TINAGL1 and B3GALNT1 are potential therapy target genes to suppress metastasis in non-small cell lung cancer

Tubulointerstitial nephritis antigen-like 1 promotes the progression of liver fibrosis after HCV eradication with direct-acting antivirals

Although therapies based on direct-acting antivirals (DAAs) effectively eradicate hepatitis C virus (HCV) in patients, there is still a high risk of liver fibrosis even after a sustained virological response. Therefore, it is of great clinical importance to understand the mechanism of potential factors that promote liver fibrosis after virological cure by treatment with DAAs. Here, we found that tubulointerstitial nephritis antigen-like 1 (TINAGL1) is significantly increased in HCV-infected hepatocytes and in the liver of patients with liver fibrosis, and that higher TINAGL1 expression persists in HCV-eradicated hepatocytes after treatment with DAAs. Overexpression of TINAGL1 in the liver triggers and exacerbates liver fibrosis, and xenotransplantation of HCV-eradicated Huh7.5 cells leads to a higher risk of hepatocellular carcinoma. Conversely, knockdown of TINAGL1 expression prevents and attenuates the progression of liver fibrosis in mice. TINAGL1 binds and stabilizes platelet-derived growth factor-BB (PDGF-BB) in hepatocytes, leading to an increase in intracellular and extracellular PDGF-BB, which sensitizes the PDGF-BB/PDGFRβ pathway to activate hepatic stellate cells. This study highlights that TINAGL1 is a new factor contributing to liver fibrosis after injury, including but not limited to HCV infection, even after virological cure by DAAs, and emphasizes the therapeutic potential of TINAGL1 as an innovative target for the treatment of liver fibrosis.

Implications of ZNF334 gene in lymph node metastasis of lung SCC: potential bypassing of cellular senescence

BACKGROUND

The primary goal of this work is to identify biomarkers associated with lung squamous cell carcinoma and assess their potential for early detection of lymph node metastasis.

METHODS

This study investigated gene expression in lymph node metastasis of lung squamous cell carcinoma using data from the Cancer Genome Atlas and R software. Protein-protein interaction networks, hub genes, and enriched pathways were analyzed. ZNF334 and TINAGL1, two less explored genes, were further examined through in vitro, ex vivo, and in vivo experiments to validate the findings from bioinformatics analyses. The role of ZNF334 and TINAGL1 in senescence induction was assessed after H2O2 and UV induced senescence phenotype determined using β-galactosidase activity and cell cycle status assay.

RESULTS

We identified a total of 611 up- and 339 down-regulated lung squamous cell carcinoma lymph node metastasis-associated genes (FDR < 0.05). Pathway enrichment analysis highlighted the central respiratory pathway within mitochondria for the subnet genes and the nuclear DNA-directed RNA polymerases for the hub genes. Significantly down regulation of ZNF334 gene was associated with malignancy lymph node progression and senescence induction has significantly altered ZNF334 expression (with consistency in bioinformatics, in vitro, ex vivo, and in vivo results). Deregulation of TINAGL1 expression with inconsistency in bioinformatics, in vitro (different types of lung squamous cancer cell lines), ex vivo, and in vivo results, was also associated with malignancy lymph node progression and altered in senescence phenotype.

CONCLUSIONS

ZNF334 is a highly generalizable gene to lymph node metastasis of lung squamous cell carcinoma and its expression alter certainly under senescence conditions.

Complexation of histone deacetylase inhibitor belinostat to Cu(II) prevents premature metabolic inactivation in vitro and demonstrates potent anti-cancer activity in vitro and ex vivo in colon cancer

PURPOSE

The histone deacetylase inhibitor (HDACi), belinostat, has had limited therapeutic impact in solid tumors, such as colon cancer, due to its poor metabolic stability. Here we evaluated a novel belinostat prodrug, copper-bis-belinostat (Cubisbel), in vitro and ex vivo, designed to overcome the pharmacokinetic challenges of belinostat.

METHODS

The in vitro metabolism of each HDACi was evaluated in human liver microsomes (HLMs) using mass spectrometry. Next, the effect of belinostat and Cubisbel on cell growth, HDAC activity, apoptosis and cell cycle was assessed in three colon cancer cell lines. Gene expression alterations induced by both HDACis were determined using RNA-Seq, followed by in silico analysis to identify master regulators (MRs) of differentially expressed genes (DEGs). The effect of both HDACis on the viability of colon cancer patient-derived tumor organoids (PDTOs) was also examined.

RESULTS

Belinostat and Cubisbel significantly reduced colon cancer cell growth mediated through HDAC inhibition and apoptosis induction. Interestingly, the in vitro half-life of Cubisbel was significantly longer than belinostat. Belinostat and its Cu derivative commonly dysregulated numerous signalling and metabolic pathways while genes downregulated by Cubisbel were potentially controlled by VEGFA, ERBB2 and DUSP2 MRs. Treatment of colon cancer PDTOs with the HDACis resulted in a significant reduction in cell viability and downregulation of stem cell and proliferation markers.

CONCLUSIONS

Complexation of belinostat to Cu(II) does not alter the HDAC activity of belinostat, but instead significantly enhances its metabolic stability in vitro and targets anti-cancer pathways by perturbing key MRs in colon cancer. Complexation of HDACis to a metal ion might improve the efficacy of clinically used HDACis in patients with colon cancer.

Tubulointerstitial nephritis antigen-like 1 from cancer-associated fibroblasts contribute to the progression of diffuse-type gastric cancers through the interaction with integrin β1

BACKGROUND

Tumor cells of diffuse-type gastric cancer (DGC) are discohesive and infiltrate into the stroma as single cells or small subgroups, so the stroma significantly impacts DGC progression. Cancer-associated fibroblasts (CAFs) are major components of the tumor stroma. Here, we identified CAF-specific secreted molecules and investigated the mechanism underlying CAF-induced DGC progression.

METHODS

We conducted transcriptome analysis for paired normal fibroblast (NF)-CAF isolated from DGC patient tissues and proteomics for conditioned media (CM) of fibroblasts. The effects of fibroblasts on cancer cells were examined by transwell migration and soft agar assays, western blotting, and in vivo. We confirmed the effect of blocking tubulointerstitial nephritis antigen-like 1 (TINAGL1) in CAFs using siRNA or shRNA. We evaluated the expression of TINAGL1 protein in frozen tissues of DGC and paired normal stomach and mRNA in formalin-fixed, paraffin-embedded (FFPE) tissue using RNA in-situ hybridization (RNA-ISH).

RESULTS

CAFs more highly expressed TINAGL1 than NFs. The co-culture of CAFs increased migration and tumorigenesis of DGC. Moreover, CAFs enhanced the phosphorylation of focal adhesion kinase (FAK) and mesenchymal marker expression in DGC cells. In an animal study, DGC tumors co-injected with CAFs showed aggressive phenotypes, including lymph node metastasis. However, increased phosphorylation of FAK and migration were reduced by blocking TINAGL1 in CAFs. In the tissues of DGC patients, TINAGL1 was higher in cancer than paired normal tissues and detected with collagen type I alpha 1 chain (COL1A1) in the same spot. Furthermore, high TINAGL1 expression was significantly correlated with poor prognosis in several public databases and our patient cohort diagnosed with DGC.

CONCLUSIONS

These results indicate that TINAGL1 secreted by CAFs induces phosphorylation of FAK in DGC cells and promotes tumor progression. Thus, targeting TINAGL1 in CAFs can be a novel therapeutic strategy for DGC.

A four-gene-based methylation signature associated with lymph node metastasis predicts overall survival in lung squamous cell carcinoma

We aimed to identify prognostic methylation genes associated with lymph node metastasis (LNM) in lung squamous cell carcinoma (LUSC). Bioinformatics methods were used to obtain optimal prognostic genes for risk model construction using data from the Cancer Genome Atlas database. ROC curves were adopted to predict the prognostic value of the risk model. Multivariate regression was carried out to identify independent prognostic factors and construct a prognostic nomogram. The differences in overall survival, gene mutation and pathways between high- and low-risk groups were analyzed. Finally, the expression and methylation level of the optimal prognostic genes among different LNM stages were analyzed. FGA, GPR39, RRAD and TINAGL1 were identified as the optimal prognostic genes and were applied to establish a prognostic risk model. Significant differences were found among the different LNM stages. The risk model could predict overall survival, showing a moderate performance with AUC of 0.64-0.68. The model possessed independent prognostic value, and could accurately predict 1-, 3- and 5-year survival. Patients with a high risk score showed poorer survival. Lower gene mutation frequencies and enrichment of leukocyte transendothelial migration and the VEGF signaling pathway in the high-risk group may lead to the poor prognosis. This study identified several specific methylation markers associated with LNM in LUSC and generated a prognostic model to predict overall survival for LUSC patients.

Functional analysis reveals that Tinagl1 is required for normal muscle development in mice through the activation of ERK signaling

Tinagl1 (tubulointerstitial nephritis antigen-like 1) is a matricellular protein involved in female infertility and breast cancer tumorigenesis. In this study, we analyzed the function of Tinagl1 in skeletal muscle using knockout mice and cell experiments. Although primary myoblasts isolated from Tinagl1-decifient (Tinagl1^-/-) mice differentiated into normal myotubes, and treatment with recombinant Tinagl1 did not affect the proliferation or differentiation of C2C12 myoblasts, Tinagl1^-/- mice exhibited reduced body mass and calf muscle weights compared to the control group (Tinagl1^flox/flox). Furthermore, Tinagl1^-/- mice showed myofibers with centrally located nuclei, which is a morphological marker of regenerating muscle or myopathy. In addition, the capillary density in the soleus muscle of Tinagl1^-/- mice showed a decreasing trend compared to that of the control group. Importantly, si-RNA-mediated knockdown of TINAGL1 resulted in reduced tube formation in human umbilical vein endothelial cells (HUVECs), whereas treatment with Tinagl1 promoted tube formation. Immunoblot analysis revealed that Tinagl1 activates ERK signaling in both HUVECs and C2C12 myoblasts and myotubes, which are involved in the regulation of myogenic differentiation, proliferation, metabolism, and angiogenesis. Our results demonstrate that Tinagl1 may be required for normal muscle and capillary development through the activation of ERK signaling.

Down-regulated TINAGL1 in fibroblasts impairs wound healing in diabetes

Matricellular proteins, a group of extracellular matrix (ECM) proteins, are key regulators of skin repair and their dysregulation impairs wound healing in diabetes. Tubulointerstitial nephritis antigen like 1 (TINAGL1) is a new member of matricellular protein family, and the understanding of its functional role is still relatively limited. In the current study, we detected the expression of TINAGL1 in diabetic skin wound tissues through RT-PCR, ELISA and Western blot analysis, investigated the contribution of TINAGL1 to wound healing through cutaneous administration of recombinant TINAGL1 protein, and characterized its regulation by hyperglycemia through RNA-seq and signal pathway inhibition assay. We showed that TINAGL1 expression has dynamic change and reaching a peak on day-9 after wound during the wound healing process in wild-type (WT) mice. Interestingly, decreased TINAGL1 expression is detected in skin tissues of diabetic patients and mice after wound. Then, we found that high glucose (HG), an important factor that impairs wound healing, reduces the expression of TINAGL1 in fibroblasts through JNK pathway. Notably, the histology analysis, Masson trichrome assay and IHC assay showed that exogenous TINAGL1 promotes wound healing in diabetic mice by accelerating the formation of granulation tissues. Our study provides evidence that TINAGL1 has an essential role in diabetic wound healing, and meanwhile, indicates that manipulation of TINAGL1 might be a possible therapeutic approach.

Lipid metabolism-related gene prognostic index (LMRGPI) reveals distinct prognosis and treatment patterns for patients with early-stage pulmonary adenocarcinoma

Background: Lipid metabolism plays a pivotal role in cancer progression and metastasis. This study aimed to investigate the prognostic value of lipid metabolism-related genes (LMRGs) in early-stage lung adenocarcinoma (LUAD) and develop a lipid metabolism-related gene prognostic index (LMRGPI) to predict their overall survival (OS) and treatment response. Methods: A total of 774 early-stage LUAD patients were identified from The Cancer Genome Atlas (TCGA, 403 patients) database and Gene Expression Omnibus (GEO, 371 patients) database. The non-negative Matrix Factorization (NMF) algorithm was used to identify different population subtypes based on LMRGs. The Least Absolute Shrinkage and Selection Operator (LASSO) and multivariate Cox regression analyses were used to develop the LMRGPI, with receiver operating characteristic (ROC) curves and concordance index being used to evaluate its performance. The characteristics of mutation landscape, enriched pathways, tumor microenvironment (TME), and treatment response between different LMRGPI groups were also investigated. Results: We identified two population subtypes based on LMRGs in the TCGA-LUAD cohort, with distinct prognosis, TME, and immune status being observed. LMRGPI was developed based on the expression levels of six LMRGs, including ANGPTL4, NPAS2, SLCO1B3, ACOXL, ALOX15, and B3GALNT1. Higher LMRGPI was correlated with poor OS both in TCGA and GSE68465 cohorts. Two nomograms were established to predict the survival probability of early-stage LUAD, with higher consistencies being observed between the predicted and actual OS. Higher LMRGPI was significantly correlated with more frequent TP53 mutation, higher tumor mutation burden (TMB), and up-regulation of CD274. Besides, patients with higher LMRGPI presented unremarkable responses for gefitinib, erlotinib, cisplatin, and vinorelbine, while they tend to have a favorable response for immune checkpoint inhibitors (ICIs). The opposite results were observed in the low-LMRGPI group. Conclusions: We comprehensively investigated the prognostic value of LMRGs in early-stage LUAD. Given its good prognostic ability, LMRGPI could serve as a promising biomarker to predict the OS and treatment response of these patients.

PCA-based unsupervised feature extraction for gene expression analysis of COVID-19 patients

Coronavirus disease 2019 (COVID-19) is raging worldwide. This potentially fatal infectious disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the complete mechanism of COVID-19 is not well understood. Therefore, we analyzed gene expression profiles of COVID-19 patients to identify disease-related genes through an innovative machine learning method that enables a data-driven strategy for gene selection from a data set with a small number of samples and many candidates. Principal-component-analysis-based unsupervised feature extraction (PCAUFE) was applied to the RNA expression profiles of 16 COVID-19 patients and 18 healthy control subjects. The results identified 123 genes as critical for COVID-19 progression from 60,683 candidate probes, including immune-related genes. The 123 genes were enriched in binding sites for transcription factors NFKB1 and RELA, which are involved in various biological phenomena such as immune response and cell survival: the primary mediator of canonical nuclear factor-kappa B (NF-κB) activity is the heterodimer RelA-p50. The genes were also enriched in histone modification H3K36me3, and they largely overlapped the target genes of NFKB1 and RELA. We found that the overlapping genes were downregulated in COVID-19 patients. These results suggest that canonical NF-κB activity was suppressed by H3K36me3 in COVID-19 patient blood.

Upregulation of Tubulointerstitial nephritis antigen like 1 promotes gastric cancer growth and metastasis by regulating multiple matrix metallopeptidase expression

BACKGROUND AND AIM

Tubulointerstitial nephritis antigen-like 1 (TINAGL1), as a novel matricellular protein, has been demonstrated to participate in cancer progression, whereas the potential function of TINAGL1 in gastric cancer (GC) remains unknown.

METHODS

The expression pattern of TINAGL1 in GC was examined by immunohistochemistry, ELISA, real-time polymerase chain reaction, and Western blot. Correlation between TINAGL1 and matrix metalloproteinases (MMPs) was analyzed by the GEPIA website and Kaplan-Meier plots database. The lentivirus-based TINAGL1 knockdown, CCK-8, and transwell assays were used to test the function of TINAGL1 in vitro. The role of TINAGL1 was confirmed by subcutaneous xenograft, abdominal dissemination, and lung metastasis model. Microarray experiments, ELISA, real-time polymerase chain reaction, and Western blot were used to identify molecular mechanism.

RESULTS

TINAGL1 was increased in GC tumor tissues and associated with poor patient survival. Moreover, TINAGL1 significantly promoted GC cell proliferation and migration in vitro as well as facilitated GC tumor growth and metastasis in vivo. TINAGL1 expression in GC cells was accompanied with increasing MMPs including MMP2, MMP9, MMP11, MMP14, and MMP16. GEPIA database revealed that these MMPs were correlated with TINAGL1 in GC tumors and that the most highly expressed MMP was MMP2. Mechanically, TINAGL1 regulated MMP2 through the JNK signaling pathway activation.

CONCLUSIONS

Our data highlight that TINAGL1 promotes GC growth and metastasis and regulates MMP2 expression, indicating that TINAGL1 may serve as a therapeutic target for GC.

Crosstalk between Epidermal Growth Factor Receptors (EGFR) and integrins in resistance to EGFR tyrosine kinase inhibitors (TKIs) in solid tumors

Cell adhesion to the extracellular matrix (ECM) is important in a variety of physiological and pathologic processes, including development, tumor invasion, and metastasis. Integrin-mediated attachment to ECM proteins has emerged to cue events primitively important for the transformed phenotype of human cancer cells. Cross-talk between integrins and growth factor receptors takes an increasingly prominent role in defining adhesion, motility, and cell growth. This functional interaction has expanded beyond to link integrins with resistance to Tyrosine kinase inhibitors (TKIs) of Epidermal Growth Factor Receptors (EGFRs). In this regard, integrin-mediated adhesion has two separate functions one as a clear collaborator with growth factor receptor signaling and the second as a basic mechanism contributing in Epithelial to Mesenchymal Transition (EMT) which affects response to chemotherapy. This review provides an overview of these mechanisms and describes treatment options for selectively targeting and disrupting integrin interaction to EGFR for cancer therapy.

An Unsupervised Strategy for Identifying Epithelial-Mesenchymal Transition State Metrics in Breast Cancer and Melanoma

Digital cytometry aims to identify different cell types in the tumor microenvironment, with the current focus on immune cells. Yet, identifying how changes in tumor cell phenotype, such as the epithelial-mesenchymal transition, influence the immune contexture is emerging as an important question. To extend digital cytometry, we developed an unsupervised feature extraction and selection strategy to capture functional plasticity tailored to breast cancer and melanoma separately. Specifically, principal component analysis coupled with resampling helped develop gene expression-based state metrics that characterize differentiation within an epithelial to mesenchymal-like state space and independently correlate with metastatic potential. First developed using cell lines, the orthogonal state metrics were refined to exclude the contributions of normal fibroblasts and provide tissue-level state estimates using bulk tissue RNA-seq measures. The resulting metrics for differentiation state aim to inform a more holistic view of how the malignant cell phenotype influences the immune contexture within the tumor microenvironment.

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Unsupervised strategy to generate epithelial and mesenchymal state metrics

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Refined metrics for use with bulk RNA-seq data by removing normal fibroblasts genes

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Validated state predictions against independent measures of metastatic potential

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Breast cancer and melanoma share more common genes in de-differentiated metrics

Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis

Although single-cell RNA sequencing (scRNA-seq) technology is newly invented and a promising one, but because of lack of enough information that labels individual cells, it is hard to interpret the obtained gene expression of each cell. Because of insufficient information available, unsupervised clustering, for example, t-distributed stochastic neighbor embedding and uniform manifold approximation and projection, is usually employed to obtain low-dimensional embedding that can help to understand cell–cell relationship. One possible drawback of this strategy is that the outcome is highly dependent upon genes selected for the usage of clustering. In order to fulfill this requirement, there are many methods that performed unsupervised gene selection. In this study, a tensor decomposition (TD)-based unsupervised feature extraction (FE) was applied to the integration of two scRNA-seq expression profiles that measure human and mouse midbrain development. TD-based unsupervised FE could select not only coincident genes between human and mouse but also biologically reliable genes. Coincidence between two species as well as biological reliability of selected genes is increased compared with that using principal component analysis (PCA)-based FE applied to the same data set in the previous study. Since PCA-based unsupervised FE outperformed the other three popular unsupervised gene selection methods, highly variable genes, bimodal genes, and dpFeature, TD-based unsupervised FE can do so as well. In addition to this, 10 transcription factors (TFs) that might regulate selected genes and might contribute to midbrain development were identified. These 10 TFs, BHLHE40, EGR1, GABPA, IRF3, PPARG, REST, RFX5, STAT3, TCF7L2, and ZBTB33, were previously reported to be related to brain functions and diseases. TD-based unsupervised FE is a promising method to integrate two scRNA-seq profiles effectively.

Drug candidate identification based on gene expression of treated cells using tensor decomposition-based unsupervised feature extraction for large-scale data

Background

Although in silico drug discovery is necessary for drug development, two major strategies, a structure-based and ligand-based approach, have not been completely successful. Currently, the third approach, inference of drug candidates from gene expression profiles obtained from the cells treated with the compounds under study requires the use of a training dataset. Here, the purpose was to develop a new approach that does not require any pre-existing knowledge about the drug–protein interactions, but these interactions can be inferred by means of an integrated approach using gene expression profiles obtained from the cells treated with the analysed compounds and the existing data describing gene–gene interactions.

Results

In the present study, using tensor decomposition-based unsupervised feature extraction, which represents an extension of the recently proposed principal-component analysis-based feature extraction, gene sets and compounds with a significant dose-dependent activity were screened without any training datasets. Next, after these results were combined with the data showing perturbations in single-gene expression profiles, genes targeted by the analysed compounds were inferred. The set of target genes thus identified was shown to significantly overlap with known target genes of the compounds under study.

Conclusions

The method is specifically designed for large-scale datasets (including hundreds of treatments with compounds), not for conventional small-scale datasets. The obtained results indicate that two compounds that have not been extensively studied, WZ-3105 and CGP-60474, represent promising drug candidates targeting multiple cancers, including melanoma, adenocarcinoma, liver carcinoma, and breast, colon, and prostate cancers, which were analysed in this in silico study.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2395-8) contains supplementary material, which is available to authorized users.

TINAGL1 promotes hepatocellular carcinogenesis through the activation of TGF-β signaling-medicated VEGF expression

Background and purpose

Tubulointerstitial nephritis antigen-like 1 (TINAGL1) is an extracellular matrix protein that plays an important role in cell adhesion and therefore modulates cell proliferation, migration, and differentiation. In addition, it is frequently upregulated in highly metastatic tumors. The aim of our study was to determine the role of TINAGL1 in the progression and metastasis of hepatocellular carcinoma (HCC).

Materials and methods

TINAGL1 mRNA levels were analyzed in HCC and adjacent non-tumorous samples by reverse transcription polymerase chain reaction (RT-PCR). Human HCC cell lines were transfected with lentiviral plasmids expressing either si-TINAGL1 or TINAGL1 and subjected to CCK-8, colony forming, transwell migration, Annexin V/propidium iodide, and 5-ethynyl-2′-deoxyuridine uptake assays. Suitably transfected HCC cells were injected into athymic nude mice to establish xenograft tumors that were imaged and measured on a weekly basis. Mediators of the TGF-β signaling pathway were analyzed by Western blot.

Results

TINAGL1 was upregulated in human HCC tissues and associated with poor prognosis. TINAGL1 knockdown suppressed HCC cell growth, proliferation, and migration and induced apoptosis in HCC cells, whereas TINAGL1 overexpression had opposite effects. In addition, inhibition of TINAGL1 retarded xenograft tumor growth in a nude mouse model. Mechanistically, TINAGL1 activated the TGF-β signaling pathway and increased VEGF secretion.

Conclusion

TINAGL1 promotes hepatocellular carcinogenesis and metastasis via the TGF-β/Smad3/VEGF axis and is a potential new biomarker of HCC.

Activation of PI3K/AKT is involved in TINAG-mediated promotion of proliferation, invasion and migration of hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) is a highly aggressive malignancy that has a poor prognosis. Through the literatures, TINAG significantly participated in the processes of the renal-associated diseases, but there were no studies about the roles of TINAG in the HCC development. Hence, we attempted to use the HCC samples collected by ourselves to reveal the clinical significance and prognostic impact of TINAG in HCC.

METHODS

We first measured the expression level of TINAG in HCC on the basis of TCGA database. Then, real time quantitative reverse transcription PCR (RT-qPCR) was used to examine the expression level of TINAG in 100 pairs of HCC tissues and corresponding adjacent non-tumor tissues, as well as HCC cell lines (HepG2, HB611, HHCC, and Hep3B). Moreover, Kaplan-Meier method and COX's proportional hazards model were utilized to perform the survival and prognosis analyses using the clinical data collected by ourselves. After knockdown of TINAG, the cell proliferation, invasion and migration capacities of HepG2 and Hep3B cells were evaluate by counting kit-8 (CCK-8) assay (24 h, 48 h, 72 h, and 96 h post-cultivation), clone formation experiment, would-healing, and invasion as well as migration assays. To further explore whether the dys-regulated TINAG expression regulates the HCC progression and prognosis, protein biomarkers of PI3K signaling pathway, including AKT, p-AKT, PI3K, p-PI3K, p70S6K, and p-p70S6K were measured based on western blotting analysis.

RESULTS

According to the data of TCGA database, clinical patients, and HCC cell lines, TINAG was highly expressed in HCC compared with normal. Relationship of TINAG expression level with the clinicopathological factors implicated that the high expression of TINAG was significantly associated with pathologic stage, pathologic-node, and pathologic-metastasis. Univariate as well as multivariate COX analysis indicated that TINAG expression and pathologic metastasis can serve as the independent prognostic factor for overall survival of HCC. After TINAG knockdown in HepG2 and Hep3B cells, cell proliferation rate, the colony numbers, and the invasive and migratory capacity were found to be suppressed. Remarkably, western blot results showed that reduction of TINAG remarkably decreased p-AKT, p-PI3K, and p-p70S6K expression level in HepG2 and Hep3B cells.

CONCLUSION

Collectively, our results underscore the significance of TINAG in HCC progression and prognosis, and TINAG might be a novel candidate oncogene in HCC. These results propose that targeting TINAG might offer future clinical utility in HCC.

Detecting Rare Mutations with Heterogeneous Effects Using a Family-Based Genetic Random Field Method

The genetic etiology of many complex diseases is highly heterogeneous. A complex disease can be caused by multiple mutations within the same gene or mutations in multiple genes at various genomic loci. Although these disease-susceptibility mutations can be collectively common in the population, they are often individually rare or even private to certain families. Family-based studies are powerful for detecting rare variants enriched in families, which is an important feature for sequencing studies due to the heterogeneous nature of rare variants. In addition, family designs can provide robust protection against population stratification. Nevertheless, statistical methods for analyzing family-based sequencing data are underdeveloped, especially those accounting for heterogeneous etiology of complex diseases. In this article, we introduce a random field framework for detecting gene-phenotype associations in family-based sequencing studies, referred to as family-based genetic random field (FGRF). Similar to existing family-based association tests, FGRF could utilize within-family and between-family information separately or jointly to test an association. We demonstrate that FGRF has comparable statistical power with existing methods when there is no genetic heterogeneity, but can improve statistical power when there is genetic heterogeneity across families. The proposed method also shares the same advantages with the conventional family-based association tests (e.g., being robust to population stratification). Finally, we applied the proposed method to a sequencing data from the Minnesota Twin Family Study, and revealed several genes, including SAMD14, potentially associated with alcohol dependence.

Tensor Decomposition-Based Unsupervised Feature Extraction Can Identify the Universal Nature of Sequence-Nonspecific Off-Target Regulation of mRNA Mediated by MicroRNA Transfection

MicroRNA (miRNA) transfection is known to degrade target mRNAs and to decrease mRNA expression. In contrast to the notion that most of the gene expression alterations caused by miRNA transfection involve downregulation, they often involve both up- and downregulation; this phenomenon is thought to be, at least partially, mediated by sequence-nonspecific off-target effects. In this study, I used tensor decomposition-based unsupervised feature extraction to identify genes whose expression is likely to be altered by miRNA transfection. These gene sets turned out to largely overlap with one another regardless of the type of miRNA or cell lines used in the experiments. These gene sets also overlap with the gene set associated with altered expression induced by a Dicer knockout. This result suggests that the off-target effect is at least as important as the canonical function of miRNAs that suppress translation. The off-target effect is also suggested to consist of competition for the protein machinery between transfected miRNAs and miRNAs in the cell. Because the identified genes are enriched in various biological terms, these genes are likely to play critical roles in diverse biological processes.

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.

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.

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.

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.

Molecular and cellular events during blastocyst implantation in the receptive uterus: clues from mouse models

The success of implantation is an interactive process between the blastocyst and the uterus. Synchronized development of embryos with uterine differentiation to a receptive state is necessary to complete pregnancy. The period of uterine receptivity for implantation is limited and referred to as the “implantation window”, which is regulated by ovarian steroid hormones. Implantation process is complicated due to the many signaling molecules in the hierarchical mechanisms with the embryo-uterine dialogue. The mouse is widely used in animal research, and is uniquely suited for reproductive studies, i.e., having a large litter size and brief estrous cycles. This review first describes why the mouse is the preferred model for implantation studies, focusing on uterine morphology and physiological traits, and then highlights the knowledge on uterine receptivity and the hormonal regulation of blastocyst implantation in mice. Our recent study revealed that selective proteolysis in the activated blastocyst is associated with the completion of blastocyst implantation after embryo transfer. Furthermore, in the context of blastocyst implantation in the mouse, this review discusses the window of uterine receptivity, hormonal regulation, uterine vascular permeability and angiogenesis, the delayed-implantation mouse model, morphogens, adhesion molecules, crosslinker proteins, extracellular matrix, and matricellular proteins. A better understanding of uterine and blastocyst biology during the peri-implantation period should facilitate further development of reproductive technology.

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.

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.

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.

Identification of aberrant gene expression associated with aberrant promoter methylation in primordial germ cells between E13 and E16 rat F3 generation vinclozolin lineage

Background

Transgenerational epigenetics (TGE) are currently considered important in disease, but the mechanisms involved are not yet fully understood. TGE abnormalities expected to cause disease are likely to be initiated during development and to be mediated by aberrant gene expression associated with aberrant promoter methylation that is heritable between generations. However, because methylation is removed and then re-established during development, it is not easy to identify promoter methylation abnormalities by comparing normal lineages with those expected to exhibit TGE abnormalities.

Methods

This study applied the recently proposed principal component analysis (PCA)-based unsupervised feature extraction to previously reported and publically available gene expression/promoter methylation profiles of rat primordial germ cells, between E13 and E16 of the F3 generation vinclozolin lineage that are expected to exhibit TGE abnormalities, to identify multiple genes that exhibited aberrant gene expression/promoter methylation during development.

Results

The biological feasibility of the identified genes were tested via enrichment analyses of various biological concepts including pathway analysis, gene ontology terms and protein-protein interactions. All validations suggested superiority of the proposed method over three conventional and popular supervised methods that employed t test, limma and significance analysis of microarrays, respectively. The identified genes were globally related to tumors, the prostate, kidney, testis and the immune system and were previously reported to be related to various diseases caused by TGE.

Conclusions

Among the genes reported by PCA-based unsupervised feature extraction, we propose that chemokine signaling pathways and leucine rich repeat proteins are key factors that initiate transgenerational epigenetic-mediated diseases, because multiple genes included in these two categories were identified in this study.

Impaired female fertility in tubulointerstitial antigen-like 1-deficient mice

Tubulointerstitial nephritis antigen-like 1 (Tinagl1, also known as adrenocortical zonation factor 1 [AZ-1] or lipocalin 7) is a matricellular protein. Previously, we demonstrated that Tinagl1 expression was restricted to extraembryonic regions during the postimplantation period and detected marked expression in mouse Reichert's membranes. In uteri, Tinagl1 is markedly expressed in the decidual endometrium during the postimplantation period, suggesting that it plays a physical and physiological role in embryo development and/or decidualization of the uterine endometrium during pregnancy. In the present study, in order to determine the role of Tinagl1 during embryonic development and pregnancy, we generated Tinagl1-deficient mice. Although Tinagl1(-/-) embryos were not lethal during development to term, homologous matings of Tinagl1(-/-) females and Tinagl1(-/-) males showed impaired fertility during pregnancy, including failure to carry pregnancy to term and perinatal lethality. To examine ovarian function, ovulation was induced with equine chorionic gonadotropin (eCG) and human chorionic gonadotropin (hCG); the number of ovulated oocytes did not differ between Tinagl1(-/-) and Tinagl1(flox/flox). In vitro fertilization followed by embryo culture also demonstrated the normal developmental potential of Tinagl1-null embryos during the preimplantation period. Our results demonstrate that Tinagl1 deficiency affects female mice and results in subfertility phenotypes, and they suggest that although the potential of Tinagl1(-/-) oocytes is normal, Tinagl1 is related to fertility in adult females but is not essential for either fertilization or preimplantation development in vitro.

Principal component analysis-based unsupervised feature extraction applied to in silico drug discovery for posttraumatic stress disorder-mediated heart disease

Background

Feature extraction (FE) is difficult, particularly if there are more features than samples, as small sample numbers often result in biased outcomes or overfitting. Furthermore, multiple sample classes often complicate FE because evaluating performance, which is usual in supervised FE, is generally harder than the two-class problem. Developing sample classification independent unsupervised methods would solve many of these problems.

Results

Two principal component analysis (PCA)-based FE, specifically, variational Bayes PCA (VBPCA) was extended to perform unsupervised FE, and together with conventional PCA (CPCA)-based unsupervised FE, were tested as sample classification independent unsupervised FE methods. VBPCA- and CPCA-based unsupervised FE both performed well when applied to simulated data, and a posttraumatic stress disorder (PTSD)-mediated heart disease data set that had multiple categorical class observations in mRNA/microRNA expression of stressed mouse heart. A critical set of PTSD miRNAs/mRNAs were identified that show aberrant expression between treatment and control samples, and significant, negative correlation with one another. Moreover, greater stability and biological feasibility than conventional supervised FE was also demonstrated. Based on the results obtained, in silico drug discovery was performed as translational validation of the methods.

Conclusions

Our two proposed unsupervised FE methods (CPCA- and VBPCA-based) worked well on simulated data, and outperformed two conventional supervised FE methods on a real data set. Thus, these two methods have suggested equivalence for FE on categorical multiclass data sets, with potential translational utility for in silico drug discovery.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0574-4) contains supplementary material, which is available to authorized users.

InCoB2014: mining biological data from genomics for transforming industry and health

The 13th International Conference on Bioinformatics (InCoB2014) was held for the first time in Australia, at Sydney, July 31-2 August, 2014. InCoB is the annual scientific gathering of the Asia-Pacific Bioinformatics Network (APBioNet), hosted since 2002 in the Asia-Pacific region. Of 106 full papers submitted to the BMC track of InCoB2014, 50 (47.2%) were accepted in BMC Bioinformatics, BMC Genomics and BMC Systems Biology supplements, with three papers in a new BMC Medical Genomics supplement. While the majority of presenters and authors were from Asia and Australia, the increasing number of US and European conference attendees augurs well for the international flavour of InCoB. Next year's InCoB will be held jointly with the Genome Informatics Workshop (GIW), September 9-11, 2015 in Tokyo, Japan, with a view to integrate bioinformatics communities in the region.