Multiple Gene Expression Datasets Research Articles

Identification and validation of a novel 9-gene signature of non-specific classification to predict prognosis in glioma patients.

This study aimed to identify and validate a 9-gene signature for predicting overall survival (OS) in glioma patients. Analysis of multiple gene expression datasets led to the identification of 135 candidate genes associated with OS in glioma patients. Further analysis revealed that IGFBP2, PBK, NRXN3, TGIF1, DNAJA4, and LGALS3BP were identified as risk factors for OS, while ENAH, PPP2R2C, and SPHKAP were found to be protective factors. Multifaceted validation using different databases confirmed their differential expression patterns in glioma tissues compared to normal brain tissue. By utilizing LASSO regression and multivariate Cox regression analysis, a risk score was developed based on the expression levels of the 9 crucial genes. The risk score showed a significant correlation with OS in both training and validation cohorts and yielded superior predictive accuracy compared to individual gene expression. Moreover, a predictive nomogram incorporating the risk score, WHO grade, age, IDH mutation, and 1p/19q co-deletion was constructed and validated, which exhibited high predictive capabilities for survival rates at different time points. Enrichment analysis revealed the involvement of extracellular matrix-related pathways and immune system signaling in glioma prognosis. Furthermore, the risk score showed a strong correlation with immune cell infiltration and immune checkpoint expression, suggesting its potential role in the tumor immune microenvironment. In conclusion, our study provides a robust 9-gene signature and a predictive nomogram for evaluating the prognosis of glioma patients, offering valuable insights into personalized treatment strategies.

GeneCompete: an integrative tool of a novel union algorithm with various ranking techniques for multiple gene expression data

Background Identifying the genes responsible for diseases requires precise prioritization of significant genes. Gene expression analysis enables differentiation between gene expressions in disease and normal samples. Increasing the number of high-quality samples enhances the strength of evidence regarding gene involvement in diseases. This process has led to the discovery of disease biomarkers through the collection of diverse gene expression data. Methods This study presents GeneCompete, a web-based tool that integrates gene expression data from multiple platforms and experiments to identify the most promising biomarkers. GeneCompete incorporates a novel union strategy and eight well-established ranking methods, including Win-Loss, Massey, Colley, Keener, Elo, Markov, PageRank, and Bi-directional PageRank algorithms, to prioritize genes across multiple gene expression datasets. Each gene in the competition is assigned a score based on log-fold change values, and significant genes are determined as winners. Results We tested the tool on the expression datasets of Hypertrophic cardiomyopathy (HCM) and the datasets from Microarray Quality Control (MAQC) project, which include both microarray and RNA-Sequencing techniques. The results demonstrate that all ranking scores have more power to predict new occurrence datasets than the classical method. Moreover, the PageRank method with a union strategy delivers the best performance for both up-regulated and down-regulated genes. Furthermore, the top-ranking genes exhibit a strong association with the disease. For MAQC, the two-sides ranking score shows a high relationship with TaqMan validation set in all log-fold change thresholds. Conclusion GeneCompete is a powerful web-based tool that revolutionizes the identification of disease-causing genes through the integration of gene expression data from multiple platforms and experiments.

Silencing of GRHL2 induces epithelial‑to‑mesenchymal transition in lung cancer cell lines with different effects on proliferation and clonogenic growth.

Grainyhead-like 2 (GRHL2) is a transcription factor that suppresses epithelial-to-mesenchymal transition (EMT). It has been previously shown that GRHL2 can confer both oncogenic and tumor-suppressive roles in human cancers, including breast, pancreatic and colorectal cancers. However, its role in lung cancer remains elusive. In the present study, a meta-analysis of multiple gene expression datasets with clinical data revealed that GRHL2 expression was increased in lung cancer compared with that in the normal tissues. Copy number analysis of GRHL2, performed using datasets of whole exome sequencing involving 151 lung cancer cell lines, revealed frequent amplifications, suggesting that the increased GRHL2 expression may have resulted from gene amplification. A survival meta-analysis of GRHL2 using The Cancer Genome Atlas (TCGA) dataset showed no association of GRHL2 expression with overall survival. GRHL2 expression was found to be associated with EMT status in lung cancer in TCGA dataset and lung cancer cell lines. GRHL2 knockdown induced partial EMT in the hTERT/Cdk4-immortalized normal lung epithelial cell line HBEC4KT without affecting proliferation measured by CCK-8 assays. In addition, GRHL2 silencing caused three lung cancer cell lines, H1975, H2009 and H441, to undergo partial EMT. However, the proliferative effects differed significantly. GRHL2 silencing promoted proliferation but not colony formation in H1975 cells whilst suppressing colony formation without affecting proliferation in H2009 cells, but it did not affect proliferation in H441 cells. These results suggest cell type-dependent effects of GRHL2 knockdown. Downstream, GRHL2 silencing enhanced the phosphorylation of AKT and ERK, assessed by western blotting with phospho-specific antibodies, in HBEC4KT, H1975 and H2009 cell lines but not in the H441 cell line. By contrast, transient GRHL2 overexpression did not affect A549 cell proliferation, which lack detectable endogenous expression of the GRHL2 protein. However, GRHL2 overexpression did suppress E-cadherin expression in A549 cells. These results suggested that GRHL2 does not only function as a tumor suppressor of EMT but can also behave as an oncogene depending on the lung cancer cell-type context.

Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma

Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One of the main goals in the related bioinformatics analyses is to provide stable genetic signatures able to include genes whose expression levels can be effective to predict the prognosis of the patients. In this study, we collected the prognostic signatures for neuroblastoma published in the biomedical literature, and noticed that the most frequent genes present among them were three: AHCY, DPYLS3, and NME1. We therefore investigated the prognostic power of these three genes by performing a survival analysis and a binary classification on multiple gene expression datasets of different groups of patients diagnosed with neuroblastoma. Finally, we discussed the main studies in the literature associating these three genes with neuroblastoma. Our results, in each of these three steps of validation, confirm the prognostic capability of AHCY, DPYLS3, and NME1, and highlight their key role in neuroblastoma prognosis. Our results can have an impact on neuroblastoma genetics research: biologists and medical researchers can pay more attention to the regulation and expression of these three genes in patients having neuroblastoma, and therefore can develop better cures and treatments which can save patients’ lives.

Scaffold protein MAPK8IP2 expression is a robust prognostic factor in prostate cancer associated with AR signaling activity.

Mitogen-activated protein kinase-8-interacting protein 2 (MAPK8IP2) is a scaffold protein that modulates MAPK signal cascades. Although MAPK pathways were heavily implicated in prostate cancer progression, the regulation of MAPK8IP2 expression in prostate cancer is not yet reported. We assessed MAPK8IP2 gene expression in prostate cancer related to disease progression and patient survival outcomes. MAPK8IP2 expression was analyzed using multiple genome-wide gene expression datasets derived from The Cancer Genome Atlas (TCGA) RNA-sequence project and complementary DNA (cDNA) microarrays. Multivariable Cox regressions and log-rank tests were used to analyze the overall survival outcome and progression-free interval. MAPK8IP2 protein expression was evaluated using the immunohistochemistry approach. The quantitative PCR and Western blot methods analyzed androgen-stimulated MAPK8IP2 expression in LNCaP cells. In primary prostate cancer tissues, MAPK8IP2 mRNA expression levels were significantly higher than those in the case-matched benign prostatic tissues. Increased MAPK8IP2 expression was strongly correlated with late tumor stages, lymph node invasion, residual tumors after surgery, higher Gleason scores, and preoperational serum prostate-specific antigen (PSA) levels. MAPK8IP2 upregulation was significantly associated with worse overall survival outcomes and progression-free intervals. In castration-resistant prostate cancers, MAPK8IP2 expression strongly correlated with androgen receptor (AR) signaling activity. In cell culture-based experiments, MAPK8IP2 expression was stimulated by androgens in AR-positive prostate cancer cells. However, MAPK8IP2 expression was blocked by AR antagonists only in androgen-sensitive LNCaP but not castration-resistant C4-2B and 22RV1 cells. These results indicate that MAPK8IP2 is a robust prognostic factor and therapeutic biomarker for prostate cancer. The potential role of MAPK8IP2 in the castration-resistant progression is under further investigation.

Integrative analysis to identify shared mechanisms between schizophrenia and bipolar disorder and their comorbidities

Schizophrenia and bipolar disorder are characterized by highly similar neuropsychological signatures, implying shared neurobiological mechanisms between these two disorders. These disorders also have comorbidities, such as type 2 diabetes mellitus (T2DM). To date, an understanding of the mechanisms that mediate the link between these two disorders remains incomplete. In this work, we identify and investigate shared patterns across multiple schizophrenia, bipolar disorder and T2DM gene expression datasets through multiple strategies. Firstly, we investigate dysregulation patterns at the gene-level and compare our findings against disease-specific knowledge graphs (KGs). Secondly, we analyze the concordance of co-expression patterns across datasets to identify disease-specific as well as common pathways. Thirdly, we examine enriched pathways across datasets and disorders to identify common biological mechanisms between them. Lastly, we investigate the correspondence of shared genetic variants between these two disorders and T2DM as well as the disease-specific KGs. In conclusion, our work reveals several shared candidate genes and pathways, particularly those related to the immune system, such as TNF signaling pathway, IL-17 signaling pathway and NF-kappa B signaling pathway and nervous system, such as dopaminergic synapse and GABAergic synapse, which we propose mediate the link between schizophrenia and bipolar disorder and its shared comorbidity, T2DM.

Parkinson's Disease rs117896735 Variant Regulates INPP5F Expression in Brain Tissues and Increases Risk of Alzheimer's Disease.

Both INPP5D and INPP5F are members of INPP5 family. INPP5F rs117896735 variant was associated with Parkinson's disease (PD) risk, and INPP5D was an Alzheimer's disease (AD) risk gene. However, it remains unclear about the roles of INPP5F rs117896735 variant in AD. We aim to investigate the roles of rs117896735 in AD. First, we conducted a candidate variant study to evaluate the association of rs117896735 variant with AD risk using the large-scale AD GWAS dataset. Second, we conducted a gene expression analysis of INPP5F to investigate the expression difference of INPP5F in different human tissues using two large-scale gene expression datasets. Third, we conducted an expression quantitative trait loci analysis to evaluate whether rs117896735 variant regulate the expression of INPP5F. Fourth, we explore the potentially differential expression of INPP5F in AD and control using multiple AD-control gene expression datasets in human brain tissues and whole blood. We found that 1) rs117896735 A allele was associated with the increased risk of AD with OR = 1.15, 95% CI 1.005-1.315, p = 0.042; 2) rs117896735 A allele could increase INPP5F expression in multiple human tissues; 3) INPP5F showed different expression in different human tissues, especially in brain tissues; 4) INPP5F showed significant expression dysregulation in AD compared with controls in human brain tissues. Conclusion: We demonstrate that PD rs117896735 variant could regulate INPP5F expression in brain tissues and increase the risk of AD. These finding may provide important information about the role of rs117896735 in AD.

A novel meta-analysis based on data augmentation and elastic data shared lasso regularization for gene expression

BackgroundGene expression analysis can provide useful information for analyzing complex biological mechanisms. However, many reported findings are unrepeatable due to small sample sizes relative to a large number of genes and the low signal-to-noise ratios of most gene expression datasets.ResultsMeta-analysis of multi-data sets is an efficient method for tackling the above problem. To improve the performance of meta-analysis, we propose a novel meta-analysis framework. It consists of two parts: (1) a novel data augmentation strategy. Various cross-platform normalization methods exist, which can preserve original biological information of gene expression datasets from different angles and add different “perturbations” to the dataset. Using such perturbation, we provide a feasible means for gene expression data augmentation; (2) elastic data shared lasso (DSL-{{varvec{L}}}_{mathbf{2}}). The DSL-{mathbf{L}}_{mathbf{2}} method spans the continuum between individual models for each dataset and one model for all datasets. It also overcomes the shortcomings of the data shared lasso method when dealing with highly correlated features. Comprehensive simulation experiment results show that the proposed method has high prediction and gene selection performance. We then apply the proposed method to non-small cell lung cancer (NSCLC) blood gene expression data in order to identify key tumor-related genes. The outcomes of our experiment indicate that the method could be used for identifying a set of robust disease-related gene signatures that may be used for NSCLC early diagnosis or prognosis or even targeting.ConclusionWe propose a novel and effective meta-analysis method for biological research, extrapolating and integrating information from multiple gene expression datasets.

A Diagnostic Gene Expression Signature for Bladder Cancer Can Stratify Cases into Prescribed Molecular Subtypes and Predict Outcome.

Bladder cancer is a biologically heterogeneous disease with variable clinical presentations, outcomes and responses to therapy. Thus, the clinical utility of single biomarkers for the detection and prediction of biological behavior of bladder cancer is limited. We have previously identified and validated a bladder cancer diagnostic signature composed of 10 biomarkers, which has been incorporated into a multiplex immunoassay bladder cancer test, Oncuria™. In this study, we evaluate whether these 10 biomarkers can assist in the prediction of bladder cancer clinical outcomes. Tumor gene expression and patient survival data from bladder cancer cases from The Cancer Genome Atlas (TCGA) were analyzed. Alignment between the mRNA expression of 10 biomarkers and the TCGA 2017 subtype classification was assessed. Kaplan–Meier analysis of multiple gene expression datasets indicated that high expression of the combined 10 biomarkers correlated with a significant reduction in overall survival. The analysis of three independent, publicly available gene expression datasets confirmed that multiplex prognostic models outperformed single biomarkers. In total, 8 of the 10 biomarkers from the Oncuria™ test were significantly associated with either luminal or basal molecular subtypes, and thus, the test has the potential to assist in the prediction of clinical outcome.

Un-biased housekeeping gene panel selection for high-validity gene expression analysis

Differential gene expression normalised to a single housekeeping (HK) is used to identify disease mechanisms and therapeutic targets. HK gene selection is often arbitrary, potentially introducing systematic error and discordant results. Here we examine these risks in a disease model of brain hypoxia. We first identified the eight most frequently used HK genes through a systematic review. However, we observe that in both ex-vivo and in vivo, their expression levels varied considerably between conditions. When applying these genes to normalise expression levels of the validated stroke target gene, inducible Nox4, we obtained opposing results. As an alternative tool for unbiased HK gene selection, software tools exist but are limited to individual datasets lacking genome-wide search capability and user-friendly interfaces. We, therefore, developed the HouseKeepR algorithm to rapidly analyse multiple gene expression datasets in a disease-specific manner and rank HK gene candidates according to stability in an unbiased manner. Using a panel of de novo top-ranked HK genes for brain hypoxia, but not single genes, Nox4 induction was consistently reproduced. Thus, differential gene expression analysis is best normalised against a HK gene panel selected in an unbiased manner. HouseKeepR is the first user-friendly, bias-free, and broadly applicable tool to automatically propose suitable HK genes in a tissue- and disease-dependent manner.

Mutational Activation of the NRF2 Pathway Upregulates Kynureninase Resulting in Tumor Immunosuppression and Poor Outcome in Lung Adenocarcinoma.

Simple SummaryActivation of the Nuclear factor-erythroid factor 2-related factor 2 (NRF2) pathway through gain-of-function mutations or loss-of-function of its suppressor Kelch-like ECH-associated protein 1 (KEAP1) is frequent in lung cancer. NRF2 activation has also been reported to alter the tumor microenvironment. Proteomic profiles of 47 lung adenocarcinoma (LUAD) cell lines (11 KEAP1 mutant and 36 KEAP1 wild-type) revealed the tryptophan-kynurenine enzyme kynureninase (KYNU) as a top overexpressed protein associated with activated NRF2. Mechanistic studies demonstrated that NRF2 is a regulator of enzymatically functional KYNU in LUAD. Analysis of multiple independent gene expression datasets of human lung cancer and a LUAD tumor microarray demonstrated that elevated tumor KYNU expression was associated with immunosuppression, including potent induction of T-regulatory cells, increased levels of PD1 and PD-L1, and poorer overall survival. Our findings indicate a novel mechanism of NRF2 tumoral immunosuppression through upregulation of KYNU.Activation of the NRF2 pathway through gain-of-function mutations or loss-of-function of its suppressor KEAP1 is a frequent finding in lung cancer. NRF2 activation has been reported to alter the tumor microenvironment. Here, we demonstrated that NRF2 alters tryptophan metabolism through the kynurenine pathway that is associated with a tumor-promoting, immune suppressed microenvironment. Specifically, proteomic profiles of 47 lung adenocarcinoma (LUAD) cell lines (11 KEAP1 mutant and 36 KEAP1 wild-type) revealed the tryptophan-kynurenine enzyme kynureninase (KYNU) as a top overexpressed protein associated with activated NRF2. The siRNA-mediated knockdown of NFE2L2, the gene encoding for NRF2, or activation of the NRF2 pathway through siRNA-mediated knockdown of KEAP1 or via chemical induction with the NRF2-activator CDDO-Me confirmed that NRF2 is a regulator of KYNU expression in LUAD. Metabolomic analyses confirmed KYNU to be enzymatically functional. Analysis of multiple independent gene expression datasets of LUAD, as well as a LUAD tumor microarray demonstrated that elevated KYNU was associated with immunosuppression, including potent induction of T-regulatory cells, increased levels of PD1 and PD-L1, and resulted in poorer survival. Our findings indicate a novel mechanism of NRF2 tumoral immunosuppression through upregulation of KYNU.

Shambhala-2: A Protocol for Uniformly Shaped Harmonization of Gene Expression Profiles of Various Formats.

Uniformly shaped harmonization of gene expression profiles is central for the simultaneous comparison of multiple gene expression datasets. It is expected to operate with the gene expression data obtained using various experimental methods and equipment, and to return harmonized profiles in a uniform shape. Such uniformly shaped expression profiles from different initial datasets can be further compared directly. However, current harmonization techniques have strong limitations that prevent their broad use for bioinformatic applications. They can either operate with only up to two datasets/platforms or return data in a dynamic format that will be different for every comparison under analysis. This also does not allow for adding new data to the previously harmonized dataset(s), which complicates the analysis and increases calculation costs. We propose here a new method termed Shambhala-2 that can transform multi-platform expression data into a universal format that is identical for all harmonizations made using this technique. Shambhala-2 is based on sample-by-sample cubic conversion of the initial expression dataset into a preselected shape of the reference definitive dataset. Using 8390 samples of 12 healthy human tissue types and 4086 samples of colorectal, kidney, and lung cancer tissues, we verified Shambhala-2's capacity in restoring tissue-specific expression patterns for seven microarray and three RNA sequencing platforms. Shambhala-2 performed well for all tested combinations of RNAseq and microarray profiles, and retained gene-expression ranks, as evidenced by high correlations between different single- or aggregated gene expression metrics in pre- and post-Shambhalized samples, including preserving cancer-specific gene expression and pathway activation features. © 2022 Wiley Periodicals LLC. Basic Protocol: Shambhala-2 harmonizer Alternate Protocol 1: Linear Shambhala/Shambhala-1 Alternate Protocol 2: Alternative (flexible-format and uniformly shaped) normalization methods Support Protocol 1: Watermelon multisection (WM) Support Protocol 2: Calculation of cancer-to-normal log-fold-change (LFC) and pathway activation level (PAL).

RegEnrich gene regulator enrichment analysis reveals a key role of the ETS transcription factor family in interferon signaling

Changes in a few key transcriptional regulators can lead to different biological states. Extracting the key gene regulators governing a biological state allows us to gain mechanistic insights. Most current tools perform pathway/GO enrichment analysis to identify key genes and regulators but tend to overlook the gene/protein regulatory interactions. Here we present RegEnrich, an open-source Bioconductor R package, which combines differential expression analysis, data-driven gene regulatory network inference, enrichment analysis, and gene regulator ranking to identify key regulators using gene/protein expression profiling data. By benchmarking using multiple gene expression datasets of gene silencing studies, we found that RegEnrich using the GSEA method to rank the regulators performed the best. Further, RegEnrich was applied to 21 publicly available datasets on in vitro interferon-stimulation of different cell types. Collectively, RegEnrich can accurately identify key gene regulators from the cells under different biological states, which can be valuable in mechanistically studying cell differentiation, cell response to drug stimulation, disease development, and ultimately drug development.

Identification and validation of CALCRL-associated prognostic genes in acute myeloid leukemia

In the past few decades, several advances have been made in the field of acute myeloid leukemia (AML), especially in the development of novel drugs. However, the overall survival rate remains particularly disappointing due to a high rate of chemotherapy resistance and relapse. The calcitonin receptor-like receptor (CALCRL) is a novel promising therapeutic target of AML and has been indicated to be strongly correlated with chemotherapy resistance and relapse driven by leukemic stem cells. Nevertheless, the CALCRL downstream genes associated with the drug resistance and relapse of AML remain to be elucidated. Within this study, we used multiple gene expression datasets from the Gene Expression Omnibus (GEO) database and cBioPortal to explore the candidate CALCRL-associated genes that could potentially mediate the chemoresistance and relapse of AML. Then, we investigated the prognostic value, coexpression relationship with CALCRL, and expression characteristics of these genes using independent data from The Cancer Genome Atlas (TCGA). Eventually, three genes were screened out as CALCRL-associated prognostic genes. The expression of AGTPBP1 and LYST was negatively correlated with CALCRL, high expression of which was associated with favorable prognosis in AML. In contrast, the expression of ETS2 was positively correlated with CALCRL, high expression of which was associated with poor prognosis in AML. The results indicated that the three prognostic genes are potential CALCRL downstream genes that mediate drug resistance and relapse in AML. This study helps to further explore the role and molecular pathways of CALCRL in mediating drug resistance and relapse of AML.

HIST1H1B Promotes Basal-Like Breast Cancer Progression by Modulating CSF2 Expression.

BackgroundBasal-like breast cancer (BLBC) is associated with a poor clinical outcome; however, the mechanism of BLBC aggressiveness is still unclear. It has been shown that a linker histone functions as either a positive or negative regulator of gene expression in tumors. Here, we aimed to investigate the possible involvement and mechanism of HIST1H1B in BLBC progression.Experimental designWe analyzed multiple gene expression datasets to determine the relevance of HIST1H1B expression with BLBC. We employed quantitative real-time PCR, transwell assay, colony formation assay, and mammosphere assay to dissect the molecular events associated with the expression of HIST1H1B in human breast cancer. We studied the association of HIST1H1B with CSF2 by ChIP assay. Using tumorigenesis assays, we determine the effect of HIST1H1B expression on tumorigenicity of BLBC cells.ResultsHere, we show that the linker histone HIST1H1B is dramatically elevated in BLBC due to HIST1H1B copy number amplification and promoter hypomethylation. HIST1H1B upregulates colony-stimulating factor 2 (CSF2) expression by binding the CSF2 promoter. HIST1H1B expression promotes, whereas knockdown of HIST1H1B expression suppresses tumorigenicity. In breast cancer patients, HIST1H1B expression is positively correlated with large tumor size, high grade, metastasis and poor survival.ConclusionHIST1H1B contributes to basal-like breast cancer progression by modulating CSF2 expression, indicating a potential prognostic marker and therapeutic target for this disease.

The long non-coding RNA HOTAIRM1 promotes tumor aggressiveness and radiotherapy resistance in glioblastoma

Glioblastoma is the most common malignant primary brain tumor. To date, clinically relevant biomarkers are restricted to isocitrate dehydrogenase (IDH) gene 1 or 2 mutations and O6-methylguanine DNA methyltransferase (MGMT) promoter methylation. Long non-coding RNAs (lncRNAs) have been shown to contribute to glioblastoma pathogenesis and could potentially serve as novel biomarkers. The clinical significance of HOXA Transcript Antisense RNA, Myeloid-Specific 1 (HOTAIRM1) was determined by analyzing HOTAIRM1 in multiple glioblastoma gene expression data sets for associations with prognosis, as well as, IDH mutation and MGMT promoter methylation status. Finally, the role of HOTAIRM1 in glioblastoma biology and radiotherapy resistance was characterized in vitro and in vivo. We identified HOTAIRM1 as a candidate lncRNA whose up-regulation is significantly associated with shorter survival of glioblastoma patients, independent from IDH mutation and MGMT promoter methylation. Glioblastoma cell line models uniformly showed reduced cell viability, decreased invasive growth and diminished colony formation capacity upon HOTAIRM1 down-regulation. Integrated proteogenomic analyses revealed impaired mitochondrial function and determination of reactive oxygen species (ROS) levels confirmed increased ROS levels upon HOTAIRM1 knock-down. HOTAIRM1 knock-down decreased expression of transglutaminase 2 (TGM2), a candidate protein implicated in mitochondrial function, and knock-down of TGM2 mimicked the phenotype of HOTAIRM1 down-regulation in glioblastoma cells. Moreover, HOTAIRM1 modulates radiosensitivity of glioblastoma cells both in vitro and in vivo. Our data support a role for HOTAIRM1 as a driver of biological aggressiveness, radioresistance and poor outcome in glioblastoma. Targeting HOTAIRM1 may be a promising new therapeutic approach.

Comparison of merging strategies for building machine learning models on multiple independent gene expression data sets

AbstractHigh‐dimensional gene expression data are regularly studied for their ability to separate different groups of samples by means of machine learning (ML) models. Meanwhile, a large number of such data are publicly available. Several approaches for meta‐analysis on independent sets of gene expression data have been proposed, mainly focusing on the step of feature selection, a typical step in fitting a ML model. Here, we compare different strategies of merging the information of such independent data sets to train a classifier model. Specifically, we compare the strategy of merging data sets directly (strategy A), and the strategy of merging the classification results (strategy B). We use simulations with pure artificial data as well as evaluations based on independent gene expression data from lung fibrosis studies to compare the two merging approaches. In the simulations, the number of studies, the strength of batch effects, and the separability are varied. The comparison incorporates five standard ML techniques typically used for high‐dimensional data, namely discriminant analysis, support vector machines, least absolute shrinkage and selection operator, random forest, and artificial neural networks. Using cross‐study validations, we found that direct data merging yields higher accuracies when having training data of three or four studies, and merging of classification results performed better when having only two training studies. In the evaluation with the lung fibrosis data, both strategies showed a similar performance.

Identification of Hub Genes and Pathways Associated With Idiopathic Pulmonary Fibrosis via Bioinformatics Analysis.

Idiopathic pulmonary fibrosis (IPF) is a progressive disease whose etiology remains unknown. The purpose of this study was to explore hub genes and pathways related to IPF development and prognosis. Multiple gene expression datasets were downloaded from the Gene Expression Omnibus database. Weighted correlation network analysis (WGCNA) was performed and differentially expressed genes (DEGs) identified to investigate Hub modules and genes correlated with IPF. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, and protein-protein interaction (PPI) network analysis were performed on selected key genes. In the PPI network and cytoHubba plugin, 11 hub genes were identified, including ASPN, CDH2, COL1A1, COL1A2, COL3A1, COL14A1, CTSK, MMP1, MMP7, POSTN, and SPP1. Correlation between hub genes was displayed and validated. Expression levels of hub genes were verified using quantitative real-time PCR (qRT-PCR). Dysregulated expression of these genes and their crosstalk might impact the development of IPF through modulating IPF-related biological processes and signaling pathways. Among these genes, expression levels of COL1A1, COL3A1, CTSK, MMP1, MMP7, POSTN, and SPP1 were positively correlated with IPF prognosis. The present study provides further insights into individualized treatment and prognosis for IPF.

Differential Expression and Copy Number Variation of Gasdermin (GSDM) Family Members, Pore-Forming Proteins in Pyroptosis, in Normal and Malignant Serous Ovarian Tissue.

Gasdermins (GSDM) are members of a family of pore-forming effector proteins which lead to membrane permeabilization and pyroptosis, a lytic cell death with pro-inflammatory characteristics. Recently, two members of the gasdermin family, gasdermin B (GSDMB) and gasdermin E (GSDME), were shown to suppress tumor growth, through the involvement of cytotoxic lymphocytes. Other studies also reported the important functions of gasdermins in various cancer types including gastric cancer, hepatocarcinoma, and cervix and breast cancer. However, gasdermins have not been previously studied in the context of serous ovarian cancer. Here, we showed that gasdermin D (GSDMD) and gasdermin C (GSDMC) expression increases in serous ovarian cancer; in contrast, the expression of GSDME and PJVK (Pejvakin, DFNB59) is downregulated, compared to healthy ovaries, in multiple independent gene expression datasets. We found that copy number gains are highly frequent (present in approximately 50% of patients) in genes encoding GSDMD and GSDMC in ovarian cancer, in line with their upregulated expression in serous ovarian cancer. Moreover, we observed that the expression of GSDMB and GSDMD, but not of GSDME, is different among several histotypes of epithelial ovarian cancer. Therefore, we propose that differential expression and copy number variations of certain gasdermins might be associated with the development of serous ovarian cancer, in which different members of the family have distinct functions; however, further research is required in in vivo models to understand how changes in gasdermin family members mechanistically contribute to serous ovarian cancer.

Prognostic Relevance of Expression of EMP1, CASP1, and NLRP3 Genes in Pediatric B-Lineage Acute Lymphoblastic Leukemia.

Glucocorticoid (GC), such as prednisolone, is an essential component of multidrug chemotherapy regimen for pediatric acute lymphoblastic leukemia (ALL). Resistance to GC in leukemia cells is associated with disease progression and poor prognosis. Despite the extensive use of GC for many years, molecular mechanisms underlying its resistance in ALL have not been fully uncovered. Recent studies have shown a potential role of EMP1, CASP1, and NLRP3 genes in prednisolone response. In this study on 148 pediatric B-ALL patients, we studied these three genes to assess their association with prednisolone response measured by day 8 blast count after 7 days of induction therapy with prednisolone. Intriguingly, ALL samples exhibited higher expression of EMP1 along with a low expression of CASP1 and NLRP3 compared to disease free normal bone marrow collected from patients with solid tumors. Among the three analyzed genes, only EMP1 was found to be overexpressed in prednisolone poor responders (p=0.015). Further, a comparison of gene expression between cytogenetic subtypes revealed higher expression of EMP1 in BCR-ABL subtype. Expression of EMP1 in multiple gene expression datasets was used for gene set enrichment analysis, which revealed TNF-α, IL-2-STAT5 signaling, inflammatory responses and hypoxia as the major positively associated pathways and E2F targets as negatively associated pathways. Interestingly, the clinical remission rate was higher in CASP1 high patients (p=0.048). In univariate survival analysis, higher EMP1 expression was associated with poor prognostic measures while higher expression of NLRP3 and CASP1 was associated with better prognostic measures in our data. Further, multivariate analysis revealed an independent association of high CASP1 and NLRP3 with a better prognosis. This study strengthens the available evidence that mRNA expression of EMP1, CASP1, and NLRP3 may serve as potential biomarkers for risk stratification of pediatric B-ALL patients.