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.

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.

[Identification of oxidative stress-related biomarkers in chronic rhinosinusitis with nasal polyps using WGCNA combined with machine learning algorithms].

Objective: To identify diagnostic markers related to oxidative stress in chronic rhinosinusitis with nasal polyps (CRSwNP) by analyzing transcriptome sequencing data, and to investigate their roles in CRSwNP. Methods: Utilizing four CRSwNP sequencing datasets, differentially expressed genes (DEGs) analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning methods for Hub gene selection were performed in this study. Subsequent validation was carried out using external datasets, as well as real-time quantitative polymerase chain reaction (Real-time qPCR), and immunofluorescence staining of clinical samples. Moreover, the diagnostic efficacy of the genes was assessed by receiver operating characteristic (ROC) curve, followed by functional and pathway enrichment analysis, immune-related analysis, and cell population localization. Additionally, a competing endogenous RNA (CeRNA) network was constructed to predict potential drug targets. Statistical analysis and plotting were conducted using SPSS 26.0 and Graphpad Prism9 software. Results: Through data analysis and clinical validation, CP, SERPINF1 and GSTO2 were identified among 4 138 DEGs as oxidative stress markers related to CRSwNP. Specifically, the expression of CP and SERPINF1 increased in CRSwNP, whereas that of GSTO2 decreased, with statistically significant differences (P<0.05). Additionally, an area under the curve (AUC)>0.7 indicated their effectiveness as diagnostic indicators. Importantly, functional analysis indicated that these genes were mainly related to lipid metabolism, cell adhesion migration, and immunity. Single-cell data analysis revealed that SERPINF1 was mainly distributed in epithelial cells, stromal cells, and fibroblasts, while CP was primarily located in epithelial cells, and GSTO2 was minimally present in the epithelial cells and fibroblasts of nasal polyps. Consequently, a CeRNA regulatory network was constructed for the genes CP and GSTO2. This construction allowed for the prediction of potential drugs that could target CP. Conclusion: This study successfully identifies CP, SERPINF1 and GSTO2 as diagnostic and therapeutic markers related to oxidative stress in CRSwNP.

Identifying Hub Genes Related to Vascular Endothelial Cell Permeability Through Bioinformatics and Verification.

BACKGROUND: In this study, we aimed to identify the hub genes responsible for increased vascular endothelial cell permeability. METHODS: We applied the weighted Gene Expression Omnibus (GEO) database to mine dataset GSE178331 and ob-tained the most relevant high-throughput sequenced genes for an increased permeability of vascular endothelial cells due to inflammation. We constructed two weighted gene co-expression network analysis (WGCNA) networks, and the differential expression of high-throughput sequenced genes related to endothelial cell permeability were screened from the GEO database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differential genes. Their degree values were obtained from the topological properties of protein-protein interaction (PPI) networks of differential genes, and the hub genes associated with an increased endothelial cell permeability were analyzed. Reverse transcription-polymerase chain reaction (RT-PCR) and western blotting techniques were used to detect the presence of these hub genes in TNF-α induced mRNA and the protein expression in endothelial cells. RESULTS: In total, 1,475 differential genes were mainly enriched in the cell adhesion and TNF-α signaling pathway. With TNF-α inducing an increase in the endothelial cell permeability and significantly increasing mRNA and protein expression levels, we identified three hub genes, namely PTGS2, ICAM1, and SNAI1. There was a significant difference in the high-dose TNF-α group and in the low-dose TNF-α group compared to the control group, in the endothelial cell permeability experiment (p = 0.008 vs. p = 0.02). Measurement of mRNA and protein levels of PTGS2, ICAM1, and SNAI1 by western blotting analysis showed that there was a significant impact on TNF-α and that there was a significant dose-dependent relationship (p < 0.05 vs. p < 0.01). CONCLUSIONS: The three hub genes identified through bioinformatics analyses in the present study may serve as biomarkers of increased vascular endothelial cell permeability. The findings offer valuable insights into the progress and mechanism of vascular endothelial cell permeability.

Exploring the potential of predicted miRNAs on the genes involved in the expansion of hematopoietic stem cells.

A major challenge in therapeutic approaches applying hematopoietic stem cells (HSCs) is the cell quantity. The primary objective of this study was to predict the miRNAs and anti-miRNAs using bioinformatics tools and investigate their effects on the expression levels of key genes predicted in the improvement of proliferation, and the inhibition of differentiation in HSCs isolated from Human umbilical cord blood (HUCB). A network including genes related to the differentiation and proliferation stages of HSCs was constructed by enriching data of text (PubMed) and StemChecker server with KEGG signaling pathways, and was improved using GEO datasets. Bioinformatics tools predicted a profile from miRNAs containing miR-20a-5p, miR-423-5p, and chimeric anti-miRNA constructed from 5'-miR-340/3'-miR-524 for the high-score genes (RB1, SMAD4, STAT1, CALML4, GNG13, and CDKN1A/CDKN1B genes) in the network. The miRNAs and anti-miRNA were transferred into HSCs using polyethylenimine (PEI). The gene expression levels were estimated using the RT-qPCR technique in the PEI + (miRNA/anti-miRNA)-contained cell groups (n = 6). Furthermore, CD markers (90, 16, and 45) were evaluated using flow cytometry. Strong relationships were found between the high-score genes, miRNAs, and chimeric anti-miRNA. The RB1, SMAD4, and STAT1 gene expression levels were decreased by miR-20a-5p (P < 0.05). Additionally, the anti-miRNA increased the gene expression level of GNG13 (P < 0.05), whereas the miR-423-5p decreased the CDKN1A gene expression level (P < 0.01). The cellular count also increased significantly (P < 0.05) but the CD45 differentiation marker did not change in the cell groups. The study revealed the predicted miRNA/anti-miRNA profile expands HSCs isolated from HUCB. While miR-20a-5p suppressed the RB1, SMAD4, and STAT1 genes involved in cellular differentiation, the anti-miRNA promoted the GNG13 gene related to the proliferation process. Notably, the mixed miRNA/anti-miRNA group exhibited the highest cellular expansion. This approach could hold promise for enhancing the cell quantity in HSC therapy.

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 and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning.

There is a growing body of evidence suggesting that Hashimoto's thyroiditis (HT) may contribute to an increased risk of papillary thyroid carcinoma (PTC). However, the exact relationship between HT and PTC is still not fully understood. The objective of this study was to identify potential common biomarkers that may be associated with both PTC and HT. Three microarray datasets from the GEO database and RNA-seq dataset from TCGA database were collected to identify shared differentially expressed genes (DEGs) between HT and PTC. A total of 101 genes was identified as common DEGs, primarily enriched inflammation- and immune-related pathways through GO and KEGG analysis. We performed protein-protein interaction analysis and identified six significant modules comprising a total of 29 genes. Subsequently, tree hub genes (CD53, FCER1G, TYROBP) were selected using random forest (RF) algorithms for the development of three diagnostic models. The artificial neural network (ANN) model demonstrates superior performance. Notably, CD53 exerted the greatest influence on the ANN model output. We analyzed the protein expressions of the three genes using the Human Protein Atlas database. Moreover, we observed various dysregulated immune cells that were significantly associated with the hub genes through immune infiltration analysis. Immunofluorescence staining confirmed the differential expression of CD53, FCER1G, and TYROBP, as well as the results of immune infiltration analysis. Lastly, we hypothesise that benzylpenicilloyl polylysine and aspirinmay be effective in the treatment of HT and PTC and may prevent HT carcinogenesis. This study indicates that CD53, FCER1G, and TYROBP play a role in the development of HT and PTC, and may contribute to the progression of HT to PTC. These hub genes could potentially serve as diagnostic markers and therapeutic targets for PTC and HT.

Investigation on the molecular mechanism of SPA interference with osteogenic differentiation of bone marrow mesenchymal stem cells.

Binding of Staphylococcus aureus protein A (SPA) to osteoblasts induces apoptosis and inhibits bone formation. Bone marrow-derived mesenchymal stem cells (BMSCs) have the ability to differentiate into bone, fat and cartilage. Therefore, it was important to analyze the molecular mechanism of SPA on osteogenic differentiation. We introduced transcript sequence data to screen out differentially expressed genes (DEGs) related to SPA-interfered BMSC. Protein-protein interaction (PPI) network of DEGs was established to screen biomarkers associated with SPA-interfered BMSC. Receiver operating characteristic (ROC) curve was plotted to evaluate the ability of biomarkers to discriminate between two groups of samples. Finally, we performed GSEA and regulatory analysis based on biomarkers. We identified 321 DEGs. Subsequently, 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap and Kif14) were identified by hubba algorithm in PPI. ROC analysis showed that six biomarkers could clearly discriminate between normal differentiated and SPA-interfered BMSC. Moreover, we found that these biomarkers were mainly enriched in the pyrimidine metabolism pathway. We also constructed '71 circRNAs-14 miRNAs-5 mRNAs' and '10 lncRNAs-5 miRNAs-2 mRNAs' networks. Kntc1 and Asf1b genes were associated with rno-miR-3571. Nek2 and Asf1b genes were associated with rno-miR-497-5p. Finally, we found significantly lower expression of six biomarkers in the SPA-interfered group compared to the normal group by RT-qPCR. Overall, we obtained 6 biomarkers (Cenpf, Kntc1, Nek2, Asf1b, Troap, and Kif14) related to SPA-interfered BMSC, which provided a theoretical basis to explore the key factors of SPA affecting osteogenic differentiation.

Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma.

The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches.

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.

[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.

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.

Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA).

This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.

Exploring the potential biological significance of KDELR family genes in lung adenocarcinoma.

The Lys-Asp-Glu-Leu receptor (KDELR) family genes play critical roles in a variety of biological processes in different tumors. Our study aimed to provide a comprehensive analysis of the potential roles of KDELRs in lung adenocarcinoma (LUAD). Utilizing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, as well as clinical samples, we conducted a series of analyses and validations using R software tools and various online resources. The results showed that KDELR family genes and proteins were highly expressed and associated with a poor prognosis of LUAD. Promoter hypomethylation and the competing endogenous RNA (ceRNA) network of PCAT6/hsa-miR-326/KDELR1 might be potential causes of aberrant KDELR1 overexpression in LUAD. Three key Transcription factors (TFs) (SPI1, EP300, and MAZ) and a TFs-miRNAs-KDELRs network (involving 11 TFs) might be involved in modulating KDELRs expression abnormalities. Gene Set Enrichment Analysis (GSEA) indicated enrichment of genes highly expressing KDELR1, KDELR2, and KDELR3 in MTORC1_SIGNALING, P53_PATHWAY, and ANGIOGENESIS. Negative correlations between KDELRs expression and CD8 + T cell infiltration, as well as CTLA-4 expression. Our multiple analyses suggested that the KDELRs are important signaling molecules in LUAD. These results provided novel insights for developing prognostic markers and novel therapies of LUAD.

Screening of potential hub genes involved in Kidney Wilms tumor via bioinformatics analysis and experimental validation.

BACKGROUND: Wilms tumor (WT) is the most common pediatric embryonal tumor. Improving patient outcomes requires advances in understanding and targeting the multiple genes and cellular control pathways, but its pathogenesis is currently not well-researched. We aimed to identify the potential molecular biological mechanism of WT and develop new prognostic markers and molecular targets by comparing gene expression profiles of Wilms tumors and fetal normal kidneys. METHODS: Differential gene expression analysis was performed on Wilms tumor transcriptomic data from the GEO and TARGET databases. For biological functional analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were utilized. Out of 24 hub genes identified, nine were found to be prognostic-related through univariate Cox regression analysis. These nine genes underwent LASSO regression analysis to enhance the predictive capability of the model. The key hub genes were validated in the GSE73209 datasets, and cell function experiments were conducted to identify the genes' functions in WiT-49 cells. RESULTS: The enrichment analysis revealed that DEGs were significantly involved in the regulation of angiogenesis and regulation of cell differentiation. 24 DEGs were identified through PPI networks and the MCODE algorithm, and 9 of 24 genes were related to WT patients' prognosis. EMCN and CCNA1 were identified as key hub genes, and related to the progression of WT. Functionally, over-expression of EMCN and CCNA1 knockdown inhibited cell viability, proliferation, migration, and invasion of Wilms tumor cells. CONCLUSIONS: EMCN and CCNA1 were identified as key prognostic markers in Wilms tumor, suggesting their potential as therapeutic targets. Differential gene expression and enrichment analyses indicate significant roles in angiogenesis and cell differentiation.

Identification of shared disease marker genes and underlying mechanisms between rheumatoid arthritis and Crohn disease through bioinformatics analysis.

As chronic autoimmune inflammatory diseases, rheumatoid arthritis (RA) and Crohn disease (CD) are closely associated and display a significant positive correlation. However, the underlying mechanisms and disease markers responsible for their cooccurrence remain unknown and have not been systematically studied. Therefore, this study aimed to identify key molecules and pathways commonly involved in both RA and CD through bioinformatic analysis of public sequencing databases. Datasets for RA and CD were downloaded from the GEO database. Overlapping genes were identified using weighted gene co-expression network analysis and differential analysis crossover, and enrichment analysis was conducted for these genes. Protein-protein interaction networks were then constructed using these overlapping genes to identify hub genes. Expression validation and receiver operating characteristic curve validation were performed for these hub genes using different datasets. Additionally, the immune cell correlation, single-cell expression cluster, and the immune cell expression cluster of the core gene were analyzed. Furthermore, upstream shared microRNAs (miRNA) were predicted and a miRNA-gene network was constructed. Finally, drug candidates were analyzed and predicted. These core genes were found to be positively correlated with multiple immune cells that are infiltrated by the disease. Analysis of gene expression clusters revealed that these genes were mostly associated with inflammatory and immune responses. The miRNA-genes network analysis suggested that hsa-miR-31-5p may play an important role in the common mechanism of RA and CD. Finally, tamibarotene, retinoic acid, and benzo[a]pyrene were identified as potential treatment options for patients with both RA and CD. This bioinformatics study has identified ITGB2, LCP2, and PLEK as key diagnostic genes in patients with both RA and CD. The study has further confirmed that inflammation and immune response play a central role in the development of both RA and CD. Interestingly, the study has highlighted hsa-miR-31-5p as a potential key player in the common mechanism of both diseases, representing a new direction in research and a potential therapeutic target. These shared genes, potential mechanisms, and regulatory networks offer new opportunities for further research and may provide hope for future treatment of patients with both RA and CD.