Identification of immune infiltration and cuproptosis-related subgroups in Crohn's disease

Identifying disulfidptosis-related biomarkers in epilepsy based on integrated bioinformatics and experimental analyses

One of the underlying mechanisms of epilepsy (EP), a brain disease characterized by recurrent seizures, is considered to be cell death. Disulfidptosis, a proposed novel cell death mechanism, is thought to play a part in the pathogenesis of epilepsy, but the exact role is unclear. The gene expression omnibus series (GSE) 33000 and GSE63808 datasets were used to search for differentially expressed disulfidptosis-related molecules (DE-DRMs). A correlation between the DE-DRMs was discovered. Individuals with epilepsy were then used to investigate molecular clusters based on the expression of DE-DRMs. Following that, the best machine learning model which is validated by GSE143272 dataset and predictor molecules were identified. The correlation between predictive molecules and clinical traits was determined. Based on the in vitro and in vivo seizures models, experimental analyses were applied to verify the DE-DRMs expressions and the correlation between them. Nine molecules were identified as DE-DRMs: glycogen synthase 1 (GYS1), solute carrier family 3 member 2 (SLC3A2), solute carrier family 7 member 11 (SLC7A11), NADH:ubiquinone oxidoreductase core subunit S1 (NDUFS1), 3-oxoacyl-ACP synthase, mitochondrial (OXSM), leucine rich pentatricopeptide repeat containing (LRPPRC), NADH:ubiquinone oxidoreductase subunit A11 (NDUFA11), NUBP iron‑sulfur cluster assembly factor, mitochondrial (NUBPL), and NCK associated protein 1 (NCKAP1). NDUFS1 interacted with NDUFA11, NUBPL, and LRPPRC, while SLC3A2 interacted with SLC7A11. The optimal machine learning model was revealed to be the random forest (RF) model. G protein guanine nucleotide-binding protein alpha subunit q (GNAQ) was linked to sodium valproate resistance. The experimental analyses suggested an upregulated SLC7A11 expression, an increased number of formed SLC3A2 and SLC7A11 complexes, and a decreased number of formed NDUFS1 and NDUFA11 complexes. This study provides previously undocumented evidence of the relationship between disulfidptosis and EP. In addition to suggesting that SLC7A11 may be a specific DRM for EP, this research demonstrates the alterations in two disulfidptosis-related protein complexes: SLC7A11-SLC3A2 and NDUFS1-NDUFA11.

Comprehensive Bioinformatics Analysis Reveals the Role of Shared Cuproptosis- and Ferroptosis-Related DEG DLD in Abdominal Aortic Aneurysm

Ferroptosis plays a crucial role in the progression of abdominal aortic aneurysm (AAA). Cuproptosis, as a new mode of death, has some similarities with ferroptosis. The primary objective of this study was to develop the role of shared cuproptosis-related differentially expressed genes (CRDEGs) and ferroptosis-related differentially expressed genes (FRDEGs) in AAA. RNA sequencing and bioinformatic analyses of human AAA tissue were used to identify dihydrolipoamide dehydrogenase (DLD), which is involved in cuproptosis and ferroptosis. qRT-PCR and IHC assays further confirmed that the DLD level was substantially higher in the AAA group than in the control group. Finally, experimental verification was conducted to identify that DLD could promote the necrosis, apoptosis and mitophagy of SMCs. In summary, our research identified DLD, linked to cuproptosis and ferroptosis, as differentially expressed in AAA across human and murine samples. DLD's role in regulating SMC necrosis, apoptosis and mitophagy positions it as a potential AAA biomarker and therapeutic target, warranting further investigation for clinical applications.

Identification of cuproptosis-related genes in septic shock based on bioinformatic analysis

BACKGROUND

Septic shock is a life-threatening condition characterized by a failure of organ systems and a high mortality rate. Cuproptosis is a new form of cell death that is triggered by copper overload. However, the relationship between cuproptosis-related genes and septic shock remains unclear.

METHODS

The GSE26440 dataset from the GEO database was used to screen differentially expressed genes (DEGs) between control and septic shock samples. Additionally, hub genes related to the progression of septic shock and cuproptosis were screened by Venn analysis. RT-qPCR was utilized to validate the expression of hub genes in peripheral blood lymphocytes from septic shock patients and healthy controls. Next, functional analysis and immune cells infiltration were performed.

RESULTS

SLC31A1 and MTF1 levels were obviously elevated and LIAS and LIPT1 levels were downregulated in septic shock samples, compared to normal controls. The diagnostic values of the four genes were confirmed with receiver operating characteristic (ROC) curves. Additionally, SLC31A1 and MTF1 showed a positive correlation with natural killer cells and LIAS and LIPT1 exhibited a positive correlation with CD8+ T cells. Furthermore, compared to low-level groups, MAPK signaling was activated in the high-SLC31A1 level group, VEGF signaling was activated in the high-MTF1 level group and lipoic acid metabolism was activated in high-LIAS and high-LIPT1 level groups.

CONCLUSION

This study demonstrates that SLC31A1, MTF1, LIAS, and LIPT1 are dysregulated in septic shock samples, and these genes exhibit potential diagnostic efficacy in septic shock, suggesting that these genes may be potential biomarkers for the diagnosis of septic shock.

Identification and Analysis of Potential Biomarkers Associated with Neutrophil Extracellular Traps in Cervicitis

Early diagnosis of cervicitis is important. Previous studies have found that neutrophil extracellular traps (NETs) play pro-inflammatory and anti-inflammatory roles in many diseases, suggesting that they may be involved in the inflammation of the uterine cervix and NETs-related genes may serve as biomarkers of cervicitis. However, what NETs-related genes are associated with cervicitis remains to be determined. Transcriptome analysis was performed using samples of exfoliated cervical cells from 15 patients with cervicitis and 15 patients without cervicitis as the control group. First, the intersection of differentially expressed genes (DEGs) and neutrophil extracellular trap-related genes (NETRGs) were taken to obtain genes, followed by functional enrichment analysis. We obtained hub genes through two machine learning algorithms. We then performed Artificial Neural Network (ANN) and nomogram construction, confusion matrix, receiver operating characteristic (ROC), gene set enrichment analysis (GSEA), and immune cell infiltration analysis. Moreover, we constructed ceRNA network, mRNA-transcription factor (TF) network, and hub genes-drug network. We obtained 19 intersecting genes by intersecting 1398 DEGs and 136 NETRGs. 5 hub genes were obtained through 2 machine learning algorithms, namely PKM, ATG7, CTSG, RIPK3, and ENO1. Confusion matrix and ROC curve evaluation ANN model showed high accuracy and stability. A nomogram containing the 5 hub genes was established to assess the disease rate in patients. The correlation analysis revealed that the expression of ATG7 was synergistic with RIPK3. The GSEA showed that most of the hub genes were related to ECM receptor interactions. It was predicted that the ceRNA network contained 2 hub genes, 3 targeted miRNAs, and 27 targeted lnRNAs, and that 5 mRNAs were regulated by 28 TFs. In addition, 36 small molecule drugs that target hub genes may improve the treatment of cervicitis. In this study, five hub genes (PKM, ATG7, CTSG, RIPK3, ENO1) provided new directions for the diagnosis and treatment of patients with cervicitis.

Resveratrol suppresses liver cancer progression by downregulating AKR1C3: targeting HCC with HSA nanomaterial as a carrier to enhance therapeutic efficacy

The enzyme AKR1C3 plays a crucial role in hormone and drug metabolism and is associated with abnormal expression in liver cancer, leading to tumor progression and poor prognosis. Nanoparticles modified with HSA can modulate the tumor microenvironment by enhancing photodynamic therapy to induce apoptosis in tumor cells and alleviate hypoxia. Therefore, exploring the potential regulatory mechanisms of resveratrol on AKR1C3 through the construction of HSA-RSV NPs carriers holds significant theoretical and clinical implications for the treatment of liver cancer. The aim of this study is to investigate the targeted regulation of AKR1C3 expression through the loading of resveratrol (RSV) on nanomaterials HSA-RSV NPs (Nanoparticles) in order to alleviate tumor hypoxia and inhibit the progression of hepatocellular carcinoma (HCC), and to explore its molecular mechanism. PubChem database and PharmMapper server were used to screen the target genes of RSV. HCC-related differentially expressed genes (DEGs) were analyzed through the GEO dataset, and relevant genes were retrieved from the GeneCards database, resulting in the intersection of the three to obtain candidate DEGs. GO and KEGG enrichment analyses were performed on the candidate DEGs to analyze the potential cellular functions and molecular signaling pathways affected by the main target genes. The cytohubba plugin was used to screen the top 10 target genes ranked by Degree and further intersected the results of LASSO and Random Forest (RF) to obtain hub genes. The expression analysis of hub genes and the prediction of malignant tumor prognosis were conducted. Furthermore, a pharmacophore model was constructed using PharmMapper. Molecular docking simulations were performed using AutoDockTools 1.5.6 software, and ROC curve analysis was performed to determine the core target. In vitro cell experiments were carried out by selecting appropriate HCC cell lines, treating HCC cells with different concentrations of RSV, or silencing or overexpressing AKR1C3 using lentivirus. CCK-8, clone formation, flow cytometry, scratch experiment, and Transwell were used to measure cancer cell viability, proliferation, migration, invasion, and apoptosis, respectively. Cellular oxygen consumption rate was analyzed using the Seahorse XF24 analyzer. HSA-RSV NPs were prepared, and their characterization and cytotoxicity were evaluated. The biological functional changes of HCC cells after treatment were detected. An HCC subcutaneous xenograft model was established in mice using HepG2 cell lines. HSA-RSV NPs were injected via the tail vein, with a control group set, to observe changes in tumor growth, tumor targeting of NPs, and biological safety. TUNEL, Ki67, and APC-hypoxia probe staining were performed on excised tumor tissue to detect tumor cell proliferation, apoptosis, and hypoxia. Lentivirus was used to silence or overexpress AKR1C3 simultaneously with the injection of HSA-RSV NPs via the tail vein to assess the impact of AKR1C3 on the regulation of HSA-RSV NPs in HCC progression. Bioinformatics analysis revealed that AKR1C3 is an important target gene involved in the regulation of HCC by RSV, which is associated with the prognosis of HCC patients and upregulated in expression. In vitro cell experiments showed that RSV significantly inhibits the respiratory metabolism of HCC cells, suppressing their proliferation, migration, and invasion and promoting apoptosis. Silencing AKR1C3 further enhances the toxicity of RSV towards HCC cells. The characterization and cytotoxicity experiments of nanomaterials demonstrated the successful construction of HSA-RSV NPs, which exhibited stronger inhibitory effects on HCC cells. In vivo, animal experiments further confirmed that targeted downregulation of AKR1C3 by HSA-RSV NPs suppresses the progression of HCC and tumor hypoxia while exhibiting tumor targeting and biological safety. Targeted downregulation of AKR1C3 by HSA-RSV NPs can alleviate HCC tumor hypoxia and inhibit the progression of HCC.

A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer

BACKGROUND

The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI.

METHODS

Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes.

RESULTS

This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells.

CONCLUSION

We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

Identification of diagnostic markers and molecular clusters of cuproptosis-related genes in alcohol-related liver disease based on machine learning and experimental validation

Background and aims

Alcohol-related liver disease (ALD) is a worldwide burden. Cuproptosis has been shown to play a key role in the development of several diseases. However, the role and mechanisms of cuproptosis in ALD remain unclear.

Methods

The RNA-sequencing data of ALD liver samples were downloaded from the Gene Expression Omnibus (GEO) database. Bioinformatical analyses were performed using the R data package. We then identified key genes through multiple machine learning methods. Immunoinfiltration analyses were used to identify different immune cells in ALD patients and controls. The expression levels of key genes were further verified.

Results

We identified three key cuproptosis-related genes (CRGs) (DPYD, SLC31A1, and DBT) through an in-depth analysis of two GEO datasets, including 28 ALD samples and eight control samples. The area under the curve (AUC) value of these three genes combined in determining ALD was 1.0. In the external datasets, the three key genes had AUC values as high as 1.0 and 0.917, respectively. Nomogram, decision curve, and calibration curve analyses also confirmed these genes' ability to predict the diagnosis. These three key genes were found to be involved in multiple pathways associated with ALD progression. We confirmed the mRNA expression of these three key genes in mouse ALD liver samples. Regarding immune cell infiltration, the numbers of B cells, CD8 (+) T cells, NK cells, T-helper cells, and Th1 cells were significantly lower in ALD patient samples than in control liver samples. Single sample gene set enrichment analysis (ssGSEA) was then used to estimate the immune microenvironment of different CRG clusters and CRG-related gene clusters. In addition, we calculated CRG scores through principal component analysis (PCA) and selected Sankey plots to represent the correlation between CRG clusters, gene clusters, and CRG scores. Finally, the three key genes were confirmed in mouse ALD liver samples and liver cells treated with ethanol.

Conclusions

We first established a prognostic model for ALD based on 3 CRGs and robust prediction efficacy was confirmed. Our investigation contributes to a comprehensive understanding of the role of cuproptosis in ALD, presenting promising avenues for the exploration of therapeutic strategies.

Exploring cuproptosis-related molecular clusters and immunological characterization in ischemic stroke through machine learning

Objective

Ischemic stroke (IS) is a significant health concern with high disability and fatality rates despite available treatments. Immune cells and cuproptosis are associated with the onset and progression of IS. Investigating the interaction between cuproptosis-related genes (CURGs) and immune cells in IS can provide a theoretical basis for IS treatment.

Methods

We obtained IS datasets from the Gene Expression Omnibus (GEO) and employed machine learning to identify CURGs. The diagnostic efficiency of the CURGs was evaluated using receiver operating characteristic (ROC) curves. KEGG and gene set enrichment analysis (GSEA) were also conducted to identify biologically relevant pathways associated with CURGs in IS patients. Single-cell analysis was used to confirm the expression of 19 CURGs, and pathway activity calculations were performed using the AUCell package. Additionally, a risk prediction model for IS patients was developed, and core modules and hub genes related to IS were identified using weighted gene coexpression network analysis (WGCNA). We classified IS patients using a method of consensus clustering.

Results

We established a precise diagnostic model for IS. Enrichment analysis revealed major pathways, including oxidative phosphorylation, the NF-kappa B signaling pathway, the apoptosis pathway, and the Wnt signaling pathway. At the single-cell level, compared to those in non-IS samples, 19 CURGs were primarily overexpressed in the immune cells of IS samples and exhibited high activity in natural killer cell-mediated cytotoxicity, steroid hormone biosynthesis, and oxidative phosphorylation. Two clusters were obtained through consensus clustering. Notably, immune cell types including B cells, plasma cells, and resting NK cells, varied between the two clusters. Furthermore, the red module and hub genes associated with IS were uncovered. The expression patterns of CURGs varied over time.

Conclusion

This study developed a precise diagnostic model for IS by identifying CURGs and evaluating their interaction with immune cells. Enrichment analyses revealed key pathways involved in IS, and single-cell analysis confirmed CURG overexpression in immune cells. A risk prediction model and core modules associated with IS were also identified.

Copper metabolism in osteoarthritis and its relation to oxidative stress and ferroptosis in chondrocytes

Ferroptosis, an iron-ion-dependent process of lipid peroxidation, damages the plasma membrane, leading to non-programmed cell death. Osteoarthritis (OA), a prevalent chronic degenerative joint disease among middle-aged and older adults, is characterized by chondrocyte damage or loss. Emerging evidence indicates that chondrocyte ferroptosis plays a role in OA development. However, most research has concentrated on ferroptosis regulation involving typical iron ions, potentially neglecting the significance of elevated copper ions in both serum and joint fluid of patients with OA. This review aims to fill this gap by systematically examining the interplay between copper metabolism, oxidative stress, ferroptosis, and copper-associated cell death in OA. It will provide a comprehensive overview of copper ions' role in regulating ferroptosis and their dual role in OA. This approach seeks to offer new insights for further research, prevention, and treatment of OA.

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

BACKGROUND

The present study aimed to dissect the cellular complexity of Crohn's disease (CD) using single-cell RNA sequencing, focusing on identifying key cell populations and their transcriptional profiles in inflamed tissue.

METHODS

We applied scRNA-sequencing to compare the cellular composition of CD patients with healthy controls, utilizing Seurat for clustering and annotation. Differential gene expression analysis and protein-protein interaction networks were constructed to identify crucial genes and pathways.

RESULTS

Our study identified eight distinct cell types in CD, highlighting crucial fibroblast and T cell interactions. The analysis revealed key cellular communications and identified significant genes and pathways involved in the disease's pathology. The role of fibroblasts was underscored by elevated expression in diseased samples, offering insights into disease mechanisms and potential therapeutic targets, including responses to ustekinumab treatment, thus enriching our understanding of CD at a molecular level.

CONCLUSIONS

Our findings highlight the complex cellular and molecular interplay in CD, suggesting new biomarkers and therapeutic targets, offering insights into disease mechanisms and treatment implications.

Identification of Key Disulfidptosis-Related Genes and Their Association with Gene Expression Subtypes in Crohn's Disease

Background

Crohn's disease (CD) is a persistent inflammatory condition that impacts the gastrointestinal system and is characterized by a multifaceted pathogenesis involving genetic, immune, and environmental components. This study primarily investigates the relationship between gene expression and immune cell infiltration in CD, focusing on disulfidptosis-a novel form of cell death caused by abnormal disulfide accumulation-and its impact on various immune cell populations. By identifying key disulfidptosis-related genes (DRGs) and exploring their association with distinct gene expression subtypes, this research aims to enhance our understanding of CD and potentially other autoimmune diseases.

Methods

Gene expression data from intestinal biopsy samples were collected from both individuals with CD and healthy controls, and these data were retrieved from the GEO database. Through gene expression level comparisons, various differentially expressed genes (DEGs) were identified. Subsequently, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed to reveal the biological processes and pathways linked to these DEGs. Later, immune cell infiltration was evaluated. Hub candidate DRGs were identified using machine learning algorithms. Validation of the expression of hub DRGs was carried out using quantitative real-time polymerase chain reaction. The hub DRGs were subjected to unsupervised hierarchical clustering to classify CD patients into subtypes. The characteristics of each subtype were then analyzed.

Results

Two hub DRGs (NDUFA11 and LRPPRC) were identified. NDUFA11 showed a significantly positive association with the abundance of Th17 cells. Conversely, higher expression levels of LRPPRC were associated with a reduced abundance of various immune cells, particularly monocytes. CD patients were classified into two disulfidptosis-related subtypes. Cluster B patients exhibited lower immune infiltration and milder clinical presentation.

Conclusion

LRPPRC and NDUFA11 are identified as hub DRGs in CD, with potential roles in disulfidptosis and immune regulation. The disulfidptosis subtypes provide new insights into disease progression.

Unveiling the role of copper metabolism and STEAP2 in idiopathic pulmonary fibrosis molecular landscape

Idiopathic pulmonary fibrosis (IPF) is a debilitating interstitial lung disease characterized by progressive fibrosis and poor prognosis. Despite advancements in treatment, the pathophysiological mechanisms of IPF remain elusive. Herein, we conducted an integrated bioinformatics analysis combining clinical data and carried out experimental validations to unveil the intricate molecular mechanism of IPF. Leveraging three IPF datasets, we identified 817 upregulated and 560 downregulated differentially expressed genes (DEGs). Of these, 14 DEGs associated with copper metabolism were identified, shedding light on the potential involvement of disrupted copper metabolism in IPF progression. Immune infiltration analysis revealed dysregulated immune cell infiltration in IPF, with a notable correlation between copper metabolism-related genes and immune cells. Weighted gene co-expression network analysis (WGCNA) identified a central module correlated with IPF-associated genes, among which STEAP2 emerged as a key hub gene. Subsequent in vivo and in vitro studies confirmed the upregulation of STEAP2 in IPF model. Knockdown of STEAP2 using siRNA alleviated fibrosis in vitro, suggesting potential pathway related to copper metabolism in the pathophysiological progression of IPF. Our study established a novel link between immune cell infiltration and dysregulated copper metabolism. The revelation of intracellular copper overload and upregulated STEAP2 unravelled a potential therapeutic option. These findings offer valuable insights for future research and therapeutic interventions targeting STEAP2 and associated pathways in IPF.

Characterization of PANoptosis-related genes in Crohn's disease by integrated bioinformatics, machine learning and experiments

Currently, the biological understanding of Crohn's disease (CD) remains limited. PANoptosis is a revolutionary form of cell death reported to participate in numerous diseases, including CD. In our study, we aimed to uncover the roles of PANoptosis in CD. Differentially expressed PANoptosis-related genes (DE-PRGs) were identified by overlapping PANoptosis-related genes and differentially expressed genes between CD and normal samples in a combined microarray dataset. Three machine learning algorithms were adopted to detect hub DE-PRGs. To stratify the heterogeneity within CD patients, nonnegative matrix factorization clustering was conducted. In terms of immune landscape analysis, the "ssGSEA" method was applied. qRT-PCR was performed to examine the expression levels of the hub DE-PRGs in CD patients and colitis model mice. Ten hub DE-PRGs with satisfactory diagnostic performance were identified and validated: CD44, CIDEC, NDRG1, NUMA1, PEA15, RAG1, S100A8, S100A9, TIMP1 and XBP1. These genes displayed significant associations with certain immune cell types and CD-related genes. We also constructed gene‒microRNA, gene‒transcription factor and drug‒gene interaction networks. CD samples were classified into two PANoptosis patterns according to the expression levels of the hub DE-PRGs. Our results suggest that PANoptosis plays a nonnegligible role in CD by modulating the immune system and interacting with CD-related genes.

Transcriptomic analysis identifies the shared diagnostic biomarkers and immune relationship between Atherosclerosis and abdominal aortic aneurysm based on fatty acid metabolism gene set

Background

Epidemiological research has demonstrated that there is a connection between lipid metabolism disorder and an increased risk of developing arteriosclerosis (AS) and abdominal aortic aneurysm (AAA). However, the precise relationship between lipid metabolism, AS, and AAA is still not fully understood. The objective of this study was to examine the pathways and potential fatty acid metabolism-related genes (FRGs) that are shared between AS and AAA.

Methods

AS- and AAA-associated datasets were retrieved from the Gene Expression Omnibus (GEO) database, and the limma package was utilized to identify differentially expressed FRGs (DFRGs) common to both AS and AAA patients. Functional enrichment analysis was conducted on the (DFRGs), and a protein-protein interaction (PPI) network was established. The selection of signature genes was performed through the utilization of least absolute shrinkage and selection operator (LASSO) regression and random forest (RF). Subsequently, a nomogram was developed using the results of the screening process, and the crucial genes were validated in two separate external datasets (GSE28829 and GSE17901) as well as clinical samples. In the end, single-sample gene set enrichment analysis (ssGSEA) was utilized to assess the immune cell patterns in both AS and AAA. Additionally, the correlation between key crosstalk genes and immune cell was evaluated.

Results

In comparison to control group, both AS and AAA patients exhibited a decrease in fatty acid metabolism score. We found 40 DFRGs overlapping in AS and AAA, with lipid and amino acid metabolism critical in their pathogenesis. PCBD1, ACADL, MGLL, BCKDHB, and IDH3G were identified as signature genes connecting AS and AAA. Their expression levels were confirmed in validation datasets and clinical samples. The analysis of immune infiltration showed that neutrophils, NK CD56dim cells, and Tem cells are important in AS and AAA development. Correlation analysis suggested that these signature genes may be involved in immune cell infiltration.

Conclusion

The fatty acid metabolism pathway appears to be linked to the development of both AS and AAA. Furthermore, PCBD1, ACADL, MGLL, BCKDHB, and IDH3G have the potential to serve as diagnostic markers for patients with AS complicated by AAA.

Immune patterns of cuproptosis in ischemic heart failure: A transcriptome analysis

Cuproptosis is a recently discovered programmed cell death pattern that affects the tricarboxylic acid (TCA) cycle by disrupting the lipoylation of pyruvate dehydrogenase (PDH) complex components. However, the role of cuproptosis in the progression of ischemic heart failure (IHF) has not been investigated. In this study, we investigated the expression of 10 cuproptosis-related genes in samples from both healthy individuals and those with IHF. Utilizing these differential gene expressions, we developed a risk prediction model that effectively distinguished healthy and IHF samples. Furthermore, we conducted a comprehensive evaluation of the association between cuproptosis and the immune microenvironment in IHF, encompassing infiltrated immunocytes, immune reaction gene-sets and human leukocyte antigen (HLA) genes. Moreover, we identified two different cuproptosis-mediated expression patterns in IHF and explored the immune characteristics associated with each pattern. In conclusion, this study elucidates the significant influence of cuproptosis on the immune microenvironment in ischemic heart failure (IHF), providing valuable insights for future mechanistic research exploring the association between cuproptosis and IHF.

ARDS and aging: TYMS emerges as a promising biomarker and therapeutic target

Background

Acute Respiratory Distress Syndrome (ARDS) is a common condition in the intensive care unit (ICU) with a high mortality rate, yet the diagnosis rate remains low. Recent studies have increasingly highlighted the role of aging in the occurrence and progression of ARDS. This study is committed to investigating the pathogenic mechanisms of cellular and genetic changes in elderly ARDS patients, providing theoretical support for the precise treatment of ARDS.

Methods

Gene expression profiles for control and ARDS samples were obtained from the Gene Expression Omnibus (GEO) database, while aging-related genes (ARGs) were sourced from the Human Aging Genomic Resources (HAGR) database. Differentially expressed genes (DEGs) were subjected to functional enrichment analysis to understand their roles in ARDS and aging. The Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning pinpointed key modules and marker genes, with ROC curves illustrating their significance. The expression of four ARDS-ARDEGs was validated in lung samples from aged mice with ARDS using qRT-PCR. Gene set enrichment analysis (GSEA) investigated the signaling pathways and immune cell infiltration associated with TYMS expression. Single-nucleus RNA sequencing (snRNA-Seq) explored gene-level differences among cells to investigate intercellular communication during ARDS onset and progression.

Results

ARDEGs are involved in cellular responses to DNA damage stimuli, inflammatory reactions, and cellular senescence pathways. The MEmagenta module exhibited a significant correlation with elderly ARDS patients. The LASSO, RRF, and XGBoost algorithms were employed to screen for signature genes, including CKAP2, P2RY14, RBP2, and TYMS. Further validation emphasized the potential role of TYMS in the onset and progression of ARDS. Immune cell infiltration indicated differential proportion and correlations with TYMS expression. SnRNA-Seq and cell-cell communication analysis revealed that TYMS is highly expressed in endothelial cells, and the SEMA3 signaling pathway primarily mediates cell communication between endothelial cells and other cells.

Conclusion

Endothelial cell damage associated with aging could contribute to ARDS progression by triggering inflammation. TYMS emerges as a promising diagnostic biomarker and potential therapeutic target for ARDS.

Identification of cuproptosis-related genes and immune infiltration in dilated cardiomyopathy

BACKGROUND

Dilated cardiomyopathy (DCM) is a leading cause of heart failure. Cuproptosis is involved in various diseases, although its role in DCM is still unclear. Here, this study aims to investigate the feasibility of using genes related to cuproptosis as diagnostic biomarkers for DCM and the association of their expression with immune infiltration and drug target in cardiac tissue.

METHODS

Gene expression data from nonfailure (NF) and DCM samples were retrieved from the GEO database. Cuproptosis scores were calculated using single-sample gene set enrichment analysis (ssGSEA). Weighted gene co-expression network analysis (WGCNA) was used to screen key modules associated with DCM and cuproptosis. Random forest and least absolute shrinkage and selection operator (LASSO) were applied to identify signature genes. Finally, immune cell infiltration was assessed using ssGSEA. mRNA-miRNA-lncRNA regulatory networks and chemical-drug regulatory networks based on signature genes were analyzed by Cytoscape.

RESULTS

8 modules were aggregated by WGCNA, among which MEblue was significantly associated with cuproptosis scores and DCM. A diagnostic model made up of six signature genes including SEPTIN1, CLEC11A, ISG15, P3H3, SDSL, and INKA1 was selected. Furthermore, immune infiltration studies showed significant differences between DCM and NF. Drugs networks and ceRNA regulatory network based on six signature genes were successfully constructed.

CONCLUSION

Six signature genes (SEPTIN1, CLEC11A, ISG15, P3H3, SDSL, and INKA1) were identified as novel diagnostic biomarkers in DCM. In addition, the expression of these genes was associated with immune cell infiltration, suggesting that cuproptosis may be involved in the immune regulation of DCM.

Development and validation of a novel biomarker panel for Crohn's disease and rheumatoid arthritis diagnosis and treatment

BACKGROUND

Crohn's disease (CD) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases. However, the molecular mechanisms linking these two diseases remain unclear.

METHODS

To identify shared core genes between CD and RA, we employed differential gene analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Functional annotation of these core biomarkers was performed using consensus clustering and gene set enrichment analysis. We also constructed a protein-protein network and a miRNA-mRNA network using multiple databases, and potential therapeutic agents targeting the core biomarkers were predicted. Finally, we confirmed the expression of the genes in the biomarker panel in both CD and RA using quantitative PCR.

RESULTS

A total of five shared core genes, namely C-X-C motif chemokine ligand 10 (CXCL10), C-X-C motif chemokine ligand 9 (CXCL9), aquaporin 9 (AQP9), secreted phosphoprotein 1 (SPP1), and metallothionein 1M (MT1M), were identified as core biomarkers. These biomarkers activate classical pro-inflammatory and immune signaling pathways, influencing immune cell aggregation. Additionally, testosterone was identified as a potential therapeutic agent targeting the biomarkers identified in this study. The expression of genes in the biomarker panel in CD and RA was confirmed through quantitative PCR.

CONCLUSION

Our study revealed some core genes shared between CD and RA and established a novel biomarker panel with potential implications for the diagnosis and treatment of these diseases.

A comprehensive bioinformatics analysis identifies mitophagy biomarkers and potential Molecular mechanisms in hypertensive nephropathy

Mitophagy, the selective removal of damaged mitochondria, plays a critical role in kidney diseases, but its involvement in hypertensive nephropathy (HTN) is not well understood. To address this gap, we investigated mitophagy-related genes in HTN, identifying potential biomarkers for diagnosis and treatment. Transcriptome datasets from the Gene Expression Omnibus database were analyzed, resulting in the identification of seven mitophagy related differentially expressed genes (MR-DEGs), namely PINK1, ULK1, SQSTM1, ATG5, ATG12, MFN2, and UBA52. Further, we explored the correlation between MR-DEGs, immune cells, and inflammatory factors. The identified genes demonstrated a strong correlation with Mast cells, T-cells, TGFβ3, IL13, and CSF3. Machine learning techniques were employed to screen important genes, construct diagnostic models, and evaluate their accuracy. Consensus clustering divided the HTN patients into two mitophagy subgroups, with Subgroup 2 showing higher levels of immune cell infiltration and inflammatory factors. The functions of their proteins primarily involve complement, coagulation, lipids, and vascular smooth muscle contraction. Single-cell RNA sequencing revealed that mitophagy was most significant in proximal tubule cells (PTC) in HTN patients. Pseudotime analysis of PTC confirmed the expression changes observed in the transcriptome. Intercellular communication analysis suggested that mitophagy might regulate PTC's participation in intercellular crosstalk. Notably, specific transcription factors such as HNF4A, PPARA, and STAT3 showed strong correlations with mitophagy-related genes in PTC, indicating their potential role in modulating PTC function and influencing the onset and progression of HTN. This study offers a comprehensive analysis of mitophagy in HTN, enhancing our understanding of the pathogenesis, diagnosis, and treatment of HTN.Communicated by Ramaswamy H. Sarma.

Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model

Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored. Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis. Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS. Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration.

Identification of Novel Cuproptosis-Related Genes Mediating the Prognosis and Immune Microenvironment in Cholangiocarcinoma

BACKGROUND

Cuproptosis is a novel type of mediated cell death strongly associated with the progression of several cancers and has been implicated as a potential therapeutic target. However, the role of cuproptosis in cholangiocarcinoma for prognostic prediction, subgroup classification, and therapeutic strategies remains largely unknown.

METHODS

A systematic analysis was conducted among 146 cuproptosis-related genes and clinical information based on independent mRNA and protein datasets to elucidate the potential mechanisms and prognostic prediction value of cuproptosis-related genes. A 10-cuproptosis-related gene prediction model was constructed, and its effects on cholangiocarcinoma prognosis were significantly connected to poor patient survival. Additionally, the expression patterns of our model included genes that were validated with several cholangiocarcinoma cancer cell lines and a normal biliary epithelial cell line.

RESULTS

First, a 10-cuproptosis-related gene signature (ADAM9, ADAM17, ALB, AQP1, CDK1, MT2A, PAM, SOD3, STEAP3, and TMPRSS6) displayed excellent predictive performance for the overall survival of cholangiocarcinoma. The low-cuproptosis group had a significantly better prognosis than the high-cuproptosis group with transcriptome and protein cohorts. Second, compared with the high-risk and low-risk groups, the 2 groups displayed distinct tumor microenvironments, reduced proportions of endothelial cells, and increased levels of cancer-associated fibroblasts based on CIBERSORTx and EPIC analyses. Third, patients' sensitivities to chemotherapeutic drugs and immune checkpoints revealed distinctive differences between the 2 groups. Finally, in replicating the expression patterns of the 10 genes, these results were validated with quantitative real-time polymerase chain reaction results validating the abnormal expression pattern of the target genes in cholangiocarcinoma.

CONCLUSIONS

Collectively, we established and verified an effective prognostic model that could separate cholangiocarcinoma patients into 2 heterogeneous cuproptosis subtypes based on the molecular or protein characteristics of 10 cuproptosis-related genes. These findings may provide potential benefits for unveiling molecular characteristics and defining subgroups could improve the early diagnosis and individualized treatment of cholangiocarcinoma patients.

Identification and validation of potential diagnostic signature and immune cell infiltration for NAFLD based on cuproptosis-related genes by bioinformatics analysis and machine learning

Background and aims

Cuproptosis has been identified as a key player in the development of several diseases. In this study, we investigate the potential role of cuproptosis-related genes in the pathogenesis of nonalcoholic fatty liver disease (NAFLD).

Method

The gene expression profiles of NAFLD were obtained from the Gene Expression Omnibus database. Differential expression of cuproptosis-related genes (CRGs) were determined between NAFLD and normal tissues. Protein-protein interaction, correlation, and function enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was analyzed in both NAFLD patients and controls. Quantitative real-time PCR was employed to validate the expression of hub genes.

Results

Four datasets containing 115 NAFLD and 106 control samples were included for bioinformatics analysis. Three hub CRGs (NFE2L2, DLD, and POLD1) were identified through the intersection of three machine learning algorithms. The receiver operating characteristic curve was plotted based on these three marker genes, and the area under the curve (AUC) value was 0.704. In the external GSE135251 dataset, the AUC value of the three key genes was as high as 0.970. Further nomogram, decision curve, calibration curve analyses also confirmed the diagnostic predictive efficacy. Gene set enrichment analysis and gene set variation analysis showed these three marker genes involved in multiple pathways that are related to the progression of NAFLD. CIBERSORT and single-sample gene set enrichment analysis indicated that their expression levels in macrophages, mast cells, NK cells, Treg cells, resting dendritic cells, and tumor-infiltrating lymphocytes were higher in NAFLD compared with control liver samples. The ceRNA network demonstrated a complex regulatory relationship between the three hub genes. The mRNA level of these hub genes were further confirmed in a mouse NAFLD liver samples.

Conclusion

Our study comprehensively demonstrated the relationship between NAFLD and cuproptosis, developed a promising diagnostic model, and provided potential targets for NAFLD treatment and new insights for exploring the mechanism for NAFLD.

Construction of gene subgroups of Crohn disease based on transcriptome data

BACKGROUND

The global prevalence of Crohn disease (CD), a chronic inflammatory disease of the intestine, has been increasing; however, the etiology and pathogenesis of this disease have not been fully elucidated. Therefore, in the present study, we aimed to better understand the molecular mechanisms underlying CD to aid the development of novel therapeutic strategies for this condition.

METHODS

Based on the transcriptome data from patients with CD, this study used an unsupervised learning method to construct gene co-expression molecular subgroups and the R and SPSS software to identify the biological, clinical, and genetic characteristics and signatures of each subgroup.

RESULTS

Two subgroups were analyzed. Compared to subgroup II, subgroup I consisted of older patients with a more limited range of disease presentation and had a higher number of smokers. The specific genes associated with this subgroup, including CDKN2B, solute carrier family 22 member 5, and phytanoyl-CoA 2-hydroxylase, can be targeted for managing intestinal dysbacteriosis. The number of patients showing infiltrating lesions was higher, the number of smokers was lower, and CD severity was worse in patients in subgroup II than those in subgroup I. The specific genes relevant to subgroup II included cluster of differentiation 44, tryptophanyl-tRNA synthetase, and interleukin 10 receptor, alpha subunit, which may be related to viral infection.

CONCLUSION

The present study segregated patients with CD into 2 subgroups; the findings reported herein provide a new theoretical basis for the diagnosis and treatment of CD and could aid a thorough identification of potential therapeutic targets for this disease.

Identification of aging-related biomarkers and immune infiltration characteristics in osteoarthritis based on bioinformatics analysis and machine learning

Background

Osteoarthritis (OA) is a degenerative disease closely related to aging. Nevertheless, the role and mechanisms of aging in osteoarthritis remain unclear. This study aims to identify potential aging-related biomarkers in OA and to explore the role and mechanisms of aging-related genes and the immune microenvironment in OA synovial tissue.

Methods

Normal and OA synovial gene expression profile microarrays were obtained from the Gene Expression Omnibus (GEO) database and aging-related genes (ARGs) from the Human Aging Genomic Resources database (HAGR). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO), and Gene set variation analysis (GSVA) enrichment analysis were used to uncover the underlying mechanisms. To identify Hub ARDEGs with highly correlated OA features (Hub OA-ARDEGs), Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning methods were used. Furthermore, we created diagnostic nomograms and receiver operating characteristic curves (ROC) to assess Hub OA-ARDEGs' ability to diagnose OA and predict which miRNAs and TFs they might act on. The Single sample gene set enrichment analysis (ssGSEA) algorithm was applied to look at the immune infiltration characteristics of OA and their relationship with Hub OA-ARDEGs.

Results

We discovered 87 ARDEGs in normal and OA synovium samples. According to functional enrichment, ARDEGs are primarily associated with inflammatory regulation, cellular stress response, cell cycle regulation, and transcriptional regulation. Hub OA-ARDEGs with excellent OA diagnostic ability were identified as MCL1, SIK1, JUND, NFKBIA, and JUN. Wilcox test showed that Hub OA-ARDEGs were all significantly downregulated in OA and were validated in the validation set and by qRT-PCR. Using the ssGSEA algorithm, we discovered that 15 types of immune cell infiltration and six types of immune cell activation were significantly increased in OA synovial samples and well correlated with Hub OA-ARDEGs.

Conclusion

Synovial aging may promote the progression of OA by inducing immune inflammation. MCL1, SIK1, JUND, NFKBIA, and JUN can be used as novel diagnostic biomolecular markers and potential therapeutic targets for OA.

Identification of immune infiltration and cuproptosis-related molecular clusters in tuberculosis

Background

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) infection. Cuproptosis is a novel cell death mechanism correlated with various diseases. This study sought to elucidate the role of cuproptosis-related genes (CRGs) in TB.

Methods

Based on the GSE83456 dataset, we analyzed the expression profiles of CRGs and immune cell infiltration in TB. Based on CRGs, the molecular clusters and related immune cell infiltration were explored using 92 TB samples. The Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was utilized to identify the co-expression modules and cluster-specific differentially expressed genes. Subsequently, the optimal machine learning model was determined by comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB). The predictive performance of the machine learning model was assessed by generating calibration curves and decision curve analysis and validated in an external dataset.

Results

11 CRGs were identified as differentially expressed cuproptosis genes. Significant differences in immune cells were observed in TB patients. Two cuproptosis-related molecular clusters expressed genes were identified. Distinct clusters were identified based on the differential expression of CRGs and immune cells. Besides, significant differences in biological functions and pathway activities were observed between the two clusters. A nomogram was generated to facilitate clinical implementation. Next, calibration curves were generated, and decision curve analysis was conducted to validate the accuracy of our model in predicting TB subtypes. XGB machine learning model yielded the best performance in distinguishing TB patients with different clusters. The top five genes from the XGB model were selected as predictor genes. The XGB model exhibited satisfactory performance during validation in an external dataset. Further analysis revealed that these five model-related genes were significantly associated with latent and active TB.

Conclusion

Our study provided hitherto undocumented evidence of the relationship between cuproptosis and TB and established an optimal machine learning model to evaluate the TB subtypes and latent and active TB patients.

Identification of cuproptosis-associated subtypes and signature genes for diagnosis and risk prediction of Ulcerative colitis based on machine learning

Background

Ulcerative colitis (UC) is a chronic and debilitating inflammatory bowel disease that impairs quality of life. Cuproptosis, a recently discovered form of cell death, has been linked to many inflammatory diseases, including UC. This study aimed to examine the biological and clinical significance of cuproptosis-related genes in UC.

Methods

Three gene expression profiles of UC were obtained from the Gene Expression Omnibus (GEO) database to form the combined dataset. Differential analysis was performed based on the combined dataset to identify differentially expressed genes, which were intersected with cuproptosis-related genes to obtain differentially expressed cuproptosis-related genes (DECRGs). Machine learning was conducted based on DECRGs to identify signature genes. The prediction model of UC was established using signature genes, and the molecular subtypes related to cuproptosis of UC were identified. Functional enrichment analysis and immune infiltration analysis were used to evaluate the biological characteristics and immune infiltration landscape of signature genes and molecular subtypes.

Results

Seven signature genes (ABCB1, AQP1, BACE1, CA3, COX5A, DAPK2, and LDHD) were identified through the machine learning algorithms, and the nomogram built from these genes had excellent predictive performance. The 298 UC samples were divided into two subtypes through consensus cluster analysis. The results of the functional enrichment analysis and immune infiltration analysis revealed significant differences in gene expression patterns, biological functions, and enrichment pathways between the cuproptosis-related molecular subtypes of UC. The immune infiltration analysis also showed that the immune cell infiltration in cluster A was significantly higher than that of cluster B, and six of the characteristic genes (excluding BACE1) had higher expression levels in subtype B than in subtype A.

Conclusions

This study identified several promising signature genes and developed a nomogram with strong predictive capabilities. The identification of distinct subtypes of UC enhances our current understanding of UC's underlying pathogenesis and provides a foundation for personalized diagnosis and treatment in the future.

Classification of tuberculosis-related programmed cell death-related patient subgroups and associated immune cell profiling

Background

Tuberculosis (TB) is the deadliest communicable disease in the world with the exception of the ongoing COVID-19 pandemic. Programmed cell death (PCD) patterns play key roles in the development and progression of many disease states such that they may offer value as effective biomarkers or therapeutic targets that can aid in identifying and treating TB patients.

Materials and methods

The Gene Expression Omnibus (GEO) was used to gather TB-related datasets after which immune cell profiles in these data were analyzed to examine the potential TB-related loss of immune homeostasis. Profiling of differentially expressed PCD-related genes was performed, after which candidate hub PCD-associated genes were selected via a machine learning approach. TB patients were then stratified into two subsets based on the expression of PCD-related genes via consensus clustering. The potential roles of these PCD-associated genes in other TB-related diseases were further examined.

Results

In total, 14 PCD-related differentially expressed genes (DEGs) were identified and highly expressed in TB patient samples and significantly correlated with the abundance of many immune cell types. Machine learning algorithms enabled the selection of seven hub PCD-related genes that were used to establish PCD-associated patient subgroups, followed by the validation of these subgroups in independent datasets. These findings, together with GSVA results, indicated that immune-related pathways were significantly enriched in TB patients exhibiting high levels of PCD-related gene expression, whereas metabolic pathways were significantly enriched in the other patient group. Single cell RNA-seq (scRNA-seq) further highlighted significant differences in the immune status of these different TB patient samples. Furthermore, we used CMap to predict five potential drugs for TB-related diseases.

Conclusion

These results highlight clear enrichment of PCD-related gene expression in TB patients and suggest that this PCD activity is closely associated with immune cell abundance. This thus indicates that PCD may play a role in TB progression through the induction or dysregulation of an immune response. These findings provide a foundation for further research aimed at clarifying the molecular drivers of TB, the selection of appropriate diagnostic biomarkers, and the design of novel therapeutic interventions aimed at treating this deadly infectious disease.

Pediatric Crohn's disease diagnosis aid via genomic analysis and machine learning

Introduction

Determination of pediatric Crohn's disease (CD) remains a major diagnostic challenge. However, the rapidly emerging field of artificial intelligence has demonstrated promise in developing diagnostic models for intractable diseases.

Methods

We propose an artificial neural network model of 8 gene markers identified by 4 classification algorithms based on Gene Expression Omnibus database for diagnostic of pediatric CD.

Results

The model achieved over 85% accuracy and area under ROC curve value in both training set and testing set for diagnosing pediatric CD. Additionally, immune infiltration analysis was performed to address why these markers can be integrated to develop a diagnostic model.

Conclusion

This study supports further clinical facilitation of precise disease diagnosis by integrating genomics and machine learning algorithms in open-access database.