[Show abstract][Hide abstract] ABSTRACT: MicroRNAs (miRNAs) are short, non-coding RNA molecules that play critical roles in human malignancy. However, the regulatory characteristics of miRNAs in triple-negative breast cancer, a phenotype of breast cancer that does not express the genes for estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2, are still poorly understood.
In this study, miRNA expression profiles of 24 triple-negative breast cancers and 14 adjacent normal tissues were analyzed using deep sequencing technology. Expression levels of miRNA reads were normalized with the quantile-quantile scaling method. Deregulated miRNAs in triple-negative breast cancer were identified from the sequencing data using the Student's t-test. Quantitative reverse transcription PCR validations were carried out to examine miRNA expression levels. Potential target candidates of a miRNA were predicted using published target prediction algorithms. Luciferase reporter assay experiments were performed to verify a putative miRNA-target relationship. Validated molecular targets of the deregulated miRNAs were retrieved from curated databases and their associations with cancer progression were discussed.
A novel 25-miRNA expression signature was found to effectively distinguish triple-negative breast cancers from surrounding normal tissues in a hierarchical clustering analysis. We documented the evidence of seven polycistronic miRNA clusters preferentially harboring deregulated miRNAs in triple-negative breast cancer. Two of these miRNA clusters (miR-143-145 at 5q32 and miR-497-195 at 17p13.1) were markedly down-regulated in triple-negative breast cancer, while the other five miRNA clusters (miR-17-92 at 13q31.3, miR-183-182 at 7q32.2, miR-200-429 at 1p36.33, miR-301b-130b at 22q11.21, and miR-532-502 at Xp11.23) were up-regulated in triple-negative breast cancer. Moreover, miR-130b-5p from the miR-301b-130b cluster was shown to directly repress the cyclin G2 (CCNG2) gene, a crucial cell cycle regulator, in triple-negative breast cancer cells. Luciferase reporter assays showed that miR-130b-5p-mediated repression of CCNG2 was dependent on the sequence of the 3'-untranslated region. The findings described in this study implicate a miR-130b-5p-CCNG2 axis that may be involved in the malignant progression of triple-negative breast cancer.
Our work delivers a clear picture of the global miRNA regulatory characteristics in triple-negative breast cancer and extends the current knowledge of microRNA regulatory network.
Molecular Cancer 12/2015; 14(1). DOI:10.1186/s12943-015-0301-9 · 4.26 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Background
Although much research effort has been devoted to elucidating lung cancer, the molecular mechanism of tumorigenesis still remains unclear. A major challenge to improve the understanding of lung cancer is the difficulty of identifying reproducible differentially expressed genes across independent studies, due to their low consistency. To enhance the reproducibility of the findings, an integrated analysis was performed to identify regulatory SNPs. Thirty-two pairs of tumor and adjacent normal lung tissue specimens were analyzed using Affymetrix U133plus2.0, Affymetrix SNP 6.0, and Illumina Infinium Methylation microarrays. Copy number variations (CNVs) and methylation alterations were analyzed and paired t-tests were used to identify differentially expressed genes.
A total of 505 differentially expressed genes were identified, and their dysregulated patterns moderately correlated with CNVs and methylation alterations based on the hierarchical clustering analysis. Subsequently, three statistical approaches were performed to explore regulatory SNPs, which revealed that the genotypes of 551 and 66 SNPs were associated with CNV and changes in methylation, respectively. Among them, downstream transcriptional dysregulation was observed in 9 SNPs for CNVs and 4 SNPs for methylation alterations.
In summary, these identified SNPs concurrently showed the same direction of gene expression changes with genetic modifications, suggesting their pivotal roles in the genome for non-smoking women with lung adenocarcinoma.
Electronic supplementary material
The online version of this article (doi:10.1186/s13104-015-1053-8) contains supplementary material, which is available to authorized users.
BMC Research Notes 12/2015; 8(1). DOI:10.1186/s13104-015-1053-8
[Show abstract][Hide abstract] ABSTRACT: Although clinical features, cytogenetics, and mutations are widely used to predict prognosis in patients with acute myeloid leukemia (AML), further refinement of risk stratification is necessary for optimal treatment, especially in cytogenetically normal (CN) patients. We sought to generate a simple gene expression signature as a predictor of clinical outcome through analyzing the mRNA arrays of 158 de novo CN AML patients. We compared the gene expression profiles of patients with poor response to induction chemotherapy with those who responded well. Forty-six genes expressed differentially between the two groups. Among them, expression of 11 genes was significantly associated with overall survival (OS) in univariate Cox regression analysis in 104 patients who received standard intensive chemotherapy. We integrated the z-transformed expression levels of these 11 genes to generate a risk scoring system. Higher risk scores were significantly associated with shorter OS (median 17.0 months vs. not reached, P < 0.001) in ours and another 3 validation cohorts. In addition, it was an independent unfavorable prognostic factor by multivariate analysis (HR 1.116, 95% CI 1.035~1.204, P = 0.004). In conclusion, we developed a simple mRNA expression signature for prognostication in CN-AML patients. This prognostic biomarker will help refine the treatment strategies for this group of patients.
[Show abstract][Hide abstract] ABSTRACT: Distinct microRNA (miRNA) and mRNA signatures were reported in NPM1-mutated AML. However, it remains unknown whether the mutation participates in the dynamic interaction between miRNA and mRNA. In this study, we aimed to investigate the role of NPM1 mutation in modulating miRNA-mRNA regulation (MMR). From the sample-paired miRNA/mRNA microarrays of 181 de novo AML patients, we found that MMR was dynamic and could be affected by NPM1 mutation. By a systematic framework we identified 493 NPM1 mutation-modulated MMR pairs, where the strength of MMR was significantly attenuated in patients carrying NPM1 mutations, compared to those with wild-type NPM1. These miRNAs/mRNAs were associated with pathways implicated in cancer and known functions of NPM1 mutation. Such modulation of MMR was validated in two independent cohorts as well as in cells with different NPM1 mutant burdens. Furthermore, we showed that the regulatory strength of nine MMR pairs could predict patients' outcomes. Combining these pairs, a scoring system was proposed and shown to predict survival in discovery and validations datasets, independent of other known prognostic factors. Our study provides novel biological insights into the role of NPM1 mutation as a modulator of MMR, based on which a novel prognostic marker is proposed in AML.Leukemia accepted article preview online, 16 September 2015. doi:10.1038/leu.2015.253.
Leukemia: official journal of the Leukemia Society of America, Leukemia Research Fund, U.K 09/2015; DOI:10.1038/leu.2015.253 · 10.43 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Lung adenocarcinoma possesses distinct patterns of EGFR/KRAS mutations between East Asian and Western, male and female patients. However, beyond the well-known EGFR/KRAS distinction, gender and ethnic specific molecular aberrations and their effects on prognosis remain largely unexplored. Association modules capture the dependency of an effector molecular aberration and target gene expressions. We established association modules from the copy number variation (CNV), DNA methylation and mRNA expression data of a Taiwanese female cohort. The inferred modules were validated in four external datasets of East Asian and Caucasian patients by examining the coherence of the target gene expressions and their associations with prognostic outcomes. Modules 1 (cis-acting effects with chromosome 7 CNV) and 3 (DNA methylations of UBIAD1 and VAV1) possessed significantly negative associations with survival times among two East Asian patient cohorts. Module 2 (cis-acting effects with chromosome 18 CNV) possessed significantly negative associations with survival times among the East Asian female subpopulation alone. By examining the genomic locations and functions of the target genes, we identified several putative effectors of the two cis-acting CNV modules: RAC1, EGFR, CDK5 and RALBP1. Furthermore, module 3 targets were enriched with genes involved in cell proliferation and division and hence were consistent with the negative associations with survival times. We demonstrated that association modules in lung adenocarcinoma with significant links of prognostic outcomes were ethnic and/or gender specific. This discovery has profound implications in diagnosis and treatment of lung adenocarcinoma and echoes the fundamental principles of the personalized medicine paradigm.
[Show abstract][Hide abstract] ABSTRACT: Gene regulation is dynamic across cellular conditions and disease subtypes. From the aspect of regulation under modulation, regulation strength between a pair of genes can be modulated by (dependent on) expression abundance of another gene (modulator gene). Previous studies have demonstrated the involvement of genes modulated by single modulator genes in cancers, including breast cancer. However, analysis of multi-modulator co-modulation that can further delineate the landscape of complex gene regulation is, to our knowledge, unexplored previously. In the present study we aim to explore the joint effects of multiple modulator genes in modulating global gene regulation and dissect the biological functions in breast cancer.
To carry out the analysis, we proposed the Covariability-based Multiple Regression (CoMRe) method. The method is mainly built on a multiple regression model that takes expression levels of multiple modulators as inputs and regulation strength between genes as output. Pairs of genes were divided into groups based on their co-modulation patterns. Analyzing gene expression profiles from 286 breast cancer patients, CoMRe investigated ten candidate modulator genes that interacted and jointly determined global gene regulation. Among the candidate modulators, ESR1, ERBB2, and ADAM12 were found modulating the most numbers of gene pairs. The largest group of gene pairs was composed of ones that were modulated by merely ESR1. Functional annotation revealed that the group was significantly related to tumorigenesis and estrogen signaling in breast cancer. ESR1-ERBB2 co-modulation was the largest group modulated by more than one modulators. Similarly, the group was functionally associated with hormone stimulus, suggesting that functions of the two modulators are performed, at least partially, through modulation. The findings were validated in majorities of patients (> 99%) of two independent breast cancer datasets.
We have showed CoMRe is a robust method to discover critical modulators in gene regulatory networks, and it is capable of achieving reproducible and biologically meaningful results. Our data reveal that gene regulatory networks modulated by single modulator or co-modulated by multiple modulators play important roles in breast cancer. Findings of this report illuminate complex and dynamic gene regulation under modulation and its involvement in breast cancer.
[Show abstract][Hide abstract] ABSTRACT: Esophageal cancer patients with pathological complete response (pCR) to neoadjuvant chemoradiation (CRT) have favorable outcomes. Currently, there was no reliable biomarker predicting the response to CRT. Perioperative circulating mRNA may be associated with prognosis, but its application for predicting treatment response is unclear. We prospectively assessed the value of circulating messenger RNA (mRNA) profiling in predicting pCR for esophageal squamous cell carcinoma (ESCC). Patients with ESCC completing CRT followed by surgery were enrolled for analysis. Venous peripheral blood was obtained before and after CRT, and total RNA was extracted for hybridization-based whole genome expression analysis and quantitative RT-PCR. We found circulating expression profiling was significantly altered after CRT. Altered FAM84B expression was significantly predictive of pCR. The decrease of serum FAM84B protein level after CRT was also associated with pCR. Immunohistochemistry and western blot confirmed that FAM84B protein was overexpressed in the majority of patients and ESCC cell lines. Furthermore, knockdown of FAM84B delayed tumor growth in ectopic xenografts. We demonstrated the decreased of circulating FAM84B mRNA and protein after neoadjuvant CRT may predict pCR, and FAM84B protein is overexpressed in ESCC. The potential of FAM84B as a novel predictive biomarker, and its biological functions deserve further investigation.
[Show abstract][Hide abstract] ABSTRACT: 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.
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.
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.
[Show abstract][Hide abstract] ABSTRACT: Next generation sequencing (NGS) has been widely used in biological and medical researches. NGS facilitates identifying mutations and differentially expressed genes that are causative to diseases. However, its high-throughput feature poses major challenges in performing the analysis, especially the alignment step. The alignment step is critical in the NGS gene expression analysis since the correctness of the advanced steps heavily depends on it. Therefore, we evaluated the performances of the four popular alignment algorithms including Tophat, STAR, MapSplice, and GNSAP based on the simulated data generated by Flux-Simulator. Shorter and longer reference genomes and read lengths were evaluated in the four scenarios. Three indices including time cost, alignment accuracy, and junction detection were utilized to consider the performances of different algorithms. The result shows that the Tophat algorithm has highest alignment accuracy and the STAR algorithm is the fastest one with a little lower accuracy. The MapSplice and GNSAP algorithms are not stable as the STAR and Tophat algorithms, and might encounter more problems in doing the alignment.
[Show abstract][Hide abstract] ABSTRACT: As a highly heterogeneous disease, acute myeloid leukemia (AML) needs fine risk stratification to get an optimal outcome of patients. MicroRNAs have florid biological functions and play critical roles in the pathogenesis and prognosis in AML. Expression levels of some single microRNAs are influential for prognosis, but a system integrating several together and considering the weight of each should be more powerful. We thus analyzed the clinical, genetic, and microRNA profiling data of 138 de novo AML patients of our institute. By multivariate analysis we identified high expression of hsa-miR-9-5p and hsa-miR-155-5p were independent poor prognostic factors, while that of hsa-miR-203 had a trend to be a favorable factor. We constructed a scoring system from expression of these 3 microRNAs by considering the weight of each. The scores correlated with distinct clinical and biological features and outperformed single microRNA expression in prognostication. In both ours and another validation cohort, higher scores were associated with shorter overall survival, independent of other well-known prognostic factors. By analyzing the mRNA expression profiles, we sorted out several cancer-related pathways highly correlated with the microRNA prognostic signature. We conclude this 3-microRNA scoring system is simple and powerful for risk stratification of de novo AML patients.Leukemia accepted article preview online, 27 November 2014. doi:10.1038/leu.2014.333.
[Show abstract][Hide abstract] ABSTRACT: 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.
Journal of Medical Genetics 11/2014; 52(1). DOI:10.1136/jmedgenet-2014-102618 · 6.34 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Brugada syndrome (BrS) is one of the ion channelopathies associated with sudden cardiac death (SCD). The most common BrS-associated gene (SCN5A) only accounts for approximately 20-25% of BrS patients. This study aims to identify novel mutations across human ion channels in non-familial BrS patients without SCN5A variants through disease-targeted sequencing. We performed disease-targeted multi-gene sequencing across 133 human ion channel genes and 12 reported BrS-associated genes in 15 unrelated, non-familial BrS patients without SCN5A variants. Candidate variants were validated by mass spectrometry and Sanger sequencing. Five de novo mutations were identified in four genes (SCNN1A, KCNJ16, KCNB2, and KCNT1) in three BrS patients (20%). Two of the three patients presented SCD and one had syncope. Interestingly, the two patients presented with SCD had compound mutations (SCNN1A:Arg350Gln and KCNB2:Glu522Lys; SCNN1A:Arg597* and KCNJ16:Ser261Gly). Importantly, two SCNN1A mutations were identified from different families. The KCNT1:Arg1106Gln mutation was identified in a patient with syncope. Bioinformatics algorithms predicted severe functional interruptions in these four mutation loci, suggesting their pivotal roles in BrS. This study identified four novel BrS-associated genes and indicated the effectiveness of this disease-targeted sequencing across ion channel genes for non-familial BrS patients without SCN5A variants.
[Show abstract][Hide abstract] ABSTRACT: A need for more accurate and reliable radiation dosimetry has become increasingly important due to the possibility of a large-scale radiation emergency resulting from terrorism or nuclear accidents. Although traditional approaches provide accurate measurements, such methods usually require tedious effort and at least two days to complete. Therefore, we provide a new method for rapid prediction of radiation exposure. Eleven microarray datasets were classified into two groups based on their radiation doses and utilized as the training samples. For the two groups, Student's t-tests and resampling tests were used to identify biomarkers, and their gene expression ratios were used to develop a prediction model. The performance of the model was evaluated in four independent datasets, and Ingenuity pathway analysis was performed to characterize the associated biological functions. Our meta-analysis identified 29 biomarkers, showing approximately 90% and 80% accuracy in the training and validation samples. Furthermore, the 29 genes significantly participated in the regulation of cell cycle, and 19 of them are regulated by three well-known radiation-modulated transcription factors: TP53, FOXM1 and ERBB2. In conclusion, this study demonstrates a reliable method for identifying biomarkers across independent studies and high and reproducible prediction accuracy was demonstrated in both internal and external datasets.
[Show abstract][Hide abstract] ABSTRACT: Several prognostic signatures have been identified for breast cancer. However, these signatures vary extensively in their gene compositions, and the poor concordance of the risk groups defined by the prognostic signatures hinders their clinical applicability. Breast cancer risk prediction was refined with a novel approach to finding concordant genes from leading edge analysis of prognostic signatures. Each signature was split into two gene sets, which contained either up-regulated or down-regulated genes, and leading edge analysis was performed within each array study for all up-/down-regulated gene sets of the same signature from all training datasets. Consensus of leading edge subsets among all training microarrays was used to synthesize a predictive model, which was then tested in independent studies by partial least squares regression. Only a small portion of six prognostic signatures (Amsterdam, Rotterdam, Genomic Grade Index, Recurrence Score, and Hu306 and PAM50 of intrinsic subtypes) was significantly enriched in the leading edge analysis in five training datasets (n = 2,380), and that the concordant leading edge subsets (43 genes) could identify the core signature genes that account for the enrichment signals providing prognostic power across all assayed samples. The proposed concordant leading edge algorithm was able to discriminate high-risk from low-risk patients in terms of relapse-free or distant metastasis-free survival in all training samples (hazard ratios: 1.84-2.20) and in three out of four independent studies (hazard ratios: 3.91-8.31). In some studies, the concordant leading edge subset remained a significant prognostic factor independent of clinical ER, HER2, and lymph node status. The present study provides a statistical framework for identifying core consensus across microarray studies with leading edge analysis, and a breast cancer risk predictive model was established.
Breast Cancer Research and Treatment 08/2014; 147(2). DOI:10.1007/s10549-014-3104-6 · 3.94 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Hypoxia and reoxygenation are common characteristics of solid tumors, which lead to oxidative stress and activation of stress-response genes. Previously, we observed that N-myc downstream-regulated gene 1 (NDRG1) was strongly down-regulated after shifting to reoxygenation, but the regulatory mechanism of NDRG1 remained elusive. Here we focused on the regulation of NDRG1 by microRNAs (miRNAs). Breast cancer MCF-7 cells were cultured under hypoxia for 24 h followed by 24 h of reoxygenation. The miRNA profiles were examined by Nanostring nCounter assays. Forty-three miRNAs had significant changes upon reoxygenation. In silico analysis identified four oxygen-sensitive miRNAs whose seed regions perfectly matched the 3'-UTR of NDRG1. In particular, miR-769-3p was able to inhibit the expression of NDRG1, which caused a significant reduction of NDRG1 protein upon reoxygenation. Furthermore, overexpression of miR-769-3p significantly inhibited cell proliferation and enhanced apoptosis. Our results revealed that miR-769-3p can functionally regulate NDRG1 during changes in oxygen concentration.