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The pathogenesis of sepsis, with a high mortality rate and often poor prognosis, has not been fully elucidated. Therefore, an in-depth study on the pathogenesis of sepsis at the molecular level is essential to identify key sepsis-related genes. The aim of this study was to explore the key genes and potential molecular mechanisms of sepsis using a bioinformatics approach. In addition, key genes with miRNA network correlation analysis and immune infiltration correlation analysis were investigated. The scRNA dataset (GSE167363) and RNA-seq dataset (GSE65682, GSE134347) from GEO database were used for screening out differentially expressed genes using single-cell sequencing and transcriptome sequencing. The analysis of immune infiltration was evaluated by the CIBERSORT method. Key genes and possible mechanisms were identified by WGCNA analysis, GSVA analysis, GSEA enrichment analysis and regulatory network analysis, and miRNA networks associated with key genes were constructed. Nine key genes associated with the development of sepsis, namely IL7R, CD3D, IL32, GPR183, HLA-DPB1, CD81, PEBP1, NCL, and ETS1 were screened, and the specific signaling mechanisms associated with the key genes causing sepsis were predicted. Immune profiling showed immune heterogeneity between control and sepsis samples. A regulatory network of 82 miRNAs, 266 pairs of mRNA-miRNA relationship pairs was also constructed. These nine key genes have the potential to become biomarkers for the diagnosis of sepsis and provide new targets and research directions for the treatment of sepsis. [ABSTRACT FROM AUTHOR]

Simple Summary: This study aimed to conduct a comprehensive analysis of the factors involved in muscle growth and development, along with their associated biological functions and pathways, in cattle, pigs, and sheep. This was achieved by integrating an interpretable machine learning model with a comparative transcriptomic approach. The findings revealed that the elements closely linked to muscle growth and development across these three species exhibited high repeatability. Specifically, we identified 8 unique factors in cattle, 2 in pigs, and 1 in sheep, thereby providing novel insights into the mechanisms underlying muscle growth and development within these species. The growth and development of muscle tissue play a pivotal role in the economic value and quality of meat in agricultural animals, garnering close attention from breeders and researchers. The quality and palatability of muscle tissue directly determine the market competitiveness of meat products and the satisfaction of consumers. Therefore, a profound understanding and management of muscle growth is essential for enhancing the overall economic efficiency and product quality of the meat industry. Despite this, systematic research on muscle development-related genes across different species still needs to be improved. This study addresses this gap through extensive cross-species muscle transcriptome analysis, combined with interpretable machine learning models. Utilizing a comprehensive dataset of 275 publicly available transcriptomes derived from porcine, bovine, and ovine muscle tissues, encompassing samples from ten distinct muscle types such as the semimembranosus and longissimus dorsi, this study analyzes 113 porcine (n = 113), 94 bovine (n = 94), and 68 ovine (n = 68) specimens. We employed nine machine learning models, such as Support Vector Classifier (SVC) and Support Vector Machine (SVM). Applying the SHapley Additive exPlanations (SHAP) method, we analyzed the muscle transcriptome data of cattle, pigs, and sheep. The optimal model, adaptive boosting (AdaBoost), identified key genes potentially influencing muscle growth and development across the three species, termed SHAP genes. Among these, 41 genes (including NANOG, ADAMTS8, LHX3, and TLR9) were consistently expressed in all three species, designated as homologous genes. Specific candidate genes for cattle included SLC47A1, IGSF1, IRF4, EIF3F, CGAS, ZSWIM9, RROB1, and ABHD18; for pigs, DRP2 and COL12A1; and for sheep, only COL10A1. Through the analysis of SHAP genes utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, relevant pathways such as ether lipid metabolism, cortisol synthesis and secretion, and calcium signaling pathways have been identified, revealing their pivotal roles in muscle growth and development. [ABSTRACT FROM AUTHOR]

Objectives: To provide a basis for further research on the molecular mechanisms underlying type 2 diabetes-associated mild cognitive impairment (DCI) using two bioinformatics methods to screen key genes involved in the progression of mild cognitive impairment (MCI) and type 2 diabetes. Methods: RNA sequencing data of MCI and normal cognition groups, as well as expression profile and sample information data of clinical characteristic data of GSE63060, which contains 160 MCI samples and 104 normal samples, were downloaded from the GEO database. Hub genes were identified using weighted gene co-expression network analysis (WGCNA). Protein–protein interaction (PPI) analysis, combined with least absolute shrinkage and selection operator (LASSO) and receiver operating characteristic (ROC) curve analyses, was used to verify the genes. Moreover, RNA sequencing and clinical characteristic data for GSE166502 of 13 type 2 diabetes samples and 13 normal controls were downloaded from the GEO database, and the correlation between the screened genes and type 2 diabetes was verified by difference and ROC curve analyses. In addition, we collected clinical biopsies to validate the results. Results: Based on WGCNA, 10 modules were integrated, and six were correlated with MCI. Six hub genes associated with MCI (TOMM7, SNRPG, COX7C, UQCRQ, RPL31, and RPS24) were identified using the LASSO algorithm. The ROC curve was screened by integrating the GEO database, and revealed COX7C, SNRPG, TOMM7, and RPS24 as key genes in the progression of type 2 diabetes. Conclusions: COX7C, SNRPG, TOMM7, and RPS24 are involved in MCI and type 2 diabetes progression. Therefore, the molecular mechanisms of these four genes in the development of type 2 diabetes-associated MCI should be studied. [ABSTRACT FROM AUTHOR]

Background: Our previous study demonstrated that long intergenic noncoding RNA 02454 (LINC02454) may act as an oncogene to promote the proliferation and inhibit the apoptosis of papillary thyroid cancer (PTC) cells. This study was designed to investigate the mechanisms whereby LINC02454 is related to PTC tumorigenesis. Methods: Thyroid cancer RNA sequence data were obtained from The Cancer Genome Atlas (TCGA) database. Weighted gene coexpression network analysis (WGCNA) was applied to identify modules closely associated with PTC. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the key pathways, and the maximal clique centrality (MCC) topological method was used to identify the hub genes. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to compare expression levels of key genes between PTC samples and normal samples and explore the prognostic value of key genes. The key genes were further validated with GEO dataset. Results: The top 5000 variable genes were investigated, followed by an analysis of 8 modules, and the turquoise module was the most positively correlated with the clinical stage of PTC. KEGG pathway analysis found the top two pathways of the ECM − receptor interaction and MAPK signaling pathway. In addition, five key genes (FN1, LAMB3, ITGA3, SDC4, and IL1RAP) were identified through the MCC algorithm and KEGG analysis. The expression levels of the five key genes were significantly upregulated in thyroid cancer in both TCGA and GEO datasets, and of these five genes, FN1 and ITGA3 were associated with poor disease-free prognosis. Conclusions: Our study identified five key genes and two key pathways associated with LINC02454, which might shed light on the underlying mechanism of LINC02454 action in PTC. [ABSTRACT FROM AUTHOR]

Huikang Wang,1– 3,* Xinjun Xu,1– 3,* Haoran Lu,1– 3 Yang Zheng,1– 3 Liting Shao,1– 3 Zhaoyang Lu,2– 4 Yu Zhang,2,3 Xicheng Song2,3 1Department of Otorhinolaryngology, Head and Neck Surgery, Yantai Yuhuangding Hospital, QingdaoUniversity, Yantai, People’s Republic of China; 2Shandong Provincial Clinical Research Center for Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, People’s Republic of China; 3Yantai Key Laboratory of Otorhinolaryngologic Diseases, Yantai Yuhuangding Hospital, Yantai, People’s Republic of China; 4Second Clinical Medicine College, Binzhou Medical University, Yantai, Shandong, 264003, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xicheng Song; Yu Zhang, Department of Otolaryngology, Head and Neck Surgery. Yantai Yuhuangding Hospital, Qingdao University, Yantai, 264000, People’s Republic of China, Tel +86535 6691999, Fax +86535 6240341, Email drxchsong@163.com; superzhang013@163.comPurpose: The pathogenesis of CRSwNP is complex and not yet fully explored, so we aimed to identify the pivotal hub genes and associated pathways of CRSwNP, to facilitate the detection of novel diagnostic or therapeutic targets.Methods: Utilizing two CRSwNP sequencing datasets from GEO, differential expression gene analysis, WGCNA, and three machine learning methods (LASSO, RF and SVM-RFE) were applied to screen for hub genes. A diagnostic model was then formulated utilizing hub genes, and the AUC was generated to evaluate the performance of the prognostic model and candidate genes. Hub genes were validated through the validation set and qPCR performed on normal mice and CRSwNP mouse model. Lastly, the ssGSEA algorithm was employed to assess the differences in immune infiltration levels.Results: A total of 239 DEGs were identified, with 170 upregulated and 69 downregulated in CRSwNP. Enrichment analysis revealed that these DEGs were primarily enriched in pathways related to nucleocytoplasmic transport and HIF-1 signaling pathway. Data yielded by WGCNA analysis contained 183 DEGs. The application of three machine learning algorithms identified 11 hub genes. Following concurrent validation analysis with the validation set and qPCR performed after establishing the mouse model confirmed the overexpression of BTBD10, ERAP1, GIPC1, and PEX6 in CRSwNP. The examination of immune cell infiltration suggested that the infiltration rate of type 2 T helper cell and memory B cell experienced a decline in the CRSwNP group. Conversely, the infiltration rates of Immature dendritic cell and Effector memory CD8 T cell were positive correlation.Conclusion: This study successfully identified and validated BTBD10, ERAP1, GIPC1, and PEX6 as potential novel diagnostic or therapeutic targets for CRSwNP, which offers a fresh perspective and a theoretical foundation for the diagnostic prediction and therapeutic approach to CRSwNP.Keywords: chronic rhinosinusitis with nasal polyposis, key genes, machine learning, immune cell infiltration

Abstract Background Our previous study demonstrated that long intergenic noncoding RNA 02454 (LINC02454) may act as an oncogene to promote the proliferation and inhibit the apoptosis of papillary thyroid cancer (PTC) cells. This study was designed to investigate the mechanisms whereby LINC02454 is related to PTC tumorigenesis. Methods Thyroid cancer RNA sequence data were obtained from The Cancer Genome Atlas (TCGA) database. Weighted gene coexpression network analysis (WGCNA) was applied to identify modules closely associated with PTC. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was used to identify the key pathways, and the maximal clique centrality (MCC) topological method was used to identify the hub genes. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to compare expression levels of key genes between PTC samples and normal samples and explore the prognostic value of key genes. The key genes were further validated with GEO dataset. Results The top 5000 variable genes were investigated, followed by an analysis of 8 modules, and the turquoise module was the most positively correlated with the clinical stage of PTC. KEGG pathway analysis found the top two pathways of the ECM − receptor interaction and MAPK signaling pathway. In addition, five key genes (FN1, LAMB3, ITGA3, SDC4, and IL1RAP) were identified through the MCC algorithm and KEGG analysis. The expression levels of the five key genes were significantly upregulated in thyroid cancer in both TCGA and GEO datasets, and of these five genes, FN1 and ITGA3 were associated with poor disease-free prognosis. Conclusions Our study identified five key genes and two key pathways associated with LINC02454, which might shed light on the underlying mechanism of LINC02454 action in PTC.

Objective To gain a deeper understanding of molecular mechanisms underlying acute kidney injury （AKI）, explore traditional Chinese medicine strategies for managing AKI, employ bioinformatics to analyze key genes associated with AKI and predict potential Chinese herbal medicines with therapeutic effects. Methods The AKI gene expression microarray dataset GSE30718 were downloaded from the database of Gene Expression Omnibus （GEO） and GEO2R online tool was utilized for analysis. Simultaneously, AKI target genes were obtained from the Comparative Toxicogenomics Database （CTD） and the intersection of these datasets yielded the differential expression genes （DEGs） associated with AKI. Enrichment analysis and protein-protein interaction analysis were performed on DEGs. Cytoscape software was employed for identifying key genes. Subsequently, a medical information retrieval platform （Coremine Medical） was utilized for searching for traditional Chinese medicines corresponding to these key genes with the most frequently occurring ones identified. Then the database of BATMAN-TCM was employed for detailed analysis of molecular mechanisms involved in their therapeutic effects on AKI. Results For 206 DEGs, functional enrichment analysis confirmed their involvements in extracellular matrix regulation, immune-inflammatory responses and transcriptional regulation. FN1, Alb, COL1A1, JUN, TGFB1, FOS, PXDN, COL1A2, EGF and TIMP1 were identified as key genes for AKI. The potential traditional Chinese medicines for treating AKI included Danshen （Salvia Miltiorrhiza）, Renshen （Radix Ginseng）, Danggui （Angelica Sinensis） and Sanqi （Radix Notoginseng）. And Danshen had the highest frequency. The primary signaling pathway associated with Danshen's therapeutic effects on AKI was signaling pathway of phosphatidylinositide 3-kinase/protein kinase B （PI3K/AKT）. Conclusion This study has identified 10 key genes involved in AKI and their associated traditional Chinese medicines. Specific analysis of Danshen's molecular mechanisms in treating AKI provides new insights for clinical interventions and establishes theoretical rationales for subsequent researches.

Abstract Introduction Hepatocellular carcinoma (HCC) is a common and fatal cancer, and its molecular mechanisms are still not fully understood. This study aimed to explore the potential molecular mechanisms and immune infiltration characteristics of celecoxib combined with sorafenib in the treatment of HCC by analyzing the differentially expressed genes (DEGs) from the GSE45340 dataset in the GEO database and identifying key genes. Methods The GSE45340 dataset was downloaded from the GEO database, and DEGs were screened using GEO2R, and visualization and statistical analysis were performed. Metascape was used to perform functional annotation and protein–protein interaction network analysis of DEGs. The immune infiltration was analyzed using the TIMER database, and the expression of key genes and their relationship with patient survival were analyzed and verified using the UALCAN database. Results A total of 2181 DEGs were screened through GEO2R analysis, and heat maps were drawn for the 50 genes with the highest expression. Metascape was used for enrichment analysis, and the enrichment results of KEGG and GO and the PPI network were obtained, and 44 core genes were screened. Analysis of the TIMER database found that 12 genes were closely related to tumor immune infiltration. UALCAN analysis further verified the differential expression of these genes in HCC and was closely related to the overall survival of patients. Conclusions Through comprehensive bioinformatics analysis, this study identified a group of key genes related to the treatment of HCC with celecoxib combined with sorafenib. These genes play an important role in tumor immune infiltration and patient survival, providing important clues for further studying the molecular mechanism of HCC and developing potential therapeutic targets.

Abstract Background Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. Methods The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. Results Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. Conclusion The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

Abstract In order to resolve the key genes for weed control by Trichoderma polysporum at the genomic level, we extracted the genomic DNA and sequenced the whole genome of T. polysporum strain HZ-31 on the Illumina Hiseq platform. The raw data was cleaned up using Trimmomatic and checked for quality using FastQC. The sequencing data was assembled using SPAdes, and GeneMark was used to perform gene prediction on the assembly results. The results showed that the genome size of T. polysporum HZ-31 was 39,325,746 bp, with 48% GC content, and the number of genes encoded was 11,998. A total of 148 tRNAs and 45 rRNAs were predicted. A total of 782 genes were annotated in the Carbohydrase Database, 757 genes were annotated to the Pathogen-Host Interaction Database, and 67 gene clusters were identified. In addition, 1023 genes were predicted to be signal peptide proteins. The annotation and functional analysis of the whole genome sequence of T. polymorpha HZ-31 provide a basis for the in-depth study of the molecular mechanism of its herbicidal action and more effective utilization for weed control.

Aim To explore the key genes in the blood transcriptome of atherosclerosis patients based on blood transcriptomics. Methods Three datasets GSE12288, GSE27034 and GSE90074 were extracted from the GEO database and performed the merging and normalization processing. The differential genes in peripheral blood samples of atherosclerosis patients and controls were analyzed, and enrichment analysis of differentially expressed genes were performed. Then weighted gene co-expression network analysis was performed for all genes. Using differentially expressed genes to construct protein-protein interaction (PPI) network, and using CytoHubba to screen key genes based on co-expression network and PPI network. And the expression levels of key genes were detected by RT-qPCR. Results 74 down-regulated genes and 145 up-regulated genes were identified between atherosclerosis patients and controls. GO and KEGG enrichment analyses revealed that they were significantly enriched in neutrophil activation, granulocyte activation, cytokinecytokine receptor interaction and chemokine signaling pathway. In addition, the top 10 genes in the co-expression network and the top 20 genes in the PPI network were also identified, in which PRF1, NKG7, GZMB and CCL5 played a high core role in the PPI network and co-expression network. The RT-qPCR results showed that compared with the non coronary atherosclerosis controls, the mRNA expression levels of PRF1 and GZMB in peripheral venous blood peripheral blood mononuclear cell (PBMC) of coronary atherosclerosis patients were significantly reduced (P<0. 05), while the mRNA expression levels of NKG7 and CCL5 were significantly increased ( P< 0. 05). Conclusion PRF1, GZMB, NKG7 and CCL5 may be key genes in the blood transcriptome of atherosclerosis patients, and are expected to be potential biomarkers for diagnosis and treatment of atherosclerosis. [ABSTRACT FROM AUTHOR]

Introduction: Hepatocellular carcinoma (HCC) is a common and fatal cancer, and its molecular mechanisms are still not fully understood. This study aimed to explore the potential molecular mechanisms and immune infiltration characteristics of celecoxib combined with sorafenib in the treatment of HCC by analyzing the differentially expressed genes (DEGs) from the GSE45340 dataset in the GEO database and identifying key genes. Methods: The GSE45340 dataset was downloaded from the GEO database, and DEGs were screened using GEO2R, and visualization and statistical analysis were performed. Metascape was used to perform functional annotation and protein–protein interaction network analysis of DEGs. The immune infiltration was analyzed using the TIMER database, and the expression of key genes and their relationship with patient survival were analyzed and verified using the UALCAN database. Results: A total of 2181 DEGs were screened through GEO2R analysis, and heat maps were drawn for the 50 genes with the highest expression. Metascape was used for enrichment analysis, and the enrichment results of KEGG and GO and the PPI network were obtained, and 44 core genes were screened. Analysis of the TIMER database found that 12 genes were closely related to tumor immune infiltration. UALCAN analysis further verified the differential expression of these genes in HCC and was closely related to the overall survival of patients. Conclusions: Through comprehensive bioinformatics analysis, this study identified a group of key genes related to the treatment of HCC with celecoxib combined with sorafenib. These genes play an important role in tumor immune infiltration and patient survival, providing important clues for further studying the molecular mechanism of HCC and developing potential therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. Methods: The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. Results: Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. Conclusion: The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future. [ABSTRACT FROM AUTHOR]

In order to resolve the key genes for weed control by Trichoderma polysporum at the genomic level, we extracted the genomic DNA and sequenced the whole genome of T. polysporum strain HZ-31 on the Illumina Hiseq platform. The raw data was cleaned up using Trimmomatic and checked for quality using FastQC. The sequencing data was assembled using SPAdes, and GeneMark was used to perform gene prediction on the assembly results. The results showed that the genome size of T. polysporum HZ-31 was 39,325,746 bp, with 48% GC content, and the number of genes encoded was 11,998. A total of 148 tRNAs and 45 rRNAs were predicted. A total of 782 genes were annotated in the Carbohydrase Database, 757 genes were annotated to the Pathogen-Host Interaction Database, and 67 gene clusters were identified. In addition, 1023 genes were predicted to be signal peptide proteins. The annotation and functional analysis of the whole genome sequence of T. polymorpha HZ-31 provide a basis for the in-depth study of the molecular mechanism of its herbicidal action and more effective utilization for weed control. [ABSTRACT FROM AUTHOR]

Background: Skin cancer, a prevalent form of cancer that is on the rise worldwide, requires proactive prevention strategies to reduce the burden of screening, treatment, and mortality. The KEGG research highlighted the significant involvement of red module genes in protein digestion and absorption. These findings provide valuable insights into the underlying molecular mechanisms associated with skin cancer susceptibility, offering potential targets for further research and development of preventive strategies. Materials and methods: Hub genes numbered 130. "limma" in R found 600 DEGs from GSE66359 dataset. DEGs are enriched in BP: chromosome segregation, CC: chromosomal region, and MF: DNA replication origin binding, according to GO analysis. Cell cycle was enriched in DEGs by KEGG and GSEA. Finally, significant genes were COL5A1, CTHRC1, ECM1, FSTL1, KDELR3, and WIPI1. Results: ECM1 and WIPI1 greatly prevented skin cancer. This study created a coexpression network using WGCNA to investigate skin cancer susceptibility modules and cardiovascular disease genes. Conclusion: Our study finds a module and many important genes that are essential building blocks in the etiology of skin cancer, which may help us understand the molecular mechanisms of disease prevention. [ABSTRACT FROM AUTHOR]

Objective To identify the key genes associated with recurrent breast cancer in pregnancy by transcriptome data analysis. Methods The gene expression profiles of the GSE53031 gene set, which was associated with pregnancy-associated breast cancer, were downloaded from the GEO database. Gene expression differences between recurrent and non-recurrent breast cancer patients in pregnancy were analyzed using the Wilcoxon test method in the “limma” software package. The R package “ClusterProfiler” was used to perform Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of these differentially expressed genes. The protein-protein interaction (PPI) network of differentially expressed genes was constructed using the STRING online database, and the “igraph” package was used to analyze the PPI network to identify key genes of breast cancer recurrence during pregnancy. Kaplan- Meier(KM) method and univariate Cox regression analysis were used to evaluate the relationship between the key genes and recurrence-free survival of breast cancer patients during pregnancy. Results In the GSE63514 dataset, 840 genes with significant differences in the expression of recurrent breast cancer samples in pregnancy were obtained, including 390 up-regulated genes and 450 down-regulated genes. GO functional annotation and KEGG pathway enrichment analysis showed that multiple differentially expressed genes were significant enriched in cell cycle regulation, mismatch repair, RNA degradation, DNA replication, breast cancer, and p53 signaling pathway. PPI network analysis showed that the top 10 node genes in the network were PLK1, STAT3, SRC, UHRF1, UBE2C, UBE2T, TRIP13, RAD51, MYC, and TPX2, which were initially defined as the key genes for the recurrence of breast cancer in pregnancy. KM analysis and univariate Cox analysis showed that PLK1, UBE2C, UBE2T, TRIP13, RAD51 and TPX2 were risk genes for the recurrence of breast cancer in pregnancy [P <0. 05, hazard raiol(HR)>1], while STAT3 was a protective gene for the recurrence of breast cancer in pregnancy (P <0. 05, HR <1). UBE2C, TPX2, UBE2T, TRIP13, PLK1, RAD51, and STAT3 were identified as the key genes for recurrence of breast cancer in pregnancy. Conclusion Seven key genes (UBE2C, TPX2, UBE2T, TRIP13, PLK1, RAD51, and STAT3) of breast cancer recurrence in pregnancy were identified, which provided candidate molecular for future research on the recurrence of breast cancer in pregnancy. [ABSTRACT FROM AUTHOR]

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Acute myocardial infarction (AMI) continues to be a leading cause of death globally, with distinct immune cell dynamics in ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI) playing a critical role in disease progression and patient outcomes. Sample data for STEMI and NSTEMI were downloaded from the Sequence Read Archive (SRA) database (https://www.ncbi.nlm.nih.gov/sra). Differences and correlations of immune infiltrating cells were assessed by CIBERSORT. Differentially expressed genes (DEGs) were identified between STEMI and NSTEMI, followed by functional analysis. Immune-related DEGs were further identified. Some immune-related DEGs were selected to perform expression verification using real-time PCR. There was a significant difference in immune cells between STEMI and NSTEMI, including activated dendritic cells, memory CD4 T cells, mast cells, and CD8 T cells. A total of 229 DEGs were identified, with functions related to inflammatory regulation and drug metabolism. A total of 21 immune-related DEGs, which may play important roles in STEMI and NSTEMI, were identified. Among the 21 immune-related DEGs, genes like CCL18, NRP2, CXCR2, CXCL9, KIR2DL4, BPIFB1, and IL33 were significantly correlated with immune cells and had a tendency for differential expression between STEMI and NSTEMI patients. Our study reveals differences in the distribution of immune cell subsets between STEMI and NSTEMI, highlighting key immune-related genes and their association with immune cells, which may provide new insights into the treatment of AMI.

Purpose: Osteoarthritis (OA) is a prevalent cause of disability in older adults. Identifying diagnostic markers for OA is essential for elucidating its mechanisms and facilitating early diagnosis. Methods: We analyzed 53 synovial tissue samples (n = 30 for OA, n = 23 for the control group) from two datasets in the Gene Express Omnibus (GEO) database. We identified differentially expressed genes (DEGs) between the groups and applied dimensionality reduction using six machine learning algorithms to pinpoint characteristic genes (key genes). We classified the OA samples into subtypes based on these key genes and explored the differences in biological functions and immune characteristics among subtypes, as well as the roles of the key genes. Additionally, we constructed a protein-protein interaction network to predict small molecules that target these genes. Further, we accessed synovial tissue sample data from the single-cell RNA dataset GSE152805, categorized the cells into various types, and examined variations in gene expression and their correlation with OA progression. Validation of key gene expression was conducted in cellular experiments using the qPCR method. Results: Four genes AGMAT, MAP3K8, PER1, and XIST, were identified as characteristic genes of OA. All can independently predict the occurrence of OA. With these genes, the OA samples can be clustered into two subtypes, which showed significant differences in functional pathways and immune infiltration. Eight cell types were obtained by analyzing the single-cell RNA data, with synovial intimal fibroblasts (SIF) accounting for the highest proportion in each sample. The key genes were found over-expressed in SIF and significantly correlated with OA progression and the content of immune cells (ICs). We validated the relative levels of key genes in OA and normal cartilage tissue cells, which showed an expression trend consistency with the bioinformatics result except for XIST. Conclusion: Four genes, AGMAT, MAP3K8, PER1, and XIST are closely related to the progression of OA, and play as diagnostic and predictive markers in early OA.

Abstract Background Studies have shown that mitochondrial function and macrophages may play a role in the development of idiopathic pulmonary fibrosis (IPF). However, the understanding of the interactions and specific mechanisms between mitochondrial function and macrophages in pulmonary fibrosis is still very limited. Methods To construct a prognostic model for IPF based on Macrophage- related genes (MaRGs) and Mitochondria-related genes (MitoRGs), differential analysis was performed to achieve differentially expressed genes (DEGs) between IPF and Control groups in the GSE28042 dataset. Then, MitoRGs, MaRGs and DEGs were overlapped to screen out the signature genes. The univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm were implemented to achieve key genes. Furthermore, the independent prognostic analysis was employed. The ingenuity pathway analysis (IPA) was employed to further understand the molecular mechanisms of key genes.Next, the immune infiltration analysis was implemented to identify differential immune cells between two risk subgroups. Results There were 4791 DEGs between IPF and Control groups. Furthermore, 26 signature genes were achieved by the intersection processing. Three key genes including ALDH2, MCL1, and BCL2A1 were achieved, and the risk model based on the key genes was created. In addition, a nomogram for survival forecasting of IPF patients was created based on riskScore, Age, and Gender, and we found that key genes were associated with classical pathways including ‘Apoptosis Signaling’, ‘PI3K/AKT Signaling’, and so on. Next, two differential immune cells including Monocytes and CD8 T cells were identified between two risk subgroups. Moreover, we found that MIR29B2CHG and hsa-mir-1-3p could regulate the expression of ALDH2. Conclusion We achieved 3 key genes including ALDH2, MCL1,, and BCL2A1 associated with IPF, providing a new theoretical basis for clinical treatment of IPF.

Biological control is a scientific management method used in modern agricultural production, and microbially derived biopesticides are one effective method with which to control weeds in agricultural fields. In order to determine the key genes for weed control by Trichoderma polysporum, transcriptome sequencing was carried out by high-throughput sequencing technology, and the strains of T. polysporum HZ-31 infesting Avena fatua L. at 24, 48, and 72 h were used as the experimental group, with 0 h as the control group. A total of 690,713,176 clean reads were obtained, and the sequencing results for each experimental group and the control group (0 h) were analyzed. In total, 3464 differentially expressed genes were found after 24 h of infection with the pathogen, including 1283 down-regulated genes and 2181 up-regulated genes. After 48 h of infection, the number of differentially expressed genes was 3885, of which 2242 were up-regulated and 1643 were down-regulated. The number of differentially expressed genes after 72 h of infection was the highest among all the groups, with 4594 differentially expressed genes, of which 2648 were up-regulated and 1946 were down-regulated. The up-regulated genes were analyzed by GO and KEGG, and the results showed that the up-regulated differentially expressed genes were mainly enriched in the biosynthesis of phenylalanine, tyrosine, and tryptophan; the degradation of aromatic compounds; methane metabolism; and other pathways. Among them, the PHA2, GDH, ADH2, and AROF genes were significantly enriched in the above-mentioned pathways, so they were hypothesized to play an important role in the synthesis of the herbicidally active substances of T. polysporum HZ-31. The results of this study can provide a theoretical basis for further studies on the pathogenicity of T. polysporum to A. fatua L., and accelerate the development and utilization of new and efficient bioherbicides. [ABSTRACT FROM AUTHOR]

The morbidity and mortality associated with vascular cognitive impairment (VCI) generally increase steeply, and health systems will face increasing demand for services. The present study aims to screen key genes to give new insight into the mechanisms and treatment of VCI based on bioinformatic approaches combined with biological experiments in rats. The gene expression data of VCI patients contained in the GSE122063 data set were downloaded from the Gene Expression Omnibus. We performed a weighted gene co-expression network analysis to identify a hub module and 44 hub genes. Two hundred and seventy-seven differentially expressed genes (DEGs) were analyzed using R software by the "limma" package. STRING database was used to construct protein–protein interaction (PPI) network, after which 36 hub genes were identified through Cytoscape. Functional enrichment analysis revealed that these genes from the yellow module and 277 DEGs were mainly associated with these pathways, such as Staphylococcus aureus infection, complement, and coagulation cascades. These biological functions are related to inflammatory cell activation and inflammatory response. The key genes of VCI were the overlapping hub genes from the yellow module and the PPI network. The expressions of hub genes in rats were determined by quantitative reverse transcription-polymerase chain reaction, Western blot, immunohistochemistry, and immunofluorescence. In conclusion, C1QA, C1QB, C1QC, CD163, and FCGR2A were highly expressed in the hippocampus of VCI rats, and they can serve as candidate biomarkers for the diagnosis and prognosis of VCI. Finally, molecular docking results suggested that 5 genes interact with Bisphenol A. These findings open a new avenue to investigate molecular mechanisms for preventing or treating VCI. [ABSTRACT FROM AUTHOR]

Background: Studies have shown that mitochondrial function and macrophages may play a role in the development of idiopathic pulmonary fibrosis (IPF). However, the understanding of the interactions and specific mechanisms between mitochondrial function and macrophages in pulmonary fibrosis is still very limited. Methods: To construct a prognostic model for IPF based on Macrophage- related genes (MaRGs) and Mitochondria-related genes (MitoRGs), differential analysis was performed to achieve differentially expressed genes (DEGs) between IPF and Control groups in the GSE28042 dataset. Then, MitoRGs, MaRGs and DEGs were overlapped to screen out the signature genes. The univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) algorithm were implemented to achieve key genes. Furthermore, the independent prognostic analysis was employed. The ingenuity pathway analysis (IPA) was employed to further understand the molecular mechanisms of key genes.Next, the immune infiltration analysis was implemented to identify differential immune cells between two risk subgroups. Results: There were 4791 DEGs between IPF and Control groups. Furthermore, 26 signature genes were achieved by the intersection processing. Three key genes including ALDH2, MCL1, and BCL2A1 were achieved, and the risk model based on the key genes was created. In addition, a nomogram for survival forecasting of IPF patients was created based on riskScore, Age, and Gender, and we found that key genes were associated with classical pathways including 'Apoptosis Signaling', 'PI3K/AKT Signaling', and so on. Next, two differential immune cells including Monocytes and CD8 T cells were identified between two risk subgroups. Moreover, we found that MIR29B2CHG and hsa-mir-1-3p could regulate the expression of ALDH2. Conclusion: We achieved 3 key genes including ALDH2, MCL1,, and BCL2A1 associated with IPF, providing a new theoretical basis for clinical treatment of IPF. [ABSTRACT FROM AUTHOR]

Objective: To screen key differentially expressed genes （DEGs） in dead mice with sepsis by spleen high-throughput sequencing combined with bioinformatics. Methods: ①A mouse sepsis model was set up by intraperitoneal injection of lipopolysaccharide （LPS）, a 7-day survival curve of mice was drawn, and the modeling doses of survival group and death group were screened out. ②Expressions of TNF-α, IL-1β, IL-6 and IL-10 in peripheral blood of mice in control group, survival group and death group were verified by ELISA. ③High-throughput sequencing was conducted on spleens of survival group and death group, and the key genes were screened by bioinformatics analysis of DEGs. ④Expressions of key genes and proteins were detected by RT-PCR and Western blot. Results: ①LPS dosage in survival group was 15 mg/kg （with a mortality of 30%）, and LPS dosage in death group was 30 mg/kg（ with a mortality of 80%）. ②Expression levels of IL-6, TNF-α and IL-1β in sepsis mice were significantly higher than those of control group, while expression level of IL-10 was decreased （P<0.05）. Comparison of sepsis model groups showed that levels of pro-inflammatory factors in death group were higher than those in survival group, while level of IL-10 was lower than that in survival group （P<0.05）. ③A total of 2 999 DEGs in survival group and death group were screened out by bioinformatics, among which 1 185 genes were up-regulated and 1 814 genes were down-regulated. Top 5 DEGs enrichment pathways were screened out: "hematopoietic cell lineage" "primary immunodeficiency" "African trypanosomiasis" "leishmaniasis" and "B-cell receptor signaling pathway". Ifit1, Ifit3 and Mx1 were three key genes that were screened out. ④Compared with survival group, expressions of genes and proteins of Ifit1, Ifit3 and Mx1 were down-regulated in spleen tissues of the death group （P<0.05）. Conclusion: By high-throughput sequencing and bioinformatics, Ifit1, Ifit3 and Mx1 are screened out as key genes related to the death outcome of sepsis, which probably influence the outcome of sepsis through the immune mechanism related to virus infection. [ABSTRACT FROM AUTHOR]

Invasive Lobular Carcinoma (ILC) is a common form of breast cancer that begins in milk-producing glands lobules and spreads to other parts of the breast. According to the American Cancer Society, about 10–15% of breast cancer cases are ILC. ILC risk rises with age. The number of deaths caused by this cancer each year can be decreased through early diagnosis and if accurate therapy is given. However, diagnosis of ILC is difficult due to its development pattern as it grows as single file strands and not as lumps. Treatments of ILC involve chemotherapy, hormonal therapy and radiation therapy. Drugs that are being used for ILC, are commonly used to treat all types of breast cancer and there are no specific drugs that target receptors of ILC are available. Microarray technology's emergence helps in finding the differentially expressed genes (DEGs) in malignant cells. From the DEGs, highly interacting genes were identified using the online tool, string. Seven key genes were identified based on the interaction and they are FN1, CDKN2A, COL1A1, COL3A1, COL11A1, LEF1 and IL1B. Thus, the drugs targeting these biomarkers were identified by doing molecular docking using the tool Autodock and molecular dynamic (MD) simulation using the tool iMODs. The response of the identified drugs to the ILC cell line was compared with the control drugs by in silico pharmacogenomic analysis and it was found that the identified drugs have a good response to the ILC cell line. This current research focuses on the search for key genes for the diagnosis and therapy of invasive lobular carcinoma using computational methods. The data on the differentially expressed genes of the ILC cell lines help in finding the key genes. Proteins expressed by these key genes are the targets of ILC therapy. Drugs targeting these proteins were found by molecular docking and molecular dynamic simulations. Pharmacogenomic in silico analysis was performed for dose-response prediction. [ABSTRACT FROM AUTHOR]

Background: Dysfunction in myocardial energy metabolism plays a vital role in the pathological process of Dilated Cardiomyopathy (DCM). However, the precise mechanisms remain unclear. This study aims to investigate the key molecular mechanisms of energy metabolism and potential therapeutic agents in the progression of dilated cardiomyopathy with heart failure. Methods: Gene expression profiles and clinical data for patients with dilated cardiomyopathy complicated by heart failure, as well as healthy controls, were sourced from the Gene Expression Omnibus (GEO) database. Gene sets associated with energy metabolism were downloaded from the Molecular Signatures Database (MSigDB) for subsequent analysis. Weighted Gene Coexpression Network Analysis (WGCNA) and differential expression analysis were employed to identify key modules and genes related to heart failure. Potential biological mechanisms were investigated through Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and the construction of a competing endogenous RNA (ceRNA) network. Molecular docking simulations were then conducted to explore the binding affinity and conformation of potential therapeutic drugs with hub genes. Results: Analysis of the left ventricular tissue expression profiles revealed that, compared to healthy controls, patients with dilated cardiomyopathy exhibited 234 differentially expressed genes and 2 genes related to myocardial energy metabolism. Additionally, Benzoylaconine may serve as a potential therapeutic agent for the treatment of dilated cardiomyopathy. Conclusion: The study findings highlight the crucial role of myocardial energy metabolism in the progression of Dilated Cardiomyopathy. Notably, Benzoylaconine emerges as a potential candidate for treating Dilated Cardiomyopathy, potentially exerting its therapeutic effects by targeted modulation of myocardial energy metabolism through NRK and NT5. [ABSTRACT FROM AUTHOR]

Aim The key genes for necroptosis in atherosclerosis were screened by bioinformatics methods and verified with the help of in vitro experiments to provide new strategies for the prevention and treatment of atherosclerosis from the perspective of necroptosis. Methods Genes related to atherosclerotic plaques were downloaded from GEO database, and genes related to necroptosis were downloaded from GeneCards database and intersected to obtain atherosclerotic necroptosis genes, and the mechanism of action and signalling pathways of the genes were further analysed by GO and KEGG enrichment analysis, and the protein-protein interaction (PPI) network was constructed and screened for key genes. Finally, macrophages were treated with oxidized low density lipoprotein (ox-LDL) at a final concentration of 100 mg/L, and the expression of key genes was detected by RT-PCR and Western blot. Results A total of 81 atherosclerotic necroptosis genes were obtained. GO and KEGG enrichment analyses revealed that they were mainly enriched in the positive regulation of endopeptidase activity, IκB kinase (IKK) / nuclear factor-κB (NF-κB) signalling, and autophagy signalling pathway. Five key genes including HSPA8, STAT3, HMOX1, SQSTM1 and FAS were obtained by using five computational methods of Cytoscape software cytoHubba plug-in. Compared with the normal control group, the HMOX1 gene was highly expressed in THP-1 macrophages treated with ox-LDL (P<0. 05), while the expression of the HSPA8, STAT3, SQSTM1 and FAS genes showed no significant changes (P>0. 05); the HMOX1 and SQSTM1 genes were highly expressed in RAW264. 7 macrophages treated with ox-LDL (P<0. 05), while HSPA8, STAT3 and FAS genes showed no significant changes (P>0. 05). The expression of HMOX1 protein in THP-1 macrophages was also increased. Conclusion HMOX1 may be the key gene of atherosclerotic necroptosis, and it is expected to become a new target for the prevention and treatment of atherosclerosis. [ABSTRACT FROM AUTHOR]

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Colorectal cancer (CC) is a significant public health concern and make it necessary to identify reliable biomarkers and elucidate their molecular and biological mechanisms. This study proposed a system by integrating weighted gene co-expression network analysis (WGCNA) and machine learning-based integrative bioinformatics (ML-IB) analysis to identify key genes for CC. WGCNA was implemented to find a co-expression network of genes and identify important genes by intersecting gene sets obtained using module membership and gene significance criteria across datasets. WGCNA-based significant genes were determined by intersecting important genes between two datasets. ML-IB based approach primarily identified differentially expressed genes (DEGs), then employed support vector machine to determine differentially expressed discriminative genes (DEDGs) and took their common DEDGs across datasets. Protein-protein interaction networks were built and identified hub genes based on the degrees of connectivity and hub module genes using MCODE scores. The ML-IB based significant genes were determined by intersecting hub genes and hub module genes. Four common significant genes were found by intersecting significant genes derived from WGCNA and ML-IB based perspectives. Finally, two genes (AURKA and CCNA2) were determined as key genes for showing strong correlation with survival of CC patients and validated their discriminative capability on an independent test dataset using AUC analysis. The key genes of AURKA and CCNA2 may be used for the early detection of patients with CC. This study will helpful for physicians and doctors to determine and understand the associated the molecular mechanisms and pathway of patients with CC.

Objectives:To identify key genes common to lung cancer and rheumatoid arthritis through WGCNA co-expression network and MCC algorithm analysis.

The growth and development of muscle tissue play a pivotal role in the economic value and quality of meat in agricultural animals, garnering close attention from breeders and researchers. The quality and palatability of muscle tissue directly determine the market competitiveness of meat products and the satisfaction of consumers. Therefore, a profound understanding and management of muscle growth is essential for enhancing the overall economic efficiency and product quality of the meat industry. Despite this, systematic research on muscle development-related genes across different species still needs to be improved. This study addresses this gap through extensive cross-species muscle transcriptome analysis, combined with interpretable machine learning models. Utilizing a comprehensive dataset of 275 publicly available transcriptomes derived from porcine, bovine, and ovine muscle tissues, encompassing samples from ten distinct muscle types such as the semimembranosus and longissimus dorsi, this study analyzes 113 porcine (n = 113), 94 bovine (n = 94), and 68 ovine (n = 68) specimens. We employed nine machine learning models, such as Support Vector Classifier (SVC) and Support Vector Machine (SVM). Applying the SHapley Additive exPlanations (SHAP) method, we analyzed the muscle transcriptome data of cattle, pigs, and sheep. The optimal model, adaptive boosting (AdaBoost), identified key genes potentially influencing muscle growth and development across the three species, termed SHAP genes. Among these, 41 genes (including NANOG, ADAMTS8, LHX3, and TLR9) were consistently expressed in all three species, designated as homologous genes. Specific candidate genes for cattle included SLC47A1, IGSF1, IRF4, EIF3F, CGAS, ZSWIM9, RROB1, and ABHD18; for pigs, DRP2 and COL12A1; and for sheep, only COL10A1. Through the analysis of SHAP genes utilizing Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, relevant pathways such as ether lipid metabolism, cortisol synthesis and secretion, and calcium signaling pathways have been identified, revealing their pivotal roles in muscle growth and development.

In order to identify key genes related to energy metabolism in ulcerative colitis(UC), the expression of energy metabolism-related genes was extracted from the GSE87466 dataset and enriched by differential analysis, and the key genes were identified by using the least absolute shrinkage and selection operator(LASSO) algorithm and support vector machine recursive feature elimination (SVM-RFE) algorithm to identify the key genes of UC energy metabolism, and then enrichment analysis, immune infiltration analysis, prediction of key genes targeting drugs, and construction of ceRNA network were performed on the key genes. Finally, GSE75214 was used as a validation set to verify the expression of key genes. The results show that a total of 32 genes related to energy metabolism are screened, and 5 key genes (SLC16A1, ACSF2, NR1H4, CHST11 and CBR3) are identified by LASSO and SVM algorithms. The single gene enrichment results show that the key genes are involved in the development of UC through glycolysis/ glucose de novo, butyric acid metabolism, and pyruvate metabolism. The validation set GSE75214 on the key genes reveals that the expressions are all differential, providing new ideas and directions for the treatment of ulcerative colitis from the perspective of energy metabolism. [ABSTRACT FROM AUTHOR]

Aims: Autophagy plays a significant role in the development of acute myocardial infarction (AMI), and cardiomyocyte autophagy is of major importance in maintaining cardiac function. We aimed to identify key genes associated with autophagy in AMI through bioinformatics analysis and verify them through clinical validation. Materials and Methods: We downloaded an AMI expression profile dataset GSE166780 from Gene Expression Omnibus (GEO). Autophagy-associated genes potentially differentially expressed in AMI were screened using R software. Then, to identify key autophagy-related genes, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, protein-protein interaction (PPI) analysis, Receiver Operating Characteristic (ROC) curve analysis, and correlation analysis were performed on the differentially expressed autophagy-related genes in AMI. Finally, we used quantificational real-time polymerase chain reaction (qRT-PCR) to verify the RNA expression of the screened key genes. Results: TSC2, HSPA8, and HIF1A were screened out as key autophagy-related genes. qRT-PCR results showed that the expression levels of HSPA8 and TSC2 in AMI blood samples were lower, while the expression level of HIF1A was higher than that in the healthy controls. Conclusions: TSC2, HSPA8, and HIF1A were identified as key autophagy-related genes in this study. They may influence the development of AMI through autophagy. These findings may help deepen our understanding of AMI and may be useful for the treatment of AMI. [ABSTRACT FROM AUTHOR]

Primary ovarian insufficiency (POI) is a common condition leading to the pathological decline of ovarian function in women of reproductive age, resulting in amenorrhea, hypogonadism, and infertility. Biochemical premature ovarian insufficiency (bPOI) is an intermediate stage in the pathogenesis of POI in which the fertility of patients has been reduced. Previous studies suggest that granulosa cells (GCs) play an essential role in the pathogenesis of POI, but their pathogenetic mechanisms remain unclear. To further explore the potential pathophysiological mechanisms of GCs in POI, we constructed a molecular long non-coding RNA (lncRNA)-microRNA (miRNA)-messenger RNA (mRNA) network using GC expression data collected from biochemical premature ovarian failure (bPOI) patients in the GEO database. We discovered that the GCs of bPOI patients had differential expression of 131 mRNAs, 191 lncRNAs, and 28 miRNAs. By systematic network analysis, we identified six key genes, including SRSF1, PDIA5, NEURL1B, UNK, CELF2, and CFL2, and five hub miRNAs, namely hsa-miR-27a-3p, hsa-miR-24-3p, hsa-miR-22-3p, hsa-miR-129-5p, and hsa-miR-17-5p, and the results suggest that the expression of these key genes may be regulated by two hub miRNAs, hsa-miR-27a-3p and hsa-miR-17-5p. Additionally, a POI model in vitro was created to confirm the expression of a few important genes. In this study, we discovered a unique lncRNA-miRNA-mRNA network based on the ceRNA mechanism in bPOI for the first time, and we screened important associated molecules, providing a partial theoretical foundation to better understand the pathogenesis of POI. [ABSTRACT FROM AUTHOR]

Background: Nasopharyngeal carcinoma (NPC) is a highly aggressive and metastatic malignancy originating in the nasopharyngeal tissue. Pyroptosis is a relatively newly discovered, regulated form of necrotic cell death induced by inflammatory caspases that is associated with a variety of diseases. However, the role and mechanism of pyroptosis in NPC are not fully understood. Methods: We analyzed the differential expression of pyroptosis‐related genes (PRGs) between patients with and without NPC from the GSE53819 and GSE64634 datasets of the Gene Expression Omnibus (GEO) database. We mapped receptor operating characteristic profiles for these key PRGs to assess the accuracy of the genes for disease diagnosis and prediction of patient prognosis. In addition, we constructed a nomogram based on these key PRGs and carried out a decision curve analysis. The NPC patients were classified into different pyroptosis gene clusters by the consensus clustering method based on key PRGs, whereas the expression profiles of the key PRGs were analyzed by applying principal component analysis. We also analyzed the differences in key PRGs, immune cell infiltration and NPC‐related genes between the clusters. Finally, we performed differential expression analysis for pyroptosis clusters and obtained differentially expressed genes (DEGs) and performed Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Results: We obtained 14 differentially expressed PRGs from GEO database. Based on these 14 differentially expressed PRGs, we applied least absolute shrinkage and selection operator analysis and the random forest algorithm to obtain four key PRGs (CHMP7, IL1A, TP63 and GSDMB). We completely distinguished the NPC patients into two pyroptosis gene clusters (pyroptosis clusters A and B) based on four key PRGs. Furthermore, we determined the immune cell abundance of each NPC sample, estimated the association between the four PRGs and immune cells, and determined the difference in immune cell infiltration between the two pyroptosis gene clusters. Finally, we obtained and functional enrichment analyses 259 DEGs by differential expression analysis for both pyroptosis clusters. Conclusions: PRGs are critical in the development of NPC, and our research on the pyroptosis gene cluster may help direct future NPC therapeutic approaches. [ABSTRACT FROM AUTHOR]

Ferroptosis is closely associated with the development of disease in the body. However, there are few studies on ferroptosis-related genes (FRGs) in obesity. Therefore, key genes and signalling pathways related to ferroptosis in obesity were screened. Briefly, the RNA sequencing data of obesity and the non-obesity human samples and 259 FRGs were downloaded from GEO database and FerrDb database, respectively. The obesity-related module genes were firstly screened by weighted gene co-expression network analysis (WGCNA) and crossed with differentially expressed genes (DEGs) of obesity/normal samples and FRGs to obtain obesity-ferroptosis related (OFR) DEGs. Then, key genes were screened by PPI network. Next, the correlation of key genes and differential immune cells between obesity and normal samples were further explored by immune infiltration analysis. Finally, microRNA (miRNA)-messenger RNA (mRNA), transcription factor (TF)-mRNA networks and drug-gene interaction networks were constructed. As a result, 17 OFR DEGs were obtained, which mainly participated in processes such as lipid metabolism or adipocyte differentiation. The 4 key genes, STAT3, IL-6, PTGS2, and VEGFA, constituted the network. M2 macrophages, T cells CD8, mast cells activated, and T cells CD4 memory resting had significant differences between obesity and normal samples. Moreover, 51 miRNAs and 164 drugs were predicted for 4 key genes. All in all, this study has screened 4 FRGs, including IL-6, VEGFA, STAT3, and PTGS2, in obesity patients. [ABSTRACT FROM AUTHOR]

Abstract Background In children with CKD, Protein Energy Wasting (PEW) is common, which affects the outcome of children and is an important cause of poor prognosis. We are aiming to explore the pathogenesis of muscle wasting in CKD-PEW children. Methods Blood samples of 32 children diagnosed with chronic kidney disease (CKD) and protein energy wasting (PEW) in our hospital from January 2016 to June 2021 were collected. RNA sequencing and bioinformatics analysis were performed. Results Based on GO (Gene Ontology) functional enrichment analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis and differential gene expression analysis, a total of 25 CKD-PEW related genes were obtained including CRP, IL6, TNF, IL1B, CXCL8, IL12B, IL12A, IL18, IL1A, IL4, IL10, TGFB2, TGFB1, TGFB3, ADIPOQ, NAMPT, RETN, RETNLB, LEP, CD163, ICAM1, VCAM1, SELE, NF-κB1, NF-κB2. The most significantly differentially expressed gene was NF-κB2 (adjusted P = 2.81 × 10–16), and its expression was up-regulated by 3.92 times (corresponding log2FoldChange value was 1.979). Followed by RETN (adjusted P = 1.63 × 10–7), and its expression was up-regulated by 8.306 times (corresponding log2FoldChange value was 2.882). SELE gene were secondly significant (adjusted P = 5.81 × 10–7), and its expression was down-regulated by 22.05 times (corresponding log2FoldChange value was -4.696). Conclusions A variety of inflammatory factors are involved in the pathogenesis of CKD-PEW in children, and chronic inflammation may lead to the development of muscle atrophy in CKD-PEW. It is suggested for the first time that NF-κB is a key gene in the pathogenesis of muscle wasting in CKD-PEW children, and its increased expression may play an important role in the pathogenesis of muscle wasting in children with CKD-PEW.

Jian Jun Gu,1,2,&ast; Tian Jian Du,1,2,&ast; Li Na Zhang,3,&ast; Jing Zhou,4 Xiang Gu,2 Ye Zhu2 1Department of Cardiology, Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, Jiangsu, People’s Republic of China; 2Department of Cardiology, Northern Jiangsu People’s Hospital, Yangzhou, Jiangsu, People’s Republic of China; 3Department of Cardiology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, People’s Republic of China; 4Key Laboratory of Animal Breeding Reproduction and Molecular Design for Jiangsu Province, College of Animal Science and Technology, Yangzhou University, Yangzhou, Jiangsu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Ye Zhu; Xiang Gu, Department of Cardiology, Northern Jiangsu People’s Hospital, 98 Nantong West Road, Yangzhou, Jiangsu, People’s Republic of China, Email 307971331@qq.com; DZ120210035@yzu.edu.cnBackground: Heart failure (HF) is a common clinical syndrome due to ventricular dysfunction and is a major cause of mortality worldwide. Ferroptosis, marked by excessive iron-dependent lipid peroxidation, is closely related to HF. Therefore, the purpose of this study is to explore and validate ferroptosis-related markers in HF by bioinformatics analysis and animal experiments validation.Materials and Methods: The gene expression profiles (GSE36074) of murine transverse aortic constriction (TAC) were obtained from the Gene Expression Omnibus (GEO); From the FerrDb database, ferroptosis-related genes (FRGs) were identified. Using GEO2R, differential expressed genes (DEGs) were screened. An overlapping analysis was conducted among DEGs and FRGs to identify ferroptosis-related DEGs (FRDEGs). We then performed clustering, functional enrichment analysis, and protein-protein interaction (PPI) analyses. In addition, the key FRDEGs were extracted by cytoHubba plugin and the networks of transcription factors (TFs)-key FRDEGs and microRNA-key FRDEGs were constructed. Lastly, the key FRDEGs were carried by quantitative reverse transcription PCR (RT-qPCR) and immunohistochemistry (IHC).Results: Fifty-nine FRGs showing significantly different expression were identified from a total of 1918 DEGs in mice heart by transverse aortic constriction. GO and KEGG functional enrichment analysis revealed that these 59 ferroptosis-related DEGs mostly associated with positive regulation of apoptotic process, FoxO signaling pathway, VEGF signaling pathway, Apoptosis, Ferroptosis. Five key FRDEGs (Mapk14, Hif1a, Ddit3, Tlr4 and Ptgs2) were identified using PPI networks; Based on TFs-key FRDEGs networks, we found that Mapk14, Hif1a, Tlr4 and Ptgs2 were regulated by 3, 4, 5, and 29 TFs, respectively; however, Ddit3 was not regulated by any TF; By analyzing the miRNA–key FRDEGs networks, we found that 39, 74, 11, 28, and 18 miRNAs targets regulate the expression of Mapk14, Hif1a, Ddit3, Tlr4 and Ptgs2, respectively. Lastly, five key FRDEGs were validated at the mRNA and protein levels by RT-qPCR and IHC, which were in line with our bioinformatics analysis.Conclusion: Our findings reveal that Mapk14, Hif1a, Ddit3, Tlr4 and Ptgs2 may be involved in the development of HF through regulating ferroptosis and as potential targets for HF.Keywords: heart failure, transverse aortic constriction, ferroptosis, bioinformatics, key genes

Background: Gastric cancer (GC) is a digestive system tumor with high morbidity and mortality. It is urgently required to identify genes to elucidate the underlying molecular mechanisms. The aim of this study is to identify the key genes which may affect the prognosis of GC patients and be a therapeutic strategy for GC patients by bioinformatic analysis. Methods: The significant prognostic differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets. The protein-protein interaction (PPI) network was established by STRING and screening key genes by MCODE and CytoNCA plug-ins in Cytoscape. Functional enrichment analysis, construction of a prognostic risk model, and nomograms verify key genes as potential therapeutic targets. Results: In total, 997 genes and 805 genes were related to the prognosis of GC in the GSE84437 and TCGA datasets, respectively. We define the 128 genes shared by the two datasets as prognostic DEGs (P-DEGs). Then, the first four genes (MYLK, MYL9, LUM, and CAV1) with great node importance in the PPI network of P-DEGs were identified as key genes. Independent prognostic risk analysis found that patients with high key gene expression had a poor prognosis, excluding their age, gender, and TNM stage. GO and KEGG enrichment analyses showed that key genes may exert influence through the PI3K-Akt pathway, in which extracellular matrix organization and focal adhesion may play important roles in key genes influencing the prognosis of GC patients. Conclusion: We found that MYLK, MYL9, LUM, and CAV1 are potential and reliable prognostic key genes that affect the invasion and migration of gastric cancer.

Background: Streptomyces lavendulae is a bacterium that produces 1-deoxynojirimycin (DNJ), an alkaloid with various beneficial pharmacological properties. While a putative DNJ biosynthesis metabolite pathway has been proposed for this bacterium, the related key genes have not yet been identified. Results: In this study, DNJ production during S. lavendulae fermentation was induced by mulberry seed extracts and the key genes related to DNJ biosynthesis under those experimental conditions were identified using transcriptome sequencing. Gene expression was then analyzed using real-time qPCR, and related to metabolite levels in the DNJ pathway. The mulberry seed polysaccharide extract (MSP) induced DNJ yield in S. lavendulae broth more effectively than the mulberry seed alkaloid and flavonoid extracts. Supplementation with 3.00 mg/mL MSP resulted in 27.19 μg/mL DNJ, which was 8.55 times than that in the control group (3.18 μg/mL). Transcriptome sequencing revealed 2328 differentially expressed genes (DEGs) in MSP vs. unsupplemented-control comparisons, of which 1151 DEGs were up-regulated and 1177 DEGs were down-regulated in MSP. Functional enrichment analysis revealed four pathways of DNJ biosynthesis. Six key genes related to DNJ biosynthesis were then validated using real-time qPCR and the analysis of intermediate metabolites. These were, aspC, encoding a putative transaminase that produces 2-amino-2-deoxy-d-mannitol-6-phosphate; MJ0374-1 and MJ0374-2, encoding a phosphatase that produces 2-amino-2-deoxy-d-mannitol; and frmA-1, frmA-2, and adhD, which encode a Zn-dependent dehydrogenase that produces mannojirimycin. Conclusions: This is the first study to identify the genes relevant for DNJ biosynthesis in S. lavendulae, and it paves the way for engineering the bacterium to achieve for industrial-scale DNJ production. How to cite: Xin X, Jiang X, Niu B, et al. Identification and expression of key genes related to 1-deoxynojirimycin biosynthesis in Streptomyces lavendulae. Electron J Biotechnol 2023; 64. https://doi.org/10.1016/j.ejbt.2023.03.003.

IntroductionAutophagy refers to the process of breaking down and recycling damaged or unnecessary components within a cell to maintain cellular homeostasis. Heart failure (HF) is a severe medical condition that poses a serious threat to the patient's life. Autophagy is known to play a pivotal role in the pathogenesis of HF. However, our understanding of the specific mechanisms involved remains incomplete. Here, we identify autophagy-related genes (ARGs) associated with HF, which we believe will contribute to further comprehending the pathogenesis of HF.MethodsBy searching the GEO (Gene Expression Omnibus) database, we found the GSE57338 dataset, which was related to HF. ARGs were obtained from the HADb and HAMdb databases. Annotation of GO and enrichment analysis of KEGG pathway were carried out on the differentially expressed ARGs (AR-DEGs). We employed machine learning algorithms to conduct a thorough screening of significant genes and validated these genes by analyzing external dataset GSE76701 and conducting mouse models experimentation. At last, immune infiltration analysis was conducted, target drugs were screened and a TF regulatory network was constructed.ResultsThrough processing the dataset with R language, we obtained a total of 442 DEGs. Additionally, we retrieved 803 ARGs from the database. The intersection of these two sets resulted in 15 AR-DEGs. Upon performing functional enrichment analysis, it was discovered that these genes exhibited significant enrichment in domains related to “regulation of cell growth”, “icosatetraenoic acid binding”, and “IL-17 signaling pathway”. After screening and verification, we ultimately identified 4 key genes. Finally, an analysis of immune infiltration illustrated significant discrepancies in 16 distinct types of immune cells between the HF and control group and up to 194 potential drugs and 16 TFs were identified based on the key genes.DiscussionIn this study, TPCN1, MAP2K1, S100A9, and CD38 were considered as key autophagy-related genes in HF. With these relevant data, further exploration of the molecular mechanisms of autophagy in HF can be carried out.

ABSTRACTFerroptosis is closely associated with the development of disease in the body. However, there are few studies on ferroptosis-related genes (FRGs) in obesity. Therefore, key genes and signalling pathways related to ferroptosis in obesity were screened. Briefly, the RNA sequencing data of obesity and the non-obesity human samples and 259 FRGs were downloaded from GEO database and FerrDb database, respectively. The obesity-related module genes were firstly screened by weighted gene co-expression network analysis (WGCNA) and crossed with differentially expressed genes (DEGs) of obesity/normal samples and FRGs to obtain obesity-ferroptosis related (OFR) DEGs. Then, key genes were screened by PPI network. Next, the correlation of key genes and differential immune cells between obesity and normal samples were further explored by immune infiltration analysis. Finally, microRNA (miRNA)-messenger RNA (mRNA), transcription factor (TF)-mRNA networks and drug-gene interaction networks were constructed. As a result, 17 OFR DEGs were obtained, which mainly participated in processes such as lipid metabolism or adipocyte differentiation. The 4 key genes, STAT3, IL-6, PTGS2, and VEGFA, constituted the network. M2 macrophages, T cells CD8, mast cells activated, and T cells CD4 memory resting had significant differences between obesity and normal samples. Moreover, 51 miRNAs and 164 drugs were predicted for 4 key genes. All in all, this study has screened 4 FRGs, including IL-6, VEGFA, STAT3, and PTGS2, in obesity patients.

GBM is the most common and aggressive type of brain tumor. It is classified as a grade IV tumor by the WHO, the highest grade. Prognosis is generally poor, with most patients surviving only about a year. Only 5% of patients survive longer than 5 years. Understanding the molecular mechanisms that drive GBM progression is critical for developing better diagnostic and treatment strategies. Identifying key genes involved in GBM pathogenesis is essential to fully understand the disease and develop targeted therapies. In this study two datasets, GSE108474 and GSE50161, were obtained from the Gene Expression Omnibus (GEO) to compare gene expression between GBM and normal samples. Differentially expressed genes (DEGs) were identified and analyzed. To construct a protein-protein interaction (PPI) network of the commonly up-regulated and down-regulated genes, the STRING 11.5 and Cytoscape 3.9.1 were utilized. Key genes were identified through this network analysis. The GEPIA database was used to confirm the expression levels of these key genes and their association with survival. Functional and pathway enrichment analyses on the DEGs were conducted using the Enrichr server. In total, 698 DEGs were identified, consisting of 377 up-regulated genes and 318 down-regulated genes. Within the PPI network, 11 key up-regulated genes and 13 key down-regulated genes associated with GBM were identified. NOTCH1, TOP2A, CD44, PTPRC, CDK4, HNRNPU, and PDGFRA were found to be important targets for potential drug design against GBM. Additionally, functional enrichment analysis revealed the significant impact of Epstein-Barr virus (EBV), Cell Cycle, and P53 signaling pathways on GBM.

Background: In children with CKD, Protein Energy Wasting (PEW) is common, which affects the outcome of children and is an important cause of poor prognosis. We are aiming to explore the pathogenesis of muscle wasting in CKD-PEW children. Methods: Blood samples of 32 children diagnosed with chronic kidney disease (CKD) and protein energy wasting (PEW) in our hospital from January 2016 to June 2021 were collected. RNA sequencing and bioinformatics analysis were performed. Results: Based on GO (Gene Ontology) functional enrichment analysis, KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analysis and differential gene expression analysis, a total of 25 CKD-PEW related genes were obtained including CRP, IL6, TNF, IL1B, CXCL8, IL12B, IL12A, IL18, IL1A, IL4, IL10, TGFB2, TGFB1, TGFB3, ADIPOQ, NAMPT, RETN, RETNLB, LEP, CD163, ICAM1, VCAM1, SELE, NF-κB1, NF-κB2. The most significantly differentially expressed gene was NF-κB2 (adjusted P = 2.81 × 10–16), and its expression was up-regulated by 3.92 times (corresponding log2FoldChange value was 1.979). Followed by RETN (adjusted P = 1.63 × 10–7), and its expression was up-regulated by 8.306 times (corresponding log2FoldChange value was 2.882). SELE gene were secondly significant (adjusted P = 5.81 × 10–7), and its expression was down-regulated by 22.05 times (corresponding log2FoldChange value was -4.696). Conclusions: A variety of inflammatory factors are involved in the pathogenesis of CKD-PEW in children, and chronic inflammation may lead to the development of muscle atrophy in CKD-PEW. It is suggested for the first time that NF-κB is a key gene in the pathogenesis of muscle wasting in CKD-PEW children, and its increased expression may play an important role in the pathogenesis of muscle wasting in children with CKD-PEW. [ABSTRACT FROM AUTHOR]

River-lake ecosystems are indispensable hubs for water transfers and flow regulation engineering, which have frequent and complex artificial hydrological regulation processes, and the water quality is often unstable. Microorganisms usually aect these systems by driving the nutrient cycling process. Thus, understanding the key biochemical rate-limiting steps under highly regulated conditions was critical for the water quality stability of river-lake ecosystems. This study investigated how the key microorganisms and genes involving nitrogen and phosphorus cycling contributed to the stability of water by combining 16S rRNA and metagenomic sequencing using the Dongping river-lake system as the case study. The results showed that nitrogen and phosphorus concentrations were significantly lower in lake zones than in river inflow and outflow zones (p < 0.05). Pseudomonas, Acinetobacter, and Microbacterium were the key microorganisms associated with nitrate and phosphate removal. These microorganisms contributed to key genes that promote denitrification (nirB/narG/narH/nasA) and phosphorus absorption and transport (pstA/pstB/pstC/pstS). Partial least squares path modeling (PLS-PM) revealed that environmental factors (especially flow velocity and COD concentration) have a significant negative eect on the key microbial abundance (p < 0.001). Our study provides theoretical support for the eective management and protection of water transfer and the regulation function of the river-lake system. [ABSTRACT FROM AUTHOR]

Apocyni Veneti Folium (AVF) is a salt-tolerant medicinal halophyte and soil salinity is a main stress affecting its quality. Molecular bases involved in quality evaluation and ecological adaptations to abiotic constraints can be explored using omics tools. In the study, AVF was treated with four levels of salt stress (control, 100, 200 and 300 mM NaCl, respectively) and subjected to de novo-based RNA-sequencing. We constructed GO and KEGG analysis on the obtained DEGs. After molecular phylogenetic analysis, we isolated and characterized one representative and up-regulated candidate gene AvUGT (Tr_AVENL_25169) encoding UDP-glucosyltransferase under salt stress. Results showed that the obtained clean reads were assembled into a total of 54,276 high-quality unigenes. Notably, specific genes related to flavonoid glucoside biosynthesis and salt-tolerant regulation, such as genes encoding transcription factors, transporters, glucosyltransferase, heat shock protein and plant hormone signal transduction-related protein, preferentially up-regulated by low level of salt. Combined with previous metabonomic analysis, the results revealed key genes that contribute to elucidate the reduced salt toxicity in AVF. Furthermore, the transcript profiles of UGT genes were consistent with the accumulation of flavonoid glycosides in AVF; the candidate gene AvUGT probably plays a critical role in biosynthesis of flavonoid glucosides in response to salt environment. These results provided a basis for future research on the regulatory mechanism of salt stress of medicinal halophyte AVF. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) represents profound degenerative conditions of the brain that cause significant deterioration in memory and cognitive function. Despite extensive research on the significant contribution of lipid metabolism to AD progression, the precise mechanisms remain incompletely understood. Hence, this study aimed to identify key differentially expressed lipid metabolism-related genes (DELMRGs) in AD progression. Methods: Comprehensive analyses were performed to determine key DELMRGs in AD compared to controls in GSE122063 dataset from Gene Expression Omnibus. Additionally, the ssGSEA algorithm was utilized for estimating immune cell levels. Subsequently, correlations between key DELMRGs and each immune cell were calculated specifically in AD samples. The key DELMRGs expression levels were validated via two external datasets. Furthermore, gene set enrichment analysis (GSEA) was utilized for deriving associated pathways of key DELMRGs. Additionally, miRNA-TF regulatory networks of the key DELMRGs were constructed using the miRDB, NetworkAnalyst 3.0, and Cytoscape software. Finally, based on key DELMRGs, AD samples were further segmented into two subclusters via consensus clustering, and immune cell patterns and pathway differences between the two subclusters were examined. Results: Seventy up-regulated and 100 down-regulated DELMRGs were identified. Subsequently, three key DELMRGs (DLD, PLPP2, and PLAAT4) were determined utilizing three algorithms [(i) LASSO, (ii) SVM-RFE, and (iii) random forest]. Specifically, PLPP2 and PLAAT4 were up-regulated, while DLD exhibited downregulation in AD cerebral cortex tissue. This was validated in two separate external datasets (GSE132903 and GSE33000). The AD group exhibited significantly altered immune cell composition compared to controls. In addition, GSEA identified various pathways commonly associated with three key DELMRGs. Moreover, the regulatory network of miRNA-TF for key DELMRGs was established. Finally, significant differences in immune cell levels and several pathways were identified between the two subclusters. Conclusion: This study identified DLD, PLPP2, and PLAAT4 as key DELMRGs in AD progression, providing novel insights for AD prevention/treatment. [ABSTRACT FROM AUTHOR]

Oncidium, an important tropical orchid, has high ornamental value due to its specific color and occupies a significant market position for the worldwide flower. Transcriptome analysis of flower and leaf color formation provides new sources for producing novel Oncidium hybridum cultivars. We sequenced 12 samples of flowers (yellow and white) and leaves (striped and regular) of O. hybridum and assembled 381,136 and 453,566 unigene sequences from RNA-seq data, respectively. Among unigenes, 662 and 1,324 differentially expressed genes were identified in flower and leaf samples, respectively. Gene ontology and pathway enrichment showed that secondary metabolite biosynthetic pathways were responsible for flower and leaf color formation. It was determined that UGT75C1, E2.4.1.115, CCD7, E2.1.1.76, and CCoAOMT are involved in regulating flower color, and UGT75C1, LHCB, UGT, RP-L18Ae, and ABCB1 play crucial roles in regulating leaf color. Kyoto Encyclopedia of Genes and Genomes analysis revealed that UGT75C1 was significantly enriched in the anthocyanin biosynthetic pathway, showing effects on flower and leaf colors. This study was the first detailed analysis of the molecular mechanisms of O. hybridum flower and leaf colors, and the results advanced the understanding of the genetic basis of flower and leaf colors; they also provided additional support for improving commercial value and producing novel cultivars of O. hybridum. [ABSTRACT FROM AUTHOR]

Background Cardiovascular disease (CVD) is a common and frequently occurring disease, and the prevalence and mortality of which are increasing rapidly. Atherosclerosis (AS) is the pathological basis of ischemic CVD. Studies have shown that epicardial adipose tissue (EAT) promotes the progression of AS by secreting exosomes and bioactive substances, but the mechanism of action still needs to be further studied. Objective To perform a bioinformatics analysis of role of EAT in coronary artery disease (CAD) at cellular and molecular levels by identifying the differentially expressed genes (DEGs) in EAT to explore the status of immune cell infiltration, and to assess and verify whether EAT is derived from DEGs in exosomes in CAD patients. Methods We downloaded GSE64554 and GSE120774 datasets about EAT from the GEO database and performed a bioinformatics analysis using R language and related packages. We first used R language to screen the DEGs in EAT and CAD patients, then used GO/KEGG enrichment analysis to establish a protein interaction network to explore biological functions of the screened genes and transcription factors potentially involved in their regulation process. After that, we conducted a weighted gene co-expression network analysis (WGCNA) of EAT in GSE64554 dataset to obtain a gene module related to CAD phenotype, then crossed the hub genes in this module and DEGs in EAT to obtain the key common genes. We used Cibersort to characterize the immune cell infiltration in EAT. Then we obtained DEGs from blood exosomes of CAD patients and healthy controls included in the exoRbase database, crossed DEGs in EAT and blood exosomes to identify the common genes to be used as diagnostic and therapeutic markers for CAD, and their values were tested by qRT-PCR measurement of clinical samples. The selected genes were analyzed by GO/KEGG and Metascape enrichment analyses. Results A total of 1 511 DEGs in EAT of CAD patients were identified, including 956 with up-regulated expression and 555 with down-regulated expression. By crossing the DEGs in EAT and hub genes in modules associated with CAD closely, we identified DDX47, FEM1C, NOL11, SRP54, ABI1, PATL1, BNIP2, C1orf159, and CHCHD4 as key genes in the development of CAD. Immune cell infiltration analysis showed that the abundance of immature CD4+ T cells increased while expression abundance of resting dendritic cells decreased in EAT of CAD patients (P0.05), and decreased F2RmRNA expression level (P

Abstract Alzheimer’s disease (AD) is the most prevalent cause of dementia and is primarily associated with memory impairment and cognitive decline, but the etiology of AD has not been elucidated. In recent years, evidence has shown that immune cells play critical roles in AD pathology. In the current study, we collected the transcriptomic data of the hippocampus from gene expression omnibus database, and investigated the effect of immune cell infiltration in the hippocampus on AD, and analyzed the key genes that influence the pathogenesis of AD patients. The results revealed that the relative abundance of immune cells in the hippocampus of AD patients was altered. Of all given 28 kinds of immune cells, monocytes were the important immune cell associated with AD. We identified 4 key genes associated with both AD and monocytes, including KDELR1, SPTAN1, CDC16 and RBBP6, and they differentially expressed in 5XFAD mice and WT mice. The logistic regression and random forest models based on the 4 key genes could effectively distinguish AD from healthy samples. Our research provided a new perspective on immunotherapy for AD patients.