Unlocking the Genetic Code: A Systematic Review and Bioinformatic Analysis of the MS4A Gene Cluster’s Role in Alzheimer’s Disease

HSPH1 and DNAJB1 as potential key regulators in hepatic ischemia-reperfusion injury.

Insights into the molecular mechanisms and signalling pathways of epithelial to mesenchymal transition (EMT) in colorectal cancer: A systematic review and bioinformatic analysis of gene expression

Several studies have identified CD163 as a potential mediator of diabetes mellitus through an immune-inflammation. Further study is necessary to identify its specific mechanism. In this study, we aimed to investigate CD163 as a potential biomarker associated with immune inflammation in diabetes mellitus through a systematic review and bioinformatics analysis. We searched PubMed, Web of Science, the Cochrane Library, and Embase databases with a time limit of September 2, 2022. Furthermore, we conducted a systematic search and review based on PRISMA guidelines. Additionally, diabetic gene expression microarray datasets GSE29221, GSE30528, GSE30529, and GSE20966 were downloaded from the GEO database (http://www.ncbi.nlm.nih.gov/geo) for bioinformatics analysis. The PROSPERO number for this study is CRD420222347160. Following the inclusion and exclusion criteria, seven articles included 1607 patients, comprising 912 diabetic patients and 695 non-diabetic patients. This systematic review found significantly higher levels of CD163 in diabetic patients compared to non-diabetic patients. People with diabetes had higher levels of CRP expression compared to the control group. Similarly, two of the three papers that used TNF- α as an outcome indicator showed higher expression levels in diabetic patients. Furthermore, IL-6 expression levels were higher in diabetic patients than in the control group. A total of 62 samples were analyzed by bioinformatics (33 case controls and 29 experimental groups), and 85 differential genes were identified containing CD163. According to the immune cell correlation analysis, CD163 was associated with macrophage M2, γδ T lymphocytes, macrophage M1, and other immune cells. Furthermore, to evaluate the diagnostic performance of CD163, we validated it using the GSE20966 dataset. In the validation set, CD163 showed high diagnostic accuracy. This study suggests CD163 participates in the inflammatory immune response associated with diabetes mellitus and its complications by involving several immune cells. Furthermore, the results suggest CD163 may be a potential biomarker reflecting immune inflammation in diabetic mellitus.

Traumatic brain injury (TBI) is a common brain injury with a high morbidity and mortality. The complex injury cascade triggered by TBI can result in permanent neurological dysfunction such as cognitive impairment. In order to provide new insights for elucidating the underlying molecular mechanisms of TBI, this study systematically analyzed the transcriptome data of the rat hippocampus in the subacute phase of TBI. Two datasets (GSE111452 and GSE173975) were downloaded from the Gene Expression Omnibus (GEO) database. Systematic bioinformatics analyses were performed, including differentially expressed genes (DEGs) analysis, gene set enrichment analysis (GSEA), Gene Ontology (GO) enrichment analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, protein-protein interaction (PPI) network construction, and hub gene identification. In addition, hematoxylin and eosin (HE), Nissl, and immunohistochemical staining were performed to assess the injured hippocampus in a TBI rat model. The hub genes identified by bioinformatics analyses were verified at the mRNA expression level. A total of 56 DEGs were shared in the two datasets. GSEA results suggested significant enrichment in the MAPK and PI3K/Akt pathways, focal adhesion, and cellular senescence. GO and KEGG analyses showed that the common DEGs were predominantly related to immune and inflammatory processes, including antigen processing and presentation, leukocyte-mediated immunity, adaptive immune response, lymphocyte-mediated immunity, phagosome, lysosome, and complement and coagulation cascades. A PPI network of the common DEGs was constructed, and 15 hub genes were identified. In the shared DEGs, we identified two transcription co-factors and 15 immune-related genes. The results of GO analysis indicated that these immune-related DEGs were mainly enriched in biological processes associated with the activation of multiple cells such as microglia, astrocytes, and macrophages. HE and Nissl staining results demonstrated overt hippocampal neuronal damage. Immunohistochemical staining revealed a marked increase in the number of Iba1-positive cells in the injured hippocampus. The mRNA expression levels of the hub genes were consistent with the transcriptome data. This study highlighted the potential pathological processes in TBI-related hippocampal impairment. The crucial genes identified in this study may serve as novel biomarkers and therapeutic targets, accelerating the pace of developing effective treatments for TBI-related hippocampal impairment.

Identification of circulatory miRNAs as candidate biomarkers in prediabetes - A systematic review and bioinformatics analysis

Different experimental results suggest the presence of an interplay between global transcriptional regulation and chromosome spatial organization in bacteria. The identification and clear visualization of spatial clusters of contiguous genes targeted by specific DNA-binding proteins or sensitive to nucleoid perturbations can elucidate links between nucleoid structure and gene expression patterns. Similarly, statistical analysis to assess correlations between results from independent experiments can provide the integrated analysis needed in this line of research. NuST (Nucleoid Survey tools), based on the Escherichia coli genome, gives the non-expert the possibility to analyze the aggregation of genes or loci sets along the genome coordinate, at different scales of observation. It is useful to discover correlations between different sources of data (e.g. expression, binding or genomic data) and genome organization. A user can use it on datasets in the form of gene lists coming from his/her own experiments or bioinformatic analyses, but also make use of the internal database, which collects data from many published studies. NuST is a web server (available at http://www.lgm.upmc.fr/nust/). The website is implemented in PHP, SQLite and Ajax, with all major browsers supported, while the core algorithms are optimized and implemented in C. NuST has an extensive help page and provides a direct visualization of results as well as different downloadable file formats. A template Perl code for automated access to the web server can be downloaded at http://www.lgm.upmc.fr/nust/downloads/, in order to allow the users to use NuST in systematic bioinformatic analyses.

Diabetic kidney disease (DKD) is a long-term complication of diabetes and causes renal microvascular disease. It is also one of the main causes of end-stage renal disease (ESRD), which has a complex pathophysiological process. Timely prevention and treatment are of great significance for delaying DKD. This study aimed to use bioinformatics analysis to find key diagnostic markers that could be possible therapeutic targets for DKD. We downloaded DKD datasets from the Gene Expression Omnibus (GEO) database. Overexpression enrichment analysis (ORA) was used to explore the underlying biological processes in DKD. Algorithms such as WGCNA, LASSO, RF, and SVM_RFE were used to screen DKD diagnostic markers. The reliability and practicability of the the diagnostic model were evaluated by the calibration curve, ROC curve, and DCA curve. GSEA analysis and correlation analysis were used to explore the biological processes and significance of candidate markers. Finally, we constructed a mouse model of DKD and diabetes mellitus (DM), and we further verified the reliability of the markers through experiments such as PCR, immunohistochemistry, renal pathological staining, and ELISA. Biological processes, such as immune activation, T-cell activation, and cell adhesion were found to be enriched in DKD. Based on differentially expressed oxidative stress and inflammatory response-related genes (DEOIGs), we divided DKD patients into C1 and C2 subtypes. Four potential diagnostic markers for DKD, including tenascin C, peroxidasin, tissue inhibitor metalloproteinases 1, and tropomyosin (TNC, PXDN, TIMP1, and TPM1, respectively) were identified using multiple bioinformatics analyses. Further enrichment analysis found that four diagnostic markers were closely related to various immune cells and played an important role in the immune microenvironment of DKD. In addition, the results of the mouse experiment were consistent with the bioinformatics analysis, further confirming the reliability of the four markers. In conclusion, we identified four reliable and potential diagnostic markers through a comprehensive and systematic bioinformatics analysis and experimental validation, which could serve as potential therapeutic targets for DKD. We performed a preliminary examination of the biological processes involved in DKD pathogenesis and provide a novel idea for DKD diagnosis and treatment.

Background Existing research lacks a systematic investigation of the complete gene expression profile in hypertrophic scars (HS). This study aimed to systematically explore the differences in mRNA expression profiles between human HS and normal skin tissues using bioinformatics analysis methods. Methods GSE236983 and GSE229848 were downloaded from Gene Expression Omnibus (GEO) to screen differentially expressed genes (DEGs). Protein-protein interaction (PPI) network was constructed using STRING (search tool for recurring instances of neighboring genes), followed by gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the DEGs. Three surgical samples of HSs and adjacent normal skin tissues were selected from 3 HS patients for comparative analysis of DEGs. Results Venn diagram of two datasets showed an intersection, revealing 31 DEGs. GO enrichment results unveiled that DEGs were mainly involved in keratinization, keratinization cell differentiation, epidermal cell differentiation (ECD), and skin development. Cellular components (CCs) were mainly enriched in cell desmosomes, intermediate filament cytoskeleton (IFC), keratin filament (KF), and intermediate filament (IF). KEGG analysis signified that the main involved pathways encompassed tight junctions, extracellular matrix-receptor interaction, arachidonic acid metabolism, and terpenoid backbone biosynthesis. mRNA levels of 10 core genes (MVD, ACAT2, HMGCS1, HMGCR, FASN, MSMO1, DHCR7, DHCR24, and AACS) in HS and normal skin tissues were different (P < 0.05), with MVK showing non-significant differences (P > 0.05). Conclusion this study identified ten core DEGs associated with HS through systematic bioinformatics analysis. These genes are involved in biological processes and cellular components such as keratinization, cell differentiation, lipid metabolism, cholesterol biosynthesis, and cell adhesion.

Obesity is a growing global health problem; it has been forecasted that over half of the global population will be obese by 2030. Obesity is complicated with many diseases, such as diabetes and cardiovascular diseases, leading to an economic impact on society. Other than diet, exposure to environmental pollutants is considered a risk factor for obesity. Exposure to perfluorooctanoic acid (PFOA) was found to impair hepatic lipid metabolism, resulting in obesity. In this study, we applied network pharmacology and systematic bioinformatics analysis, such as gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, together with molecular docking, to investigate the targets of fucoidan for treating PFOA-associated obesity through the regulation of endoplasmic reticulum stress (ERS). Our results identified ten targets of fucoidan, such as glucosylceramidase beta (GBA), glutathione-disulfide reductase (GSR), melanocortin 4 receptor (MC4R), matrix metallopeptidase (MMP)2, MMP9, nuclear factor kappa B subunit 1 (NFKB1), RELA Proto-Oncogene, NF-KB Subunit (RELA), nuclear receptor subfamily 1 group I member 2 (NR1I2), proliferation-activated receptor delta (PPARD), and cellular retinoic acid binding protein 2 (CRABP2). GO and KEGG enrichment analyses highlighted their involvement in the pathogenesis of obesity, such as lipid and fat metabolisms. More importantly, the gene cluster is responsible for obesity-associated diseases and disorders, such as insulin resistance (IR), non-alcoholic fatty liver disease, and diabetic cardiomyopathy, via the control of signaling pathways. The findings of this report provide evidence that fucoidan is a potential nutraceutical product against PFOA-associated obesity through the regulation of ERS.

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high‑ and low‑risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.

Introduction: Inborn errors of amino acid metabolism (IEAAM) are genetic defects that lead to the toxic accumulation of metabolites. While the genetic basis of these intoxication-type disorders is well-established, the regulatory role of microRNAs in their pathogenesis remains poorly synthesized. This systematic bioinformatic analysis aims to identify and validate specific miRNA-gene interactions that modulate key metabolic pathways in IEAAM. Methods: A systematic literature search was conducted across PubMed and Scopus databases. We integrated identified miRNAs with metabolic genes using prediction tools (e.g., miRWalk, miRDB) and validated these interactions through functional pathway analysis using KEGG, DisGeNET, and PubChem database integration. Results: Our analysis identified a consistent network of miRNAs associated with amino acid metabolism. Specifically, six miRNAs (mmu-miR-409-5p, hsa-miR-3944-3p, rno-miR-125b-5p, hsa-miR-145-5p, hsa-miR-5195-3p, and hsa-miR-1202) were bioinformatically validated to target key genes such as FAH, DBT, CBS, PSAT1, and ARG1. These miRNAs are significantly enriched in metabolic pathways (KEGG) and associated with clinical phenotypes including epilepsy and intoxication-related metabolic crises. Conclusion: This computational study provides the first systematic evidence of a conserved miRNA-gene regulatory network in aminoacidopathies. By identifying these six key regulatory miRNAs, our findings offer novel insights into the epigenetic modulation of metabolic blocks and highlight potential targets for future miRNA-based therapeutic interventions in IEAAM.

MicroRNA hsa-miR-1301-3p Regulates Human ADH6, ALDH5A1 and ALDH8A1 in the Ethanol-Acetaldehyde-Acetate Metabolic Pathway.

BackgroundActivating transcription factor 5 (ATF5) is a transcription regulator closely associated with cancers. Our previous studies have identified the expression and function of ATF5 in various malignant tumors. However, a comprehensive study of ATF5 remains lacking across different tumor types. This study will focus on a comprehensive analysis of ATF5 across various cancers, while further investigating the function of the ATF5 protein in gliomas and its protein characteristics.MethodsWe investigated ATF5 expression across various tumors using systematic bioinformatics analysis. We further examined the correlations between ATF5 expression and patient prognosis, tumor staging, and immunological characteristics. Additionally, functional enrichment analysis was performed on proteins interacting with or co-expressed with ATF5. We employed flow cytometry to analyze apoptosis and investigate the function of ATF5 in glioma cell lines. Furthermore, immunohistochemistry (IHC) was employed to validate the relationship between ATF5 expression and distinct glioma grades. Finally, we explored the protein phase-separation capacity of ATF5 and its influencing factors in vitro.ResultsATF5 expression levels exhibited a strong correlation with prognosis in multiple cancers. Functional clustering analysis of the proteins interacting with ATF5 and co-expressed with ATF5 suggested that ATF5 might influence tumor cell proliferation, differentiation, and apoptosis. Immunohistochemical results demonstrated a strong association between ATF5 expression and glioma pathological grades. Additionally, flow cytometry analysis showed that interfering with ATF5 expression using small interfering RNA (siRNA) promoted apoptosis in glioma cells. Interestingly, droplet formation experiments confirmed that ATF5 is capable of aggregation in vitro and is influenced by factors such as salt concentration.ConclusionsOur findings indicate that ATF5 has prognostic value in cancer treatment, providing a potential therapeutic target for subsequent treatment of related cancers.

Bladder cancer (BLCA) is the most common type of malignancy affecting the urinary tract, characterized by high recurrence rates, propensity for progression, metastatic potential, and multidrug resistance, all of which ultimately contribute to an unfavorable prognosis. RNA-binding proteins (RBPs) play a critical role in cancer development and have been associated with the progression and prognosis of the disease. However, comprehensive investigations into the biological functions and molecular mechanisms of RBPs in BLCA remain limited. The study aims to explore the relationship between RBPs and prognosis in BLCA, and to develop and validate an RBPs-based prognostic signature, providing new insights for the diagnosis and treatment of BLCA. Clinical data and RBPs expression profiles of BLCA patients were sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A systematic bioinformatics analysis was conducted to identify differentially expressed RBPs and assess their prognostic significance. The optimal predictive model was selected by integrating multiple machine learning algorithms, enabling the identification of hub genes associated with BLCA prognosis and developing an RBP-related gene signature. To evaluate the prognostic signature's efficacy, survival curves and receiver operating characteristic (ROC) curves were generated. A nomogram was constructed and validated to predict the survival of BLCA patients at 1, 3, and 5 years. Furthermore, analyses of immune infiltration and gene set enrichment analysis (GSEA) were conducted to explore the roles of RBPs in immune cell interactions and elucidate underlying biological pathways. A prognostic signature was effectively developed using nine RBPs (OAS1, MTG1, DUS4L, IGF2BP3, NOL12, PABPC1L, ZC3HAV1L, TRMT2A and TRMU), represented as risk score, through the integration of 13 combinatorial machine learning algorithms. Kaplan-Meier analysis revealed that the high-risk group exhibited a significantly poorer overall survival (OS) probability compared to the low-risk group. The areas under the ROC curves for the risk score model at 1, 3, and 5 years were 0.661, 0.655, and 0.676, respectively. The nomogram, which integrated clinical characteristics and risk scores, demonstrated robust prognostic accuracy. Furthermore, single-sample gene set enrichment analysis (ssGSEA) demonstrated significant correlations between both the risk score model and hub RBPs with the immune status of BLCA patients. GSEA indicated that major signaling pathways enriched in the high-risk group included extracellular matrix (ECM) components and interaction, as well as cytokine and receptor interaction. This study successfully identified and developed a prognostic signature based on nine RBPs, accompanied by a nomogram for predicting survival probability in BLCA patients. Our findings demonstrate that these nine RBPs function as significant biomarkers for forecasting the prognosis and immune status in BLCA, suggesting their potential as therapeutic targets for BLCA.

The coronavirus disease 2019 (COVID-19) pandemic has led to 4,255,892 deaths worldwide. Although COVID-19 vaccines are available, mutant forms of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have reduced the effectiveness of vaccines. Patients with cancer are more vulnerable to COVID-19 than patients without cancer. Identification of new drugs to treat COVID-19 could reduce mortality rate, and traditional Chinese Medicine(TCM) has shown potential in COVID-19 treatment. In this study, we focused on lung adenocarcinoma (LUAD) patients with COVID-19. We aimed to investigate the use of curcumol, a TCM, to treat LUAD patients with COVID-19, using network pharmacology and systematic bioinformatics analysis. The results showed that LUAD and patients with COVID-19 share a cluster of common deregulated targets. The network pharmacology analysis identified seven core targets (namely, AURKA, CDK1, CCNB1, CCNB2, CCNE1, CCNE2, and TTK) of curcumol in patients with COVID-19 and LUAD. Clinicopathological analysis of these targets demonstrated that the expression of these targets is associated with poor patient survival rates. The bioinformatics analysis further highlighted the involvement of this target cluster in DNA damage response, chromosome stability, and pathogenesis of LUAD. More importantly, these targets influence cell-signaling associated with the Warburg effect, which supports SARS-CoV-2 replication and inflammatory response. Comparative transcriptomic analysis on in vitro LUAD cell further validated the effect of curcumol for treating LUAD through the control of cell cycle and DNA damage response. This study supports the earlier findings that curcumol is a potential treatment for patients with LUAD and COVID-19.