Microarray Meta-Analysis and Cross-Platform Normalization: Integrative Genomics for Robust Biomarker Discovery

BMC3PM: bioinformatics multidrug combination protocol for personalized precision medicine and its application in cancer treatment

BACKGROUND

In recent years, drug screening has been one of the most significant challenges in the field of personalized medicine, particularly in cancer treatment. However, several new platforms have been introduced to address this issue, providing reliable solutions for personalized drug validation and safety testing. In this study, we developed a personalized drug combination protocol as the primary input to such platforms.

METHODS

To achieve this, we utilized data from whole-genome expression profiles of 6173 breast cancer patients, 312 healthy individuals, and 691 drugs. Our approach involved developing an individual pattern of perturbed gene expression (IPPGE) for each patient, which was used as the basis for drug selection. An algorithm was designed to extract personalized drug combinations by comparing the IPPGE and drug signatures. Additionally, we employed the concept of drug repurposing, searching for new benefits of existing drugs that may regulate the desired genes.

RESULTS

Our study revealed that drug combinations obtained from both specialized and non-specialized cancer medicines were more effective than those extracted from only specialized medicines. Furthermore, we observed that the individual pattern of perturbed gene expression (IPPGE) was unique to each patient, akin to a fingerprint.

CONCLUSIONS

The personalized drug combination protocol developed in this study offers a methodological interface between drug repurposing and combination drug therapy in cancer treatment. This protocol enables personalized drug combinations to be extracted from hundreds of drugs and thousands of drug combinations, potentially offering more effective treatment options for cancer patients.

Silencing of genes by promoter hypermethylation shapes tumor microenvironment and resistance to immunotherapy in clear-cell renal cell carcinomas

The efficacy of immune checkpoint inhibitors varies in clear-cell renal cell carcinoma (ccRCC), with notable primary resistance among patients. Here, we integrate epigenetic (DNA methylation) and transcriptome data to identify a ccRCC subtype characterized by cancer-specific promoter hypermethylation and epigenetic silencing of Polycomb targets. We develop and validate an index of methylation-based epigenetic silencing (iMES) that predicts primary resistance to immune checkpoint inhibition (ICI) in the BIONIKK trial. High iMES is associated with VEGF pathway silencing, endothelial cell depletion, immune activation/suppression, EZH2 activation, BAP1/SETD2 deficiency, and resistance to ICI. Combination therapy with hypomethylating agents or tyrosine kinase inhibitors may benefit patients with high iMES. Intriguingly, tumors with low iMES exhibit increased endothelial cells and improved ICI response, suggesting the importance of angiogenesis in ICI treatment. We also develop a transcriptome-based analogous system for extended applicability of iMES. Our study underscores the interplay between epigenetic alterations and tumor microenvironment in determining immunotherapy response.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers

Background

The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear.

Methodology

In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response.

Results and discussion

The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers.

Conclusions

Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

Adjustment of p-value expression to ontology using machine learning for genetic prediction, prioritization, interaction, and its validation in glomerular disease

The results of gene expression analysis based on p-value can be extracted and sorted by their absolute statistical significance and then applied to multiple similarity scores of their gene ontology (GO) terms to promote the combination and adjustment of these scores as essential predictive tasks for understanding biological/clinical pathways. The latter allows the possibility to assess whether certain aspects of gene function may be associated with other varieties of genes, to evaluate regulation, and to link them into networks that prioritize candidate genes for classification by applying machine learning techniques. We then detect significant genetic interactions based on our algorithm to validate the results. Finally, based on specifically selected tissues according to their normalized gene expression and frequencies of occurrence from their different biological and clinical inputs, a reported classification of genes under the subject category has validated the abstract (glomerular diseases) as a case study.

Methodology to identify a gene expression signature by merging microarray datasets

A vast number of microarray datasets have been produced as a way to identify differentially expressed genes and gene expression signatures. A better understanding of these biological processes can help in the diagnosis and prognosis of diseases, as well as in the therapeutic response to drugs. However, most of the available datasets are composed of a reduced number of samples, leading to low statistical, predictive and generalization power. One way to overcome this problem is by merging several microarray datasets into a single dataset, which is typically a challenging task. Statistical methods or supervised machine learning algorithms are usually used to determine gene expression signatures. Nevertheless, statistical methods require an arbitrary threshold to be defined, and supervised machine learning methods can be ineffective when applied to high-dimensional datasets like microarrays. We propose a methodology to identify gene expression signatures by merging microarray datasets. This methodology uses statistical methods to obtain several sets of differentially expressed genes and uses supervised machine learning algorithms to select the gene expression signature. This methodology was validated using two distinct research applications: one using heart failure and the other using autism spectrum disorder microarray datasets. For the first, we obtained a gene expression signature composed of 117 genes, with a classification accuracy of approximately 98%. For the second use case, we obtained a gene expression signature composed of 79 genes, with a classification accuracy of approximately 82%. This methodology was implemented in R language and is available, under the MIT licence, at https://github.com/bioinformatics-ua/MicroGES.

Potential target identification for osteosarcoma treatment: Gene expression re-analysis and drug repurposing

Survival rate of osteosarcoma has remained plateaued for the past three decades. New treatment is needed to improve survival rate. Drug repurposing, a method to identify new indications of previous drugs, which saves time and cost compared to the de novo drug discovery. Data mining from gene expression profile was carried out and new potential targets were identified by using drug repurposing strategy. Selected data were newly categorized as pathophysiology and metastasis groups. Data were normalized and calculated the differential gene expression. Genes with log fold change ≥ 2 and adjusted p-value ≤ 0.05 were selected as primary candidate genes (PCGs). PCGs were further enriched to determine the secondary candidate genes (SCGs) by protein interaction analysis, upstream transcription factor and related-protein kinase identification. PCGs and SCGs were further matched with gene targeted of corresponding drugs from the Drug Repurposing Hub. A total of 778 targets were identified (360 from PCGs, and 418 from SCGs). This newly identified KLHL13 is a new candidate target based on its molecular function. KLHL13 was upregulated in clinical samples. We found 256 drugs from matching processes (50anti-cancerand206non-anticancerdrugs). Clinical trials of anti-cancer drugs from 5 targets (CDK4, BCL-2, JUN, SRC, PIK3CA) are being performed for osteosarcoma treatment. Niclosamide and synthetic PPARɣ ligands are candidates for repurposing due to the possibility based on their mechanism and pharmacology properties. Re-analysis of gene expression profile could identify new potential targets, confirm a current implication, and expand the chance of repurposing drugs for osteosarcoma treatment.

New Transcriptomic Biomarkers of 5-Fluorouracil Resistance

The overall response rate to fluoropyrimidine monotherapy in colorectal cancer (CRC) is limited. Transcriptomic datasets of CRC patients treated with 5-fluorouracil (5FU) could assist in the identification of clinically useful biomarkers. In this research, we aimed to analyze transcriptomic cohorts of 5FU-treated cell lines to uncover new predictive biomarker candidates and to validate the strongest hits in 5FU-treated human colorectal cancer samples with available clinical response data. We utilized an in vitro dataset of cancer cell lines treated with 5FU and used the reported area under the dose-response curve values to determine the therapeutic response to 5FU treatment. Mann-Whitney and ROC analyses were performed to identify significant genes. The strongest genes were combined into a single signature using a random forest classifier. The compound 5-fluorouracil was tested in 592 cell lines (294 nonresponders and 298 responders). The validation cohort consisted of 157 patient samples with 5FU monotherapy from three datasets. The three strongest associations with treatment outcome were observed in SHISA4 (AUC = 0.745, p-value = 5.5 × 10^-25), SLC38A6 (AUC = 0.725, p-value = 3.1 × 10^-21), and LAPTM4A (AUC = 0.723, p-value = 6.4 × 10^-21). A random forest model utilizing the top genes reached an AUC value of 0.74 for predicting therapeutic sensitivity. The model correctly identified 83% of the nonresponder and 73% of the responder patients. The cell line cohort is available and the entire human colorectal cohort have been added to the ROCPlot analysis platform. Here, by using in vitro and in vivo data, we present a framework enabling the ranking of future biomarker candidates of 5FU resistance. A future option is to conduct an independent validation of the established predictors of resistance.

Long non-coding RNAs as potential biomarkers or therapeutic targets in gastric cancer

Aim

This study aimed to find lncRNAs and mRNAs that were expressed differently by combining microarray datasets from different studies. This was done to find important target genes in gastric cancer for anti-cancer therapy.

Background

Gastric cancer (GC) is the fourth most frequent and second-most deadly malignancy worldwide. Thus, genetic diagnosis and treatment should focus on genetic and epigenetic variables. Based on several studies, disordered expression of non-coding RNAs (ncRNAs), such as lncRNAs, regulate gastric cancer invasion and metastasis. Besides, lncRNAs cooperatively regulate gene expression and GC progression.

Methods

We obtained differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) from three GC tissue microarray datasets by meta-analysis and screened genes using the "Limma" package. Then, using the RNAInter database, we allocated DEmRNAs to each DElncRNA. ClusterProfiler and GOplot programs were used to analyze function enrichment pathways and gene ontologies for final DEmRNAs.

Results

A total of 9 differentially expressed lncRNAs (DElncRNAs) (5 up-regulated and 4 down-regulated), and 856 DEmRNAs (451 up-regulated and 405 down-regulated) between tumor and adjacent normal samples were found. Finally, 117 differentially expressed mRNAs were predicted as interactors of six DElncRNAs (H19, WT1-AS, EMX2OS, HOTAIR, ZEB1-AS1, and LINC00261).

Conclusion

In order to promote cancer therapeutics and give knowledge on the process of carcinogenesis, our study projected a network of drug-gene interactions for discovered genes and presented relevant prospective biomarkers for the prognosis of patients with stomach cancer.

SIRPα maintains macrophage homeostasis by interacting with PTK2B kinase in Mycobacterium tuberculosis infection and through autophagy and necroptosis

Background

To determine whether SIRPα can be a diagnostic marker of pulmonary tuberculosis (PTB) and the molecular mechanism of SIRPα regulating macrophages to kill Mycobacterium tuberculosis (MTB).

Methods

Meta-analysis combined with subsequent qRT-PCR, western-blotting and flow cytometry assay were used to detect SIRPα expression in PTB patients. Cell-based assays were used to explore the regulation of macrophage function by SIRPα. SIRPα^−/- and wide type macrophages transplanted C57BL/6J mice were used to determine the function of SIRPα on MTB infection in vivo.

Findings

SIRPα levels are closely correlated with the treatment outcomes among PTB patients. Cell-based assay demonstrated that MTB significantly induces the expression of SIRPα on macrophages. SIRPα deficiency enhances the killing ability of macrophages against MTB through processes that involve enhanced autophagy and reduced necroptosis of macrophages. Mechanistically, SIRPα forms a direct interaction with PTK2B through its intracellular C-terminal domain, thus inhibiting PTK2B activation in macrophages. Necroptosis inhibition due to SIRPα deficiency requires PTK2B activity. The transfer of SIRPα-deficient bone marrow-derived macrophages (BMDMs) into wild type mice resulted in a drop of bacterial load in the lungs but an enhancement of inflammatory lung damage, and the combination of ulinastatin and SIRPα^−/−→WT treatment could decrease the inflammation and maintain the bactericidal capacity.

Interpretation

Our data define SIRPα a novel biomarker for tuberculosis infection and underlying mechanisms for maintaining macrophage homeostasis.

Funding

This work was financially supported by the Chinese National Natural Science Foundation project (No.81401635). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

AIM2 as a putative target in acute kidney graft rejection

Acute rejection (AR) is a process triggered via the recognition of grafted organ-derived antigens by the immune system, which could present as a life-threatening condition. In the context of a kidney transplant, despite improvement with immunosuppressive therapies, AR maintains a significant incidence of 10%, and currently available drugs generally act in similar and canonical pathways of lymphocyte activation. This prompted the research for different approaches to identify potential novel targets that could improve therapeutic interventions. Here, we conducted a transcriptome analysis comparing groups of acute rejection (including T cell-mediated rejection and antibody-mediated rejection) to stable grafts that included differentially expressed genes, transcription factor and kinase enrichment, and Gene Set Enrichment Analysis. These analyses revealed inflammasome enhancement in rejected grafts and AIM2 as a potential component linked to acute rejection, presenting a positive correlation to T-cell activation and a negative correlation to oxidative phosphorylation metabolism. Also, the AIM2 expression showed a global accuracy in discerning acute rejection grafts (area under the curve (AUC) = 0.755 and 0.894, p < 0.0001), and meta-analysis comprising different studies indicated a considerable enhancement of AIM2 in rejection (standardized mean difference (SMD) = 1.45, [CI 95%, 1.18 to 1.71]), especially for T cell-mediated rejection (TCMR) (SMD = 2.01, [CI 95%, 1.58 to 2.45]). These findings could guide future studies of AIM2 as either an adjuvant target for immunosuppression or a potential biomarker for acute rejection and graft survival.

Assessing equivalent and inverse change in genes between diverse experiments

Background: It is important to identify when two exposures impact a molecular marker (e.g., a gene's expression) in similar ways, for example, to learn that a new drug has a similar effect to an existing drug. Currently, statistically robust approaches for making comparisons of equivalence of effect sizes obtained from two independently run treatment vs. control comparisons have not been developed. Results: Here, we propose two approaches for evaluating the question of equivalence between effect sizes of two independent studies: a bootstrap test of the Equivalent Change Index (ECI), which we previously developed, and performing Two One-Sided t-Tests (TOST) on the difference in log-fold changes directly. The ECI of a gene is computed by taking the ratio of the effect size estimates obtained from the two different studies, weighted by the maximum of the two p-values and giving it a sign indicating if the effects are in the same or opposite directions, whereas TOST is a test of whether the difference in log-fold changes lies outside a region of equivalence. We used a series of simulation studies to compare the two tests on the basis of sensitivity, specificity, balanced accuracy, and F1-score. We found that TOST is not efficient for identifying equivalently changed gene expression values (F1-score = 0) because it is too conservative, while the ECI bootstrap test shows good performance (F1-score = 0.95). Furthermore, applying the ECI bootstrap test and TOST to publicly available microarray expression data from pancreatic cancer showed that, while TOST was not able to identify any equivalently or inversely changed genes, the ECI bootstrap test identified genes associated with pancreatic cancer. Additionally, when investigating publicly available RNAseq data of smoking vs. vaping, no equivalently changed genes were identified by TOST, but ECI bootstrap test identified genes associated with smoking. Conclusion: A bootstrap test of the ECI is a promising new statistical approach for determining if two diverse studies show similarity in the differential expression of genes and can help to identify genes which are similarly influenced by a specific treatment or exposure. The R package for the ECI bootstrap test is available at https://github.com/Hecate08/ECIbootstrap.

Identifying novel host-based diagnostic biomarker panels for COVID-19: a whole-blood/nasopharyngeal transcriptome meta-analysis

BACKGROUND

Regardless of improvements in controlling the COVID-19 pandemic, the lack of comprehensive insight into SARS-COV-2 pathogenesis is still a sophisticated challenge. In order to deal with this challenge, we utilized advanced bioinformatics and machine learning algorithms to reveal more characteristics of SARS-COV-2 pathogenesis and introduce novel host response-based diagnostic biomarker panels.

METHODS

In the present study, eight published RNA-Seq datasets related to whole-blood (WB) and nasopharyngeal (NP) swab samples of patients with COVID-19, other viral and non-viral acute respiratory illnesses (ARIs), and healthy controls (HCs) were integrated. To define COVID-19 meta-signatures, Gene Ontology and pathway enrichment analyses were applied to compare COVID-19 with other similar diseases. Additionally, CIBERSORTx was executed in WB samples to detect the immune cell landscape. Furthermore, the optimum WB- and NP-based diagnostic biomarkers were identified via all the combinations of 3 to 9 selected features and the 2-phases machine learning (ML) method which implemented k-fold cross validation and independent test set validation.

RESULTS

The host gene meta-signatures obtained for SARS-COV-2 infection were different in the WB and NP samples. The gene ontology and enrichment results of the WB dataset represented the enhancement in inflammatory host response, cell cycle, and interferon signature in COVID-19 patients. Furthermore, NP samples of COVID-19 in comparison with HC and non-viral ARIs showed the significant upregulation of genes associated with cytokine production and defense response to the virus. In contrast, these pathways in COVID-19 compared to other viral ARIs were strikingly attenuated. Notably, immune cell proportions of WB samples altered in COVID-19 versus HC. Moreover, the optimum WB- and NP-based diagnostic panels after two phases of ML-based validation included 6 and 8 markers with an accuracy of 97% and 88%, respectively.

CONCLUSIONS

Based on the distinct gene expression profiles of WB and NP, our results indicated that SARS-COV-2 function is body-site-specific, although according to the common signature in WB and NP COVID-19 samples versus controls, this virus also induces a global and systematic host response to some extent. We also introduced and validated WB- and NP-based diagnostic biomarkers using ML methods which can be applied as a complementary tool to diagnose the COVID-19 infection from non-COVID cases.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Comprehensive multi-cohort transcriptional meta-analysis of muscle diseases identifies a signature of disease severity

Muscle diseases share common pathological features suggesting common underlying mechanisms. We hypothesized there is a common set of genes dysregulated across muscle diseases compared to healthy muscle and that these genes correlate with severity of muscle disease. We performed meta-analysis of transcriptional profiles of muscle biopsies from human muscle diseases and healthy controls. Studies obtained from public microarray repositories fulfilling quality criteria were divided into six categories: (i) immobility, (ii) inflammatory myopathies, (iii) intensive care unit (ICU) acquired weakness (ICUAW), (iv) congenital muscle diseases, (v) chronic systemic diseases, (vi) motor neuron disease. Patient cohorts were separated in discovery and validation cohorts retaining roughly equal proportions of samples for the disease categories. To remove bias towards a specific muscle disease category we repeated the meta-analysis five times by removing data sets corresponding to one muscle disease class at a time in a "leave-one-disease-out" analysis. We used 636 muscle tissue samples from 30 independent cohorts to identify a 52 gene signature (36 up-regulated and 16 down-regulated genes). We validated the discriminatory power of this signature in 657 muscle biopsies from 12 additional patient cohorts encompassing five categories of muscle diseases with an area under the receiver operating characteristic curve of 0.91, 83% sensitivity, and 85.3% specificity. The expression score of the gene signature inversely correlated with quadriceps muscle mass (r = -0.50, p-value = 0.011) in ICUAW and shoulder abduction strength (r = -0.77, p-value = 0.014) in amyotrophic lateral sclerosis (ALS). The signature also positively correlated with histologic assessment of muscle atrophy in ALS (r = 0.88, p-value = 1.62 × 10^-3) and fibrosis in muscular dystrophy (Jonckheere trend test p-value = 4.45 × 10^-9). Our results identify a conserved transcriptional signature associated with clinical and histologic muscle disease severity. Several genes in this conserved signature have not been previously associated with muscle disease severity.

MAGE: An Open-Source Tool for Meta-Analysis of Gene Expression Studies

MAGE (Meta-Analysis of Gene Expression) is a Python open-source software package designed to perform meta-analysis and functional enrichment analysis of gene expression data. We incorporate standard methods for the meta-analysis of gene expression studies, bootstrap standard errors, corrections for multiple testing, and meta-analysis of multiple outcomes. Importantly, the MAGE toolkit includes additional features for the conversion of probes to gene identifiers, and for conducting functional enrichment analysis, with annotated results, of statistically significant enriched terms in several formats. Along with the tool itself, a web-based infrastructure was also developed to support the features of this package.

Blood-based gene-expression biomarkers identification for the non-invasive diagnosis of Parkinson's disease using two-layer hybrid feature selection

Parkinson's disease (PD) is one of the most prevalent neurodegenerative diseases. Understanding the molecular mechanism and identifying potential biomarkers of PD promote effective treatments to the patients. Due to less invasiveness and easy accessibility, biomarkers from blood support early detection and diagnosis of PD. This study combined three independent PD microarray gene expression data from blood samples applying the early integration approach. Moderated t-statistics was employed to identify differentially expressed genes (DEGs). Relevant genes were selected using a two-layer embedded wrapper feature selection method with gradient boosting machine (GBM) in the first layer followed by an ensemble of wrappers including Recursive Feature Elimination (RFE), Genetic algorithm (GA) and Bi-directional elimination (Stepwise). All three wrappers were based on logistic regression classifier (LR). The PD-predictability of the generated signature was tested using nine supervised classification models, including eight shallow machine learning and one deep learning. On an independent dataset, GSE72267, Support Vector Machine-Radial (SVMR), and Deep Neural Network (DNN) showed the best performance with AUC 0.821 and 0.82, respectively. Comparison with existing blood-based PD signatures and the biological analysis verified the reliability of the proposed signature.

A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction

The clinical utility of blood transcriptomic biosignatures for the treatment monitoring and outcome prediction of tuberculosis (TB) remains limited. In this study, we aimed to discover and validate biomarkers for pulmonary TB treatment monitoring and outcome prediction based on kinetic responses of gene expression during treatment. In particular, differentially expressed genes (DEGs) were identified by time-series comparison. Subsequently, DEGs with the monotonic expression alterations during the treatment were selected. Ten consistently down-regulated genes (CD274, KIF1B, IL15, TLR1, TLR5, FCGR1A, GBP1, NOD2, GBP2, EGF) exhibited significant potential in treatment monitoring, demonstrated via biological and technical validation. Additionally, the biosignature showed potential in predicting the cured versus relapsed patients. Furthermore, the biosignature could be utilized for TB diagnosis, latent tuberculosis infection/active TB differential diagnosis, and risk of progression to active TB. Benchmarking analysis of the 10-gene biosignature with other biosignatures showed equivalent performance in tested data sets. In conclusion, we established a 10-gene transcriptomic biosignature that represents the kinetic responses of TB treatment. Subsequent studies are warranted to validate, refine and translate the biosignature into a precise assay to assist clinical decisions in a broad spectrum of TB management.

LIGHT of pulmonary NKT cells annihilates tissue protective alveolar macrophages in augmenting severe influenza pneumonia

CD1d-restricted natural killer T (NKT) cells are innate-like T lymphocytes with protective or pathogenic roles in the development of influenza pneumonia. Here, we show that lung-infiltrated and activated NKT cells are the major cellular source of LIGHT/TNFSF14, which determines the severity of pulmonary pneumonia by highly deteriorative influenza A virus (IAV) infection. Compared to wild-type mice, LIGHT^-/- mice exhibit much lower morbidity and mortality to IAV, due to alleviated lung damage and reduced apoptosis of alveolar macrophages (AMs). LIGHT preferentially promotes cell death of lymphotoxin β receptors positive (LTβR⁺) AMs but not herpesvirus entry mediator positive (HVEM⁺) AMs. Therefore, these results suggest that NKT-derived LIGHT augments cell death of the tissue protective AMs in exacerbating lung pathology and susceptibility to fatal influenza infection. Suppression of LIGHT signaling might be a viable option in the treatment of influenza-associated acute respiratory distress syndrome.

Meta-analysis of gene expression disease signatures in colonic biopsy tissue from patients with ulcerative colitis

Publicly available ulcerative colitis (UC) gene expression datasets from observational studies and clinical trials include inherently heterogeneous disease characteristics and methodology. We used meta-analysis to identify a robust UC gene signature from inflamed biopsies. Eight gene expression datasets derived from biopsy tissue samples from noninflammatory bowel disease (IBD) controls and areas of active inflammation from patients with UC were publicly available. Expression- and meta-data were downloaded with GEOquery. Differentially expressed genes (DEG) in individual datasets were defined as those with fold change > 1.5 and a Benjamini–Hochberg adjusted P value < .05. Meta-analysis of all DEG used a random effects model. Reactome pathway enrichment analysis was conducted. Meta-analysis identified 946 up- and 543 down-regulated genes in patients with UC compared to non-IBD controls (1.2 and 1.7 times fewer up- and down-regulated genes than the median of the individual datasets). Top-ranked up- and down-regulated DEG were LCN2 and AQP8. Multiple immune-related pathways (e.g., ‘Chemokine receptors bind chemokine’ and ‘Interleukin-10 signaling’) were significantly up-regulated in UC, while ‘Biological oxidations’ and ‘Fatty acid metabolism’ were downregulated. A web-based data-mining tool with the meta-analysis results was made available (https://premedibd.com/genes.html). A UC inflamed biopsy disease gene signature was derived. This signature may be an unbiased reference for comparison and improve the efficiency of UC biomarker studies by increasing confidence for identification of disease-related genes and pathways.

A merged microarray meta-dataset for transcriptionally profiling colorectal neoplasm formation and progression

Transcriptional profiling of pre- and post-malignant colorectal cancer (CRC) lesions enable temporal monitoring of molecular events underlying neoplastic progression. However, the most widely used transcriptomic dataset for CRC, TCGA-COAD, is devoid of adenoma samples, which increases reliance on an assortment of disparate microarray studies and hinders consensus building. To address this, we developed a microarray meta-dataset comprising 231 healthy, 132 adenoma, and 342 CRC tissue samples from twelve independent studies. Utilizing a stringent analytic framework, select datasets were downloaded from the Gene Expression Omnibus, normalized by frozen robust multiarray averaging and subsequently merged. Batch effects were then identified and removed by empirical Bayes estimation (ComBat). Finally, the meta-dataset was filtered for low variant probes, enabling downstream differential expression as well as quantitative and functional validation through cross-platform correlation and enrichment analyses, respectively. Overall, our meta-dataset provides a robust tool for investigating colorectal adenoma formation and malignant transformation at the transcriptional level with a pipeline that is modular and readily adaptable for similar analyses in other cancer types.

Functional genomics meta-analysis to identify gene set enrichment networks in cardiac hypertrophy

In order to take advantage of the continuously increasing number of transcriptome studies, it is important to develop strategies that integrate multiple expression datasets addressing the same biological question to allow a robust analysis. Here, we propose a meta-analysis framework that integrates enriched pathways identified through the Gene Set Enrichment Analysis (GSEA) approach and calculates for each meta-pathway an empirical p-value. Validation of our approach on benchmark datasets showed comparable or even better performance than existing methods and an increase in robustness with increasing number of integrated datasets. We then applied the meta-analysis framework to 15 functional genomics datasets of physiological and pathological cardiac hypertrophy. Within these datasets we grouped expression sets measured at time points that represent the same hallmarks of heart tissue remodeling ('aggregated time points') and performed meta-analysis on the expression sets assigned to each aggregated time point. To facilitate biological interpretation, results were visualized as gene set enrichment networks. Here, our meta-analysis framework identified well-known biological mechanisms associated with pathological cardiac hypertrophy (e.g., cardiomyocyte apoptosis, cardiac contractile dysfunction, and alteration in energy metabolism). In addition, results highlighted novel, potentially cardioprotective mechanisms in physiological cardiac hypertrophy involving the down-regulation of immune cell response, which are worth further investigation.

Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis

Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface.

Bioinformatics Analysis Reveals Crosstalk Among Platelets, Immune Cells, and the Glomerulus That May Play an Important Role in the Development of Diabetic Nephropathy

Diabetic nephropathy (DN) is the main cause of end stage renal disease (ESRD). Glomerulus damage is one of the primary pathological changes in DN. To reveal the gene expression alteration in the glomerulus involved in DN development, we screened the Gene Expression Omnibus (GEO) database up to December 2020. Eleven gene expression datasets about gene expression of the human DN glomerulus and its control were downloaded for further bioinformatics analysis. By using R language, all expression data were extracted and were further cross-platform normalized by Shambhala. Differentially expressed genes (DEGs) were identified by Student's t-test coupled with false discovery rate (FDR) (P < 0.05) and fold change (FC) ≥1.5. DEGs were further analyzed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to enrich the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. We further constructed a protein-protein interaction (PPI) network of DEGs to identify the core genes. We used digital cytometry software CIBERSORTx to analyze the infiltration of immune cells in DN. A total of 578 genes were identified as DEGs in this study. Thirteen were identified as core genes, in which LYZ, LUM, and THBS2 were seldom linked with DN. Based on the result of GO, KEGG enrichment, and CIBERSORTx immune cells infiltration analysis, we hypothesize that positive feedback may form among the glomerulus, platelets, and immune cells. This vicious cycle may damage the glomerulus persistently even after the initial high glucose damage was removed. Studying the genes and pathway reported in this study may shed light on new knowledge of DN pathogenesis.

A nomogram for predicting metabolic steatohepatitis: The combination of NAMPT, RALGDS, GADD45B, FOSL2, RTP3, and RASD1

Objective

To identify differentially expressed and clinically significant mRNAs and construct a potential prediction model for metabolic steatohepatitis (MASH).

Method

We downloaded four microarray datasets, GSE89632, GSE24807, GSE63067, and GSE48452, from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis were performed to screen significant genes. Finally, we constructed a nomogram of six hub genes in predicting MASH and assessed it through receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, qRT-PCR was used for relative quantitative detection of RNA in QSG-7011 cells to further verify the expression of the selected mRNA in fatty liver cells.

Results

Based on common DEGs and brown and yellow modules, seven hub genes were identified, which were NAMPT, PHLDA1, RALGDS, GADD45B, FOSL2, RTP3, and RASD1. After logistic regression analysis, six hub genes were used to establish the nomogram, which were NAMPT, RALGDS, GADD45B, FOSL2, RTP3, and RASD1. The area under the ROC of the nomogram was 0.897. The DCA showed that when the threshold probability of MASH was 0–0.8, the prediction model was valuable to GSE48452. In QSG-7011 fatty liver model cells, the relative expression levels of NAMPT, GADD45B, FOSL2, RTP3, RASD1 and RALGDS were lower than the control group.

Conclusion

We identified seven hub genes NAMPT, PHLDA1, RALGDS, GADD45B, FOSL2, RTP3, and RASD1. The nomogram showed good performance in the prediction of MASH and it had clinical utility in distinguishing MASH from simple steatosis.

Identification of key biomarkers for thyroid cancer by integrative gene expression profiles

Thyroid cancer is a frequently diagnosed malignancy and the incidence has been increased rapidly in recent years. Despite the favorable prognosis of most thyroid cancer patients, advanced patients with metastasis and recurrence still have poor prognosis. Therefore, the molecular mechanisms of progression and targeted biomarkers were investigated for developing effective targets for treating thyroid cancer. Eight chip datasets from the gene expression omnibus database were selected and the inSilicoDb and inSilicoMerging R/Bioconductor packages were used to integrate and normalize them across platforms. After merging the eight gene expression omnibus datasets, we obtained one dataset that contained the expression profiles of 319 samples (188 tumor samples plus 131 normal thyroid tissue samples). After screening, we identified 594 significantly differentially expressed genes (277 up-regulated genes plus 317 down-regulated genes) between the tumor and normal tissue samples. The differentially expressed genes exhibited enrichment in multiple signaling pathways, such as p53 signaling. By building a protein-protein interaction network and module analysis, we confirmed seven hub genes, and they were all differentially expressed at all the clinical stages of thyroid cancer. A diagnostic seven-gene signature was established using a logistic regression model with the area under the receiver operating characteristic curve (AUC) of 0.967. Seven robust candidate biomarkers predictive of thyroid cancer were identified, and the obtained seven-gene signature may serve as a useful marker for thyroid cancer diagnosis and prognosis.

A computational analysis of transcriptional profiles from CD8(+) T lymphocytes reveals potential mechanisms of HIV/AIDS control and progression

Cytotoxic and noncytotoxic CD8+ T lymphocyte responses are essential for the control of HIV infection. Understanding the mechanisms underlying HIV control in elite controllers (ECs), which maintain undetectable viral load in the absence of antiretroviral therapy, may facilitate the development of new effective therapeutic strategies. We developed an original pipeline for an analysis of the transcriptional profiles of CD8+ cells from ECs, treated and untreated progressors. Hierarchical cluster analysis of CD8+ cells' transcription profiles allowed us to identify five distinct groups (EC groups 1-5) of ECs. The transcriptional profiles of EC group 1 were opposite to those of groups 2-4 and similar to those of the treated progressors, which can be associated with residual activation and dysfunction of CD8+ T-lymphocytes. The profiles of groups 2-4 were associated with different numbers of differentially expressed genes compared to healthy controls, but the corresponding genes shared the same cellular processes. These three groups were associated with increased metabolism, survival, proliferation, and the absence of an "exhausted" phenotype, compared to both untreated progressors and healthy controls. The CD8+ lymphocytes from these groups of ECs may contribute to the control under HIV replication and slower disease progression. The EC group 5 was indistinguishable from normal. Application of master regulator analysis allowed us to identify 22 receptors, including interferon-gamma, interleukin-2, and androgen receptors, which may be responsible for the observed expression changes and the functional states of CD8+ cells from ECs. These receptors can be considered potential targets of therapeutic intervention, which may decelerate disease progression.

Differential gene expression data from the human central nervous system across Alzheimer's disease, Lewy body diseases, and the amyotrophic lateral sclerosis and frontotemporal dementia spectrum

In Noori et al. [1], we hypothesized that there is a shared gene expression signature underlying neurodegenerative proteinopathies including Alzheimer's disease (AD), Lewy body diseases (LBD), and the amyotrophic lateral sclerosis and frontotemporal dementia (ALS-FTD) spectrum. To test this hypothesis, we performed a systematic review and meta-analysis of 60 human central nervous system transcriptomic datasets in the public Gene Expression Omnibus and ArrayExpress repositories, comprising a total of 2,600 AD, LBD, and ALS-FTD patients and age-matched controls which passed our stringent quality control pipeline. Here, we provide the results of differential expression analyses with data quality reports for each of these 60 datasets. This atlas of differential expression across AD, LBD, and ALS-FTD may guide future work to elucidate the pathophysiological drivers of these individual diseases as well as the common substrate of neurodegeneration.

CuBlock: a cross-platform normalization method for gene-expression microarrays

Motivation

Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.

Results

We present CuBlock, an algorithm addressing this objective, together with a strategy to validate cross-platform normalization methods. To validate the algorithm and benchmark it against existing methods, we used two distinct datasets, one specifically generated for testing and standardization purposes and one from an actual experimental study. Using these datasets, we benchmarked CuBlock against ComBat (Johnson et al., 2007), UPC (Piccolo et al., 2013), YuGene (Lê Cao et al., 2014), DBNorm (Meng et al., 2017), Shambhala (Borisov et al., 2019) and a simple log₂ transform as reference. We note that many other popular normalization methods are not applicable in this context. CuBlock was the only algorithm in this group that could always and clearly differentiate the underlying biological groups after mixing the data, from up to six different platforms in this study.

Availability and implementation

CuBlock can be downloaded from https://www.mathworks.com/matlabcentral/fileexchange/77882-cublock.

Supplementary information

Supplementary data are available at Bioinformatics online.

A meta-analysis of comorbidities in COVID-19: Which diseases increase the susceptibility of SARS-CoV-2 infection?

Comorbidities in COVID-19 patients often lead to more severe outcomes. The disease-specific molecular events, which may induce susceptibility to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, are being investigated. To assess this, we retrieved array-based gene expression datasets from patients of 30 frequently occurring acute, chronic, or infectious diseases. Comparative analyses of the datasets were performed after quantile normalization and log2 transformation. Among the 78 host genes prominently implicated in COVID-19 infection, ACE2 (receptor for SARS-CoV-2) was positively regulated in several cases, namely, leukemia, psoriasis, lung cancer, non-alcoholic fatty liver disease (NAFLD), breast cancer, and pulmonary arterial hypertension (PAH). FURIN was positively regulated in some cases, such as leukemia, psoriasis, NAFLD, lung cancer, and type II diabetes (T2D), while TMPRSS2 was positively regulated in only 3 cases, namely, leukemia, lung cancer, and T2D. Genes encoding various interferons, cytokines, chemokines, and mediators of JAK-STAT pathway were positively regulated in leukemia, NAFLD, and T2D cases. Among the 161 genes that are positively regulated in the lungs of COVID-19 patients, 99–111 genes in leukemia (including various studied subtypes), 77 genes in NAFLD, and 48 genes in psoriasis were also positively regulated. Because of the high similarity in gene expression patterns, the patients of leukemia, NAFLD, T2D, psoriasis, and PAH may need additional preventive care against acquiring SARS-CoV-2 infections. Further, two genes CARBONIC ANHYDRASE 11 (CA11) and CLUSTERIN (CLU) were positively regulated in the lungs of patients infected with either SARS-CoV-2, or SARS-CoV or Middle East Respiratory Syndrome Coronavirus (MERS-CoV).

Prediction of future gene expression profile by analyzing its past variation pattern

A number of initial Hematopoietic Stem Cells (HSC) are considered in a container that are able to divide into HSCs or differentiate into various types of descendant cells. In this paper, a method is designed to predict an approximate gene expression profile (GEP) for future descendant cells resulted from HSC division/differentiation. First, the GEP prediction problem is modeled into a multivariate time series prediction problem. A novel method called EHSCP (Extended Hematopoietic Stem Cell Prediction) is introduced which is an artificial neural machine to solve the problem. EHSCP accepts the initial sequence of measured GEPs as input and predicts GEPs of future descendant cells. This prediction can be performed for multiple stages of cell division/differentiation. EHSCP considers the GEP sequence as time series and computes correlation between input time series. Two novel artificial neural units called PLSTM (Parametric Long Short Term Memory) and MILSTM (Multi-Input LSTM) are designed. PLSTM makes EHSCP able to consider this correlation in output prediction. Since there exist thousands of time series in GEP prediction, a hierarchical encoder is proposed that computes this correlation using 101 MILSTMs. EHSCP is trained using 155 datasets and is evaluated on 39 test datasets. These evaluations show that EHSCP surpasses existing methods in terms of prediction accuracy and number of correctly-predicted division/differentiation stages. In these evaluations, number of correctly-predicted stages in EHSCP was 128 when as many as 8 initial stages were given.

Meta-Analysis Reveals the Prognostic Relevance of Nuclear and Membrane-Associated Bile Acid Receptors in Gastric Cancer

INTRODUCTION

Bile acids (BAs) arising from duodenogastric reflux are known to facilitate gastric cancer (GC) development. Although BAs traditionally contribute to carcinogenesis through direct cellular cytotoxicity, increasing evidence implicates nuclear and membrane BA receptors (BARs) as additional factors influencing cancer risk. Indeed, some BARs are already linked with GC, but conflicting evidence and lack of information regarding other endogenous BARs warrant further investigation. In this study, we meta-analyzed multiple data sets to identify clinically relevant relationships between BAR expression and prognosis, clinicopathology, and activity in GC.

METHODS

We collected transcriptomic data from the Gene Expression Omnibus and The Cancer Genome Atlas to analyze associations between BAR expression and GC prognosis, subtype, and clinicopathology. We also used Ingenuity Pathway Analysis to assess and predict functions, upstream regulators, and downstream mediators of membrane and nuclear BARs in GC.

RESULTS

BARs showed differential distribution in GC; membrane BARs (G protein-coupled BAR 1, sphingosine-1-phosphate receptor 2, and cholinergic receptor muscarinic 2) were enriched in diffuse-, genome-stable, and mesenchymal-type tumors, whereas nuclear BARs (pregnane-X-receptor, constitutive androstane receptor, and farnesoid-X-receptor) were enriched in chromosome instability and metabolic subtypes. High expression of all membrane but not nuclear BARs was associated with poor prognosis and unfavorable GC clinicopathologic features. Similarly, expression patterns of membrane but not nuclear BARs varied geographically, aligning with Helicobacter pylori infection and GC mortality rates. Finally, GC-related oncogenes, namely transforming growth factor β1, were associated with membrane BARs, whereas many metabolic-associated genes were associated with nuclear BARs.

DISCUSSION

Through transcriptomic meta-analysis, we identified distinct expression profiles between nuclear and membrane BARs that demonstrate prognostic relevance and warrant further investigation.

Systematic review and meta-analysis of human transcriptomics reveals neuroinflammation, deficient energy metabolism, and proteostasis failure across neurodegeneration

Neurodegenerative disorders such as Alzheimer's disease (AD), Lewy body diseases (LBD), and the amyotrophic lateral sclerosis and frontotemporal dementia (ALS-FTD) spectrum are defined by the accumulation of specific misfolded protein aggregates. However, the mechanisms by which each proteinopathy leads to neurodegeneration remain elusive. We hypothesized that there is a common "pan-neurodegenerative" gene expression signature driving pathophysiology across these clinically and pathologically diverse proteinopathies. To test this hypothesis, we performed a systematic review of human CNS transcriptomics datasets from AD, LBD, and ALS-FTD patients and age-matched controls in the Gene Expression Omnibus (GEO) and ArrayExpress databases, followed by consistent processing of each dataset, meta-analysis, pathway enrichment, and overlap analyses. After applying pre-specified eligibility criteria and stringent data pre-processing, a total of 2600 samples from 26 AD, 21 LBD, and 13 ALS-FTD datasets were included in the meta-analysis. The pan-neurodegenerative gene signature is characterized by an upregulation of innate immunity, cytoskeleton, and transcription and RNA processing genes, and a downregulation of the mitochondrial electron transport chain. Pathway enrichment analyses also revealed the upregulation of neuroinflammation (including Toll-like receptor, TNF, and NFκB signaling) and phagocytosis, and the downregulation of mitochondrial oxidative phosphorylation, lysosomal acidification, and ubiquitin-proteasome pathways. Our findings suggest that neuroinflammation and a failure in both neuronal energy metabolism and protein degradation systems are consistent features underlying neurodegenerative diseases, despite differences in the extent of neuronal loss and brain regions involved.

Meta-analysis of Transcriptomic Data Reveals Pathophysiological Modules Involved with Atrial Fibrillation

BACKGROUND

Atrial fibrillation (AF) is a complex disease and affects millions of people around the world. The biological mechanisms that are involved with AF are complex and still need to be fully elucidated. Therefore, we performed a meta-analysis of transcriptome data related to AF to explore these mechanisms aiming at more sensitive and reliable results.

METHODS

Ten public transcriptomic datasets were downloaded, analyzed for quality control, and individually pre-processed. Differential expression analysis was carried out for each dataset, and the results were meta-analytically aggregated using the rth ordered p value method. We analyzed the final list of differentially expressed genes through network analysis, namely topological and modularity analysis, and functional enrichment analysis.

RESULTS

The meta-analysis of transcriptomes resulted in 1197 differentially expressed genes, whose protein-protein interaction network presented 39 hubs-bottlenecks and four main identified functional modules. These modules were enriched for 39, 20, 64, and 10 biological pathways involved with the pathophysiology of AF, especially with the disease's structural and electrical remodeling processes. The stress of the endoplasmic reticulum, protein catabolism, oxidative stress, and inflammation are some of the enriched processes. Among hub-bottlenecks genes, which are highly connected and probably have a key role in regulating these processes, HSPA5, ANK2, CTNNB1, and MAPK1 were identified.

CONCLUSION

Our approach based on transcriptome meta-analysis revealed a set of key genes that demonstrated consistent overall changes in expression patterns associated with AF despite data heterogeneity related, among others, to type of tissue. Further experimental investigation of our findings may shed light on the pathophysiology of the disease and contribute to the identification of new therapeutic targets.

Microarray Normalization Revisited for Reproducible Breast Cancer Biomarkers

Precision medicine for breast cancer relies on biomarkers to select therapies. However, the reliability of biomarkers drawn from gene expression arrays has been questioned and calls for reassessment, in particular for large datasets. We revisit widely used data-normalization procedures and evaluate differences in outcome in order to pinpoint the most reliable reprocessing methods biomarkers can be based upon. We generated a database of 3753 breast cancer patients out of 38 studies by downloading and curating patient samples from NCBI-GEO. As gene-expression biomarkers, we select the assessment of receptor status and breast cancer subtype classification. Each normalization procedure is applied separately, and biomarkers are then evaluated for each patient. Differences between normalization pipelines are quantified as percentages of patients having outcomes different for each pipeline. Some normalization procedures lead to quite consistent biomarkers, differing only in 1-2% of patients. Other normalization procedures—some of them have been used in many clinical studies—end up with distrusting discrepancies (10% and more). A good deal of doubt regarding the reliability of microarrays may root in the haphazard application of inadequate preprocessing pipelines. Several modes of batch corrections are evaluated regarding a possible improvement of receptor prediction from gene expression versus the golden standard of immunohistochemistry. Finally, we nominate those normalization methods yielding consistent and trustable results. Adequate bioinformatics data preprocessing is key and crucial for any subsequent statistics to arrive at trustable results. We conclude with a suggestion for future bioinformatics development to further increase the reliability of cancer biomarkers.

Meta-analysis of gene expression profiles of lean and obese PCOS to identify differentially regulated pathways and risk of comorbidities

Polycystic ovary syndrome (PCOS) is a complex multigenic disorder and women with PCOS suffer from several comorbidities. Although, obesity is a known risk factor for PCOS, the incidence of lean women with PCOS is on the rise. A systematic and comparative study on lean and obese PCOS with respect to genes, pathways and comorbidity analysis has not been attempted so far. Analysis of differentially expressed genes (DEGs) across tissue types for lean and obese PCOS revealed that the majority of them were downregulated for lean and obese PCOS. Ovarian and endometrial tissues shared several commonly dysregulated genes, suggesting shared PCOS pathophysiology mechanisms exist across tissues. Several pathways for cellular homeostasis, such as inflammation and immune response, insulin signaling, steroidogenesis, hormonal and metabolic signaling, regulation of gonadotrophic hormone secretion, cell structure and signaling that are known to be affected in PCOS were found to be enriched in our gene expression analysis of lean and obese PCOS. The gene-disease network is denser for obese PCOS with a higher comorbidity score as compared to lean PCOS.

Reanalysis and integration of public microarray datasets reveals novel host genes modulated in leprosy

Due to multiple hypothesis testing with often limited sample size, microarrays and other-omics technologies can sometimes produce irreproducible findings. Complementary to better experimental design, reanalysis and integration of gene expression datasets may help overcome reproducibility issues by identifying consistent differentially expressed genes from independent studies. In this work, after a systematic search, nine microarray datasets evaluating host gene expression in leprosy were reanalyzed and the information was integrated to strengthen evidence of differential expression for several genes. Our results are relevant in prioritizing genes and pathways for further investigation, whether in functional studies or in biomarker discovery. Reanalysis of individual datasets revealed several differentially expressed genes (DEGs) in accordance with original reports. Then, five integration methods (P value and effect size based) were tested. In the end, random-effects model and ratio association were selected as the main methods to pinpoint DEGs. Overall, classic pathways were found corroborating previous findings and validating this approach. Also, we identified some novel DEG involved especially with skin development processes (AQP3, AKR1C3, CYP27B1, LTB, VDR) and keratinocyte biology (CSTA, DSG1, KRT14, KRT5, PKP1, IVL), both still poorly understood in leprosy context. In addition, here we provide aggregated evidence towards some gene candidates that should be prioritized in further leprosy research, as they are likely important in immunopathogenesis. Altogether, these data are useful in better understanding host responses to the disease and, at the same time, provide a list of potential host biomarkers that could be useful in complementing leprosy diagnosis based on transcriptional levels.

Transcriptional meta-analysis of regulatory B cells

Regulatory B cells (Bregs) have the ability to regulate inflammation in various pathological situations, making them key players in immune regulation. Several mechanisms have been described and we recently identified a GZMB expressing Breg population in kidney transplanted patients who tolerate a kidney graft. To further investigate their biology and mechanisms, we conducted a transcriptomic analysis by RNAseq of these cells and we performed the first weighted meta-analysis of publicly available transcriptomic data from published Breg studies both in humans and mice. We identified two distinct and unique transcriptional signatures of 126 and 93 genes, respectively, associated with these Bregs. While we highlighted genes coding for proteins with potent involvement in regulatory functions, proliferation, and coding for transcription factors, the comparison between humans and mice did not allow identifying a common pattern. Thus, our results suggest distinct species-restricted Breg transcriptional signatures in humans and mice.

An integrative Bayesian network approach to highlight key drivers in systemic lupus erythematosus

BACKGROUND

A comprehensive intuition of the systemic lupus erythematosus (SLE), as a complex and multifactorial disease, is a biological challenge. Dealing with this challenge needs employing sophisticated bioinformatics algorithms to discover the unknown aspects. This study aimed to underscore key molecular characteristics of SLE pathogenesis, which may serve as effective targets for therapeutic intervention.

METHODS

In the present study, the human peripheral blood mononuclear cell (PBMC) microarray datasets (n = 6), generated by three platforms, which included SLE patients (n = 220) and healthy control samples (n = 135) were collected. Across each platform, we integrated the datasets by cross-platform normalization (CPN). Subsequently, through BNrich method, the structures of Bayesian networks (BNs) were extracted from KEGG-indexed SLE, TCR, and BCR signaling pathways; the values of the node (gene) and edge (intergenic relationships) parameters were estimated within each integrated datasets. Parameters with the FDR < 0.05 were considered significant. Finally, a mixture model was performed to decipher the signaling pathway alterations in the SLE patients compared to healthy controls.

RESULTS

In the SLE signaling pathway, we identified the dysregulation of several nodes involved in the (1) clearance mechanism (SSB, MACROH2A2, TRIM21, H2AX, and C1Q gene family), (2) autoantigen presentation by MHCII (HLA gene family, CD80, IL10, TNF, and CD86), and (3) end-organ damage (FCGR1A, ELANE, and FCGR2A). As a remarkable finding, we demonstrated significant perturbation in CD80 and CD86 to CD28, CD40LG to CD40, C1QA and C1R to C2, and C1S to C4A edges. Moreover, we not only replicated previous studies regarding alterations of subnetworks involved in TCR and BCR signaling pathways (PI3K/AKT, MAPK, VAV gene family, AP-1 transcription factor) but also distinguished several significant edges between genes (PPP3 to NFATC gene families). Our findings unprecedentedly showed that different parameter values assign to the same node based on the pathway topology (the PIK3CB parameter values were 1.7 in TCR vs - 0.5 in BCR signaling pathway).

CONCLUSIONS

Applying the BNrich as a hybridized network construction method, we highlight under-appreciated systemic alterations of SLE, TCR, and BCR signaling pathways in SLE. Consequently, having such a systems biology approach opens new insights into the context of multifactorial disorders.

Human Pluripotent Stem Cell-Derived Hepatocytes Show Higher Transcriptional Correlation with Adult Liver Tissue than with Fetal Liver Tissue

Human pluripotent stem cell-derived hepatocytes (hPSC-HEP) display many properties of mature hepatocytes, including expression of important genes of the drug metabolizing machinery, glycogen storage, and production of multiple serum proteins. To this date, hPSC-HEP do not, however, fully recapitulate the complete functionality of in vivo mature hepatocytes. In this study, we applied versatile bioinformatic algorithms, including functional annotation and pathway enrichment analyses, transcription factor binding-site enrichment, and similarity and correlation analyses, to datasets collected from different stages during hPSC-HEP differentiation and compared these to developmental stages and tissues from fetal and adult human liver. Our results demonstrate a high level of similarity between the in vitro differentiation of hPSC-HEP and in vivo hepatogenesis. Importantly, the transcriptional correlation of hPSC-HEP with adult liver (AL) tissues was higher than with fetal liver (FL) tissues (0.83 and 0.70, respectively). Functional data revealed mature features of hPSC-HEP including cytochrome P450 enzymes activities and albumin secretion. Moreover, hPSC-HEP showed expression of many genes involved in drug absorption, distribution, metabolism, and excretion. Despite the high similarities observed, we identified differences of specific pathways and regulatory players by analyzing the gene expression between hPSC-HEP and AL. These findings will aid future intervention and improvement of in vitro hepatocyte differentiation protocol in order to generate hepatocytes displaying the complete functionality of mature hepatocytes. Finally, on the transcriptional level, our results show stronger correlation and higher similarity of hPSC-HEP to AL than to FL. In addition, potential targets for further functional improvement of hPSC-HEP were also identified.

Dysregulated NF-κB signal promotes the hub gene PCLAF expression to facilitate nasopharyngeal carcinoma proliferation and metastasis

BACKGROUND

Nasopharyngeal carcinoma (NPC) is common in Southern China. The molecular mechanism underlying NPC genesis and progression has been comprehensively investigated, but the key gene (s) or pathway (s) pertaining to NPC are unidentified.

METHODS

We explored some key genes and pathways involved in NPC through using meta-analysis of deposited expression of microarray data of NPC. The expression of proliferating cell nuclear antigen clamp associated factor (PCLAF) was determined by real-time PCR and western blots. CCK-8 assay, colony formation assay, transwell migration assay, cell wound healing assay, cell cycle analysis and cell apoptosis were carried out to assess biological behaviors caused by downregulation and overexpression of PCLAF in vitro. CHIP was utilized to determine the direct upstream regulatory transcription factors of PCLAF.

RESULTS

PCLAF was the key gene of NPC, which was significantly up-regulated in NPC cell line compared to the normal nasopharyngeal cell line. Additionally, in vitro assay has demonstrated the down-regulation and overexpression of PCLAF, resulted in significantly suppressed and enhanced NPC proliferation, metastasis and invasion respectively. Furthermore, the up-regulation of PCLAF in NPC is induced by direct binding of dysregulated NF-κB p50/RelB complex to the promoter of PCLAF.

CONCLUSION

Our results offer a strategy for re-using the deposited data to find the key genes and pathways involved in pathogenesis of cancer. Our study has provided evidence of supporting the role of PCLAF in NPC genesis and progression.

Integrative Analysis of Axolotl Gene Expression Data from Regenerative and Wound Healing Limb Tissues

Axolotl (Ambystoma mexicanum) is a urodele amphibian endowed with remarkable regenerative capacities manifested in scarless wound healing and restoration of amputated limbs, which makes it a powerful experimental model for regenerative biology and medicine. Previous studies have utilized microarrays and RNA-Seq technologies for detecting differentially expressed (DE) genes in different phases of the axolotl limb regeneration. However, sufficient consistency may be lacking due to statistical limitations arising from intra-laboratory analyses. This study aims to bridge such gaps by performing an integrative analysis of publicly available microarray and RNA-Seq data from axolotl limb samples having comparable study designs using the "merging" method. A total of 351 genes were found DE in regenerative samples compared to the control in data of both technologies, showing an adjusted p-value < 0.01 and log fold change magnitudes >1. Downstream analyses illustrated consistent correlations of the directionality of DE genes within and between data of both technologies, as well as concordance with the literature on regeneration related biological processes. qRT-PCR analysis validated the observed expression level differences of five of the top DE genes. Future studies may benefit from the utilized concept and approach for enhanced statistical power and robust discovery of biomarkers of regeneration.

Available Software for Meta-analyses of Genome-wide Expression Studies

Advances in transcriptomic methods have led to a large number of published Genome-Wide Expression Studies (GWES), in humans and model organisms. For several years, GWES involved the use of microarray platforms to compare genome-expression data for two or more groups of samples of interest. Meta-analysis of GWES is a powerful approach for the identification of differentially expressed genes in biological topics or diseases of interest, combining information from multiple primary studies. In this article, the main features of available software for carrying out meta-analysis of GWES have been reviewed and seven packages from the Bioconductor platform and five packages from the CRAN platform have been described. In addition, nine previously described programs and four online programs are reviewed. Finally, advantages and disadvantages of these available programs and proposed key points for future developments have been discussed.

The role and robustness of the Gini coefficient as an unbiased tool for the selection of Gini genes for normalising expression profiling data

We recently introduced the Gini coefficient (GC) for assessing the expression variation of a particular gene in a dataset, as a means of selecting improved reference genes over the cohort ('housekeeping genes') typically used for normalisation in expression profiling studies. Those genes (transcripts) that we determined to be useable as reference genes differed greatly from previous suggestions based on hypothesis-driven approaches. A limitation of this initial study is that a single (albeit large) dataset was employed for both tissues and cell lines. We here extend this analysis to encompass seven other large datasets. Although their absolute values differ a little, the Gini values and median expression levels of the various genes are well correlated with each other between the various cell line datasets, implying that our original choice of the more ubiquitously expressed low-Gini-coefficient genes was indeed sound. In tissues, the Gini values and median expression levels of genes showed a greater variation, with the GC of genes changing with the number and types of tissues in the data sets. In all data sets, regardless of whether this was derived from tissues or cell lines, we also show that the GC is a robust measure of gene expression stability. Using the GC as a measure of expression stability we illustrate its utility to find tissue- and cell line-optimised housekeeping genes without any prior bias, that again include only a small number of previously reported housekeeping genes. We also independently confirmed this experimentally using RT-qPCR with 40 candidate GC genes in a panel of 10 cell lines. These were termed the Gini Genes. In many cases, the variation in the expression levels of classical reference genes is really quite huge (e.g. 44 fold for GAPDH in one data set), suggesting that the cure (of using them as normalising genes) may in some cases be worse than the disease (of not doing so). We recommend the present data-driven approach for the selection of reference genes by using the easy-to-calculate and robust GC.

Multi-view based integrative analysis of gene expression data for identifying biomarkers

The widespread applications in microarray technology have produced the vast quantity of publicly available gene expression datasets. However, analysis of gene expression data using biostatistics and machine learning approaches is a challenging task due to (1) high noise; (2) small sample size with high dimensionality; (3) batch effects and (4) low reproducibility of significant biomarkers. These issues reveal the complexity of gene expression data, thus significantly obstructing microarray technology in clinical applications. The integrative analysis offers an opportunity to address these issues and provides a more comprehensive understanding of the biological systems, but current methods have several limitations. This work leverages state of the art machine learning development for multiple gene expression datasets integration, classification and identification of significant biomarkers. We design a novel integrative framework, MVIAm - Multi-View based Integrative Analysis of microarray data for identifying biomarkers. It applies multiple cross-platform normalization methods to aggregate multiple datasets into a multi-view dataset and utilizes a robust learning mechanism Multi-View Self-Paced Learning (MVSPL) for gene selection in cancer classification problems. We demonstrate the capabilities of MVIAm using simulated data and studies of breast cancer and lung cancer, it can be applied flexibly and is an effective tool for facing the four challenges of gene expression data analysis. Our proposed model makes microarray integrative analysis more systematic and expands its range of applications.

MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

We present a new update to MetaboAnalyst (version 4.0) for comprehensive metabolomic data analysis, interpretation, and integration with other omics data. Since the last major update in 2015, MetaboAnalyst has continued to evolve based on user feedback and technological advancements in the field. For this year's update, four new key features have been added to MetaboAnalyst 4.0, including: (1) real-time R command tracking and display coupled with the release of a companion MetaboAnalystR package; (2) a MS Peaks to Pathways module for prediction of pathway activity from untargeted mass spectral data using the mummichog algorithm; (3) a Biomarker Meta-analysis module for robust biomarker identification through the combination of multiple metabolomic datasets and (4) a Network Explorer module for integrative analysis of metabolomics, metagenomics, and/or transcriptomics data. The user interface of MetaboAnalyst 4.0 has been reengineered to provide a more modern look and feel, as well as to give more space and flexibility to introduce new functions. The underlying knowledgebases (compound libraries, metabolite sets, and metabolic pathways) have also been updated based on the latest data from the Human Metabolome Database (HMDB). A Docker image of MetaboAnalyst is also available to facilitate download and local installation of MetaboAnalyst. MetaboAnalyst 4.0 is freely available at http://metaboanalyst.ca.

Array expression meta-analysis of cancer stem cell genes identifies upregulation of PODXL especially in DCC low expression meningiomas

BACKGROUND

Meningiomas are the most common intracranial tumors, with a subset of cases bearing a progressive phenotype. The DCC netrin 1 receptor (DCC) is a candidate gene for early meningioma progression. Cancer stem cell (CSC) genes are emerging as cancer therapeutic targets, as their expression is frequently associated with aggressive tumor phenotypes. The main objective of the study was to identify deregulated CSC genes in meningiomas.

MATERIALS AND METHODS

Interrogating two expression data repositories, significantly differentially expressed genes (DEGs) were determined using DCC low vs. DCC high expression groups and WHO grade I (GI) vs. grade II + grade III (GII + GIII) comparison groups. Human stem cell (SC) genes were compiled from two published data sets and were extracted from the DEG lists. Biofunctional analysis was performed to assess associations between genes or molecules.

RESULTS

In the DCC low vs. DCC high expression groups, we assessed seven studies representing each between seven and 58 samples. The type I transmembrane protein podocalyxin like (PODXL) was markedly upregulated in DCC low expression meningiomas in six studies. Other CSC genes repeatedly deregulated included, e.g., BMP/retinoic acid inducible neural specific 1 (BRINP1), prominin 1 (PROM1), solute carrier family 24 member 3 (SLC24A3), rRho GTPase activating protein 28 (ARHGAP28), Kruppel like factor 5 (KLF5), and leucine rich repeat containing G protein-coupled receptor 4 (LGR4). In the GI vs. GII + GIII comparison groups, we assessed six studies representing each between nine and 68 samples. DNA topoisomerase 2-alpha (TOP2A) was markedly upregulated in GII + GIII meningiomas in four studies. Other CSC genes repeatedly deregulated included, e.g., ARHGAP28 and PODXL. Network analysis revealed associations of molecules with, e.g., cellular development and movement; nervous system development and function; and cancer.

CONCLUSIONS

This meta-analysis on meningiomas identified a comprehensive list of deregulated CSC genes across different array expression studies. Especially, PODXL is of interest for functional assessment in progressive meningiomas.