NCBI GEO: mining tens of millions of expression profiles--database and tools update

Identification of mitophagy-related hub genes during the progression of spinal cord injury by integrated multinomial bioinformatics analysis

Spinal cord injury (SCI) is a disturbance of peripheral and central nerve conduction that causes disability in sensory and motor function. Currently, there is no effective treatment for SCI. Mitophagy plays a vital role in mitochondrial quality control during various physiological and pathological processes. The study aimed to elucidate the role of mitophagy and identify potential mitophagy-related hub genes in SCI pathophysiology. Two datasets (GSE15878 and GSE138637) were analyzed. Firstly, the differentially expressed genes (DEGs) were identified and mitophagy-related genes were obtained from GeneCards, then the intersection between SCI and mitophagy-related genes was determined. Next, we performed gene set enrichment analysis (GSEA), weighted gene co-expression network analysis (WGCNA), protein-protein interaction network (PPI network), least absolute shrinkage and selection operator (LASSO), and cluster analysis to identify and define the hub genes in SCI. Finally, the link between hub genes and infiltrating immune cells was investigated and the potential transcriptional regulation/small molecular compounds to target hub genes were predicted. In total, SKP1 and BAP1 were identified as hub genes of mitophagy-related DEGs during SCI development and regulatory T cells (Tregs)/resting NK cells/activated mast cells may play an essential role in the progression of SCI. LINC00324 and SNHG16 may regulate SKP1 and BAP1, respectively, through miRNAs. Eleven and eight transcriptional factors (TFs) regulate SKP1 and BAP1, respectively, and six small molecular compounds target BAP1. Then, the mRNA expression levels of BAP1 and SKP1 were detected in the injured sites of spinal cord of SD rats at 6 h and 72 h after injury using RT-qPCR, and found that the level were decreased. Therefore, the pathways of mitophagy are downregulated during the pathophysiology of SCI, and SKP1 and BAP1 could be accessible targets for diagnosing and treating SCI.

MZB1 regulates cellular proliferation, mitochondrial dysfunction, and inflammation and targets the PI3K-Akt signaling pathway in acute pancreatitis

BACKGROUND

Acute pancreatitis (AP) is a pathological condition characterized by the premature release and activation of trypsinogens and other enzyme precursors. In severe cases, the mortality rates are in the range of 20-30% and may even be as high as 50%. Though various prophylaxes are available for AP, the mechanism of its progression is unclear. Marginal zone B and B-1 cell-specific protein 1 (MZB1) is found in the endoplasmic reticulum (ER) where it is expressed exclusively in the B cells there. MZB1 promotes proliferation, inhibits apoptosis, invasion, and inflammation, and mitigates mitochondrial damage in cells. However, the importance of MZB1 in AP has not yet been determined.

METHODS

Differentially expressed genes (DEGs) between healthy pancreatic cells and those affected by AP were identified using datasets from Gene Expression Omnibus (GEO) datasets. Relative differences in MZB1 expression between normal and diseased tissues and cells were validated in vivo using a rat AP model induced with 4% (w/v) sodium taurocholate and in vitro using the AR42J rat pancreatic cell line exposed to caerulein (CAE). Cell Counting Kit-8 (CCK-8) and 5-ethynyl-2`-deoxyuridine (EdU) assays were performed to detect and compare normal and pathological cell proliferation. Flow cytometry was employed to assess and compare cellular apoptosis. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) and western blot (WB) were applied to evaluate the apoptotic factors Bax and Bcl. The inflammatory factors interleukin (IL)-6 and IL-1β were quantified using Enzyme-linked immunosorbent assay (ELISA) and qRT-PCR techniques. Mitochondrial function was evaluated using assays for reactive oxygen species (ROS) and tetramethylrhodamine methyl ester (TMRM). WB and qRT-PCR were utilized to measure the expression levels of the PI3K-Akt signaling pathway, followed by a rescue experiment involving the inhibitor of wortmannin.

RESULTS

MZB1 was upregulated in the AP cases screened from the GEO datasets, the rat AP model, and the AR42J cells exposed to CAE. Overexpression of MZB1 enhanced the growth and supressed the cell death of AR42J cells while also activating the PI3K-Akt signaling pathway. MZB1 knockdown led to mitochondrial dysfunction and exacerbated inflammation. The rescue experiment demonstrated that MZB1 enhanced proliferation and inhibited apoptosis, mitochondrial dysfunction, and inflammation in pancreatic cells through the PI3K-Akt pathway.

CONCLUSIONS

AP cells and tissues exhibited markedly elevated levels of MZB1 expression compared to their healthy counterparts. MZB1 overexpression promoted proliferation and supressed apoptosis, mitochondrial dysfunction, and inflammation in pancreatic cells through the positive regulation of the PI3K-Akt signaling pathway.

Construction of competing endogenous RNA networks in systemic lupus erythematosus by integrated analysis

Objective

Systemic lupus erythematosus (SLE) is a disease characterised by immune inflammation and damage to multiple organs. Recent investigations have linked competing endogenous RNAs (ceRNAs) to lupus. However, the exact mechanism through which the ceRNAs network affects SLE is still unclear. This study aims to investigate the regulatory functions of the ceRNAs network, which are important pathways that control the pathophysiological processes of SLE.

Methods

CircRNA microarray for our tested assays were derived from bone marrow samples from three healthy individuals and three SLE patients in our hospital. The other sequencing data of circRNA, miRNA and mRNA were obtained from Gene Expression Omnibus (GEO) datasets. Using the limma package of R program, the differential expression of mRNA and miRNA in the GEO database was discovered. Then predicted miRNA-mRNA and circRNA-miRNA were established using miRMap, miRanda, miRDB, TargetScan, and miTarBase. CircRNA-miRNA-mRNA ceRNA network was constructed using Cytoscape, and hub genes were screened using a protein-protein interaction network. Immune infiltration analysis of the hub gene was also performed by CIBERSORT and GSEA.

Results

230 overlapped circRNAs, 86 DEmiRNAs and 2083 DEmRNAs were identified in SLE patients as compared to healthy controls. We constructed a circRNA-miRNA-mRNA ceRNAs network contained 11 overlapped circRNAs, 9 miRNAs and 51 mRNAs. ESR1 and SIRT1 were the most frequently associated protein-protein interactions in the PPI network. KEGG analysis showed that DEGs was enriched in FoxO signaling pathway as well as lipids and atherosclerosis. We constructed a novel circRNA-miRNA-mRNA ceRNA network (HSA circ 0000345- HSA miR-22-3-P-ESR1/SIRT1) that may have a major impact on SLE.

Conclusion

Through this bioinformatics and integrated analysis, we suggest a regulatory role for ceRNA network in the pathogenesis and treatment of SLE.

Exploration of the Shared Gene Signatures and Molecular Mechanisms Between Ischemic Stroke and Atherosclerosis

Purpose

Atherosclerosis (AS) is a chronic inflammatory vascular disease and the predominant cause of ischemic stroke (IS). AS is a potential pathogenetic factor in IS. However, the processes by which they interact remain unknown. The purpose of this paper was to investigate the shared gene signatures and putative molecular processes in AS and IS.

Methods

Gene Expression Omnibus (GEO) data for AS and IS microarrays were retrieved. The co-expression modules associated with AS and IS were identified using the Weighted Gene Co-Expression Network Analysis (WGCNA). We constructed an interaction network of shared differentially expressed genes in AS and IS and conducted an enrichment analysis using ClueGO software. We validated the results in a separate cohort through differential gene analysis. Additionally, we retrieved AS and IS-related miRNAs from the Human microRNA Disease Database (HMDD) and predicted their target genes using miRWalk. We then built a network of miRNAs-mRNAs-KEGG pathways using the shared genes.

Results

Through WGCNA, we identified five modules and six modules as significant in AS and IS, respectively. A ClueGO enrichment analysis of common genes showed that highly active CCR1 chemokine receptor binding is critical to AS and IS pathogenesis. The differential analysis expression results in another cohort closely matched these findings. The miRNA-mRNA network suggested that hsa-miR-330-5p, hsa-miR-143-3p, hsa-miR-16-5p, hsa-miR-152-3p might regulate the shared gene KRAS, which could be a key player in AS and IS.

Conclusion

We integrated ischemic stroke and carotid atherosclerosis public database data and found that ATF3, CCL3, CCL4, JUNB, KRAS, and ZC3H12A may affect both, making them novel biomarkers or therapeutic target genes. Clinical samples and expression trends supported our analyses of pivotal genes.

WGCNA and machine learning analysis identifi ed SAMD9 and IFIT3 as primary Sjögren's Syndrome key genes

Background

Current treatments for primary Sjögren's Syndrome (pSS) are with limited effect, partially due to the heterogeneity and uncleared mechanism.

Methods

We got GSE40568 (Japan) and GSE40611 (USA), and analyzed them with WGCNA to find key Differentially expressed genes (DEGs) between pSS and healthy salivary glands (SG). Key pSS genes (KPGs) were further selected through 3 machine-learning methods. The expression of KPGs was validated via two other GEO datasets (GSE127952 and GSE154926). Infiltrated immune cells, ceRNA network, and potential compounds were explored.

Results

Our study identified 376 DEGs from the pSS patients, with 186 genes located in the "plum2" module, showing the strongest correlation with clinical characteristics. SAMD9 and IFIT3 emerged as KPGs with excellent diagnostic potential. SAMD9 demonstrated close association with immune cell infiltration. We constructed a lncRNA-miRNA-mRNA network comprising 2 KPGs, 12 miRNAs, 124 lncRNAs, and potential therapeutic targets.

Conclusion

In the investigation of pSS public datasets, our study revealed two potential critical mediators in the pathological process of pSS salivary glands, namely SAMD9 and IFIT3. Furthermore, we put forth a hypothesis regarding the ceRNA network and made predictions regarding potential therapeutic drugs targeting these two genes.

Comprehensive analysis of lncRNA-miRNA-mRNA ceRNA network in ischemic stroke

Objective

Competitive endogenous RNA (ceRNA) networks have uncovered a novel mode of RNA interaction, and are implicated in various biological processes and the pathogenesis of IS. This study aimed to explore the potential mechanisms underlying the ceRNA network in IS.

Methods

Four public datasets containing lncRNA and mRNA (GSE22255 and GSE16561) and miRNA (GSE55937 and GSE43618) expression profiles from the GEO database were systematically analyzed to explore the role of RNAs in ischemic stroke (IS). Differentially expressed mRNAs (DEmRNAs), lncRNAs (DElncRNAs), and miRNAs (DEmiRNAs) between IS and normal control samples were identified. LncRNA-miRNA and miRNA-mRNA interactions were predicted, and the competing endogenous RNA (ceRNA) regulatory network was constructed using the Cytoscape software. The correlation between the RNAs in the ceRNA network and the clinical features of the samples was evaluated. Finally, principal component analysis was performed on the RNAs that constitute the ceRNA regulatory network, and their differential expression and principal component relationships among different types of samples were observed.

Results

A total of 224 DEmRNAs, 7 DEmiRNAs, and four DElncRNAs related to IS in four datasets were identified. Then, through target gene prediction, a lncRNA-miRNA-mRNA ceRNA network that contained 3 DElncRNAs, 2 DEmiRNAs, and 24 DEmRNAs was constructed. Correlations of the clinical characteristics showed that PART1 and SERPINH1 were related to clinical diseases, WNK1 was related to lifestyle, and seven RNAs were related to age. PCA results indicate that three principal components of PC1, PC2, and PC3 can clearly distinguish between control and IS samples.

Conclusion

Overall, we constructed a ceRNA network in IS, which could offer insights into the molecular mechanism and potential prognostic biomarkers for further research.

A Novel Neutrophil Extracellular Trap Signature Predicts Patient Chemotherapy Resistance and Prognosis in Lung Adenocarcinoma

Chemoresistance is a key obstacle in the long-term survival of patients with locally and advanced lung adenocarcinoma (LUAD). This study used bioinformatic analysis to reveal the chemoresistance of gene-neutrophil extracellular traps (NETs) associated with LUAD. RNA sequencing data and LUAD expression patterns were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, respectively. The GeneCards database was used to identify NETosis-related genes (NRGs). To identify hub genes with significant and consistent expression, differential analysis was performed using the TCGA-LUAD and GEO datasets. LUAD subtypes were determined based on these hub genes, followed by prognostic analysis. Immunological scoring and infiltration analysis were conducted using NETosis scores (N-scores) derived from the TCGA-LUAD dataset. A clinical prognostic model was established and analyzed, and its clinical applications explored. Twenty-two hub genes were identified, and consensus clustering was used to identify two subgroups based on their expression levels. The Kaplan-Meier (KM) curves demonstrated statistically significant differences in prognosis between the two LUAD subtypes. Based on the median score, patients were further divided into high and low N-score groups, and KM curves showed that the N-scores were more precise at predicting the prognosis of patients with LUAD for overall survival (OS). Immunological infiltration analysis revealed significant differences in the abundances of 10 immune cell infiltrates between the high and low N-score groups. Risk scores indicated significant differences in prognosis between the two extreme score groups. The risk scores for the prognostic model also indicated significant differences between the two groups. The results provide new insights into NETosis-related differentially expressed genes (NRDEGs) associated with chemotherapy resistance in patients with LUAD. The established prognostic model is promising and could help with clinical applications to evaluate patient survival and therapeutic efficiency.

CFHR1 involvement in bile duct carcinoma: Insights from a data mining study

The aim of this study is to investigate the role of CFHR1 in bile duct carcinoma (BDC) and its mechanism of action, and we hope that our analysis and research will contribute to a better understanding of cholangiocarcinoma (BDC) disease genesis, progression and the development of new therapeutic strategies. The prognostic receiver operating characteristic curve of CFHR1 was generated using survival ROC. The ROC curve for CFHR1 showed that there is a correlation between CFHR1 expression and clinicopathological parameters and has an impact on poor prognosis. STRING was used to predict the protein-protein interaction network of the identified genes, and the Microenvironment Cell Populations counter algorithm was used to analyze immune cell infiltration within the BDC. The combined analysis showed that CFHR1 was found to be upregulated in BDC tissues, along with a total of 20 related differentially expressed genes (DEGs) (8 downregulated and 12 upregulated genes). Also, the results showed that the expression of CFHR1 is correlated with immune cell infiltration in tumor and immune cell markers in BDC (P < 0.05). In addition, we have verified experimentally the biological function of CFHR1. These findings suggest that CFHR1 may be a prognostic marker and a potential therapeutic target for BDC. Information regarding the detailed roles of CFHR1 in BDC could be valuable for improving the diagnosis and treatment of this rare cancer.

Unveiling the mitophagy puzzle in non-alcoholic fatty liver disease (NAFLD): Six hub genes for early diagnosis and immune modulatory roles

Background

Non-alcoholic fatty liver disease (NAFLD) stands as a predominant chronic liver ailment globally, yet its pathogenesis remains elusive. This study aims to identify Hub mitophagy-related genes (MRGs), and explore the underlying pathological mechanisms through which these hub genes regulate NAFLD.

Methods

A total of 3 datasets were acquired from the GEO database and integrated to identify differentially expressed genes (DEGs) in NAFLD and perform Gene Set Enrichment Analysis (GSEA). By intersecting DEGs with MRGs, mitophagy-related differentially expressed genes (MRDEGs) were obtained. Then, hub MRGs with diagnostic biomarker capability for NAFLD were screened and a diagnostic prediction model was constructed and assessed using Nomogram, Decision Curve Analysis (DCA), and ROC curves. Functional enrichment analysis was conducted on the identified hub genes to explore their biological significance. Additionally, regulatory networks were constructed using databases. NAFLD was stratified into high and low-risk groups based on the Riskscore from the diagnostic prediction model. Furthermore, single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were employed to analyze immune cell infiltration patterns and the relationship between Hub MRGs and immune cells.

Results

The integrated dataset comprised 122 NAFLD samples and 31 control samples. After screening, 18 MRDEGs were identified. Subsequently, six hub MRGs (NR4A1, PPP2R2A, P4HA1, TUBB6, DUSP1, NAMPT) with diagnostic potential were selected through WGCNA, logistic regression, SVM, RF, and LASSO models, all significantly downregulated in NAFLD samples compared to the control group. A diagnostic prediction model based on these six genes demonstrated robust predictive performance. Functional enrichment analysis of the six hub genes revealed involvement in processes such as protein phosphorylation or dephosphorylation. Correlation analysis demonstrated a significant association between hub MRGs and infiltrating immune cells.

Conclusion

We identified six hub MRGs in NAFLD and constructed a diagnostic prediction model based on these six genes, applicable for early NAFLD diagnosis. These genes may participate in regulating NAFLD progression through the modulation of mitophagy and immune activation. Our findings may contribute to subsequent clinical and basic research on NAFLD.

Integrated transcriptome sequencing and weighted gene co-expression network analysis reveals key genes of papillary thyroid carcinomas

Objective

Papillary thyroid carcinoma (PTC) accounts for the majority of thyroid cancers and has a high recurrence rate. We aimed to screen key genes involved in PTC to provide novel insights into the mechanisms of PTC.

Methods

Seven microarray datasets of PTC were downloaded from gene expression omnibus database. Differentially expressed genes (DEGs) between PTC and normal samples were screened in the merged dataset. Then, protein-protein interaction (PPIs) functional modules analysis and weighted gene co-expression network analysis (WGCNA) were utilized to identify PTC-associated key genes. The identified key genes were then characterized from various aspects, including gene set enrichment analysis (GSEA) and the associations with immune infiltration, methylation levels and prognosis.

Results

A large numbers of DEGs were identified, and these DEGs are involved in several cancer pathways. Nine key genes (including down-regulated genes GNA14, AVPR1A, and WFS1, and up-regulated genes LAMB3, PLAU, MET, MFGE8, PRSS23, and SERPINA1) were identified. Patients in the AVPR1A and GNA14 high expression groups had better disease-free survival (DFS) than those in the low expression group. Key genes were mainly involved in P53 pathway, estrogen response, apoptosis, glycolysis, NOTCH signaling, epithelial mesenchymal transition, WNT_beta catenin signaling, and inflammatory response. The expression of key genes was associated with immune cell infiltration and corresponding methylation levels. The verification results of key gene proteins and mRNA expression levels using external validation datasets were consistent with our expectations, implying the involvements of key genes in PTC.

Conclusion

The key genes may serve as potential therapeutic targets for PTC. This study provides novel insights into the mechanisms underlying PTC development.

Development and experimental validation of an energy metabolism-related gene signature for diagnosing of osteoporosis

Osteoporosis is usually caused by excessive bone resorption and energy metabolism plays a critical role in the development of osteoporosis. However, little is known about the role of energy metabolism-related genes in osteoporosis. This study aimed to explore the important energy metabolism-related genes involved in the development of osteoporosis and develop a diagnosis signature for osteoporosis. The GSE56814, GSE62402, and GSE7158 datasets were downloaded from the NCBI Gene Expression Omnibus. The intersection of differentially expressed genes between high and low levels of body mineral density (BMD) and genes related to energy metabolism were screened as differentially expressed energy metabolism genes (DE-EMGs). Subsequently, a DE-EMG-based diagnostic model was constructed and differential expression of genes in the model was validated by RT-qPCR. Furthermore, a receiver operating characteristic curve and nomogram model were constructed to evaluate the predictive ability of the diagnostic model. Finally, the immune cell types in the merged samples and networks associated with the selected optimal DE-EMGs were constructed. A total of 72 overlapped genes were selected as DE-EMGs, and a five DE-EMG based diagnostic model consisting B4GALT4, ADH4, ACAD11, B4GALT2, and PPP1R3C was established. The areas under the curve of the five genes in the merged training dataset and B4GALT2 in the validation dataset were 0.784 and 0.790, respectively. Moreover, good prognostic prediction ability was observed using the nomogram model (C index = 0.9201; P = 5.507e-14). Significant differences were observed in five immune cell types between the high- and low-BMD groups. These included central memory, effector memory, and activated CD8 T cells, as well as regulatory T cells and activated B cells. A network related to DE-EMGs was constructed, including hsa-miR-23b-3p, DANCR, 17 small-molecule drugs, and two Kyoto Encyclopedia of Genes and Genomes pathways, including metabolic pathways and pyruvate metabolism. Our findings highlighted the important roles of DE-EMGs in the development of osteoporosis. Furthermore, the DANCR/hsa-miR-23b-3p/B4GALT4 axis might provide novel molecular insights into the process of osteoporosis development.

Roles of pyroptosis and immune infiltration in aortic dissection

Introduction: Aortic dissection (AD) is often fatal, and its pathogenesis involves immune infiltration and pyroptosis, though the molecular pathways connecting these processes remain unclear. This study aimed to investigate the role of immune infiltration and pyroptosis in AD pathogenesis using bioinformatics analysis. Methods: Two Gene Expression Omnibus datasets and a Gene Cards dataset of pyroptosis-related genes (PRGs) were utilized. Immunological infiltration was assessed using CIBERSORT, and AD diagnostic markers were identified through univariate logistic regression and least absolute shrinkage and selection operator regression. Interaction networks were constructed using STRING, and weighted gene correlation network analysis (WGCNA) was employed to identify important modules and essential genes. Single-sample gene set enrichment analysis determined immune infiltration, and Pearson correlation analysis assessed the association of key genes with infiltrating immune cells. Results: Thirty-one PRGs associated with inflammatory response, vascular epidermal growth factor receptor, and Rap1 signaling pathways were identified. WGCNA revealed seven important genes within a critical module. CIBERSORT detected immune cell infiltration, indicating significant changes in immune cell infiltration and pyroptosis genes in AD and their connections. Discussion: Our findings suggest that key PRGs may serve as indicators for AD or high-risk individuals. Understanding the role of pyroptosis and immune cell infiltration in AD pathogenesis may lead to the development of novel molecular-targeted therapies for AD. Conclusion: This study provides insights into the molecular mechanisms underlying AD pathogenesis, highlighting the importance of immune infiltration and pyroptosis. Identification of diagnostic markers and potential therapeutic targets may improve the management of AD and reduce associated morbidity and mortality.

Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis

Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.

Transcriptional profile and immune infiltration in colorectal cancer reveal the significance of inducible T-cell costimulator as a crucial immune checkpoint molecule

BACKGROUND

Emergence of novel immuno-therapeutics has shown promising improvement in the clinical outcome of colorectal cancer (CRC).

OBJECTIVE

To identify robust immune checkpoints based on expression and immune infiltration profiles of clinical CRC samples.

METHODS

One dataset from The Cancer Genome Atlas database and two from Gene Expression Omnibus were independently employed for the analysis. Genes associated with overall survival were identified, and distribution of each immune checkpoint with respect to different clinical features was determined to explore key immune checkpoints. Multiple staining methods were used to verify the correlation between key immune checkpoint ICOS and clinical pathological features. Differentially expressed mRNA and long non-coding RNA (lncRNA) were then detected for gene set enrichment analysis and gene set variation analysis to investigate the differentially enriched biological processes between low- and high-expression groups. Significant immune-related mRNAs and lncRNA were subjected to competing endogenous RNA (ceRNA) network analysis. Correlation of inducible T-cell costimulator (ICOS) and top 10 genes in ceRNA network were further considered for validation.

RESULTS

ICOS was identified from 14 immune checkpoints as the most highly correlated gene with survival and clinical features in CRC. The expression of ICOS protein in the poorly differentiated group was lower than that in the moderately differentiated group, and the expression in different pathological stages was significant. In addition, the expressions of ICOS were negatively correlated with Ki67. A conspicuous number of immune-related pathways were enriched in differentially expressed genes in the ICOS high- and low-expression groups. Integration with immune infiltration data revealed a multitude of differentially expressed immune-related genes enriched for ceRNA network. Furthermore, expression of top 10 genes investigated from ceRNA network showed high correlation with ICOS.

CONCLUSION

ICOS might serve as a robust immune checkpoint for prognosis with several genes being potential targets of ICOS-directed immunotherapy in CRC.

Integrated bioinformatics approaches and expression assays identified new markers in pituitary adenomas

Pituitary adenomas (PA) include about one third of primary central nervous tumors in adolescent and young adult. Despite extensive research, the underlying mechanism of PA tumorigenesis is still unknown. In the present study, through bioinformatics analysis of a PA-related dataset downloaded from GEO database, we attempted to identify pair(s) of lncRNA/target mRNA whose expression changes may be involved in the tumorigenesis of PAs. For this end, we evaluated expression of a set of bioinformatically obtained genes in 46 PA tissues against adjacent non-tumor pituitary tissues. The bioinformatics step led to selection of four genes for validation through expression assays. Expression levels of HIF1A and MAPK1 were increased in NFPA tissues (P < 0.0001 and =0.0042, respectively). Expression level of BANCR was significantly decreased in tumor tissues (P < 0.0001). However, expression of STAT3 was not meaningfully different between the two tissue types (P = 0.56). Since there was no significant correlation between MAPK1 and BANCR expressions in either tumor or adjacent normal tissues, the regulatory effect of BANCR on MAPK1 was not confirmed. In conclusion, this study offers information about deregulation of bioinformatically identified genes in PA tumors and indicates that further studies in this field is needed to understand the involved molecular mechanisms.

Establishment of a diagnostic model based on immune-related genes in children with asthma

Objective

Allergic asthma is driven by an antigen-specific immune response. This study aimed to identify immune-related differentially expressed genes in childhood asthma and establish a classification diagnostic model based on these genes.

Methods

GSE65204 and GSE19187 were downloaded and served as training set and validation set. The immune cell composition was evaluated with ssGSEA algorithm based on the immune-related gene set. Modules that significantly related to the asthma were selected by WGCNA algorithm. The immune-related differentially expressed genes (DE-IRGs) were screened, the protein-protein interaction network and diagnostic model of DE-IRGs was constructed. The pathway and immune correlation analysis of hub DE-IRGs was analyzed.

Results

Eight immune cell types exhibited varying levels of abundance between the asthma and control groups. A total of 112 differentially expressed immune-related genes (DE-IRGs) was identified. Through the application of four ranking methods (MCC, MNC, DEGREE, and EPC), 17 hub DE-IRGs with overlapping significance were further selected. Subsequently, 8 optimized were identified using univariate logistic regression analysis and the LASSO regression algorithm, based on which a robust diagnostic model was constructed. Notably, TNF and CD40LG emerged as direct participants in asthma-related signaling pathways, displaying a positive correlation with the immune cell types of immature B cells, activated B cells, activated CD8 T cells, activated CD4 T cells, and myeloid-derived suppressor cells.

Conclusion

The diagnostic model constructed using the DE-IRGs (CCL5, CCR5, CD40LG, CD8A, IL2RB, PDCD1, TNF, and ZAP70) exhibited high and specific diagnostic value for childhood asthma. The diagnostic model may contribute to the diagnosis of childhood asthma.

Machine learning and experimental validation identified autophagy signature in hepatic fibrosis

Background

The molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.

Methods

Gene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.

Results

A total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.

Conclusion

ATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell.

Time-series analysis of hematopoietic stem cells

This study aimed to investigate the molecular mechanisms underlying the aging of hematopoietic stem cells (HSCs). Gene expression profile GSE32719 was downloaded from the Gene Expression Omnibus database, including 14 young, 5 middle, and 8 old HSCs. Differential expression analysis, short time-series expression miner analysis, and weighted co-expression network analysis were conducted to screen for hub genes whose expression changed over time during HSC aging. Subsequently, functional enrichment and multiple regulatory network analyses of the hub genes were performed. A total of 124 intersecting time-dependent differentially expressed and module genes were obtained, which were considered hub genes whose expression changed over time during HSC aging. Hub genes were significantly enriched in pathways such as the Hippo and AMP-activated protein kinase (AMPK) signaling pathways. Moreover, AP-1 Transcription Factor Subunit (FOS) and sirtuin 1 (SIRT1) had higher degrees in the protein-protein interaction network, were regulated by more transcription factors (TFs), such as Sp1 transcription factor (SP1) and BRCA1 DNA repair-associated (BRCA1), in the TF-mRNA-miRNA network, were associated with more diseases in the disease-gene network, and could be targeted by more drugs in the drug-gene network. Furthermore, SIRT1 was targeted by miR-9-5p in the TF-mRNA-miRNA network. Hub genes such as FOS and SIRT1 and key pathways such as the Hippo and AMPK signaling pathways may play crucial roles in HSC aging. Moreover, FOS and SIRT1 were regulated by SP1 and BRCA1, respectively, during HSC aging. Furthermore, miR-9-5p may modulate HSC aging by targeting SIRT1. Thus, FOS and SIRT1 may be potential therapeutic targets for age-related hematopoietic dysfunction.

Identification of a shared gene signature and biological mechanism between diabetic foot ulcers and cutaneous lupus erythemnatosus by transcriptomic analysis

Diabetic foot ulcers (DFU) and cutaneous lupus erythematosus (CLE) are both diseases that can seriously affect a patient's quality of life and generate economic pressure in society. Symptomatically, both DLU and CLE exhibit delayed healing and excessive inflammation; however, there is little evidence to support a molecular and cellular connection between these two diseases. In this study, we investigated potential common characteristics between DFU and CLE at the molecular level to provide new insights into skin diseases and regeneration, and identify potential targets for the development of new therapies. The gene expression profiles of DFU and CLE were obtained from the Gene Expression Omnibus (GEO) database and used for analysis. A total of 41 common differentially expressed genes (DEGs), 16 upregulated genes and 25 downregulated genes, were identified between DFU and CLE. GO and KEGG analysis showed that abnormalities in epidermal cells and the activation of inflammatory factors were both involved in the occurrence and development of DFU and CLE. Protein-protein interaction network (PPI) and sub-module analysis identified enrichment in seven common key genes which is KRT16, S100A7, KRT77, OASL, S100A9, EPGN and SAMD9. Based on these seven key genes, we further identified five miRNAs(has-mir-532-5p, has-mir-324-3p,has-mir-106a-5p,has-mir-20a-5p,has-mir-93-5p) and7 transcription factors including CEBPA, CEBPB, GLI1, EP30D, JUN,SP1, NFE2L2 as potential upstream molecules. Functional immune infiltration assays showed that these genes were related to immune cells. The CIBERSORT algorithm and Pearson method were used to determine the correlations between key genes and immune cells, and reverse key gene-immune cell correlations were found between DFU and CLE. Finally, the DGIbd database demonstrated that Paquinimod and Tasquinimod could be used to target S100A9 and Ribavirin could be used to target OASL. Our findings highlight common gene expression characteristics and signaling pathways between DFU and CLE, indicating a close association between these two diseases. This provides guidance for the development of targeted therapies and mutual interactions.

DIRECTEUR: transcriptome-based prediction of small molecules that replace transcription factors for direct cell conversion

MOTIVATION

Direct reprogramming (DR) is a process that directly converts somatic cells to target cells. Although DR via small molecules is safer than using transcription factors (TFs) in terms of avoidance of tumorigenic risk, the determination of DR-inducing small molecules is challenging.

RESULTS

Here we present a novel in silico method, DIRECTEUR, to predict small molecules that replace TFs for DR. We extracted DR-characteristic genes using transcriptome profiles of cells in which DR was induced by TFs, and performed a variant of simulated annealing to explore small molecule combinations with similar gene expression patterns with DR-inducing TFs. We applied DIRECTEUR to predicting combinations of small molecules that convert fibroblasts into neurons or cardiomyocytes, and were able to reproduce experimentally verified and functionally related molecules inducing the corresponding conversions. The proposed method is expected to be useful for practical applications in regenerative medicine.

AVAILABILITY AND IMPLEMENTATION

The code and data are available at the following link: https://github.com/HamanoLaboratory/DIRECTEUR.git.

Immune Cell Infiltration Types as Biomarkers for the Recurrence Diagnosis and Prognosis of Bladder Cancer

This study aimed to investigate the role of infiltrating immune cell types in diagnosing and predicting bladder cancer recurrence. This study mainly applied some algorithms, including Estimate the Proportion of Immune and Cancer Cells (EPIC), support vector machine-recursive feature elimination (SVM-RFE), random forest out-of-bag (RF-OOB) and least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. We found six immune infiltrating cell types significantly associated with recurrence prognosis and two independent clinical prognostic factors. Infiltrating immune cell types (IICTs) based on the prognostic immune risk score (pIRS) models may provide significant biomarkers for the diagnosis and prognostic prediction of bladder cancer recurrence.

Growth signaling autonomy in circulating tumor cells aids metastatic seeding

Self-sufficiency (autonomy) in growth signaling, the earliest recognized hallmark of cancer, is fueled by the tumor cell's ability to "secrete-and-sense" growth factors (GFs); this translates into cell survival and proliferation that is self-sustained by autocrine/paracrine secretion. A Golgi-localized circuitry comprised of two GTPase switches has recently been implicated in the orchestration of growth signaling autonomy. Using breast cancer cells that are either endowed or impaired (by gene editing) in their ability to assemble the circuitry for growth signaling autonomy, here we define the transcriptome, proteome, and phenome of such an autonomous state, and unravel its role during cancer progression. We show that autonomy is associated with enhanced molecular programs for stemness, proliferation, and epithelial-mesenchymal plasticity. Autonomy is both necessary and sufficient for anchorage-independent GF-restricted proliferation and resistance to anticancer drugs and is required for metastatic progression. Transcriptomic and proteomic studies show that autonomy is associated, with a surprising degree of specificity, with self-sustained epidermal growth factor receptor (EGFR)/ErbB signaling. Derivation of a gene expression signature for autonomy revealed that growth signaling autonomy is uniquely induced in circulating tumor cells (CTCs), the harshest phase in the life of tumor cells when it is deprived of biologically available epidermal growth factor (EGF). We also show that autonomy in CTCs tracks therapeutic response and prognosticates outcome. These data support a role for growth signaling autonomy in multiple processes essential for the blood-borne dissemination of human breast cancer.

Novel ferroptosis gene biomarkers and immune infiltration profiles in diabetic kidney disease via bioinformatics

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, exhibiting high disability and mortality rates. Ferroptosis is vital for the progression of DKD, but the exact mechanism remains unclear. This study aimed to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with the immune and to identify new diagnostic biomarkers to help treat and diagnose DKD. GSE30122 and GSE47185 were obtained from the Gene Expression Omnibus database and were integrated into a merged dataset, followed by functional enrichment analysis. Then potential differentially expressed genes were screened. Ferroptosis-related differentially-expressed genes were identified, followed by gene ontology analysis. Protein-protein interaction networks were constructed and hub genes were screened. The immune cell-infiltrating state in the dataset was assessed using appropriate algorithms. Immune signature subtypes were constructed using the consensus clustering analysis. Hub gene expression was validated using qRT-PCR and immunohistochemistry. A total of Eleven screened ferroptosis-related differentially expressed genes were screened. Six potentially diagnostically favorable ferroptosis-related hub genes were identified. Significantly increased expression of γδT cells, resting mast cells, and macrophages infiltration was observed in the DKD group. Additionally, two distinct immune signature subgroups were identified. Ferroptosis-related hub genes were significantly correlated with differentially infiltrated immune cells. Six hub genes were significantly upregulated in HK-2 cells following high glucose treatment and in human kidney tissues of patients with DKD. Six ferroptosis-related hub genes were identified as potential biomarkers of diabetic kidney disease, but further validation is needed.

Comprehensive analysis of an endoplasmic reticulum stress-related gene prediction model and immune infiltration in idiopathic pulmonary fibrosis

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive interstitial lung disease. This study aimed to investigate the involvement of endoplasmic reticulum stress (ERS) in IPF and explore its correlation with immune infiltration.

Methods

ERS-related differentially expressed genes (ERSRDEGs) were identified by intersecting differentially expressed genes (DEGs) from three Gene Expression Omnibus datasets with ERS-related gene sets. Gene Set Variation Analysis and Gene Ontology were used to explore the potential biological mechanisms underlying ERS. A nomogram was developed using the risk signature derived from the ERSRDEGs to perform risk assessment. The diagnostic value of the risk signature was evaluated using receiver operating characteristics, calibration, and decision curve analyses. The ERS score of patients with IPF was measured using a single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. Subsequently, a prognostic model based on the ERS scores was established. The proportion of immune cell infiltration was assessed using the ssGSEA and CIBERSORT algorithms. Finally, the expression of ERSRDEGs was validated in vivo and in vitro via RT-qPCR.

Results

This study developed an 8-ERSRDEGs signature. Based on the expression of these genes, we constructed a diagnostic nomogram model in which agouti-related neuropeptide had a significantly greater impact on the model. The area under the curve values for the predictive value of the ERSRDEGs signature were 0.975 and 1.000 for GSE70866 and GSE110147, respectively. We developed a prognostic model based on the ERS scores of patients with IPF. Furthermore, we classified patients with IPF into two subtypes based on their signatures. The RT-qPCR validation results supported the reliability of most of our conclusions.

Conclusion

We developed and verified a risk model using eight ERSRDEGs. These eight genes can potentially affect the progression of IPF by regulating ERS and immune responses.

Identification of TNFRSF1A as a potential biomarker for osteosarcoma

BACKGROUND

Osteosarcoma (OS) is a relatively rare malignant bone tumor in teenagers; however, its molecular mechanisms are not yet understood comprehensively.

OBJECTIVE

The study aimed to use necroptosis-related genes (NRGs) and their relationships with immune-related genes to construct a prognostic signature for OS.

METHODS

TARGET-OS was used as the training dataset, and GSE 16091 and GSE 21257 were used as the validation datasets. Univariate regression, survival analysis, and Kaplan-Meier curves were used to screen for hub genes. The immune-related targets were screened using immune infiltration assays and immune checkpoints. The results were validated using nomogram and decision curve analyses (DCA).

RESULTS

Using univariate Cox regression analysis, TNFRSF1A was screened from 14 NRGs as an OS prognostic signature. Functional enrichment was analyzed based on the median expression of TNFRSF1A. The prognosis of the TNFRSF1A low-expression group in the Kaplan-Meier curve was notably worse. Immunohistochemistry analysis showed that the number of activated T cells and tumor purity increased considerably. Furthermore, the immune checkpoint lymphocyte activation gene 3 (LAG-3) is a possible target for intervention. The nomogram accurately predicted 1-, 3-, and 5-year survival rates. DCA validated the model (C = 0.669).

Conclusion

TNFRSF1A can be used to elucidate the potential relationship between the immune microenvironment and NRGs in OS pathogenesis.

Integrative analysis of an endoplasmic reticulum stress-related signature in multiple myeloma

BACKGROUND

Multiple myeloma (MM) is a malignancy in which plasma cells proliferate abnormally, and it remains incurable. The cells are characterized by high levels of endoplasmic reticulum stress (ERS) and depend on the ERS response for survival. Thus, we aim to find an ERS-related signature of MM and assess its diagnostic value.

METHODS

We downloaded three datasets of MM from the Gene Expression Omnibus database. After identifying ERS-related differentially expressed genes (ERDEGs), we analyzed them using Gene Ontology enrichment analysis. A protein-protein interaction network, a transcription factor-mRNA network, a miRNA-mRNA network and a drug-mRNA network were constructed to explore the ERDEGs. The clinical application of these genes was identified by calculating the infiltration of immune cells and using receiver operating characteistic analyses. Finally, qPCR was performed to further confirm the roles of ERDEGs.

RESULTS

We obtained nine ERDEGs of MM. Gene Ontology enrichment indicated that the ERDEGs played a role in the endoplasmic reticulum membrane. Additionally, the protein-protein interaction network showed interaction among the ERDEGs, and there were 20 proteins, 107 transcription factors, 42 drugs or molecular compounds and 51 miRNAs which were likely to interact with the nine genes. In addition, immune cell infiltration analyses showed that there was a strong correlation between the nine genes and immune cells, and these potential biomarkers exhibited good diagnostic values. Finally, the expression of ERDEGs in MM cells was different from that in healthy donor samples.

CONCLUSION

The nine ERS-related genes, CR2, DHCR7, DNAJC3, KDELR2, LPL, OSBPL3, PINK1, VCAM1 and XBP1 are potential biomarkers of MM, and this supports further clinical development of the diagnosis and treatment of MM.

Machine learning identifies ferroptosis-related gene ANXA2 as potential diagnostic biomarkers for NAFLD

Introduction

Non-alcoholic fatty liver disease (NAFLD), a major cause of chronic liver disease, still lacks effective therapeutic targets today. Ferroptosis, a type of cell death characterized by lipid peroxidation, has been linked to NAFLD in certain preclinical trials, yet the exact molecular mechanism remains unclear. Thus, we analyzed the relationship between ferroptosis genes and NAFLD using high-throughput data.

Method

We utilized a total of 282 samples from five datasets, including two mouse ones, one human one, one single nucleus dataset and one single cell dataset from Gene Expression Omnibus (GEO), as the data basis of our study. To filter robust treatment targets, we employed four machine learning methods (LASSO, SVM, RF and Boruta). In addition, we used an unsupervised consensus clustering algorithm to establish a typing scheme for NAFLD based on the expression of ferroptosis related genes (FRGs). Our study is also the first to investigate the dynamics of FRGs throughout the disease process by time series analysis. Finally, we validated the relationship between core gene and ferroptosis by in vitro experiments on HepG2 cells.

Results

We discovered ANXA2 as a central focus in NAFLD and indicated its potential to boost ferroptosis in HepG2 cells. Additionally, based on the results obtained from time series analysis, ANXA2 was observed to significantly define the disease course of NAFLD. Our results demonstrate that implementing a ferroptosis-based staging method may hold promise for the diagnosis and treatment of NAFLD.

Conclusion

Our findings suggest that ANXA2 may be a useful biomarker for the diagnosis and characterization of NAFLD.

Coiled-coil domain-containing 154 promotes colorectal cancer proliferation and metastasis via interacting with minichromosome maintenance complex component 2

Coiled-Coil Domain-Containing (CCDC) is a large class of structural proteins containing left-handed supercoiled structure. The clinical value and the functional implication of CCDC in colorectal cancer (CRC) remain unknown. Based on the genetic, transcriptional, and clinical data from The Cancer Genome Atlas, five of thirty-six CCDC proteins were differentially expressed in the CRC and associated with the survival of patients with CRC. A CCDC-score model was established to evaluate the prognosis of patients. The potential function of Coiled-Coil Domain-Containing 154 (CCDC154) was investigated using bioinformatical methods, which unveiled that high expression of CCDC154 indicates poor survival for patients with CRC and correlates with low infiltration of CD8+ T cells and high infiltration of neutrophils, indicating that CCDC154 enhances tumor growth and metastasis. CCDC154 interacts with Minichromosome Maintenance Complex Component 2 (MCM2) protein and promotes malignant phenotype via MCM2. We validated the expression level and survival prediction value of CCDC154 in clinical samples, and analyzed its co-expression of MCM2, Ki-67 and p53. This work discloses the role of CCDC in clinical setting and CCDC154 functions in CRC.

Prognostic and immune correlation analysis of mitochondrial autophagy and aging-related genes in lung adenocarcinoma

PURPOSE

Mitophagy and aging (MiAg) are very important pathophysiological mechanisms contributing to tumorigenesis. MiAg-related genes have prognostic value in lung adenocarcinoma (LUAD). However, prognostic, and immune correlation studies of MiAg-related genes in LUAD are lacking.

METHODS

MiAg differentially expressed genes (DEGs) in LUAD were obtained from public sequencing datasets. A prognostic model including MiAg DEGs was constructed according to patients divided into low- and high-risk groups. Gene Ontology, gene set enrichment analysis, gene set variation analysis, CIBERSORT immune infiltration analysis, and clinical characteristic correlation analyses were performed for functional annotation and correlation of MiAgs with prognosis in patients with LUAD.

RESULTS

Seven MiAg DEGs of LUAD were identified: CAV1, DSG2, DSP, MYH11, NME1, PAICS, PLOD2, and the expression levels of these genes were significantly correlated (P < 0.05). The RiskScore of the MiAg DEG prognostic model demonstrated high predictive ability of overall survival of patients diagnosed with LUAD. Patients with high and low MiAg phenotypic scores exhibited significant differences in the infiltration levels of eight types of immune cells (P < 0.05). The multi-factor DEG regression model showed higher efficacy in predicting 5-year survival than 3- and 1-year survival of patients with LUAD.

CONCLUSIONS

Seven MiAg-related genes were identified to be significantly associated with the prognosis of patients diagnosed with LUAD. Moreover, the identified MiAg DEGs might affect the immunotherapy strategy of patients with LUAD.

Integrative bioinformatics analysis of biomarkers and pathways for exploring the mechanisms and molecular targets associated with pyroptosis in type 2 diabetes mellitus

Introduction

Research has shown that pyroptosis contributes greatly to the progression of diabetes and its complications. However, the exact relationship between this particular cell death process and the pathology of type 2 diabetes mellitus (T2DM) remains unclear. In this study, we used bioinformatic tools to identify the pyroptosis-related genes (PRGs) associated with T2DM and to analyze their roles in the disease pathology.

Methods

Two microarray datasets, GSE7014 and GSE25724, were obtained from the GEO database and assessed for differentially expressed genes (DEGs). The T2DM-associated DEGs that overlapped with differentially expressed PRGs were noted as T2DM-PRGs. Subsequently, 25 T2DM-PRGs were validated and subjected to functional enrichment analysis through Gene Ontology annotation analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis, and gene set enrichment analysis (GSEA). The diagnostic and predictive value of the T2DM-PRGs was evaluated using receiver operating characteristic curves (ROC). Additionally, a single-sample GSEA algorithm was applied to study immune infiltration in T2DM and assess immune infiltration levels.

Results

We identified 25 T2DM-PRGs that were significantly enriched in the nuclear factor-kappa B signaling and prostate cancer pathways. The top five differentially expressed prognostic T2DM-PRGs targeted by miRNAs were PTEN, BRD4, HSP90AB1, VIM, and PKN2. The top five differentially expressed T2DM-PRGs associated with transcription factors were HSP90AB1, VIM, PLCG1, SCAF11, and PTEN. The genes PLCG1, PTEN, TP63, CHI3L1, SDHB, DPP8, BCL2, SERPINB1, ACE2, DRD2, DDX58, and BTK showed excellent diagnostic performance. The immune infiltration analysis revealed notable differences in immune cells between T2DM and normal tissues in both datasets. These findings suggest that T2DM-PRGs play a crucial role in the development and progression of T2DM and could be used as potential diagnostic biomarkers and therapeutic targets.

Discussion

Investigating the mechanisms and biomarkers associated with pyroptosis may offer valuable insights into the pathophysiology of T2DM and lead to novel therapeutic approaches to treat the disease.

Prediction of lncRNA functions using deep neural networks based on multiple networks

BACKGROUND

More and more studies show that lncRNA is widely involved in various physiological processes of the organism. However, the functions of the vast majority of them continue to be unknown. In addition, data related to lncRNAs in biological databases are constantly increasing. Therefore, it is quite urgent to develop a computing method to make the utmost of these data.

RESULTS

In this paper, we propose a new computational method based on global heterogeneous networks to predict the functions of lncRNAs, called DNGRGO. DNGRGO first calculates the similarities among proteins, miRNAs, and lncRNAs, and annotates the functions of lncRNAs according to its similar protein-coding genes, which have been labeled with gene ontology (GO). To evaluate the performance of DNGRGO, we manually annotated GO terms to lncRNAs and implemented our method on these data. Compared with the existing methods, the results of DNGRGO show superior predictive performance of maximum F-measure and coverage.

CONCLUSIONS

DNGRGO is able to annotate lncRNAs through capturing the low-dimensional features of the heterogeneous network. Moreover, the experimental results show that integrating miRNA data can help to improve the predictive performance of DNGRGO.

Exploration of the common pathogenic link between COVID-19 and diabetic foot ulcers: An in silico approach

Background and Aims

The Coronavirus Disease-19 (COVID-19) is posing an ongoing threat to human health. Patients of diabetic foot ulcer (DFU) are susceptible to COVID-19-induced adverse outcomes. Nevertheless, investigations into their mutual molecular mechanisms have been limited to date. In the present work, we tried to uncover the shared pathogenesis and regulatory gene targets of COVID-19 and DFU.

Methods

In this study, we chose GSE161281 as the COVID-19 data set, which contained severe acute respiratory syndrome coronavirus 2 infected human induced embryonic stem cell-derived peripheral neurons (n = 2) with uninfected controls (n = 2). The GSE134431 designated as the DFU data set, comprising full-thickness DFU (n = 13) and diabetic foot skin (n = 8) samples from diabetic patients. The differential expressed genes (DEGs) were identified from GSE161281 and GSE134431, and the common DEGs between COVID-19 and DFU were extracted. Multifactor regulatory network and co-expression network of the common DEGs were analyzed, along with candidate drug prediction.

Results

Altogether, six common DEGs (dickkopf-related protein 1 [DKK1], serine proteinase inhibitor A3 [SERPINA3], ras homolog family member D [RHOD], myelin protein zero like 3 [MPZL3], Claudin-11 [CLDN11], and epidermal growth factor receptor pathway substrate 8-like 1 [EPS8L1]) were found between COVID-19 and DFU. Functional analyses indicated that pathways of apoptotic and Wnt signaling may contribute to progression of COVID-19. Gene co-expression network implied the shared pathways of immune regulation and cytokine response participated collectively in the development of DFU and COVID-19. A multifactor regulatory network was constructed integrating the corresponding microRNAs (miRNAs) and transcription factors. Additionally, we proposed potential drug objects for the combined therapy.

Conclusion

Our study revealed the shared molecular mechanisms underlying COVID-19 and DFU. The identified pivotal targets and common pathways can provide new perspectives for further research and assist the development of management strategies in patients of DFU complicated with COVID-19.

Single-cell sequencing and transcriptome analyses in the construction of a liquid-liquid phase separation-associated gene model for rheumatoid arthritis

Background: Rheumatoid arthritis (RA) is a disabling autoimmune disease that affects multiple joints. Accumulating evidence suggests that imbalances in liquid-liquid phase separation (LLPS) can lead to altered spatiotemporal coordination of biomolecular condensates, which play important roles in carcinogenesis and inflammatory diseases. However, the role of LLPS in the development and progression of RA remains unclear. Methods: We screened RA and normal samples from GSE12021, GSE55235, and GSE55457 transcriptome datasets and GSE129087 and GSE109449 single-cell sequencing datasets from Gene Expression Omnibus database to investigate the pathogenesis of LLPS-related hub genes at the transcriptome and single cell sequencing levels. Machine learning algorithms and weighted gene co-expression network analysis were applied to screen hub genes, and hub genes were validated using correlation studies. Results: Differential analysis showed that 36 LLPS-related genes were significantly differentially expressed in RA, further random forest and support vector machine identified four and six LLPS-related genes, respectively, and weighted gene co-expression network analysis identified 396 modular genes. Hybridization of the three sets revealed two hub genes, MYC and MAP1LC3B, with AUCs of 0.907 and 0.911, respectively. Further ROC analysis of the hub genes in the GSE55457 dataset showed that the AUCs of MYC and MAP1LC3B were 0.815 and 0.785, respectively. qRT-PCR showed that the expression of MYC and MAP1LC3B in RA synovial tissues was significantly lower than that in the normal control synovial tissues. Correlation analysis between hub genes and the immune microenvironment and single-cell sequencing analysis revealed that both MYC and MAP1LC3B were significantly correlated with the degree of infiltration of various innate and acquired immune cells. Conclusion: Our study reveals a possible mechanism for LLPS in RA pathogenesis and suggests that MYC and MAP1LC3B may be potential novel molecular markers for RA with immunological significance.

Use of a ferroptosis-related gene signature to construct diagnostic and prognostic models for assessing immune infiltration in metabolic dysfunction-associated fatty liver disease

Introduction: Metabolic dysfunction-associated fatty liver disease (MAFLD), a serious health problem worldwide, can involve ferroptosis. This study aimed to comprehensively analyze the ferroptosis-related genes associated with MAFLD. Methods: Ferroptosis-related differentially expressed genes (FRDEGs) were identified in patients with MAFLD and healthy individuals. Gene ontology functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis, and gene set enrichment analysis (GSEA) were used to analyze the relevant action pathways of the FRDEGs. The Encyclopedia of RNA Interactomes, CHIPBase, and comparative toxicogenomics databases were used to build mRNA-miRNA, mRNA-transcription factor (TF), and mRNA-drug interaction networks, respectively. A diagnostic model was constructed and bioinformatics analysis methods, such as least absolute shrinkage and selection operator regression analysis, Cox regression analysis, nomogram-based analysis, consensus clustering analysis, and single-sample GSEA, were used to systematically investigate the prognostic values and immunologic characteristics. Results: A total of 13 FRDEGs were obtained and eight were used to construct a diagnostic model and perform a prognostic analysis. Hub genes were also used to construct mRNA-miRNA and mRNA-TF interaction networks and potential drug or molecular compounds. Two MAFLD subtypes were identified: cluster2, which represents an "immunoactive" type, and cluster1, which represents an "immunosuppressive" type; a significant correlation was observed between the immune cell contents and the expression of three FRDEGs (NR4A1, FADS2, and SCD). Conclusion: A ferroptosis-related gene signature was constructed to diagnose MAFLD-associated steatohepatitis, predict the prognosis of MAFLD patients, and analyze the immunologic characteristics of MAFLD. Our findings may provide insights into developing innovative MAFLD treatment techniques.

Applying a Gene Reversal Rate Computational Methodology to Identify Drugs for a Rare Cancer: Inflammatory Breast Cancer

The aim of this study was to utilize a computational methodology based on Gene Reversal Rate (GRR) scoring to repurpose existing drugs for a rare and understudied cancer: inflammatory breast cancer (IBC). This method uses IBC-related gene expression signatures (GES) and drug-induced gene expression profiles from the LINCS database to calculate a GRR score for each candidate drug, and is based on the idea that a compound that can counteract gene expression changes of a disease may have potential therapeutic applications for that disease. Genes related to IBC with associated differential expression data (265 up-regulated and 122 down-regulated) were collated from PubMed-indexed publications. Drug-induced gene expression profiles were downloaded from the LINCS database and candidate drugs to treat IBC were predicted using their GRR scores. Thirty-two (32) drug perturbations that could potentially reverse the pre-compiled list of 297 IBC genes were obtained using the LINCS Canvas Browser (LCB) analysis. Binary combinations of the 32 perturbations were assessed computationally to identify combined perturbations with the highest GRR scores, and resulted in 131 combinations with GRR greater than 80%, that reverse up to 264 of the 297 genes in the IBC-GES. The top 35 combinations involve 20 unique individual drug perturbations, and 19 potential drug candidates. A comprehensive literature search confirmed 17 of the 19 known drugs as having either anti-cancer or anti-inflammatory activities. AZD-7545, BMS-754807, and nimesulide target known IBC relevant genes: PDK, Met, and COX, respectively. AG-14361, butalbital, and clobenpropit are known to be functionally relevant in DNA damage, cell cycle, and apoptosis, respectively. These findings support the use of the GRR approach to identify drug candidates and potential combination therapies that could be used to treat rare diseases such as IBC.

m6A Regulator-mediated RNA Methylation Modulates Immune Microenvironment of Hepatitis B Virus-related Acute Liver Failure

Acute liver failure (ALF) is a rare and complicated disease with a high mortality rate. Emergency liver transplantation is the only treatment method that can improve the ALF prognosis. However, its clinical application remains limited owing to the aggressive nature of liver transplantation, limited donors, and high postoperative mortality. The study investigated the effect of m6A on the immune microenvironment of hepatitis B virus-related ALF (HBV-ALF). In this study, the gene expression data of 47 normal people and 42 HBV-ALF patients were downloaded from the Gene Expression Omnibu (GEO) database. The known 23 m6A regulators which mediated RNA modification patterns were compared and analyzed in these two groups, and the gene diagnosis model of HBV-ALF patients was established based on the analysis results. In addition, we used unsupervised clustering to identify different m6A RNA methylation modification patterns in HBV-ALF based on m6A regulators, and evaluated the immune infiltration and biological differences in these subtypes. In addition, the relationship between m6A genes and immune cell activation in HBV-ACLF patients was explored by immune infiltration analysis. Nineteen m6A regulators mediated RNA methylation (m6A regulators for short) were differentially expressed in HBV-ALF and control groups. m6A regulators could well distinguish control samples from HBV-ALF samples, and m6A regulators might be used as a basis for diagnosing HBV-ALF patients. Immune cells such as activated CD8 T cells, activated B cells, and activated CD4 T cells might play important roles in HBV-ALF, and m6A regulators were closely associated with immune cell infiltration. ALKBH15, CBLL1, IGF2BP2, IGF2BP3, and ZC3H13 were significantly associated with immune cells. Considering 23 m6A regulators, HBV-ALF patients could be classified into two subtypes (cluster 1 and cluster 2) based on different immune cell infiltration. m6A regulators of the IGFBP and YHDF families have extremely different levels in these two subtypes. Differential immune cell infiltration among these subtypes was observed, a total of 913 differentially expressed genes among different m6A modification patterns was identified, and their biological functions were explored. m6A modification might play a crucial role in the diverse and complex immune microenvironment of HBV-ALF patients.

Identification of LOX as a candidate prognostic biomarker in Glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most malignant type of glioma. GBM tumors grow rapidly, have a high degree of malignancy, and are characterized by a fast disease progression. Unfortunately, there is a lack of effective treatments. An effective strategy for the treatment of GBM would be to identify key biomarkers correlating with the occurrence and progression of GBM and developing these biomarkers into therapeutic targets.

METHOD AND RESULTS

In this study, using integrated bioinformatics analysis, we identified differentially expressed genes (DEGs), including 130 genes that were upregulated in GBM compared to normal brain tissue, and 128 genes that were downregulated in GBM. Based on Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, these genes were associated with regulation of tumor cell adhesion, differentiation, morphology in GBM and were mainly enriched in Complement and coagulation cascades pathway. The Search Tool for the Retrieval of Interacting Genes (STRING) database was used to construct a Protein-Protein Interaction network. Ten hub genes were identified, including FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2, all of which were significantly upregulated in GBM, these results were confirmed by oncomine database exploration. Alteration analysis of hub genes found that patients with alteration in at least one of the hub genes showed shorter median survival times (p = 0.013) and shorter median disease-free survival times (p = 2.488E-3) than patients without alterations in any of the hub genes. Multiple tests for survival analysis showed that among individual hub genes only expression of LOX was correlated with patient survival (P < 0.05).GDS4467 data set was used to analyze the expression of LOX in gliomas with different degrees of malignancy, and it was found that the expression level of LOX was positively correlated with the malignant degree of gliomas.By analyzing GDS 4535 data set showed that the expression level of LOX was positively correlated with the differentiation degree of GBM cells CONCLUSION: This research suggests that FN1, CD44, MYC, CDK1, SERPINE1, COL3A1, COL1A2, LOX, POSTN and EZH2 are key genes in GBM. However, only LOX is correlated with patient survival and promotes glioblastoma cell differentiation and tumor recurrence. LOX may be a candidate prognostic biomarker and potential therapeutic target for GBM.

Identification and validation of a prognostic signature of cuproptosis-related genes for esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is a highly lethal form of cancer. Cuproptosis is a recently discovered form of regulated cell death. However, its significance in ESCC remains largely unknown. In this study, we observed significant expression differences in most of the 12 cuproptosis-related genes (CRGs) in the TCGA-ESCC dataset, which was validated using GSE20347, GSE38129, and individual ESCC datasets. We were able to divide patients in the TCGA-ESCC cohort into two subgroups based on disease, and found significant differences in survivor outcomes and biological functions between these subgroups. Additionally, we identified 11 prognosis-related genes from the 12 CRGs using LASSO COX regression analysis and constructed a CRGs signature for ESCC. Patients were categorized into high- and low-risk subgroups based on their median risk score, with those in the high-risk subgroup having significantly worse overall survival than those in the low-risk subgroup. The CRGs signature was also highly accurate in predicting prognosis and survival outcomes. Univariate and multivariate Cox regression analyses revealed that 8 of the 11 CRGs were independent prognostic factors for predicting survival in ESCC patients. Furthermore, our nomogram performed well and could serve as a useful tool for predicting prognosis. Finally, our risk model was found to be relevant to the sensitivity of targeted agents and immune infiltration. Functional enrichment analysis demonstrated that the risk model was associated with biological pathways of tumor migration and invasion. In summary, our study may provide a promising prognostic signature based on CRGs and offers potential targets for personalized therapy.

Brain-wide transcriptome-based metabolic alterations in Parkinson's disease: human inter-region and human-experimental model correlations

Alterations in brain metabolism are closely associated with the molecular hallmarks of Parkinson's disease (PD). A clear understanding of the main metabolic perturbations in PD is therefore important. Here, we retrospectively analysed the expression of metabolic genes from 34 PD-control post-mortem human brain transcriptome data comparisons from literature, spanning multiple brain regions. We found high metabolic correlations between the Substantia nigra (SN)- and cerebral cortex-derived tissues. Moreover, three clusters of PD patient cohorts were identified based on perturbed metabolic processes in the SN - each characterised by perturbations in (a) bile acid metabolism (b) omega-3 fatty acid metabolism, and (c) lipoic acid and androgen metabolism - metabolic themes not comprehensively addressed in PD. These perturbations were supported by concurrence between transcriptome and proteome changes in the expression patterns for CBR1, ECI2, BDH2, CYP27A1, ALDH1B1, ALDH9A1, ADH5, ALDH7A1, L1CAM, and PLXNB3 genes, providing a valuable resource for drug targeting and diagnosis. Also, we analysed 58 PD-control transcriptome data comparisons from in vivo/in vitro disease models and identified experimental PD models with significant correlations to matched human brain regions. Collectively, our findings suggest metabolic alterations in several brain regions, heterogeneity in metabolic alterations between study cohorts for the SN tissues and the need to optimize current experimental models to advance research on metabolic aspects of PD.

Identification of Potential Biomarkers of Septic Shock Based on Pathway and Transcriptome Analyses of Immune-Related Genes

Immunoregulation is crucial to septic shock (SS) but has not been clearly explained. Our aim was to explore potential biomarkers for SS by pathway and transcriptional analyses of immune-related genes to improve early detection. GSE57065 and GSE95233 microarray data were used to screen differentially expressed genes (DEGs) in SS. Gene Ontology and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment analyses of DEGs were performed, and correlations between immune cell and pathway enrichment scores were analyzed. The predictive value of candidate genes was evaluated by receiver operating characteristic (ROC) curves. GSE66099, GSE4607, and GSE13904 datasets were used for external validation. Blood samples from six patients and six controls were collected for validation by qRT-PCR and western blotting. In total, 550 DEGs in SS were identified; these genes were involved in the immune response, inflammation, and infection. Immune-related pathways and levels of infiltration of CD4 + TCM, CD8 + T cells, and preadipocytes differed between SS cases and controls. Seventeen genes were identified as potential biomarkers of SS (areas under ROC curves >0.9). The downregulation of CD8A, CD247, CD3G, LCK, and HLA-DRA in SS was experimentally confirmed. We identified several immune-related biomarkers in SS that may improve early identification of disease risk.

Idiopathic Pulmonary Fibrosis Molecular Substrates Revealed by Competing Endogenous RNA Regulatory Networks

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrotic disease of the lung with poor prognosis. Fibrosis results from remodeling of the interstitial tissue. A wide range of gene expression changes are observed, but the role of micro RNAs (miRNAs) and circular RNAs (circRNA) is still unclear. Therefore, this study aimed to establish an messenger RNA (mRNA)-miRNA-circRNA competing endogenous RNA (ceRNA) regulatory network to uncover novel molecular signatures using systems biology tools. Six datasets were used to determine differentially expressed genes (DEGs) and miRNAs (DEmiRNA). Accordingly, protein-protein, mRNA-miRNA, and miRNA-circRNA interactions were constructed. Modules were determined and further analyzed in the Drug Gene Budger platform to identify potential therapeutic compounds. We uncovered common 724 DEGs and 278 DEmiRNAs. In the protein-protein interaction network, TMPRSS4, ESR2, TP73, CLEC4E, and TP63 were identified as hub protein coding genes. The mRNA-miRNA interaction network revealed two modules composed of ADRA1A, ADRA1B, hsa-miR-484 and CDH2, TMPRSS4, and hsa-miR-543. The DEmiRNAs in the modules further analyzed to propose potential circRNA regulators in the ceRNA network. These results help deepen the understanding of the mechanisms of IPF. In addition, the molecular leads reported herein might inform future innovations in diagnostics and therapeutics research and development for IPF.

Identifying circRNA-associated-ceRNA networks in juvenile spondyloarthropathies patients

BACKGROUND

Juvenile spondyloarthropathies (JSpA) are defined as a heterogeneous group of diseases that start before the age of 16. The study aimed to identify key genes and pathways that are influenced by circRNAs and to screen potential therapeutic agents for JSpA. The study involved the analysis of circRNA expression profiles, identification of circRNA-miRNA-mRNA regulatory networks, and functional annotation of differentially expressed genes. The results of the study may have provided insights into the molecular mechanisms underlying JSpA and potential therapeutic targets for this disease.

METHODS

In this study, sequencing data of circRNA, miRNA, and mRNA were obtained from the GEO datasets. The data were then analyzed to identify candidates for constructing a circRNA-miRNA-mRNA network based on circRNA-miRNA interactions and miRNA-mRNA interactions. Functional enrichments of genes were performed using the DAVID database. A PPI network was constructed using the STRING database and visualized using Cytoscape software. The MCODE plugin app was used to explore hub genes in the PPI network. The expression changes in immune cells were assessed using the online CIBERSORT algorithm to obtain the proportion of various types of immune cells. Finally, the Connectivity Map L1000 platform was used to identify potential agents for JSpA treatment. Overall, this study aimed to provide a comprehensive understanding of the molecular mechanisms underlying JSpA and to identify potential therapeutic agents for this disease.

RESULTS

A total of 225 differentially expressed circRNAs (DEcircRNAs), 23 differentially expressed miRNAs (DEmiRNAs) and 1324 differentially expressed mRNAs (DEmRNAs) were identified. We integrated 5 overlapped circRNAs, 7 miRNAs and 299 target mRNAs into a circRNA-miRNA-mRNA network. We next identified 10 hub genes based on the PPI network. KEGG pathway analysis revealed that the DEGs were mainly associated with JAK-STAT signal pathway. We found that neutrophils accounted for the majority of all enriched cells. In addition, we discovered several chemicals as potential treatment options for JSpA.

CONCLUSIONS

Through this bioinformatics analysis, we suggest a regulatory role for circRNAs in the pathogenesis and treatment of JSpA from the view of a competitive endogenous RNA (ceRNA) network.

Analysis of lactate metabolism-related genes and their association with immune infiltration in septic shock via bioinformatics method

Background: Lactate, as an essential clinical evaluation index of septic shock, is crucial in the incidence and progression of septic shock. This study aims to investigate the differential expression, regulatory relationship, clinical diagnostic efficacy, and immune infiltration of lactate metabolism-related genes (LMGs) in septic shock. Methods: Two sepsis shock datasets (GSE26440 and GSE131761) were screened from the GEO database, and the common differentially expressed genes (DEGs) of the two datasets were screened out. LMGs were selected from the GeneCards database, and lactate metabolism-related DEGs (LMDEGs) were determined by integrating DEGs and LMGs. Protein-protein interaction networks, mRNA-miRNA, mRNA-RBP, and mRNA-TF interaction networks were constructed using STRING, miRDB, ENCORI, and CHIPBase databases, respectively. Receiver operating characteristic (ROC) curves were constructed for each of the LMDEGs to evaluate the diagnostic efficacy of the expression changes in relation to septic shock. Finally, immune infiltration analysis was performed using ssGSEA and CIBERSORT. Results: This study identified 10 LMDEGs, including LDHB, STAT3, LDHA, GSR, FOXM1, PDP1, GCDH, GCKR, ABCC1, and CDKN3. Enrichment analysis revealed that DEGs were significantly enriched in pathways such as pyruvate metabolism, hypoxia pathway, and immune-inflammatory pathways. PPI networks based on LMDEGs, as well as 148 pairs of mRNA-miRNA interactions, 243 pairs of mRNA-RBP interactions, and 119 pairs of mRNA-TF interactions were established. ROC curves of eight LMDEGs (LDHA, GSR, STAT3, CDKN3, FOXM1, GCKR, PDP1, and LDHB) with consistent expression patterns in two datasets had an area under the curve (AUC) ranging from 0.662 to 0.889. The results of ssGSEA and CIBERSORT both showed significant differences in the infiltration of various immune cells, including CD8 T cells, T regulatory cells, and natural killer cells, and LMDEGs such as STAT3, LDHB, LDHA, PDP1, GSR, FOXM1, and CDKN3 were significantly associated with various immune cells. Conclusion: The LMDEGs are significantly associated with the immune-inflammatory response in septic shock and have a certain diagnostic accuracy for septic shock.

Genome-Wide Identification and Characterization of the Biosynthesis of the Polyamine Gene Family in Citrus unshiu

Polyamines (PAs) contribute to diverse plant processes, environmental interaction, and stress responses. In citrus, the mechanism underlying the biosynthesis of polyamines is poorly understood. The present study aims to identify the biosynthesis of PA gene family members in satsuma mandarin (Citrus unshiu) and investigate their response against various stresses. The identified biosynthesis of PA genes in C. unshiu showed clustering in six groups, i.e., SPMS, SPDS, ACL5, ADC, ODC, and SAMDC. Syntenic analysis revealed that segmental duplication was prevalent among the biosynthesis of PA genes compared to tandem duplication. Thus, it might be the main reason for diversity in the gene family in C. unshiu. Almost all biosynthesis of PA gene family members in C. unshiu showed syntenic blocks in the genome of Arabidopsis, Citrus sinensis, Poncirus trifoliata, and Citrus reticulata. Analysis of Cis-regulatory elements (CREs) indicated the occurrence of hormones, light, defense, and environmental stress responses as well as the development and other plant mechanisms-related elements in the upstream sequence of the biosynthesis of PA genes. Expression profiling revealed that the biosynthesis of PA gene expression modulates in different organs during various developmental stages and stress in C. unshiu. This information will provide a deep understanding of genomic information and its expression in multiple tissues to better understand its potential application in functional genomics.

Neurodegenerative Disease Associated Pathways in the Brains of Triple Transgenic Alzheimer's Model Mice Are Reversed Following Two Weeks of Peripheral Administration of Fasudil

The pan Rho-associated coiled-coil-containing protein kinase (ROCK) inhibitor fasudil acts as a vasodilator and has been used as a medication for post-cerebral stroke for the past 29 years in Japan and China. More recently, based on the involvement of ROCK inhibition in synaptic function, neuronal survival, and processes associated with neuroinflammation, it has been suggested that the drug may be repurposed for neurodegenerative diseases. Indeed, fasudil has demonstrated preclinical efficacy in many neurodegenerative disease models. To facilitate an understanding of the wider biological processes at play due to ROCK inhibition in the context of neurodegeneration, we performed a global gene expression analysis on the brains of Alzheimer's disease model mice treated with fasudil via peripheral IP injection. We then performed a comparative analysis of the fasudil-driven transcriptional profile with profiles generated from a meta-analysis of multiple neurodegenerative diseases. Our results show that fasudil tends to drive gene expression in a reverse sense to that seen in brains with post-mortem neurodegenerative disease. The results are most striking in terms of pathway enrichment analysis, where pathways perturbed in Alzheimer's and Parkinson's diseases are overwhelmingly driven in the opposite direction by fasudil treatment. Thus, our results bolster the repurposing potential of fasudil by demonstrating an anti-neurodegenerative phenotype in a disease context and highlight the potential of in vivo transcriptional profiling of drug activity.

Effects of Helicobacter pylori Infection on the Development of Chronic Gastritis

BACKGROUND/AIMS

Based on the gene expression profiles of gastric epithelial tissue at different stages of Helicobacter pylori-infected gastritis, key long noncoding RNAs and genes in the development of Helicobacter pylori infection-induced gastritis were screened to provide a basis for early diagnosis and treatment.

MATERIALS AND METHODS

We downloaded 2 sets of sample data from the database, including gastric epithelial tissue samples from gastritis patients from Bhutan and Dominican, and screened mRNAs in the differentially expressed RNAs of the 2 regions. Mfuzz clustering algorithm was used to screen RNAs related to the 3 stages of chronic gastritis. The competing endogenous RNA (ceRNA) regulation network was constructed, and the selected key RNAs were verified. Samples from Bhutan and Dominican were subdivided into the chronic gastritis/ normal comparison groups, and the differentially expressed RNAs were screened to obtain 1067 overlapping RNAs, containing 21 long noncoding RNAs and 1046 mRNAs.

RESULTS

Thirty-eight significant gene ontology functional nodes and 6 expression pattern clusters were obtained. Two ceRNA regulatory networks were constructed, and 4 shared miRNAs (hsa-miR-320b, hsa-miR-320c, hsa-miR-320d, and hsa-miR-155-5p) were obtained. Eleven important long noncoding RNAs (AFAP1-AS1, MIR155HG, LINC00472, and FAM201A) and mRNAs (CASP10, SLC26A2, TRIB1, BMP2K, SCAMP1, TNKS1BP1, and MBOAT2) regulated by these 4 miRNAs were obtained. These results indicated that Helicobacter pylori infection had a certain influence on the development of gastritis.

CONCLUSIONS

The 11 key RNAs can provide a target for the early diagnosis and treatment of chronic gastritis following Helicobacter pylori infection.

Identification of glycogen phosphorylase L as a potential target for lung cancer

Traditional Chinese medicine (TCM) has been widely used for cancer treatment. Identification of anti-cancer targets of TCM is the first and principal step in discovering molecular mechanisms of TCM as well as obtaining novel targets for cancer therapy. In this study, glycogen phosphorylase L (PYGL) was identified as one of the targeted proteins for several TCMs and was upregulated in various cancer types. The expression level of PYGL was positively correlated with the stage of lung cancer and the poor prognosis of patients. Meanwhile, knockdown of PYGL significantly inhibited proliferation and migration in lung cancer cells. In addition, PYGL was associated with spindle, kinetochore, and microtubule, the cellular components that are closely related to mitosis, in lung cancer. Moreover, PYGL was more susceptible to be upregulated by 144 mutated genes. Taken together, PYGL is a potential target for lung cancer treatment and its molecular mechanism probably influences the mitotic function of cells by regulating energy metabolism.

Microenvironment modulation by key regulators of RNA N6-methyladenosine modification in respiratory allergic diseases

BACKGROUND

RNA N6-methyladenosine (m6A) regulators are considered post-transcriptional regulators that affect several biological functions, and their role in immunity, in particular, is emerging. However, the role of m6A regulators in respiratory allergic diseases remains unclear. Therefore, we aimed to investigate the role of key m6A regulators in mediating respiratory allergic diseases and immune microenvironment infiltration characteristics.

METHODS

We downloaded gene expression profiles of respiratory allergies from the Gene Expression Omnibus (GEO) database and we performed hierarchical clustering, difference analysis, and construction of predictive models to identify hub m6A regulators that affect respiratory allergies. Next, we investigate the underlying biological mechanisms of key m6A regulators by performing PPI network analysis, functional enrichment analysis, and immune microenvironment infiltration analysis. In addition, we performed a drug sensitivity analysis on the key m6A regulator, hoping to be able to provide some implications for clinical medication.

RESULTS

In this study, we identified four hub m6A regulators that affect the respiratory allergy and investigated the underlying biological mechanisms. In addition, studies on the characteristics of immune microenvironment infiltration revealed that the expression of METTL14, METTL16, and RBM15B correlated with the infiltration of the mast and Th2 cells in respiratory allergy, and METTL16 expression was found to be significantly negatively correlated with macrophages for the first time (R = -0.53, P < 0.01). Finally, a key m6A regulator, METTL14, was screened by combining multiple algorithms. In addition, by performing a drug sensitivity analysis on METTL14, we hypothesized that it may play an important role in the improvement of allergic symptoms in the upper and lower airways with topical nasal glucocorticoids.

CONCLUSIONS

Our findings suggest that m6A regulators, particularly METTL14, play a crucial role in the development of respiratory allergic diseases and the infiltration of immune cells. These results may provide insight into the mechanism of action of methylprednisolone in treating respiratory allergic diseases.

Establishment of Risk Model and Analysis of Immunoinfiltration Based on Mitophagy-Related Associated Genes in Atrial Fibrillation

Objective

Atrial fibrillation (AF) is a common tachyarrhythmia whose pathogenesis remains elusive. In the present study, we aimed to investigate the pathological mechanism of mitophagy and immunoinfiltration in AF.

Methods

First, we identified differentially expressed mitophagy-related genes (DEMRGs) based on the GSE79768 and GSE115574 datasets, subjecting them to functional enrichment analysis. STRING, TRRUST, miRNet, miRwalk, and Cytoscape were used to explore the potential regulatory roles of downstream signaling pathways. Subsequently, the random forest method was used to construct the AF risk model, and the DEMRGs most correlated with AF risk were determined by combining the Gini index. ssGSEA algorithm, NMF algorithm, and unsupervised clustering were used to subdivide AF molecular types. We then studied the characteristics of mitophagy- and immune infiltration-related genes in AF. Ultimately, we detected the expression of key genes in canine atrial tissues and HL-1 cells by immunofluorescence and Western blot.

Results

Mitophagy and immune infiltration were significantly enriched and activated in AF samples. Thirty-seven DEMRGs were screened, of which MAPK1, VDAC1, MAPK14, and MTERF3 were most associated with AF risk. The risk model based on these could identify patients at a high risk of AF. The infiltration of immunocells such as mast cells and neutrophils was significantly different among AF types. Finally, expression verification indicated that the expression trend of four key genes in canine atrial muscle tissue and HL-1 cells was consistent.

Conclusion

We found that mitophagy may participate in AF progression through immune activation. In addition, the AF risk prediction model composed of VDAC1, MAPK1, MAPK14, and MTERF3 has a good AF prediction performance, which provides new ideas for the study of AF pathogenesis and potential therapeutic targets.

Identification of immunosuppressive signature subtypes and prognostic risk signatures in triple-negative breast cancer

Purpose

Immune checkpoint blockade (ICB) therapy has transformed the treatment of triple-negative breast cancer (TNBC) in recent years. However, some TNBC patients with high programmed death-ligand 1 (PD-L1) expression levels develop immune checkpoint resistance. Hence, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to construct prognostic models of patient survival outcomes in order to understand biological mechanisms operating within the tumor microenvironment.

Patients and methods

RNA sequence (RNA-seq) data from 303 TNBC samples were analyzed using an unsupervised cluster analysis approach to reveal distinctive cellular gene expression patterns within the TNBC tumor microenvironment (TME). A panel of T cell exhaustion signatures, immunosuppressive cell subtypes and clinical features were correlated with the immunotherapeutic response, as assessed according to gene expression patterns. The test dataset was then used to confirm the occurrence of immune depletion status and prognostic features and to formulate clinical treatment recommendations. Concurrently, a reliable risk prediction model and clinical treatment strategy were proposed based on TME immunosuppressive signature differences between TNBC patients with good versus poor survival status and other clinical prognostic factors.

Results

Significantly enriched TNBC microenvironment T cell depletion signatures were detected in the analyzed RNA-seq data. A high proportion of certain immunosuppressive cell subtypes, 9 inhibitory checkpoints and enhanced anti-inflammatory cytokine expression profiles were noted in 21.4% of TNBC patients that led to the designation of this group of immunosuppressed patients as the immune depletion class (IDC). Although IDC group TNBC samples contained tumor-infiltrating lymphocytes present at high densities, IDC patient prognosis was poor. Notably, PD-L1 expression was relatively elevated in IDC patients that indicated their cancers were resistant to ICB treatment. Based on these findings, a set of gene expression signatures predicting IDC group PD-L1 resistance was identified then used to develop risk models for use in predicting clinical therapeutic outcomes.

Conclusion

A novel TNBC immunosuppressive tumor microenvironment subtype associated with strong PD-L1 expression and possible resistance to ICB treatment was identified. This comprehensive gene expression pattern may provide fresh insights into drug resistance mechanisms for use in optimizing immunotherapeutic approaches for TNBC patients.