediately frozen samples. A linear discriminant analysis effect size (LEfSe) analysis showed that the relative abundances of Faecalibacterium were significantly higher in samples stored in Stratec kits. CONCLUSION: Our study reveals that both Stratec and OMNIgene.GUT kits provide good microbiome preservation for up to 7 days in ambient temperature and would represent good options for fecal sample collection in large scale, population-based studies.

anamiR: integrated analysis of MicroRNA and gene expression profiling.

BACKGROUND: With advancements in high-throughput technologies, the cost of obtaining expression profiles of both mRNA and microRNA in the same individual has substantially decreased. Integrated analysis of these profiles can help to elucidate the functional effects of RNA expression in complex diseases, such as cancer. However, fundamental discrepancies are observed in the results from microRNA-mRNA target gene prediction algorithms, and few packages can be used to analyze microRNA and mRNA expression levels simultaneously. RESULTS: To address these issues, an R package, anamiR, was developed. A total of 10 experimental/prediction databases were integrated. Two analytical functions are provided in anamiR, including the single marker test and functional gene set enrichment analysis, and several parameters can be changed by users. Here we demonstrate the potential application of the anamiR package to 2 publicly available microarray datasets. CONCLUSION: The anamiR package is effective for an integrated analysis of both RNA and microRNA profiles. By characterizing biological functions and signaling pathways, this package helps identify dysregulated genes/miRNAs from biological and medical experiments. The source code and manual of the anamiR package are freely available at https://bioconductor.org/packages/release/bioc/html/anamiR.html .

miR-338-5p inhibits cell proliferation, colony formation, migration and cisplatin resistance in esophageal squamous cancer cells by targeting FERMT2.

Esophageal cancer is one of the leading causes of cancer death in the male population of Eastern Asia. In addition, esophageal squamous cell carcinoma (ESCC) is the major type of esophageal cancer among the world. Owing to the poor overall 5-year survival rate, novel effective treatment strategies are needed. MicroRNAs are important gene regulators that are dysregulated in many cancer types. In our previous study, we applied next-generation sequencing to demonstrate that miR-338-5p was downregulated in the tumor tissue of patients with versus without recurrence. In this study, we further studied the roles of miR-338-5p in ESCC. The expression of endogenous miR-338-5p was at lower levels in ESCC cells compared with normal cells. Functional assays showed that miR-338-5p reduced cell proliferation, colony formation, migration and cisplatin resistance in an ESCC cell line, CE-81T. Potential target genes of miR-338-5p were identified by microarray and prediction tools, and 31 genes were selected. Among these, Fermitin family homolog 2 (FERMT2) plays an oncogenic role in ESCC, so it was chosen for further study. Luciferase assays showed the direct binding between miR-338-5p and the 3' untranslated region of FERMT2. Silencing of FERMT2 inhibited cell proliferation, colony formation, migration and cisplatin resistance. Pathway analysis revealed that the integrin-linked protein kinase signaling pathway, in which FERMT2 participates, was significantly affected by a miR-338-5p mimic. Our results suggest that miR-338-5p may play an antioncogenic role in ESCC via repressing FERMT2.

Altered gut microbiota and inflammatory cytokine responses in patients with Parkinson's disease.

OBJECTIVE: Emerging evidence suggests that gut microbiome composition alterations affect neurodegeneration through neuroinflammation in the pathogenesis of Parkinson's disease (PD). Here, we evaluate gut microbiota alterations and host cytokine responses in a population of Taiwanese patients with PD. METHODS: Fecal microbiota communities from 80 patients with PD and 77 age and gender-matched controls were assessed by sequencing the V3-V4 region of the 16S ribosomal RNA gene. Diet and comorbidities were controlled in the analyses. Plasma concentrations of IL-1ß, IL-2, IL-4, IL-6, IL-13, IL-18, GM-CSF, IFNγ, and TNFα were measured by a multiplex immunoassay and relationships between microbiota, clinical characteristics, and cytokine levels were analyzed in the PD group. We further examined the cytokine changes associated with the altered gut microbiota seen in patients with PD in another independent cohort of 120 PD patients and 120 controls. RESULTS: Microbiota from patients with PD was altered relative to controls and dominated by Verrucomicrobia, Mucispirillum, Porphyromonas, Lactobacillus, and Parabacteroides. In contrast, Prevotella was more abundant in controls. The abundances of Bacteroides were more increased in patients with non-tremor PD subtype than patients with tremor subtype. Bacteroides abundance was correlated with motor symptom severity defined by UPDRS part III motor scores (rho = 0.637 [95% confidence interval 0.474 to 0.758], P < 0.01). Altered microbiota was correlated with plasma concentrations of IFNγ and TNFα. There was a correlation between Bacteroides and plasma level of TNFα (rho = 0.638 [95% CI: 0.102-0.887], P = 0.02); and a correlation between Verrucomicrobia abundance and plasma concentrations of IFNγ (rho = 0.545 [95% CI - 0.043-0.852], P = 0.05). The elevated plasma cytokine responses were confirmed in an additional independent 120 patients with PD and 120 controls (TNFα: PD vs. control 8.51 ± 4.63 pg/ml vs. 4.82 ± 2.23 pg/ml, P < 0.01; and IFNγ: PD vs. control: 38.45 ± 7.12 pg/ml vs. 32.79 ± 8.03 pg/ml, P = 0.03). CONCLUSIONS: This study reveals altered gut microbiota in PD and its correlation with clinical phenotypes and severity in our population. The altered plasma cytokine profiles associated with gut microbiome composition alterations suggest aberrant immune responses may contribute to inflammatory processes in PD.

Using proteomic profiling to characterize protein signatures of different thymoma subtypes.

BACKGROUND: Histology is a traditional way to classify subtypes of thymoma, because of low cost and convenience. Yet, due to the diverse morphology of thymoma, this method increases the complexity of histopathologic classification, and requires experienced experts to perform correct diagnosis. Therefore, in this study, we developed an alternative method by identifying protein biomarkers in order to assist clinical practitioners to make right classification of thymoma subtypes. METHODS: In total, 204 differentially expressed proteins in three subtypes of thymoma, AB, B2, and B3, were identified using mass spectrometry. Pathway analysis showed that the differentially expressed proteins in the three subtypes were involved in activation-related, signaling transduction-related and complement system-related pathways. To predict the subtypes of thymoma using the identified protein signatures, a support vector machine algorithm was used. Leave-one-out cross validation methods and receiver operating characteristic analysis were used to evaluate the predictive performance. RESULTS: The mean accuracy rates were > 80% and areas under the curve were ≧0.93 across these three subtypes. Especially, subtype B3 had the highest accuracy rate (96%) and subtype AB had the greatest area under the curve (0.99). One of the differentially expressed proteins COL17A2 was further validated using immunohistochemistry. CONCLUSIONS: In summary, we identified specific protein signatures for accurately classifying subtypes of thymoma, which could facilitate accurate diagnosis of thymoma patients.

An automated microfluidic DNA microarray platform for genetic variant detection in inherited arrhythmic diseases.

In this study, we developed an automated microfluidic DNA microarray (AMDM) platform for point mutation detection of genetic variants in inherited arrhythmic diseases. The platform allows for automated and programmable reagent sequencing under precise conditions of hybridization flow and temperature control. It is composed of a commercial microfluidic control system, a microfluidic microarray device, and a temperature control unit. The automated and rapid hybridization process can be performed in the AMDM platform using Cy3 labeled oligonucleotide exons of SCN5A genetic DNA, which produces proteins associated with sodium channels abundant in the heart (cardiac) muscle cells. We then introduce a graphene oxide (GO)-assisted DNA microarray hybridization protocol to enable point mutation detection. In this protocol, a GO solution is added after the staining step to quench dyes bound to single-stranded DNA or non-perfectly matched DNA, which can improve point mutation specificity. As proof-of-concept we extracted the wild-type and mutant of exon 12 and exon 17 of SCN5A genetic DNA from patients with long QT syndrome or Brugada syndrome by touchdown PCR and performed a successful point mutation discrimination in the AMDM platform. Overall, the AMDM platform can greatly reduce laborious and time-consuming hybridization steps and prevent potential contamination. Furthermore, by introducing the reciprocating flow into the microchannel during the hybridization process, the total assay time can be reduced to 3 hours, which is 6 times faster than the conventional DNA microarray. Given the automatic assay operation, shorter assay time, and high point mutation discrimination, we believe that the AMDM platform has potential for low-cost, rapid and sensitive genetic testing in a simple and user-friendly manner, which may benefit gene screening in medical practice.

Different effects of long noncoding RNA NDRG1-OT1 fragments on NDRG1 transcription in breast cancer cells under hypoxia.

Hypoxia plays a crucial role in the aggressiveness of solid tumors by driving multiple signaling pathways. Recently, long non-coding RNA (lncRNA) has been reported to promote or inhibit tumor aggressiveness by regulating gene expression. Previous studies in our laboratory found that the lncRNA NDRG1-OT1 is significantly up-regulated under hypoxia and inhibits its target gene NDRG1 at both the mRNA and protein levels. At the protein level, NDRG1-OT1 increases NDRG1 degradation via ubiquitin-mediated proteolysis. However, the repressive mechanism of NDRG1 at the RNA level is still unknown. Therefore, the purpose of this study was to study how NDRG1-OT1 transcriptionally regulates its target gene NDRG1. Luciferase reporter assays showed that NDRG1-OT1 decreased NDRG1 promoter activities. Mass spectrometry, bioinformatics tools, genetic manipulation, and immunoblotting were used to identify the interacting proteins. Surprisingly, different fragments of NDRG1-OT1 had opposite effects on NDRG1. The first quarter fragment (1-149 nt) of NDRG1-OT1 had no effect on the NDRG1 promoter; the second quarter fragment (150-263 nt) repressed NDRG1 by increasing the binding affinity of HNRNPA1; the third quarter fragment (264-392 nt) improved NDRG1 promoter activity by recruiting HIF-1α; the fourth quarter fragment (393-508 nt) down-regulated NDRG1 promoter activity via down-regulation of KHSRP under hypoxia. In summary, we have found a novel mechanism by which different fragments of the same lncRNA can cause opposite effects within the same target gene.

Differential correlation analysis of glioblastoma reveals immune ceRNA interactions predictive of patient survival.

BACKGROUND: Recent studies illuminated a novel role of microRNA (miRNA) in the competing endogenous RNA (ceRNA) interaction: two genes (ceRNAs) can achieve coexpression by competing for a pool of common targeting miRNAs. Individual biological investigations implied ceRNA interaction performs crucial oncogenic/tumor suppressive functions in glioblastoma multiforme (GBM). Yet, a systematic analysis has not been conducted to explore the functional landscape and prognostic significance of ceRNA interaction. RESULTS: Incorporating the knowledge that ceRNA interaction is highly condition-specific and modulated by the expressional abundance of miRNAs, we devised a ceRNA inference by differential correlation analysis to identify the miRNA-modulated ceRNA pairs. Analyzing sample-paired miRNA and gene expression profiles of GBM, our data showed that this alternative layer of gene interaction is essential in global information flow. Functional annotation analysis revealed its involvement in activated processes in brain, such as synaptic transmission, as well as critical tumor-associated functions. Notably, a systematic survival analysis suggested the strength of ceRNA-ceRNA interactions, rather than expressional abundance of individual ceRNAs, among three immune response genes (CCL22, IL2RB, and IRF4) is predictive of patient survival. The prognostic value was validated in two independent cohorts. CONCLUSIONS: This work addresses the lack of a comprehensive exploration into the functional and prognostic relevance of ceRNA interaction in GBM. The proposed efficient and reliable method revealed its significance in GBM-related functions and prognosis. The highlighted roles of ceRNA interaction provide a basis for further biological and clinical investigations.

iGC-an integrated analysis package of gene expression and copy number alteration.

BACKGROUND: With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. RESULTS: We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. CONCLUSION: The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package's source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html .

Genome-wide identification of key modulators of gene-gene interaction networks in breast cancer.

BACKGROUND: With the advances in high-throughput gene profiling technologies, a large volume of gene interaction maps has been constructed. A higher-level layer of gene-gene interaction, namely modulate gene interaction, is composed of gene pairs of which interaction strengths are modulated by (i.e., dependent on) the expression level of a key modulator gene. Systematic investigations into the modulation by estrogen receptor (ER), the best-known modulator gene, have revealed the functional and prognostic significance in breast cancer. However, a genome-wide identification of key modulator genes that may further unveil the landscape of modulated gene interaction is still lacking. RESULTS: We proposed a systematic workflow to screen for key modulators based on genome-wide gene expression profiles. We designed four modularity parameters to measure the ability of a putative modulator to perturb gene interaction networks. Applying the method to a dataset of 286 breast tumors, we comprehensively characterized the modularity parameters and identified a total of 973 key modulator genes. The modularity of these modulators was verified in three independent breast cancer datasets. ESR1, the encoding gene of ER, appeared in the list, and abundant novel modulators were illuminated. For instance, a prognostic predictor of breast cancer, SFRP1, was found the second modulator. Functional annotation analysis of the 973 modulators revealed involvements in ER-related cellular processes as well as immune- and tumor-associated functions. CONCLUSIONS: Here we present, as far as we know, the first comprehensive analysis of key modulator genes on a genome-wide scale. The validity of filtering parameters as well as the conservativity of modulators among cohorts were corroborated. Our data bring new insights into the modulated layer of gene-gene interaction and provide candidates for further biological investigations.

Next-generation sequencing of nine atrial fibrillation candidate genes identified novel de novo mutations in patients with extreme trait of atrial fibrillation.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. Genome-wide association studies (GWAS) have identified common variants in nine genomic regions associated with AF (KCNN3, PRRX1, PITX2, WNT8A, CAV1, C9orf3, SYNE2, HCN4 and ZFHX3 genes); however, the genetic variability of these risk variants does not explain the entire genetic susceptibility to AF. Rare variants missed by GWAS may also contribute to genetic risk of AF. METHODS: We used an extreme trait design to sequence carefully selected probands with extreme phenotypes and their unaffected parents to identify rare de novo variants or mutations. Based on the hypothesis that common and rare variants may colocate in the same disease susceptibility gene, we used next-generation sequencing to sequence these nine published AF susceptibility genes identified by GWAS (a total of 179 exons) in 20 trios, 200 unrelated patients with AF and 200 non-AF controls. RESULTS: We identified a novel mutation in the 5' untranslated region of the PITX2 gene, which localised in the transcriptionally active enhancer region. We also identified one missense exon mutation in KCNN3, two in ZFHX3 and one in SYNE2. None of these mutations were present in other unrelated patients with AF, healthy controls, unaffected parents and are thus novel and de novo (p<10(-4)). Functional study showed that the mutation in the 5' untranslated region of the PITX2 gene significantly downregulated PITX2 expression in atrial myocytes, either in basal condition or during rapid pacing. In silico analysis showed that the missense mutation in ZFHX3 results in damage of the ZFHX3 protein structure. CONCLUSIONS: The genetic architecture of subjects with extreme phenotypes of AF is similar to that of rare or Mendelian diseases, and mutations may be the underlying cause.

Parameter optimization for constructing competing endogenous RNA regulatory network in glioblastoma multiforme and other cancers.

BACKGROUND: In addition to direct targeting and repressing mRNAs, recent studies reported that microRNAs (miRNAs) can bridge up an alternative layer of post-transcriptional gene regulatory networks. The competing endogenous RNA (ceRNA) regulation depicts the scenario where pairs of genes (ceRNAs) sharing, fully or partially, common binding miRNAs (miRNA program) can establish coexpression through competition for a limited pool of the miRNA program. While the dynamics of ceRNA regulation among cellular conditions have been verified based on in silico and in vitro experiments, comprehensive investigation into the strength of ceRNA regulation in human datasets remains largely unexplored. Furthermore, pan-cancer analysis of ceRNA regulation, to our knowledge, has not been systematically investigated. RESULTS: In the present study we explored optimal conditions for ceRNA regulation, investigated functions governed by ceRNA regulation, and evaluated pan-cancer effects. We started by investigating how essential factors, such as the size of miRNA programs, the number of miRNA program binding sites, and expression levels of miRNA programs and ceRNAs affect the ceRNA regulation capacity in tumors derived from glioblastoma multiforme patients captured by The Cancer Genome Atlas (TCGA). We demonstrated that increased numbers of common targeting miRNAs as well as the abundance of binding sites enhance ceRNA regulation and strengthen coexpression of ceRNA pairs. Also, our investigation revealed that the strength of ceRNA regulation is dependent on expression levels of both miRNA programs and ceRNAs. Through functional annotation analysis, our results indicated that ceRNA regulation is highly associated with essential cellular functions and diseases including cancer. Furthermore, the highly intertwined ceRNA regulatory relationship enables constitutive and effective intra-function regulation of genes in diverse types of cancer. CONCLUSIONS: Using gene and microRNA expression datasets from TCGA, we successfully quantified the optimal conditions for ceRNA regulation, which hinge on four essential parameters of ceRNAs. Our analysis suggests optimized ceRNA regulation is related to disease pathways and essential cellular functions. Furthermore, although the strength of ceRNA regulation is dynamic among cancers, its governing functions are stably maintained. The findings of this report contribute to better understanding of ceRNA dynamics and its crucial roles in cancers.