Unraveling the immunogenetic landscape of autism spectrum disorder: a comprehensive bioinformatics approach.

Background: Autism spectrum disorder (ASD) is a disease characterized by social disorder. Recently, the population affected by ASD has gradually increased around the world. There are great difficulties in diagnosis and treatment at present. Methods: The ASD datasets were obtained from the Gene Expression Omnibus database and the immune-relevant genes were downloaded from a previously published compilation. Subsequently, we used WGCNA to screen the modules related to the ASD and immune. We also choose the best combination and screen out the core genes from Consensus Machine Learning Driven Signatures (CMLS). Subsequently, we evaluated the genetic correlation between immune cells and ASD used GNOVA. And pleiotropic regions identified by PLACO and CPASSOC between ASD and immune cells. FUMA was used to identify pleiotropic regions, and expression trait loci (EQTL) analysis was used to determine their expression in different tissues and cells. Finally, we use qPCR to detect the gene expression level of the core gene. Results: We found a close relationship between neutrophils and ASD, and subsequently, CMLS identified a total of 47 potential candidate genes. Secondly, GNOVA showed a significant genetic correlation between neutrophils and ASD, and PLACO and CPASSOC identified a total of 14 pleiotropic regions. We annotated the 14 regions mentioned above and identified a total of 6 potential candidate genes. Through EQTL, we found that the CFLAR gene has a specific expression pattern in neutrophils, suggesting that it may serve as a potential biomarker for ASD and is closely related to its pathogenesis. Conclusions: In conclusion, our study yields unprecedented insights into the molecular and genetic heterogeneity of ASD through a comprehensive bioinformatics analysis. These valuable findings hold significant implications for tailoring personalized ASD therapies.

OMD Curation Toolkit: a workflow for in-house curation of public omics datasets.

BACKGROUND: Major advances in sequencing technologies and the sharing of data and metadata in science have resulted in a wealth of publicly available datasets. However, working with and especially curating public omics datasets remains challenging despite these efforts. While a growing number of initiatives aim to re-use previous results, these present limitations that often lead to the need for further in-house curation and processing. RESULTS: Here, we present the Omics Dataset Curation Toolkit (OMD Curation Toolkit), a python3 package designed to accompany and guide the researcher during the curation process of metadata and fastq files of public omics datasets. This workflow provides a standardized framework with multiple capabilities (collection, control check, treatment and integration) to facilitate the arduous task of curating public sequencing data projects. While centered on the European Nucleotide Archive (ENA), the majority of the provided tools are generic and can be used to curate datasets from different sources. CONCLUSIONS: Thus, it offers valuable tools for the in-house curation previously needed to re-use public omics data. Due to its workflow structure and capabilities, it can be easily used and benefit investigators in developing novel omics meta-analyses based on sequencing data.

Identification of Breast Cancer Subtypes Based on Endoplasmic Reticulum Stress-Related Genes and Analysis of Prognosis and Immune Microenvironment in Breast Cancer Patients.

Introduction: Endoplasmic reticulum stress (ERS) was a response to the accumulation of unfolded proteins and plays a crucial role in the development of tumors, including processes such as tumor cell invasion, metastasis, and immune evasion. However, the specific regulatory mechanisms of ERS in breast cancer (BC) remain unclear. Methods: In this study, we analyzed RNA sequencing data from The Cancer Genome Atlas (TCGA) for breast cancer and identified 8 core genes associated with ERS: ELOVL2, IFNG, MAP2K6, MZB1, PCSK6, PCSK9, IGF2BP1, and POP1. We evaluated their individual expression, independent diagnostic, and prognostic values in breast cancer patients. A multifactorial Cox analysis established a risk prognostic model, validated with an external dataset. Additionally, we conducted a comprehensive assessment of immune infiltration and drug sensitivity for these genes. Results: The results indicate that these eight core genes play a crucial role in regulating the immune microenvironment of breast cancer (BRCA) patients. Meanwhile, an independent diagnostic model based on the expression of these eight genes shows limited independent diagnostic value, and its independent prognostic value is unsatisfactory, with the time ROC AUC values generally below 0.5. According to the results of logistic regression neural networks and risk prognosis models, when these eight genes interact synergistically, they can serve as excellent biomarkers for the diagnosis and prognosis of breast cancer patients. Furthermore, the research findings have been confirmed through qPCR experiments and validation. Conclusion: In conclusion, we explored the mechanisms of ERS in BRCA patients and identified 8 outstanding biomolecular diagnostic markers and prognostic indicators. The research results were double-validated using the GEO database and qPCR.

Diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease.

The purpose of this study was to explore the diagnostic implications of ubiquitination-related gene signatures in Alzheimer's disease. In this study, we first collected 161 samples from the GEO database (including 87 in the AD group and 74 in the normal group). Subsequently, through differential expression analysis and the iUUCD 2.0 database, we obtained 3450 Differentially Expressed Genes (DEGs) and 806 Ubiquitin-related genes (UbRGs). After taking the intersection, we obtained 128 UbR-DEGs. Secondly, by conducting GO and KEGG enrichment analysis on these 128 UbR-DEGs, we identified the main molecular functions and biological pathways related to AD. Furthermore, through the utilization of GSEA analysis, we have gained insight into the enrichment of functions and pathways within both the AD and normal groups. Further, using lasso regression analysis and cross-validation techniques, we identified 22 characteristic genes associated with AD. Subsequently, we constructed a logistic regression model and optimized it, resulting in the identification of 6 RUbR-DEGs: KLHL21, WDR82, DTX3L, UBTD2, CISH, and ATXN3L. In addition, the ROC result showed that the diagnostic model we built has excellent accuracy and reliability in identifying AD patients. Finally, we constructed a lncRNA-miRNA-mRNA (competing endogenous RNA, ceRNA) regulatory network for AD based on six RUbR-DEGs, further elucidating the interaction between UbRGs and lncRNA, miRNA. In conclusion, our findings will contribute to further understanding of the molecular pathogenesis of AD and provide a new perspective for AD risk prediction, early diagnosis and targeted therapy in the population.

Pathway-based, reaction-specific annotation of disease variants for elucidation of molecular phenotypes.

Germline and somatic mutations can give rise to proteins with altered activity, including both gain and loss-of-function. The effects of these variants can be captured in disease-specific reactions and pathways that highlight the resulting changes to normal biology. A disease reaction is defined as an aberrant reaction in which a variant protein participates. A disease pathway is defined as a pathway that contains a disease reaction. Annotation of disease variants as participants of disease reactions and disease pathways can provide a standardized overview of molecular phenotypes of pathogenic variants that is amenable to computational mining and mathematical modeling. Reactome (https://reactome.org/), an open source, manually curated, peer-reviewed database of human biological pathways, in addition to providing annotations for >11 000 unique human proteins in the context of ∼15 000 wild-type reactions within more than 2000 wild-type pathways, also provides annotations for >4000 disease variants of close to 400 genes as participants of ∼800 disease reactions in the context of ∼400 disease pathways. Functional annotation of disease variants proceeds from normal gene functions, described in wild-type reactions and pathways, through disease variants whose divergence from normal molecular behaviors has been experimentally verified, to extrapolation from molecular phenotypes of characterized variants to variants of unknown significance using criteria of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Reactome's data model enables mapping of disease variant datasets to specific disease reactions within disease pathways, providing a platform to infer pathway output impacts of numerous human disease variants and model organism orthologs, complementing computational predictions of variant pathogenicity. Database URL: https://reactome.org/.

AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

Unlocking the genetic tapestry of autoimmune diseases: Unveiling common genes across multiple conditions.

OBJECTIVES: This study aimed to unravel the complexities of autoimmune diseases by conducting a comprehensive analysis of gene expression data across 10 conditions, including systemic lupus erythematosus (SLE), psoriasis, Sjögren's syndrome, sclerosis, immune-associated diseases, osteoarthritis, cystic fibrosis, inflammatory bowel disease (IBD), type 1 diabetes, and Guillain-Barré syndrome. METHODS: Gene expression profiles were rigorously examined to identify both upregulated and downregulated genes specific to each autoimmune disease. The study employed visual representation techniques such as heatmaps, volcano plots, and contour-MA plots to provide an intuitive understanding of the complex gene expression patterns in these conditions. RESULTS: Distinct gene expression profiles for each autoimmune condition were uncovered, with psoriasis and osteoarthritis standing out due to a multitude of both upregulated and downregulated genes, indicating intricate molecular interplays in these disorders. Notably, common upregulated and downregulated genes were identified across various autoimmune conditions, with genes like SELENBP1, MMP9, BNC1, and COL1A1 emerging as pivotal players. CONCLUSION: This research contributes valuable insights into the molecular signatures of autoimmune diseases, highlighting the unique gene expression patterns characterizing each condition. The identification of common genes shared among different autoimmune conditions, and their potential role in mitigating the risk of rare diseases in patients with more prevalent conditions, underscores the growing significance of genetics in healthcare and the promising future of personalized medicine.

Identification of potential biomarkers of leprosy: A study based on GEO datasets.

Leprosy has a high rate of cripplehood and lacks available early effective diagnosis methods for prevention and treatment, thus novel effective molecule markers are urgently required. In this study, we conducted bioinformatics analysis with leprosy and normal samples acquired from the GEO database(GSE84893, GSE74481, GSE17763, GSE16844 and GSE443). Through WGCNA analysis, 85 hub genes were screened(GS > 0.7 and MM > 0.8). Through DEG analysis, 82 up-regulated and 3 down-regulated genes were screened(|Log2FC| > 3 and FDR < 0.05). Then 49 intersection genes were considered as crucial and subjected to GO annotation, KEGG pathway and PPI analysis to determine the biological significance in the pathogenesis of leprosy. Finally, we identified a gene-pathway network, suggesting ITK, CD48, IL2RG, CCR5, FGR, JAK3, STAT1, LCK, PTPRC, CXCR4 can be used as biomarkers and these genes are active in 6 immune system pathways, including Chemokine signaling pathway, Th1 and Th2 cell differentiation, Th17 cell differentiation, T cell receptor signaling pathway, Natural killer cell mediated cytotoxicity and Leukocyte transendothelial migration. We identified 10 crucial gene markers and related important pathways that acted as essential components in the etiology of leprosy. Our study provides potential targets for diagnostic biomarkers and therapy of leprosy.

PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Phenome-wide identification of therapeutic genetic targets, leveraging knowledge graphs, graph neural networks, and UK Biobank data.

The ongoing expansion of human genomic datasets propels therapeutic target identification; however, extracting gene-disease associations from gene annotations remains challenging. Here, we introduce Mantis-ML 2.0, a framework integrating AstraZeneca's Biological Insights Knowledge Graph and numerous tabular datasets, to assess gene-disease probabilities throughout the phenome. We use graph neural networks, capturing the graph's holistic structure, and train them on hundreds of balanced datasets via a robust semi-supervised learning framework to provide gene-disease probabilities across the human exome. Mantis-ML 2.0 incorporates natural language processing to automate disease-relevant feature selection for thousands of diseases. The enhanced models demonstrate a 6.9% average classification power boost, achieving a median receiver operating characteristic (ROC) area under curve (AUC) score of 0.90 across 5220 diseases from Human Phenotype Ontology, OpenTargets, and Genomics England. Notably, Mantis-ML 2.0 prioritizes associations from an independent UK Biobank phenome-wide association study (PheWAS), providing a stronger form of triaging and mitigating against underpowered PheWAS associations. Results are exposed through an interactive web resource.

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.

Background: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration. Methods: We downloaded four datasets containing 51 RA and 36 healthy synovium samples from the Gene Expression Omnibus database. Differentially expressed genes were identified using R. Then, various enrichment analyses were conducted. Subsequently, weighted gene co-expression network analysis (WGCNA), random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and least absolute shrinkage and selection operator (LASSO) were used to identify the hub genes for RA diagnosis. Receiver operating characteristic curves and nomogram models were used to validate the specificity and sensitivity of hub genes. Additionally, we analyzed the infiltration levels of 28 immune cells in the expression profile and their relationship with the hub genes using single-sample gene set enrichment analysis. Results: Three hub genes, namely, ribonucleotide reductase regulatory subunit M2 (RRM2), DLG-associated protein 5 (DLGAP5), and kinesin family member 11 (KIF11), were identified through WGCNA, LASSO, SVM-RFE, and RF algorithms. These hub genes correlated strongly with T cells, natural killer cells, and macrophage cells as indicated by immune cell infiltration analysis. Conclusion: RRM2, DLGAP5, and KIF11 could serve as potential diagnostic indicators and treatment targets for RA. The infiltration of immune cells offers additional insights into the underlying mechanisms involved in the progression of RA.

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.

BACKGROUND: Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics. METHODS: The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed. RESULTS: A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM. CONCLUSIONS: The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.

Identification and validation of potential diagnostic signature and immune cell infiltration for HIRI based on cuproptosis-related genes through bioinformatics analysis and machine learning.

Background and aims: Cuproptosis has emerged as a significant contributor in the progression of various diseases. This study aimed to assess the potential impact of cuproptosis-related genes (CRGs) on the development of hepatic ischemia and reperfusion injury (HIRI). Methods: The datasets related to HIRI were sourced from the Gene Expression Omnibus database. The comparative analysis of differential gene expression involving CRGs was performed between HIRI and normal liver samples. Correlation analysis, function enrichment analyses, and protein-protein interactions were employed to understand the interactions and roles of these genes. Machine learning techniques were used to identify hub genes. Additionally, differences in immune cell infiltration between HIRI patients and controls were analyzed. Quantitative real-time PCR and western blotting were used to verify the expression of the hub genes. Results: Seventy-five HIRI and 80 control samples from three databases were included in the bioinformatics analysis. Three hub CRGs (NLRP3, ATP7B and NFE2L2) were identified using three machine learning models. Diagnostic accuracy was assessed using a receiver operating characteristic (ROC) curve for the hub genes, which yielded an area under the ROC curve (AUC) of 0.832. Remarkably, in the validation datasets GSE15480 and GSE228782, the three hub genes had AUC reached 0.904. Additional analyses, including nomograms, decision curves, and calibration curves, supported their predictive power for diagnosis. Enrichment analyses indicated the involvement of these genes in multiple pathways associated with HIRI progression. Comparative assessments using CIBERSORT and gene set enrichment analysis suggested elevated expression of these hub genes in activated dendritic cells, neutrophils, activated CD4 memory T cells, and activated mast cells in HIRI samples versus controls. A ceRNA network underscored a complex regulatory interplay among genes. The genes mRNA and protein levels were also verified in HIRI-affected mouse liver tissues. Conclusion: Our findings have provided a comprehensive understanding of the association between cuproptosis and HIRI, establishing a promising diagnostic pattern and identifying latent therapeutic targets for HIRI treatment. Additionally, our study offers novel insights to delve deeper into the underlying mechanisms of HIRI.

De novo transcriptome assembly database for 100 tissues from each of seven species of domestic herbivore.

Domesticated herbivores are an important agricultural resource that play a critical role in global food security, particularly as they can adapt to varied environments, including marginal lands. An understanding of the molecular basis of their biology would contribute to better management and sustainable production. Thus, we conducted transcriptome sequencing of 100 to 105 tissues from two females of each of seven species of herbivore (cattle, sheep, goats, sika deer, horses, donkeys, and rabbits) including two breeds of sheep. The quality of raw and trimmed reads was assessed in terms of base quality, GC content, duplication sequence rate, overrepresented k-mers, and quality score distribution with FastQC. The high-quality filtered RNA-seq raw reads were deposited in a public database which provides approximately 54 billion high-quality paired-end sequencing reads in total, with an average mapping rate of ~93.92%. Transcriptome databases represent valuable resources that can be used to study patterns of gene expression, and pathways that are related to key biological processes, including important economic traits in herbivores.

Exploring the common mechanism of vascular dementia and inflammatory bowel disease: a bioinformatics-based study.

Objective: Emerging evidence has shown that gut diseases can regulate the development and function of the immune, metabolic, and nervous systems through dynamic bidirectional communication on the brain-gut axis. However, the specific mechanism of intestinal diseases and vascular dementia (VD) remains unclear. We designed this study especially, to further clarify the connection between VD and inflammatory bowel disease (IBD) from bioinformatics analyses. Methods: We downloaded Gene expression profiles for VD (GSE122063) and IBD (GSE47908, GSE179285) from the Gene Expression Omnibus (GEO) database. Then individual Gene Set Enrichment Analysis (GSEA) was used to confirm the connection between the two diseases respectively. The common differentially expressed genes (coDEGs) were identified, and the STRING database together with Cytoscape software were used to construct protein-protein interaction (PPI) network and core functional modules. We identified the hub genes by using the Cytohubba plugin. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were applied to identify pathways of coDEGs and hub genes. Subsequently, receiver operating characteristic (ROC) analysis was used to identify the diagnostic ability of these hub genes, and a training dataset was used to verify the expression levels of the hub genes. An alternative single-sample gene set enrichment (ssGSEA) algorithm was used to analyze immune cell infiltration between coDEGs and immune cells. Finally, the correlation between hub genes and immune cells was analyzed. Results: We screened 167 coDEGs. The main articles of coDEGs enrichment analysis focused on immune function. 8 shared hub genes were identified, including PTPRC, ITGB2, CYBB, IL1B, TLR2, CASP1, IL10RA, and BTK. The functional categories of hub genes enrichment analysis were mainly involved in the regulation of immune function and neuroinflammatory response. Compared to the healthy controls, abnormal infiltration of immune cells was found in VD and IBD. We also found the correlation between 8 shared hub genes and immune cells. Conclusions: This study suggests that IBD may be a new risk factor for VD. The 8 hub genes may predict the IBD complicated with VD. Immune-related coDEGS may be related to their association, which requires further research to prove.

Identifying allergic-rhinitis-associated genes with random-walk-based method in PPI network.

Allergic rhinitis is a common allergic disease with a complex pathogenesis and many unresolved issues. Studies have shown that the incidence of allergic rhinitis is closely related to genetic factors, and research on the related genes could help further understand its pathogenesis and develop new treatment methods. In this study, 446 allergic rhinitis-related genes were obtained on the basis of the DisGeNET database. The protein-protein interaction network was searched using the random-walk-with-restart algorithm with these 446 genes as seed nodes to assess the linkages between other genes and allergic rhinitis. Then, this result was further examined by three screening tests, including permutation, interaction, and enrichment tests, which aimed to pick up genes that have strong and special associations with allergic rhinitis. 52 novel genes were finally obtained. The functional enrichment test confirmed their relationships to the biological processes and pathways related to allergic rhinitis. Furthermore, some genes were extensively analyzed to uncover their special or latent associations to allergic rhinitis, including IRAK2 and MAPK, which are involved in the pathogenesis of allergic rhinitis and the inhibition of allergic inflammation via the p38-MAPK pathway, respectively. The new found genes may help the following investigations for understanding the underlying molecular mechanisms of allergic rhinitis and developing effective treatments.

Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.

BACKGROUND: There is mounting evidence that asthma might exacerbate depression. We sought to examine candidates for diagnostic genes in patients suffering from asthma and depression. METHODS: Microarray data were downloaded from the Gene Expression Omnibus(GEO) database and used to screen for differential expressed genes(DEGs) in the SA and MDD datasets. A weighted gene co-expression network analysis(WGCNA) was used to identify the co-expression modules of SA and MDD. The least absolute shrinkage and selection operatoes(LASSO) and support vector machine(SVM) were used to determine critical biomarkers. Immune cell infiltration analysis was used to investigate the correlation between immune cell infiltration and common biomarkers of SA and MDD. Finally, validation of these analytical results was accomplished via the use of both in vivo and in vitro studies. RESULTS: The number of DEGs that were included in the MDD dataset was 5177, whereas the asthma dataset had 1634 DEGs. The intersection of DEGs for SA and MDD included 351 genes, the strongest positive modules of SA and MDD was 119 genes, which played a function in immunity. The intersection of DEGs and modular hub genes was 54, following the analysis using machine learning algorithms,three hub genes were identified and employed to formulate a nomogram and for the evaluation of diagnostic effectiveness, which demonstrated a significant diagnostic value (area under the curve from 0.646 to 0.979). Additionally, immunocyte disorder was identified by immune infiltration. In vitro studies have revealed that STK11IP deficiency aggravated the LPS/IFN-γinduced up-regulation in M1 macrophage activation. CONCLUSION: Asthma and MDD pathophysiology may be associated with alterations in inflammatory processes and immune pathways. Additionally, STK11IP may serve as a diagnostic marker for individuals with the two conditions.

Mining and exploration of rehabilitation nursing targets for colorectal cancer.

BACKGROUND: There are often subtle early symptoms of colorectal cancer, a common malignancy of the intestinal tract. However, it is not yet clear how MYC and NCAPG2 are involved in colorectal cancer. METHOD: We obtained colorectal cancer datasets GSE32323 and GSE113513 from the Gene Expression Omnibus (GEO). After downloading, we identified differentially expressed genes (DEGs) and performed Weighted Gene Co-expression Network Analysis (WGCNA). We then undertook functional enrichment assay, gene set enrichment assay (GSEA) and immune infiltration assay. Protein-protein interaction (PPI) network construction and analysis were undertaken. Survival analysis and Comparative Toxicogenomics Database (CTD) analysis were conducted. A gene expression heat map was generated. We used TargetScan to identify miRNAs that are regulators of DEGs. RESULTS: 1117 DEGs were identified. Their predominant enrichment in activities like the cellular phase of the cell cycle, in cell proliferation, in nuclear and cytoplasmic localisation and in binding to protein-containing complexes was revealed by Gene Ontology (GO). When the enrichment data from GSE32323 and GSE113513 colon cancer datasets were merged, the primary enriched DEGs were linked to the cell cycle, protein complex, cell cycle control, calcium signalling and P53 signalling pathways. In particular, MYC, MAD2L1, CENPF, UBE2C, NUF2 and NCAPG2 were identified as highly expressed in colorectal cancer samples. Comparative Toxicogenomics Database (CTD) demonstrated that the core genes were implicated in the following processes: colorectal neoplasia, tumour cell transformation, inflammation and necrosis. CONCLUSIONS: High MYC and NCAPG2 expression has been observed in colorectal cancer, and increased MYC and NCAPG2 expression correlates with worse prognosis.

ITGAM is a critical gene in ischemic stroke.

BACKGROUND: Globally, ischemic stroke (IS) is ranked as the second most prevailing cause of mortality and is considered lethal to human health. This study aimed to identify genes and pathways involved in the onset and progression of IS. METHODS: GSE16561 and GSE22255 were downloaded from the Gene Expression Omnibus (GEO) database, merged, and subjected to batch effect removal using the ComBat method. The limma package was employed to identify the differentially expressed genes (DEGs), followed by enrichment analysis and protein-protein interaction (PPI) network construction. Afterward, the cytoHubba plugin was utilized to screen the hub genes. Finally, a ROC curve was generated to investigate the diagnostic value of hub genes. Validation analysis through a series of experiments including qPCR, Western blotting, TUNEL, and flow cytometry was performed. RESULTS: The analysis incorporated 59 IS samples and 44 control samples, revealing 226 DEGs, of which 152 were up-regulated and 74 were down-regulated. These DEGs were revealed to be linked with the inflammatory and immune responses through enrichment analyses. Overall, the ROC analysis revealed the remarkable diagnostic potential of ITGAM and MMP9 for IS. Quantitative assessment of these genes showed significant overexpression in IS patients. ITGAM modulation influenced the secretion of critical inflammatory cytokines, such as IL-1ß, IL-6, and TNF-α, and had a distinct impact on neuronal apoptosis. CONCLUSIONS: The inflammation and immune response were identified as potential pathological mechanisms of IS by bioinformatics and experiments. In addition, ITGAM may be considered a potential therapeutic target for IS.