Systems biology-based analysis exploring shared biomarkers and pathogenesis of myocardial infarction combined with osteoarthritis.

Background: More and more evidence supports the association between myocardial infarction (MI) and osteoarthritis (OA). The purpose of this study is to explore the shared biomarkers and pathogenesis of MI complicated with OA by systems biology. Methods: Gene expression profiles of MI and OA were downloaded from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-Expression Network Analysis (WGCNA) and differentially expressed genes (DEGs) analysis were used to identify the common DEGs. The shared genes related to diseases were screened by three public databases, and the protein-protein interaction (PPI) network was built. GO and KEGG enrichment analyses were performed on the two parts of the genes respectively. The hub genes were intersected and verified by Least absolute shrinkage and selection operator (LASSO) analysis, receiver operating characteristic (ROC) curves, and single-cell RNA sequencing analysis. Finally, the hub genes differentially expressed in primary cardiomyocytes and chondrocytes were verified by RT-qPCR. The immune cell infiltration analysis, subtypes analysis, and transcription factors (TFs) prediction were carried out. Results: In this study, 23 common DEGs were obtained by WGCNA and DEGs analysis. In addition, 199 common genes were acquired from three public databases by PPI. Inflammation and immunity may be the common pathogenic mechanisms, and the MAPK signaling pathway may play a key role in both disorders. DUSP1, FOS, and THBS1 were identified as shared biomarkers, which is entirely consistent with the results of single-cell RNA sequencing analysis, and furher confirmed by RT-qPCR. Immune infiltration analysis illustrated that many types of immune cells were closely associated with MI and OA. Two potential subtypes were identified in both datasets. Furthermore, FOXC1 may be the crucial TF, and the relationship of TFs-hub genes-immune cells was visualized by the Sankey diagram, which could help discover the pathogenesis between MI and OA. Conclusion: In summary, this study first revealed 3 (DUSP1, FOS, and THBS1) novel shared biomarkers and signaling pathways underlying both MI and OA. Additionally, immune cells and key TFs related to 3 hub genes were examined to further clarify the regulation mechanism. Our study provides new insights into shared molecular mechanisms between MI and OA.

Identifying the key hub genes linked with lung squamous cell carcinoma by examining the differentially expressed and survival genes.

Lung Squamous Cell Carcinoma is characterised by significant alterations in RNA expression patterns, and a lack of early symptoms and diagnosis results in poor survival rates. Our study aimed to identify the hub genes involved in LUSC by differential expression analysis and their influence on overall survival rates in patients. Thus, identifying genes with the potential to serve as biomarkers and therapeutic targets. RNA sequence data for LUSC was obtained from TCGA and analysed using R Studio. Survival analysis was performed on DE genes. PPI network and hub gene analysis was performed on survival-relevant genes. Enrichment analysis was conducted on the PPI network to elucidate the functional roles of hub genes. Our analysis identified 2774 DEGs in LUSC patient datasets. Survival analysis revealed 511 genes with a significant impact on patient survival. Among these, 20 hub genes-FN1, ACTB, HGF, PDGFRB, PTEN, SNAI1, TGFBR1, ESR1, SERPINE1, THBS1, PDGFRA, VWF, BMP2, LEP, VTN, PXN, ABL1, ITGA3 and ANXA5-were found to have lower expression levels associated with better patient survival, whereas high expression of SOX2 correlated with longer survival. Enrichment analysis indicated that these hub genes are involved in critical cellular and cancer-related pathways. Our study has identified six key hub genes that are differentially expressed and exhibit significant influence over LUSC patient survival outcomes. Further, in vitro and in vivo studies must be conducted on the key genes for their utilisation as therapeutic targets and biomarkers in LUSC.

A comparative cross-platform analysis of cuproptosis-related genes in human nonobstructive azoospermia: An observational study.

This study aimed to identify novel biomarkers associated with cuproptosis in human nonobstructive azoospermia (NOA). We obtained 4 NOA microarray datasets (GSE145467, GSE9210, GSE108886, and GSE45885) from the NCBI Gene Expression Omnibus database and merged them into training set. Another NOA dataset (GSE45887) was used as validation set. Differentially expressed cuproptosis-related genes were identified from training set. Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway analyses were conducted. Least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination were used to identify hub cuproptosis-related genes. We calculated the expression of the hub cuproptosis-related genes in both validation set and patients with NOA. Gene set variation analysis was used to explore their potential biological functions. The risk prediction model was built by logistic regression analysis and was evaluated in the validation set. Finally, we constructed a competing endogenous RNA network. The training set included 29 patents in the control group and 92 in the NOA group, and 10 cuproptosis-related differentially expressed genes were identified. Subsequently, we screened 6 hub cuproptosis-related genes (DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1) by least absolute shrinkage and selection operator regression and support vector machine-recursive feature elimination. GCSH, NFE2L2, NLRP3, and SLC31A1 expressed higher in NOA group than in control group (P < .05) in the validation set (4 patients in control and 16 in NOA groups), while the expression levels of GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 were higher in NOA group than in control group (P < .05) in our patients (3 patients in control and 4 in NOA groups). The model based on the 6-gene signature showed superior performance with an AUC value of 0.970 in training set, while 1.0 in validation set. Gene set variation analysis revealed a higher enrichment score of "homologous recombination" in the high expression groups of the 6 hub genes. Finally, we constructed a competing endogenous RNA network and found hsa-miR-335-3p and hsa-miR-1-3p were the most frequently related to the 6 hub genes. DBT, GCSH, NFE2L2, NLRP3, PDHA1, and SLC31A1 may serve as predictors of cuproptosis and play important roles in the NOA pathogenesis.

Identification and verification of immune-related genes for diagnosing the progression of atherosclerosis and metabolic syndrome.

BACKGROUND: Atherosclerosis and metabolic syndrome are the main causes of cardiovascular events, but their underlying mechanisms are not clear. In this study, we focused on identifying genes associated with diagnostic biomarkers and effective therapeutic targets associated with these two diseases. METHODS: Transcriptional data sets of atherosclerosis and metabolic syndrome were obtained from GEO database. The differentially expressed genes were analyzed by RStudio software, and the function-rich and protein-protein interactions of the common differentially expressed genes were analyzed.Furthermore, the hub gene was screened by Cytoscape software, and the immune infiltration of hub gens was analyzed. Finally, relevant clinical blood samples were collected for qRT-PCR verification of the three most important hub genes. RESULTS: A total of 1242 differential genes (778 up-regulated genes and 464 down-regulated genes) were screened from GSE28829 data set. A total of 1021 differential genes (492 up-regulated genes and 529 down-regulated genes) were screened from the data set GSE98895. Then 23 up-regulated genes and 11 down-regulated genes were screened by venn diagram. Functional enrichment analysis showed that cytokines and immune activation were involved in the occurrence and development of these two diseases. Through the construction of the Protein-Protein Interaction(PPI) network and Cytoscape software analysis, we finally screened 10 hub genes. The immune infiltration analysis was further improved. The results showed that the infiltration scores of 7 kinds of immune cells in GSE28829 were significantly different among groups (Wilcoxon Test < 0.05), while in GSE98895, the infiltration scores of 4 kinds of immune cells were significantly different between groups (Wilcoxon Test < 0.05). Spearman method was used to analyze the correlation between the expression of 10 key genes and 22 kinds of immune cell infiltration scores in two data sets. The results showed that there were 42 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE28829 (|Cor| > 0.3 & P < 0.05). There were 41 pairs of significant correlations between 10 genes and 22 kinds of immune cells in GSE98895 (|Cor| > 0.3 & P < 0.05). Finally, our results identified 10 small molecules with the highest absolute enrichment value, and the three most significant key genes (CX3CR1, TLR5, IL32) were further verified in the data expression matrix and clinical blood samples. CONCLUSION: We have established a co-expression network between atherosclerotic progression and metabolic syndrome, and identified key genes between the two diseases. Through the method of bioinformatics, we finally obtained 10 hub genes in As and MS, and selected 3 of the most significant genes (CX3CR1, IL32, TLR5) for blood PCR verification. This may be helpful to provide new research ideas for the diagnosis and treatment of AS complicated with MS.

Integrating bioinformatics and machine learning methods to analyze diagnostic biomarkers for HBV-induced hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a malignant tumor. It is estimated that approximately 50-80% of HCC cases worldwide are caused by hepatitis b virus (HBV) infection, and other pathogenic factors have been shown to promote the development of HCC when coexisting with HBV. Understanding the molecular mechanisms of HBV-induced hepatocellular carcinoma (HBV-HCC) is crucial for the prevention, diagnosis, and treatment of the disease. In this study, we analyzed the molecular mechanisms of HBV-induced HCC by combining bioinformatics and deep learning methods. Firstly, we collected a gene set related to HBV-HCC from the GEO database, performed differential analysis and WGCNA analysis to identify genes with abnormal expression in tumors and high relevance to tumors. We used three deep learning methods, Lasso, random forest, and SVM, to identify key genes RACGAP1, ECT2, and NDC80. By establishing a diagnostic model, we determined the accuracy of key genes in diagnosing HBV-HCC. In the training set, RACGAP1(AUC:0.976), ECT2(AUC:0.969), and NDC80 (AUC: 0.976) showed high accuracy. They also exhibited good accuracy in the validation set: RACGAP1(AUC:0.878), ECT2(AUC:0.731), and NDC80(AUC:0.915). The key genes were found to be highly expressed in liver cancer tissues compared to normal liver tissues, and survival analysis indicated that high expression of key genes was associated with poor prognosis in liver cancer patients. This suggests a close relationship between key genes RACGAP1, ECT2, and NDC80 and the occurrence and progression of HBV-HCC. Molecular docking results showed that the key genes could spontaneously bind to the anti-hepatocellular carcinoma drugs Lenvatinib, Regorafenib, and Sorafenib with strong binding activity. Therefore, ECT2, NDC80, and RACGAP1 may serve as potential biomarkers for the diagnosis of HBV-HCC and as targets for the development of targeted therapeutic drugs.

Identification of HTRA1, DPT and MXRA5 as potential biomarkers associated with osteoarthritis progression and immune infiltration.

BACKGROUND: Our study aimed to identify potential specific biomarkers for osteoarthritis (OA) and assess their relationship with immune infiltration. METHODS: We utilized data from GSE117999, GSE51588, and GSE57218 as training sets, while GSE114007 served as a validation set, all obtained from the GEO database. First, weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were performed to identify hub modules and potential functions of genes. We subsequently screened for potential OA biomarkers within the differentially expressed genes (DEGs) of the hub module using machine learning methods. The diagnostic accuracy of the candidate genes was validated. Additionally, single gene analysis and ssGSEA was performed. Then, we explored the relationship between biomarkers and immune cells. Lastly, we employed RT-PCR to validate our results. RESULTS: WGCNA results suggested that the blue module was the most associated with OA and was functionally associated with extracellular matrix (ECM)-related terms. Our analysis identified ALB, HTRA1, DPT, MXRA5, CILP, MPO, and PLAT as potential biomarkers. Notably, HTRA1, DPT, and MXRA5 consistently exhibited increased expression in OA across both training and validation cohorts, demonstrating robust diagnostic potential. The ssGSEA results revealed that abnormal infiltration of DCs, NK cells, Tfh, Th2, and Treg cells might contribute to OA progression. HTRA1, DPT, and MXRA5 showed significant correlation with immune cell infiltration. The RT-PCR results also confirmed these findings. CONCLUSIONS: HTRA1, DPT, and MXRA5 are promising biomarkers for OA. Their overexpression strongly correlates with OA progression and immune cell infiltration.

Ant colony optimization for the identification of dysregulated gene subnetworks from expression data.

BACKGROUND: High-throughput experimental technologies can provide deeper insights into pathway perturbations in biomedical studies. Accordingly, their usage is central to the identification of molecular targets and the subsequent development of suitable treatments for various diseases. Classical interpretations of generated data, such as differential gene expression and pathway analyses, disregard interconnections between studied genes when looking for gene-disease associations. Given that these interconnections are central to cellular processes, there has been a recent interest in incorporating them in such studies. The latter allows the detection of gene modules that underlie complex phenotypes in gene interaction networks. Existing methods either impose radius-based restrictions or freely grow modules at the expense of a statistical bias towards large modules. We propose a heuristic method, inspired by Ant Colony Optimization, to apply gene-level scoring and module identification with distance-based search constraints and penalties, rather than radius-based constraints. RESULTS: We test and compare our results to other approaches using three datasets of different neurodegenerative diseases, namely Alzheimer's, Parkinson's, and Huntington's, over three independent experiments. We report the outcomes of enrichment analyses and concordance of gene-level scores for each disease. Results indicate that the proposed approach generally shows superior stability in comparison to existing methods. It produces stable and meaningful enrichment results in all three datasets which have different case to control proportions and sample sizes. CONCLUSION: The presented network-based gene expression analysis approach successfully identifies dysregulated gene modules associated with a certain disease. Using a heuristic based on Ant Colony Optimization, we perform a distance-based search with no radius constraints. Experimental results support the effectiveness and stability of our method in prioritizing modules of high relevance. Our tool is publicly available at github.com/GhadiElHasbani/ACOxGS.git.

Identification of potential biomarkers for atrial fibrillation and stable coronary artery disease based on WGCNA and machine algorithms.

BACKGROUND: Patients with atrial fibrillation (AF) often have coronary artery disease (CAD), but the biological link between them remains unclear. This study aims to explore the common pathogenesis of AF and CAD and identify common biomarkers. METHODS: Gene expression profiles for AF and stable CAD were downloaded from the Gene Expression Omnibus database. Overlapping genes related to both diseases were identified using weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Hub genes were then identified using the machine learning algorithm. Immune cell infiltration and correlations with hub genes were explored, followed by drug predictions. Hub gene expression in AF and CAD patients was validated by real-time qPCR. RESULTS: We obtained 28 common overlapping genes in AF and stable CAD, mainly enriched in the PI3K-Akt, ECM-receptor interaction, and relaxin signaling pathway. Two hub genes, COL6A3 and FKBP10, were positively correlated with the abundance of MDSC, plasmacytoid dendritic cells, and regulatory T cells in AF and negatively correlated with the abundance of CD56dim natural killer cells in CAD. The AUCs of COL6A3 and FKBP10 were all above or close to 0.7. Drug prediction suggested that collagenase clostridium histolyticum and ocriplasmin, which target COL6A3, may be potential drugs for AF and stable CAD. Additionally, COL6A3 and FKBP10 were upregulated in patients with AF and CAD. CONCLUSION: COL6A3 and FKBP10 may be key biomarkers for AF and CAD, providing new insights into the diagnosis and treatment of this disease.

Identification of ENTPD1 as a novel biomarker linking allergic rhinitis and systemic lupus erythematosus.

Several studies reveal that allergic rhinitis (AR) is a significant risk factor of systemic lupus erythematosus (SLE). However, studies investigating the common pathogenesis linking AR and SLE are lacking. Our study aims to search for the shared biomarkers and mechanisms that may provide new therapeutic targets for preventing AR from developing SLE. GSE50223 for AR and GSE103760 for SLE were downloaded from the Gene Expression Omnibus (GEO) database to screen differentially expressed genes (DEGs). The Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to explore the functions of shared DEGs. Hub genes were screened by cytoHubba (a plugin of Cytoscape) and validated in another two datasets. Gene set enrichment analysis (GSEA) and single-sample Gene set enrichment analysis (ssGSEA) algorithm were applied to understand the functions of hub gene. ENTPD1 was validated as a hub gene between AR and SLE. GSEA results revealed that ENTPD1 was associated with KRAS_SIGNALING_UP pathway in AR and related to HYPOXIA, TGF_BETA_SIGNALING and TNFA_SIGNALING_VIA_NFKB pathways in SLE. The expression of ENTPD1 was positively correlated with activated CD8 T cell in both diseases. Thus, ENTPD1 may be a novel therapeutic target for preventing AR from developing SLE.

Prediction of lung adenocarcinoma prognosis and diagnosis with a novel model anchored in circadian clock-related genes.

Lung adenocarcinoma is the most common primary lung cancer seen in the world, and identifying genetic markers is essential for predicting the prognosis of lung adenocarcinoma and improving treatment outcomes. It is well known that alterations in circadian rhythms are associated with a higher risk of cancer. Moreover, circadian rhythms play a regulatory role in the human body. Therefore, studying the changes in circadian rhythms in cancer patients is crucial for optimizing treatment. The gene expression data and clinical data were sourced from TCGA database, and we identified the circadian clock-related genes. We used the obtained TCGA-LUAD data set to build the model, and the other 647 lung adenocarcinoma patients' data were collected from two GEO data sets for external verification. A risk score model for circadian clock-related genes was constructed, based on the identification of 8 genetically significant genes. Based on ROC analyses, the risk model demonstrated a high level of accuracy in predicting the overall survival times of lung adenocarcinoma patients in training folds, as well as external data sets. This study has successfully constructed a risk model for lung adenocarcinoma prognosis, utilizing circadian rhythm as its foundation. This model demonstrates a dependable capacity to forecast the outcome of the disease, which can further guide the relevant mechanism of lung adenocarcinoma and combine behavioral therapy with treatment to optimize treatment decision-making.

SoDCoD: a comprehensive database of Cu/Zn superoxide dismutase conformational diversity caused by ALS-linked gene mutations and other perturbations.

A structural alteration in copper/zinc superoxide dismutase (SOD1) is one of the common features caused by amyotrophic lateral sclerosis (ALS)-linked mutations. Although a large number of SOD1 variants have been reported in ALS patients, the detailed structural properties of each variant are not well summarized. We present SoDCoD, a database of superoxide dismutase conformational diversity, collecting our comprehensive biochemical analyses of the structural changes in SOD1 caused by ALS-linked gene mutations and other perturbations. SoDCoD version 1.0 contains information about the properties of 188 types of SOD1 mutants, including structural changes and their binding to Derlin-1, as well as a set of genes contributing to the proteostasis of mutant-like wild-type SOD1. This database provides valuable insights into the diagnosis and treatment of ALS, particularly by targeting conformational alterations in SOD1. Database URL: https://fujisawagroup.github.io/SoDCoDweb/.

Database size positively correlates with the loss of species-level taxonomic resolution for the 16S rRNA and other prokaryotic marker genes.

For decades, the 16S rRNA gene has been used to taxonomically classify prokaryotic species and to taxonomically profile microbial communities. However, the 16S rRNA gene has been criticized for being too conserved to differentiate between distinct species. We argue that the inability to differentiate between species is not a unique feature of the 16S rRNA gene. Rather, we observe the gradual loss of species-level resolution for other nearly-universal prokaryotic marker genes as the number of gene sequences increases in reference databases. This trend was strongly correlated with how represented a taxonomic group was in the database and indicates that, at the gene-level, the boundaries between many species might be fuzzy. Through our study, we argue that any approach that relies on a single marker to distinguish bacterial taxa is fraught even if some markers appear to be discriminative in current databases.

Effects of HOX family regulator-mediated modification patterns and immunity characteristics on tumor-associated cell type in endometrial cancer.

Endometrial cancer (UCEC) is one of three major malignant tumors in women. The HOX gene regulates tumor development. However, the potential roles of HOX in the expression mechanism of multiple cell types and in the development and progression of tumor microenvironment (TME) cell infiltration in UCEC remain unknown. In this study, we utilized both the The Cancer Genome Atlas (TCGA) database and International Cancer Genome Consortium (ICGC) database to analyze transcriptome data of 529 patients with UCEC based on 39 HOX genes, combing clinical information, we discovered HOX gene were a pivotal factor in the development and progression of UCEC and in the formation of TME diversity and complexity. Here, a new scoring system was developed to quantify individual HOX patterns in UCEC. Our study found that patients in the low HOX score group had abundant anti-tumor immune cell infiltration, good tumor differentiation, and better prognoses. In contrast, a high HOX score was associated with blockade of immune checkpoints, which enhances the response to immunotherapy. The Real-Time quantitative PCR (RT-qPCR) and Immunohistochemistry (IHC) exhibited a higher expression of the HOX gene in the tumor patients. We revealed that the significant upregulation of the HOX gene in the epithelial cells can activate signaling pathway associated with tumour invasion and metastasis through single-cell RNA sequencing (scRNA-seq), such as nucleotide metabolic proce and so on. Finally, a risk prognostic model established by the positive relationship between HOX scores and cancer-associated fibroblasts (CAFs) can predict the prognosis of individual patients by scRNA-seq and transcriptome data sets. In sum, HOX gene may serve as a potential biomarker for the diagnosis and prediction of UCEC and to develop more effective therapeutic strategies.

HPC-T-Annotator: an HPC tool for de novo transcriptome assembly annotation.

BACKGROUND: The availability of transcriptomic data for species without a reference genome enables the construction of de novo transcriptome assemblies as alternative reference resources from RNA-Seq data. A transcriptome provides direct information about a species' protein-coding genes under specific experimental conditions. The de novo assembly process produces a unigenes file in FASTA format, subsequently targeted for the annotation. Homology-based annotation, a method to infer the function of sequences by estimating similarity with other sequences in a reference database, is a computationally demanding procedure. RESULTS: To mitigate the computational burden, we introduce HPC-T-Annotator, a tool for de novo transcriptome homology annotation on high performance computing (HPC) infrastructures, designed for straightforward configuration via a Web interface. Once the configuration data are given, the entire parallel computing software for annotation is automatically generated and can be launched on a supercomputer using a simple command line. The output data can then be easily viewed using post-processing utilities in the form of Python notebooks integrated in the proposed software. CONCLUSIONS: HPC-T-Annotator expedites homology-based annotation in de novo transcriptome assemblies. Its efficient parallelization strategy on HPC infrastructures significantly reduces computational load and execution times, enabling large-scale transcriptome analysis and comparison projects, while its intuitive graphical interface extends accessibility to users without IT skills.

Identification and Validation of Aging Related Genes Signature in Chronic Obstructive Pulmonary Disease.

PURPOSE: Chronic Obstructive Pulmonary Disease (COPD) is regarded as an accelerated aging disease. Aging-related genes in COPD are still poorly understood. METHOD: Data set GSE76925 was obtained from the Gene Expression Omnibus (GEO) database. The "limma" package identified the differentially expressed genes. The weighted gene co-expression network analysis (WGCNA) constructes co-expression modules and detect COPD-related modules. The least absolute shrinkage and selection operator (LASSO) and the support vector machine recursive feature elimination (SVM-RFE) algorithms were chosen to identify the hub genes and the diagnostic ability. Three external datasets were used to identify differences in the expression of hub genes. Real-time reverse transcription polymerase chain reaction (RT-qPCR) was used to verify the expression of hub genes. RESULT: We identified 15 differentially expressed genes associated with aging (ARDEGs). The SVM-RFE and LASSO algorithms pinpointed four potential diagnostic biomarkers. Analysis of external datasets confirmed significant differences in PIK3R1 expression. RT-qPCR results indicated decreased expression of hub genes. The ROC curve demonstrated that PIK3R1 exhibited strong diagnostic capability for COPD. CONCLUSION: We identified 15 differentially expressed genes associated with aging. Among them, PIK3R1 showed differences in external data sets and RT-qPCR results. Therefore, PIK3R1 may play an essential role in regulating aging involved in COPD.

Comparative transcriptome database for Camellia sinensis reveals genes related to the cold sensitivity and albino mechanism of 'Anji Baicha'.

Tea, a globally popular beverage, contains various beneficial secondary metabolites. Tea plants (Camellia sinensis) exhibit diverse genetic traits across cultivars, impacting yield, adaptability, morphology, and secondary metabolite composition. Many tea cultivars have been the subject of much research interest, which have led to the accumulation of publicly available RNA-seq data. As such, it has become possible to systematically summarize the characteristics of different cultivars at the transcriptomic level, identify functional genes, and infer gene functions through co-expression analysis. Here, the transcriptomes of 9 tea cultivars were assembled, and comparative analysis was conducted on the coding sequences of 13 cultivars. To give access to this data, we present TeaNekT (https://teanekt.sbs.ntu.edu.sg/), a web resource that facilitates the prediction of gene functions of various tea cultivars. We used TeaNekT to perform a cross-cultivar comparison of co-expressed gene clusters and tissue-specific gene expression. We observed that 'Anji Baicha' possesses the highest number of cultivar-specific genes and the second-highest number of expanded genes. These genes in 'Anji Baicha' tend to be enriched in functions associated with cold stress response, chloroplast thylakoid structure, and nitrogen metabolism. Notably, we identified three significantly expanded homologous genes in 'Anji Baicha' encoding the ICE1, SIZ1, and MAPKK2, which are closely associated with the cold sensitivity of 'Anji Baicha'. Additionally, one significantly expanded homologous gene in 'Anji Baicha' encoding regulatory factor RIQ may play a crucial role in the abnormal chloroplast structure and absence of thylakoid membranes in 'Anji Baicha'.

Deciphering the Molecular Complexity of Hepatocellular Carcinoma: Unveiling Novel Biomarkers and Therapeutic Targets Through Advanced Bioinformatics Analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) represents a primary liver tumor characterized by a bleak prognosis and elevated mortality rates, yet its precise molecular mechanisms have not been fully elucidated. This study uses advanced bioinformatics techniques to discern differentially expressed genes (DEGs) implicated in the pathogenesis of HCC. The primary objective is to discover novel biomarkers and potential therapeutic targets that can contribute to the advancement of HCC research. METHODS: The bioinformatics analysis in this study primarily utilized the Gene Expression Omnibus (GEO) database as data source. Initially, the Transcriptome analysis console (TAC) screened for DEGs. Subsequently, we constructed a protein-protein interaction (PPI) network of the proteins associated to the identified DEGs with the STRING database. We obtained our hub genes using Cytoscape and confirmed the results through the GEPIA database. Furthermore, we assessed the prognostic significance of the identified hub genes using the GEPIA database. To explore the regulatory interactions, a miRNA-gene interaction network was also constructed, incorporating information from the miRDB database. For predicting the impact of gene overexpression on drug effects, we utilized CANCER DP. RESULTS: A comprehensive analysis of HCC gene expression profiles revealed a total of 4716 DEGs, consisting of 2430 upregulated genes and 2313 downregulated genes in HCC sample compared to healthy control group. These DEGs exhibited significant enrichment in key pathways such as the PI3K-Akt signaling pathway, nuclear receptors meta-pathway, and various metabolism-related pathways. Further exploration of the PPI network unveiled the P53 signaling pathway and pyrimidine metabolism as the most prominent pathways. We identified 10 hub genes (ASPM, RRM2, CCNB1, KIF14, MKI67, SHCBP1, CENPF, ANLN, HMMR, and EZH2) that exhibited significant upregulation in HCC samples compared to healthy control group. Survival analysis indicated that elevated expression levels of these genes were strongly associated with changes in overall survival in HCC patients. Lastly, we identified specific miRNAs that were found to influence the expression of these genes, providing valuable insights into potential regulatory mechanisms underlying HCC progression. CONCLUSION: The findings of this study have successfully identified pivotal genes and pathways implicated in the pathogenesis of HCC. These novel discoveries have the potential to significantly enhance our understanding of HCC at the molecular level, opening new ways for the development of targeted therapies and improved prognosis evaluation.

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients.

BACKGROUND: Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention. MATERIALS AND METHODS: Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI). RESULTS: A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores. CONCLUSION: High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

Setting Up the JBrowse 2 Genome Browser.

JBrowse 2 is a modular genome browser that can visualize many common genomic file formats. While JBrowse 2 supports a variety of different usages, it is particularly suited for deployment on websites, such as model organism databases or other web-based genomic data resources. This protocol provides detailed instructions for setting up JBrowse 2 on an Ubuntu Linux web server, loading a reference genome from a FASTA format file, and adding a gene annotation track from a GFF3 format file. By the end of the protocol, users will have a working JBrowse 2 instance that is accessible via the web. © 2024 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Setting up JBrowse 2 on your web server.

Analysis of the prognostic value of mitochondria-related genes in patients with acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) is a leading cause of death worldwide. Mitochondrial dysfunction is a key determinant of cell death post-AMI. Preventing mitochondrial dysfunction is thus a key therapeutic strategy. This study aimed to explore key genes and target compounds related to mitochondrial dysfunction in AMI patients and their association with major adverse cardiovascular events (MACE). METHODS: Differentially expressed genes in AMI were identified from the Gene Expression Omnibus (GEO) datasets (GSE166780 and GSE24519), and mitochondria-related genes were obtained from MitoCarta3.0 database. By intersection of the two gene groups, mitochondria-related genes in AMI were identified. Next, the identified genes related to mitochondria were subject to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Protein-protein interaction (PPI) network was constructed, and key genes were screened. Then, targeted drug screening and molecular docking were performed. Blood samples from AMI patients and healthy volunteers were analyzed for the key genes expressions using quantitative real time polymerase chain reaction (qRT-PCR). Later, receiver operating characteristic (ROC) curves assessed the diagnostic value of key genes, and univariate and multivariate COX analyses identified risk factors and protective factors for MACE in AMI patients. RESULTS: After screening and identification, 138 mitochondria-related genes were identified, mainly enriched in the processes and pathways of cellular respiration, redox, mitochondrial metabolism, apoptosis, amino acid and fatty acid metabolism. According to the PPI network, 5 key mitochondria-related genes in AMI were obtained: translational activator of cytochrome c oxidase I (TACO1), cytochrome c oxidase subunit Va (COX5A), PTEN-induced putative kinase 1 (PINK1), SURF1, and NDUFA11. Molecular docking showed that Cholic Acid, N-Formylmethionine interacted with COX5A, nicotinamide adenine dinucleotide + hydrogen (NADH) and NDUFA11. Subsequent basic experiments revealed that COX5A and NDUFA11 expressions were significantly lower in the blood of patients with AMI than those in the corresponding healthy volunteers; also, AMI patients with MACE had lower COX5A and NDUFA11 expressions in the blood than those without MACE (P < 0.01). ROC analysis also showed high diagnostic value for COX5A and NDUFA11 [area under the curve (AUC) > 0.85]. In terms of COX results, COX5A, NDUFA11 and left ventricular ejection fraction (LVEF) were protective factors for MACE in AMI, while C-reactive protein (CRP) was a risk factor. CONCLUSION: COX5A and NDUFA11, key mitochondria-related genes in AMI, may be used as biomarkers to diagnose AMI and predict MACE.