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PubMed2026

Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.

Humans↗

PubMed2024

Decoding the genetic landscape of allergic rhinitis: a comprehensive network analysis revealing key genes and potential therapeutic targets.

BACKGROUND: Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe. METHODS: We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes. RESULTS: Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR. CONCLUSION: By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

Humans↗

PubMed2024

Identification of cuproptosis-realated key genes and pathways in Parkinson's disease via bioinformatics analysis.

INTRODUCTION: Parkinson's disease (PD) is the second most common worldwide age-related neurodegenerative disorder without effective treatments. Cuproptosis is a newly proposed conception of cell death extensively studied in oncological diseases. Currently, whether cuproptosis contributes to PD remains largely unclear. METHODS: The dataset GSE22491 was studied as the training dataset, and GSE100054 was the validation dataset. According to the expression levels of cuproptosis-related genes (CRGs) and differentially expressed genes (DEGs) between PD patients and normal samples, we obtained the differentially expressed CRGs. The protein-protein interaction (PPI) network was achieved through the Search Tool for the Retrieval of Interacting Genes. Meanwhile, the disease-associated module genes were screened from the weighted gene co-expression network analysis (WGCNA). Afterward, the intersection genes of WGCNA and PPI were obtained and enriched using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the key genes were identified from the datasets. The receiver operating characteristic curves were plotted and a PPI network was constructed, and the PD-related miRNAs and key genes-related miRNAs were intersected and enriched. Finally, the 2 hub genes were verified via qRT-PCR in the cell model of the PD and the control group. RESULTS: 525 DEGs in the dataset GSE22491 were identified, including 128 upregulated genes and 397 downregulated genes. Based on the PPI network, 41 genes were obtained. Additionally, the dataset was integrated into 34 modules by WGCNA. 36 intersection genes found from WGCNA and PPI were significantly abundant in 7 pathways. The expression levels of the genes were validated, and 2 key genes were obtained, namely peptidase inhibitor 3 (PI3) and neuroserpin family I member 1 (SERPINI1). PD-related miRNAs and key genes-related miRNAs were intersected into 29 miRNAs including hsa-miR-30c-2-3p. At last, the qRT-PCR results of 2 hub genes showed that the expressions of mRNA were up-regulated in PD. CONCLUSION: Taken together, this study demonstrates the coordination of cuproptosis in PD. The key genes and miRNAs offer novel perspectives in the pathogenesis and molecular targeting treatment for PD.

Humans↗

PubMed2026

Identification and expression validation of key genes of Xiaozhengtongluo formula in the treatment of diabetic nephropathy by Mendelian randomization.

Xiaozhengtongluo formula (XZTL) has a positive effect on the treatment of diabetic nephropathy (DN), but its mechanism is not fully understood. Therefore, it is important to explore the key genes of XZTL in the treatment of DN. Differentially expressed genes (DEGs) between DN and control obtained from GSE96804, drug target genes of XZTL, and disease target genes of DN obtained from public databases were intersected. Genes of intersection were defined as candidate genes. Next, Mendelian randomization (MR) analysis was used to ascertain the causal associations between candidate genes and DN. Afterwards, key genes were confirmed through receiver operating characteristic (ROC) curve analysis and expression validation. Subsequently, enrichment analysis, molecular regulatory network analysis, and molecular docking were conducted. Finally, experimental verification of the expression levels of key genes was performed through reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Altogether, 29 candidate genes were screened via MR analysis, identifying APOD, IGFBP3, and LPL as significantly associated with DN. IGFBP3 and APOD were risk factors, whereas LPL was protective. Consistent expression trends across training and validation datasets defined them as key genes. All three were co-enriched in 26 pathways, including oxidative phosphorylation. Regulatory networks showed MIR497HG/hsa-miR-19a-3p regulated IGFBP3, and NEAT1/hsa-miR-29a-3p regulated LPL; IGFBP3 and LPL were co-targeted by SP3 and SP1. Molecular docking revealed APOD-baicalein, LPL-oleic acid, and IGFBP3-quercetin binding, suggesting therapeutic potential. RT-qPCR confirmed aberrant expression of these genes in DN, which was normalized by XZTL intervention. In this study, three key genes (APOD, IGFBP3, and LPL) of XZTL in the treatment of DN were finally obtained, providing mechanistic clues for understanding XZTL's multi-target mechanism and providing experimentally tractable candidate targets for DN molecular subtyping, targeted therapeutic development, and precision medicine approaches in TCM.

Diabetic Nephropathies↗

PubMed2026

Screening and identification of key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods.

OBJECTIVE: Radiotherapy (RT) plays a crucial role in the comprehensive treatment of rectal cancer. However, the impact of radiotherapy on the tumor microenvironment (TME), especially its effect on immune cell infiltration and immune-related gene expression, has not been fully studied. This study aims to screen and analyze key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods for the purpose of identifying potential biomarkers and providing new insights for the personalized therapy of rectal cancer. METHODS: Using data from the Public Gene Expression Database (GEO) and the Cancer Genomics Database (TCGA), the impact of radiotherapy on the immune microenvironment of rectal cancer was explored using bioinformatics tools. Through screening differentially expressed genes (DEGs), correlation analysis, TIMER database analysis, immune infiltration score, and correlation analysis between key genes and prognosis, the effects of radiotherapy on the immune microenvironment of rectal cancer were investigated. RESULTS: Totally 7 upregulated and 4 downregulated differentially expressed genes were identified, among which MASP1, LTK, SLC9A3R2 were negatively correlated with myeloid suppressor cell infiltration (MDSCs), while ZP2 was positively correlated. The expression of MASP1 and SLC9A3R2 was closely related to the level of immune cell infiltration and played significant roles in the immune microenvironment. High expression of MASP1 was significantly correlated with survival benefits from immune checkpoint inhibitor therapy, while SLC9A3R2 was closely related to the efficacy of PD-L1 inhibitors and CTLA4 inhibitors. CONCLUSIONS: MASP1 and SLC9A3R2, as two key genes that may be related to the immune microenvironment of rectal cancer radiotherapy, deserve further exploration of their roles in the mechanism. The combination of radiotherapy and immunotherapy holds promising prospects in the treatment of rectal cancer, and exploration of related mechanisms will provide new strategies and targets for the treatment of various tumors and rectal cancer.

Bioinformatics↗

PubMed2026

Integrated transcriptome analysis and machine learning to construct a homeostatic model of acetylation for bladder cancer and validate the key gene CES1.

BACKGROUND: Bladder cancer (BLCA) is one of the most common malignant tumors of the urinary system. Protein acetylation (PA) plays a critical role in regulating multiple biological processes (BPs), cellular homeostasis, and cancer-related signaling pathways. This study aimed to construct a homeostatic model of acetylation for BLCA using integrated transcriptome analysis and machine learning and to validate the key gene CES1. METHODS: RNA sequencing (RNA-seq) and clinical data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Acetylation-related differentially expressed genes (DEGs) in BLCA were screened using differential expression analysis (DEA). An acetylation homeostatic model was constructed via univariate, machine learning-based least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses, followed by validation in multiple cohorts. Single-cell RNA-seq analysis was used to explore gene expression patterns in diverse cell types. Enrichment analysis (EA), immune infiltration, and drug sensitivity analysis (DSA) were performed to characterize molecular features of different risk groups. Finally, the biological function of CES1 as the key gene was verified by in vitro knockdown experiments. RESULTS: We established a robust acetylation homeostatic model consisting of five genes, which effectively predicted overall survival (OS) and served as an independent prognostic factor in BLCA. High-risk patients showed significantly poorer prognosis, distinct immune infiltration profiles, and differential drug sensitivity. CES1 was identified and validated as the key gene in this model, which was highly expressed in BLCA and associated with poor prognosis. Knockdown of CES1 markedly suppressed cell proliferation, invasion, and migration, and reduced intracellular coenzyme A (CoA) levels, thereby regulating PA homeostasis. CONCLUSIONS: We developed and validated a novel acetylation homeostatic model for survival stratification and personalized treatment guidance in BLCA, based on integrated transcriptome analysis and machine learning. CES1 is closely associated with intracellular CoA levels and the malignant progression of BLCA. Its potential association with PA homeostasis requires further mechanistic validation, and it may act as a candidate therapeutic biomarker for BLCA.

Bladder cancer (BLCA)↗

PubMed2026

Protein-protein interactions reveal key genes in rice response to salt stress: a meta-analysis.

The salt-tolerant genes (STGs) play important roles in protecting plants against salt stress. Although various types of STGs have been systematically characterized in plant species, the key genes (KGs) regulating salt stress tolerance in rice (Oryza sativa L.) remain elusive. This study focused on the identification and characterization of the members of STGs in rice through integrated bioinformatic and molecular approaches, including chromosomal location, physicochemical characteristics, protein-protein interaction, and expression profiles of the identified genes. A total of 164 differentially expressed genes (DEGs) were systematically identified as responsive to salt tolerance and sorted out potential 12 kg (OsHSP20.2, OsGFP2, OsBBTI2, OsEN20.6, OsUBC17, OsACD5, OsPEAB5, OsDP11, OsDFP5, OsWD40.7, OsEP11.1, and OsGRAM12) through the CytoHubba algorithms analysis. Physicochemical characterization indicated substantial variation among KGs, including genomic sequences (824-4051 bp), amino acid length (148-659 aa), molecular weight (16.39-71.35 kDa), and isoelectric point (4.66-10.37). Protein-protein interaction (PPI) network prediction indicated intricate functional associations among key STGs. Gene Ontology (GO) enrichment analysis revealed that the KGs are involved in numerous biological processes and molecular functions. Moreover, gene homology results revealed that KGs have multiple relationships with other plant species. Co-expression network analysis revealed that 12 kg are potentially involved in the regulatory mechanisms underlying the biological process. Relative gene expression through the comparative threshold (ΔΔCT) of qRT-PCR revealed that the KGs are salt-induced and may play crucial roles in rice responses to salt stress. Tissue-specific expression patterns revealed that the KGs significantly altered expression levels across different tissues and under stress. This systematic investigation demonstrated that the 12 identified genes may play roles in the development of salt-tolerant rice varieties.

Oryza↗

PubMed2025

Exploring diagnostic m6A regulators in primary open-angle glaucoma: insight from gene signature and possible mechanisms by which key genes function.

PURPOSE: The purpose of this study was to interrogate the potential role of N6-methyladenosine (m6A) regulators in the process of trabecular meshwork (TM) tissue damage in patients with primary open-angle glaucoma (POAG). METHODS: Firstly, the expression profile of m6A regulators in TM tissues of POAG patients was comprehensively analyzed by bioinformatics analysis; Plasmid transfection and siRNA gene interference were used to enhance or weaken the expression levels of YTHDC2 in human trabecular meshwork cells (HTMCs); Cell migration ability was detected by transwell chamber assay; Immunofluorescence staining assay was used to evaluate the expression of extracellular matrix (ECM) related proteins. RESULTS: Through the analysis of GSE27276 database, 5 m6A regulators with different expression in POAG were screened out. The results of random forest model showed that these 5 m6A regulators exhibited diagnostic potential and were characteristic genes of POAG. All POAG samples could be effectively divided into two groups based on the expression levels of these 5 hub m6A regulators. Immune cell infiltration analysis indicated that the levels of activated CD8+ T cells and regulatory T cells were different in the two subtypes. HTMC oxidative stress cell model and TGF-β2 stimulation cell model were further constructed to verify the expression of the aforementioned hub m6A regulators, and it was found that YTHDC2 mRNA showed the same expression trend in both models. The silencing of YTHDC2 enhanced the migration ability of HTMCs and increased the synthesis ability of ECM. However, when YTHDC2ΔYTH, which lacks the YTH domain, is overexpressed in HTMCs, there is no significant change in the ECM synthesis ability. CONCLUSIONS: The differentially expressed m6A regulators in TM tissues may serve as potential diagnostic biomarkers for POAG. And, in HTMCs, the expression level of YTHDC2 mRNA was changed under oxidative stress or TGF-β2 intervention, and then exerted its regulation on cell migration and ECM synthesis capability through m6A modification, which may be an important part of the disease process of POAG.

Humans↗

PubMed2025

Identification of key genes related to bone metastasis of breast cancer using bioinformatics methods and construction of a prognostic model.

Breast cancer (BC) ranks among the most prevalent cancers in females, with bone metastasis significantly compromising patients' quality of life and survival rates. Enhancing our comprehension of BC bone metastasis mechanisms at the molecular level holds promise for improving BC treatment and prognosis. Leveraging bioinformatics tools, we integrated multiple datasets, conducted comprehensive analyses across various databases, identified biomarkers associated with BC bone metastasis, and constructed a prognostic model. Firstly, 3 BC bone metastasis-related datasets were downloaded from gene expression omnibus, the data were merged, and batch effects were removed, followed by identification of differentially expressed genes (DEGs). Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the DEGs. A protein-protein interaction network was constructed using the STRING database to screen hub genes. Then, survival analysis of hub genes was performed using the Cancer Genome Atlas (TCGA) database. A prognostic model was constructed using key genes with survival differences, and the model was evaluated. Two hundred ninety-two DEGs were identified. Gene ontology and KEGG pathway enrichment analysis yielded 769 biological processes (BPs), 78 cellular components, 43 molecular functions, and 50 KEGG pathways. Fifteen hub genes were selected from the protein-protein interaction network. Survival analysis revealed 6 genes related to BC survival. The prognostic model identified 4 genes with important predictive value for BC prognosis. Our study utilized bioinformatics analysis to identify a series of DEGs related to BC bone metastasis. Based on further selection of hub genes, we constructed a relatively ideal prognostic model for BC, and identified 4 genes (DLGAP5, TPX2, PLK1, and CENPN) with valuable predictive value for BC prognosis.

Humans↗

PubMed2026

Multi-omics analysis identifies key genes and functional loci affecting teat number in American Large White and Landrace pigs and their application in optimizing genomic selection models.

BACKGROUND: Teat number is a crucial economic trait in pigs. It directly affects the ability of sows to lactate, which in turn influences the survival and health of piglets. The teat number of French Large White pigs is close to 16, while the teat number of American Large White and Landrace pigs is about 14. In order to improve the teat number of American Landrace and Large White pigs through molecular approaches and precise breeding techniques, we genotyped 2,131 American Landrace and 4,564 American Large White with teat number phenotype using a 50 K SNP chip. Then, the SNP-chip data was imputed to the level of whole-genome sequencing (iWGS). Based on iWGS data, we conducted GWAS to identify novel, significant SNPs associated with teat number and to incorporate them into genomic selection. RESULTS: In Landrace pigs, significant SNPs for TTN mapped to SSC2, SSC7, SSC8, and SSC14; the SSC8 and SSC14 effects are novel. LTN mapped to SSC7, RTN to SSC7 and SSC8. The lead SSC7 SNP explained 2.60% of TTN phenotypic variance. In Large White pigs, significant SNPs were detected on SSC7 and SSC10 for TTN; SSC7, SSC10, and SSC12 for LTN; and SSC7 and SSC10 for RTN. The most significant locus on SSC7 accounted for 2.99% of the phenotypic variance in TTN. Additionally, a multi-population meta-analysis detected significant novel SNPs for LTN on SSC1 and SSC8. By utilizing Bayesian fine mapping, the most precise QTL confidence interval on SSC7 for both TTN and RTN in Large White pigs was reduced to 40 kb. By integrating functional gene annotation with RNA-seq and ATAC-seq data from Erhualian and Bamaxiang pigs mammary placodes at embryonic day 26, we prioritized PTPN13, TRPV3, ZDHHC13, and BRD2 as novel candidate genes for teat number. We then incorporated the significant SNPs to GBLUP and benchmarked genomic-selection accuracy. In both breeds, fitting the top SNP as fixed maximized prediction for TTN and RTN, whereas treating all significant loci as an additional random effect optimized LTN. CONCLUSIONS: Our findings provide a theoretical basis for dissecting new key genes affecting teat number and for advancing molecular breeding of teat number in pigs.

Animals↗

PubMed1985

A small region on the X chromosome of Drosophila regulates a key gene that controls sex determination and dosage compensation.

In Drosophila, flies with two X chromosomes are females, with one X chromosome, males. We investigated the presence of sex determining factors on the X chromosome by constructing genotypes with one X and various X-chromosomal duplications. We found that female determining factors are not evenly distributed along the X chromosome as had been previously postulated. A distal duplication covering 35% of the X chromosome promotes female differentiation, a much larger proximal duplication of 60% results in male differentiation. The strong feminizing effect of distal duplications originates from a small segment that, when present in two doses, activates Sxl, a key gene for sex determination and dosage compensation. Our results suggest that Sxl can be activated to intermediate levels.

Aneuploidy↗

PubMed2026

Research on identification of key genes and immune-metabolic mechanisms in atrial fibrillation through integrated multi-cohort transcriptomic analysis and machine learning.

This study aimed to integrate multiple datasets for the identification of atrial fibrillation (AF)-related differentially expressed genes (DEGs), analyze their underlying mechanisms through functional enrichment and machine learning, construct diagnostic models, and explore immune-metabolic interactions to provide novel biomarkers and theoretical foundations. Gene expression datasets were integrated and normalized, with batch effects removed using principal component analysis. Differential expression analysis, functional enrichment analysis (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathways), and machine learning-based feature gene selection and model construction were performed. Shapley additive explanations analysis was utilized to interpret the constructed models, while gene set enrichment analysis, gene set variation analysis, and immune cell infiltration analysis were conducted to investigate the associations between feature genes and immune infiltration. After integrating and normalizing gene expression data and eliminating batch effects via principal component analysis, 6 DEGs were identified, including 4 upregulated and 2 down-regulated ones. Functional enrichment analysis showed these DEGs were significantly enriched in neuro-related biological processes and pathways, indicating their key roles in AF pathogenesis. Five key feature genes were selected using LASSO, random forest, and support vector machine-recursive feature elimination algorithms. They had significant expression differences between the AF and control groups (P < .001) and were located on distinct chromosomes. The constructed random forest and support vector machine models performed excellently (area under the curve ≥ 0.85). Shapley additive explanations analysis revealed TNNI1 contributed most to model prediction, with its expression significantly positively correlated with immune cell infiltration. Gene set enrichment analysis and gene set variation analysis analyses further showed feature genes participated in AF pathogenesis by regulating immune modulation, metabolic pathways, and autophagy. Immune cell infiltration analysis found altered proportions of T-cell subsets and M0 macrophages in the AF group, along with complex links between feature gene expression and immune cell function. This study systematically elucidated the unique gene expression patterns and key regulatory pathways associated with AF, clarifying the crucial roles of feature genes in immune regulation, metabolic imbalance, and cellular dysfunction. These findings provide a theoretical basis and potential therapeutic targets for understanding AF pathogenesis and developing targeted treatment strategies.

Atrial Fibrillation↗

PubMed2026

Functional identification of the key gene Eh-fadB in nicosulfuron degradation by Enterobacter hormaechei ES1 based on multi-omics and enzymatic characterization.

Nicosulfuron is a sulfonylurea herbicide with residues that pose ecological risks in agricultural soils. Here we elucidated the degradation mechanism of Enterobacter hormaechei ES1 through whole-genome sequencing, transcriptomics, metabolomics, gene knockout, heterologous expression, and soil bioremediation assays. Under nicosulfuron stress, ES1 upregulated antioxidant enzymes including SOD, POD, and CAT, along with glutathione synthesis, to scavenge excess reactive oxygen species. HPLC-TOF-MS identified degradation intermediates such as ADMP and ASDM, indicating initial cleavage of the sulfonylurea bridge. Integrated multi-omics prioritized Eh-fadB, encoding a fatty acid β-oxidation multifunctional enzyme, as a novel degradative gene. Targeted knockout of Eh-fadB reduced nicosulfuron degradation from 87.6% to 37.04%, while genetic complementation restored nearly full activity. Purified Eh-FadB directly converted nicosulfuron, with optimal performance at 30 °C and pH 5-6; its activity was enhanced by Na+ and Pb2+ but inhibited by Fe3+. Molecular docking and dynamics identified His-450 and Asn-427 as key residues for substrate binding. In contaminated soil, inoculation with ES1 reduced nicosulfuron content within 21 days and promoted recovery of dehydrogenase and urease activities. This study provides the first genetic and biochemical evidence that a FadB-type enzyme participates in nicosulfuron catabolism, supporting sulfonylurea bridge cleavage and its potential for soil bioremediation.

Eh-fadB↗

PubMed2026

Bioinformatics Analysis and Experimental Validation of Key Genes Associated With Hypoxia and Ischemia in Myocardial Infarction.

BACKGROUND: This study aimed to screen and identify core hypoxia-ischemia-related genes associated with myocardial infarction (MI). METHOD: Two transcriptomic datasets, GSE97320 and GSE48060, were retrieved from the Gene Expression Omnibus (GEO) database. After data integration and batch effect elimination, differential expression analysis was performed to screen differentially expressed genes (DEGs), and the corresponding visualization analysis was conducted. Hypoxia-ischemia-related genes were acquired from the GeneCards database; hypoxia-ischemia related genes (HIRGs) were subsequently identified by intersecting the retrieved genes with screened DEGs. Gene Ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented to explore the biological functions and underlying signaling pathways of HIRGs. A combination of protein-protein interaction (PPI) network analysis and random forest (RF) algorithm was applied to screen hub genes from HIRGs. The external GEO dataset GSE66360 was utilized to validate the expression patterns of candidate hub genes. Furthermore, an acute myocardial infarction (AMI) mouse model was established, and quantitative real-time polymerase chain reaction (qPCR) was performed to detect the mRNA expression levels of hub genes in myocardial tissues for in vivo validation. RESULTS: A total of 633 DEGs and 308 hypoxia-ischemia-related genes were screened in the present study, among which 21 overlapping HIRGs were obtained. PLAUR and IL1B were finally identified as two hub genes from HIRGs based on PPI network and random forest algorithm. The qPCR results revealed that the expression levels of PLAUR and IL1B were significantly upregulated in the AMI group compared with the sham operation group (p < 0.05). CONCLUSION: The present findings demonstrated that PLAUR and IL1B serve as pivotal genes involved in the pathological hypoxia-ischemia process of AMI. These two genes may act as novel biomarkers and promising therapeutic targets for the recognition and clinical intervention of hypoxia-ischemia injury following AMI.

Myocardial Infarction↗

PubMed2026

Proteomics combined with single-cell sequencing reveals key genes and computational lead compound related to ligamentum flavum hypertrophy, lactate metabolism and lactate modification.

Ligamentum flavum hypertrophy (LFH) is a hallmark pathological feature of lumbar spinal stenosis; however, its underlying molecular mechanisms remain incompletely understood. Lactate metabolism and related lactylation modifications have emerged as critical links between cellular metabolism and epigenetic regulation, with established roles in various fibrotic and inflammatory diseases. Nevertheless, the specific contribution of lactylation to LFH pathogenesis remains unexplored. In this study, we integrated proteomic profiling of ligamentum flavum tissues with single-cell transcriptomic data to identify differentially expressed proteins associated with LFH. Cross-referencing these genes with genes involved in lactate metabolism and lactylation yielded 16 candidate genes. Through functional enrichment analysis, protein-protein interaction network construction, and GraphBAN model prediction, we identified five hub genes (NDUFS2, HMOX1, SPR, FABP5, and PFKP) and two potential lead compounds (ZINC000014879975 and ZINC000242437513). Molecular docking analysis confirmed favorable binding affinities between these compounds, suggesting that they may serve as potential lead compounds worthy of further experimental investigation. Single-cell analysis further revealed that macrophages occupy a central position in the LFH microenvironment, resulting in pronounced metabolic reprogramming and remodeling of intercellular communication networks, particularly via the MIF-CD74/CD44 axis, under pathological conditions.

Proteomics↗

PubMed2026

A novel reusable transcriptome-wide association study workflow used to map key genes linked to important cattle traits.

Transcriptome-wide association studies (TWAS) are a powerful approach for studying the genes underlying complex traits by directly integrating GWAS and gene expression datasets. In cattle, they have been previously applied to identify genes driving fertility, milk production, and health. However, these studies have also highlighted several challenges, from difficulties in reproducing these complex analyses to limitations from poor genotype calls, especially when called directly from RNA sequencing data. To address these and other challenges, for the H2020 BovReg Project, we have developed a streamlined, species-agnostic, and reusable Nextflow TWAS workflow to integrate transcriptomic and GWAS summary statistic datasets. Our workflow first generates accurate genotype calls and gene expression prediction models from transcriptomic datasets and then applies these tools to impute gene expression levels into GWAS cohorts, enabling the association of genes with traits of interest. We explore optimal strategies for calling genetic variants directly from transcriptomic data and illustrate that using imputation approaches specifically designed for low-pass sequencing data can improve variant calling over previously adopted methods. We demonstrate the utility of our TWAS workflow by applying it to both novel and publicly available GWAS cohorts for cattle, detecting novel gene-trait associations for complex traits. Using a new transcriptome annotation of the cattle genome generated for the BovReg project we also illustrate how previously un-assayable associations can be detected. The results and the workflow we present, provide a new resource for the community and contribute to a better understanding of the molecular drivers of complex traits in cattle with the goal of eventually leveraging this information in future breeding decisions.

Animals↗

PubMed2026

Identification of potential key genes involved in iron deficiency for sepsis: A retrospective cohort and transcriptomic study.

Iron overload has been associated with sepsis, but the role of iron deficiency and its molecular links remain unclear. We investigated the association between iron deficiency and sepsis and identified candidate genes potentially linking these conditions. MIMIC-IV data were used to assess the association between serum iron and sepsis status. Transcriptomic datasets from dietary iron-deficient mice (GSE10421), LPS-induced septic mice (GSE267388), and a human blood sepsis cohort (GSE137340) were sequentially analyzed to identify and externally evaluate candidate genes. IEU Open GWAS summary statistics were used for exploratory Mendelian randomization (MR). Exploratory drug prediction was performed using L1000FWD, followed by molecular docking analysis. Patients with sepsis had significantly lower serum iron levels, and restricted cubic spline analysis showed a nonlinear association between serum iron and the odds of sepsis. Cross-tissue transcriptomic analysis identified Sqle, Lss, and Rdh11 as candidate genes. In the human blood cohort, SQLE and RDH11 were significantly increased, whereas LSS was not significantly altered. Exploratory MR showed that genetically proxied SQLE expression was associated with higher odds of sepsis (odds ratio [OR] = 1.23, P = 1.67 × 10-3), whereas LSS expression was associated with lower odds (OR = 0.97, P = 8.90 × 10-4); RDH11 showed no significant association (OR = 1.01, P = .90). Drug prediction identified ML106 as the top-ranked candidate drug, and molecular docking predicted potential binding poses with SQLE and LSS. Serum iron showed a nonlinear association with sepsis status. SQLE, LSS, and RDH11 emerged as candidate genes, with concordant expression changes of SQLE and RDH11 observed in human blood. MR findings for SQLE and LSS were exploratory and require further validation. ML106 was identified through exploratory drug prediction and requires experimental validation before its therapeutic relevance can be established.

Sepsis↗

PubMed2025

Genotypes of SNPs of key genes regulate susceptibility and drug sensitivity to neovascular AMD in the human population.

OBJECTIVE: To compare the genetic characteristics of the normal control group to those of neovascular age-related macular degeneration (AMD) patients and to detect single-nucleotide polymorphisms (SNPs) related to the pathogenesis of neovascular AMD and the sensitivity to anti-VEGF drug, combercept. METHOD: This is a prospective case-controlled study. A total of 104 neovascular AMD patients were treated with combercept and 106 normal subjects were served as the control group. SNPs associated with neovascular AMD and disease susceptibility and drug sensitivity were analysed. RESULTS: Significant differences existed between neovascular AMD patients and normal subjects among genotypes of the SNPs of two genes, ARMS2 (rs10490924 T) and HTRA 1 (rs11200638 A). The T alleles in rs1065489 of CFH and the rs2230205 of C3 significantly promoted neovascular AMD in males while having no significant effect in females. Six SNPs of five genes, including C3 (rs2250656 G), CFB (rs2072633 G), CFH (rs2274700 A, rs3766405 T), KDR (rs6828477 A) and FZD 4 (rs10898563 T), had significant impact in reducing neovascular AMD. Two SNPs of the CFH gene (rs2274700 A and rs3766405 T) and one SNP of the CFB gene, rs2072633 G, were statistically significantly associated with good response to combercept. Conversely, the other two SNPs of the CFH gene, rs1065489 T and rs3753396 G, and the rs7412 T of the APOE gene were associated with a relatively poor patient response to drug action. Two sets of SNPs of CFB have a combined positive effect on disease. The two SNPs of CFH (rs1065489 T and rs3753396 G) and the combination of the two SNPs of CFH and rs7412T of APOE have negative effects on the drug effectiveness. CONCLUSIONS: These genotype differences facilitate the selection of individualised treatment options towards obtaining the most efficacious clinical treatment. These findings need to be validated by studies with different ethnic populations and/or larger samples.

Humans↗