Comprehensive analysis of transcriptome and metabolome analysis in Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma

Tensor decomposition-based and principal-component-analysis-based unsupervised feature extraction applied to the gene expression and methylation profiles in the brains of social insects with multiple castes

Background

Even though coexistence of multiple phenotypes sharing the same genomic background is interesting, it remains incompletely understood. Epigenomic profiles may represent key factors, with unknown contributions to the development of multiple phenotypes, and social-insect castes are a good model for elucidation of the underlying mechanisms. Nonetheless, previous studies have failed to identify genes associated with aberrant gene expression and methylation profiles because of the lack of suitable methodology that can address this problem properly.

Methods

A recently proposed principal component analysis (PCA)-based and tensor decomposition (TD)-based unsupervised feature extraction (FE) can solve this problem because these two approaches can deal with gene expression and methylation profiles even when a small number of samples is available.

Results

PCA-based and TD-based unsupervised FE methods were applied to the analysis of gene expression and methylation profiles in the brains of two social insects, Polistes canadensis and Dinoponera quadriceps. Genes associated with differential expression and methylation between castes were identified, and analysis of enrichment of Gene Ontology terms confirmed reliability of the obtained sets of genes from the biological standpoint.

Conclusions

Biologically relevant genes, shown to be associated with significant differential gene expression and methylation between castes, were identified here for the first time. The identification of these genes may help understand the mechanisms underlying epigenetic control of development of multiple phenotypes under the same genomic conditions.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2068-7) contains supplementary material, which is available to authorized users.

Deciphering hepatocellular carcinoma through metabolomics: from biomarker discovery to therapy evaluation

Hepatocellular carcinoma (HCC) is the third most common cause of death from cancer, with increasing prevalence worldwide. The mortality rate of HCC is similar to its incidence rate, which reflects its poor prognosis. At present, the diagnosis of HCC is still mostly dependent on invasive biopsy, imaging methods, and serum α-fetoprotein (AFP) testing. Because of the asymptomatic nature of early HCC, biopsy and imaging methods usually detect HCC at the middle–late stages. AFP has limited sensitivity and specificity, as many other nonmalignant liver diseases can also result in a very high serum level of AFP. Therefore, better biomarkers with higher sensitivity and specificity at earlier stages are greatly needed. Since metabolic reprogramming is an essential hallmark of cancer and the liver is the metabolic hub of living systems, it is useful to investigate HCC from a metabolic perspective. As a noninvasive and nondestructive approach, metabolomics provides holistic information on dynamically metabolic responses of living systems to both endogenous and exogenous factors. Therefore, it would be conducive to apply metabolomics in investigating HCC. In this review, we summarize recent metabolomic studies on HCC cellular, animal, and clinicopathologic models with attention to metabolomics as a biomarker in cancer diagnosis. Recent applications of metabolomics with respect to therapeutic and prognostic evaluation of HCC are also covered, with emphasis on the potential of treatment by drugs from natural products. In the last section, the current challenges and trends of future development of metabolomics on HCC are discussed. Overall, metabolomics provides us with novel insight into the diagnosis, prognosis, and therapeutic evaluation of HCC.

A nomogram incorporating six easily obtained parameters to discriminate intrahepatic cholangiocarcinoma and hepatocellular carcinoma

Intrahepatic cholangiocarcinoma (ICC) and hepatocellular carcinoma (HCC) are the most prevalent histologic types of primary liver cancer (PLC). Although ICC and HCC share similar risk factors and clinical manifestations, ICC usually bears poorer prognosis than HCC. Confidently discriminating ICC and HCC before surgery is beneficial to both treatment and prognosis. Given the lack of effective differential diagnosis biomarkers and methods, construction of models based on available clinicopathological characteristics is in need. Nomograms present a simple and efficient way to make a discrimination. A total of 2894 patients who underwent surgery for PLC were collected. Of these, 1614 patients formed the training cohort for nomogram construction, and thereafter, 1280 patients formed the validation cohort to confirm the model's performance. Histopathologically confirmed ICC was diagnosed in 401 (24.8%) and 296 (23.1%) patients in these two cohorts, respectively. A nomogram integrating six easily obtained variables (Gender, Hepatitis B surface antigen, Aspartate aminotransferase, Alpha-fetoprotein, Carcinoembryonic antigen, Carbohydrate antigen 19-9) is proposed in accordance with Akaike's Information Criterion (AIC). A score of 15 was determined as the cut-off value, and the corresponding discrimination efficacy was sufficient. Additionally, patients who scored higher than 15 suffered poorer prognosis than those with lower scores, regardless of the subtype of PLC. A nomogram for clinical discrimination of ICC and HCC has been established, where a higher score indicates ICC and poor prognosis. Further application of this nomogram in multicenter investigations may confirm the practicality of this tool for future clinical use.

Downregulated miR-23b-3p expression acts as a predictor of hepatocellular carcinoma progression: A study based on public data and RT-qPCR verification

Mounting evidence has shown that miR-23b-3p, which is associated with cell proliferation, invasion, and apoptosis, acts as a biomarker for diagnosis and outcomes in numerous cancers. However, the clinicopathological implication of miR-23b-3p in hepatocellular carcinoma (HCC) remains unclear. Our study evaluated the role of miR-23b-3p in HCC and investigated its potential application as a marker for preliminary diagnosis and therapy in HCC. High-throughput data from the NCBI Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were collected and analyzed. One hundred and one tissue sections of HCC were paired with adjacent non-cancerous HCC as further supplements. miR-23b-3p expression was detected using quantitative real-time PCR. Additionally, the relationship between miR-23b-3p expression and HCC progression and Time-to-recurrence (months) was explored. Ten algorithms were applied to predict the prospective target genes of miR-23b-3p. Next, we conducted bioinformatics analysis for further study. miR-23b-3p expression was pronouncedly decreased in HCC tissues in contrast with their paired adjacent non-cancerous HCC (P<0.001) with RT-qPCR. In total, 405 targets, acquired with consistent prediction from at least five databases, were used for the bioinformatics analysis. According to the Gene Ontology (GO) analysis, all targets were classified into biological processes, cellular components and molecular functions. In the pathway analysis, targets of miR-23b-3p were primarily enriched in the signaling pathways of renal cell carcinoma, hepatitis B and pancreatic cancer (corrected P-value <0.05). In the protein-protein interaction (PPI) network for miR-23b-3p, a total of 8 targets, including SRC, AKT1, EGFR, CTNNB1, BCL2, SMAD3, PTEN and KDM6A, were located in the key nodes with high degree (>35). In conclusion, this study provides impressive illumination of the potential role of miR-23b-3p in HCC tumorigenesis and progression. Furthermore, miR-23b-3p may act as a predictor of HCC and could be a new treatment target.

[18F]Fluorocholine PET/CT Imaging of Liver Cancer: Radiopathologic Correlation with Tissue Phospholipid Profiling

PURPOSE

[¹⁸F]fluorocholine PET/CT can detect hepatocellular carcinoma (HCC) based on imaging the initial steps of phosphatidylcholine synthesis. To relate the diagnostic performance of [¹⁸F]fluorocholine positron emission tomography (PET)/x-ray computed tomography (CT) to the phospholipid composition of liver tumors, radiopathologic correspondence was performed in patients with early-stage liver cancer who had undergone [¹⁸F]fluorocholine PET/CT before tumor resection.

PROCEDURES

Tumor and adjacent liver were profiled by liquid chromatography mass spectrometry, quantifying phosphatidylcholine species by mass-to-charge ratio. For clinical-radiopathologic correlation, HCC profiles were reduced to two orthogonal principal component factors (PCF1 and PCF2) accounting for 80 % of total profile variation.

RESULTS

Tissues from 31 HCC patients and 4 intrahepatic cholangiocarcinoma (ICC) patients were analyzed, revealing significantly higher levels of phosphocholine, CDP-choline, and highly saturated phosphatidylcholine species in HCC tumors relative to adjacent liver and ICC tumors. Significant loading values for PCF1 corresponded to phosphatidylcholines containing poly-unsaturated fatty acids while PCF2 corresponded only to highly saturated phosphatidylcholines. Only PCF2 correlated significantly with HCC tumor-to-liver [¹⁸F]fluorocholine uptake ratio (ρ = 0.59, p < 0.0005). Sensitivity for all tumors based on an abnormal [¹⁸F]fluorocholine uptake ratio was 93 % while sensitivity for HCC based on increased tumor [¹⁸F]fluorocholine uptake was 84 %, with lower levels of highly saturated phosphatidylcholines in tumors showing low [¹⁸F]fluorocholine uptake.

CONCLUSION

Most HCC tumors contain high levels of saturated phosphatidylcholines, supporting their dependence on de novo fatty acid metabolism for phospholipid membrane synthesis. While [¹⁸F]fluorocholine PET/CT can serve to identify these lipogenic tumors, its imperfect diagnostic sensitivity implies metabolic heterogeneity across HCC and a weaker lipogenic phenotype in some tumors.

MicroRNA-124-3p expression and its prospective functional pathways in hepatocellular carcinoma: A quantitative polymerase chain reaction, gene expression omnibus and bioinformatics study

The present study aimed to explore the potential clinical significance of microRNA (miR)-124-3p expression in the hepatocarcinogenesis and development of hepatocellular carcinoma (HCC), as well as the potential target genes of functional HCC pathways. Reverse transcription-quantitative polymerase chain reaction was performed to evaluate the expression of miR-124-3p in 101 HCC and adjacent non-cancerous tissue samples. Additionally, the association between miR-124-3p expression and clinical parameters was also analyzed. Differentially expressed genes identified following miR-124-3p transfection, the prospective target genes predicted in silico and the key genes of HCC obtained from Natural Language Processing (NLP) were integrated to obtain potential target genes of miR-124-3p in HCC. Relevant signaling pathways were assessed with protein-protein interaction (PPI) networks, Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Protein Annotation Through Evolutionary Relationships (PANTHER) pathway enrichment analysis. miR-124-3p expression was significantly reduced in HCC tissues compared with expression in adjacent non-cancerous liver tissues. In HCC, miR-124-3p was demonstrated to be associated with clinical stage. The mean survival time of the low miR-124-3p expression group was reduced compared with that of the high expression group. A total of 132 genes overlapped from differentially expressed genes, miR-124-3p predicted target genes and NLP identified genes. PPI network construction revealed a total of 109 nodes and 386 edges, and 20 key genes were identified. The major enriched terms of three GO categories included regulation of cell proliferation, positive regulation of cellular biosynthetic processes, cell leading edge, cytosol and cell projection, protein kinase activity, transcription activator activity and enzyme binding. KEGG analysis revealed pancreatic cancer, prostate cancer and non-small cell lung cancer as the top three terms. Angiogenesis, the endothelial growth factor receptor signaling pathway and the fibroblast growth factor signaling pathway were identified as the most significant terms in the PANTHER pathway analysis. The present study confirmed that miR-124-3p acts as a tumor suppressor in HCC. miR-124-3p may target multiple genes, exerting its effect spatiotemporally, or in combination with a diverse range of processes in HCC. Functional characterization of miR-124-3p targets will offer novel insight into the molecular changes that occur in HCC progression.

Cholangiocarcinoma‑associated genes identified by integrative analysis of gene expression data

Cholangiocarcinoma (CCA) is characterized by delayed diagnosis and poor survival rate. Research efforts have focused on novel diagnostic technologies for this type of cancer. Transcriptomic microarray technology is a useful research strategy for investigating the molecular properties of CCA. The objective of the present study was to identify candidate biomarkers with high potential for clinical application in CCA using a meta‑analysis‑based approach. Gene expression profiles of CCA were downloaded from the Gene Expression Omnibus database for integrated analysis. All differentially expressed genes (DEGs) were analyzed by Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Protein‑protein interaction (PPI) networks were further constructed, hub proteins were identified and functional modules were extracted. Following integrated analysis of the seven eligible datasets (428 cases and 46 controls), a set of 1,080 DEGs was identified, including 710 upregulated and 370 downregulated genes. Functional enrichment analysis demonstrated that 'chromosome organization' was a significantly enriched GO term in the biological process category. 'DNA replication', 'influenza A', and 'lysosome' were the top three significantly enriched KEGG pathways. Furthermore, PPI network analysis indicated that the significant hub proteins were histone deacetylase 1, cullin‑associated NEDD8‑dissociated protein 1, ubiquitin D, early growth response protein 1 and glycogen synthase kinase 3β. The majority of these proteins are involved in CCA. These results provided a set of targets that may help researchers to clarify further the underlying mechanisms of CCA tumorigenesis.

Clinical significance of microRNA-449a in hepatocellular carcinoma with microarray data mining together with initial bioinformatics analysis

Increasing evidence has demonstrated that microRNA (miR)-449a expression is reduced in various types of tumors and that it serves as a tumor suppressor. However, the molecular mechanism of miR-449a in hepatocellular carcinoma (HCC) has not been thoroughly elucidated and is disputed. Therefore, the aim of the present work was to systematically review the current literature and to utilize the public Gene Expression Omnibus database to determine the role of miR-449a and its significance in HCC. A total of eight original papers and seven microarrays were included in the present study. Based on the evidence, miR-449a was reduced in HCC. miR-449a is likely involved in various signaling pathways and is targeted to multiple mRNA as part of its function in HCC. In addition, a preliminary bioinformatic analysis was conducted for miR-449a to investigate the novel potential pathways that miR-449a may participate in regarding HCC.

Salivary biomarkers for oral cancer and pre-cancer screening: a review

OBJECTIVE

The objective of the study was to conduct a systematic review of the literature assessing potential salivary biomarkers of oral cancer and pre-cancer and discuss emerging issues and challenges in relation to oral cancer and pre-cancer diagnostics.

MATERIALS AND METHODS

Search for articles involved the Medline, PubMed, and EMBASE. Specific terms were used from January 1995 to March 2017 by three experts.

RESULTS

This search collected 270 articles, of which 105 articles such as reviews, case reports, news, letter to editor, etc. in first round and 117 articles such as publications in other languages than English, non-human studies, etc. were excluded. The remaining 48 articles considered analyzing whole saliva as well as specific gland saliva. Thirty-one studies considered oral stimuli such as eating, drinking, and oral hygiene practices for varied periods of time prior to sample collection. The time of collection of saliva was morning in most studies, but the exact time of collection was not mentioned. Three studies showed to have evaluated the whole saliva without centrifugation. Two-dimensional gel electrophoresis and tandem mass spectrometry were the most commonly used methods. Most of the potential salivary biomarkers of oral cancer are salivary proteins.

CONCLUSION

Combination approach of salivary biomarkers could be used as screening tool to improve early detection and diagnostic precision of oral pre-cancer and cancer.

CLINICAL RELEVANCE

The current findings are of importance for clinicians and researchers to mitigate the challenges in salivary-based diagnosis of oral cancer and to evaluate reliable, specific, and sensitive salivary biomarkers for oral pre-cancer and cancer diagnosis.

Emergence of Intrahepatic Cholangiocarcinoma: How High-Throughput Technologies Expedite the Solutions for a Rare Cancer Type

Intrahepatic cholangiocarcinoma (ICC) is the cancer of the intrahepatic bile ducts, and together with hepatocellular carcinoma (HCC), constitute the majority of primary liver cancers. ICC is a rare disorder as its overall incidence is < 1/100,000 in the United States and Europe. However, it shows much higher incidence in particular geographical regions, such as northeastern Thailand, where liver fluke infection is the most common risk factor of ICC. Since the early stages of ICC are often asymptomatic, the patients are usually diagnosed at advanced stages with no effective treatments available, leading to the high mortality rate. In addition, unclear genetic mechanisms, heterogeneous nature, and various etiologies complicate the development of new efficient treatments. Recently, a number of studies have employed high-throughput approaches, including next-generation sequencing and mass spectrometry, in order to understand ICC in different biological aspects. In general, the majority of recurrent genetic alterations identified in ICC are enriched in known tumor suppressor genes and oncogenes, such as mutations in TP53, KRAS, BAP1, ARID1A, IDH1, IDH2, and novel FGFR2 fusion genes. Yet, there are no major driver genes with immediate clinical solutions characterized. Interestingly, recent studies utilized multi-omics data to classify ICC into two main subgroups, one with immune response genes as the main driving factor, while another is enriched with driver mutations in the genes associated with epigenetic regulations, such as IDH1 and IDH2. The two subgroups also show different hypermethylation patterns in the promoter regions. Additionally, the immune response induced by host-pathogen interactions, i.e., liver fluke infection, may further stimulate tumor growth through alterations of the tumor microenvironment. For in-depth functional studies, although many ICC cell lines have been globally established, these homogeneous cell lines may not fully explain the highly heterogeneous genetic contents of this disorder. Therefore, the advent of patient-derived xenograft and 3D patient-derived organoids as new disease models together with the understanding of evolution and genetic alterations of tumor cells at the single-cell resolution will likely become the main focus to fill the current translational research gaps of ICC in the future.

Tensor decomposition-based unsupervised feature extraction identifies candidate genes that induce post-traumatic stress disorder-mediated heart diseases

Background

Although post-traumatic stress disorder (PTSD) is primarily a mental disorder, it can cause additional symptoms that do not seem to be directly related to the central nervous system, which PTSD is assumed to directly affect. PTSD-mediated heart diseases are some of such secondary disorders. In spite of the significant correlations between PTSD and heart diseases, spatial separation between the heart and brain (where PTSD is primarily active) prevents researchers from elucidating the mechanisms that bridge the two disorders. Our purpose was to identify genes linking PTSD and heart diseases.

Methods

In this study, gene expression profiles of various murine tissues observed under various types of stress or without stress were analyzed in an integrated manner using tensor decomposition (TD).

Results

Based upon the obtained features, ∼ 400 genes were identified as candidate genes that may mediate heart diseases associated with PTSD. Various gene enrichment analyses supported biological reliability of the identified genes. Ten genes encoding protein-, DNA-, or mRNA-interacting proteins—ILF2, ILF3, ESR1, ESR2, RAD21, HTT, ATF2, NR3C1, TP53, and TP63—were found to be likely to regulate expression of most of these ∼ 400 genes and therefore are candidate primary genes that cause PTSD-mediated heart diseases. Approximately 400 genes in the heart were also found to be strongly affected by various drugs whose known adverse effects are related to heart diseases and/or fear memory conditioning; these data support the reliability of our findings.

Conclusions

TD-based unsupervised feature extraction turned out to be a useful method for gene selection and successfully identified possible genes causing PTSD-mediated heart diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-017-0302-1) contains supplementary material, which is available to authorized users.

Diagnostic significance and potential function of miR-338-5p in hepatocellular carcinoma: A bioinformatics study with microarray and RNA sequencing data

MicroRNA (miR)-338-5p has been studied in hepatocellular carcinoma (HCC); however, the diagnostic value and molecular mechanism underlying its actions remains to be elucidated. The present study aimed to validate the diagnostic ability of miR-338-5p and further explore the underlying molecular mechanism. Data from eligible studies, Gene Expression Omnibus (GEO) chips and The Cancer Genome Atlas (TCGA) datasets were gathered in the data mining and the integrated meta-analysis, to evaluate the significance of miR-338-5p in diagnosing HCC comprehensively. The potential target genes of miR-338-5p were achieved from the intersection of the deregulated targets of miR-338-5p from GEO and TCGA in addition to the predicted target genes from 12 online software. A protein-protein-interaction (PPI) network was drawn to illustrate the interaction between target genes and to define the hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to investigate the function of the target genes. From the results, miR-338-5p exhibited favorable value in diagnosing HCC. Types of sample and experiment were defined as the possible sources of heterogeneity in meta-analysis. A total of 423 genes were selected as the potential target genes of miR-338-5p, and five genes were defined as the hub genes from the PPI network. The GO and KEGG analyses indicated that the target genes were significantly assembled in the pathways of metabolic process and cell cycle. miR-338-5p may function as a novel diagnostic target for HCC through regulating certain target genes and signaling pathways.

Identification of candidate drugs using tensor-decomposition-based unsupervised feature extraction in integrated analysis of gene expression between diseases and DrugMatrix datasets

Identifying drug target genes in gene expression profiles is not straightforward. Because a drug targets proteins and not mRNAs, the mRNA expression of drug target genes is not always altered. In addition, the interaction between a drug and protein can be context dependent; this means that simple drug incubation experiments on cell lines do not always reflect the real situation during active disease. In this paper, I applied tensor-decomposition-based unsupervised feature extraction to the integrated analysis using a mathematical product of gene expression in various diseases and gene expression in the DrugMatrix dataset, where comprehensive data on gene expression during various drug treatments of rats are reported. I found that this strategy, in a fully unsupervised manner, enables researchers to identify a combined set of genes and compounds that significantly overlap with gene and drug interactions identified in the past. As an example illustrating the usefulness of this strategy in drug discovery experiments, I considered cirrhosis, for which no effective drugs have ever been proposed. The present strategy identified two promising therapeutic-target genes, CYPOR and HNFA4; for their protein products, bezafibrate was identified as a promising candidate drug, supported by in silico docking analysis.

Down-regulation of miR-26a-5p in hepatocellular carcinoma: A qRT-PCR and bioinformatics study

BACKGROUND

To practically verify the clinical value of miR-26a-5p and thoroughly explore its target genes as well as its potential functions in hepatocellular carcinoma (HCC).

METHODS

HCC and adjacent non-cancerous hepatic tissues of 95 HCC patients were collected for analysis using reverse transcription quantitative real-time PCR (qRT-PCR). For the bioinformatics analysis, we identified potential target genes for miR-26a-5p from differentially expressed genes (DEGs) in Gene Expression Omnibus (GEO) data sets and miRWalk predicted database. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses were applied to analyze the prospective mechanisms of the predicted target genes.

RESULTS

MiR-26a-5p showed a significantly lower expression level in HCC tissues (1.56±1.07) than adjacent benign liver tissues (2.28±1.06, P<0.001). The area under the curve (AUC) of the receiver operating characteristic (ROC) was 0.665 (95% CI: 0.588-0.743, P<0.001). Significant correlations between miR-26a-5p expression and clinicopathological features such as gender (r=0.275, P<0.01), clinical TNM stage (r=-0.306, P<0.01), and metastasis (r=-0.321, P<0.01) were observed. To examine potential target genes, we obtained 175 genes for further function analysis, by attaining the intersection of 2062 up-regulated DEGs and 1390 online-predicted target genes. The GO and KEGG pathway annotation indicated focal adhesion, regulation of actin cytoskeleton and the PI3K-Akt signaling pathway as significant prospective mechanisms. The PPI network indicated that NRAS was the most essential hub gene in the whole network.

CONCLUSION

Down-regulated miR-26a-5p was closely correlated with the status of metastasis and the progression of HCC. MiR-26a-5p might play protective roles by targeting diverse genes and pathways.

Genetic Association between Amyotrophic Lateral Sclerosis and Cancer

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. An ALS drug, Riluzole, has been shown to induce two different anticancer effects on hepatocellular carcinoma (HCC). In light of this finding, we explore the relationship between ALS and cancer, especially for HCC, from the molecular biological viewpoint. We establish biomarkers that can discriminate between ALS patients and healthy controls. A principal component analysis (PCA) based unsupervised feature extraction (FE) is used to find gene biomarkers of ALS based on microarray gene expression data. Based on this method, 101 probes were selected as biomarkers for ALS with 95% high accuracy to discriminate between ALS patients and controls. Most of the genes corresponding to these probes are shown to be related to various cancers. These findings might provide a new insight for developing new therapeutic options or drugs for both ALS and cancer.

Regulation of Pharmacogene Expression by microRNA in The Cancer Genome Atlas (TCGA) Research Network

Individual differences in drug responses are associated with genetic and epigenetic variability of pharmacogene expression. We aimed to identify the relevant miRNAs which regulate pharmacogenes associated with drug responses. The miRNA and mRNA expression profiles derived from data for normal and solid tumor tissues in The Cancer Genome Atlas (TCGA) Research Network. Predicted miRNAs targeted to pharmacogenes were identified using publicly available databases. A total of 95 pharmacogenes were selected from cholangiocarcinoma and colon adenocarcinoma, as well as kidney renal clear cell, liver hepatocellular, and lung squamous cell carcinomas. Through the integration analyses of miRNA and mRNA, 35 miRNAs were found to negatively correlate with mRNA expression levels of 16 pharmacogenes in normal bile duct, liver, colon, and lung tissues (p<0.05). Additionally, 36 miRNAs were related to differential expression of 32 pharmacogene mRNAs in those normal and tumorigenic tissues (p<0.05). These results indicate that changes in expression levels of miRNAs targeted to pharmacogenes in normal and tumor tissues may play a role in determining individual variations in drug response.

Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing

In the current era of big data, the amount of data available is continuously increasing. Both the number and types of samples, or features, are on the rise. The mixing of distinct features often makes interpretation more difficult. However, separate analysis of individual types requires subsequent integration. A tensor is a useful framework to deal with distinct types of features in an integrated manner without mixing them. On the other hand, tensor data is not easy to obtain since it requires the measurements of huge numbers of combinations of distinct features; if there are m kinds of features, each of which has N dimensions, the number of measurements needed are as many as N^m, which is often too large to measure. In this paper, I propose a new method where a tensor is generated from individual features without combinatorial measurements, and the generated tensor was decomposed back to matrices, by which unsupervised feature extraction was performed. In order to demonstrate the usefulness of the proposed strategy, it was applied to synthetic data, as well as three omics datasets. It outperformed other matrix-based methodologies.

The search for novel diagnostic and prognostic biomarkers in cholangiocarcinoma

The poor prognosis of cholangiocarcinoma (CCA) is in part due to late diagnosis, which is currently achieved by a combination of clinical, radiological and histological approaches. Available biomarkers determined in serum and biopsy samples to assist in CCA diagnosis are not sufficiently sensitive and specific. Therefore, the identification of new biomarkers, preferably those obtained by minimally invasive methods, such as liquid biopsy, is important. The development of innovative technologies has permitted to identify a significant number of genetic, epigenetic, proteomic and metabolomic CCA features with potential clinical usefulness in early diagnosis, prognosis or prediction of treatment response. Potential new candidates must be rigorously evaluated prior to entering routine clinical application. Unfortunately, to date, no such biomarker has achieved validation for these purposes. This review is an up-to-date of currently used biomarkers and the candidates with promising characteristics that could be included in the clinical practice in the next future. This article is part of a Special Issue entitled: Cholangiocytes in Health and Disease edited by Jesus Banales, Marco Marzioni, Nicholas LaRusso and Peter Jansen.

Serum metabolomics analysis for early detection of colorectal cancer

BACKGROUND

Although colorectal cancer (CRC) is one of the most common causes of cancer mortality, early-stage detection improves survival rates dramatically. Because cancer impacts important metabolic pathways, the alteration of metabolite levels as a potential biomarker of early-stage cancer has been the focus of many studies. Here, we used CE-TOFMS, a novel and promising method with small injection volume and high resolution, to separate and detect ionic compounds based on the different migration rates of charged metabolites in order to detect metabolic biomarkers in patients with CRC.

METHODS

A total of 56 patients with CRC (n = 14 each of Stages I-IV), 60 healthy controls, and 59 patients with colonic adenoma were included in this study. Metabolome analysis was conducted by CE-TOFMS on serum samples of patients and controls using the Advanced Scan package (Human Metabolome Technologies).

RESULTS

We obtained 334 metabolites in the serum, of which 139 were identified as known substances. Among these 139 known metabolites, 16 were correlated with CRC stage by upregulation and 44 by downregulation, with benzoic acid (r = -0.649, t = 11.653, p = 6.07599E-24), octanoic acid (r = 0.557, t = 9.183, p = 7.9557E-17), decanoic acid (r = 0.539, t = 8.749, p = 1.24352E-15), and histidine (r = -0.513, t = 8.194, p = 3.90224E-14) exhibiting significant correlation.

CONCLUSIONS

To the best of our knowledge, this is the first report to determine the correlation between serum metabolites and CRC stage using CE-TOFMS. Our results show that benzoic acid exhibited excellent diagnostic power and could potentially serve as a novel disease biomarker for CRC diagnosis.

Principal Components Analysis Based Unsupervised Feature Extraction Applied to Gene Expression Analysis of Blood from Dengue Haemorrhagic Fever Patients

Dengue haemorrhagic fever (DHF) sometimes occurs after recovery from the disease caused by Dengue virus (DENV), and is often fatal. However, the mechanism of DHF has not been determined, possibly because no suitable methodologies are available to analyse this disease. Therefore, more innovative methods are required to analyse the gene expression profiles of DENV-infected patients. Principal components analysis (PCA)-based unsupervised feature extraction (FE) was applied to the gene expression profiles of DENV-infected patients, and an integrated analysis of two independent data sets identified 46 genes as critical for DHF progression. PCA using only these 46 genes rendered the two data sets highly consistent. The application of PCA to the 46 genes of an independent third data set successfully predicted the progression of DHF. A fourth in vitro data set confirmed the identification of the 46 genes. These 46 genes included interferon- and heme-biosynthesis-related genes. The former are enriched in binding sites for STAT1, STAT2, and IRF1, which are associated with DHF-promoting antibody-dependent enhancement, whereas the latter are considered to be related to the dysfunction of spliceosomes, which may mediate haemorrhage. These results are outcomes that other type of bioinformatic analysis could hardly achieve.

Identification of microRNAs as biomarkers for cholangiocarcinoma detection: A diagnostic meta-analysis

BACKGROUND

Cholangiocarcinoma (CCA) is a common biliary malignancy that results in high mortality and poor survival rates. The diagnostic capacity of miRNAs in CCA detection is still controversial. Current meta-analysis evaluate the evidence on the capacity of miRNAs for CCA diagnosis.

METHODS

A systematic literature search for relevant articles in PubMed, Embase and Web of science database up to 10 April 2016 was conducted. The pooled sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR, respectively), diagnostic odds ratio (DOR) and area under the curve (AUC) were pooled to assess the overall diagnostic value of miRNA. Subgroup analysis was further applied to explore the between-study heterogeneity. Publication bias was assessed with Deeks' funnel plot. Statistical analysis was carried out using RevMan 5.2 and MetaDisc 1.4.

RESULTS

A total of 501 patients and 248 controls from 14 studies in 8 articles were included in this meta-analysis. The pooled sensitivity and specialty were 0.83 (0.80-0.86) and 0.79 (0.75-0.83), respectively. The pooled PLR was 3.96 (2.64-5.94), NLR was 0.25 (0.19-0.34), DOR was 18.1 (9.79-33.5) and pooled AUC was 0.88 (0.85-0.90). Subgroup analyses indicated that the serum miRNA assays showed highly accurate CCA diagnosis. The diagnostic capacity was more accurate in Caucasians than in Asians.

CONCLUSIONS

The miRNA assays are promising diagnostic tools for CCA detection with moderate accuracy. However, further prospective studies were needed to confirm this results before clinical application.

Microarray analysis of circulating microRNAs in familial Mediterranean fever

OBJECTIVES

Familial Mediterranean fever (FMF) is an autoinflammatory disease caused by mutations in MEFV. Mutations in exon 10 are associated with typical FMF phenotypes, whereas the pathogenic role of variants in exons 2 and 3 remains uncertain. Recent evidence suggests that circulating microRNAs (miRNAs) are potentially useful biomarkers in several diseases. Therefore, their expression was assessed in FMF.

METHODS

The subjects were 24 patients with FMF who were between attacks: eight with exon 10 mutations (group A), eight with exon 3 mutations (group B), and eight without exon 3 or 10 mutations (group C). We also investigated eight cases of PFAPA as disease controls. Exosome-rich fractionated RNA was subjected to miRNA profiling by microarray.

RESULTS

Using the expression patterns of 26 miRNAs, we classified FMF (groups A, B, and C) and PFAPA with 78.1% accuracy. In FMF patients, groups A and B, A and C, and B and C were distinguished with 93.8, 87.5, and 100% accuracy using 24, 30, and 25 miRNA expression patterns, respectively.

CONCLUSIONS

These findings suggest that expression patterns of circulating miRNAs differ among FMF subgroups based on MEFV mutations between FMF episodes. These patterns may serve as a useful biomarker for detecting subgroups of FMF.

CE-MS for metabolomics: Developments and applications in the period 2014-2016

CE–MS can be considered a useful analytical technique for the global profiling of (highly) polar and charged metabolites in various samples. Over the past few years, significant advancements have been made in CE–MS approaches for metabolomics studies. In this paper, which is a follow‐up of a previous review paper covering the years 2012–2014 (Electrophoresis 2015, 36, 212–224), recent CE–MS strategies developed for metabolomics covering the literature from July 2014 to June 2016 are outlined. Attention will be paid to new CE–MS approaches for the profiling of anionic metabolites and the potential of SPE coupled to CE–MS is also demonstrated. Representative examples illustrate the applicability of CE–MS in the fields of biomedical, clinical, microbial, plant, and food metabolomics. A complete overview of recent CE–MS‐based metabolomics studies is given in a table, which provides information on sample type and pretreatment, capillary coatings, and MS detection mode. Finally, general conclusions and perspectives are given.

SFRP1 is a possible candidate for epigenetic therapy in non-small cell lung cancer

Background

Non-small cell lung cancer (NSCLC) remains a lethal disease despite many proposed treatments. Recent studies have indicated that epigenetic therapy, which targets epigenetic effects, might be a new therapeutic methodology for NSCLC. However, it is not clear which objects (e.g., genes) this treatment specifically targets. Secreted frizzled-related proteins (SFRPs) are promising candidates for epigenetic therapy in many cancers, but there have been no reports of SFRPs targeted by epigenetic therapy for NSCLC.

Methods

This study performed a meta-analysis of reprogrammed NSCLC cell lines instead of the direct examination of epigenetic therapy treatment to identify epigenetic therapy targets. In addition, mRNA expression/promoter methylation profiles were processed by recently proposed principal component analysis based unsupervised feature extraction and categorical regression analysis based feature extraction.

Results

The Wnt/β-catenin signalling pathway was extensively enriched among 32 genes identified by feature extraction. Among the genes identified, SFRP1 was specifically indicated to target β-catenin, and thus might be targeted by epigenetic therapy in NSCLC cell lines. A histone deacetylase inhibitor might reactivate SFRP1 based upon the re-analysis of a public domain data set. Numerical computation validated the binding of SFRP1 to WNT1 to suppress Wnt signalling pathway activation in NSCLC.

Conclusions

The meta-analysis of reprogrammed NSCLC cell lines identified SFRP1 as a promising target of epigenetic therapy for NSCLC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12920-016-0196-3) contains supplementary material, which is available to authorized users.

Principal component analysis based unsupervised feature extraction applied to budding yeast temporally periodic gene expression

Background

The recently proposed principal component analysis (PCA) based unsupervised feature extraction (FE) has successfully been applied to various bioinformatics problems ranging from biomarker identification to the screening of disease causing genes using gene expression/epigenetic profiles. However, the conditions required for its successful use and the mechanisms involved in how it outperforms other supervised methods is unknown, because PCA based unsupervised FE has only been applied to challenging (i.e. not well known) problems.

Results

In this study, PCA based unsupervised FE was applied to an extensively studied organism, i.e., budding yeast. When applied to two gene expression profiles expected to be temporally periodic, yeast metabolic cycle (YMC) and yeast cell division cycle (YCDC), PCA based unsupervised FE outperformed simple but powerful conventional methods, with sinusoidal fitting with regards to several aspects: (i) feasible biological term enrichment without assuming periodicity for YMC; (ii) identification of periodic profiles whose period was half as long as the cell division cycle for YMC; and (iii) the identification of no more than 37 genes associated with the enrichment of biological terms related to cell division cycle for the integrated analysis of seven YCDC profiles, for which sinusoidal fittings failed. The explantation for differences between methods used and the necessary conditions required were determined by comparing PCA based unsupervised FE with fittings to various periodic (artificial, thus pre-defined) profiles. Furthermore, four popular unsupervised clustering algorithms applied to YMC were not as successful as PCA based unsupervised FE.

Conclusions

PCA based unsupervised FE is a useful and effective unsupervised method to investigate YMC and YCDC. This study identified why the unsupervised method without pre-judged criteria outperformed supervised methods requiring human defined criteria.

Electronic supplementary material

The online version of this article (doi:10.1186/s13040-016-0101-9) contains supplementary material, which is available to authorized users.

Metabolomic Studies of Oral Biofilm, Oral Cancer, and Beyond

Oral diseases are known to be closely associated with oral biofilm metabolism, while cancer tissue is reported to possess specific metabolism such as the ‘Warburg effect’. Metabolomics might be a useful method for clarifying the whole metabolic systems that operate in oral biofilm and oral cancer, however, technical limitations have hampered such research. Fortunately, metabolomics techniques have developed rapidly in the past decade, which has helped to solve these difficulties. In vivo metabolomic analyses of the oral biofilm have produced various findings. Some of these findings agreed with the in vitro results obtained in conventional metabolic studies using representative oral bacteria, while others differed markedly from them. Metabolomic analyses of oral cancer tissue not only revealed differences between metabolomic profiles of cancer and normal tissue, but have also suggested a specific metabolic system operates in oral cancer tissue. Saliva contains a variety of metabolites, some of which might be associated with oral or systemic disease; therefore, metabolomics analysis of saliva could be useful for identifying disease-specific biomarkers. Metabolomic analyses of the oral biofilm, oral cancer, and saliva could contribute to the development of accurate diagnostic, techniques, safe and effective treatments, and preventive strategies for oral and systemic diseases.

Identification of More Feasible MicroRNA-mRNA Interactions within Multiple Cancers Using Principal Component Analysis Based Unsupervised Feature Extraction

MicroRNA(miRNA)–mRNA interactions are important for understanding many biological processes, including development, differentiation and disease progression, but their identification is highly context-dependent. When computationally derived from sequence information alone, the identification should be verified by integrated analyses of mRNA and miRNA expression. The drawback of this strategy is the vast number of identified interactions, which prevents an experimental or detailed investigation of each pair. In this paper, we overcome this difficulty by the recently proposed principal component analysis (PCA)-based unsupervised feature extraction (FE), which reduces the number of identified miRNA–mRNA interactions that properly discriminate between patients and healthy controls without losing biological feasibility. The approach is applied to six cancers: hepatocellular carcinoma, non-small cell lung cancer, esophageal squamous cell carcinoma, prostate cancer, colorectal/colon cancer and breast cancer. In PCA-based unsupervised FE, the significance does not depend on the number of samples (as in the standard case) but on the number of features, which approximates the number of miRNAs/mRNAs. To our knowledge, we have newly identified miRNA–mRNA interactions in multiple cancers based on a single common (universal) criterion. Moreover, the number of identified interactions was sufficiently small to be sequentially curated by literature searches.