CrossHub: a tool for multi-way analysis of The Cancer Genome Atlas (TCGA) in the context of gene expression regulation mechanisms

Tumor Suppressor Properties of Small C-Terminal Domain Phosphatases in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) accounts for 80-90% of kidney cancers worldwide. Small C-terminal domain phosphatases CTDSP1, CTDSP2, and CTDSPL (also known as SCP1, 2, 3) are involved in the regulation of several important pathways associated with carcinogenesis. In various cancer types, these phosphatases may demonstrate either antitumor or oncogenic activity. Tumor-suppressive activity of these phosphatases in kidney cancer has been shown previously, but in general case, the antitumor activity may be dependent on the choice of cell line. In the present work, transfection of the Caki-1 cell line (ccRCC morphologic phenotype) with expression constructs containing the coding regions of these genes resulted in inhibition of cell growth in vitro in the case of CTDSP1 (p < 0.001) and CTDSPL (p < 0.05) but not CTDSP2. The analysis of The Cancer Genome Atlas (TCGA) data showed differential expression of some of CTDSP genes and of their target, RB1. These results were confirmed by quantitative RT-PCR using an independent sample of primary ccRCC tumors (n = 52). We observed CTDSPL downregulation and found a positive correlation of expression for two gene pairs: CTDSP1 and CTDSP2 (rs = 0.76; p < 0.001) and CTDSPL and RB1 (rs = 0.38; p < 0.05). Survival analysis based on TCGA data demonstrated a strong association of lower expression of CTDSP1, CTDSP2, CTDSPL, and RB1 with poor survival of ccRCC patients (p < 0.001). In addition, according to TCGA, CTDSP1, CTDSP2, and RB1 were differently expressed in two subtypes of ccRCC-ccA and ccB, characterized by different survival rates. These results confirm that CTDSP1 and CTDSPL have tumor suppressor properties in ccRCC and reflect their association with the more aggressive ccRCC phenotype.

Pan-Cancer Analysis of Head-to-Head Gene Pairs in Terms of Transcriptional Activity, Co-expression and Regulation

Background

Head-to-Head (H2H) gene pairs are regulated by bidirectional promoters and divergently transcribed from opposite DNA strands with transcription start sites (TSSs) separated within 1 kb. H2H organization is ancient and conserved, and H2H pairs tend to exhibit similar expression patterns. Although some H2H genes have been reported to be associated with disease and cancer, there is a lack of systematic studies on H2H organization in the scenario of cancer development.

Methods

Human H2H gene pairs were identified based on GENCODE hg19 and the functional relevance of H2H pairs was explored through function enrichment and semantic similarity analysis. To investigate the association between H2H organization and carcinogenesis, pan-cancer differential analysis of H2H genes about transcriptional activity, co-expression and transcriptional regulation by transcription factors and enhancers were performed based on data from The Cancer Genome Atlas. Cox proportional hazards regression model and log-rank test were used to determine the prognostic powers of H2H pairs.

Results

In the present study, we first updated H2H genes from 1,447 to 3,150 pairs, from which the peak group with TSS distance of 1–100 was observed as expected in our previous work. It was found that housekeeping genes, mitochondrial-functional associated genes and cancer genes tend to be organized in H2H arrangement. Pan-cancer analysis indicates that H2H genes are transcriptionally active than random genes in both normal and cancer tissues, but H2H pairs display higher correlation in cancer than in normal. Particularly, housekeeping H2H pairs are differentially correlated much more significantly than non-housekeeping H2H pairs are. Some of differentially correlated H2H pairs were found to be associated with prognosis. The alteration of TF similarity seems to contribute to differential co-expression of H2H pairs during carcinogenesis; meanwhile remote enhancers also at least partly explain the differential co-expression and co-regulation of H2H pairs.

Conclusion

H2H pairs tend to show much stronger positive expression correlation in cancer than in normal due to differential regulation of bidirectional promoters. The study provides insights into the significance of H2H organization in carcinogenesis and the underlying dysfunctional regulation mechanisms. Those differentially correlated H2H pairs associated with survival have the potential to be prognostic biomarkers and therapeutic targets for cancer.

Tumor suppressor properties of the small C-terminal domain phosphatases in non-small cell lung cancer

Non-Small Cell Lung Cancer (NSCLC) is responsible for the majority of deaths caused by cancer. Small C-terminal domain (CTD) phosphatases (SCP), CTDSP1, CTDSP2 and CTDSPL (CTDSPs) belong to SCP/CTDSP subfamily and are involved in many vital cellular processes and tumorigenesis. High similarity of their structures suggests similar functions. However their role in NSCLC remains insufficiently understood. For the first time we revealed the suppressor function of CTDSPs leading to a significant growth slowdown and senescence of A549 lung adenocarcinoma (ADC) cells in vitro. Their tumor-suppressive activity can be realized through increasing the proportion of the active form of Rb protein dephosphorylated at Ser807/811, Ser780, and Ser795 (P<0.05) thereby negatively regulating cancer cell proliferation. Moreover, we observed that a frequent (84%, 39/46) and highly concordant (Spearman's rank correlation coefficient (rs) = 0.53-0.62, P≤0.01) down-regulation of CTDSPs and RB1 is characteristic of primary NSCLC samples (n=46). A clear difference in their mRNA levels was found between lung ADCs with and without lymph node metastases, but not in squamous cell carcinomas (SCCs) (P≤0.05). Based on The Cancer Genome Atlas (TCGA) data and the results obtained using the CrossHub tool, we suggest that the well-known oncogenic cluster miR-96/182/183 could be a common expression regulator of CTDSPs. Indeed, according to our qPCR, the expression of CTDSPs negatively correlates with these miRs, but positively correlates with their intronic miR-26a/b. Our results reflect functional association of CTDSP1, CTDSP2, and CTDSPL, expand knowledge about their suppressor properties through Rb dephosphorylation and provide new insights into the regulation of NSCLC growth.

Upregulation of long non-coding RNA FOXD2-AS1 promotes progression and predicts poor prognosis in tongue squamous cell carcinoma

BACKGROUND

Accumulating evidences suggest that lncRNA FOXD2-AS1 plays an important role in tumor progression, however, its function in tongue squamous cell carcinoma (TSCC) remains unknown. This research aims to investigate the function and mechanism of FOXD2-AS1 in the modulation of tongue squamous cell carcinoma progression.

METHODS

Expression of FOXD2-AS1 was detected in TSCC tissues and TCGA data. Receiver operating characteristic curves (ROCs) analysis and bioinformatic analysis of TCGA data were performed to investigate the role of FOXD2-AS1 in TSCC prognosis. After siRNA-mediated downregulation of FOXD2-AS1, wound healing assay, Transwell migration and invasion assays, and MTS proliferation assay were conducted to explore the effects that FOXD2-AS1 exerted on SCC-9 and CAL-27 cell lines. Western blotting was performed to detect the downstream protein changes.

RESULTS

Compared to the normal tissues and samples, FOXD2-AS1 significantly highly expressed in TSCC tissues and in TSCC samples of TCGA data, and high expression of FOXD2-AS1 was associated with lymphatic metastasis and poor TNM stages. ROC analysis and bioinformatic analysis of TCGA data further suggested that high expression of FOXD2-AS1 was associated with TSCC poor prognosis. Downregulation of FOXD2-AS1 inhibited the migration and invasion of SCC-9 and CAL-27 cell lines. Western blotting showed that the expression of p-p44 and p-p65 downregulated after FOXD2-AS1 knockdown.

CONCLUSION

High expression of FOXD2-AS1 promotes TSCC progression through modulating NF-kB and ERK MAPK signaling pathways and is associated with TSCC poor prognosis, it could be a novel therapeutic target and prognostic biomarker for TSCC.

Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer

MicroRNAs (miRNAs) are small endogenous regulatory molecules that modulate gene expression post-transcriptionally. Although differential expression of miRNAs have been implicated in many diseases (including cancers), the underlying mechanisms of action remain unclear. Because each miRNA can target multiple genes, miRNAs may potentially have functional implications for the overall behavior of entire pathways. Here, we investigate the functional consequences of miRNA dysregulation through an integrative analysis of miRNA and mRNA expression data using a novel approach that incorporates pathway information a priori. By searching for miRNA-pathway associations that differ between healthy and tumor tissue, we identify specific relationships at the systems level which are disrupted in cancer. Our approach is motivated by the hypothesis that if an miRNA and pathway are associated, then the expression of the miRNA and the collective behavior of the genes in a pathway will be correlated. As such, we first obtain an expression-based summary of pathway activity using Isomap, a dimension reduction method which can articulate non-linear structure in high-dimensional data. We then search for miRNAs that exhibit differential correlations with the pathway summary between phenotypes as a means of finding aberrant miRNA-pathway coregulation in tumors. We apply our method to cancer data using gene and miRNA expression datasets from The Cancer Genome Atlas and compare ∼10⁵ miRNA-pathway relationships between healthy and tumor samples from four tissues (breast, prostate, lung and liver). Many of the flagged pairs we identify have a biological basis for disruption in cancer.

The CIMP-high phenotype is associated with energy metabolism alterations in colon adenocarcinoma

Background

CpG island methylator phenotype (CIMP) is found in 15–20% of malignant colorectal tumors and is characterized by strong CpG hypermethylation over the genome. The molecular mechanisms of this phenomenon are not still fully understood. The development of CIMP is followed by global gene expression alterations and metabolic changes. In particular, CIMP-low colon adenocarcinoma (COAD), predominantly corresponded to consensus molecular subtype 3 (CMS3, “Metabolic”) subgroup according to COAD molecular classification, is associated with elevated expression of genes participating in metabolic pathways.

Methods

We performed bioinformatics analysis of RNA-Seq data from The Cancer Genome Atlas (TCGA) project for CIMP-high and non-CIMP COAD samples with DESeq2, clusterProfiler, and topGO R packages. Obtained results were validated on a set of fourteen COAD samples with matched morphologically normal tissues using quantitative PCR (qPCR).

Results

Upregulation of multiple genes involved in glycolysis and related processes (ENO2, PFKP, HK3, PKM, ENO1, HK2, PGAM1, GAPDH, ALDOA, GPI, TPI1, and HK1) was revealed in CIMP-high tumors compared to non-CIMP ones. Most remarkably, the expression of the PKLR gene, encoding for pyruvate kinase participating in gluconeogenesis, was decreased approximately 20-fold. Up to 8-fold decrease in the expression of OGDHL gene involved in tricarboxylic acid (TCA) cycle was observed in CIMP-high tumors. Using qPCR, we confirmed the increase (4-fold) in the ENO2 expression and decrease (2-fold) in the OGDHL mRNA level on a set of COAD samples.

Conclusions

We demonstrated the association between CIMP-high status and the energy metabolism changes at the transcriptomic level in colorectal adenocarcinoma against the background of immune pathway activation. Differential methylation of at least nine CpG sites in OGDHL promoter region as well as decreased OGDHL mRNA level can potentially serve as an additional biomarker of the CIMP-high status in COAD.

Electronic supplementary material

The online version of this article (10.1186/s12881-019-0771-5) contains supplementary material, which is available to authorized users.

Functional analysis of haplotypes and promoter activity at the 5' region of the human GABRB3 gene and associations with schizophrenia

Background

This study investigated the effects of haplotypes T‐G and C‐A derived from NG_012836.1:g.4160T>C and NG_012836.1:g.4326G>A on protein expression levels in vitro and identified the functional sequence in the regulatory region of the GABRB3 gene linked to possible associations with schizophrenia.

Methods

Recombinant plasmids with haplotypes T‐G and C‐A and 10 recombinant vectors containing deletion fragments from the GABRB3 gene 5′ regulatory region were transfected into HEK‐293, SK‐N‐SH, and SH‐SY5Y cells. The relative fluorescence intensity of the two haplotypes and different sequences was compared using a dual luciferase reporter assay system.

Results

The relative fluorescence intensity of haplotype C‐A was significantly lower than that of T‐G. We shortened the core promoter sequence of the GABRB3 gene 5′ regulation region from −177 bp to −18 bp (ATG+1). We also found an expression suppression region from −1,735 bp to −1,638 bp and an enhanced regulatory region from −1,638 bp to −1,335 bp. Multiple inhibitory functional elements were identified in the region from −680 bp to −177 bp.

Conclusion

We demonstrated that haplotype C‐A might increase the risk of schizophrenia and found multiple regulatory regions that had an effect on GABRB3 receptor expression.

New functionalities in the TCGAbiolinks package for the study and integration of cancer data from GDC and GTEx

The advent of Next-Generation Sequencing (NGS) technologies has opened new perspectives in deciphering the genetic mechanisms underlying complex diseases. Nowadays, the amount of genomic data is massive and substantial efforts and new tools are required to unveil the information hidden in the data. The Genomic Data Commons (GDC) Data Portal is a platform that contains different genomic studies including the ones from The Cancer Genome Atlas (TCGA) and the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiatives, accounting for more than 40 tumor types originating from nearly 30000 patients. Such platforms, although very attractive, must make sure the stored data are easily accessible and adequately harmonized. Moreover, they have the primary focus on the data storage in a unique place, and they do not provide a comprehensive toolkit for analyses and interpretation of the data. To fulfill this urgent need, comprehensive but easily accessible computational methods for integrative analyses of genomic data that do not renounce a robust statistical and theoretical framework are required. In this context, the R/Bioconductor package TCGAbiolinks was developed, offering a variety of bioinformatics functionalities. Here we introduce new features and enhancements of TCGAbiolinks in terms of i) more accurate and flexible pipelines for differential expression analyses, ii) different methods for tumor purity estimation and filtering, iii) integration of normal samples from other platforms iv) support for other genomics datasets, exemplified here by the TARGET data. Evidence has shown that accounting for tumor purity is essential in the study of tumorigenesis, as these factors promote confounding behavior regarding differential expression analysis. With this in mind, we implemented these filtering procedures in TCGAbiolinks. Moreover, a limitation of some of the TCGA datasets is the unavailability or paucity of corresponding normal samples. We thus integrated into TCGAbiolinks the possibility to use normal samples from the Genotype-Tissue Expression (GTEx) project, which is another large-scale repository cataloging gene expression from healthy individuals. The new functionalities are available in the TCGAbiolinks version 2.8 and higher released in Bioconductor version 3.7.

The advent of Next-Generation Sequencing (NGS) technologies has been generating a massive amount of data which require continuous efforts in developing and maintain computational tool for data analyses. The Genomic Data Commons (GDC) Data Portal is a platform that contains different cancer genomic studies. Such platforms have often the primary focus on the data storage and they do not provide a comprehensive toolkit for analyses. To fulfil this urgent need, comprehensive but accessible computational protocols that do not renounce a robust statistical framework are thus required. In this context, we here present the new functions of the R/Bioconductor package TCGAbiolinks to improve the discovery of differentially expressed genes in cancer and tumor (sub)types, include the estimate of tumor purity and tumor infiltrations, use normal samples from other platforms and support more broadly other genomics datasets.

Pan-Cancer Analysis of TCGA Data Revealed Promising Reference Genes for qPCR Normalization

Quantitative PCR (qPCR) remains the most widely used technique for gene expression evaluation. Obtaining reliable data using this method requires reference genes (RGs) with stable mRNA level under experimental conditions. This issue is especially crucial in cancer studies because each tumor has a unique molecular portrait. The Cancer Genome Atlas (TCGA) project provides RNA-Seq data for thousands of samples corresponding to dozens of cancers and presents the basis for assessment of the suitability of genes as reference ones for qPCR data normalization. Using TCGA RNA-Seq data and previously developed CrossHub tool, we evaluated mRNA level of 32 traditionally used RGs in 12 cancer types, including those of lung, breast, prostate, kidney, and colon. We developed an 11-component scoring system for the assessment of gene expression stability. Among the 32 genes, PUM1 was one of the most stably expressed in the majority of examined cancers, whereas GAPDH, which is widely used as a RG, showed significant mRNA level alterations in more than a half of cases. For each of 12 cancer types, we suggested a pair of genes that are the most suitable for use as reference ones. These genes are characterized by high expression stability and absence of correlation between their mRNA levels. Next, the scoring system was expanded with several features of a gene: mutation rate, number of transcript isoforms and pseudogenes, participation in cancer-related processes on the basis of Gene Ontology, and mentions in PubMed-indexed articles. All the genes covered by RNA-Seq data in TCGA were analyzed using the expanded scoring system that allowed us to reveal novel promising RGs for each examined cancer type and identify several "universal" pan-cancer RG candidates, including SF3A1, CIAO1, and SFRS4. The choice of RGs is the basis for precise gene expression evaluation by qPCR. Here, we suggested optimal pairs of traditionally used RGs for 12 cancer types and identified novel promising RGs that demonstrate high expression stability and other features of reliable and convenient RGs (high expression level, low mutation rate, non-involvement in cancer-related processes, single transcript isoform, and absence of pseudogenes).

Key miRNAs and target genes played roles in the development of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (CCRCC) is the most aggressive form of renal cell carcinoma (RCC).

OBJECTIVE

This study was aimed to identify the differentially expressed miRNAs and target genes in CCRCC.

METHODS

The miRNA and mRNA next-generation sequencing data were downloaded from The Cancer Genome Atlas (TCGA) dataset. Differential expression analysis was performed, followed by correlation analysis of miRNA-mRNA. Functional enrichment and survival analysis was also performed.

RESULTS

Seven hundred and eighty-seven patients with CCRCC from TCGA data portal were included in this study. A total of 52 differentially expressed miRNAs were identified in CCRCC. Then 2361 differentially expressed genes (DEGs) were identified. Prediction analysis and correlation analysis revealed that 89 miRNA-mRNA pairs were not only predicted by algorithms but also had a significant inverse relationship. Several differentially expressed miRNAs such as hsa-mir-501 and their target genes including AK1, SLC25A15 and PCDHGC3 had a significant prognostic value for CCRCC patients.

CONCLUSIONS

Alterations of differentially expressed miRNAs and target genes may be involved in the development of CCRCC and can be considered as the prognostic markers for CCRCC.

Characterization and functional analyses of the human HTR1A gene: 5' regulatory region modulates gene expression in vitro

BACKGROUND

The serotonin neurotransmitter (5-HT) and its receptors have important roles in neuropsychiatric disorders such as schizophrenia. The aim of this study was to investigate the functional sequences of the 5' regulation region of the human HTR1A gene to explore the effects on the expression of the 5-HT1A receptor.

METHODS

Fourteen recombinant pGL3-basic vectors containing deletion fragments of the HTR1A gene regulatory region were transfected with HEK-293 and SK-N-SH cells. The relative chemiluminescence intensities of different length fragments were analyzed. The JASPAR software was used for the prediction of transcription factors.

RESULTS

In the HEK-293 cells, the relative chemiluminescence intensity of the - 1649 bp to - 1550 bp (ATG + 1) fragment was significantly different. Two inhibitory activity regions were found in the - 1409 bp to - 1381 bp and - 1196 bp to - 1124 bp fragments, which might be bound to the GATA or SOX10 transcription factors as predicted by the JASPAR software. In addition, the fragments located from - 1124 bp to - 1064 bp and from - 908 bp to - 722 bp up-regulated protein expression. Only the sequence from - 1550 bp to - 1409 bp demonstrated a difference in luciferase expression in the both cell lines. According to the results of the 5'-UTR truncated vectors, there was a repression region at the distal end of the 5'-UTR, an enhancer region might be present at the proximal end of the transcription start site.

CONCLUSIONS

Although the functional sequences of the HTR1A gene regulatory region were confirmed, the regulatory factors and functional components require further investigation.

regQTLs: Single nucleotide polymorphisms that modulate microRNA regulation of gene expression in tumors

Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) associated with trait diversity and disease susceptibility, yet their functional properties often remain unclear. It has been hypothesized that SNPs in microRNA binding sites may disrupt gene regulation by microRNAs (miRNAs), short non-coding RNAs that bind to mRNA and downregulate the target gene. While several studies have predicted the location of SNPs in miRNA binding sites, to date there has been no comprehensive analysis of their impact on miRNA regulation. Here we investigate the functional properties of genetic variants and their effects on miRNA regulation of gene expression in cancer. Our analysis is motivated by the hypothesis that distinct alleles may cause differential binding (from miRNAs to mRNAs or from transcription factors to DNA) and change the expression of genes. We previously identified pathways—systems of genes conferring specific cell functions—that are dysregulated by miRNAs in cancer, by comparing miRNA–pathway associations between healthy and tumor tissue. We draw on these results as a starting point to assess whether SNPs on dysregulated pathways are responsible for miRNA dysregulation of individual genes in tumors. Using an integrative regression analysis that incorporates miRNA expression, mRNA expression, and SNP genotype data, we identify functional SNPs that we term “regulatory QTLs (regQTLs)”: loci whose alleles impact the regulation of genes by miRNAs. We apply the method to breast, liver, lung, and prostate cancer data from The Cancer Genome Atlas, and provide a tool to explore the findings.

Genomics studies have identified single nucleotide polymorphisms (SNPs) associated with trait diversity and disease susceptibility, yet the mechanism of action of many genetic variants remains unclear. MicroRNAs (miRNAs) are a class of small non-coding RNA molecules that base-pair coding mRNAs to regulate gene transcription. We hypothesize that SNP variants may affect the ability of miRNAs to bind their target genes, thus influencing gene regulation. To identify these “regulatory QTLs” (regQTLs), we integrate miRNA expression, mRNA expression, and SNP data to identify miRNAs that are associated with pathway dysregulation in tumors, and assess whether SNPs on these pathways are responsible for disrupted miRNA-gene regulation. This data-driven approach enables the discovery of SNPs whose alleles impact gene regulation by miRNAs, with functional consequences for tumor biology. We detail the method, apply it to data from The Cancer Genome Atlas, and provide a tool to explore the findings.

Deep Sequencing Revealed a CpG Methylation Pattern Associated With ALDH1L1 Suppression in Breast Cancer

Hypermethylation of promoter CpG islands is generally recognized epigenetic mechanism responsible for gene silencing in cancer. However, molecular details on how this epigenetic mark triggers the process of gene downregulation are still elusive. Here, we used deep bisulfite sequencing and qPCR analysis to investigate the pattern of CpG methylation of ALDH1L1 promoter region and its association with the gene expression level in 16 paired breast cancer (BC) samples of different clinical stages. Expression of ALDH1L1 gene was suppressed in all examined BC samples up to 200-fold, and average hypermethylation level of the promoter region correlated positively with ALDH1L1 downregulation. We determined the role of every individual CpG site within the ALDH1L1 promoter, including upstream untranscribed region, first untranslated exon, and the start of the first intron, in aberrant gene expression by correlation analysis. The search revealed CpG sites which methylation has the highest impact on intensity of gene transcription. The majority of such CpG sites are located in a compact region in the first intron of the ALDH1L1 gene. These results assist in unraveling of dynamic nature of CpG promoter hypermethylation as well as demonstrate the efficiency of deep bisulfite sequencing in search for novel epigenetic markers in cancer.

Comparative analysis of protein interactome networks prioritizes candidate genes with cancer signatures

Comprehensive understanding of human cancer mechanisms requires the identification of a thorough list of cancer-associated genes, which could serve as biomarkers for diagnoses and therapies in various types of cancer. Although substantial progress has been made in functional studies to uncover genes involved in cancer, these efforts are often time-consuming and costly. Therefore, it remains challenging to comprehensively identify cancer candidate genes. Network-based methods have accelerated this process through the analysis of complex molecular interactions in the cell. However, the extent to which various interactome networks can contribute to prediction of candidate genes responsible for cancer is still enigmatic. In this study, we evaluated different human protein-protein interactome networks and compared their application to cancer gene prioritization. Our results indicate that network analyses can increase the power to identify novel cancer genes. In particular, such predictive power can be enhanced with the use of unbiased systematic protein interaction maps for cancer gene prioritization. Functional analysis reveals that the top ranked genes from network predictions co-occur often with cancer-related terms in literature, and further, these candidate genes are indeed frequently mutated across cancers. Finally, our study suggests that integrating interactome networks with other omics datasets could provide novel insights into cancer-associated genes and underlying molecular mechanisms.

MethyMer: Design of combinations of specific primers for bisulfite sequencing of complete CpG islands

We present MethyMer, a Python-based tool aimed at selecting primers for amplification of complete CpG islands. These regions are difficult in terms of selecting appropriate primers because of their low-complexity, high GC content. Moreover, bisulfite treatment, in fact, leads to the reduction of the 4-letter alphabet (ATGC) to 3-letter one (ATG, except for methylated cytosines), and this also reduces region complexity and increases mispriming potential. MethyMer has a flexible scoring system, which optimizes the balance between various characteristics such as nucleotide composition, thermodynamic features (melting temperature, dimers [Formula: see text]G, etc.), the presence of CpG sites and polyN tracts, and primer specificity, which is assessed with aligning primers to the bisulfite-treated genome using bowtie (up to three mismatches are allowed). Users are able to customize desired or limit ranges of various parameters as well as penalties for non-desired values. Moreover, MethyMer allows picking up the optimal combination of PCR primer pairs to perform the amplification of a large genomic locus, e.g. CpG island or other hard-to-study region, with minimal overlap of the individual amplicons. MethyMer incorporates ENCODE genome annotation records (promoter/enhancer/insulator), The Cancer Genome Atlas (TCGA) CpG methylation data derived with Illumina Infinium 450K microarrays, and records on correlations between TCGA RNA-Seq and CpG methylation data for 20 cancer types. These databases are included in the MethyMer release. Our tool is available at https://sourceforge.net/projects/methymer/ .

Integrative Analysis of Omics Big Data

The diversity and huge omics data take biology and biomedicine research and application into a big data era, just like that popular in human society a decade ago. They are opening a new challenge from horizontal data ensemble (e.g., the similar types of data collected from different labs or companies) to vertical data ensemble (e.g., the different types of data collected for a group of person with match information), which requires the integrative analysis in biology and biomedicine and also asks for emergent development of data integration to address the great changes from previous population-guided to newly individual-guided investigations.Data integration is an effective concept to solve the complex problem or understand the complicate system. Several benchmark studies have revealed the heterogeneity and trade-off that existed in the analysis of omics data. Integrative analysis can combine and investigate many datasets in a cost-effective reproducible way. Current integration approaches on biological data have two modes: one is "bottom-up integration" mode with follow-up manual integration, and the other one is "top-down integration" mode with follow-up in silico integration.This paper will firstly summarize the combinatory analysis approaches to give candidate protocol on biological experiment design for effectively integrative study on genomics and then survey the data fusion approaches to give helpful instruction on computational model development for biological significance detection, which have also provided newly data resources and analysis tools to support the precision medicine dependent on the big biomedical data. Finally, the problems and future directions are highlighted for integrative analysis of omics big data.

Comprehensive epigenetic analysis of the signature genes in lung adenocarcinoma

Aim: This study aimed to explore the epigenetic modifications of signature genes in lung adenocarcinoma. Materials & methods: The data of miRNA expression, mRNA expression and DNA methylation were downloaded from The Cancer Genome Atlas. Differential analysis was performed, followed by correlation analysis of miRNA–mRNA and DNA methylation-mRNA. Results: A total of 14 significant inverse correlations between gene expression and DNA methylation were identified, the expressions of which were selected for further validation via GSE27262, displaying similar pattern with that of the integrated analysis. In addition, qRT-PCR results showed that the expression profiling results of six mRNAs and one miRNA were consistent with the findings of integrated analysis. Five genes showed higher diagnostic value, which was also associated with overall survival of patients. Conclusion: Taken together, the epigenetic alterations of signature genes may hold promise for becoming biomarkers for the early detection of lung adenocarcinoma.

Clinical significance and prospective molecular mechanism of MALAT1 in pancreatic cancer exploration: a comprehensive study based on the GeneChip, GEO, Oncomine, and TCGA databases

Purpose

Long noncoding RNAs (lncRNAs) are known to function as regulators in the development and occurrence of various tumors. MALAT1 is a highly conserved lncRNA and has vital functions in diverse tumors, including pancreatic cancer (PC). However, the underlying molecular regulatory mechanism involved in the occurrence and development of PC remains largely unknown. Thus, it is important to explore MALAT1 in PC and elucidate its function, which might offer a new perspective for clinical diagnosis and therapy.

Methods

First, we used the Gene Expression Omnibus, Oncomine, and The Cancer Genome Atlas databases to determine the clinical diagnostic and prognostic values of MALAT1. We next used our own GeneChip and The Cancer Genome Atlas database to collect the possible target genes of MALAT1 and further utilized a bioinformatics analysis to explore the underlying significant pathways that might be crucial in PC. Finally, we identified several key target genes of MALAT1 and hope to offer references for future research.

Results

We found that the expression of MALAT1 was significantly elevated in patients with PC. A receiver operating characteristics curve analysis showed a moderate diagnostic value (area under the curve =0.75, sensitivity =0.66, specificity =0.72). A total of 224 important overlapping genes were collected, and six hub genes (CCND1, MAPK8, VEGFA, FOS, CDH1, and HSP90AA1) were identified, of which CCND1, MAPK8, and VEGFA, are important genes in PC. Several pathways, including the mTOR signaling pathway, pathways in cancer, and the MAPK signaling pathway, were suggested to be the vital MALAT1 pathways in PC.

Conclusion

MALAT1 is suggested to be a promising diagnostic biomarker in PC. Six hub genes (CCND1, MAPK8, VEGFA, FOS, CDH1, and HSP90AA1), and specifically CCND1, MAPK8, and VEGFA, might be key MALAT1 target genes in PC. Due to their possible clinical significance in PC, several pathways, such as the mTOR signaling pathway, pathways in cancer, and the MAPK signaling pathway, are worthy of further study.

Clinical Significance and Effect of lncRNA HOXA11-AS in NSCLC: A Study Based on Bioinformatics, In Vitro and in Vivo Verification

HOXA11 antisense RNA (HOXA11-AS) has been shown to be involved in tumorigenesis and development of different cancers. However, the role of HOXA11-AS in non-small cell lung cancer (NSCLC) remains unclear. In this study, we firstly explored and confirmed the expression of HOXA11-AS in NSCLC tissues and cells. Cytometry, CCK-8, cell scratch, migration, Matrigel invasion and flow cytometry assays were performed to determine the biological impact of HOXA11-AS in vitro. Furthermore, a chick embryo chorioallantoic membrane (CAM) model of NSCLC was constructed to explore the effect of HOXA11-AS on tumorigenicity and angiogenesis in vivo. Additionally, bioinformatics analyses were performed to investigate the prospective pathways of HOXA11-AS co-expressed genes. As results, HOXA11-AS was markedly highly expressed in NSCLC tissues and cells. Furthermore, the proliferation, migration, invasion, tumorigenic and angiogenic ability of NSCLC cells were all inhibited and apoptosis was induced after HOXA11-AS knock-down. HOXA11-AS RNAi also led to cell cycle arrest on G0/G1 or G2/M phase. In addition, the non-small cell lung cancer pathway might be involved in regulating the co-expressed genes of HOXA11-AS in NSCLC. These results indicate that HOXA11-AS plays pivotal roles in NSCLC and it can become a novel therapeutic direction for treating NSCLC.

Viral component of the human genome

Relationships between viruses and their human host are traditionally described from the point of view taking into consideration hosts as victims of viral aggression, which results in infectious diseases. However, these relations are in fact two-sided and involve modifications of both the virus and host genomes. Mutations that accumulate in the populations of viruses and hosts may provide them advantages such as the ability to overcome defense barriers of host cells or to create more efficient barriers to deal with the attack of the viral agent. One of the most common ways of reinforcing anti-viral barriers is the horizontal transfer of viral genes into the host genome. Within the host genome, these genes may be modified and extensively expressed to compete with viral copies and inhibit the synthesis of their products or modulate their functions in other ways. This review summarizes the available data on the horizontal gene transfer between viral and human genomes and discusses related problems.

Differential expression of alternatively spliced transcripts related to energy metabolism in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is one of the most common malignant tumors worldwide. CRC molecular pathogenesis is heterogeneous and may be followed by mutations in oncogenes and tumor suppressor genes, chromosomal and microsatellite instability, alternative splicing alterations, hypermethylation of CpG islands, oxidative stress, impairment of different signaling pathways and energy metabolism. In the present work, we have studied the alterations of alternative splicing patterns of genes related to energy metabolism in CRC.

RESULTS

Using CrossHub software, we analyzed The Cancer Genome Atlas (TCGA) RNA-Seq datasets derived from colon tumor and matched normal tissues. The expression of 1014 alternative mRNA isoforms involved in cell energy metabolism was examined. We found 7 genes with differentially expressed alternative transcripts whereas overall expression of these genes was not significantly altered in CRC. A set of 8 differentially expressed transcripts of interest has been validated by qPCR. These eight isoforms encoded by OGDH, COL6A3, ICAM1, PHPT1, PPP2R5D, SLC29A1, and TRIB3 genes were up-regulated in colorectal tumors, and this is in concordance with the bioinformatics data. The alternative transcript NM_057167 of COL6A3 was also strongly up-regulated in breast, lung, prostate, and kidney tumors. Alternative transcript of SLC29A1 (NM_001078177) was up-regulated only in CRC samples, but not in the other tested tumor types.

CONCLUSIONS

We identified tumor-specific expression of alternative spliced transcripts of seven genes involved in energy metabolism in CRC. Our results bring new knowledge on alternative splicing in colorectal cancer and suggest a set of mRNA isoforms that could be used for cancer diagnosis and development of treatment methods.

Dissecting the biological relationship between TCGA miRNA and mRNA sequencing data using MMiRNA-Viewer

Background

MicroRNAs (miRNA) are short nucleotides that interact with their target genes through 3′ untranslated regions (UTRs). The Cancer Genome Atlas (TCGA) harbors an increasing amount of cancer genome data for both tumor and normal samples. However, there are few visualization tools focusing on concurrently displaying important relationships and attributes between miRNAs and mRNAs of both cancer tumor and normal samples. Moreover, a deep investigation of miRNA-mRNA target and biological relationships across multiple cancer types by integrating web-based analysis has not been thoroughly conducted.

Results

We developed an interactive visualization tool called MMiRNA-Viewer that can concurrently present the co-relationships of expression between miRNA-mRNA pairs of both tumor and normal samples into a single graph. The input file of MMiRNA-Viewer contains the expression information including fold changes between normal and tumor samples for mRNAs and miRNAs, the correlation between mRNA and miRNA, and the predicted target relationship by a number of databases. Users can also load their own input data into MMiRNA-Viewer and visualize and compare detailed information about cancer-related gene expression changes, and also changes in the expression of transcription-regulating miRNAs.

To validate the MMiRNA-Viewer, eight types of TCGA cancer datasets with both normal and control samples were selected in this study and three filter steps were applied subsequently. We performed Gene Ontology (GO) analysis for genes available in final selected 238 pairs and also for genes in the top 5 % (95 percentile) for each of eight cancer types to report a significant number of genes involved in various biological functions and pathways. We also calculated various centrality measurement matrices for the largest connected component(s) in each of eight cancers and reported top genes and miRNAs with high centrality measurements.

Conclusions

With its user-friendly interface, dynamic visualization and advanced queries, we also believe MMiRNA-Viewer offers an intuitive approach for visualizing and elucidating co-relationships between miRNAs and mRNAs of both tumor and normal samples. We suggest that miRNA and mRNA pairs with opposite fold changes of their expression and with inverted correlation values between tumor and normal samples might be most relevant for explaining the decoupling of mRNAs and their targeting miRNAs in tumor samples for certain cancer types.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1219-y) contains supplementary material, which is available to authorized users.

The Dysregulation of Polyamine Metabolism in Colorectal Cancer Is Associated with Overexpression of c-Myc and C/EBPβ rather than Enterotoxigenic Bacteroides fragilis Infection

Colorectal cancer is one of the most common cancers in the world. It is well known that the chronic inflammation can promote the progression of colorectal cancer (CRC). Recently, a number of studies revealed a potential association between colorectal inflammation, cancer progression, and infection caused by enterotoxigenic Bacteroides fragilis (ETBF). Bacterial enterotoxin activates spermine oxidase (SMO), which produces spermidine and H2O2 as byproducts of polyamine catabolism, which, in turn, enhances inflammation and tissue injury. Using qPCR analysis, we estimated the expression of SMOX gene and ETBF colonization in CRC patients. We found no statistically significant associations between them. Then we selected genes involved in polyamine metabolism, metabolic reprogramming, and inflammation regulation and estimated their expression in CRC. We observed overexpression of SMOX, ODC1, SRM, SMS, MTAP, c-Myc, C/EBPβ (CREBP), and other genes. We found that two mediators of metabolic reprogramming, inflammation, and cell proliferation c-Myc and C/EBPβ may serve as regulators of polyamine metabolism genes (SMOX, AZIN1, MTAP, SRM, ODC1, AMD1, and AGMAT) as they are overexpressed in tumors, have binding site according to ENCODE ChIP-Seq data, and demonstrate strong coexpression with their targets. Thus, increased polyamine metabolism in CRC could be driven by c-Myc and C/EBPβ rather than ETBF infection.