Large-scale Gene Expression Datasets Research Articles

ScCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution.

Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME. The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296.

The MAL Family of Proteins: Normal Function, Expression in Cancer, and Potential Use as Cancer Biomarkers.

The MAL family of integral membrane proteins consists of MAL, MAL2, MALL, PLLP, CMTM8, MYADM, and MYADML2. The best characterized members are elements of the machinery that controls specialized pathways of membrane traffic and cell signaling. This review aims to help answer the following questions about the MAL-family genes: (i) is their expression regulated in cancer and, if so, how? (ii) What role do they play in cancer? (iii) Might they have biomedical applications? Analysis of large-scale gene expression datasets indicated altered levels of MAL-family transcripts in specific cancer types. A comprehensive literature search provides evidence of MAL-family gene dysregulation and protein function repurposing in cancer. For MAL, and probably for other genes of the family, dysregulation is primarily a consequence of gene methylation, although copy number alterations also contribute to varying degrees. The scrutiny of the two sources of information, datasets and published studies, reveals potential prognostic applications of MAL-family members as cancer biomarkers-for instance, MAL2 in breast cancer, MAL2 and MALL in pancreatic cancer, and MAL and MYADM in lung cancer-and other biomedical uses. The availability of validated antibodies to some MAL-family proteins sanctions their use as cancer biomarkers in routine clinical practice.

EVI2B Is a Prognostic Biomarker and Is Correlated with Monocyte and Macrophage Infiltration in Osteosarcoma Based on an Integrative Analysis.

Osteosarcoma (OS) is the most common malignant bone tumor. However, treatment strategies have not changed over the past 30 years. The relationship between OS and the immune microenvironment may provide a basis for the establishment of novel therapeutic targets. In this study, a large-scale gene expression dataset (GSE42352) was used to identify key genes in OS. A Target-OS dataset from the Cancer Genome Atlas was used as a validation set. Ecotropic viral integration site 2B (EVI2B) was significantly upregulated in OS tumor samples. Differentially expressed genes (DEGs) were identified between samples with high and low EVI2B expression in both the test and validation cohorts. The top three functions of DEGs determined by a gene set enrichment analysis (GSEA) were chemokine signaling, cytokine-cytokine receptor interaction, and Human T-cell leukemia virus 1 infection. A prognostic prediction model including EVI2B, DOCK2, and CD33 was constructed by a Cox regression analysis. This model indicated that EVI2B is an independent protective prognostic marker in OS. An analysis of immune infiltration further showed that high EVI2B expression levels were correlated with high levels of macrophage infiltration. Protein expression data derived from the Human Protein Atlas suggested EVI2B to be highly expressed in monocytes. Finally, we validated the elevated expression of EVI2B in OS cell lines and OS tissue samples; these results were consistent with those of the analyses of the GSE42352 and Target-OS datasets. Our integrative bioinformatics analysis and experimental results provide clear evidence for the prognostic value of EVI2B in OS and its close relationship with monocyte and macrophage infiltration.

ARBic: an all-round biclustering algorithm for analyzing gene expression data.

Identifying significant biclusters of genes with specific expression patterns is an effective approach to reveal functionally correlated genes in gene expression data. However, none of existing algorithms can simultaneously identify both broader and narrower biclusters due to their failure of balancing between effectiveness and efficiency. We introduced ARBic, an algorithm which is capable of accurately identifying any significant biclusters of any shape, including broader, narrower and square, in any large scale gene expression dataset. ARBic was designed by integrating column-based and row-based strategies into a single biclustering procedure. The column-based strategy borrowed from RecBic, a recently published biclustering tool, extracts narrower biclusters, while the row-based strategy that iteratively finds the longest path in a specific directed graph, extracts broader ones. Being tested and compared to other seven salient biclustering algorithms on simulated datasets, ARBic achieves at least an average of 29% higher recovery, relevance and[Formula: see text] scores than the best existing tool. In addition, ARBic substantially outperforms all tools on real datasets and is more robust to noises, bicluster shapes and dataset types.

Alzheimer’s disease large-scale gene expression portrait identifies exercise as the top theoretical treatment

Alzheimer’s disease (AD) is a complex neurodegenerative disorder that affects multiple brain regions and is difficult to treat. In this study we used 22 AD large-scale gene expression datasets to identify a consistent underlying portrait of AD gene expression across multiple brain regions. Then we used the portrait as a platform for identifying treatments that could reverse AD dysregulated expression patterns. Enrichment of dysregulated AD genes included multiple processes, ranging from cell adhesion to CNS development. The three most dysregulated genes in the AD portrait were the inositol trisphosphate kinase, ITPKB (upregulated), the astrocyte specific intermediate filament protein, GFAP (upregulated), and the rho GTPase, RHOQ (upregulated). 41 of the top AD dysregulated genes were also identified in a recent human AD GWAS study, including PNOC, C4B, and BCL11A. 42 transcription factors were identified that were both dysregulated in AD and that in turn affect expression of other AD dysregulated genes. Male and female AD portraits were highly congruent. Out of over 250 treatments, three datasets for exercise or activity were identified as the top three theoretical treatments for AD via reversal of large-scale gene expression patterns. Exercise reversed expression patterns of hundreds of AD genes across multiple categories, including cytoskeleton, blood vessel development, mitochondrion, and interferon-stimulated related genes. Exercise also ranked as the best treatment across a majority of individual region-specific AD datasets and meta-analysis AD datasets. Fluoxetine also scored well and a theoretical combination of fluoxetine and exercise reversed 549 AD genes. Other positive treatments included curcumin. Comparisons of the AD portrait to a recent depression portrait revealed a high congruence of downregulated genes in both. Together, the AD portrait provides a new platform for understanding AD and identifying potential treatments for AD.

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.

Lung tissue shows divergent gene expression between chronic obstructive pulmonary disease and idiopathic pulmonary fibrosis

BackgroundChronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are characterized by shared exposures and clinical features, but distinct genetic and pathologic features exist. These features have not been well-studied using large-scale gene expression datasets. We hypothesized that there are divergent gene, pathway, and cellular signatures between COPD and IPF.MethodsWe performed RNA-sequencing on lung tissues from individuals with IPF (n = 231) and COPD (n = 377) compared to control (n = 267), defined as individuals with normal spirometry. We grouped the overlapping differential expression gene sets based on direction of expression and examined the resultant sets for genes of interest, pathway enrichment, and cell composition. Using gene set variation analysis, we validated the overlap group gene sets in independent COPD and IPF data sets.ResultsWe found 5010 genes differentially expressed between COPD and control, and 11,454 genes differentially expressed between IPF and control (1% false discovery rate). 3846 genes overlapped between IPF and COPD. Several pathways were enriched for genes upregulated in COPD and downregulated in IPF; however, no pathways were enriched for genes downregulated in COPD and upregulated in IPF. There were many myeloid cell genes with increased expression in COPD but decreased in IPF. We found that the genes upregulated in COPD but downregulated in IPF were associated with lower lung function in the independent validation cohorts.ConclusionsWe identified a divergent gene expression signature between COPD and IPF, with increased expression in COPD and decreased in IPF. This signature is associated with worse lung function in both COPD and IPF.

FoxO3a Inhibits Tamoxifen-Resistant Breast Cancer Progression by Inducing Integrin α5 Expression.

Simple SummaryTamoxifen is a mainstay of adjuvant treatment for estrogen receptor α-positive (ERα+) breast tumors, which account for over 70% of all the diagnosed breast malignancies. Unfortunately, ~30% of patients initially responding to the therapy will eventually relapse with an endocrine-resistant disease. Here, we show that the transcription factor FoxO3a can overcome tamoxifen resistance by inhibiting cell motility and invasiveness, the main features of tumor progression. The underlying mechanism could be ascribed to FoxO3a-dependent transcriptional up-regulation of the integrin α5 subunit of the α5β1 fibronectin receptor, a well-known membrane heterodimer controlling cell adhesion. Indeed, FoxO3a and integrin α5 expression is significantly correlated in ERα+ tumors, and FoxO3a protective effects are lost in cells with reduced levels of integrin α5. Therefore, a pharmacological increase in FoxO3a levels could represent an effective approach to be exploited in combination with tamoxifen treatment in order to restore the sensitivity to the therapy in refractory tumors.Resistance to endocrine therapy is still a major clinical challenge in the management of estrogen receptor α-positive (ERα+) breast cancer (BC). Here, the role of the Forkhead box class O (FoxO)3a transcription factor in tumor progression has been evaluated in tamoxifen-resistant BC cells (TamR), expressing lower levels of FoxO3a compared to sensitive ones. FoxO3a re-expression reduces TamR motility (wound-healing and transmigration assays) and invasiveness (matrigel transwell invasion assays) through the mRNA (qRT-PCR) and protein (Western blot) induction of the integrin α5 subunit of the α5β1 fibronectin receptor, a well-known membrane heterodimer controlling cell adhesion and signaling. The induction occurs through FoxO3a binding to a specific Forkhead responsive core sequence located on the integrin α5 promoter (cloning, luciferase, and ChIP assays). Moreover, FoxO3a failed to inhibit migration and invasion in integrin α5 silenced (siRNA) cells, demonstrating integrin α5 involvement in both processes. Finally, using large-scale gene expression data sets, a strong positive correlation between FoxO3a and integrin α5 in ERα+, but not in ER-negative (ERα−), BC patients emerged. Altogether, our data show how the oncosuppressor FoxO3a, by increasing the expression of its novel transcriptional target integrin α5, reverts the phenotype of endocrine-resistant BC toward a lower aggressiveness.

HDMC: a novel deep learning-based framework for removing batch effects in single-cell RNA-seq data.

With the development of single-cell RNA sequencing (scRNA-seq) techniques, increasingly more large-scale gene expression datasets become available. However, to analyze datasets produced by different experiments, batch effects among different datasets must be considered. Although several methods have been recently published to remove batch effects in scRNA-seq data, two problems remain to be challenging and not completely solved: (i) how to reduce the distribution differences of different batches more accurately; and (ii) how to align samples from different batches to recover the cell type clusters. We proposed a novel deep-learning approach, which is a hierarchical distribution-matching framework assisted with contrastive learning to address these two problems. Firstly, we design a hierarchical framework for distribution matching based on a deep autoencoder. This framework employs an adversarial training strategy to match the global distribution of different batches. This provides an improved foundation to further match the local distributions with a maximum mean discrepancy-based loss. For local matching, we divide cells in each batch into clusters and develop a contrastive learning mechanism to simultaneously align similar cluster pairs and keep noisy pairs apart from each other. This allows to obtain clusters with all cells of the same type (true positives), and avoid clusters with cells of different type (false positives). We demonstrate the effectiveness of our method on both simulated and real datasets. Results show that our new method significantly outperforms the state-of-the-art methods and has the ability to prevent overcorrection. The python code to generate results and figures in this article is available at https://github.com/zhanglabNKU/HDMC, the data underlying this article is also available at this github repository. Supplementary data are available at Bioinformatics online.

Stratification of lncRNA modulation networks in breast cancer

BackgroundRecently, non-coding RNAs are of growing interest, and more scientists attach importance to research on their functions. Long non-coding RNAs (lncRNAs) are defined as non-protein coding transcripts longer than 200 nucleotides. We already knew that lncRNAs are related to cancers and will be dysregulated in them. But most of their functions are still left to further study. A mechanism of RNA regulation, known as competing endogenous RNAs (ceRNAs), has been proposed to explain the complex relationships among mRNAs and lncRNAs by competing for binding with shared microRNAs (miRNAs).MethodsWe proposed an analysis framework to construct the association networks among lncRNA, mRNA, and miRNAs based on their expression patterns and decipher their network modules.ResultsWe collected a large-scale gene expression dataset of 1,061 samples from breast invasive carcinoma (BRCA) patients, each consisted of the expression profiles of 4,359 lncRNAs, 16,517 mRNAs, and 534 miRNAs, and applied the proposed analysis approach to interrogate them. We have uncovered the underlying ceRNA modules and the key modulatory lncRNAs for different subtypes of breast cancer.ConclusionsWe proposed a modulatory analysis to infer the ceRNA effects among mRNAs and lncRNAs and performed functional analysis to reveal the plausible mechanisms of lncRNA modulation in the four breast cancer subtypes. Our results might provide new directions for breast cancer therapeutics and the proposed method could be readily applied to other diseases.

Deep Modeling of Regulating Effects of Small Molecules on Longevity-Associated Genes.

Aging is considered an inevitable process that causes deleterious effects in the functioning and appearance of cells, tissues, and organs. Recent emergence of large-scale gene expression datasets and significant advances in machine learning techniques have enabled drug repurposing efforts in promoting longevity. In this work, we further developed our previous approach—DeepCOP, a quantitative chemogenomic model that predicts gene regulating effects, and extended its application across multiple cell lines presented in LINCS to predict aging gene regulating effects induced by small molecules. As a result, a quantitative chemogenomic Deep Model was trained using gene ontology labels, molecular fingerprints, and cell line descriptors to predict gene expression responses to chemical perturbations. Other state-of-the-art machine learning approaches were also evaluated as benchmarks. Among those, the deep neural network (DNN) classifier has top-ranked known drugs with beneficial effects on aging genes, and some of these drugs were previously shown to promote longevity, illustrating the potential utility of this methodology. These results further demonstrate the capability of “hybrid” chemogenomic models, incorporating quantitative descriptors from biomarkers to capture cell specific drug–gene interactions. Such models can therefore be used for discovering drugs with desired gene regulatory effects associated with longevity.

FoxO3a Reduces Tamoxifen Resistant Breast Cancer Aggressiveness by Inducing Integrin a5 Expression

Resistance to endocrine therapy is still a major clinical challenge in the management of estrogen receptor positive (ER+) breast cancer (BC) patients, whose poor outcome demands additional studies. Forkhead box class O (FoxO)3a transcription factor, a bona fide oncosuppressor, has been involved in BC metastasis as well as in antiestrogen resistance. Here we demonstrate that the α5 subunit of the integrin α5β1, a well-known membrane heterodimer controlling cell surface adhesion and signaling, is a novel FoxO3a transcriptional target. FoxO3a re-expression reduces motility (wound healing and transmigration assays) of different Tamoxifen resistant BC cell lines, through the induction of α5 mRNA (qRT-PCR) and protein (Western blot) levels. FoxO3a transcriptionally regulates α5 expression by binding to specific Forkhead responsive elements located on the α5 promoter (Luciferase and ChIP assays). Accordingly, using a large-scale BC gene expression datasets from The Cancer Genome Atlas (TCGA) database, a strong positive correlation between FoxO3a and α5 in ER+ BC patients emerged. Altogether, our data unveil an additional mechanism through which FoxO3a activation/induction, by increasing α5 expression, restores a less aggressive phenotype in BC refractory to endocrine therapy.

DCI: learning causal differences between gene regulatory networks.

Designing interventions to control gene regulation necessitates modeling a gene regulatory network by a causal graph. Currently, large-scale gene expression datasets from different conditions, cell types, disease states, and developmental time points are being collected. However, application of classical causal inference algorithms to infer gene regulatory networks based on such data is still challenging, requiring high sample sizes and computational resources. Here, we describe an algorithm that efficiently learns the differences in gene regulatory mechanisms between different conditions. Our difference causal inference (DCI) algorithm infers changes (i.e. edges that appeared, disappeared, or changed weight) between two causal graphs given gene expression data from the two conditions. This algorithm is efficient in its use of samples and computation since it infers the differences between causal graphs directly without estimating each possibly large causal graph separately. We provide a user-friendly Python implementation of DCI and also enable the user to learn the most robust difference causal graph across different tuning parameters via stability selection. Finally, we show how to apply DCI to single-cell RNA-seq data from different conditions and cell states, and we also validate our algorithm by predicting the effects of interventions. Python package freely available at http://uhlerlab.github.io/causaldag/dci. Supplementary data are available at Bioinformatics online.

Combined CDK2 and CDK4/6 Inhibition Overcomes Palbociclib Resistance in Breast Cancer by Enhancing Senescence.

Simple SummaryCyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors are widely used to treat metastatic hormone receptor-positive/human epidermal growth factor receptor 2-negative breast cancer. Despite the effectiveness of CDK4/6 inhibitors, acquired resistance occurs in almost all cases. Strategies to address this issue have not been developed yet. We investigated mechanisms of resistance to CDK4/6 inhibitor in breast cancer and potential therapeutic strategies. We found that cyclin E-CDK2 mediated phosphorylation of C-MYC is responsible for resistance to CDK4/6 inhibitor by suppressing C-MYC induced senescence. On the contrary, the synergistic anti-proliferative effect of the combined inhibition of CDK2 and CDK4/6 overcomes acquired resistance to CDK4/6 inhibitors by enhancing senescence. Our findings could pave the way for the development CDK2-specific kinase inhibitor for the treatment of breast cancers that are resistant to CDK4/6 inhibitor.Breast cancer represents the number one global cancer burden in women and the hormone receptor (HR)-positive subtype comprises approximately 70% of breast cancers. Unfortunately, acquired resistance ultimately occurs in almost all cases, even though cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors are a highly effective therapy for HR-positive/human epidermal growth factor receptor 2-negative subtype. Here, we investigated mechanisms of resistance to CDK4/6 inhibitor and potential therapeutic strategies using our palbociclib-resistant preclinical model. We observed that cyclin E was significantly overexpressed in palbociclib-resistant cells, and similar association was also confirmed in pleural effusion samples collected from HR-positive breast cancer patients. After confirmation of cyclin E-CDK2 interaction by co-immunoprecipitation, we demonstrated CDK2 inhibition combined with palbociclib synergistically suppressed proliferation of palbociclib-resistant cells and growth of palbociclib-resistant xenograft in mice. We also proved that enhancing C-MYC-mediated senescence is a novel mechanism behind the synergism created by targeting both CDK2 and CDK4/6. Furthermore, the clinical relevance of cyclin E as a therapeutic target was supported by significant association between CCNE1 overexpression and poor prognosis based on large-scale public gene expression data sets in HR-positive breast cancer patients. Therefore, we propose cyclin E-CDK2 signaling as a promising therapeutic target for overcoming cyclin E-associated resistance to CDK4/6 inhibitor.

Co-expression of calcium and hERG potassium channels reduces the incidence of proarrhythmic events.

Cardiac electrical activity is extraordinarily robust. However, when it goes wrong it can have fatal consequences. Electrical activity in the heart is controlled by the carefully orchestrated activity of more than a dozen different ion conductances. While there is considerable variability in cardiac ion channel expression levels between individuals, studies in rodents have indicated that there are modules of ion channels whose expression co-vary. The aim of this study was to investigate whether meta-analytic co-expression analysis of large-scale gene expression datasets could identify modules of co-expressed cardiac ion channel genes in human hearts that are of functional importance. Meta-analysis of 3653 public human RNA-seq datasets identified a strong correlation between expression of CACNA1C (L-type calcium current, ICaL) and KCNH2 (rapid delayed rectifier K+ current, IKr), which was also observed in human adult heart tissue samples. In silico modelling suggested that co-expression of CACNA1C and KCNH2 would limit the variability in action potential duration seen with variations in expression of ion channel genes and reduce susceptibility to early afterdepolarizations, a surrogate marker for proarrhythmia. We also found that levels of KCNH2 and CACNA1C expression are correlated in human-induced pluripotent stem cell-derived cardiac myocytes and the levels of CACNA1C and KCNH2 expression were inversely correlated with the magnitude of changes in repolarization duration following inhibition of IKr. Meta-analytic approaches of multiple independent human gene expression datasets can be used to identify gene modules that are important for regulating heart function. Specifically, we have verified that there is co-expression of CACNA1C and KCNH2 ion channel genes in human heart tissue, and in silico analyses suggest that CACNA1C-KCNH2 co-expression increases the robustness of cardiac electrical activity.

Abstract 1793: Interaction of MYC and SUMOylation machinery in an aggressive pancreatic cancer subtype

Abstract Despite recent improvements in treatment regimen such as combination chemotherapies, pancreatic ductal adenocarcinoma still carries a dismal prognosis with an overall survival rate of 9%. The oncogenic transcription factor MYC is amplified in about 12% of all PDAC, which is connected to a worse survival. It is clear that hyperactivation of MYC generates dependencies that can be exploited therapeutically. Therefore, the aim of the study was to find and to target MYC-associated dependencies. Human PDAC gene expression data sets were analyzed. Moreover, analysis of the SUMO pathway in large cohort of PDAC was performed by IHC. Furthermore, a novel, pharmacological SUMO inhibitor was used and characterized by using human and murine 2D-, organoid-, and in vivo-models of PDAC. In addition, this connection was also verified by the genetically modified human and murine models. Large-scale gene expression datasets of human PDACs analysis revealed a connection of MYC to the SUMOylation machinery in PDAC. Moreover, results were also corroborated by the hyperactivation of SUMO pathway in a large patient cohort which characterized a PDAC subtype with a dismal prognosis. We furthermore corroborated these results by using the novel SUMO inhibitor ML093 by in vitro studies that hyperactivation of MYC is connected to an increased sensitivity to pharmacological SUMO inhibition. In conclusion, SUMO inhibitor-based therapies should be further developed for an aggressive PDAC subtype. Citation Format: Zonera Hassan, Alex Biederstädt, Christian Schneeweis, Markus Schick, Nilsson Jonas, Mathias Wirth, Andrea Coluccio, Felix Orben, Katja Steiger, Jolanta Slawska, Steve Langston, Dieter Saur, Roland Rad, Maximilian Reichert, Guenter Schneider, Ulrich Keller. Interaction of MYC and SUMOylation machinery in an aggressive pancreatic cancer subtype [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 1793.

Expression profiling of host long non-coding RNAs under ORF virus infection

The present study aimed at gaining insights into the expression profile of long non-coding RNAs (lncRNAs) and coding genes of Orf virus (ORFV) infected oral mucosal tissues of sheep using RNA-Seq technology. Specifically, an expression profile and lncRNA-mRNA interaction network was inferred from a large-scale gene expression data set of sheep mucosal tissues on 0, 3, 7 and 15 days after ORFV infection. RNA-Seq profiles were obtained from the Gene Expression Omnibus (GEO) database. We found that 45, 64 and 45 lncRNAs and 1796, 2182 and 1550 coding genes were differentially expressed at early (T3), intermediate (T7) and late (T15) stages of ORFV infection in sheep mucosal tissues, respectively. Functional analysis revealed that differentially expressed long non-coding RNAs (DElncRNAs) regulate immune processes by regulating the expression level of differentially expressed coding genes (DEGs) under ORFV infection.

Conventionally used reference genes are not outstanding for normalization of gene expression in human cancer research

BackgroundThe selection of reference genes is essential for quantifying gene expression. Theoretically they should be expressed stably and not regulated by experimental or pathological conditions. However, identification and validation of reference genes for human cancer research are still being regarded as a critical point, because cancerous tissues often represent genetic instability and heterogeneity. Recent pan-cancer studies have demonstrated the importance of the appropriate selection of reference genes for use as internal controls for the normalization of gene expression; however, no stably expressed, consensus reference genes valid for a range of different human cancers have yet been identified.ResultsIn the present study, we used large-scale cancer gene expression datasets from The Cancer Genome Atlas (TCGA) database, which contains 10,028 (9,364 cancerous and 664 normal) samples from 32 different cancer types, to confirm that the expression of the most commonly used reference genes is not consistent across a range of cancer types. Furthermore, we identified 38 novel candidate reference genes for the normalization of gene expression, independent of cancer type. These genes were found to be highly expressed and highly connected to relevant gene networks, and to be enriched in transcription-translation regulation processes. The expression stability of the newly identified reference genes across 29 cancerous and matched normal tissues were validated via quantitative reverse transcription PCR (RT-qPCR).ConclusionsWe reveal that most commonly used reference genes in current cancer studies cannot be appropriate to serve as representative control genes for quantifying cancer-related gene expression levels, and propose in this study three potential reference genes (HNRNPL, PCBP1, and RER1) to be the most stably expressed across various cancerous and normal human tissues.

Guidelines for using sigQC for systematic evaluation of gene signatures.

With the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of diseases. It is becoming crucial to establish whether the expression patterns and statistical properties of sets of genes, or gene signatures, are conserved across independent datasets. Conversely, it is necessary to compare established signatures on the same dataset to better understand how they capture different clinical or biological characteristics. Here we describe how to use sigQC, a tool that enables a streamlined, systematic approach for the evaluation of previously obtained gene signatures across multiple gene expression datasets. We implemented sigQC in an R package, making it accessible to users who have knowledge of file input/output and matrix manipulation in R and a moderate grasp of core statistical principles. SigQC has been adopted in basic biology and translational studies, including, but not limited to, the evaluation of multiple gene signatures for potential clinical use as cancer biomarkers. This protocol uses a previously obtained signature for breast cancer metastasis as an example to illustrate the critical quality control steps involved in evaluating its expression, variability, and structure in breast tumor RNA-sequencing data, a different dataset from that in which the signature was originally derived. We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets.

Human Mitotic Centromere-Associated Kinesin Is Targeted by MicroRNA 485-5p/181c and Prognosticates Poor Survivability of Breast Cancer.

Purpose This study aims to evaluate the prognostic value of human Mitotic Centromere-Associated Kinesin (MCAK), a microtubule-dependent molecular motor, in breast cancers. The posttranscriptional regulation of MCAK by microRNAs will also be explored. Methods The large-scale gene expression datasets of breast cancer (total n=4,677) were obtained from GEO, NKI, and TCGA database. Kaplan-Meier and Cox analyses were used for survival analysis. MicroRNAs targeting MCAK were predicted by bioinformatic analysis and validated by a dual-luciferase reporter assay. Results The expression of MCAK was significantly associated with aggressive features of breast cancer, including tumor stage, Elston grade, and molecular subtypes, for global gene expression datasets of breast cancer (p<0.05). Overexpression of MCAK was significantly associated with poor outcome in a dose-dependent manner for either ER-positive or ER-negative breast cancer. Evidence from bioinformatic prediction, coexpression assays, and gene set enrichment analyses suggested that miR-485-5p and miR-181c might target MCAK and suppress its expression. A 3'UTR dual-luciferase target reporter assay demonstrated that miR-485-5p and miR-181c mimics specifically inhibited relative Firefly/Renilla luciferase activity by about 50% in corresponding reporter plasmids. Further survival analysis also revealed that miR-485-5p (HR=0.59, 95% CI 0.37-0.92) and miR-181c (HR=0.54, 95% CI 0.34-0.84) played opposite roles of MCAK (HR=2.80, 95% CI 1.77-4.57) and were significantly associated with better outcome in breast cancers. Conclusion MCAK could serve as a prognostic biomarker for breast cancers. miR-485-5p and miR-181c could specifically target and suppress the MCAK gene expression in breast cancer cells.