[Bioinformatic analysis of immune-related transcription factors in diabetic kidney disease].

Objective To identify immune-related transcription factors (TFs) in renal glomeruli and tubules from diabetic kidney disease (DKD) patients by bioinformatics analysis. Methods Gene expression datasets from GEO (GSE30528, GSE30529) and RNA sequencing (RNA-seq) data from the Karolinska Kidney Research Center were used. Gene set enrichment analysis (GSEA) was conducted to examine differences in immune-related gene expression in the glomeruli and tubules (DKD) patients. To identify immune-related genes (IRGs) and TFs, differential expression analysis was carried out using the Limma and DESeq2 software packages. Key immune-related TFs were pinpointed through co-expression analysis. The interaction network between TFs and IRGs was constructed using the STRING database and Cytoscape software. Furthermore, the Nephroseq database was employed to investigate the correlation between the identified TFs and clinical-pathological features. Results When compared to normal control tissues, significant differences in the expression of immune genes were observed in both the glomeruli and tubules of individuals with Diabetic Kidney Disease (DKD). Through differential and co-expression analysis, 50 immune genes and 9 immune-related transcription factors (TFs) were identified in the glomeruli. In contrast, 131 immune response genes (IRGs) and 41 immune-related TFs were discovered in the renal tubules. The protein-protein interaction (PPI) network highlighted four key immune-related TFs for the glomeruli: Interferon regulatory factor 8 (IRF8), lactotransferrin (LTF), CCAAT/enhancer binding protein alpha (CEBPA), and Runt-related transcription factor 3 (RUNX3). For the renal tubules, the key immune-related TFs were FBJ murine osteosarcoma viral oncogene homolog B (FOSB), nuclear receptor subfamily 4 group A member 1 (NR4A1), IRF8, and signal transducer and activator of transcription 1 (STAT1). These identified TFs demonstrated a significant correlation with the glomerular filtration rate (GFR), highlighting their potential importance in the pathology of DKD. Conclusion Bioinformatics analysis identifies potential genes associated with DKD pathogenesis and immune dysregulation. Further validation of the expression and function of these genes may contribute to immune-based therapeutic research for DKD.

Unique regulatory network of dragon fruit simultaneously mitigates the effect of vanadium pollutant and environmental factors.

Under changing climatic conditions, plants are simultaneously facing conflicting stresses in nature. Plants can sense different stresses, induce systematic ROS signals, and regulate transcriptomic, hormonal, and stomatal responses. We performed transcriptome analysis to reveal the integrative stress response regulatory mechanism underlying heavy metal stress alone or in combination with heat and drought conditions in pitaya (dragon fruit). A total of 70 genes were identified from 31,130 transcripts with conserved differential expression. Furthermore, weighted gene co-expression network analysis (WGCNA) identified trait-associated modules. By integrating information from three modules and protein-protein interaction (PPI) networks, we identified 10 interconnected genes associated with the multifaceted defense mechanism employed by pitaya against co-occurring stresses. To further confirm the reliability of the results, we performed a comparative analysis of 350 genes identified by three trait modules and 70 conserved genes exhibiting their dynamic expression under all treatments. Differential expression pattern of genes and comparative analysis, have proven instrumental in identifying ten putative structural genes. These ten genes were annotated as PLAT/LH2, CAT, MLP, HSP, PB1, PLA, NAC, HMA, and CER1 transcription factors involved in antioxidant activity, defense response, MAPK signaling, detoxification of metals and regulating the crosstalk between the complex pathways. Predictive analysis of putative candidate genes, potentially governing single, double, and multifactorial stress response, by several signaling systems and molecular patterns. These findings represent a valuable resource for pitaya breeding programs, offering the potential to develop resilient "super pitaya" plants.

Comprehensive analysis of key host gene-microbe networks in the cecum tissues of the obese rabbits induced by a high-fat diet.

The Cecum is a key site for cellulose digestion in nutrient metabolism of intestine, but its mechanisms of microbial and gene interactions has not been fully elucidated during pathogenesis of obesity. Therefore, the cecum tissues of the New Zealand rabbits and their contents between the high-fat diet-induced group (Ob) and control group (Co) were collected and analyzed using multi-omics. The metagenomic analysis indicated that the relative abundances of Corallococcus_sp._CAG:1435 and Flavobacteriales bacterium species were significantly lower, while those of Akkermansia glycaniphila, Clostridium_sp._CAG:793, Mycoplasma_sp._CAG:776, Mycoplasma_sp._CAG:472, Clostridium_sp._CAG:609, Akkermansia_sp._KLE1605, Clostridium_sp._CAG:508, and Firmicutes_bacterium_CAG:460 species were significantly higher in the Ob as compared to those in Co. Transcriptomic sequencing results showed that the differentially upregulated genes were mainly enriched in pathways, including calcium signaling pathway, PI3K-Akt signaling pathway, and Wnt signaling pathway, while the differentially downregulated genes were mainly enriched in pathways of NF-kappaB signaling pathway and T cell receptor signaling pathway. The comparative analysis of metabolites showed that the glycine, serine, and threonine metabolism and cysteine and methionine metabolism were the important metabolic pathways between the two groups. The combined analysis showed that CAMK1, IGFBP6, and IGFBP4 genes were highly correlated with Clostridium_sp._CAG:793, and Akkermansia_glycaniphila species. Thus, the preliminary study elucidated the microbial and gene interactions in cecum of obese rabbit and provided a basis for further studies in intestinal intervention for human obesity.

CCR7 and CD48 as Predicted Targets in Acute Rejection Related to M1 Macrophage after Pediatric Kidney Transplantation.

Background: Kidney transplantation (KT) is the best treatment for end-stage renal disease. Although long and short-term survival rates for the graft have improved significantly with the development of immunosuppressants, acute rejection (AR) remains a major risk factor attacking the graft and patients. The innate immune response plays an important role in rejection. Therefore, our objective is to determine the biomarkers of congenital immunity associated with AR after KT and provide support for future research. Materials and Methods: A differential expression genes (DEGs) analysis was performed based on the dataset GSE174020 from the NCBI gene Expression Synthesis Database (GEO) and then combined with the GSE5099 M1 macrophage-related gene identified in the Molecular Signatures Database. We then identified genes in DEGs associated with M1 macrophages defined as DEM1Gs and performed gene ontology (GO) and Kyoto Encyclopedia of Genomes (KEGG) enrichment analysis. Cibersort was used to analyze the immune cell infiltration during AR. At the same time, we used the protein-protein interaction (PPI) network and Cytoscape software to determine the key genes. Dataset, GSE14328 derived from pediatric patients, GSE138043 and GSE9493 derived from adult patients, were used to verify Hub genes. Additional verification was the rat KT model, which was used to perform HE staining, immunohistochemical staining, and Western Blot. Hub genes were searched in the HPA database to confirm their expression. Finally, we construct the interaction network of transcription factor (TF)-Hub genes and miRNA-Hub genes. Results: Compared to the normal group, 366 genes were upregulated, and 423 genes were downregulated in the AR group. Then, 106 genes related to M1 macrophages were found among these genes. GO and KEGG enrichment analysis showed that these genes are mainly involved in cytokine binding, antigen binding, NK cell-mediated cytotoxicity, activation of immune receptors and immune response, and activation of the inflammatory NF-κB signaling pathway. Two Hub genes, namely CCR7 and CD48, were identified by PPI and Cytoscape analysis. They have been verified in external validation sets, originated from both pediatric patients and adult patients, and animal experiments. In the HPA database, CCR7 and CD48 are mainly expressed in T cells, B cells, macrophages, and tissues where these immune cells are distributed. In addition to immunoinfiltration, CD4+T, CD8+T, NK cells, NKT cells, and monocytes increased significantly in the AR group, which was highly consistent with the results of Hub gene screening. Finally, we predicted that 19 TFs and 32 miRNAs might interact with the Hub gene. Conclusions: Through a comprehensive bioinformatic analysis, our findings may provide predictive and therapeutic targets for AR after KT.

Single-cell analysis of Crohn's disease: Unveiling heterogeneity and evaluating ustekinumab outcomes.

BACKGROUND: The present study aimed to dissect the cellular complexity of Crohn's disease (CD) using single-cell RNA sequencing, focusing on identifying key cell populations and their transcriptional profiles in inflamed tissue. METHODS: We applied scRNA-sequencing to compare the cellular composition of CD patients with healthy controls, utilizing Seurat for clustering and annotation. Differential gene expression analysis and protein-protein interaction networks were constructed to identify crucial genes and pathways. RESULTS: Our study identified eight distinct cell types in CD, highlighting crucial fibroblast and T cell interactions. The analysis revealed key cellular communications and identified significant genes and pathways involved in the disease's pathology. The role of fibroblasts was underscored by elevated expression in diseased samples, offering insights into disease mechanisms and potential therapeutic targets, including responses to ustekinumab treatment, thus enriching our understanding of CD at a molecular level. CONCLUSIONS: Our findings highlight the complex cellular and molecular interplay in CD, suggesting new biomarkers and therapeutic targets, offering insights into disease mechanisms and treatment implications.

The bioinfomatics analysis of the M1 macrophage-related gene CXCL9 signature in cervical cancer.

BACKGROUND: The expression and function of coexpression genes of M1 macrophage in cervical cancer have not been identified. And the CXCL9-expressing tumour-associated macrophage has been poorly reported in cervical cancer. METHODS: To clarify the regulatory gene network of M1 macrophage in cervical cancer, we downloaded gene expression profiles of cervical cancer patients in TCGA database to identify M1 macrophage coexpression genes. Then we constructed the protein-protein interaction networks by STRING database and performed functional enrichment analysis to investigate the biological effects of the coexpression genes. Next, we used multiple bioinformatics databases and experiments to overall investigate coexpression gene CXCL9, including western blot assay and immunohistochemistry assay, GeneMANIA, Kaplan-Meier Plotter, Xenashiny, TISCH2, ACLBI, HPA, TISIDB, GSCA and cBioPortal databases. RESULTS: There were 77 positive coexpression genes and 5 negative coexpression genes in M1 macrophage. The coexpression genes in M1 macrophage participated in the production and function of chemokines and chemokine receptors. Especially, CXCL9 was positively correlated with M1 macrophage infiltration levels in cervical cancer. CXCL9 expression would significantly decrease and high CXCL9 levels were linked to good prognosis in the cervical cancer tumour patients, it manifestly expressed in blood immune cells, and was positively related to immune checkpoints. CXCL9 amplification was the most common type of mutation. The CXCL9 gene interaction network could regulate immune-related signalling pathways, and CXCL9 amplification was the most common mutation type in cervical cancer. Meanwhile, CXCL9 may had clinical significance for the drug response in cervical cancer, possibly mediating resistance to chemotherapy and targeted drug therapy. CONCLUSION: Our findings may provide new insight into the M1 macrophage coexpression gene network and molecular mechanisms in cervical cancer, and indicated that M1 macrophage association gene CXCL9 may serve as a good prognostic gene and a potential therapeutic target for cervical cancer therapies.

Cervical cancer is a common gynaecological malignancy, investigating the precise gene expression regulation of M1 macrophage is crucial for understanding the changes in the immune microenvironment of cervical cancer. In our study, a total of 82 coexpression genes with M1 macrophages were identified, and these genes were involved in the production and biological processes of chemokines and chemokine receptors. Especially, the chemokine CXCL9 was positively correlated with M1 macrophage infiltration levels in cervical cancer. CXCL9 as a protective factor, it manifestly expressed in blood immune cells, and was positively related to immune checkpoints. CXCL9 amplification was the most common type of mutation. And CXCL9 expression could have an effect on the sensitivity of some chemicals or targeted drugs against cervical cancer. These findings may provide new insight into the M1 macrophage coexpression gene network and molecular mechanisms, and shed light on the role of CXCL9 in cervical cancer.

SGCP: a spectral self-learning method for clustering genes in co-expression networks.

BACKGROUND: A widely used approach for extracting information from gene expression data employs the construction of a gene co-expression network and the subsequent computational detection of gene clusters, called modules. WGCNA and related methods are the de facto standard for module detection. The purpose of this work is to investigate the applicability of more sophisticated algorithms toward the design of an alternative method with enhanced potential for extracting biologically meaningful modules. RESULTS: We present self-learning gene clustering pipeline (SGCP), a spectral method for detecting modules in gene co-expression networks. SGCP incorporates multiple features that differentiate it from previous work, including a novel step that leverages gene ontology (GO) information in a self-leaning step. Compared with widely used existing frameworks on 12 real gene expression datasets, we show that SGCP yields modules with higher GO enrichment. Moreover, SGCP assigns highest statistical importance to GO terms that are mostly different from those reported by the baselines. CONCLUSION: Existing frameworks for discovering clusters of genes in gene co-expression networks are based on relatively simple algorithmic components. SGCP relies on newer algorithmic techniques that enable the computation of highly enriched modules with distinctive characteristics, thus contributing a novel alternative tool for gene co-expression analysis.

Integrated transcriptome and microRNA analysis reveals molecular responses to high-temperature stress in the liver of American shad (Alosa sapidissima).

BACKGROUND: Fish reproduction, development and growth are directly affected by temperature, investigating the regulatory mechanisms behind high temperature stress is helpful to construct a finer molecular network. In this study, we systematically analyzed the transcriptome and miRNA information of American shad (Alosa sapidissima) liver tissues at different cultivation temperatures of 24 â„ƒ (Low), 27 â„ƒ (Mid) and 30 â„ƒ (High) based on a high-throughput sequencing platform. RESULTS: The results showed that there were 1594 differentially expressed genes (DEGs) and 660 differentially expressed miRNAs (DEMs) in the LowLi vs. MidLi comparison group, 473 DEGs and 84 DEMs in the MidLi vs. HighLi group, 914 DEGs and 442 DEMs in the LowLi vs. HighLi group. These included some important genes and miRNAs such as calr, hsp90b1, hsp70, ssa-miR-125a-3p, ssa-miR-92b-5p, dre-miR-15a-3p and novel-m1018-5p. The DEGs were mainly enriched in the protein folding, processing and export pathways of the endoplasmic reticulum; the target genes of the DEMs were mainly enriched in the focal adhesion pathway. Furthermore, the association analysis revealed that the key genes were mainly enriched in the metabolic pathway. Interestingly, we found a significant increase in the number of genes and miRNAs involved in the regulation of heat stress during the temperature change from 24 °C to 27 °C. In addition, we examined the tissue expression characteristics of some key genes and miRNAs by qPCR, and found that calr, hsp90b1 and dre-miR-125b-2-3p were significantly highly expressed in the liver at 27 â„ƒ, while novel-m0481-5p, ssa-miR-125a-3p, ssa-miR-92b-5p, dre-miR-15a-3p and novel-m1018-5p had the highest expression in the heart at 30℃. Finally, the quantitative expression trends of 10 randomly selected DEGs and 10 DEMs were consistent with the sequencing data, indicating the reliability of the results. CONCLUSIONS: In summary, this study provides some fundamental data for subsequent in-depth research into the molecular regulatory mechanisms of A. sapidissima response to heat stress, and for the selective breeding of high temperature tolerant varieties.

Regulatory gene network for coffee-like color morph of TYRP1 mutant of oujiang color common carp.

BACKGROUND: Neither a TYRP1-mediated highly conserved genetic network underlying skin color towards optimum defense nor the pathological tendency of its mutation is well understood. The Oujiang Color Common Carp (Cyprinus carpio var. color) as a model organism, offering valuable insights into genetics, coloration, aquaculture practices, and environmental health. Here, we performed a comparative skin transcriptome analysis on TYRP1 mutant and wild fishes by applying a conservative categorical approach considering different color phenotypes. RESULTS: Our results reveal that an unusual color phenotype may be sensitized with TYRP1 mutation as a result of upregulating several genes related to an anti-inflammatory autoimmune system in response to the COMT-mediated catecholamine neurotransmitters in the skin. Particularly, catecholamines-derived red/brown, red with blue colored membrane attack complex, and brown/grey colored reduced eumelanin are expected to be aggregated in the regenerated cells. CONCLUSIONS: It is, thus, concluded that the regenerated cells with catecholamines, membrane attack complex, and eumelanin altogether may contribute to the formation of the unusual (coffee-like) color phenotype in TYRP1 mutant.

Single-nucleus multi-omics of Parkinson's disease reveals a glutamatergic neuronal subtype susceptible to gene dysregulation via alteration of transcriptional networks.

The genetic architecture of Parkinson's disease (PD) is complex and multiple brain cell subtypes are involved in the neuropathological progression of the disease. Here we aimed to advance our understanding of PD genetic complexity at a cell subtype precision level. Using parallel single-nucleus (sn)RNA-seq and snATAC-seq analyses we simultaneously profiled the transcriptomic and chromatin accessibility landscapes in temporal cortex tissues from 12 PD compared to 12 control subjects at a granular single cell resolution. An integrative bioinformatic pipeline was developed and applied for the analyses of these snMulti-omics datasets. The results identified a subpopulation of cortical glutamatergic excitatory neurons with remarkably altered gene expression in PD, including differentially-expressed genes within PD risk loci identified in genome-wide association studies (GWAS). This was the only neuronal subtype showing significant and robust overexpression of SNCA. Further characterization of this neuronal-subpopulation showed upregulation of specific pathways related to axon guidance, neurite outgrowth and post-synaptic structure, and downregulated pathways involved in presynaptic organization and calcium response. Additionally, we characterized the roles of three molecular mechanisms in governing PD-associated cell subtype-specific dysregulation of gene expression: (1) changes in cis-regulatory element accessibility to transcriptional machinery; (2) changes in the abundance of master transcriptional regulators, including YY1, SP3, and KLF16; (3) candidate regulatory variants in high linkage disequilibrium with PD-GWAS genomic variants impacting transcription factor binding affinities. To our knowledge, this study is the first and the most comprehensive interrogation of the multi-omics landscape of PD at a cell-subtype resolution. Our findings provide new insights into a precise glutamatergic neuronal cell subtype, causal genes, and non-coding regulatory variants underlying the neuropathological progression of PD, paving the way for the development of cell- and gene-targeted therapeutics to halt disease progression as well as genetic biomarkers for early preclinical diagnosis.

Identification and validation of miR-29b-3p and LIN7A as important diagnostic markers for bone non-union by WGCNA.

Bone non-union is a common fracture complication that can severely impact patient outcomes, yet its mechanism is not fully understood. This study used differential analysis and weighted co-expression network analysis (WGCNA) to identify susceptibility modules and hub genes associated with fracture healing. Two datasets, GSE125289 and GSE213891, were downloaded from the GEO website, and differentially expressed miRNAs and genes were analysed and used to construct the WGCNA network. Gene ontology (GO) analysis of the differentially expressed genes showed enrichment in cytokine and inflammatory factor secretion, phagocytosis, and trans-Golgi network regulation pathways. Using bioinformatic site prediction and crossover gene search, miR-29b-3p was identified as a regulator of LIN7A expression that may negatively affect fracture healing. Potential miRNA-mRNA interactions in the bone non-union mechanism were explored, and miRNA-29-3p and LIN7A were identified as biomarkers of skeletal non-union. The expression of miRNA-29b-3p and LIN7A was verified in blood samples from patients with fracture non-union using qRT-PCR and ELISA. Overall, this study identified characteristic modules and key genes associated with fracture non-union and provided insight into its molecular mechanisms. Downregulated miRNA-29b-3p was found to downregulate LIN7A protein expression, which may affect the healing process after fracture in patients with bone non-union. These findings may serve as a prognostic biomarker and potential therapeutic target for bone non-union.

A single-cell transcriptomic study of heterogeneity in human embryonic tanycytes.

Disruptions in energy homeostasis can lead to diseases like obesity and diabetes, affecting millions of people each year. Tanycytes, the adult stem cells in the hypothalamus, play crucial roles in assisting hypothalamic neurons in maintaining energy balance. Although tanycytes have been extensively studied in rodents, our understanding of human tanycytes remains limited. In this study, we utilized single-cell transcriptomics data to explore the heterogeneity of human embryonic tanycytes, investigate their gene regulatory networks, analyze their intercellular communication, and examine their developmental trajectory. Our analysis revealed the presence of two clusters of ß tanycytes and three clusters of α tanycytes in our dataset. Surprisingly, human embryonic tanycytes displayed significant similarities to mouse tanycytes in terms of marker gene expression and transcription factor activities. Trajectory analysis indicated that α tanycytes were the first to be generated, giving rise to ß tanycytes in a dorsal-ventral direction along the third ventricle. Furthermore, our CellChat analyses demonstrated that tanycytes generated earlier along the developmental lineages exhibited increased intercellular communication compared to those generated later. In summary, we have thoroughly characterized the heterogeneity of human embryonic tanycytes from various angles. We are confident that our findings will serve as a foundation for future research on human tanycytes.

Evaluation of secretome biomarkers in glioblastoma cancer stem cells: A bioinformatics analysis.

BACKGROUND: Glioblastoma (GBM) is a malignant brain tumor that frequently occurs alongside other central nervous system (CNS) conditions. The secretome of GBM cells contains a diverse array of proteins released into the extracellular space, influencing the tumor microenvironment. These proteins can serve as potential biomarkers for GBM due to their involvement in key biological processes, exploring the secretome biomarkers in GBM research represents a cutting-edge strategy with significant potential for advancing diagnostic precision, treatment monitoring, and ultimately improving outcomes for patients with this challenging brain cancer. AIM: This study was aimed to investigate the roles of secretome biomarkers and their pathwayes in GBM through bioinformatics analysis. METHODS AND RESULTS: Using data from the Gene Expression Omnibus and the Cancer Genome Atlas datasets-where both healthy and cancerous samples were analyzed-we used a quantitative analytical framework to identify differentially expressed genes (DEGs) and cell signaling pathways that might be related to GBM. Then, we performed gene ontology studies and hub protein identifications to estimate the roles of these DEGs after finding disease-gene connection networks and signaling pathways. Using the GEPIA Proportional Hazard Model and the Kaplan-Meier estimator, we widened our analysis to identify the important genes that may play a role in both progression and the survival of patients with GBM. In total, 890 DEGs, including 475 and 415 upregulated and downregulated were identified, respectively. Our results revealed that SQLE, DHCR7, delta-1 phospholipase C (PLCD1), and MINPP1 genes are highly expressed, and the Enolase 2 (ENO2) and hexokinase-1 (HK1) genes are low expressions. CONCLUSION: Hence, our findings suggest novel mechanisms that affect the occurrence of GBM development, growth, and/or establishment and may also serve as secretory biomarkers for GBM prognosis and possible targets for therapy. So, continued research in this field may uncover new avenues for therapeutic interventions and contribute to the ongoing efforts to combat GBM effectively.

Unraveling the H19/GAS1 axis in recurrent implantation failure: A potential biomarker for diagnosis and insight into immune microenvironment alteration.

Recurrent implantation failure (RIF) presents a significant clinical challenge due to the lack of established diagnostic and therapeutic guidelines. Emerging evidence underscores the crucial role of competitive endogenous RNA (ceRNA) regulatory networks in non-cancerous female reproductive disorders, yet the intricacies and operational characteristics of these networks in RIF are not fully understood. This study aims to demystify the ceRNA regulatory network and identify potential biomarkers for its diagnosis. We analyzed expression profiles of three RNA types (long noncoding RNAs [lncRNAs], microRNAs [miRNAs], and mRNAs) sourced from the GEO database, leading to the identification of the H19-hsa-miR-301a-3p-GAS1 ceRNA network. This network demonstrates significant diagnostic relevance for RIF. Notably, the H19/GAS1 axis within this ceRNA network, identified through correlation analysis, emerged as a promising diagnostic marker, as evidenced by operating receiver operator characteristic (ROC) curve analysis. Further investigation into the binding potential of miR-301a-3p with H19 and GAS1 revealed a close association of these genes with endometrial disorders and embryo loss, as per the Comparative Toxicogenomics Database. Additionally, our immune infiltration analysis revealed a lower proportion of T cells gamma delta (γδ) in RIF, along with distinct differences in the expression of immune cell type-specific markers between fertile patients and those with RIF. We also observed a correlation between aberrant expression of H19/GAS1 and these immune markers, suggesting that the H19/GAS1 axis might play a role in modifying the immune microenvironment, contributing to the pathogenesis of RIF. In conclusion, the ceRNA-based H19/GAS1 axis holds promise as a novel diagnostic biomarker for RIF, potentially enhancing our understanding of its underlying mechanisms and improving the success rates of implantation.

Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification.

Uncovering acquired drug resistance mechanisms has garnered considerable attention as drug resistance leads to treatment failure and death in patients with cancer. Although several bioinformatics studies developed various computational methodologies to uncover the drug resistance mechanisms in cancer chemotherapy, most studies were based on individual or differential gene expression analysis. However the single gene-based analysis is not enough, because perturbations in complex molecular networks are involved in anti-cancer drug resistance mechanisms. The main goal of this study is to reveal crucial molecular interplay that plays key roles in mechanism underlying acquired gastric cancer drug resistance. To uncover the mechanism and molecular characteristics of drug resistance, we propose a novel computational strategy that identified the differentially regulated gene networks. Our method measures dissimilarity of networks based on the eigenvalues of the Laplacian matrix. Especially, our strategy determined the networks' eigenstructure based on sparse eigen loadings, thus, the only crucial features to describe the graph structure are involved in the eigenanalysis without noise disturbance. We incorporated the network biology knowledge into eigenanalysis based on the network-constrained regularization. Therefore, we can achieve a biologically reliable interpretation of the differentially regulated gene network identification. Monte Carlo simulations show the outstanding performances of the proposed methodology for differentially regulated gene network identification. We applied our strategy to gastric cancer drug-resistant-specific molecular interplays and related markers. The identified drug resistance markers are verified through the literature. Our results suggest that the suppression and/or induction of COL4A1, PXDN and TGFBI and their molecular interplays enriched in the Extracellular-related pathways may provide crucial clues to enhance the chemosensitivity of gastric cancer. The developed strategy will be a useful tool to identify phenotype-specific molecular characteristics that can provide essential clues to uncover the complex cancer mechanism.

Identification of core genes shared by ischemic stroke and myocardial infarction using an integrated approach.

BACKGROUND: Both ischemic stroke (IS) and myocardial infarction (MI) are caused by vascular occlusion that results in ischemia. While there may be similarities in their mechanisms, the potential relationship between these 2 diseases has not been comprehensively analyzed. Therefore, this study explored the commonalities in the pathogenesis of IS and MI. METHODS: Datasets for IS (GSE58294, GSE16561) and MI (GSE60993, GSE61144) were downloaded from the Gene Expression Omnibus database. Transcriptome data from each of the 4 datasets were analyzed using bioinformatics, and the differentially expressed genes (DEGs) shared between IS and MI were identified and subsequently visualized using a Venn diagram. A protein-protein interaction (PPI) network was constructed using the Interacting Gene Retrieval Tool database, and identification of key core genes was performed using CytoHubba. Gene Ontology (GO) term annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the shared DEGs were conducted using prediction and network analysis methods, and the functions of the hub genes were determined using Metascape. RESULTS: The analysis revealed 116 and 1321 DEGs in the IS and MI datasets, respectively. Of the 75 DEGs shared between IS and MI, 56 were upregulated and 19 were downregulated. Furthermore, 15 core genes - S100a12, Hp, Clec4d, Cd163, Mmp9, Ormdl3, Il2rb, Orm1, Irak3, Tlr5, Lrg1, Clec4e, Clec5a, Mcemp1, and Ly96 - were identified. GO enrichment analysis of the DEGs showed that they were mainly involved in the biological functions of neutrophil degranulation, neutrophil activation during immune response, and cytokine secretion. KEGG analysis showed enrichment in pathways pertaining to Salmonella infection, Legionellosis, and inflammatory bowel disease. Finally, the core gene-transcription factor, gene-microRNA, and small-molecule relationships were predicted. CONCLUSION: These core genes may provide a novel theoretical basis for the diagnosis and treatment of IS and MI.

Detecting tipping points of complex diseases by network information entropy.

The progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development.

Decoding dysregulated genes, molecular pathways and microRNAs involved in cervical cancer.

BACKGROUND: The present study aimed to identify dysregulated genes, molecular pathways, and regulatory mechanisms in human papillomavirus (HPV)-associated cervical cancers. We have investigated the disease-associated genes along with the Gene Ontology, survival prognosis, transcription factors and the microRNA (miRNA) that are involved in cervical carcinogenesis, enabling a deeper comprehension of cervical cancer linked to HPV. METHODS: We used 10 publicly accessible Gene Expression Omnibus (GEO) datasets to examine the patterns of gene expression in cervical cancer. Differentially expressed genes (DEGs), which showed a clear distinction between cervical cancer and healthy tissue samples, were analyzed using the GEO2R tool. Additional bioinformatic techniques were used to carry out pathway analysis and functional enrichment, as well as to analyze the connection between altered gene expression and HPV infection. RESULTS: In total, 48 DEGs were identified to be differentially expressed in cervical cancer tissues in comparison to healthy tissues. Among DEGs, CCND1, CCNA2 and SPP1 were the key dysregulated genes involved in HPV-associated cervical cancer. The five common miRNAs that were identified against these genes are miR-7-5p, miR-16-5p, miR-124-3p, miR-10b-5p and miR-27a-3p. The hub-DEGs targeted by miRNA hsa-miR-27a-3p are controlled by the common transcription factor SP1. CONCLUSIONS: The present study has identified DEGs involved in HPV-associated cervical cancer progression and the various molecular pathways and transcription factors regulating them. These findings have led to a better understanding of cervical cancer resulting in the development and identification of possible therapeutic and intervention targets, respectively.

Epigenetic alterations affecting hematopoietic regulatory networks as drivers of mixed myeloid/lymphoid leukemia.

Leukemias with ambiguous lineage comprise several loosely defined entities, often without a clear mechanistic basis. Here, we extensively profile the epigenome and transcriptome of a subgroup of such leukemias with CpG Island Methylator Phenotype. These leukemias exhibit comparable hybrid myeloid/lymphoid epigenetic landscapes, yet heterogeneous genetic alterations, suggesting they are defined by their shared epigenetic profile rather than common genetic lesions. Gene expression enrichment reveals similarity with early T-cell precursor acute lymphoblastic leukemia and a lymphoid progenitor cell of origin. In line with this, integration of differential DNA methylation and gene expression shows widespread silencing of myeloid transcription factors. Moreover, binding sites for hematopoietic transcription factors, including CEBPA, SPI1 and LEF1, are uniquely inaccessible in these leukemias. Hypermethylation also results in loss of CTCF binding, accompanied by changes in chromatin interactions involving key transcription factors. In conclusion, epigenetic dysregulation, and not genetic lesions, explains the mixed phenotype of this group of leukemias with ambiguous lineage. The data collected here constitute a useful and comprehensive epigenomic reference for subsequent studies of acute myeloid leukemias, T-cell acute lymphoblastic leukemias and mixed-phenotype leukemias.

Spatial proximity and gene function: a new dimension in prokaryotic gene association network analysis with 3D-GeneNet.

Understanding the biological functions and processes of genes, particularly those not yet characterized, is crucial for advancing molecular biology and identifying therapeutic targets. The hypothesis guiding this study is that the 3D proximity of genes correlates with their functional interactions and relevance in prokaryotes. We introduced 3D-GeneNet, an innovative software tool that utilizes high-throughput sequencing data from chromosome conformation capture techniques and integrates topological metrics to construct gene association networks. Through a series of comparative analyses focused on spatial versus linear distances, we explored various dimensions such as topological structure, functional enrichment levels, distribution patterns of linear distances among gene pairs, and the area under the receiver operating characteristic curve by utilizing model organism Escherichia coli K-12. Furthermore, 3D-GeneNet was shown to maintain good accuracy compared to multiple algorithms (neighbourhood, co-occurrence, coexpression, and fusion) across multiple bacteria, including E. coli, Brucella abortus, and Vibrio cholerae. In addition, the accuracy of 3D-GeneNet's prediction of long-distance gene interactions was identified by bacterial two-hybrid assays on E. coli K-12 MG1655, where 3D-GeneNet not only increased the accuracy of linear genomic distance tripled but also achieved 60% accuracy by running alone. Finally, it can be concluded that the applicability of 3D-GeneNet will extend to various bacterial forms, including Gram-negative, Gram-positive, single-, and multi-chromosomal bacteria through Hi-C sequencing and analysis. Such findings highlight the broad applicability and significant promise of this method in the realm of gene association network. 3D-GeneNet is freely accessible at https://github.com/gaoyuanccc/3D-GeneNet.