OBJECTIVES: To elucidate the underlying mechanisms of macrophage-mediated inflammation and tissue injury in diabetic foot ulcer (DFU). METHODS: Skin tissue samples were collected from patients with DFU and with non-DFU. A total of 79 272 high-quality cell transcriptomes were obtained using single-cell RNA sequencing. An unbiased clustering approach was employed to identify cell subpopulations. Seurat functions were used to identify differentially expressed genes between DFU and non-DFU groups, and gene ontology (GO) enrichment analysis was used to reveal gene function. Furthermore, cell-cell communication network construction and ligand-receptor interaction analysis were performed to reveal the mechanisms underlying cellular interactions and signaling regulation in the DFU microenvironment from multiple perspectives. RESULTS: The results revealed a significant expansion of myeloid cells in DFU tissues, alongside a marked reduction in structural cells such as endothelial cells, epithelial cells, and smooth muscle cells. Major cell types underwent functional reprogramming, characterized by immune activation and impaired tissue remodeling. Specifically, macrophages in DFU skin tissues exhibited a shift toward a pro-inflammatory M1 phenotype, with upregulation of genes associated with inflammation and oxidative stress. Cell communication analysis further demonstrated that M1 macrophages served as both primary signal receivers and influencers in the COMPLEMENT pathway mediated communication network, and as key signal senders and mediators in the secreted phosphoprotein 1 (SPP1) pathway mediated communication network, actively shaping the inflammatory microenvironment. Key ligand-receptor interactions driving macrophage signaling were identified, including C3-(ITGAM+ITGB2) and SPP1-CD44. CONCLUSIONS This study establishes a comprehensive single-cell atlas of DFU, revealing the role of macrophage-driven cellular networks in chronic inflammation and impaired healing. These findings may offer potential novel therapeutic targets for DFU treatment.
Humans ; Macrophages/immunology* ; Diabetic Foot/pathology* ; Single-Cell Analysis ; Transcriptome ; Gene Expression Profiling ; Inflammation ; Skin ; Cell Communication ; Signal Transduction ; Cellular Reprogramming

OBJECTIVE: To detect the expression level of T-complex polypeptide 1 (TCP1) in the bone marrow of newly diagnosed acute myeloid leukemia (AML) patients, and explore its correlation with clinical characteristics and prognosis. METHODS: The bone marrow samples from 80 newly diagnosed AML patients and 30 iron deficiency anemia (IDA) patients were collected, and real time fluorescence quantitative PCR was used to detect the expression level of TCP1 . The clinical data of AML patients were collected, and the correlation of TCP1 expression with clinical characteristics and prognosis of patients were analyzed. The impact of TCP1 on overall survival (OS) of AML patients was identified by using Kaplan-Meier curve analysis. Cox regression analysis was used to identify the factors affecting prognosis of AML patients. RESULTS: Compared with IDA patients, the expression of TCP1 was significantly increased in AML patients (P < 0.01). The high expression group of TCP1 showed a higher proportion of patients with ≥60 years and non-remission after treatment, more accompanied by TET2 mutation and poor prognosis but shorter OS compared to the low expression group (all P < 0.05). The results of multivariate Cox regression analysis showed that age, chromosomal abnormalities, therapeutic efficacy and TCP1 expression were independent risk factors affecting prognosis of AML patients (all P < 0.05). CONCLUSION TCP1 is significantly upregulated in AML patients, and its expression is associated with partial clinical features and poor prognosis. It can serve as a prognostic indicator and potential therapeutic target for AML patients.
Gene Expression Regulation, Leukemic ; Leukemia, Myeloid, Acute/metabolism* ; Humans ; Gene Expression Profiling ; Bone Marrow/metabolism* ; Anemia, Iron-Deficiency/metabolism* ; Polymerase Chain Reaction ; Prognosis ; Kaplan-Meier Estimate ; Proportional Hazards Models ; Multivariate Analysis ; Risk Factors ; Chaperonin Containing TCP-1

OBJECTIVES: Hepatocellular carcinoma is one of the most common primary malignant tumors with the third highest mortality rate worldwide. This study aims to identify key genes associated with hepatocellular carcinoma prognosis using the Gene Expression Omnibus (GEO) database and provide a theoretical basis for discovering novel prognostic biomarkers for hepatocellular carcinoma. METHODS: Hepatocellular carcinoma-related datasets were retrieved from the GEO database. Differentially expressed genes (DEGs) were identified using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and key genes were identified using Cytoscape software. The University of Alabama at Birmingham Cancer Data Analysis Resource (UALCAN) was used to analyze the expression levels of key genes in normal and hepatocellular carcinoma tissues, as well as their associations with pathological grade, clinical stage, and patient survival. The Human Protein Atlas (THPA) was used to further validate the impact of key genes on overall survival. Expression levels of key genes in the blood of hepatocellular carcinoma patients were evaluated using the expression atlas of blood-based biomarkers in the early diagnosis of cancers (BBCancer). RESULTS: A total of 78 DEGs were identified from the GEO database. GO and KEGG analyses indicated that these genes may contribute to hepatocellular carcinoma progression by promoting cell division and regulating protein kinase activity. Sixteen key genes were screened via Cytoscape and validated using UALCAN and THPA. These genes were overexpressed in hepatocellular carcinoma tissues and were associated with disease progression and poor prognosis. Finally, BBCancer analysis showed that ASPM and NCAPG were also elevated in the blood of hepatocellular carcinoma patients. CONCLUSIONS This study identified 16 key genes as potential prognostic biomarkers for hepatocellular carcinoma, among which ASPM and NCAPG may serve as promising blood-based markers for hepatocellular carcinoma.
Humans ; Carcinoma, Hepatocellular/mortality* ; Liver Neoplasms/pathology* ; Prognosis ; Computational Biology/methods* ; Protein Interaction Maps/genetics* ; Biomarkers, Tumor/genetics* ; Gene Expression Regulation, Neoplastic ; Gene Expression Profiling ; Gene Ontology ; Databases, Genetic

OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS AEBP1 and COL8A2 have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
Sarcopenia/genetics* ; Osteoarthritis/genetics* ; Computational Biology/methods* ; Humans ; Protein Interaction Maps/genetics* ; Animals ; Mice ; Gene Expression Profiling ; Gene Ontology ; Transcriptome ; Male ; Signal Transduction/genetics* ; Gene Regulatory Networks

OBJECTIVES: Polycystic ovary syndrome (PCOS) is a common endocrine disorder that affects women's health. This study aims to investigate gene and transcription factor (TF) expression differences between PCOS patients and healthy individuals using bioinformatics approaches, and to verify the function of key transcription factors, with the goal of providing new insights into the pathogenesis of PCOS. METHODS: Differentially expressed genes (DEGs) and differentially expressed transcription factors (DETFs) between PCOS patients and controls were identified from the RNA sequencing dataset GSE168404 using bioinformatics methods. Functional enrichment analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The expression and function of core transcription factors were further validated in ovarian tissues of PCOS model mice and control mice using Western blotting and reverse transcription quantitative polymerase chain reaction (RT-qPCR). RESULTS: A total of 332 DEGs were identified between PCOS patients and controls, including 259 upregulated and 73 downregulated genes in the PCOS group. 19 DETFs were further screened, of which 16 were upregulated and 3 were downregulated in PCOS. The upregulated DETFs (including TFCP2L1, DACH1, ESR2, AFF3, SMAD9, ZNF331, HOPX,ATOH8, HIF3α, DPF3, HOXC4, HES1, ID1, JDP2, SOX4, and ID3) were primarily associated with lipid metabolism, development, and cell adhesion. Protein and mRNA expression analysis in PCOS model mice revealed significantly decreased levels of hypoxia-inducible factor (HIF) 1α and HIF2α, and significantly increased expression of HIF3α compared to control mice (all P<0.001). CONCLUSIONS Significant differences in gene and TF expression exist between PCOS patients and healthy individuals. HIF-3α may play a crucial role in PCOS and could serve as a novel biomarker for diagnosis and a potential therapeutic target.
Polycystic Ovary Syndrome/metabolism* ; Female ; Humans ; Animals ; Mice ; Transcription Factors/metabolism* ; Computational Biology ; Gene Expression Profiling ; Adult

OBJECTIVES: Multiple myeloma (MM) is a highly heterogeneous hematologic malignancy, with disease progression driven by cytogenetic abnormalities and a complex bone marrow microenvironment. This study aims to construct a prognostic model for MM based on transcriptomic data and lipid metabolism related genes (LRGs), and to identify potential drug targets for high-risk patients to support clinical decision-making. METHODS: In this study, 2 transcriptomic datasets covering 985 newly diagnosed MM patients were retrieved from the Gene Expression Omnibus (GEO) database. Univariate Cox regression and 101 machine learning algorithms were used for gene selection. An LRG-based prognostic model was constructed using Stepwise Cox (both directions) and random survival forest (RSF) algorithms. The association between the prognostic score and clinical events was evaluated, and model performance was assessed using time-dependent receiver operating characteristic (ROC) curves and the C-index. The added predictive value of combining prognostic scores with clinical variables and staging systems was also analyzed. Differentially expressed genes between high- and low-risk groups were identified using limma and clusterProfiler and subjected to pathway enrichment analysis. Drug sensitivity analysis was conducted using the Genomics of Drug Sensitivity in Cancer (GDSC) database and oncoPredict to identify potential therapeutic targets for high-risk patients. The functional role of key LRGs in the model was validated via in vitro cell experiments. RESULTS: An LRG-based prognostic model (LRG17) was successfully developed using transcriptomic data and machine learning. The model demonstrated robust predictive performance, with area under the curve (AUC) values of 0.962, 0.912, and 0.842 for 3-, 5-, and 7-year survival, respectively. Patients were stratified into high- and low-risk groups, with high-risk patients showing significantly shorter overall survival (OS) and event-free survival (EFS) (both P<0.001) and worse clinical profiles (e.g., lower albumin, higher β2-microglobulin and lactate dehydrogenase levels). Enrichment analysis revealed that high-risk patients were significantly enriched for pathways related to chromosome segregation and mitosis, whereas low-risk patients were enriched for immune response and immune cell activation pathways. Drug screening suggested that AURKA inhibitor BMS-754807 and FGFR3 inhibitor I-BET-762 may be more effective in high-risk patients. Functional assays demonstrated that silencing of key LRG PLA2G4A significantly inhibited cell viability and induced apoptosis. CONCLUSIONS LRGs serve as promising biomarkers for prognosis prediction and risk stratification in MM. The overexpression of chromosomal instability-related and high-risk genetic event-associated genes in high-risk patients may explain their poorer outcomes. Given the observed resistance to bortezomib and lenalidomide in high-risk patients, combination therapies involving BMS-754807 or I-BET-762 may represent effective alternatives.
Humans ; Multiple Myeloma/mortality* ; Prognosis ; Lipid Metabolism/genetics* ; Transcriptome ; Machine Learning ; Male ; Female ; Gene Expression Profiling ; Algorithms

BACKGROUND: PANoptosis has the features of pyroptosis, apoptosis, and necroptosis. Numerous studies have confirmed the diverse roles of various types of cell death in acute liver failure (ALF), but limited attention has been given to the crosstalk among them. In this study, we aimed to explore the role of PANoptosis in ALF and uncover new targets for its prevention or treatment. METHODS: Three ALF-related datasets (GSE14668, GSE62029, and GSE74000) were downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs). Hub genes were identified through intersecting DEGs, genes obtained from weighted gene co-expression network analysis (WGCNA), and genes related to PANoptosis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein‍‒‍protein interaction (PPI) analyses and gene set enrichment analysis (GSEA) were performed to determine functional roles. Verification was performed using an ALF mouse model. RESULTS: Our results showed that expression of seven hub genes (B-cell lymphoma-2-modifying factor (BMF), B-cell lymphoma-2-interacting protein 3-like (BNIP3L), Caspase-1 (CASP1), receptor-interacting protein kinase 3 (RIPK3), uveal autoantigen with coiled-coil domains and ankyrin repeats protein (UACA), uncoordinated-5 homolog B receptor (UNC5B), and Z-DNA-binding protein 1 (ZBP1)) was up-regulated in liver samples of patients. However, in the ALF mouse model, the expression of BNIP3L, RIPK3, phosphorylated RIPK3 (P-RIPK3), UACA, and cleaved caspase-1 was up-regulated, while the expression of CASP1 and UNC5B was down-regulated. The expression of ZBP1 and BMF increased only during the development of ALF, and there was no significant change in the end stage. Immunofluorescence of mouse liver tissue showed that macrophages expressed all seven markers. Western blot results showed that pyroptosis, apoptosis, and necroptosis were always involved in lipopolysaccharide (LPS)/ d-galactosamine (d-gal)‍-induced ALF mice. The ALF cell model showed that bone marrow-derived macrophages (BMDMs) form PANoptosomes after LPS stimulation. CONCLUSIONS: Our results suggest that PANoptosis of macrophages promotes the development of ALF. The seven new ALF biomarkers identified and validated in this study may contribute to further investigation of diagnostic markers or novel therapeutic targets of ALF.
Animals ; Liver Failure, Acute/genetics* ; Computational Biology ; Mice ; Pyroptosis/genetics* ; Humans ; Protein Interaction Maps ; Apoptosis/genetics* ; Necroptosis/genetics* ; Gene Regulatory Networks ; Gene Ontology ; Gene Expression Profiling ; Disease Models, Animal

Recent data suggest that vascular endothelial growth factor receptor inhibitor (VEGFRi) can enhance the anti-tumor activity of the anti-programmed cell death-1 (anti-PD-1) antibody in colorectal cancer (CRC) with microsatellite stability (MSS). However, the comparison between this combination and standard third-line VEGFRi treatment is not performed, and reliable biomarkers are still lacking. We retrospectively enrolled MSS CRC patients receiving anti-PD-1 antibody plus VEGFRi (combination group, n=54) or VEGFRi alone (VEGFRi group, n=32), and their efficacy and safety were evaluated. We additionally examined the immune characteristics of the MSS CRC tumor microenvironment (TME) through single-cell and spatial transcriptomic data, and an MSS CRC immune cell-related signature (MCICRS) that can be used to predict the clinical outcomes of MSS CRC patients receiving immunotherapy was developed and validated in our in-house cohort. Compared with VEGFRi alone, the combination of anti-PD-1 antibody and VEGFRi exhibited a prolonged survival benefit (median progression-free survival: 4.4 vs. 2.0 months, P=0.0024; median overall survival: 10.2 vs. 5.2 months, P=0.0038) and a similar adverse event incidence. Through single-cell and spatial transcriptomic analysis, we determined ten MSS CRC-enriched immune cell types and their spatial distribution, including naive CD4⁺ T, regulatory CD4⁺ T, CD4⁺ Th17, exhausted CD8⁺ T, cytotoxic CD8⁺ T, proliferated CD8⁺ T, natural killer (NK) cells, plasma, and classical and intermediate monocytes. Based on a systemic meta-analysis and ten machine learning algorithms, we obtained MCICRS, an independent risk factor for the prognosis of MSS CRC patients. Further analyses demonstrated that the low-MCICRS group presented a higher immune cell infiltration and immune-related pathway activation, and hence a significant relation with the superior efficacy of pan-cancer immunotherapy. More importantly, the predictive value of MCICRS in MSS CRC patients receiving immunotherapy was also validated with an in-house cohort. Anti-PD-1 antibody combined with VEGFRi presented an improved clinical benefit in MSS CRC with manageable toxicity. MCICRS could serve as a robust and promising tool to predict clinical outcomes for individual MSS CRC patients receiving immunotherapy.
Humans ; Colorectal Neoplasms/drug therapy* ; Male ; Female ; Immunotherapy ; Middle Aged ; Aged ; Tumor Microenvironment/immunology* ; Retrospective Studies ; Microsatellite Instability ; Transcriptome ; Single-Cell Analysis ; Programmed Cell Death 1 Receptor/immunology* ; Gene Expression Profiling ; Immune Checkpoint Inhibitors/therapeutic use* ; Adult ; Receptors, Vascular Endothelial Growth Factor/antagonists & inhibitors*

Imprinted genes play a key role in regulating mammalian placental and embryonic development. Here, we generated glutaminyl-peptide cyclotransferase-knockout (Qpct^-/-) mice utilizing the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) platform and identified Qpct as a novel anti-angiogenic factor in regulating mouse placentation. Compared with Qpct^+/+ mice, placentae and embryos (Qpct^-/+ and Qpct^-/-) showed significant overgrowth at embryonic Day 12.5 (E12.5), E15.5, and E18.5. Using single-cell transcriptome analysis of 32 309 cells from Qpct^+/+ and Qpct^-/- mouse placentae, we identified 13 cell clusters via single-nucleus RNA sequencing (snRNA-seq) (8880 Qpct^+/+ and 13 577 Qpct^-/- cells) and 20 cell clusters via single-cell RNA sequencing (scRNA-seq) (6567 Qpct^+/+ and 3285 Qpct^-/- cells). Furthermore, we observed a global up-regulation of pro-angiogenic genes in the Qpct^-/- background. Immunohistochemistry assays revealed a notable increase in the number of blood vessels in the decidual and labyrinthine layers of E15.5 Qpct^-/+ and Qpct^-/- mice. Moreover, the elevation of multiple pairs of ligand-receptor interactions was observed in decidual cells, endothelial cells, and macrophages, promoting angiogenesis and inflammatory response. Our findings indicate that loss of maternal Qpct leads to altered phenotypic characteristics of placentae and embryos and promotes angiogenesis in murine placentae.
Animals ; Female ; Pregnancy ; Mice ; Placentation/genetics* ; Single-Cell Analysis ; Gene Expression Profiling ; Mice, Knockout ; Transcriptome ; Placenta/blood supply* ; Neovascularization, Pathologic/genetics* ; Genomic Imprinting ; Single-Cell Gene Expression Analysis ; Angiogenesis

Objective:Proteins are closely associated with the development, progression, and immunotherapy of head and neck squamous cell carcinoma（HNSCC）. However, few clinical models utilize proteomics to predict prognosis and immunotherapy efficacy. In this study, we developed a protein prognostic model（PPM） to stratify survival outcomes and differential immunotherapy responses in HNSCC patients. Methods:Based on proteomic profiling, we constructed a PPM comprising 11 protein markers. Patients were classified into high-and low-risk groups according to PPM scores. The prognostic value of risk scores was evaluated using Cox regression analysis, and predictive accuracy was assessed via time-dependent receiver operating characteristic（ROC） curves. Additionally, we analyzed treatment responses to PD1/CTLA4 immunotherapy in PD1-or CTLA4-positive patients across risk groups. Results:Cox regression confirmed the risk score as an independent prognostic factor（HR=1.161, 95%CI 1.112-1.213, P<0.001）, with high-risk patients exhibiting significantly poorer survival than low-risk counterparts. The model demonstrated robust predictive accuracy, with 1-year and 3-year time-dependent ROC areas under the curve（AUC） of 0.713 and 0.707, respectively. In PD1/CTLA4-positive subgroups, low-risk patients showed superior immunotherapy responses compared to high-risk patients. Conclusion:The PPM can provide reliable prognostic stratification and preliminary guidance for immunotherapy in HNSCC. However, further clinical studies and basic experiments are needed for further verification.
Humans ; Immunotherapy ; Squamous Cell Carcinoma of Head and Neck/therapy* ; Prognosis ; Head and Neck Neoplasms/genetics* ; Proteome ; Proteomics ; Gene Expression Profiling ; Transcriptome ; CTLA-4 Antigen ; Programmed Cell Death 1 Receptor ; Male ; Female ; Middle Aged