Comprehensive analysis for the role of macrophage-driven genes in abdominal aortic aneurysm

BackgroundOsteoarthritis (OA) is a common chronic joint disease, but the association between molecular and cellular events and the pathogenic process of OA remains unclear.ObjectiveThe study aimed to identify key molecular and cellular events in the processes of immune infiltration of the synovium in OA and to provide potential diagnostic and therapeutic targets.MethodsTo identify the common differential expression genes and function analysis in OA, we compared the expression between normal and OA samples and analyzed the protein–protein interaction (PPI). Additionally, immune infiltration analysis was used to explore the differences in common immune cell types, and Gene Set Variation Analysis (GSVA) analysis was applied to analyze the status of pathways between OA and normal groups. Furthermore, the optimal diagnostic biomarkers for OA were identified by least absolute shrinkage and selection operator (LASSO) models. Finally, the key role of biomarkers in OA synovitis microenvironment was discussed through single cell and Scissor analysis.ResultsA total of 172 DEGs (differentially expressed genes) associated with osteoarticular synovitis were identified, and these genes mainly enriched eight functional categories. In addition, immune infiltration analysis found that four immune cell types, including Macrophage, B cell memory, B cell, and Mast cell were significantly correlated with OA, and LASSO analysis showed that Macrophage were the best diagnostic biomarkers of immune infiltration in OA. Furthermore, using scRNA-seq dataset, we also analyzed the cell communication patterns of Macrophage in the OA synovial inflammatory microenvironment and found that CCL, MIF, and TNF signaling pathways were the mainly cellular communication pathways. Finally, Scissor analysis identified a population of M2-like Macrophages with high expression of CD163 and LYVE1, which has strong anti-inflammatory ability and showed that the TNF gene may play an important role in the synovial microenvironment of OA.ConclusionOverall, Macrophage is the best diagnostic marker of immune infiltration in osteoarticular synovitis, and it can communicate with other cells mainly through CCL, TNF, and MIF signaling pathways in microenvironment. In addition, TNF gene may play an important role in the development of synovitis.

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous lung disease influenced by epigenetic modifications, particularly RNA methylation. Emerging evidence also suggests that autophagy plays a crucial role in immune cell infiltration and is implicated in COPD progression. This study aimed to investigate key RNA methylation regulators and explore the roles of RNA methylation and autophagy in COPD pathogenesis. We analyzed tissue-based bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) datasets from COPD and non-COPD patients, sourced from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified between COPD and non-COPD samples, and protein–protein interaction networks were constructed. Univariate logistic regression identified shared genes between DEGs and RNA methylation gene sets. Functional enrichment analyses, including Gene Ontology (GO), gene set enrichment analysis (GSEA), and gene set variation analysis (GSVA), were performed. Weighted gene co-expression network analysis (WGCNA) and immune infiltration analysis were conducted. Integration with scRNA-seq data further elucidated changes in immune cell composition, and cell communication analysis assessed interactions between macrophages and other immune cells. AddModuleScore analysis quantified RNA methylation and autophagy effects. Finally, a COPD mouse model was used to validate the expression of critical RNA methylation genes (FTO and IGF2BP2) in lung macrophages via RT-qPCR and flow cytometry. As revealed, we identified 13 RNA methylation-related genes enriched in translation and methylation processes. GSEA and GSVA revealed significant enrichment of these genes in immune and autophagy pathways. WGCNA analysis pinpointed key hub genes linking RNA methylation and autophagy. Integrated scRNA-seq analysis demonstrated a marked reduction of macrophages in COPD, with FTO and IGF2BP2 emerging as critical RNA methylation regulators. Macrophages with elevated RNA methylation and autophagy scores had increased interactions with other immune cells. In COPD mouse models, decreased expression of FTO and IGF2BP2 in lung macrophages was validated. Taken together, this study highlights the significant roles of RNA methylation in relation to autophagy pathways in the context of COPD. We identified key RNA methylation-related hub genes, such as FTO and IGF2BP2, which were found to have decreased expression in COPD macrophages. These findings provide novel genetic insights into the epigenetic mechanisms of COPD and suggest potential avenues for developing diagnostic and therapeutic strategies.

This study examines the impact of estrogenic compounds like bisphenol A (BPA), estradiol (E2), and zearalenone (ZEA) on human ovarian cancer, focusing on constructing a risk model, conducting gene set variation analysis (GSVA), and evaluating immune infiltration. Differential gene expression analysis identified 980 shared differentially expressed genes (DEGs) in human ovarian cells exposed to BPA, E2, and ZEA, indicating disruptions in ribosome biogenesis and RNA processing. Using the cancer genome atlas ovarian cancer (TCGA-OV) dataset, a least absolute shrinkage and selection operator (LASSO)-based risk model was developed incorporating prognostic genes 4-hydroxyphenylpyruvate dioxygenase like (HPDL), Thy-1 cell surface antigen (THY1), and peptidase inhibitor 3 (PI3). This model effectively stratified ovarian cancer patients into high-risk and low-risk categories, showing significant differences in overall survival, disease-specific survival, and progression-free survival. GSVA analysis linked HPDL expression to pathways related to the cell cycle, DNA damage, and repair, while THY1 and PI3 were associated with apoptosis, hypoxia, and proliferation pathways. Immune infiltration analysis revealed distinct immune cell profiles for high and low-expression groups of HPDL, THY1, and PI3, indicating their influence on the tumor microenvironment. The findings demonstrate that estrogenic compounds significantly alter gene expression and oncogenic pathways in ovarian cancer. The risk model integrating HPDL, THY1, and PI3 offers a strong prognostic tool, with GSVA and immune infiltration analyses providing insights into the interplay between these genes and the tumor microenvironment, suggesting potential targets for personalized therapies.

Globally, primary liver cancer ranks as the sixth most prevalent cancer and was the third leading cause of cancer-related deaths in 2022. Hepatocellular carcinoma (HCC) accounts for 75–85% of all cases. In total, ~70% of patients with HCC experience recurrence within 5 years, which impacts their long-term survival outcomes. Therefore, the development of a reliable predictive model for the probability of HCC recurrence represents a crucial clinical need. However, studies that integrate bulk RNA sequencing (RNA-seq) and single-cell (sc)RNA-seq to construct prognostic models are lacking. The present study analyzed bulk RNA-seq and scRNA-seq datasets of patients with HCC to identify differentially expressed genes (DEGs) that affect HCC recurrence-free survival (RFS). Subsequently, least absolute shrinkage and selection operator Cox penalized regression analysis was performed to construct a prognostic model. Enrichment analysis and immune infiltration analysis were applied to identify the underlying mechanisms involved. Univariate and multivariate Cox regression analyses were subsequently performed. Finally, independent dataset and reverse transcription-quantitative PCR (RT-qPCR) experiments were used to evaluate the prognostic model. A total of 5,586 DEGs were obtained from the bulk RNA-seq dataset and 2,320 DEGs from the scRNA-seq dataset. Moreover, 53 DEGs associated with the RFS of patients with HCC were identified. A total of 6 of these genes (cyclin-dependent kinase inhibitor 2A, complement factor H-related 3, cytochrome P450 family 2 subfamily C member 9, high mobility group box 2, immunoglobulin λ constant 2 and Jupiter microtubule-associated homolog 1) were incorporated into the prognostic model. Patients in the high-risk group had significantly worse RFS time than those in the low-risk group. Furthermore, the cell cycle and immunosuppression were identified as possible factors affecting RFS. In addition, the prognostic signature retained independent predictive value for HCC RFS, and it was validated successfully by another publicly available dataset and RT-qPCR experiments using patient tissues. In conclusion, the present study constructed a prognostic model for predicting RFS in patients with HCC via integrated analysis of scRNA-seq and bulk RNA-seq data that could serve as a valuable reference tool for clinicians.

Growing evidence highlights the critical role of chromatin remodeling in tumor development and progression. This study explores the relationship between chromatin remodeling-related genes (CRRGs) and breast cancer (BRCA). Public databases were used to retrieve the TCGA-BRCA and GSE20685 datasets. CRRGs were sourced from prior studies. Prognosis-associated CRRGs were identified using univariate Cox regression analysis. TCGA-BRCA BRCA samples were grouped into CRRG-related subtypes through consensus clustering analysis. Differential expression analysis was conducted in TCGA-BRCA (BRAC vs. control) and among subtypes to identify differentially expressed genes (DEGs). Candidate genes were obtained through the intersection of these DEGs. Prognostic genes were selected using univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Independent prognostic factors were identified, and a nomogram model was developed. Functional enrichment, immune infiltration, clinical relevance, and drug sensitivity analyses were subsequently performed. TCGA-BRCA BRCA samples were classified into two CRRG-related subtypes (clusters 1 and 2) based on prognosis-associated CRRGs. A total of 141 candidate genes were identified by intersecting 250 DEGs (cluster 1 vs. cluster 2) with 3,145 DEGs (BRCA vs. control). Five prognostic genes—LHX5, ZP2, GABRQ, APOA2, and CLCNKB—were selected, and a prognostic risk model was developed. In clinical samples, APOA2 (P = 0.0290) and GABRQ (P = 0.0132) expression were significantly up-regulated, CLCNKB (P < 0.0001) and ZP2 (P = 0.0445) expression were significantly down-regulated, while LHX5 (P = 0.1508) expression did not present a significant difference. A nomogram was created, and calibration and Receiver Operating Characteristic (ROC) curves demonstrated its superior predictive ability for BRCA. Gene Set Variation Analysis (GSVA) revealed 16 pathways, such as “mTORC1 signaling” and “E2F targets,” were enriched in the high-risk group, while 9 pathways, including “estrogen response early” and “myogenesis,” were enriched in the low-risk group. Additionally, significant differences in immune cell types, including CD8+ T cells and follicular helper T cells, were observed between the two risk groups. The risk score was also significantly associated with six drugs, including AZD1332 1463 and SB505124 1194. This study presents a prognostic model based on five genes (LHX5, ZP2, GABRQ, APOA2, and CLCNKB) for BRCA, offering a novel perspective on the link between CRRGs and BRCA.

Glycolysis plays a crucial role in fibrosis, but the specific genes involved in glycolysis in idiopathic pulmonary fibrosis (IPF) are not well understood. Three IPF gene expression datasets were obtained from the Gene Expression Omnibus (GEO), while glycolysis-related genes were retrieved from the Molecular Signatures Database (MsigDB). Differentially expressed glycolysis-related genes (DEGRGs) were identified using the "limma" R package. Diagnostic glycolysis-related genes (GRGs) were selected through least absolute shrinkage and selection operator (LASSO) regression regression and support vector machine-recursive feature elimination (SVM-RFE). A prognostic signature was developed using LASSO regression, and time-dependent receiver operating characteristic (ROC) curves were generated to evaluate predictive performance. Single-cell RNA sequencing (scRNA-seq) data were analyzed to examine GRG expression across various cell types. Immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), and Gene Set Variation Analysis (GSVA) were performed to elucidate potential molecular mechanisms. A bleomycin (BLM)-induced pulmonary fibrosis mouse model was used for experimental validation via reverse transcription-quantitative polymerase chain reaction (RT-qPCR). 14 GRGs (VCAN, MERTK, FBP2, TPBG, SDC1, AURKA, ARTN, PGP, PLOD2, PKLR, PFKM, DEPDC1, AGRN, CXCR4) were identified as diagnostic markers for IPF, with seven (ARTN, AURKA, DEPDC1, FBP2, MERTK, PFKM, SDC1) forming a prognostic model demonstrating predictive power (AUC: 0.831-0.793). scRNA-seq revealed cell-type-specific GRG expression, particularly in macrophages and fibroblasts. Immune infiltration analysis linked GRGs to imbalanced immune responses. Experimental validation in a bleomycin-induced fibrosis model confirmed the upregulation of GRGs (such as AURKA, CXCR4). Drug prediction identified inhibitors (such as Tozasertib for AURKA, Plerixafor for CXCR4) as potential therapeutic agents. This study identifies GRGs as potential prognostic biomarkers for IPF and highlights their role in modulating immune responses within the fibrotic lung microenvironment. Notably, AURKA, MERTK, and CXCR4 were associated with pathways linked to fibrosis progression and represent potential therapeutic targets. Our findings provide insights into metabolic reprogramming in IPF and suggest that targeting glycolysis-related pathways may offer novel pharmacological strategies for antifibrotic therapy.

IntroductionHeart failure (HF) and kidney failure (KF) are closely related conditions that often coexist, posing a complex clinical challenge. Understanding the shared mechanisms between these two conditions is crucial for developing effective therapies.MethodsThis study employed transcriptomic analysis to unveil molecular signatures and novel biomarkers for both HF and KF. A total of 2869 shared differentially expressed genes (DEGs) were identified in patients with HF and KF compared to healthy controls. Functional enrichment analysis was performed to explore the common mechanisms underlying these conditions. A protein-protein interaction (PPI) network was constructed, and machine learning algorithms, including Random Forest (RF), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Least Absolute Shrinkage and Selection Operator (LASSO), were used to identify key signature genes. These genes were further analyzed using Gene Set Variation Analysis (GSVA) and Gene Set Enrichment Analysis (GSEA), with their diagnostic values validated in both training and validation sets. Molecular docking studies were conducted. Additionally, immune cell infiltration and correlation analyses were performed to assess the relationship between immune responses and the identified biomarkers.ResultsThe functional enrichment analysis indicated that the common mechanisms are associated with cellular homeostasis, cell communication, cellular replication, inflammation, and extracellular matrix (ECM) production, with the PI3K-Akt signaling pathway being notably enriched. The PPI network revealed two key protein clusters related to the cell cycle and inflammation. CDK2 and CCND1 were identified as signature genes for both HF and KF. Their diagnostic value was validated in both training and validation sets. Additionally, docking studies with CDK2 and CCND1 were performed to evaluate potential drug candidates. Immune cell infiltration and correlation analyses highlighted the immune microenvironment, and that CDK2 and CCND1 are associated with immune responses in HF and KF.DiscussionThis study identifies CDK2 and CCND1 as novel biomarkers linking cell cycle regulation and inflammation in heart and kidney failure. These findings offer new insights into the molecular mechanisms of HF and KF and present potential targets for diagnosis and therapy.

ObjectiveCoronary artery disease (CAD) is a serious global health concern. Current diagnostic methods for CAD involve risk to the patient and are costly, so better diagnostic tools are needed. We defined four classifiers based on gene expression profiles in peripheral blood mononuclear cells and determined their potential for CAD detection.MethodsWe downloaded a CAD-related data set (GSE113079) from the Gene Expression Omnibus (GEO) database. We identified differentially expressed genes (DEGs) in peripheral blood mononuclear cells between CAD samples and healthy controls. DEGs were analyzed for functional enrichment. To create a robust CAD classifier, DEGs were identified by feature selection using the principal component analysis. Then, least absolute shrinkage and selection operator (LASSO) logistic regression, random forest, and support vector machine (SVM) models were created. Gene set variation analysis (GSVA) score and gene set enrichment analysis (GSEA) were also conducted. The performance of the models was evaluated in terms of the area under receiver operating characteristic curves (AUC).ResultsIn the training set, we found 135 up-regulated genes and 104 down-regulated genes in CAD patients compared with controls. The DEGs were involved in some pathways associated with CAD, such as pathways involving calcium and interleukin-17 signaling. Twenty genes were identified as optimal features and used to generate the logistic classifier based on LASSO. The AUC for the classifier was 1.00 in the training set and 0.997 in the test set. Using the 20 DEGs, SVM and random forest classifiers were also generated and showed high diagnostic efficacy, with respective AUCs of 0.997 and 1.00 against the training set. A GSVA score was also established using the top 20 significant DEGs, which showed an AUC of 0.971 in the training set and 0.989 in the test set. Furthermore, GSEA showed autophagy and the proteasome to be major pathways involving the DEGs.ConclusionWe identified a set of genes specific for CAD whose expression can be measured non-invasively. Using these genes, we defined four diagnostic classifiers using multiple methods.

Background Laryngeal cancer (LC) is a prevalent head and neck carcinoma. Extensive research has established a link between immune cells in the tumor microenvironment (TME) and cancer progression, as well as responses to immunotherapy. This study aims to develop a prognostic model based on immune cell-related genes and examine the TME in LC. Methods RNA-seq data for LC were sourced from The Cancer Genome Atlas (TCGA), and GSE27020 and GSE51985 datasets were retrieved from the Gene Expression Omnibus (GEO) database. Key genes were identified through the intersection of differentially expressed genes (DEGs) between normal and LC samples and module genes derived from weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. The prognostic risk model was constructed using univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Gene Set Variation Analysis (GSVA) was subsequently performed for hallmark and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses in high- and low-risk groups. Immune infiltration analysis between risk groups was conducted via Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and single sample gene set enrichment analysis (ssGSEA). Finally, the relationship between the risk model and immunotherapy response was explored. Results A total of 124 key genes were identified through the overlap analysis, predominantly enriched in GO terms such as defense response to viruses and regulation of response to biotic stimuli, as well as KEGG pathways related to phagosome and Epstein-Barr virus infection. Machine learning indicated that the optimal prognostic model was constructed from two biomarkers, RENBP and OLR1. GSVA revealed that in the high-risk group, epithelial-mesenchymal transition and ECM-receptor interaction were the most significantly enriched pathways, while autoimmune thyroid disease, ribosome, and oxidative phosphorylation predominated in the low-risk group. Additionally, the stromal score was significantly higher in the high-risk group, while CD8+ T cells, cytolytic activity, inflammation promotion, and T cell co-stimulation were elevated in the low-risk group. Tumor Immune Dysfunction and Exclusion (TIDE) analysis showed higher TIDE and exclusion scores in the high-risk group, whereas the CD8 score was higher in the low-risk group. Finally, CD274 (PD-L1) expression was significantly elevated in the low-risk group. Conclusions This study identified two key prognostic biomarkers, RENBP and OLR1, and characterized TME differences across risk groups, offering novel insights into the diagnosis and treatment of LC.

Establishment and validation of an immune-related genes diagnostic model and experimental validation of diagnostic biomarkers for autoimmune thyroiditis based on RNA-seq.

Background Acute type A aortic dissection (AAD) is a catastrophic disease with high mortality, but the pathogenesis has not been fully elucidated. This study is aimed at identifying hub genes and immune cells associated with the pathogenesis of AAD. Methods The datasets were downloaded from Gene Expression Omnibus (GEO). Gene Set Enrichment Analysis (GSEA), gene set variation analysis (GSVA), and differential analysis were performed. The differentially expressed genes (DEGs) were intersected with specific genes collected from MSigDB. The gene function and pathway enrichment analysis were also performed on intersecting genes. The key modules were selected by weighted gene coexpression network analysis (WGCNA). Hub genes were identified by least absolute shrinkage and selection operator (LASSO) analysis and were verified in the metadataset. The immune cell infiltration was analyzed by CIBERSORT, and the relationship between hub genes and immune cells was performed by Pearson's correlation analysis. The single-cell RNA sequencing (scRNA-seq) dataset was used to verify the differences in DNA damage and repair signaling pathways and hub genes in different cell types. Results The results of GSEA and GSVA indicated that DNA damage and repair processes were activated in the occurrence of AAD. The gene function and pathway enrichment analysis on differentially expressed DNA damage- and repair-related genes showed that these genes were mainly involved in the regulation of the cell cycle process, cellular response to DNA damage stimulus, response to wounding, p53 signaling pathway, and cellular senescence. Three key modules were identified by WGCNA. Five genes were screened as hub genes, including CDK2, EIF4A1, GLRX, NNMT, and SLCO2A1. Naive B cells and Gamma delta T cells (γδ T cells) were decreased in AAD, but monocytes and M0 macrophages were increased. scRNA-seq analysis included that DNA damage and repair processes were activated in smooth muscle cells (SMCs), tissue stem cells, and monocytes in the aortic wall of patients with AAD. Conclusions Our results suggested that DNA damage- and repair-related genes may be involved in the occurrence of AAD by regulating many biological processes. The hub genes and immune cells reported in this study also increase the understanding of AAD.

Systemic lupus erythematosus (SLE) is a complex autoimmune disease that has significant impacts on patients' quality of life and poses a substantial economic burden on society. This study aimed to elucidate the molecular mechanisms underlying SLE by analyzing glucocorticoid-related genes (GRGs) expression profiles. We examined the expression profiles of GRGs in SLE and performed consensus clustering analysis to identify stable patient clusters. We also identified differentially expressed genes (DEGs) within the clusters and between SLE patients and healthy controls. We conducted Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to investigate biological functional differences, and we also conducted CIBERSORTx to estimate the number of immune cells. Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF) algorithms to screen for hub genes. We then validated the expression of these hub genes and constructed nomograms for further validation. Moreover, we employed single-sample Gene Set Enrichment Analysis (ssGSEA) to analyze immune infiltration. We also constructed an RNA-binding protein (RBP)-mRNA network and conducted drug sensitivity analysis along with molecular docking studies. Patients with SLE were divided into two subclusters, revealing a total of 2,681 DEGs. Among these, 1,458 genes were upregulated, while 1,223 were downregulated in cluster_1. GSVA showed significant changes in the pathways associated with cluster_1. Immune infiltration analysis revealed high levels of monocyte in all samples, with greater infiltration of various immune cells in cluster_1. A comparison of SLE patients to control subjects identified 269 DEGs, which were enriched in several pathways. Hub genes, including PTX3, DYSF and F2R, were selected through LASSO and RF methods, resulting in a well-performing diagnostic model. Drug sensitivity and docking studies suggested F2R as a potential new therapeutic target. PTX3, DYSF and F2R are potentially linked to SLE and are proposed as new molecular markers for its onset and progression. Additionally, monocyte infiltration plays a crucial role in advancing SLE.

Periodontitis is a common inflammatory disease affecting the tissues surrounding and supporting the teeth, ultimately leading to tooth loss if left untreated. This study aimed to investigate the diagnostic potential of lipid metabolism-related genes (LMRGs) and characterize the immune microenvironment landscape in periodontitis. Differential expression analysis identified differentially expressed LMRGs (DELMRGs), followed by functional enrichment analyses to elucidate their biological functions. Hub DELMRGs were identified using Random Forest, least absolute shrinkage and selection operator (LASSO) regression, and XGBoost. The diagnostic performance of these genes was assessed using receiver operating characteristic (ROC) curves. Immune cell infiltration and immune function status were analyzed using ImmuCellAI and Gene Set Variation Analysis (GSVA), respectively. Single-cell RNA sequencing (scRNA-seq) was employed to decode the immune microenvironment and cell communication networks at single-cell resolution in periodontitis. Machine learning approaches revealed five hub LMRGs: FABP4, CWH43, CLN8, ADGRF5, and OSBPL6. ADGRF5 and FABP4 were significantly upregulated in periodontitis samples, while CWH43, CLN8, and OSBPL6 were downregulated. The combined LMRGs score exhibited excellent diagnostic performance with an area under the curve (AUC) of 0.954. Immune cell infiltration analysis unveiled significant positive correlations between LMRGs score and various T cell subsets in periodontitis. GSVA indicated activation of antigen presentation processes and multiple immune-related pathways in periodontitis. scRNA-seq delineated eight distinct cell types, with key LMRGs differentially expressed across cell types. Cell communication analysis highlighted significant interactions mediated by MHC-II, CXCL, and ADGRE5 signaling pathways. Monocytes and multipotent progenitor cells (MPPs) primarily contributed to the inflammatory response. Further analysis of monocyte heterogeneity identified five monocyte clusters with distinct roles, including immune and inflammatory response activation and pathways related to cell proliferation and metabolism.In summary, the integrated LMRGs score, which reflects lipid metabolism's role, represents a promising diagnostic biomarker for periodontitis. Additionally, detailed immune cell infiltration and single-cell analyses underscored the critical role of the immune microenvironment in periodontitis pathogenesis.

Metabolic reprogramming is a hallmark of the pathogenesis and progression of endometrial carcinoma (EC). This study comprehensively analyzed the expression profiles of glycine, serine, and threonine (Gly/Ser/Thr) metabolism-related genes in EC. We also established a robust prognostic model and developed a molecular subtyping framework that integrates metabolic and immune characteristics based on the identified prognostic genes. The aims of this work are to enhance diagnostic precision and improve clinical management strategies for patients with EC. Untargeted metabolomic analysis was performed on 35 EC and 15 normal tissues. The Cancer Genome Atlas (TCGA) transcriptomic data were integrated with weighted gene co-expression network analysis (WGCNA) to identify EC-related metabolic genes and construct a prognostic model using Cox proportional hazards and least absolute shrinkage and selection operator (LASSO) regression analyses. The model was validated using an independent proteomic and single-cell dataset from our institution. Consensus clustering classified patients into three molecular subtypes, which were further characterized by gene set variation analysis (GSVA) and profiling of immune infiltration. Finally, key prognostic genes were validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in EC and normal endometrial epithelial cells. Metabolomic analysis revealed significant enrichment of the Gly/Ser/Thr metabolic pathways. WGCNA identified a tumor-associated metabolic module among 1741 pathway-related genes. A prognostic model comprising methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), ribosomal protein S6 kinase A1 (RPS6KA1), and cyclin-dependent kinase inhibitor 2A (CDKN2A) was subsequently established. Consensus clustering based on risk scores stratified EC patients into three molecular subtypes: immunometabolic-suppressed (C1), proliferative-immunobalanced (C2), and immune-activated (C3). The C1 subtype had the poorest prognosis and was characterized by metabolic suppression and immune evasion. The C2 subtype showed a favorable prognosis and was defined by a "proliferation-immune balance" in which high proliferative activity coexisted with strong anti-tumor immunity. The C3 subtype was also associated with a favorable outcome, driven by upregulated DNA repair and oxidative phosphorylation pathways alongside infiltration of immune-active cells. RT-qPCR confirmed significant differences in the mRNA expression of MTHFD2, RPS6KA1, and CDKN2A between normal and EC cells (p < 0.05). This study developed a Gly/Ser/Thr pathway-based prognostic model for EC, based on the expression of MTHFD2, RPS6KA1, and CDKN2A as novel biomarkers. The resulting patient stratification framework holds significant clinical potential for guiding precise and personalized management of EC.

Alzheimer's disease (AD) is one of the most prevalent forms of dementia globally and remains an incurable condition that often leads to death. PANoptosis represents an emerging paradigm in programmed cell death, integrating three critical processes: pyroptosis, apoptosis, and necroptosis. Studies have shown that apoptosis, necroptosis, and pyroptosis play important roles in AD development. Therefore, targeting PANoptosis genes might lead to novel therapeutic targets and clinically relevant therapeutic approaches. This study aims to identify different molecular subtypes of AD and potential drugs for treating AD based on PANoptosis. Differentially expressed PANoptosis genes associated with AD were identified via Gene Expression Omnibus (GEO) dataset GSE48350, GSE5281, and GSE122063. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to construct a risk model linked to these PANoptosis genes. Consensus clustering analysis was conducted to define AD subtypes based on these genes. We further performed gene set variation analysis (GSVA), functional enrichment analysis, and immune cell infiltration analysis to investigate differences between the identified AD subtypes. Additionally, a protein-protein interaction (PPI) network was established to identify hub genes, and the DGIdb database was consulted to identify potential therapeutic compounds targeting these hub genes. Single-cell RNA sequencing analysis was utilized to assess differences in gene expression at the cellular level across subtypes. A total of 24 differentially expressed PANoptosis genes (APANRGs) were identified in AD, leading to the classification of two distinct AD subgroups. The results indicate that these subgroups exhibit varying disease progression states, with the early subtype primarily linked to dysfunctional synaptic signaling. Furthermore, we identified hub genes from the differentially expressed genes (DEGs) between the two clusters and predicted 38 candidate drugs and compounds for early AD treatment based on these hub genes. Single-cell RNA sequencing analysis revealed that key genes associated with the early subtype are predominantly expressed in neuronal cells, while the differential genes for the metabolic subtype are primarily found in endothelial cells and astrocytes. In summary, we identified two subtypes, including the AD early synaptic abnormality subtype as well as the immune-metabolic subtype. Additionally, ten hub genes, SLC17A7, SNAP25, GAD1, SLC17A6, SLC32A1, PVALB, SYP, GRIN2A, SLC12A5, and SYN2, were identified as marker genes for the early subtype. These findings may provide valuable insights for the early diagnosis of AD and contribute to the development of innovative therapeutic strategies.