Objective To explore the clinical and immunological significance of CCDC97 in hepatocellular carcinoma (HCC). Methods Clinical data and RNA sequencing results from HCC patients were retrieved from TCGA and ICGC databases. Bioinformatics analysis and in vitro experiments were performed to investigate the role of CCDC97 in HCC. Results The expression level of CCDC97 was elevated in HCC patients and HCC cells, closely associated with pathological features and prognosis. CCDC97 was identified as a novel prognostic biomarker. It is linked to the spliceosome pathway, which is significantly active in tumors and potentially promotes carcinogenesis. CCDC97 is also highly expressed in various immune cells and is associated with microenvironment. Furthermore, knocking down CCDC97 in vitro suppressed cell migration, invasion, and proliferation. Conclusion CCDC97 plays a critical role in HCC progression and the immune microenvironment, making it a potential target for prognosis and therapeutic intervention.
Humans ; Carcinoma, Hepatocellular/metabolism* ; Liver Neoplasms/metabolism* ; Tumor Microenvironment/genetics* ; Cell Movement/genetics* ; Cell Proliferation ; Prognosis ; Cell Line, Tumor ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/genetics* ; Male

Objective To explore the prognostic value of glycolysis-related genes in colorectal cancer (CRC) patients and formulate a novel glycolysis-related prognostic risk model. Methods Single-cell and bulk transcriptomic data of CRC patients, along with clinical information, were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Glycolysis scores for each sample were calculated using single-sample Gene Set Enrichment Analysis (ssGSEA). Kaplan-Meier survival curves were generated to analyze the relationship between glycolysis scores and overall survival. Novel glycolysis-related subgroups were defined among the cell type with the highest glycolysis scores. Gene enrichment analysis, metabolic activity assessment, and univariate Cox regression were performed to explore the biological functions and prognostic impact of these subgroups. A prognostic risk model was built and validated based on genes significantly affecting the prognosis. Gene Set Enrichment Analysis (GSEA) was conducted to explore differences in biological processes between high- and low-risk groups. Differences in immune microenvironment and drug sensitivity between these groups were assessed using R packages. Potential targeted agents for prognostic risk genes were predicted using the Enrichr database. Results Tumor tissues showed significantly higher glycolysis scores than normal tissues, which was associated with a poor prognosis in CRC patients. The highest glycolysis score was observed in epithelial cells, within which we defined eight novel glycolysis-related cell subpopulations. Specifically, the P4HA1⁺ epithelial cell subpopulation was associated with a poor prognosis. Based on signature genes of this subpopulation, a six-gene prognostic risk model was formulated. GSEA revealed significant biological differences between high- and low-risk groups. Immune microenvironment analysis demonstrated that the high-risk group had increased infiltration of macrophages and tumor-associated fibroblasts, along with evident immune exclusion and suppression, while the low-risk group exhibited higher levels of B cell and T cell infiltration. Drug sensitivity analysis indicated that high-risk patients were more sensitive to Abiraterone, while low-risk patients responded to Cisplatin. Additionally, Valproic acid was predicted as a potential targeted agent. Conclusion High glycolytic activity is associated with a poor prognosis in CRC patients. The novel glycolysis-related prognostic risk model formulated in this study offers significant potential for enhancing the diagnosis and treatment of CRC.
Humans ; Colorectal Neoplasms/pathology* ; Glycolysis/genetics* ; Prognosis ; Transcriptome ; Tumor Microenvironment/genetics* ; Gene Expression Profiling ; Single-Cell Analysis ; Gene Expression Regulation, Neoplastic ; Male ; Female ; Kaplan-Meier Estimate

Objective To investigate the common molecular features of immunothrombosis, thus enhancing the comprehension of thrombosis triggered by immune and inflammatory responses and offering crucial insights for identifying potential diagnostic and therapeutic targets. Methods Differential gene expression analysis and functional enrichment analysis were conducted on datasets of systemic lupus erythematosus (SLE) and venous thromboembolism (VTE). The intersection of differentially expressed genes in SLE and VTE with those of neutrophil extracellular traps (NET) yielded cross-talk genes (CG) for SLE-NET and VTE-NET interaction. Further analysis included functional enrichment and protein-protein interaction (PPI) network assessments of these CG to identify hub genes. Venn diagrams and receiver operating characteristic (ROC) curve analysis were employed to pinpoint the most effective shared diagnostic CG, which were validated using a graft-versus-host disease (GVHD) dataset. Results Differential expression genes in SLE and VTE were associated with distinct biological processes, whereas SLE-NET-CG and VTE-NET-CG were implicated in pathways related to leukocyte migration, inflammatory response, and immune response. Through PPI network analysis, several hub genes were identified, with matrix metalloproteinase 9 (MMP9) and S100 calcium-binding protein A12 (S100A12) emerging as the best shared diagnostic CG for SLE (AUC: 0.936 and 0.832) and VTE (AUC: 0.719 and 0.759). Notably, MMP9 exhibited good diagnostic performance in the GVHD dataset (AUC: 0.696). Conclusion This study unveils the common molecular features of SLE, VTE, and NET, emphasizing MMP9 and S100A12 as the optimal shared diagnostic CG, thus providing valuable evidence for the diagnosis and therapeutic strategies related to immunothrombosis. Additionally, the expression of MMP9 in GVHD highlights its critical role in the risk of VTE associated with immune system disorders.
Humans ; Computational Biology/methods* ; Lupus Erythematosus, Systemic/immunology* ; Protein Interaction Maps/genetics* ; Venous Thromboembolism/therapy* ; Matrix Metalloproteinase 9/genetics* ; Extracellular Traps/metabolism* ; Gene Regulatory Networks ; Thrombosis/immunology* ; Graft vs Host Disease/genetics* ; Gene Expression Profiling

Objective To elucidate the molecular mechanism by which exosomes (Exo) derived from cancer-associated fibroblasts (CAF) carrying HOX transcript antisense intergenic RNA (lncRNA HOTAIR) promote the metastasis of esophageal squamous cell carcinoma (ESCC). Methods CAFs were collected from tumor tissues, and non-cancer associated fibroblasts (NFs) were obtained from adjacent normal tissues at least 5 cm away from the tumor. Exosomes (CAFs-Exo and NFs-Exo) were isolated from conditioned media collected from CAFs or NFs. CAFs-Exo and NFs-Exo were incubated with human ESCC cell line TE-1 for 24 hours, and CCK-8 was used to determine the cell proliferation ability. Scratch test and Transwell test were performed to determine the cell migration and invasion ability. TE-1 cells were divided into the following two groups: NC group and KD group. The NC group and KD group were transfected with control siRNAs or siRNAs targeting HOTAIR respectively. The effects of HOTAIR knock-down on cell proliferation, migration, invasion and glycolysis were determined. Results CAFs-Exo promoted the proliferation of TE-1 cells more significantly than NFs-Exo. Compared with NFs-Exo group, the migration and invasion ability of TE-1 cells treated with CAFs-Exo were improved significantly. In addition, CAFs-Exo treatment inhibited the expression of E-cadherin and enhanced the expression of N-cadherin. The expression of HOTAIR in CAFs was significantly higher than that in NFs. Compared with NFs-Exo, the expression level of HOTAIR in CAFs-Exo increased significantly. Compared with NC group, the proliferation, migration and invasion of TE-1 cells in KD group decreased significantly. Compared with NC group, hexokinase 2 (HK2), extracellular acidification rate (ECAR) and ATP/ADP ratio of TE-1 cells in KD group decreased significantly. Conclusion HOTAIR, an exosome derived from CAFs, may be involved in metastasis and EMT by regulating glycolysis in ESCC cells.
Humans ; RNA, Long Noncoding/metabolism* ; Esophageal Neoplasms/metabolism* ; Cell Movement/genetics* ; Cell Proliferation/genetics* ; Cell Line, Tumor ; Esophageal Squamous Cell Carcinoma ; Exosomes/genetics* ; Neoplasm Metastasis ; Neoplasm Invasiveness ; Gene Expression Regulation, Neoplastic ; Glycolysis/genetics* ; Cancer-Associated Fibroblasts/metabolism* ; Carcinoma, Squamous Cell/metabolism* ; Cadherins/genetics*

Objective To investigate the expression and correlation of LY86-AS1 and KHDRBS2 in glioblastoma (GBM), and their impacts on the prognosis of patients and immune cell infiltration. Methods Based on the GSE50161 dataset from the Gene Expression Omnibus (GEO) database, LY86-AS1 and KHDRBS2, which are closely related to the development of GBM, were identified by WGCNA and differential expression analysis. The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases were used to analyze the relationship between the expression of LY86-AS1 and KHDRBS2 and the prognosis of GBM patients. Multiple datasets were employed to analyze the correlation between the expression levels of LY86-AS1 and KHDRBS2 and its relationship with immune cell infiltration. Real-time quantitative PCR was used to verify the expression of LY86-AS1 and KHDRBS2 in GBM and normal brain tissues. The Human Protein Atlas (HPA) database was accessed to obtain the protein expression of KHDRBS2, and immunohistochemical staining was conducted to verify the protein expression of KHDRBS2. Results LY86-AS1 and KHDRBS2 were lowly expressed in GBM tissues and were closely related to the development of GBM, showing a significant positive correlation. Patients with low expression levels of LY86-AS1 and KHDRBS2 had a lower overall survival rate than those with high expression levels. LY86-AS1 was positively correlated with naive B cells, plasma cells, activated NK cells, M1 macrophages, activated mast cells and monocytes. KHDRBS2 was positively correlated with naive B cells, plasma cells, helper T cells, activated NK cells and monocytes. Conclusion The low expression levels of LY86-AS1 and KHDRBS2 in GBM, which is associated with poor prognosis, affect the tumor immune microenvironment and may serve as potential new biomarkers for the diagnosis of GBM and the prognosis assessment of patients.
Humans ; Glioblastoma/metabolism* ; Prognosis ; Brain Neoplasms/pathology* ; Gene Expression Regulation, Neoplastic ; RNA-Binding Proteins/metabolism*

Objective To investigate the clinical relevance and diagnostic or prognostic value of ST3β-galactoside α-2, 3-sialyltransferase 1 (ST3GAL1) in glioma and to confirm its role in promoting malignant phenotypes. Methods Using data from The Cancer Genome Atlas (TCGA) database, we analyzed the correlation between ST3GAL1 expression levels in glioma and clinical parameters to evaluate its diagnostic and prognostic value. The impact of ST3GAL1 on malignant phenotypes of glioma cells-including proliferation, cell cycle progression, apoptosis, and invasion was further validated through ST3GAL1 knockdown experiments. Results The expression level of ST3GAL1 was significantly higher in glioma tissues compared to healthy brain tissues and showed a strong correlation with clinical characteristics of glioma patients. Survival analysis and receiver operating characteristic (ROC) curve demonstrated that ST3GAL1 could serve as a potential diagnostic and prognostic biomarker for glioma. Knockdown of ST3GAL1 suppressed proliferation, invasion, and migration capabilities of glioma cell lines, and induced G1-phase cell cycle arrest. Conclusion ST3GAL1 promotes malignant phenotypes in glioma and plays a critical role in its malignant progression, suggesting its potential as a biomarker for glioma diagnosis and prognosis.
Humans ; Sialyltransferases/metabolism* ; Glioma/diagnosis* ; Cell Proliferation/genetics* ; Cell Line, Tumor ; Brain Neoplasms/enzymology* ; beta-Galactoside alpha-2,3-Sialyltransferase ; Disease Progression ; Prognosis ; Cell Movement/genetics* ; Apoptosis/genetics* ; Male ; Female ; Gene Expression Regulation, Neoplastic ; Biomarkers, Tumor/metabolism* ; Middle Aged

Objective To investigate the gene expression and regulatory mechanisms of mouse embryonic fibroblasts (MEFs) under inflammatory conditions, aiming to elucidate the role of MEFs in inflammatory responses and provide a foundation for discovering anti-inflammatory drugs that act by modulating MEF function. Methods MEFs cultured in vitro were divided into the following groups: lipopolysaccharides (LPS)-treated group, inflammatory conditioned medium (CM)-treated group, and control group, which were treated with LPS, CM, and equal volume solvent, respectively. Transcriptome sequencing was used to analyze the effects of two stimuli on gene expression profile of MEFs. Real time fluorescence quantitative PCR (RT-qPCR) was employed to verify the transcription levels of highly expressed genes of MEFs induced by CM. ELISA was performed to determine the concentrations of cytokines in cell supernatants. Finally, the regulatory effects of CM on the activation of signaling pathways in MEFs were analyzed by immunoblotting. Results Transcriptome analysis showed that both LPS and CM induced the transcription of a large number of genes in MEFs. Compared with LPS, CM potentiated the mRNA transcription of some acute phase proteins, inflammatory cytokines, chemokines, matrix metalloproteinases (MMP), prostaglandin synthetases, and colony-stimulating factors. The transcriptome analysis was verified by RT-qPCR. The results of ELISA showed that CM treatment significantly increased the secretion of interleukin 6 (IL-6), C-C motif chemokine ligand (CCL2), and C-X-C motif chemokine ligand (CXCL1) by MEFs compared with LPS. Mechanism study showed that both LPS and CM induced the phosphorylation of nuclear factor-κB p65 (NF-κB p65), p38 mitogen-activated protein kinase (p38 MAPK), extracellular regulated protein kinases 1/2 (ERK1/2), and TANK-binding kinase (TBK) in MEFs, and CM strongly stimulated the phosphorylation of signal transducer and activator of transcription 3 (STAT3) in MEFs. Conclusion Both LPS and CM can induce transcription and protein secretion of various inflammation-related genes in MEFs. CM can partly enhance LPS-induced activation of MEFs, and the mechanism may be related to the enhancement effect of CM on the activation STAT3 signaling pathway.
Animals ; Fibroblasts/immunology* ; Mice ; Lipopolysaccharides/pharmacology* ; Inflammation/metabolism* ; Signal Transduction/drug effects* ; Gene Expression Regulation/drug effects* ; Cytokines/genetics* ; Culture Media, Conditioned/pharmacology* ; Cells, Cultured

Objective To explore macrophage subpopulations in ischemic stroke (IS) by using single-cell RNA sequencing (scRNA-seq) data analysis and High-Dimensional Weighted Gene Co-Expression Network Analysis (hdWGCNA). Methods Based on single-cell sequencing data, transcriptomic information for different cell types was obtained, and macrophages were selected for subpopulation identification. hdWGCNA, cell-cell communication, and pseudotime trajectory analysis were used to explore the characteristics of macrophage subpopulations following IS. Key genes related to IS were identified using microarray data and validated for diagnostic potential through Receiver Operating Characteristic (ROC) analysis. Gene Set Enrichment Analysis (GSEA) was conducted to investigate the potential functions of these genes. Results The scRNA-seq data analysis revealed significant changes in macrophage subpopulation composition after IS. A specific macrophage subpopulation enriched in the stroke group was identified and designated as MCAO-specific macrophages (MSM). Pseudotime trajectory analysis indicated that MSM cells were in an intermediate stage of macrophage differentiation. Cell-cell communication analysis uncovered complex interactions between MSM cells and other cells, with the CCL6-CCR1 signaling axis potentially playing a crucial role in neuroinflammation. Two gene modules associated with MSM were identified via hdWGCNA, significantly enriched in pathways related to NOD-like receptors and antigen processing. By integrating differentially expressed MSM genes with conventional transcriptomic data, three IS-related hub genes were identified: Arg1, CLEC4D, and CLEC4E. Conclusion This study reveals the characteristics and functions of macrophage subpopulations following IS and identifies three hub genes with potential diagnostic value, providing novel insights into the pathological mechanisms of IS.
Macrophages/metabolism* ; Computational Biology/methods* ; Single-Cell Analysis/methods* ; Transcriptome ; Ischemic Stroke/metabolism* ; Animals ; Gene Regulatory Networks ; Gene Expression Profiling ; Humans ; Male

Objective To explore the mechanism of TPT1-AS1 targeting miR-324/TWIST1 axis to regulate the proliferation, invasion, migration and angiogenesis of epithelial ovarian cancer (EOC) cells, thereby affecting ovarian cancer (OC) progression. Methods RT-qPCR was used to detect the expression of TPT1-AS1 and miR-324 in 29 OC lesions and adjacent tissue samples. The two OC cell models of TPT1-AS1 overexpression and miRNA324 knockdown were constructed, and the cell proliferation, invasion and migration abilities were detected by CCK-8, Transwell^TM and scratch test. Western blot analysis was used to detect the protein expression levels of TWIST1, epithelial cadherin (E-cadherin), Vimentin, and vascular endothelial growth factor A (VEGF-A) in OC cells. Fluorescence in situ hybridization (FISH) and RNA pull-down experiments were used to verify the interaction between TPT1-AS1 and miR-324. Immunohistochemistry and Targetscan bioinformatics analysis were used to verify the negative regulatory role of miR-324 in the epithelial-mesenchymal transition (EMT) process. Results The TPT1-AS1 expression was significantly higher in OC tissues than that in para-cancerous tissues, while the miR-324 expression was significantly lower. In SKOV3 cells with TPT1-AS1 overexpression, the miR-324 expression decreased significantly, and TPT1-AS1 was negatively correlated with miR-324. It was also found that TPT1-AS1 and miR-324 were co-expressed in OC cells, and there was a direct binding relationship between them. Down-regulation of miR-324 significantly promoted the proliferation, invasion and migration of SKOV3 cells. Further studies revealed that miR-324 had a binding site at the 3'-UTR end of the TWIST1, a key transcription factor for EMT. Inhibiting miR-324 expression increased the transcription level of TWIST1, leading to a decrease in E-cadherin protein expression and an increase in Vimentin protein expression. Additionally, the downregulation of miR-324 resulted in an increased expression level of VEGF-A protein, which in turn enhanced angiogenesis of OC. Conclusion TPT1-AS1 promotes EOC cell proliferation, invasion, migration and angiogenesis by negatively regulating the miR-324/TWIST1 axis, thus promoting the development of OC. These findings provide new potential targets for the diagnosis and treatment of OC.
Humans ; MicroRNAs/metabolism* ; Female ; Cell Movement/genetics* ; Ovarian Neoplasms/blood supply* ; Twist-Related Protein 1/metabolism* ; Cell Line, Tumor ; Neovascularization, Pathologic/genetics* ; Neoplasm Invasiveness ; Carcinoma, Ovarian Epithelial/metabolism* ; Nuclear Proteins/metabolism* ; Cell Proliferation/genetics* ; Epithelial-Mesenchymal Transition/genetics* ; Gene Expression Regulation, Neoplastic ; RNA, Long Noncoding/metabolism* ; Cadherins/genetics* ; Vascular Endothelial Growth Factor A/genetics* ; Vimentin/genetics* ; Angiogenesis

Objective Through integrative bioinformatics analysis of multi-source transcriptomic data, potential biomarkers to asthma epithelial cells were identified. The expression of these candidate target was subsequently validated in lung tissues and epithelial cells from asthma models. Methods The gene expression profile data of epithelial cells from three asthma patient cohorts and corresponding healthy controls were integrated from the Gene Expression Omnibus (GEO) database. Differential expression analysis and gene co-expression network analysis were performed to identify key genes and biological pathways associated with asthma. The key genes were validated in lung tissues and epithelial cells in asthma animal models. Results Differential gene expression analysis revealed 1121 upregulated and 1484 downregulated genes in epithelial cells from asthma patients compared with healthy controls. The biological pathway enrichment analysis revealed that the upregulated genes were mainly involved in glycosylation processes, whereas the downregulated genes were mainly associated with immune cell differentiation process. The gene co-expression network analysis revealed that module 9, enriched in glycosylation-related pathways, was significantly positively correlated with asthma, whereas module 17, associated with insulin and other signaling pathways, showed a significant negative correlation with asthma. We identified the genes of polypeptide N-acetylgalactosaminyltransferase 5 (GALNT5), pyrroline-5-carboxylate reductase 1 (PYCR1), and carcinoembryonic antigen-related cell adhesion molecule 5 (CEACAM5) as key genes within module 9, all of which were significantly upregulated in asthma. Finally, we validated that the expression levels of GALNT5, PYCR1, and CEACAM5 were significantly upregulated in epithelial cells from asthmatic lung tissue. Additionally, using a rat asthma model, we further confirmed that the protein levels of these three genes were significantly upregulated in lung tissues of the model group. Conclusion Through data integration and experimental validation, this study identified key genes and biological pathways closely associated with asthma pathogenesis. These findings provide a novel theoretical basis and potential targets for the diagnosis and treatment of asthma.
Asthma/metabolism* ; Humans ; Epithelial Cells/metabolism* ; Animals ; Biomarkers/metabolism* ; Gene Expression Profiling ; Transcriptome ; Gene Regulatory Networks ; Rats ; Computational Biology