Differential Gene Set Enrichment Analysis: a statistical approach to quantify the relative enrichment of two gene sets

Integrating bioinformatics and machine learning to identify glomerular injury genes and predict drug targets in diabetic nephropathy

Diabetes mellitus (DM) is a chronic metabolic disorder that poses significant challenges to public health. Among its various complications, diabetic nephropathy (DN) emerges as a critical microvascular complication associated with high mortality rates. Despite the development of diverse therapeutic strategies targeting metabolic improvement, hemodynamic regulation, and fibrosis mitigation, the precise mechanisms responsible for glomerular injury in DN are not yet fully elucidated. To explore these mechanisms, public DN datasets (GSE30528, GSE104948, and GSE96804) were obtained from the GEO database. We merged the GSE30528 and GSE104948 datasets to identify differentially expressed genes (DEGs) between DN and control groups using R software. Weighted gene co-expression network analysis (WGCNA) was subsequently employed to discern genes associated with DN in key modules. We utilized Venny software to pinpoint co-expressed genes shared between DEGs and key module genes. These co-expressed genes underwent gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses. Through LASSO, SVM, and RF methods, we isolated five significant genes: FN1, C1orf21, CD36, CD48, and SRPX2. These genes were further validated using a logistic model and 10-fold cross-validation. The external dataset GSE96804 served to validate the identified biomarkers, while receiver operating characteristic (ROC) curve analysis assessed their diagnostic efficacy for DN. Additionally, GSE104948 facilitated comparison of biomarker expression levels between DN and five other kidney diseases, highlighting their specificity for DN. These biomarkers also enabled the identification and validation of two molecular subtypes characterized by distinct immune profiles. The Nephroseq v5 database corroborated the correlation between biomarkers and clinical data. Furthermore, the GSigDB database was employed to predict protein-drug interactions, with molecular docking confirming the therapeutic potential of these drug targets. Finally, a diabetic mouse model (BKS-db) was constructed, and RT-qPCR experiments validated the reliability of the identified biomarkers. The study identified five biomarkers with robust diagnostic predictive power for DN. Subtype classification based on these biomarkers revealed distinct enrichment pathways and immune cell infiltration profiles, underscoring the close relationship between these genes and immune functions in DN. Drug prediction and molecular docking analyses demonstrated excellent binding affinities of candidate drugs to target proteins. Differential expression analysis between DN and five other kidney diseases indicated that all biomarkers, except C1orf21, were highly expressed in DN. Notably, as the mouse model lacks the C1orf21 gene, RT-qPCR confirmed the upregulated expression of FN1, CD36, CD48, and SRPX2. This study successfully identified five biomarkers with potential diagnostic and therapeutic value for DN. These biomarkers not only offer insights into the regulatory mechanisms underlying glomerular injury but also provide a theoretical foundation for the development of diagnostic biomarkers and therapeutic targets related to DN-associated glomerular injury.

Utilizing Nanopore direct RNA sequencing of blood from patients with sepsis for discovery of co- and post-transcriptional disease biomarkers

BACKGROUND

RNA sequencing of whole blood has been increasingly employed to find transcriptomic signatures of disease states. These studies traditionally utilize short-read sequencing of cDNA, missing important aspects of RNA expression such as differential isoform abundance and poly(A) tail length variation.

METHODS

We used Oxford Nanopore Technologies sequencing to sequence native mRNA extracted from whole blood from 12 patients with definite bacterial and viral sepsis and compared with results from matching Illumina short-read cDNA sequencing data. Additionally, we explored poly(A) tail length variation, novel transcript identification, and differential transcript usage.

RESULTS

The correlation of gene count data between Illumina cDNA- and Nanopore RNA-sequencing strongly depended on the choice of analysis pipeline; NanoCount for Nanopore and Kallisto for Illumina data yielded the highest mean Pearson's correlation of 0.927 at the gene level and 0.736 at the transcript isoform level. We identified 2 genes with differential polyadenylation, 9 genes with differential expression and 4 genes with differential transcript usage between bacterial and viral infection. Gene ontology gene set enrichment analysis of poly(A) tail length revealed enrichment of long tails in mRNA of genes involved in signaling and short tails in oxidoreductase molecular functions. Additionally, we detected 240 non-artifactual novel transcript isoforms.

CONCLUSIONS

Nanopore RNA- and Illumina cDNA-gene counts are strongly correlated, indicating that both platforms are suitable for discovery and validation of gene count biomarkers. Nanopore direct RNA-seq provides additional advantages by uncovering additional post- and co-transcriptional biomarkers, such as poly(A) tail length variation and transcript isoform usage.

BIRC3 RNA Editing Modulates Lipopolysaccharide-Induced Liver Inflammation: Potential Implications for Animal Health

Animals and humans are frequently infected by bacteria or exposed to bacterial derivatives in contaminated food, drinking water, or air, which significantly impacts their health. Among these bacterial sources, LPS (lipopolysaccharide) is the primary culprit. While it is widely known that LPS can cause liver inflammation and damage in animals, few studies have investigated this mechanism from the perspective of RNA editing. In this study, we administered LPS to mice via gavage to induce a liver injury model. We then used RNA editing omics approaches (RE-seq) to analyze RNA editing events potentially leading to liver inflammation following LPS administration, aiming to reveal the crucial role of RNA editing in LPS-induced processes. At the RNA editing level, we observed significant differences between the LPS group and the control (CON) group. Specifically, we identified 354 differentially edited genes, with 192 upregulated and 162 downregulated. These differentially edited genes were significantly enriched in pathways related to apoptosis, mTOR signaling, oxidative stress, and Nf-Kappa B signaling. By further integrating gene expression profiles and using a nine-quadrant analysis, we identified an important gene, Birc3, which showed significantly higher editing and expression levels in the LPS group. This gene is directly linked to liver inflammation and damage. The RNA editing of Birc3 represents a significant potential mechanism underlying LPS-induced liver damage, providing a novel approach for addressing animal and human health issues.

Identification of Shared Pathways and Molecules Between Type 2 Diabetes and Lung Adenocarcinoma and the Impact of High Glucose Environment on Lung Adenocarcinoma

Objective: This research focused on exploring the shared pathophysiological bases of lung adenocarcinoma (LUAD) and Type 2 diabetes mellitus (T2DM). Methods: The investigation into the molecular similarities between LUAD and T2DM involved querying the Gene Expression Omnibus for pertinent data. Upon pinpointing genes exhibiting differential expression, pathway enrichment analyses were executed to discern the molecular pathways shared by both conditions. In addition, GeneMANIA was employed to establish a protein interaction network, pinpointing STK26 as a critical gene. In addition, the influence of STK26 on the immune environment of the tumor was examined using tools such as the Microenvironment Cell Populations-counter to assess levels of stromal and immune cells in cancer tissues from expression profiles. Furthermore, a lung cancer cell model enriched in glucose was developed to facilitate the knockdown of STK26 using small interfering RNA. The influence of STK26 on A549 cell functionality was assessed using CCK-8, wound healing (scratch), and colony formation (cloning) assays. Results: This will help ensure accuracy and relevance in the revised version. TGF-β, HIF-1, AGE-RAGE, extracellular matrix (ECM) components and function regulation, and cell adhesion were activated in LUAD and T2DM. WGCNA identified two main modules in LUAD, three main modules in T2DM, and 44 shared genes. ClueGO and GeneMANIA analyses focused on pathways regulating cell growth and mitosis. Our analysis revealed STK26 as a central gene that exhibits elevated expression levels in tissues affected by LUAD. Elevated expression of STK26 correlates with a diminished prognosis for LUAD patients. In patients with LUAD characterized by elevated STK26 levels, gene set enrichment analysis identified a notable upregulation in numerous metabolic pathways. These include glycolysis-gluconeogenesis, oxidative phosphorylation, and the conversion pathways between pentose and glucuronic acid, as well as the pentose phosphate pathway. Gene set variation analysis suggested that a high STK26 expression was related to glycolysis, hypoxia, MYC, oxidative phosphorylation, cell cycle, and citric acid cycle pathways. In the group exhibiting elevated levels of STK26, a marked upregulation of glycolytic pathway genes, including HK2, RPIA, IDH3G, and SORD, was noted. This upregulation indicates a correlation between STK26 expression and these pivotal glycolytic genes. MCP-counter analysis suggested that the group with a high STK26 expression level had reduced immune infiltration. Laboratory studies have demonstrated that LUAD cells thrive in a high-glucose setting, where STK26 expression notably surpasses that observed under standard conditions. In addition, suppressing STK26 using siRNA significantly curtails both the growth and movement of LUAD cells. Conclusion: The research established a shared pathogenic basis between LUAD and T2DM. TGF-β, HIF-1, AGE-RAGE, ECM components and function regulation, cell adhesion, and additional signaling pathways are intricately linked with the pathophysiological mechanisms underlying both LUAD and T2DM. Thus, STK26 may affect the development of LUAD and T2DM by regulating glucose metabolism. Suppressing STK26 in a glucose-rich setting curtailed both the expansion and mobility of LUAD cells.

Functional-proteomics-based investigation of the cellular response to farnesyltransferase inhibition in lung cancer

Farnesylation is a lipid post-translational modification of proteins crucial for protein membrane anchoring and cellular signaling. Farnesyltransferase inhibitors (FTIs), such as tipifarnib, are being tested in cancer therapy. However, the full impact of FTIs on farnesylation substrates remains poorly understood, thus limiting their use in precision medicine. In this study, we performed a global proteomics analysis to investigate farnesylation and the effects of tipifarnib in lung cancer cell lines. Using metabolic labeling and mass spectrometry, we identified farnesylated proteins and mapped their subcellular localization. We also analyzed tipifarnib-dependent protein relocalization and proteome-wide changes. Key findings include the potential therapeutic value of FTIs for NRAS-mutated melanoma and GNAQ/GNA11-mutated uveal melanoma by inhibiting INPP5A farnesylation. Additionally, we identified a synergistic drug combination involving tipifarnib and a ferroptosis inducer and discovered PTP4A1 as a regulator of interferon signaling. Our data, covering 15,080 proteins, offer valuable insights for future studies of farnesylation and FTIs.

Stress-induced premature senescence in high five cell cultures: a principal factor in cell-density effects

The Baculovirus Expression Vector System (BEVS) is highly valued in vaccine development, protein engineering, and drug metabolism research due to its biosafety, operational convenience, rapid scalability, and capacity for self-assembling virus-like particles. However, increasing cell density at the time of inoculation severely compromises the production capacity of BEVS, resulting in the "cell density effect". This study aimed to explore the mechanisms of the cell density effect through time-series analysis of transcriptomes and proteomes, with the goal of overcoming or alleviating the decline in productivity caused by increased cell density. The dynamic analysis of the omics of High Five cells under different CCI (cell density at infection) conditions showed that the impact of the "cell density effect" increased over time, particularly affecting genetic information processing, error repair, protein expression regulation, and material energy metabolism. Omics analysis of the growth stage of High Five cells showed that after 36 h of culture (cell density of about 1 × 106 cells/mL), the expression of ribosome-related proteins decreased, resulting in a rapid decrease in protein synthesis capacity, which was a key indicator of cell aging. Senescence verification experiments showed that cells began to show obvious early aging characteristics after 36 h, resulting in a decrease in the host cell's ability to resist stress. Overexpression and siRNA inhibition studies showed that the ndufa12 gene was a potential regulatory target for restricting the "cell density effect". Our results suggested that stress-induced premature senescence in High Five cell cultures, resulting in reduced energy metabolism and protein synthesis capabilities, was a critical factor contributing to cell density effects, and ultimately affecting virus production. In conclusion, this study provided new insights into managing virus production limitations due to cell density effects and offered innovative strategies to mitigate the adverse effects of cellular aging in biomanufacturing technologies.

RAD51 High-Expressed Hepatocellular Carcinomas Are Associated With High Cell Proliferation

INTRODUCTION

RAD51 is a pivotal DNA repair gene managing double-stranded DNA break recognition and repair. RAD51 high expression was associated with adverse outcomes in other cancer types. This study aims to investigate the tumor microenvironment and immune landscape in the RAD51 high-expressed Hepatocellular Carcinoma (HCCs).

METHODS

A total of 467 patients from two large independent cohorts with clinical and transcriptomic data were obtained. The cohort was dichotomized based on the median RAD51 gene expression. xCell and Gene Set Enrichment Analysis (GSEA) were used.

RESULTS

RAD51 high-expressed HCCs were associated with worse recurrence-free, progression-free, disease-specific, and overall survival (all P < 0.05). While RAD51 high-expressed HCCs were associated with intratumoral heterogeneity, homologous recombination deficiency, and fraction altered scores, mutation or neoantigens were not increased in this group. xCell analysis demonstrated inconsistent immune cell infiltration between two cohorts. Cytolytic activity as well as GSEA with immune-related gene sets also demonstrated inconsistent results between two cohorts as well. On the other hand, RAD51 expression was significantly increased in higher-grade tumors, larger tumors, and higher clinical stages. RAD51 high-expressed HCCs were found to have elevated proliferation score. Furthermore, GSEA exhibited significant enrichment of all the cell proliferation-related gene sets in the Hallmark collection, including E2F targets, G2M checkpoint, Mitotic spindle, MYC targets, and MTORC1 signaling consistently in both cohorts (all false discovery rate < 0.25).

CONCLUSIONS

RAD51 high-expressed HCCs were associated with worse survival and with increased cell proliferation and were not necessarily associated with immune infiltration or inflammation.

Identification and Immune Characteristics Study of Pyroptosis‑Related Genes in Endometriosis

Endometriosis (EMT) is a prevalent gynecological disorder characterized by pain and infertility associated with the menstrual cycle. Pyroptosis, an emerging cell death mechanism, has been implicated in the pathogenesis of diverse diseases, highlighting its pivotal role in disease progression. Therefore, our study aimed to investigate the impact of pyroptosis in EMT using a comprehensive bioinformatics approach. We initially obtained two datasets from the Gene Expression Omnibus database and performed differential expression analysis to identify pyroptosis-related genes (PRGs) that were differentially expressed between EMT and non-EMT samples. Subsequently, several machine learning algorithms, namely least absolute shrinkage selection operator regression, support vector machine-recursive feature elimination, and random forest algorithms were used to identify a hub gene to construct an effective diagnostic model for EMT. Receiver operating characteristic curve analysis, nomogram, calibration curve, and decision curve analysis were applied to validate the performance of the model. Based on the selected hub gene, differential expression analysis between high- and low-expression groups was conducted to explore the functions and signaling pathways related to it. Additionally, the correlation between the hub gene and immune cells was investigated to gain insights into the immune microenvironment of EMT. Finally, a pyroptosis-related competing endogenous RNA network was constructed to elucidate the regulatory interactions of the hub gene. Our study revealed the potential contribution of a specific PRG to the pathogenesis of EMT, providing a novel perspective for clinical diagnosis and treatment of EMT.

Identification of novel genes associated with atherosclerosis in Bama miniature pig

BACKGROUND

Atherosclerosis is a chronic cardiovascular disease of great concern. However, it is difficult to establish a direct connection between conventional small animal models and clinical practice. The pig's genome, physiology, and anatomy reflect human biology better than other laboratory animals, which is crucial for studying the pathogenesis of atherosclerosis.

METHODS

We used whole-genome sequencing data from nine Bama minipigs to perform a genome-wide linkage analysis, and further used bioinformatic tools to filter and identify underlying candidate genes. Candidate gene function prediction was performed using the online prediction tool STRING 12.0. Immunohistochemistry and immunofluorescence were used to detect the expression of proteins encoded by candidate genes.

RESULTS

We mapped differential single nucleotide polymorphisms (SNPs) to genes and obtained a total of 102 differential genes, then we used GO and KEGG pathway enrichment analysis to identify four candidate genes, including SLA-1, SLA-2, SLA-3, and TAP2. nsSNPs cause changes in the primary and tertiary structures of SLA-I and TAP2 proteins, the primary structures of these two proteins have undergone amino acid changes, and the tertiary structures also show slight changes. In addition, immunohistochemistry and immunofluorescence results showed that the expression changes of TAP2 protein in coronary arteries showed a trend of increasing from the middle layer to the inner layer.

CONCLUSIONS

We have identified SLA-I and TAP2 as potential susceptibility genes of atherosclerosis, highlighting the importance of antigen processing and immune response in atherogenesis.

Prioritization of genes for translation: a computational approach

INTRODUCTION

Gene identification for genetic diseases is critical for the development of new diagnostic approaches and personalized treatment options. Prioritization of gene translation is an important consideration in the molecular biology field, allowing researchers to focus on the most promising candidates for further investigation.

AREAS COVERED

In this paper, we discussed different approaches to prioritize genes for translation, including the use of computational tools and machine learning algorithms, as well as experimental techniques such as knockdown and overexpression studies. We also explored the potential biases and limitations of these approaches and proposed strategies to improve the accuracy and reliability of gene prioritization methods. Although numerous computational methods have been developed for this purpose, there is a need for computational methods that incorporate tissue-specific information to enable more accurate prioritization of candidate genes. Such methods should provide tissue-specific predictions, insights into underlying disease mechanisms, and more accurate prioritization of genes.

EXPERT OPINION

Using advanced computational tools and machine learning algorithms to prioritize genes, we can identify potential targets for therapeutic intervention of complex diseases. This represents an up-and-coming method for drug development and personalized medicine.

Single-cell transcriptome profiling highlights the role of APP in blood vessels in assessing the risk of patients with proliferative diabetic retinopathy developing Alzheimer's disease

Introduction: The incidence of diabetic retinopathy (DR) has been found to be associated with the risk of developing Alzheimer's disease (AD). In addition to the common properties of neurodegeneration, their progressions are involved with abnormal vascular functions. However, the interactions between them have not been fully understood. This study aimed to investigate the key factor for the underlying interactions and shared signaling pathways in the vasculature of DR and AD. Methods: We retrieved single-cell RNA sequencing (scRNA-seq) data regarding human fibrovascular membrane (FVM) of proliferative diabetic retinopathy (PDR) and human hippocampus vessels of AD from the NCBI-GEO database. GSEA analysis was performed to analyze AD-related genes in endothelial cells and pericytes of PDR. CellChat was used for predicting cell-cell communication and the signaling pathway. Results: The data suggested that amyloid-beta precursor protein (APP) signaling was found crucial in the vasculature of PDR and AD. Endothelial cells and pericytes could pose influences on other cells mainly via APP signaling in PDR. The endothelial cells were mainly coordinated with macrophages in the hippocampus vasculature of AD via APP signaling. The bulk RNA-seq in mice with PDR validated that the expression of APP gene had a significant correlation with that of the AD genome-wide association studies (GWAS) gene. Discussion: Our study demonstrates that the vasculopathy of PDR and AD is likely to share a common signaling pathway, of which the APP-related pathway is a potential target.

Identification of potential biological processes and key genes in diabetes-related stroke through weighted gene co-expression network analysis

BACKGROUND

Type 2 diabetes mellitus (T2DM) is an established risk factor for acute ischemic stroke (AIS). Although there are reports on the correlation of diabetes and stroke, data on its pathogenesis is limited. This study aimed to explore the underlying biological mechanisms and promising intervention targets of diabetes-related stroke.

METHODS

Diabetes-related datasets (GSE38642 and GSE44035) and stroke-related datasets (GSE16561 and GSE22255) were obtained from the Gene Expression omnibus (GEO) database. The key modules for stroke and diabetes were identified by weight gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in the key module. Genes in stroke- and diabetes-related key modules were intersected to obtain common genes for T2DM-related stroke. In order to discover the key genes in T2DM-related stroke, the Cytoscape and protein-protein interaction (PPI) network were constructed. The key genes were functionally annotated in the Reactome database.

RESULTS

By intersecting the diabetes- and stroke-related crucial modules, 24 common genes for T2DM-related stroke were identified. Metascape showed that neutrophil extracellular trap formation was primarily enriched. The hub gene was granulin precursor (GRN), which had the highest connectivity among the common genes. In addition, functional enrichment analysis indicated that GRN was involved in neutrophil degranulation, thus regulating neutrophil extracellular trap formation.

CONCLUSIONS

This study firstly revealed that neutrophil extracellular trap formation may represent the common biological processes of diabetes and stroke, and GRN may be potential intervention targets for T2DM-related stroke.

Transcriptome analysis reveals the impact of NETs activation on airway epithelial cell EMT and inflammation in bronchiolitis obliterans

Bronchiolitis obliterans (BO) is a chronic airway disease that was often indicated by the pathological presentation of narrowed and irreversible airways. However, the molecular mechanisms of BO pathogenesis remain unknown. Although neutrophil extracellular traps (NETs) can contribute to inflammatory disorders, their involvement in BO is unclear. This study aims to identify potential signaling pathways in BO by exploring the correlations between NETs and BO. GSE52761 and GSE137169 datasets were downloaded from gene expression omnibus (GEO) database. A series of bioinformatics analyses such as differential expression analysis, gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and gene set enrichment analysis (GSEA) were performed on GSE52761 and GSE137169 datasets to identify BO potential signaling pathways. Two different types of BO mouse models were constructed to verify NETs involvements in BO. Additional experiments and bioinformatics analysis using human small airway epithelial cells (SAECs) were also performed to further elucidate differential genes enrichment with their respective signaling pathways in BO. Our study identified 115 differentially expressed genes (DEGs) that were found up-regulated in BO. Pathway enrichment analysis revealed that these genes were primarily involved in inflammatory signaling processes. Besides, we found that neutrophil extracellular traps (NETs) were formed and activated during BO. Our western blot analysis on lung tissue from BO mice further confirmed NETs activation in BO, where neutrophil elastase (NE) and myeloperoxidase (MPO) expression were found significantly elevated. Transcriptomic and bioinformatics analysis of NETs treated-SAECs also revealed that NETs-DEGs were primarily associated through inflammatory and epithelial-to-mesenchymal transition (EMT) -related pathways. Our study provides novel clues towards the understanding of BO pathogenesis, in which NETs contribute to BO pathogenesis through the activation of inflammatory and EMT associated pathways. The completion of our study will provide the basis for potential novel therapeutic targets in BO treatment.

Identification of key biomarkers and associated pathways of pancreatic cancer using integrated transcriptomic and gene network analysis

Pancreatic cancer shows malignancy around the world standing in 4th position for causing death globally. This cancer is majorly divided into exocrine and neuroendocrine where exocrine pancreatic ductal adenocarcinoma is observed to be nearly 85% of cases. The lack of diagnosis of pancreatic cancer is considered to be one of the major drawbacks to the prognosis and treatment of pancreatic cancer patients. The survival rate after diagnosis is very low, due to the higher incidence of drug resistance to cancer which leads to an increase in the mortality rate. The transcriptome analysis for pancreatic cancer involves dataset collection from the ENA database, incorporating them into quality control analysis to the quantification process to get the summarized read counts present in collected samples and used for further differential gene expression analysis using the DESeq2 package. Additionally, explore the enriched pathways using GSEA software and represented them by utilizing the enrichment map finally, the gene network has been constructed by Cytoscape software. Furthermore, explored the hub genes that are present in the particular pathways and how they are interconnected from one pathway to another has been analyzed. Finally, we identified the CDKN1A, IL6, and MYC genes and their associated pathways can be better biomarker for the clinical processes to increase the survival rate of of pancreatic cancer.

Identification of key genes in the pathogenesis of preeclampsia via bioinformatic analysis and experimental verification

Background

Preeclampsia (PE) is the primary cause of perinatal maternal-fetal mortality and morbidity. The exact molecular mechanisms of PE pathogenesis are largely unknown. This study aims to identify the hub genes in PE and explore their potential molecular regulatory network.

Methods

We downloaded the GSE148241, GSE190971, GSE74341, and GSE114691 datasets for the placenta and performed a differential expression analysis to identify hub genes. We performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), Gene Set Enrichment Analysis (GSEA), and Protein-Protein Interaction (PPI) Analysis to determine functional roles and regulatory networks of differentially expressed genes (DEGs). We then verified the DEGs at transcriptional and translational levels by analyzing the GSE44711 and GSE177049 datasets and our clinical samples, respectively.

Results

We identified 60 DEGs in the discovery phase, consisting of 7 downregulated genes and 53 upregulated genes. We then identified seven hub genes using Cytoscape software. In the verification phase, 4 and 3 of the seven genes exhibited the same variation patterns at the transcriptional level in the GSE44711 and GSE177049 datasets, respectively. Validation of our clinical samples showed that CADM3 has the best discriminative performance for predicting PE.

Conclusion

These findings may enhance the understanding of PE and provide new insight into identifying potential therapeutic targets for PE.

Integrative epigenome profiling of 47XXY provides insights into whole genomic DNA hypermethylation and active chromatin accessibility

Klinefelter syndrome (KS, 47XXY) is a disorder characterized by sex chromosomal aneuploidy, which may lead to changes in epigenetic regulations of gene expression. To define epigenetic architectures in 47XXY, we annotated DNA methylation in euploid males (46XY) and females (46XX), and 47XXY individuals using whole genome bisulfite sequencing (WGBS) and integrated chromatin accessbilty, and detected abnormal hypermethylation in 47XXY. Furthermore, we detected altered chromatin accessibility in 47XXY, in particular in chromosome X, using Assay for Transposase-Accessible Chromatin sequencing (ATAC-seq) in cultured amniotic cells. Our results construct the whole genome-wide DNA methylation map in 47XXY, and provide new insights into the early epigenomic dysregulation resulting from an extra chromosome X in 47XXY.

Risk scoring based on DNA methylation-driven related DEGs for colorectal cancer prognosis with systematic insights

Colorectal cancer is a common malignant tumor of the digestive tract. Despite advances in diagnostic techniques and medications. Its prognosis remains challenging. DNA methylation-driven related circulating tumor cells have attracted enormous interest in diagnosing owing to their non-invasive nature and early recognition properties. However, the mechanism through which risk biomarkers act remains elusive. Here, we designed a risk model based on differentially expressed genes, DNA methylation, robust, and survival-related factors in the framework of Cox regression. The model has satisfactory performance and is independently verified by an external and isolated dataset in terms of C-index value, ROC, and tROC. The model was applied to Colorectal cancer patients who were subsequently divided into high- and low-risk groups. Functional annotations, genomic alterations, tumor immune environment, and drug sensitivity were analyzed. We observed that up-regulated genes are associated with epithelial cell differentiation and MAPK signaling pathways. The down-regulated genes are related to IL-7 signaling and apoptosis-induced DNA fragmentation. Interestingly, the immune system was inhibited in high-risk groups. High-frequency mutation genes tend to co-occur. High-risk score patients are related to copy number amplification events. To address the challenges, we suggested eleven and twenty-one drugs that are sensitive to low- and high-risk patients. Finally, an artificial neural network was provided to evaluate the immunotherapeutic efficiency. Taken together, the findings demonstrated that our risk score model is robust and reliable for evaluating the prognosis with novel diagnostic and treatment targets. It also yields benefits for the treatment and provides unique insights into developing therapeutic strategies.

Prognostic Significance of mRNA Expression RBBP8 or Its Methylation in Gliomas

Retinoblastoma-binding protein 8 (RBBP8) affects the prognosis of patients with malignancies through various mechanisms. However, its function in gliomas is unknown. Our study explored the effects of RBBP8 on the prognosis of glioma patients, as well as its regulatory role in the glioma immune microenvironment. We used various bioinformatics methods to analyze the transcriptional profiles and methylation data of RBBP8 in gliomas from multiple databases. Our results showed that the mRNA and protein expression of RBBP8 in gliomas was higher than that in normal tissues and positively correlated with malignant clinical features such as age and WHO grade. A Kaplan-Meier analysis showed that patients with high RBBP8 expression had a poor prognosis. Cox regression demonstrated that RBBP8 was an independent risk indicator and had good diagnostic value for the poor prognosis of glioma. Importantly, RBBP8 was positively correlated with many well-known immune checkpoints (e.g., CTLA4 and PDL-1). Finally, a gene set enrichment analysis revealed that RBBP8 was remarkably enriched in cancer-related pathways such as cell cycle, DNA replication and so on. In conclusion, this study is the first to elaborate on the value of RBBP8 in the pathological process of glioma for anti-tumor immunotherapy. In addition, the expression of RBBP8 and its methylation site, cg05513509, may provide potential targets for glioma therapy.

Identification of the effects of COVID-19 on patients with pulmonary fibrosis and lung cancer: a bioinformatics analysis and literature review

Coronavirus disease 2019 (COVID-19) poses a serious threat to human health and life. The effective prevention and treatment of COVID-19 complications have become crucial to saving patients’ lives. During the phase of mass spread of the epidemic, a large number of patients with pulmonary fibrosis and lung cancers were inevitably infected with the SARS-CoV-2 virus. Lung cancers have the highest tumor morbidity and mortality rates worldwide, and pulmonary fibrosis itself is one of the complications of COVID-19. Idiopathic lung fibrosis (IPF) and various lung cancers (primary and metastatic) become risk factors for complications of COVID-19 and significantly increase mortality in patients. Therefore, we applied bioinformatics and systems biology approaches to identify molecular biomarkers and common pathways in COVID-19, IPF, colorectal cancer (CRC) lung metastasis, SCLC and NSCLC. We identified 79 DEGs between COVID-19, IPF, CRC lung metastasis, SCLC and NSCLC. Meanwhile, based on the transcriptome features of DSigDB and common DEGs, we identified 10 drug candidates. In this study, 79 DEGs are the common core genes of the 5 diseases. The 10 drugs were found to have positive effects in treating COVID-19 and lung cancer, potentially reducing the risk of pulmonary fibrosis.

Identification of tubulointerstitial genes and ceRNA networks involved in diabetic nephropathy via integrated bioinformatics approaches

Background

Diabetic nephropathy (DN) is the major cause of end-stage renal disease worldwide. The mechanism of tubulointerstitial lesions in DN is not fully elucidated. This article aims to identify novel genes and clarify the molecular mechanisms for the progression of DN through integrated bioinformatics approaches.

Method

We downloaded microarray datasets from Gene Expression Omnibus (GEO) database and identified the differentially expressed genes (DEGs). Enrichment analyses, construction of Protein–protein interaction (PPI) network, and visualization of the co-expressed network between mRNAs and microRNAs (miRNAs) were performed. Additionally, we validated the expression of hub genes and analyzed the Receiver Operating Characteristic (ROC) curve in another GEO dataset. Clinical analysis and ceRNA networks were further analyzed.

Results

Totally 463 DEGs were identified, and enrichment analyses demonstrated that extracellular matrix structural constituents, regulation of immune effector process, positive regulation of cytokine production, phagosome, and complement and coagulation cascades were the major enriched pathways in DN. Three hub genes (CD53, CSF2RB, and LAPTM5) were obtained, and their expression levels were validated by GEO datasets. Pearson analysis showed that these genes were negatively correlated with the glomerular filtration rate (GFR). After literature searching, the ceRNA networks among circRNAs/IncRNAs, miRNAs, and mRNAs were constructed. The predicted RNA pathway of NEAT1/XIST-hsa-miR-155-5p/hsa-miR-486-5p-CSF2RB provides an important perspective and insights into the molecular mechanism of DN.

Conclusion

In conclusion, we identified three genes, namely CD53, CSF2RB, and LAPTM5, as hub genes of tubulointerstitial lesions in DN. They may be closely related to the pathogenesis of DN and the predicted RNA regulatory pathway of NEAT1/XIST-hsa-miR-155-5p/hsa-miR-486-5p-CSF2RB presents a biomarker axis to the occurrence and development of DN.

Identification of the Shared Gene Signatures and Biological Mechanism in Type 2 Diabetes and Pancreatic Cancer

BACKGROUND

The relationship between pancreatic cancer (PC) and type 2 diabetes mellitus (T2DM) has long been widely recognized, but the interaction mechanisms are still unknown. This study was aimed to investigate the shared gene signatures and molecular processes between PC and T2DM.

METHODS

The Gene Expression Omnibus (GEO) database was used to retrieve the RNA sequence and patient information of PC and T2DM. Weighted gene co-expression network analysis (WGCNA) was performed to discover a co-expression network associated with PC and T2DM. Enrichment analysis of shared genes present in PC and T2DM was performed by ClueGO software. These results were validated in the other four cohorts based on differential gene analysis. The predictive significance of S100A6 in PC was evaluated using univariate and multivariate Cox analyses, as well as Kaplan-Meier plots. The biological process of S100A6 enrichment in PC was detected using Gene Set Enrichment Analysis (GSEA). The involvement of S100A6 in the tumor immune microenvironment (TIME) was assessed by CIBERSORT. In vitro assays were used to further confirm the function of S100A6 in PC.

RESULTS

WGCNA recognized three major modules for T2DM and two major modules for PC. There were 44 shared genes identified for PC and T2DM, and Gene Ontology (GO) analysis showed that regulation of endodermal cell fate specification was primarily enriched. In addition, a key shared gene S100A6 was derived in the validation tests. S100A6 was shown to be highly expressed in PC compared to non-tumor tissues. PC patients with high S100A6 expression had worse overall survival (OS) than those with low expression. GSEA revealed that S100A6 is involved in cancer-related pathways and glycometabolism-related pathways. There is a strong relationship between S100A6 and TIME. In vitro functional assays showed that S100A6 helped to induce the PC cells' proliferation and migration. We also proposed a diagram of common mechanisms of PC and T2DM.

CONCLUSIONS

This study firstly revealed that the regulation of endodermal cell fate specification may be common pathogenesis of PC and T2DM and identified S100A6 as a possible biomarker and therapeutic target for PC and T2DM patients.

Copy number variation analysis of m6 A regulators identified METTL3 as a prognostic and immune-related biomarker in bladder cancer

PURPOSE

Growing evidence has demonstrated an indispensable role for N⁶ -methyladenosine (m⁶ A) in human diseases, but the copy number variations (CNVs) of m⁶ A regulatory genes in bladder cancer (BLCA) remains largely unknown.

METHODS

We investigated the CNVs on all known m⁶ A regulatory genes using the Cancer Genome Atlas (TCGA) database. The association between CNV events and clinicopathological as well as molecular characteristics of BLCA patients were explored. Gene set enrichment analysis (GSEA) was implemented to reveal relative cellular processes. Association between m⁶ A regulatory genes and immune infiltrates was analyzed by The Tumor Immune Estimation Resource (TIMER) database.

RESULTS

CNV events of m⁶ A regulatory genes were frequently observed in BLCA. CNVs of METTL3, METTL14, and METTL16 correlated with molecular characteristics of BLCA patients including TP53 mutation. CNVs of METTL3 associated with the overall survival (OS) of BLCA patients. METTL3 was also associated with several cancer-related cellular processes, including mitotic spindle assembly, G2/M checkpoint, and E2F targets signaling pathway. Besides, the CNVs of m⁶ A regulatory genes were correlated with specific kinds of immune infiltrates.

CONCLUSIONS

There are significant correlations between m⁶ A regulatory genes with CNVs and clinicopathological characteristics. METTL3 with CNVs were associated with the immune infiltrates and performed as a prognostic marker in BLCA.

Evaluating microbiome-directed fibre snacks in gnotobiotic mice and humans

Changing food preferences brought about by westernization that have deleterious health effects1,2-combined with myriad forces that are contributing to increased food insecurity-are catalysing efforts to identify more nutritious and affordable foods3. Consumption of dietary fibre can help to prevent cardiovascular disease, type 2 diabetes and obesity4-6. A substantial number of reports have explored the effects of dietary fibre on the gut microbial community7-9. However, the microbiome is complex, dynamic and exhibits considerable intra- and interpersonal variation in its composition and functions. The large number of potential interactions between the components of the microbiome makes it challenging to define the mechanisms by which food ingredients affect community properties. Here we address the question of how foods containing different fibre preparations can be designed to alter functions associated with specific components of the microbiome. Because a marked increase in snack consumption is associated with westernization, we formulated snack prototypes using plant fibres from different sustainable sources that targeted distinct features of the gut microbiomes of individuals with obesity when transplanted into gnotobiotic mice. We used these snacks to supplement controlled diets that were consumed by adult individuals with obesity or who were overweight. Fibre-specific changes in their microbiomes were linked to changes in their plasma proteomes indicative of an altered physiological state.