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Statistically overrepresented Gene Ontology functional classes. Top-20 statistically overrepresented Gene Ontology functional classes based on overexpressed genes in the UC literature (left) and in the UC microarray dataset (right)
Source publication
Background
Differential gene expression is important to understand the biological differences between healthy and diseased states. Two common sources of differential gene expression data are microarray studies and the biomedical literature.
Methods
With the aid of text mining and gene expression analysis we have examined the comparative properties...
Context in source publication
Context 1
... measure this quantitatively, we looked at the level of enrichment of Gene Ontology (GO) functional classes associated to the genes overexpressed in microarray data and in the litera- ture. Figure 6 shows the top 20 statistically overrepre- sented GO functional classes in microarray and literature data for UC based on the PANTHER statistical overrepresentation test with Bonferroni correction [30]. For UC and FC > 0, 16 functional classes were shared between the 38 overrepresented in the literature and the 36 overrepresented in the microarray dataset. ...
Citations
... We identifed the diferential compositions of Korean and Brazilian propolis and used the identifed active ingredients to conduct in silico and network analyses. Additionally, recent studies have extensively performed DEG analyses using RNA-Seq data from diseaseinduced models and healthy individuals to identify biomarkers and therapeutic target genes [34,35]. ...
Atopic dermatitis (AD) is a chronic inflammatory skin disorder requiring continuous care and treatment. Therefore, exploring the therapeutic potential of natural ingredients for AD is essential. This study conducted a network analysis to investigate the anti-AD effects of propolis and its underlying mechanism, with a focus on the compositional differences between Korean and Brazilian propolis. To identify the bioactive components and related mechanisms, differentially expressed genes (DEGs) in AD-induced HaCaT cells with and without propolis treatment were identified. NCBI, SwissTargetPrediction, STITCH, and the Comparative Toxicogenomics Database (CTD) were used to identify target genes of the propolis compounds, and these genes were compared with the DEGs to identify the shared target genes. Notably, CXCL10 and CCL2 were highly associated with target genes shared between Korean and Brazilian propolis, with Korean propolis affecting TLR4, RIPK2, and PYCARD and Brazilian propolis influencing CEBPB, PTGS2, and DAB2IP. Korean propolis was found to predominantly impact the regulation of mast cell activation and the cytosolic DNA-sensing pathway, whereas Brazilian propolis primarily affects Type I interferon–mediated regulation and the TNF signaling pathway. Additionally, both the TNF and IL-17 signaling pathways were implicated in the mechanisms of both Brazilian propolis and Korean propolis. Furthermore, our study validated the therapeutic potential of propolis in AD treatment, as evidenced by significant reductions in TNF-α, IFN-γ, IL-4, IL-13, CXCL10, CCL2, and histamine release in an AD-induced model. This study confirms the efficacy of Korean and Brazilian propolis in treating AD and reveals molecular mechanism differences due to variations in major components and target genes.
... The upregulation or downregulation of genes can instigate changes in metabolic, immune, and other physiological processes, contributing to the onset of diseases 21,22 . Therefore, the identification of differentially expressed genes (DEGs) is a crucial avenue for unraveling altered biological pathways in various diseases, including neurological disorders and cancers 23 . ...
Alzheimer’s disease (AD) stands as the most prevalent neurodegenerative ailment, presently lacking a definitive cure. Given that primary medications for AD patients in the early or middle stages demonstrate optimal efficacy, it becomes crucial to delve into the identification of risk genes associated with early onset. In our study, we compiled and integrated three transcriptomics datasets (GSE48350, GSE36980, GSE5281) originating from the hippocampus of 37 AD patients and 66 healthy controls (CTR) for comprehensive bioinformatics analysis. Comparative analysis with CTR revealed 25 up-regulated genes and 291 down-regulated genes in AD. Those down-regulated genes were notably enriched in processes related to the transmission and transport of synaptic signals. Intriguingly, 27 differentially expressed genes implicated in AD were also correlated with the Braak stage, establishing a connection with various immune cell types that exhibit differences in AD, including cytotoxic T cells, neutrophils, CD4 T cells, Th1, Th2, and Tfh. Significantly, a Cox model, constructed using nine feature genes, effectively stratified AD samples (HR = 2.72, 95% CI 1.94 ~ 3.81, P = 3.6e–10), highlighting their promising potential for risk assessment. In conclusion, our investigation sheds light on novel genes intricately linked to the onset and progression of AD, offering potential biomarkers for the early detection of this debilitating condition. This study contributes valuable insights toward enhancing the strategies for preventing and treating AD.
... The identification of differentially expressed genes (DEGs) through RNA-Seq analysis is an essential part of the study of biological pathways implicated in various neurological disorders. The purpose of conducting Differential Expression Gene (DEG) analysis is to identify genes that exhibit potential overexpression or underexpression in the context of a disease state, relative to a control group that remains unaffected 18 . Dysregulation of gene expression, whether it be overexpression or underexpression, can lead to disruptions in various biological pathways such as metabolic and immune pathways, which eventually result in the development of diseases 19 . ...
Alzheimer’s disease (AD) poses a major challenge due to its impact on the elderly population and the lack of effective early diagnosis and treatment options. In an effort to address this issue, a study focused on identifying potential biomarkers and therapeutic agents for AD was carried out. Using RNA-Seq data from AD patients and healthy individuals, 12 differentially expressed genes (DEGs) were identified, with 9 expressing upregulation (ISG15, HRNR, MTATP8P1, MTCO3P12, DTHD1, DCX, ST8SIA2, NNAT, and PCDH11Y) and 3 expressing downregulation (LTF, XIST, and TTR). Among them, TTR exhibited the lowest gene expression profile. Interestingly, functional analysis tied TTR to amyloid fiber formation and neutrophil degranulation through enrichment analysis. These findings suggested the potential of TTR as a diagnostic biomarker for AD. Additionally, druggability analysis revealed that the FDA-approved drug Levothyroxine might be effective against the Transthyretin protein encoded by the TTR gene. Molecular docking and dynamics simulation studies of Levothyroxine and Transthyretin suggested that this drug could be repurposed to treat AD. However, additional studies using in vitro and in vivo models are necessary before these findings can be applied in clinical applications.
... Biases for reporting genes associated with higher fold changes should be considered [84], and novel algorithms for detecting gene signatures should be developed [85]. Comprehensive and updated gene signature databases should be established, so that researchers can upload their own gene signatures and compare their results with the published data [86], by using different measures that calculate the degree of overlap between gene signatures [4]. ...
Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and inconsistencies among gene signatures across different cohorts pose challenges. The dynamic nature of the transcriptome, encompassing diverse RNA species and functional complexities at gene and isoform levels, introduces intricacies, and current gene signatures face reproducibility issues due to the unique transcriptomic landscape of each patient. In this context, discrepancies arising from diverse sequencing technologies, data analysis algorithms, and software tools further hinder consistency. While careful experimental design, analytical strategies, and standardized protocols could enhance reproducibility, future prospects lie in multiomics data integration, machine learning techniques, open science practices, and collaborative efforts. Standardized metrics, quality control measures, and advancements in single-cell RNA-seq will contribute to unbiased gene signature identification. In this perspective article, we outline some thoughts and insights addressing challenges, standardized practices, and advanced methodologies enhancing the reliability of gene signatures in disease transcriptomic research.
... Identifikasi gen yang terlibat dalam penyakit adalah alat penting untuk mengungkapkan mekanisme molekuler perkembangan penyakit. Dalam penelitian farmasi dan klinis, DEG juga berharga untuk menentukan kandidat biomarker, target terapeutik, dan tanda tangan gen untuk diagnostik (Rodriguez-Esteban & Jiang, 2017). Selain analisis DEG gen individu, analisis gen-gen yang diekspresikan secara bersama juga penting untuk melihat korelasi atau hubungan pengaturan yang membedakan keadaan sehat dan sakit. ...
Obesitas sering dikaitkan dengan inflamasi, yang memicu pelepasan mediator inflamasi dan menurunkan produksi adiponektin. Beberapa teori menyebutkan bahwa peradangan kronis akibat obesitas merupakan mekanisme dominan diabetes tipe 2 (T2D). Penelitian ini melakukan analisis Differentially Genes Expressed (DEG) terkait inflamasi menggunakan basis data Gene Expression Omnibus (GEO) untuk melihat perubahan transkripsi (ekspresi gen) pada hati manusia yang berkontribusi pada akumulasi lipid hati dan resistensi insulin terkait T2D. DEG yang dihasilkan kemudian digunakan untuk melihat apakah whey protein (WP) dapat mempengaruhi DEG tersebut. Konsumsi WP dipercaya dapat menurunkan kelebihan berat badan dan kadar gula darah. Hasil analisis DEG menunjukkan terdapat 22 gen terkait inflamasi pada liver yang berbeda ekspresinya antara Lean dengan kelompok pasien obesitas tanpa T2D (Obese_noT2D) dan dengan kelompok pasien obesitas dengan T2D yang tidak terkontrol (Obese_T2D-poorly controlled). Berdasarkan penelitian ini, tidak satu-pun gen-gen tersebut yang dipengaruhi oleh asupan WP. Namun demikian beberapa gen yang diduga berperan dalam inflamasi, obesitas, dan T2D seperti FOXJ1, NLRP1, LGALS2, LIME1, NOTCH3, ARBB2, SIM2, dan TAT berpotensi dipengaruhi oleh asupan WP.
... Therefore, characterizing macrophage subtypes and their cytokine profiles is crucial for developing cancer therapies and predicting patient outcomes. Quantifying gene expression facilitates the exploration of various human diseases such as inflammation and infection [2,12]. ...
These authors contributed equally to this work. Abstract: Current quantitative gene expression detection in genomic and transcriptomic research heavily relies on quantitative real-time PCR (qPCR). While existing multiplex gene detection techniques offer simultaneous analysis of multiple targets, we present an alternative assay capable of detecting gene expression simultaneously within a single well. This highly sensitive method utilizes πCode MicroDiscs, featuring unique identification patterns and fluorescent detection. Our study compared this multiplex πCode platform with a qPCR platform for profiling cytokine gene expression. The πCode MicroDisc assay successfully demonstrated the expression of polymerization markers for M1-and M2-like macrophages generated from THP-1-derived macrophages in a qualitative assay. Additionally, our findings suggest a pattern agreement between the πCode assay and the qPCR assay, indicating the potential of the πCode technology for comparative gene expression analysis. Regarding the inherent sensitivity and linearity, the developed πCode assay primarily provides qualitative gene expression to discriminate the polarization of macrophages. This remarkable capability presents substantial advantages for researchers, rendering the technology highly suitable for high-throughput applications in clinical diagnosis and disease monitoring.
... The molecular basis for these disparities remains poorly understood. While gene expression changes may not always impact biological activity, identifying differential expression present in pathologies often yields valuable insights into understanding pathological disparities [6]. The need exists for further investigation into ethnicity and sex-based variations in gene expression patterns in GBM. ...
Glioblastoma (GBM), an aggressive form of brain cancer, has a higher incidence in non-Hispanics when compared to the US Hispanic population. Using data from RT-PCR analysis of 21 GBM tissue from Hispanic patients in Puerto Rico, we identified significant correlations in the gene expression of focal adhesion kinase and proline-rich tyrosine kinase (PTK2 and PTK2B) with NGFR (nerve growth factor receptor), PDGFRB (platelet-derived growth factor receptor B), EGFR (epithelial growth factor receptor), and CXCR1 (C-X-C motif chemokine receptor 1). This study further explores these correlations found in gene expression while accounting for sex and ethnicity. Statistically significant (p < 0.05) correlations with an r value > ±0.7 were subsequently contrasted with mRNA expression data acquired from cBioPortal for 323 GBM specimens. Significant correlations in Puerto Rican male patients were found between PTK2 and PTK2B, NGFR, PDGFRB, EGFR, and CXCR1, which did not arise in non-Hispanic male patient data. The data for Puerto Rican female patients showed correlations in PTK2 with PTK2B, NGFR, PDGFRB, and EGFR, all of which did not appear in the data for non-Hispanic female patients. The data acquired from cBioPortal for non-Puerto Rican Hispanic patients supported the correlations found in the Puerto Rican population for both sexes. Our findings reveal distinct correlations in gene expression patterns, particularly involving PTK2, PTK2B, NGFR, PDGFRB, and EGFR among Puerto Rican Hispanic patients when compared to non-Hispanic counterparts.
... Identifying potential tumor markers promoting lymph node metastasis is crucial to understanding the molecular characteristics underlying lung SCC [6] that can help with clinical diagnosis, prognostic assessment, and treatment strategies for such patients. Bioinformatics-based approaches have become important research tools in recent decades, enabling the identification of novel genes and key pathways involved in cancer progression and metastasis [7,8]. ...
... Thus, reliable biomarkers are urgently required for the early prediction of lung SCC progression to lymph nodes. With the growing availability of high-throughput technology, the identification of DEGs in disease is one particular focus of the investigation to pinpoint candidate biomarkers [7,8]. The cells were treated for 2 h without (0µM) or with 25µM and 50µM H2O2, and then treated with supplemented medium for an additional 72 h. ...
Background
The primary goal of this work is to identify biomarkers associated with lung squamous cell carcinoma and assess their potential for early detection of lymph node metastasis.
Methods
This study investigated gene expression in lymph node metastasis of lung squamous cell carcinoma using data from the Cancer Genome Atlas and R software. Protein-protein interaction networks, hub genes, and enriched pathways were analyzed. ZNF334 and TINAGL1, two less explored genes, were further examined through in vitro, ex vivo, and in vivo experiments to validate the findings from bioinformatics analyses. The role of ZNF334 and TINAGL1 in senescence induction was assessed after H2O2 and UV induced senescence phenotype determined using β-galactosidase activity and cell cycle status assay.
Results
We identified a total of 611 up- and 339 down-regulated lung squamous cell carcinoma lymph node metastasis-associated genes (FDR < 0.05). Pathway enrichment analysis highlighted the central respiratory pathway within mitochondria for the subnet genes and the nuclear DNA-directed RNA polymerases for the hub genes. Significantly down regulation of ZNF334 gene was associated with malignancy lymph node progression and senescence induction has significantly altered ZNF334 expression (with consistency in bioinformatics, in vitro, ex vivo, and in vivo results). Deregulation of TINAGL1 expression with inconsistency in bioinformatics, in vitro (different types of lung squamous cancer cell lines), ex vivo, and in vivo results, was also associated with malignancy lymph node progression and altered in senescence phenotype.
Conclusions
ZNF334 is a highly generalizable gene to lymph node metastasis of lung squamous cell carcinoma and its expression alter certainly under senescence conditions.
... Although -omics technologies can provide insights on numerous genes across the genome at a time and thus offer the promise to counter historically acquired research patterns (Collins et al., 2003;Shendure et al., 2019;Lloyd et al., 2020;Kustatscher et al., 2022), this discrepancy has persisted (Haynes et al., 2018;Rodriguez-Esteban and Jiang, 2017;Oprea et al., 2018;Sinha et al., 2018;Wood et al., 2019;Donohue and Love, 2024) even as the popularity of -omics technologies has risen Peña-Castillo and Hughes, 2007;Ellens et al., 2017). We therefore sought to use bibliometric data to delineate where and why understudied human proteincoding genes are abandoned as research targets following -omics experiments. ...
Present-day publications on human genes primarily feature genes that already appeared in many publications prior to completion of the Human Genome Project in 2003. These patterns persist despite the subsequent adoption of high-throughput technologies, which routinely identify novel genes associated with biological processes and disease. Although several hypotheses for bias in the selection of genes as research targets have been proposed, their explanatory powers have not yet been compared. Our analysis suggests that understudied genes are systematically abandoned in favor of better-studied genes between the completion of -omics experiments and the reporting of results. Understudied genes remain abandoned by studies that cite these -omics experiments. Conversely, we find that publications on understudied genes may even accrue a greater number of citations. Among 45 biological and experimental factors previously proposed to affect which genes are being studied, we find that 33 are significantly associated with the choice of hit genes presented in titles and abstracts of -omics studies. To promote the investigation of understudied genes, we condense our insights into a tool, find my understudied genes (FMUG), that allows scientists to engage with potential bias during the selection of hits. We demonstrate the utility of FMUG through the identification of genes that remain understudied in vertebrate aging. FMUG is developed in Flutter and is available for download at fmug.amaral.northwestern.edu as a MacOS/Windows app.
... DEGs represent one of the key findings arising from research in this area. By definition, DEGs refer to how all cells contain identical genomic DNA but variably express encoded genes depending on their function, environment, and host's disease state [62]. Section 2.1 highlighted how DEGs such as Lgals3bp, Clec7a, and Lpl were highly expressed by microglia in both subacute TBI patients and neurodegenerative patients; however, these constitute only a portion of the clinically important DEGs [20,23]. ...
Traumatic brain injury (TBI) is a frequently encountered form of injury that can have lifelong implications. Despite advances in prevention, diagnosis, monitoring, and treatment, the degree of recovery can vary widely between patients. Much of this is explained by differences in severity of impact and patient-specific comorbidities; however, even among nearly identical patients, stark disparities can arise. Researchers have looked to genetics in recent years as a means of explaining this phenomenon. It has been hypothesized that individual genetic factors can influence initial inflammatory responses, recovery mechanisms, and overall prognoses. In this review, we focus on cytokine polymorphisms, mitochondrial DNA (mtDNA) haplotypes, immune cells, and gene therapy given their associated influx of novel research and magnitude of potential. This discussion is prefaced by a thorough background on TBI pathophysiology to better understand where each mechanism fits within the disease process. Cytokine polymorphisms causing unfavorable regulation of genes encoding IL-1β, IL-RA, and TNF-α have been linked to poor TBI outcomes like disability and death. mtDNA haplotype H has been correlated with deleterious effects on TBI recovery time, whereas haplotypes K, T, and J have been depicted as protective with faster recovery times. Immune cell genetics such as microglial differentially expressed genes (DEGs), monocyte receptor genes, and regulatory factors can be both detrimental and beneficial to TBI recovery. Gene therapy in the form of gene modification, inactivation, and editing show promise in improving post-TBI memory, cognition, and neuromotor function. Limitations of this study include a large proportion of cited literature being focused on pre-clinical murine models. Nevertheless, favorable evidence on the role of genetics in TBI recovery continues to grow. We aim for this work to inform interested parties on the current landscape of research, highlight promising targets for gene therapy, and galvanize translation of findings into clinical trials.
