Mining data and metadata from the gene expression omnibus

RummaGEO: Automatic Mining of Human and Mouse Gene Sets from GEO

The Gene Expression Omnibus (GEO) is a major open biomedical research repository for transcriptomics and other omics datasets. It currently contains millions of gene expression samples from tens of thousands of studies collected by many biomedical research laboratories from around the world. While users of the GEO repository can search the metadata describing studies for locating relevant datasets, there are currently no methods or resources that facilitate global search of GEO at the data level. To address this shortcoming, we developed RummaGEO, a webserver application that enables gene expression signature search of a large collection of human and mouse RNA-seq studies deposited into GEO. To develop the search engine, we performed offline automatic identification of sample conditions from the uniformly aligned GEO studies available from ARCHS4. We then computed differential expression signatures to extract gene sets from these studies. In total, RummaGEO currently contains 135,264 human and 158,062 mouse gene sets extracted from 23,395 GEO studies. Next, we analyzed the contents of the RummaGEO database to identify statistical patterns and perform various global analyses. The contents of the RummaGEO database are provided as a web-server search engine with signature search, PubMed search, and metadata search functionalities. Overall, RummaGEO provides an unprecedented resource for the biomedical research community enabling hypothesis generation for many future studies. The RummaGEO search engine is available from: https://rummageo.com/.

Lansoprazole Ameliorates Isoniazid-Induced Liver Injury

Isoniazid is a first-line drug in antitubercular therapy. Isoniazid is one of the most commonly used drugs that can cause liver injury or acute liver failure, leading to death or emergency liver transplantation. Therapeutic approaches for the prevention of isoniazid-induced liver injury are yet to be established. In this study, we identified the gene expression signature for isoniazid-induced liver injury using a public transcriptome dataset, focusing on the differences in susceptibility to isoniazid in various mouse strains. We predicted that lansoprazole is a potentially protective drug against isoniazid-induced liver injury using connectivity mapping and an adverse event reporting system. We confirmed the protective effects of lansoprazole against isoniazid-induced liver injury using zebrafish and patients' electronic health records. These results suggest that lansoprazole can ameliorate isoniazid-induced liver injury. The integrative approach used in this study may be applied to identify novel functions of clinical drugs, leading to drug repositioning.

Discovering Promising Biomarkers and Therapeutic Targets for Duchenne Muscular Dystrophy: a Multiomics Meta-Analysis Approach

Duchenne muscular dystrophy (DMD) is a genetic disorder that causes muscle weakness and degeneration. In this study, we identified potential biomarkers and drug targets for DMD through a comprehensive meta-analysis of mRNA profiles. We conducted an in-depth analysis of three microarray datasets from the GEO database, utilizing the Affymetrix platform. A rigorous data pre-processing pipeline encompassed background correction, normalization, log2 transformation and probe-to-gene symbol mapping. Robust multi-array average method followed by Limma package in R was employed to ensure differential expression analysis within individual datasets, yielding gene-specific p-values. We identified 63 genes exhibiting statistically significant differential expression across the three datasets (p < 0.05) and an absolute log fold change > 1.5. Functional enrichment analyses of these differentially expressed genes were done, followed by pathway analyses. Our results suggested pertinent biological processes, molecular functions and cellular components associated with DMD. Finally, eight hub genes-COL6A3, COL1A1, COL3A1, COL1A2, POSTN, TIMP1, THBS2 and SPP1-were pinpointed as central players in the network. Two differentially expressed genes with substantial absolute log-fold changes, namely, DMD, downregulated and MYH3, upregulated, were identified as potential therapeutic candidates. In light of these findings, our work contributes not only to understanding DMD at the molecular level but also presents potential targets for therapeutic strategies. Finally, our study facilitates the development of therapeutic interventions that can effectively control and mitigate the impact of DMD.

VExD: a curated resource for human gene expression alterations following viral infection

Much of the host antiviral response is mediated through changes to host gene expression levels. Likewise, viruses induce changes to host gene expression levels in order to promote the viral life cycle and evade the host immune system. However, there is no resource that specifically collects human gene expression levels pre- and post-virus infection. Further, public gene expression repositories do not contain enough specialized metadata to easily find relevant experiments. Here, we present the Virus Expression Database (VExD), a freely available website and database, that collects human gene expression datasets in response to viral infection. VExD contains ∼8,000 uniformly processed samples obtained from 289 studies examining 51 distinct human viruses. We show that the VExD processing pipeline captures known antiviral responses in the form of interferon-stimulated genes. We further show that the datasets collected in VExD can be used to quickly identify supporting data for experiments performed in human cells or model organisms. VExD is freely available at https://vexd.cchmc.org/.

Integrated Bioinformatics Investigation of Novel Biomarkers of Uterine Leiomyosarcoma Diagnosis and Outcome

Uterine leiomyosarcomas (uLMS) have a poor prognosis and a high percentage of recurrent disease. Bioinformatics has become an integral element in rare cancer studies by overcoming the inability to collect a large enough study population. This study aimed to investigate and highlight crucial genes, pathways, miRNAs, and transcriptional factors (TF) on uLMS samples from five Gene Expression Omnibus datasets and The Cancer Genome Atlas Sarcoma study. Forty-one common differentially expressed genes (DEGs) were enriched and annotated by the DAVID software. With protein-protein interaction (PPI) network analysis, we selected ten hub genes that were validated with the TNMplotter web tool. We used the USCS Xena browser for survival analysis. We also predicted TF-gene and miRNA-gene regulatory networks along with potential drug molecules. TYMS and TK1 correlated with overall survival in uLMS patients. Finally, our results propose further validation of hub genes (TYMS and TK1), miR-26b-5p, and Sp1 as biomarkers of pathogenesis, prognosis, and differentiation of uLMS. Regarding the aggressive behavior and poor prognosis of uLMS, with the lack of standard therapeutic regimens, in our opinion, the results of our study provide enough evidence for further investigation of the molecular basis of uLMS occurrence and its implication in the diagnosis and therapy of this rare gynecological malignancy.

Deciphering the sex bias in housekeeping gene expression in adipose tissue: a comprehensive meta-analysis of transcriptomic studies

BACKGROUND

As the housekeeping genes (HKG) generally involved in maintaining essential cell functions are typically assumed to exhibit constant expression levels across cell types, they are commonly employed as internal controls in gene expression studies. Nevertheless, HKG may vary gene expression profile according to different variables introducing systematic errors into experimental results. Sex bias can indeed affect expression display, however, up to date, sex has not been typically considered as a biological variable.

METHODS

In this study, we evaluate the expression profiles of six classical housekeeping genes (four metabolic: GAPDH, HPRT, PPIA, and UBC, and two ribosomal: 18S and RPL19) to determine expression stability in adipose tissues (AT) of Homo sapiens and Mus musculus and check sex bias and their overall suitability as internal controls. We also assess the expression stability of all genes included in distinct whole-transcriptome microarrays available from the Gene Expression Omnibus database to identify sex-unbiased housekeeping genes (suHKG) suitable for use as internal controls. We perform a novel computational strategy based on meta-analysis techniques to identify any sexual dimorphisms in mRNA expression stability in AT and to properly validate potential candidates.

RESULTS

Just above half of the considered studies informed properly about the sex of the human samples, however, not enough female mouse samples were found to be included in this analysis. We found differences in the HKG expression stability in humans between female and male samples, with females presenting greater instability. We propose a suHKG signature including experimentally validated classical HKG like PPIA and RPL19 and novel potential markers for human AT and discarding others like the extensively used 18S gene due to a sex-based variability display in adipose tissue. Orthologs have also been assayed and proposed for mouse WAT suHKG signature. All results generated during this study are readily available by accessing an open web resource ( https://bioinfo.cipf.es/metafun-HKG ) for consultation and reuse in further studies.

CONCLUSIONS

This sex-based research proves that certain classical housekeeping genes fail to function adequately as controls when analyzing human adipose tissue considering sex as a variable. We confirm RPL19 and PPIA suitability as sex-unbiased human and mouse housekeeping genes derived from sex-specific expression profiles, and propose new ones such as RPS8 and UBB.

New Insight Into Mechanisms of Hepatic Encephalopathy: An Integrative Analysis Approach to Identify Molecular Markers and Therapeutic Targets

Hepatic encephalopathy (HE) is a set of complex neurological complications that arise from advanced liver disease. The precise molecular and cellular mechanism of HE is not fully understood. Differentially expressed genes (DEGs) from microarray technologies are powerful approaches to obtain new insight into the pathophysiology of HE. We analyzed microarray data sets of cirrhotic patients with HE from Gene Expression Omnibus to identify DEGs in postmortem cerebral tissues. Consequently, we uploaded significant DEGs into the STRING to specify protein-protein interactions. Cytoscape was used to reconstruct the genetic network and identify hub genes. Target genes were uploaded to different databases to perform comprehensive enrichment analysis and repurpose new therapeutic options for HE. A total of 457 DEGs were identified in 2 data sets totally from 12 cirrhotic patients with HE compared with 12 healthy subjects. We found that 274 genes were upregulated and 183 genes were downregulated. Network analyses on significant DEGs indicated 12 hub genes associated with HE. Enrichment analysis identified fatty acid beta-oxidation, cerebral organic acidurias, and regulation of actin cytoskeleton as main involved pathways associated with upregulated genes; serotonin receptor 2 and ELK-SRF/GATA4 signaling, GPCRs, class A rhodopsin-like, and p38 MAPK signaling pathway were related to downregulated genes. Finally, we predicted 39 probable effective drugs/agents for HE. This study not only confirms main important involved mechanisms of HE but also reveals some yet unknown activated molecular and cellular pathways in human HE. In addition, new targets were identified that could be of value in the future study of HE.

Metadata retrieval from sequence databases with ffq

MOTIVATION

Several genomic databases host data and metadata for an ever-growing collection of sequence datasets. While these databases have a shared hierarchical structure, there are no tools specifically designed to leverage it for metadata extraction.

RESULTS

We present a command-line tool, called ffq, for querying user-generated data and metadata from sequence databases. Given an accession or a paper's DOI, ffq efficiently fetches metadata and links to raw data in JSON format. ffq's modularity and simplicity make it extensible to any genomic database exposing its data for programmatic access.

AVAILABILITY AND IMPLEMENTATION

ffq is free and open source, and the code can be found here: https://github.com/pachterlab/ffq.

Deciphering cervical cancer-associated biomarkers by integrated multi-omics approach

OBJECTIVES

Cervical Squamous Cell Carcinoma (CESC) is one of the most fatal female malignancies, and the underlying molecular mechanisms governing this disease have not been fully explored. In this research, we planned to conduct the analysis of Gene Expression Omnibus (GEO) cervical squamous cell carcinoma microarray datasets by a detailed in silico approach and to explore some novel biomarkers of CESC.

METHODS

The top commonly differentially expressed genes (DEGs) from the GSE138080 and GSE113942 datasets were analyzed by Limma package-based GEO2R tool. The protein-protein interaction (PPI) network of the DEGs was drawn through Search Tool for the Retrieval of Interacting Genes (STRING), and top 6 hub genes were obtained from Cytoscape. Expression analysis and validation of hub genes expression in CESC samples and cell lines were done using UALCAN, OncoDB, GENT2, and HPA. Additionally, cBioPortal, Gene set enrichment analysis (GSEA) tool, Kaplan-Meier (KM) plotter, ShinyGO, and DGIdb databases were also used to check some important values of hub genes in CESC.

RESULTS

Out of 79 DEGs, the minichromosome maintenance complex component 4 (MCM4), nucleolar and spindle-associated protein 1 (NUSAP1), cell division cycle associated 5 (CDCA5), cell division cycle 45 (CDC45), denticleless E3 ubiquitin protein ligase homolog (DTL), and chromatin licensing and DNA replication factor 1 (CDT1) genes were regarded as hub genes in CESC. Further analysis revealed that the expressions of all these hub genes were significantly elevated in CESC cell lines and samples of diverse clinical attributes. In this study, we also documented some important correlations between hub genes and some other diverse measures, including DNA methylation, genetic alterations, and Overall Survival (OS). Last, we also identify hub genes associated ceRNA network and 31 important chemotherapeutic drugs.

CONCLUSION

Through detailed in silico methodology, we identified 6 hub genes, including MCM4, NUSAP1, CDCA5, CDC45, DTL, and CDT1, which are likely to be associated with CESC development and diagnosis.

Systematic tissue annotations of genomics samples by modeling unstructured metadata

There are currently >1.3 million human -omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The major impediment is that sample attributes are routinely described using varied terminologies written in unstructured natural language. We propose a natural-language-processing-based machine learning approach (NLP-ML) to infer tissue and cell-type annotations for genomics samples based only on their free-text metadata. NLP-ML works by creating numerical representations of sample descriptions and using these representations as features in a supervised learning classifier that predicts tissue/cell-type terms. Our approach significantly outperforms an advanced graph-based reasoning annotation method (MetaSRA) and a baseline exact string matching method (TAGGER). Model similarities between related tissues demonstrate that NLP-ML models capture biologically-meaningful signals in text. Additionally, these models correctly classify tissue-associated biological processes and diseases based on their text descriptions alone. NLP-ML models are nearly as accurate as models based on gene-expression profiles in predicting sample tissue annotations but have the distinct capability to classify samples irrespective of the genomics experiment type based on their text metadata. Python NLP-ML prediction code and trained tissue models are available at https://github.com/krishnanlab/txt2onto .

Decoding Diabetes Biomarkers and Related Molecular Mechanisms by Using Machine Learning, Text Mining, and Gene Expression Analysis

The molecular basis of diabetes mellitus is yet to be fully elucidated. We aimed to identify the most frequently reported and differential expressed genes (DEGs) in diabetes by using bioinformatics approaches. Text mining was used to screen 40,225 article abstracts from diabetes literature. These studies highlighted 5939 diabetes-related genes spread across 22 human chromosomes, with 112 genes mentioned in more than 50 studies. Among these genes, HNF4A, PPARA, VEGFA, TCF7L2, HLA-DRB1, PPARG, NOS3, KCNJ11, PRKAA2, and HNF1A were mentioned in more than 200 articles. These genes are correlated with the regulation of glycogen and polysaccharide, adipogenesis, AGE/RAGE, and macrophage differentiation. Three datasets (44 patients and 57 controls) were subjected to gene expression analysis. The analysis revealed 135 significant DEGs, of which CEACAM6, ENPP4, HDAC5, HPCAL1, PARVG, STYXL1, VPS28, ZBTB33, ZFP37 and CCDC58 were the top 10 DEGs. These genes were enriched in aerobic respiration, T-cell antigen receptor pathway, tricarboxylic acid metabolic process, vitamin D receptor pathway, toll-like receptor signaling, and endoplasmic reticulum (ER) unfolded protein response. The results of text mining and gene expression analyses used as attribute values for machine learning (ML) analysis. The decision tree, extra-tree regressor and random forest algorithms were used in ML analysis to identify unique markers that could be used as diabetes diagnosis tools. These algorithms produced prediction models with accuracy ranges from 0.6364 to 0.88 and overall confidence interval (CI) of 95%. There were 39 biomarkers that could distinguish diabetic and non-diabetic patients, 12 of which were repeated multiple times. The majority of these genes are associated with stress response, signalling regulation, locomotion, cell motility, growth, and muscle adaptation. Machine learning algorithms highlighted the use of the HLA-DQB1 gene as a biomarker for diabetes early detection. Our data mining and gene expression analysis have provided useful information about potential biomarkers in diabetes.

Identification and validation of novel biomarker TRIM8 related to cervical cancer

Background

Cervical cancer, as a common gynecological disease, endangers female health. Give the lack of effective biomarkers for the diagnosis and treatment of cervical cancer, this paper aims to analyze the Gene Expression Omnibus (GEO) data sets using comprehensive bioinformatics tools, and to identify biomarkers associated with the cancer in patient samples.

Methods

The bioinformatics methods were used to extract genes related to cervical cancer from GSE39001, while the GEO2R online tool to elaborate on differentially expressed genes (DEGs) in normal and cancer samples, and to clarify related genes and functions. The results were verified by IHC, WB, CCK-8, clone formation and flow cytometry experiments.

Results

A total of 2,859 DEGs were identified in the GEO microarray dataset. We extracted genes associated with both ubiquitination and autophagy from the key modules of weighted gene co-expression network analysis (WGCNA), and the analysis showed that TRIM8 was of great significance for the diagnosis and prognosis of cervical cancer. Besides, experimental validation showed the high TRIM8 expression in cervical cancer, as well as its involvement in the proliferation of cervical cancer cells.

Conclusion

We identified a biomarker (TRIM8) that may be related to cervical cancer through a series of analyses on the GEO dataset. Experimental verification confirmed the inhibition of cervical cancer cells proliferation by lowering TRIM8 expression. Therefore, TRIM8 can be adopted as a new biomarker of cervical cancer to develop new therapeutic targets.

A specific immune signature for predicting the prognosis of glioma patients with IDH1-mutation and guiding immune checkpoint blockade therapy

Isocitrate dehydrogenase (IDH1) is frequently mutated in glioma tissues, and this mutation mediates specific tumor-promoting mechanisms in glioma cells. We aimed to identify specific immune biomarkers for IDH1-mutation (IDH1mt) glioma. The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) were used to obtain RNA sequencing data and clinical characteristics of glioma tissues, while the stromal and immune scores of TCGA glioma tissues were determined using the ESTIMATE algorithm. Differentially expressed genes (DEGs), the protein–protein interaction(PPI) network, and least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were used to select hub genes associated with stroma and immune scores and the prognoses of patients and to construct the risk model. The practicability and specificity of the risk model in both IDH1mt and IDH1-wildtype (wtIDH1) gliomas in TCGA and CGGA were evaluated. Molecular mechanisms, immunological characteristics and benefits of immune checkpoint blockade therapy in glioma tissues with IDH1mt were analyzed using GSEA, immunohistochemical staining, CIBERSORT, and T-cell dysfunction and exclusion (TIDE) analysis. The overall survival rate for IDH1mt-glioma patients with high stroma/immune scores was lower than that for those with low stroma/immune scores. A total of 222 DEGs were identified in IDH1mt glioma tissues with high stroma/immune scores. Among them, 72 genes had interactions in the PPI network, while three genes, HLA-DQA2, HOXA3, and SAA2, were selected as hub genes and used to construct risk models classifying patients into high- and low-risk score groups, followed by LASSO and Cox regression analyses. This risk model showed prognostic value in IDH1mt glioma in both TCGA and CCGA; nevertheless, the model was not suitable for wtIDH1 glioma. The risk model may act as an independent prognostic factor for IDH1mt glioma. IDH1mt glioma tissues from patients with high-risk scores showed more infiltration of M1 and CD8 T cells than those from patients with low-risk scores. Moreover, TIDE analysis showed that immune checkpoint blockade(ICB) therapy was highly beneficial for IDH1mt patients with high-risk scores. The risk model showed specific potential to predict the prognosis of IDH1mt-glioma patients, as well as guide ICB, contributing to the diagnosis and therapy of IDH1mt-glioma patients.

GeMI: interactive interface for transformer-based Genomic Metadata Integration

The Gene Expression Omnibus (GEO) is a public archive containing >4 million digital samples from functional genomics experiments collected over almost two decades. The accompanying metadata describing the experiments suffer from redundancy, inconsistency and incompleteness due to the prevalence of free text and the lack of well-defined data formats and their validation. To remedy this situation, we created Genomic Metadata Integration (GeMI; http://gmql.eu/gemi/), a web application that learns to automatically extract structured metadata (in the form of key-value pairs) from the plain text descriptions of GEO experiments. The extracted information can then be indexed for structured search and used for various downstream data mining activities. GeMI works in continuous interaction with its users. The natural language processing transformer-based model at the core of our system is a fine-tuned version of the Generative Pre-trained Transformer 2 (GPT2) model that is able to learn continuously from the feedback of the users thanks to an active learning framework designed for the purpose. As a part of such a framework, a machine learning interpretation mechanism (that exploits saliency maps) allows the users to understand easily and quickly whether the predictions of the model are correct and improves the overall usability. GeMI’s ability to extract attributes not explicitly mentioned (such as sex, tissue type, cell type, ethnicity and disease) allows researchers to perform specific queries and classification of experiments, which was previously possible only after spending time and resources with tedious manual annotation. The usefulness of GeMI is demonstrated on practical research use cases.

Database URL

http://gmql.eu/gemi/

Past and future uses of text mining in ecology and evolution

Ecology and evolutionary biology, like other scientific fields, are experiencing an exponential growth of academic manuscripts. As domain knowledge accumulates, scientists will need new computational approaches for identifying relevant literature to read and include in formal literature reviews and meta-analyses. Importantly, these approaches can also facilitate automated, large-scale data synthesis tasks and build structured databases from the information in the texts of primary journal articles, books, grey literature, and websites. The increasing availability of digital text, computational resources, and machine-learning based language models have led to a revolution in text analysis and natural language processing (NLP) in recent years. NLP has been widely adopted across the biomedical sciences but is rarely used in ecology and evolutionary biology. Applying computational tools from text mining and NLP will increase the efficiency of data synthesis, improve the reproducibility of literature reviews, formalize analyses of research biases and knowledge gaps, and promote data-driven discovery of patterns across ecology and evolutionary biology. Here we present recent use cases from ecology and evolution, and discuss future applications, limitations and ethical issues.

Identification of a Diagnostic Signature and Immune Cell Infiltration Characteristics in Keloids

Background: Keloid disorder is a recurrent fibroproliferative cutaneous tumor. Due to the lack of early identification of keloid patients before the formation of keloids, it is impossible to carry out pre-traumatic intervention and prevention for these patients. This led us to identify and determine signatures with diagnostic significance for keloids.

Methods: Public series of matrix files were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were calculated from expression profiling data, and the diagnostic signature was identified by constructing a protein-protein interaction (PPI) network. The diagnostic efficacy of the screened signature was assessed by employing receiver operating characteristic (ROC) curves. Furthermore, we calculated the proportion of different immune cells in the gene expression matrix microenvironment by the “ssGSEA” algorithm, and assessed the difference in immune cell abundance between keloids and control groups and the relationship between the signature and immune cell infiltration. Clinical keloid and normal skin tissues were collected, and the expression of the screened diagnostic signature was validated by RT-qPCR and immunohistochemical assay.

Results: By screening the key genes in PPI, TGM2 was recognized and validated as a diagnostic signature and the infiltrating abundance of 10 immune cells was significantly correlated with TGM2 expression. Gene ontology enrichment analysis demonstrated that TGM2 and molecules interacting with it were mainly enriched in processes involving wound healing and collagen fiber organization. TGM2 correlated positively with HIF-1A (R = 0.82, p-value = 1.4e-05), IL6 (R = 0.62, p-value = 0.0053), and FN1 (R = 0.66, p-value = 0.0019). Besides, TGM2 was significantly upregulated in clinical keloid samples compared to normal skin tissues.

Conclusion: TGM2 may serve as an auxiliary diagnostic indicator for keloids. However, the role of TGM2 in keloids has not been adequately reported in the current literature, which may provide a new direction for molecular studies of keloids.

SKIOME Project: a curated collection of skin microbiome datasets enriched with study-related metadata

Large amounts of data from microbiome-related studies have been (and are currently being) deposited on international public databases. These datasets represent a valuable resource for the microbiome research community and could serve future researchers interested in integrating multiple datasets into powerful meta-analyses. However, this huge amount of data lacks harmonization and it is far from being completely exploited in its full potential to build a foundation that places microbiome research at the nexus of many subdisciplines within and beyond biology. Thus, it urges the need for data accessibility and reusability, according to findable, accessible, interoperable and reusable (FAIR) principles, as supported by National Microbiome Data Collaborative and FAIR Microbiome. To tackle the challenge of accelerating discovery and advances in skin microbiome research, we collected, integrated and organized existing microbiome data resources from human skin 16S rRNA amplicon-sequencing experiments. We generated a comprehensive collection of datasets, enriched in metadata, and organized this information into data frames ready to be integrated into microbiome research projects and advanced post-processing analyses, such as data science applications (e.g. machine learning). Furthermore, we have created a data retrieval and curation framework built on three different stages to maximize the retrieval of datasets and metadata associated with them. Lastly, we highlighted some caveats regarding metadata retrieval and suggested ways to improve future metadata submissions. Overall, our work resulted in a curated skin microbiome datasets collection accompanied by a state-of-the-art analysis of the last 10 years of the skin microbiome field. Database URL:  https://github.com/giuliaago/SKIOMEMetadataRetrieval.

Rebalance of the Polyamine Metabolism Suppresses Oxidative Stress and Delays Senescence in Nucleus Pulposus Cells

Intervertebral disk degeneration (IDD) is a major cause of low back pain that becomes a prevalent age-related disease. However, the pathophysiological processes behind IDD are rarely known. Here, we used bioinformatics analysis based on the microarray datasets (GSE34095) to identify the differentially expressed genes (DEGs) as biomarkers and therapeutic targets in degenerated discs. From the previous studies, oxidative stress has been notified as a positive inducement of IDD, which causes DNA damage and accelerates cell senescence. Polyamine oxidase (PAOX), a member of the observed 1057 DEGs, is involved in polyamine metabolism and influences the oxidative balance in cells. However, it is uncertain if the IDD is implicated in the dysregulation of PAOX and polyamine metabolism. This study firstly verified the PAOX upregulation in human degenerated disc samples and applied an IL-1β-induced nucleus pulposus (NP) cell degeneration model to demonstrate that spermidine supplementation balanced polyamine metabolism and delayed NP cell senescence. Moreover, we confirmed that spermidine/N-acetylcysteine supplementation or Cdkn2a gene deletion stabilized the polyamine metabolism, suppressed oxidative stress, and therefore delayed the progress of IDD in older mice. Collectively, our study highlights the role of polyamine metabolism in IDD and foresees spermidine would be the advanced therapeutical drug for IDD.

COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets

Background: To develop anti-viral drugs and vaccines, it is crucial to understand the molecular basis and pathology of COVID-19. An increase in research output is required to generate data and results at a faster rate, therefore bioinformatics plays a crucial role in COVID-19 research. There is an abundance of transcriptomic data from studies carried out on COVID-19, however, their use is limited by the confounding factors pertaining to each study. The reanalysis of all these datasets in a unified approach should help in understanding the molecular basis of COVID-19. This should allow for the identification of COVID-19 biomarkers expressed in patients and the presence of markers specific to disease severity and condition.

Aim: In this study, we aim to use the multiple publicly available transcriptomic datasets retrieved from the Gene Expression Omnibus (GEO) database to identify consistently differential expressed genes in different tissues and clinical settings.

Materials and Methods: A list of datasets was generated from NCBI’s GEO using the GEOmetadb package through R software. Search keywords included SARS-COV-2 and COVID-19. Datasets in human tissues containing more than ten samples were selected for this study. Differentially expressed genes (DEGs) in each dataset were identified. Then the common DEGs between different datasets, conditions, tissues and clinical settings were shortlisted.

Results: Using a unified approach, we were able to identify common DEGs based on the disease conditions, samples source and clinical settings. For each indication, a different set of genes have been identified, revealing that a multitude of factors play a role in the level of gene expression.

Conclusion: Unified reanalysis of publically available transcriptomic data showed promising potential in identifying core targets that can explain the molecular pathology and be used as biomarkers for COVID-19.

The bioinformatic approach identifies PARM1 as a new potential prognostic factor in osteosarcoma

Objective

To explore the key factors affecting the prognosis of osteosarcoma patients.

Methods

Based on the GEO dataset and differential expression analysis of normal and osteosarcoma tissues, the gene modules related to the prognosis of osteosarcoma patients were screened by WGCNA, and intersecting genes were taken with differential genes, and the risk prognosis model of osteosarcoma patients was constructed by LASSO regression analysis of intersecting genes, and the prognosis-related factors of osteosarcoma patients were obtained by survival analysis, followed by target for validation, and finally, the expression of prognostic factors and their effects on osteosarcoma cell migration were verified by cellular assays and lentiviral transfection experiments.

Results

The prognosis-related gene module of osteosarcoma patients were intersected with differential genes to obtain a total of 9 common genes. PARM1 was found to be a prognostic factor in osteosarcoma patients by LASSO regression analysis, followed by cellular assays to verify that PARM1 was lowly expressed in osteosarcoma cells and that overexpression of PARM1 in osteosarcoma cells inhibited cell migration. Pan-cancer analysis showed that PARM1 was lowly expressed in most cancers and that low expression of PARM1 predicted poor prognosis for patients.

Conclusion

The data from this study suggest that PARM1 is closely associated with the prognosis of osteosarcoma patients, and PARM1 may serve as a novel potential prognostic target for osteosarcoma, providing a heartfelt direction for the prevention and treatment of osteosarcoma.

Druggable genetic targets in endometrial cancer✰,✰✰

BACKGROUND

FBXW7 is frequently somatically mutated in grade 3 endometrioid endometrial cancers (G3EECs) and serous endometrial cancers (SECs), high-risk cancers associated with poor prognosis. CRISPR-edited cell lines identified the proteomic and phosphoproteomic effects of FBXW7 mutation in 3 high-risk endometrial cancers (ECs), including altered protein levels of L1CAM and TGM2. This result is important because L1CAM and TGM2 are druggable proteins that could represent new therapeutic targets.

METHODS

We used cBioPortal for Cancer Genomics to analyze data in The Cancer Genome Atlas (TCGA). We used the UCSC Xena Browser to analyze gene expression. For differential gene expression analysis, the gene ontology molecular function 2018 version was used. The analysis was focused on determined genes.

RESULTS

FBXW7 mutations affect gene expression of L1CAM but are unrelated to TGM2 gene expression. L1CAM gene expression is significantly related to survival. Patients with lower L1CAM gene expression have better survival. FBXW7 mutations are unrelated to survival. TGM2 gene expression is unrelated to FBXW7 mutations. TGM2 gene expression is unrelated to survival, all tumor grades or grade 3 alone.

CONCLUSION

We agree with Urick et al. that L1CAM may be a promising druggable target in endometrial carcinoma. The lack of relationship of TGM2 expression with FBXW7 mutations and endometrial cancer survival suggests that TGM2 might not be of as much value as a druggable target, compared to L1CAM. However, the fact that a certain alteration is not prognostic for cancer survival does not necessarily mean that the alteration will not be targetable. More data, such as inhibition of each gene by calculating drug targetability, may be required to support this conclusion.

SysInflam HuDB, a Web Resource for Mining Human Blood Cells Transcriptomic Data Associated with Systemic Inflammatory Responses to Sepsis

Sepsis develops after a dysregulated host inflammatory response to a systemic infection. Identification of sepsis biomarkers has been challenging because of the multifactorial causes of disease susceptibility and progression. Public transcriptomic data are a valuable resource for mechanistic discoveries and cross-studies concordance of heterogeneous diseases. Nonetheless, the approach requires structured methodologies and effective visualization tools for meaningful data interpretation. Currently, no such database exists for sepsis or systemic inflammatory diseases in human. Hence we curated SysInflam HuDB (http://sepsis.gxbsidra.org/dm3/geneBrowser/list), a unique collection of human blood transcriptomic datasets associated with systemic inflammatory responses to sepsis. The transcriptome collection and the associated clinical metadata are integrated onto a user-friendly and Web-based interface that allows the simultaneous exploration, visualization, and interpretation of multiple datasets stemming from different study designs. To date, the collection encompasses 62 datasets and 5719 individual profiles. Concordance of gene expression changes with the associated literature was assessed, and additional analyses are presented to showcase database utility. Combined with custom data visualization at the group and individual levels, SysInflam HuDB facilitates the identification of specific human blood gene signatures in response to infection (e.g., patients with sepsis versus healthy control subjects) and the delineation of major genetic drivers associated with inflammation onset and progression under various conditions.

Drug Repositioning Based on the Reversal of Gene Expression Signatures Identifies TOP2A as a Therapeutic Target for Rectal Cancer

Rectal cancer is a common disease with high mortality rates and limited therapeutic options. Here we combined the gene expression signatures of rectal cancer patients with the reverse drug-induced gene-expression profiles to identify drug repositioning candidates for cancer therapy. Among the predicted repurposable drugs, topoisomerase II inhibitors (doxorubicin, teniposide, idarubicin, mitoxantrone, and epirubicin) presented a high potential to reverse rectal cancer gene expression signatures. We showed that these drugs effectively reduced the growth of colorectal cancer cell lines closely representing rectal cancer signatures. We also found a clear correlation between topoisomerase 2A (TOP2A) gene copy number or expression levels with the sensitivity to topoisomerase II inhibitors. Furthermore, CRISPR-Cas9 and shRNA screenings confirmed that loss-of-function of the TOP2A has the highest efficacy in reducing cellular proliferation. Finally, we observed significant TOP2A copy number gains and increased expression in independent cohorts of rectal cancer patients. These findings can be translated into clinical practice to evaluate TOP2A status for targeted and personalized therapies based on topoisomerase II inhibitors in rectal cancer patients.

ARGEOS: A New Bioinformatic Tool for Detailed Systematics Search in GEO and ArrayExpress

Simple Summary

A systematic search for datasets of transcriptome data is a hefty task. Therefore, we developed the ARGEOS web tool, which simplifies the search and selection of datasets from various public databases. In addition, the service carries out an advanced analysis of a dataset, including collecting detailed protocols, information on the number of datasets, and providing additional reference information. An example of a cell polarization study exemplifies the effectiveness of the tool.

Abstract

Conduct a reanalysis of transcriptome data for studying intracellular signaling or solving other experimental problems is becoming increasingly popular. Gene expression data are archived as microarray or RNA-seq datasets mainly in two public databases: Gene Expression Omnibus (GEO) and ArrayExpress (AE). These databases were not initially intended to systematically search datasets, making it challenging to conduct a secondary study. Therefore, we have created the ARGEOS service, which has the following advantages that facilitate the search: (1) Users can simultaneously send several requests that are supposed to be used for systematic searches, and it is possible to correct the requests; (2) advanced analysis of information about the dataset is available. The service collects detailed protocols, information on the number of datasets, analyzes the availability of raw data, and provides other reference information. All this contributes to both rapid data analysis with the search for the most relevant datasets and to the systematic search with detailed analysis of the information of the datasets. The efficiency of the service is shown in the example of analyzing transcriptome data of activated (polarized) cells. We have performed a systematic search of studies of cell polarization (when cells are exposed to different immune stimuli). The web interface for ARGEOS is user-friendly and straightforward. It can be used by a person who is not familiar with database searching.

Randomized trial drug controlled compendious transcriptome analysis supporting broad and phase specific therapeutic potential of multiple candidates in COVID-19

Effective therapies for coronavirus disease 2019 (COVID-19) are urgently needed. Maladaptive hyperinflammation and excessive cytokine release underlie the disease severity, with antiinflammatory and cytokine inhibiting agents expected to exert therapeutic effects. A major present challenge is identification of appropriate phase of the illness for a given intervention to yield optimum outcomes. Considering its established disease biomarker and drug discovery potential, a compendious analysis of existing transcriptomic data is presented here toward addressing this gap. The analysis is based on COVID-19 data related to intensive care unit (ICU) and non-ICU admissions, discharged and deceased patients, ventilation and non-ventilation phases, and high oxygen supplementation. It integrates transcriptomic data related to the effects of, in various cellular treatment models, the COVID-19 randomized clinical trial (RCT) successful drug dexamethasone, and the failed drug, with a potential to harm, hydroxychloroquine/chloroquine. Similarly, effects of various COVID-19 candidate drugs/anticytokines as well as proinflammatory cytokines implicated in the illness are also examined. The underlying assumption was that compared to COVID-19, an effective drug/anticytokine and a disease aggravating agent would affect gene regulation in opposite and same direction, in that order. Remarkably, the assumption was supported with respect to both the RCT drugs. With this control validation, etanercept, followed by tofacitinib and adalimumab, showed transcriptomic effects predictive of benefits in both ventilation and non-ventilation ICU stages as well as in non-ICU phase. On the other hand, canakinumab showed potential for effectiveness in high oxygen supplementation phase. These findings may inform experimental and clinical studies toward drug repurposing in COVID-19.

Transcriptional profiling of macaque microglia reveals an evolutionary preserved gene expression program

Microglia are tissue-resident macrophages of the central nervous system (CNS), and important for CNS development and homeostasis. In the adult CNS, microglia monitor environmental changes and react to tissue damage, cellular debris, and pathogens. Here, we present a gene expression profile of purified microglia isolated from the rhesus macaque, a non-human primate, that consists of 666 transcripts. The macaque microglia transcriptome was intersected with the transcriptional programs of microglia from mouse, zebrafish, and human CNS tissues, to determine (dis)similarities. This revealed an extensive overlap of 342 genes between the transcriptional profile of macaque and human microglia, and showed that the gene expression profile of zebrafish is most distant when compared to other species. Furthermore, an evolutionair core based on the overlapping gene expression signature from all four species was identified. This study presents a macaque microglia transcriptomics profile, and identifies a gene expression program in microglia that is preserved across species, underscoring their CNS-tailored tissue macrophage functions as innate immune cells with CNS-surveilling properties.

Construction of Adipogenic ceRNA Network Based on lncRNA Expression Profile of Adipogenic Differentiation of Human MSC Cells

The Long non-coding RNA (lncRNA) expression profile data of ten samples including human Mesenchymal Stem Cell (MSC) adipogenic differentiation 0, 3, and 6 days from the GEO database, and then perform gene ID conversion, BLAST comparison, and annotation marking. Finally, group A (treatment group on day 3 of differentiation and control group on day 0 of differentiation) obtained a total of 1180 mRNA and 185 lncRNA; group B (treatment group on day 6 of differentiation and control group on day 0 of differentiation). A total of 1376 mRNA and 206 lncRNA were obtained. Finally, we processed the differential lncRNAs and mRNAs obtained in the two groups, and obtained 113 shared differential lncRNAs to further predict the targeted miRNA, a total of 815 lncRNA-miRNA pairs. The targeted mRNA was further predicted, and the grouped differential mRNAs were combined to obtain 64 differential mRNAs. In the end, we obtained 216 ceRNAs containing 26 lncRNAs, 27 miRNAs and 64 mRNAs. We found that the mRNAs in the ceRNA network were mainly enriched with 45 Gene Ontology (GO) terms, mainly including glucose homeostasis mechanism and insulin stimulation response. 69 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were mainly enriched. It mainly includes many pathways related to lipid metabolism such as Adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK), Rap1, cAMP, mitogen-activated protein kinase (MAPK), Ras, hypoxia inducible factor-1 (HIF-1), PI3K-Akt, insulin signaling and so on. In the end, we identified 216 ceRNA regulatory relationships related to obesity research. Our research provides a clearer direction for understanding the molecular mechanism of obesity, the screening and determination of drug targets biomarkers in the future.

A comprehensive database for integrated analysis of omics data in autoimmune diseases

BACKGROUND

Autoimmune diseases are heterogeneous pathologies with difficult diagnosis and few therapeutic options. In the last decade, several omics studies have provided significant insights into the molecular mechanisms of these diseases. Nevertheless, data from different cohorts and pathologies are stored independently in public repositories and a unified resource is imperative to assist researchers in this field.

RESULTS

Here, we present Autoimmune Diseases Explorer ( https://adex.genyo.es ), a database that integrates 82 curated transcriptomics and methylation studies covering 5609 samples for some of the most common autoimmune diseases. The database provides, in an easy-to-use environment, advanced data analysis and statistical methods for exploring omics datasets, including meta-analysis, differential expression or pathway analysis.

CONCLUSIONS

This is the first omics database focused on autoimmune diseases. This resource incorporates homogeneously processed data to facilitate integrative analyses among studies.

Increasing metadata coverage of SRA BioSample entries using deep learning-based named entity recognition

High-quality metadata annotations for data hosted in large public repositories are essential for research reproducibility and for conducting fast, powerful and scalable meta-analyses. Currently, a majority of sequencing samples in the National Center for Biotechnology Information's Sequence Read Archive (SRA) are missing metadata across several categories. In an effort to improve the metadata coverage of these samples, we leveraged almost 44 million attribute-value pairs from SRA BioSample to train a scalable, recurrent neural network that predicts missing metadata via named entity recognition (NER). The network was first trained to classify short text phrases according to 11 metadata categories and achieved an overall accuracy and area under the receiver operating characteristic curve of 85.2% and 0.977, respectively. We then applied our classifier to predict 11 metadata categories from the longer TITLE attribute of samples, evaluating performance on a set of samples withheld from model training. Prediction accuracies were high when extracting sample Genus/Species (94.85%), Condition/Disease (95.65%) and Strain (82.03%) from TITLEs, with lower accuracies and lack of predictions for other categories highlighting multiple issues with the current metadata annotations in BioSample. These results indicate the utility of recurrent neural networks for NER-based metadata prediction and the potential for models such as the one presented here to increase metadata coverage in BioSample while minimizing the need for manual curation. Database URL: https://github.com/cartercompbio/PredictMEE.

Inferring molecular mechanisms of dexamethasone therapy in severe COVID-19 from existing transcriptomic data

Dexamethasone, a synthetic glucocorticoid, has previously shown mortality benefit in severe coronavirus disease 2019 (COVID-19) in a randomized controlled trial. As the illness is considered to reflect a hyperinflammatory state, this therapeutic effectiveness is presumably ascribed to broad anti-inflammatory activities of glucocorticoids. Here, an unbiased analysis of available transcriptomic data on lung and blood immune cells from severe COVID-19 patients and matching cellular models of dexamethasone treatment is presented that supports this presumption. Comparison of differentially expressed genes in severe COVID-19 with that in dexamethasone treated cells reveals a small set of genes that are regulated in opposite direction between the disease and the drug, and are enriched for genes and processes related to glucocorticoid pathway and receptor binding. This expression signature differentiates as a whole various cytokines from a set of anti-cytokine/anti-inflammatory agents, with the former resembling COVID-19 and the latter dexamethasone in gene regulation. The signature apparently relates to TNF- α, IL-1α, IL-1β, IFN-α, IFN-β, and IFN-γ signaling, but not IL-6 signaling, suggesting that therapeutic effect of dexamethasone in COVID-19 does not involve IL-6 pathway. However, as all these observations are purely based on bioinformatic analysis, experimental evidence will be required to validate the inferences drawn. In conclusion, the present analysis seems to provide a proof of concept for therapeutic mechanisms of dexamethasone in COVID-19.

Epidemiological transcriptomic data supports BCG protection in viral diseases including COVID-19

Epidemiological and clinical evidence suggests that Bacille Calmette-Guérin (BCG) vaccine induced trained immunity protects against non-specific infections. Multiple clinical trials are currently underway to assess effectiveness of the vaccine in the coronavirus disease 2019 (COVID-19). However, the durability and mechanism of BCG trained immunity remain unclear. Here, an integrative analysis of available epidemiological transcriptomic data related to BCG vaccination and respiratory tract viral infections as well as of reported transcriptomic alterations in COVID-19 is presented toward addressing this gap. Results suggest that the vaccine induces very long-lasting transcriptomic changes that mimic viral infections by, consistent with the present concept of trained immunity, upregulation of antiviral defense response, and oppose viral infections by, inconsistent with the concept, downregulation of myeloid cell activation. These durability and mechanistic insights argue against possible indiscriminate use of the vaccine and activated innate immune response associated safety concerns in COVID-19, in that order.

Identification of Hub Genes as Potential Prognostic Biomarkers in Cervical Cancer Using Comprehensive Bioinformatics Analysis and Validation Studies

Background

Cervical cancer belongs to one of the most common female cancers; yet, the exact underlying mechanisms are still elusive. Recently, microarray and sequencing technologies have been widely used for screening biomarkers and molecular mechanism discovery in cancer studies. In this study, we aimed to analyse the microarray datasets using comprehensive bioinformatics tools and identified novel biomarkers associated with the prognosis of patients with cervical cancer.

Methods

The differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) datasets including GSE138080, GSE113942 and GSE63514 were analysed using GEO2R tool. The functional enrichment analysis was performed using g:Profiler tool. The protein-protein interaction (PPI) network construction and hub genes identification were performed using the STRING database and Cytoscape software, respectively. The hub genes were subjected to expression and survival analysis in the cervical cancer. The EdU incorporation and Cell Counting Kit-8 assays were performed to evaluate the effects of hub gene knockdown on the proliferation of cervical cancer cells.

Results

A total of 89 overlapping DEGs (63 up-regulated and 26 down-regulated genes) were identified in the microarray datasets. The functional enrichment analysis indicated that the overlapping DEGs were mainly associated with "DNA replication" and "cell cycle". Furthermore, the PPI network analysis revealed that the network contains 87 nodes and 309 edges. Sub-module analysis using the Molecular Complex Detection tool identified 21 hub genes from the PPI network. The expression levels of the 21 hub genes were all up-regulated in the cervical cancer tissues when compared to normal cervical tissues as analysed by GEPIA tool. The survival analysis showed that the low expression of cell division cycle 45 (CDC45), GINS complex subunit 2 (GINS2), minichromosome maintenance complex component 2 (MCM2) and proliferating cell nuclear antigen (PCNA) was significantly correlated with the shorter overall survival of patients with cervical cancer. Moreover, the protein expression levels of GINS2, MCM2 and PCNA, but not CDC45, were significantly up-regulated in the cervical cancer tissues when compared to normal cervical tissues. Finally, knockdown of MCM2 significantly suppressed the proliferation of HeLa and SiHa cells.

Conclusion

In conclusion, we screened a total of 89 overlapping DEGs from the GEO datasets, and further analysis identified four hub genes (CDC45, GINS2, MCM2 and PCNA) that were likely associated with the prognosis of patients with cervical cancer. MCM2 knockdown repressed the cervical cancer cell proliferation. The current findings may provide novel insights into understanding the pathophysiology of cervical cancer and develop therapeutic targets for patients with cervical cancer.

An Integrated In Silico and In Vivo Approach to Identify Protective Effects of Palonosetron in Cisplatin-Induced Nephrotoxicity

Cisplatin is widely used to treat various types of cancers, but it is often limited by nephrotoxicity. Here, we employed an integrated in silico and in vivo approach to identify potential treatments for cisplatin-induced nephrotoxicity (CIN). Using publicly available mouse kidney and human kidney organoid transcriptome datasets, we first identified a 208-gene expression signature for CIN and then used the bioinformatics database Cmap and Lincs Unified Environment (CLUE) to identify drugs expected to counter the expression signature for CIN. We also searched the adverse event database, Food and Drug Administration. Adverse Event Reporting System (FAERS), to identify drugs that reduce the reporting odds ratio of developing cisplatin-induced acute kidney injury. Palonosetron, a serotonin type 3 receptor (5-hydroxytryptamine receptor 3 (5-HT3R)) antagonist, was identified by both CLUE and FAERS analyses. Notably, clinical data from 103 patients treated with cisplatin for head and neck cancer revealed that palonosetron was superior to ramosetron in suppressing cisplatin-induced increases in serum creatinine and blood urea nitrogen levels. Moreover, palonosetron significantly increased the survival rate of zebrafish exposed to cisplatin but not to other 5-HT3R antagonists. These results not only suggest that palonosetron can suppress CIN but also support the use of in silico and in vivo approaches in drug repositioning studies.

Identification of hub genes in unstable atherosclerotic plaque by conjoint analysis of bioinformatics

AIMS

Unstable atherosclerotic plaque is the main pathological basis of acute coronary syndrome, which is the leading cause of death and disability worldwide. Therefore, we combined multiple bioinformatics tools to identify key genes related to unstable plaque.

MAIN METHODS

GSE94605 contained 7 plasma sample pools of 175 healthy and 6 sample pools of 150 unstable angina pectoris (UAP) patients, and detected with miRNA array while GSE60993 collected peripheral blood from 7 normal and 9 UAP, and detected with mRNA array. GSE120521 collected carotid plaques from 4 patients and dissected in stable and unstable regions, then detected with RNA-seq. Differentially expressed miRNAs (DEMs) and genes (DEGs) in UAP were re-analyzed. Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) and Protein-protein interaction (PPI) network were applied on top 10 up-regulated or down-regulated DEMs targets, and whole DEGs. MiRNAs-mRNAs network was constructed with these DEMs and DEGs, and the expression profile of genes within the network was finally validated in GSE120521.

KEY FINDINGS

Totally, 263 up-regulated and 201 down-regulated DEMs were identified in GSE94605, and 78 up-regulated and 29 down-regulated DEGs were identified in GSE60993. Subsequently, a miRNAs-mRNAs network was constructed with 6 up-regulated miRNAs targeted to 12 down-regulated genes, and 4 down-regulated miRNAs targeted to 8 up-regulated genes. Finally, MORF4L2, RAB3IL1 and MMP9 within the network were considered as hub genes in unstable plaque progression after being validated in GSE120521.

SIGNIFICANCE

These 3 genes may provide new targets for diagnosis and therapy of unstable atherosclerotic plaque.

Geneshot: search engine for ranking genes from arbitrary text queries

The frequency by which genes are studied correlates with the prior knowledge accumulated about them. This leads to an imbalance in research attention where some genes are highly investigated while others are ignored. Geneshot is a search engine developed to illuminate this gap and to promote attention to the under-studied genome. Through a simple web interface, Geneshot enables researchers to enter arbitrary search terms, to receive ranked lists of genes relevant to the search terms. Returned ranked gene lists contain genes that were previously published in association with the search terms, as well as genes predicted to be associated with the terms based on data integration from multiple sources. The search results are presented with interactive visualizations. To predict gene function, Geneshot utilizes gene–gene similarity matrices from processed RNA-seq data, or from gene–gene co-occurrence data obtained from multiple sources. In addition, Geneshot can be used to analyze the novelty of gene sets and augment gene sets with additional relevant genes. The Geneshot web-server and API are freely and openly available from https://amp.pharm.mssm.edu/geneshot.

Transcriptomics in Alzheimer's Disease: Aspects and Challenges

Alzheimer's disease (AD) is the most common cause of dementia. Although the heritability of AD is high, the knowledge of the disease-associated genes, their expression, and their disease-related pathways remain limited. Hence, finding the association between gene dysfunctions and pathological mechanisms, such as neuronal transports, APP processing, calcium homeostasis, and impairment in mitochondria, should be crucial. Emerging studies have revealed that changes in gene expression and gene regulation may have a strong impact on neurodegeneration. The mRNA-transcription factor interactions, non-coding RNAs, alternative splicing, or copy number variants could also play a role in disease onset. These facts suggest that understanding the impact of transcriptomes in AD may improve the disease diagnosis and also the therapies. In this review, we highlight recent transcriptome investigations in multifactorial AD, with emphasis on the insights emerging at their interface.

Identification of Aberrantly Expressed Genes in Murine Glioblastoma During Radiotherapy via Bioinformatic Data Mining

Objective

Glioblastoma (GBM) is an aggressive tumor with a fast growth rate. Radioresistance of GBM can lead to high recurrence. In general, due to the protection of the blood-brain barrier, the immune environment of the central nervous system is unique. The immune response induced by radiotherapy is weak in GBM. In the present study, aberrantly expressed genes during radiotherapy were assessed in murine models based on microarray RNA data.

Methods

The microarray data were extracted from the Intergovernmental Group on Earth Observations and differentially expressed genes (DEGs) screened out. Gene expression profiles of 115 samples in GSE56113 were analyzed and 104 genes were identified as aberrantly expressed based on GEO2R 8 d after radiotherapy. Then, the Database for Annotation, Visualization, and Integrated Discovery was used to analyze Genome Kyoto Encyclopedia of Gene pathways and Gene Ontology (GO) terms. The 20 core candidate genes were identified using protein-protein interaction network analysis and Cytoscape software with Molecular Complex Detection plug-in.

Results

Post-irradiated tumor tissues expressed significantly more immune-associated genes than contralateral brain tissues. GO and pathway analyses showed core DEGs were mainly enriched in the chemokine signaling and IL-6 signaling pathways, which could lead to immunosuppressive inflammatory monocyte infiltration and radioresistance. Chemokine signaling and IL-6 signaling pathway-associated genes were increased in the irradiated U87 cell strain.

Conclusion

Chemokine signaling and IL-6 signaling pathways were activated after radiation in murine glioma and human glioma cell lines which could lead to changes in the immune microenvironment and treatment failure. The results of the present study could provide potential therapeutic targets especially when immune therapy and radiotherapy are combined to treat GBM patients.

Immune-related key gene CLDN10 correlates with lymph node metastasis but predicts favorable prognosis in papillary thyroid carcinoma

The functions of immune cells in lymph node metastasis (LNM) have attracted considerable attention. This study aimed to screen the key immune-related and LNM-related genes in PTC. In the discovery phase, the immune-related genes in LNM were screened by using bioinformatics methods. In the validation phases, the association of the genes with LNM was first confirmed in a cohort from The Cancer Genome Atlas and a cohort based on a tissue chip. Then, the relationship of the genes with immune cell infiltration was further explored. Consequently, CLDN10 was identified, and its high expression was correlated with the presence of LNM in PTC but predicted a favorable prognosis. High CLDN10 expression was positively correlated with the infiltration of several immune cells, such as B cells, CD8+T cells, and macrophages. High CLDN10 expression may improve the outcomes of patients with PTC by increasing immune cell infiltration, although it might be associated with LNM. In conclusion, although CLDN1 might be correlated with LNM, it may also increase the infiltration of immune cells, including CD8+T cells and macrophages, and improve the clinical outcomes of patients with PTC. The effects of tumor purity and immune cell infiltration need to be considered in prognosis evaluation.

PulmonDB: a curated lung disease gene expression database

Chronic Obstructive Pulmonary Disease (COPD) and Idiopathic Pulmonary Fibrosis (IPF) have contrasting clinical and pathological characteristics and interesting whole-genome transcriptomic profiles. However, data from public repositories are difficult to reprocess and reanalyze. Here, we present PulmonDB, a web-based database (http://pulmondb.liigh.unam.mx/) and R library that facilitates exploration of gene expression profiles for these diseases by integrating transcriptomic data and curated annotation from different sources. We demonstrated the value of this resource by presenting the expression of already well-known genes of COPD and IPF across multiple experiments and the results of two differential expression analyses in which we successfully identified differences and similarities. With this first version of PulmonDB, we create a new hypothesis and compare the two diseases from a transcriptomics perspective.

Understanding the Molecular Mechanisms of Asthma through Transcriptomics

The transcriptome represents the complete set of RNA transcripts that are produced by the genome under a specific circumstance or in a specific cell. High-throughput methods, including microarray and bulk RNA sequencing, as well as recent advances in biostatistics based on machine learning approaches provides a quick and effective way of identifying novel genes and pathways related to asthma, which is a heterogeneous disease with diverse pathophysiological mechanisms. In this manuscript, we briefly review how to analyze transcriptome data and then provide a summary of recent transcriptome studies focusing on asthma pathogenesis and asthma drug responses. Studies reviewed here are classified into 2 classes based on the tissues utilized: blood and airway cells.