Your browser does not allow saving data (e.g. private mode): bookmarks will be lost when the tab closes.
Search PubMed
Keywords, author, journal, years — results from PubMed, abstracts with Russian translation, bookmarks in your browser.
Found: 4 939
Page 324 of 494-
An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes.
Ulcerative colitis (UC) is a long-lasting inflammatory bowel disease that causes inflammation in the intestines and triggers autoimmune responses. This study aims to identify immune-related biomarkers for ulcerative colitis (UC) and explore potential therapeutic targets. First, we downloaded the expression profiles of datasets GSE87466, GSE87473, and GSE92415 from the GEO database. Next, we identified differentially expressed genes (DEGs) that are associated with UC. Using the WGCNA algorithm, we screened key module genes in UC and retrieved immune-related genes (IRGs) from the ImmPort database. We identified immune-related differentially expressed genes by intersecting the results from WGCNA, DEGs, and IRGs. To build a diagnostic model for UC, we applied 113 combinations of 12 machine learning algorithms. This included 10-fold cross-validation on the training set and external validation on the test set. The single-cell results presented the cellular profile of UC and indicated that the key genes were significantly associated with macrophages, epithelial cells, and fibroblasts. The single-cell results presented the cell atlas of UC and suggested that key genes were significantly associated with macrophages, epithelial cells and fibroblasts. Quantitative polymerase chain reaction (q-PCR) was used to verify the expression levels of the core biomarkers screened out by machine learning. We conducted enrichment analysis using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA), which showed biological processes and signaling pathways associated with UC. Immune cell infiltration analysis based on CIBERSORT was also performed. We also screened potential drugs from the DSigDB drug database. To evaluate their effectiveness, we performed molecular docking and dynamics simulations. The results suggested that compounds like thalidomide and troglitazone are promising candidates for new UC drug development. Our findings provide insights into the pathogenesis of UC, its clinical treatment, and potential drug development.
-
Multiomic single-nucleus profiling reveals cell-type-specific epigenetic and transcriptional dysregulation in major depressive disorder brain.
OBJECTIVE: Major depressive disorder (MDD) is a leading global cause of disability, marked by persistent mood disturbances, cognitive deficits, and changes in prefrontal cortex neural circuitry. In this study, we aimed to define cell-type-specific molecular and regulatory mechanisms underlying MDD by mapping gene-expression and chromatin-accessibility changes in the dorsolateral prefrontal cortex (PFC) (dlPFC). METHODS: Postmortem dlPFC (BA9) tissue from 7 MDD and 8 well-matched controls was analyzed using 10× Genomics snRNA-seq and paired ATAC+RNA multiome sequencing. Sequencing data were processed with Cell Ranger pipelines, nuclei were filtered for quality and doublets/debris, and datasets were integrated and clustered using Seurat/Signac packages. Differential gene expression, chromatin accessibility, and transcription factor motif activity were tested between MDD and controls within each cell type, followed by peak-to-gene linkage and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and PsyGeNET enrichment to interpret dysregulated regulatory mechanisms. RESULTS: A total of 20 distinct clusters encompassing major neuronal and non-neuronal populations were identified. Differential analyses uncovered extensive cell type-specific changes in chromatin accessibility and gene expression, particularly within excitatory layer 5/6 and inhibitory Pvalb neurons, as well as glial and vascular populations. Functional enrichment indicated dysregulation of synaptic organization, neurotransmission, myelination, stress-response, and immune-regulatory pathways across neuronal and non-neuronal cells. Notably, glucocorticoid-responsive transcription factors NR3C1/NR3C2 exhibited conserved regulatory networks implicating stress signaling in MDD pathophysiology. CONCLUSIONS: Together, these findings provide a comprehensive single-nucleus atlas of gene regulation in the MDD PFC, highlighting coordinated dysfunction across neurons, glia, and vascular cells.
-
A prognostic model based on Scissor+ cancer-associated macrophages identified from bulk and single cell RNA sequencing data in ovarian cancer.
BACKGROUND: Ovarian cancer (OC) represents the most lethal malignancy in gynecological oncology. This study analyzes functional subsets of tumor-associated macrophages (TAMs) using a single-cell sequencing database to construct a precise prognostic model and investigate its associated immune response mechanisms. METHODS: We characterized differences in cell types and TAM subtypes between OC and normal ovarian tissue using the GSE184880 single-cell RNA sequencing (scRNA-seq) dataset. By integrating scissor analysis with The Cancer Genome Atlas (TCGA) survival data, we identified prognostic Scissor+ macrophage signatures and extracted key differentially expressed genes (DEGs). Core genes selected via least absolute shrinkage and selection operator (LASSO)-Cox regression and Random Forest survival modeling were used to build a prognostic risk model, which was validated in TCGA and external cohorts (GSE26712, GSE63885, and GSE140082). Further analyses examined the single-cell expression patterns, immune microenvironment associations, and microRNA (miRNA)/transcription factor (TF) co-expression networks of these core genes. RESULTS: The macrophage proportion was increased in OC, with enriched M2-type TAMs and reduced M1-type TAMs; 94 Scissor+ macrophage DEGs were identified, including five core genes: IL1B, pro-inflammatory cytokine; ISG20, antiviral immune ribonuclease; ABCC3, ATP-binding cassette transporter; ZFP36, mRNA stability RNA-binding protein; IL2RG, immune cell cytokine receptor. The constructed model showed significantly reduced overall survival (OS) in the high-risk group across external cohorts (P<0.05). The high-risk group exhibited enrichment of immunosuppressive cells such as M2-type TAMs and regulatory T cells (Tregs), while the low-risk group showed enrichment of immune-promoting cells including CD4+ memory T cells. The core genes participated in immune regulation via miRNA/TF networks. CONCLUSIONS: This study establishes the first Scissor+ macrophage-based prognostic model for OC, demonstrating consistent predictive performance. It reveals how TAM subtype imbalance contributes to immune microenvironment remodeling, offering new insights for prognostic stratification and immunotherapeutic targeting in OC.
-
Identification and validation of a novel predictive signature based on hepatocyte-specific genes in hepatocellular carcinoma by integrated analysis of single-cell and bulk RNA sequencing.
BACKGROUND: Hepatocellular carcinoma represents a significant global burden in terms of cancer-related mortality, posing a substantial risk to human health. Despite the availability of various treatment modalities, the overall survival rates for patients with hepatocellular carcinoma remain suboptimal. The objective of this study was to explore the potential of novel biomarkers and to establish a novel predictive signature utilizing multiple transcriptome profiles. METHODS: The GSE115469 and CNP0000650 cohorts were utilized for single cell analysis and gene identification. The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were utilized in the development and evaluation of a predictive signature. The expressions of hepatocyte-specific genes were further validated using the GSE135631 cohort. Furthermore, immune infiltration results, immunotherapy response prediction, somatic mutation frequency, tumor mutation burden, and anticancer drug sensitivity were analyzed based on various risk scores. Subsequently, functional enrichment analysis was performed on the differential genes identified in the risk model. Moreover, we investigated the expression of particular genes in chronic liver diseases utilizing datasets GSE135251 and GSE142530. RESULTS: Our findings revealed hepatocyte-specific genes (ADH4, LCAT) with notable alterations during cell maturation and differentiation, leading to the development of a novel predictive signature. The analysis demonstrated the efficacy of the model in predicting outcomes, as evidenced by higher risk scores and poorer prognoses in the high-risk group. Additionally, a nomogram was devised to forecast the survival rates of patients at 1, 3, and 5 years. Our study demonstrated that the predictive model may play a role in modulating the immune microenvironment and impacting the anti-tumor immune response in hepatocellular carcinoma. The high-risk group exhibited a higher frequency of mutations and was more likely to benefit from immunotherapy as a treatment option. Additionally, we confirmed that the downregulation of hepatocyte-specific genes may indicate the progression of hepatocellular carcinoma and aid in the early diagnosis of the disease. CONCLUSION: Our research findings indicate that ADH4 and LCAT are genes that undergo significant changes during the differentiation of hepatocytes into cancer cells. Additionally, we have created a unique predictive signature based on genes specific to hepatocytes.
-
Exploring molecular mechanisms of radioactive iodine therapy in thyroid cancer using single-cell RNA sequencing data.
OBJECTIVE: To investigate the role of ubiquitination-related differentially expressed genes (URDEGs) in thyroid carcinoma (THCA) and their implications for predicting responses to radioactive iodine (RAI) therapy. METHODS: We analyzed data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases using TCGA biolinks and GEO query tools for data acquisition and normalization. Differential expression analysis was performed with DESeq2 to identify URDEGs between RAI-sensitive and RAI- refractory groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using ClusterProfiler. A prognostic risk model was built using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Single-cell RNA sequencing data were analyzed with Seurat for clustering and annotation. Gene Set Variation Analysis (GSVA) was used to assess pathway enrichment across various cell subpopulations. RESULTS: A total of 25 URDEGs were identified, which were enriched in processes related to cytoskeleton organization and pathways including the peroxisome proliferator-activated receptor (PPAR) signaling pathway. A prognostic model based on four URDEGs—INSM2, TAGLN3, MDGA2, and SIK1—demonstrated strong predictive capability. Immune infiltration analysis revealed significant differences in immune cell composition between high- and low-risk groups. Single-cell analysis identified 74,497 cells across 22 clusters and nine cell types, revealing gene expression patterns that highlight cellular heterogeneity. CONCLUSION: URDEGs show potential as biomarkers for predicting RAI therapy response and prognosis in THCA. These findings provide insights for personalized therapeutic strategies targeting specific molecular pathways in THCA patients.
-
Exploring the prognostic value of HK3 and its association with immune infiltration in glioblastoma multiforme.
Background: Hexokinase 3 (HK3) is one of the key enzymes involved in glucose phosphorylation (the first step in most glucose metabolic pathways). Many studies have demonstrated the vital role of dysregulation of HK3 in several tumors. However, there is a need for in-depth characterization of the role of HK3 in glioblastoma multiforme (GBM). Methods: All data were sourced from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA). Kaplan-Meier analysis and univariate regression were applied for survival analysis. Gene set enrichment analysis (GSEA) was used for enrichment analysis. Tumor Immune Single Cell Hub (TISCH) database was applied for single-cell analysis. Tumor Immune Dysfunction and Exclusion (TIDE) analysis was applied to evaluate the immune response. Results: HK3 expression was upregulated in GBM and correlated with poor prognosis. The high HK3 expression group was primarily enriched in adaptive immune response, chemokine signaling pathway, and cytokine-cytokine receptor interaction. The high HK3 expression group showed significantly greater enrichment of the majority of immune cells and immune-related pathways. HK3 showed significant correlation with most immune cells, especially macrophages (p < .001, R = .81). TISCH analysis showed that HK3 was predominantly expressed in macrophages in most cancers. HK3 showed significant correlation with most immune-related genes, such as PD-1 (p < .001, R = .41), PDL-1 (p < .001, R = .27), and CTLA-4 (p < .001, R = .29). TIDE analysis revealed that the low HK3 expression group has a lower TIDE score and may benefit from immunotherapy. Drug sensitivity analysis showed that patients with high HK3 expression frequently showed drug resistance. Conclusion: HK3 was associated with poor prognosis and may serve as a biomarker of macrophages in GBM. HK3 was also associated with immune response and drug resistance. Our findings may provide novel insights for GBM immunotherapy.
-
Dedifferentiated Schwann cell-derived TGF-β3 is essential for the neural system to promote wound healing.
Rationale: Wound healing is among the most complicated physiological processes and requires the synchronization of various cell types with distinct roles to re-establish the condition of the original skin. Patients affected by peripheral neuropathies often experience failure to heal. Loss of Schwann cells (SCs), a crucial population of peripheral nervous system cells in skin, may contribute to chronic wounds. However, the role of SCs in wound healing are poorly understood. Methods: The activity of SCs was investigated by using a cell atlas of the wound healing process, which was generated by integrating single-cell RNA sequencing (scRNA-seq) libraries covering different states of mouse back skin. The results of in silico analysis were validated by in vitro cell culture and in vivo mouse model. Selective inhibitors and conditional RNAi by virus transfection were utilized to investigate the role of SCs in wound healing. Findings from mouse experiments were further verified in scRNA-seq analysis of diabetic patients. Results: Our in silico analysis revealed the heterogeneous cellular components of skin and the dynamic interactions of neural crest derived cells (NCs) with other cell types. We found that SCs dedifferentiated at an early stage of wound repair with upregulated Wnt signaling. We also identified dedifferentiated SC (dSC) defect in diabetic wounds in both mouse and human. Wnt inhibition at the wound site repressed SC dedifferentiation, leading to defective repair. Furthermore, dSCs derived TGF-β3, which is context-dependent, promoted the migration of fibroblasts and keratinocytes. Moreover, TGF-β3 supplementation enhanced the healing of chronic wounds in diabetic mice with impaired SCs. Conclusion: Our study thus advances the understanding of the roles of neural-derived cells in skin regeneration and suggests a potential therapeutic strategy for wound healing disorders.
-
Transcriptomic Analysis Reveals the Role of TRIM26 in Hepatocellular Carcinoma and Its Association With the Wnt/β-catenin Signaling Pathway.
BACKGROUND: Hepatocellular carcinoma (HCC) shows high incidence and mortality worldwide. TRIM26, an E3 ubiquitin ligase within the TRIM family, exerts regulatory functions in various tumors. This study analyzed the expression patterns and potential functions of TRIM26 in HCC based on transcriptomic data. METHODS: First, the differential expression of TRIM26 between tumor and normal tissues was analyzed using the TCGA dataset and cross-validated using TIMER 2.0 and HCCDB. Enrichment analysis evaluated its association with hallmark pathways including Wnt/β-catenin. A gene functional interaction network was built via GeneMANIA to explore TRIM26 and the Wnt/β-catenin pathway. Immune cell infiltration was quantified by ssGSEA for immune microenvironment correlation. scRNA-seq data established an HCC single-cell atlas to define TRIM26 distribution across cell subsets. AUCell was used to assess TRIM26-pathway associations within specific cell types. RESULTS: TRIM26 was significantly upregulated in HCC tissues, and its high expression correlated with enrichment of oncogenic pathways including Wnt/β-catenin, G2/M checkpoint, and TGF-β. GeneMANIA showed that TRIM26 interacted directly or indirectly with Wnt/β-catenin core molecules, implying its regulatory role. TRIM26 expression was closely linked to infiltration of activated B cells, CD8+ T cells, and NKT cells. Single-cell analysis revealed TRIM26 was mainly expressed in hepatocytes, T/NK cells, myeloid cells, and B cells. Importantly, in hepatocytes, TRIM26 strongly correlated with Wnt/β-catenin activity, which was much higher in tumor hepatocytes than normal ones. CONCLUSION: In HCC, TRIM26 was abnormally overexpressed. TRIM26 may regulate tumor progression via the Wnt/β-catenin pathway and is linked to immune infiltration. Thus, TRIM26 is a potential therapeutic target for HCC.
-
Identification and validation of subclusters of papillary thyroid carcinoma based on Human Phenotype Ontology.
BACKGROUND: The increase in the diagnosis of papillary thyroid carcinoma (PTC) has prompted researchers to establish a diagnostic model and identify functional subclusters. The Human Phenotype Ontology (HPO) platform is widely available for differential diagnostics and phenotype-driven investigations based on next-generation sequence-variation data. However, a systematic and comprehensive study to identify and validate PTC subclusters based on HPO is lacking. METHODS: We first used the HPO platform to identify the PTC subclusters. An enrichment analysis was then conducted to examine the key biological processes and pathways associated with the subclusters, and a gene mutation analysis of the subclusters was conducted. For each subcluster, the differentially expressed genes (DEGs) were selected and validated. Finally, a single-cell RNA-sequencing data set was used to verify the DEGs. RESULTS: In our study, 489 PTC patients from The Cancer Genome Atlas (TCGA) were included. Our analysis demonstrated that distinct subclusters of PTC are associated with different survival times and have different functional enrichment, and that C-C motif chemokine ligand 21 (CCL21) and zinc finger CCHC-type containing 12 (ZCCHC12) were the common down- and upregulated genes, respectively, in the 4 subclusters. Additionally, 20 characteristic genes were identified in the 4 subclusters, some of which have previously been reported to have roles in PTC. Further, we found that these characteristic genes were mainly expressed in thyrocytes, endothelial cells, and fibroblasts, and were rarely expressed in immune cells. CONCLUSIONS: We first identified subclusters in PTC based on HPO and found that patients with distinct subclusters have different prognoses. We then identified and validated the characteristic genes in the 4 subclusters. These findings are expected to serve as a crucial reference that will improve our understanding of PTC heterogeneity and the use of novel targets.
-
Integrative single-cell transcriptome analysis reveals a subpopulation of fibroblasts associated with favorable prognosis of liver cancer patients.
Single-cell transcriptome analysis has provided detailed insights into the ecosystem of liver cancer. However, the changes of the cellular and molecular components of liver tumors in comparison with normal livers have not been described at single-cell level. Here, we performed an integrative single-cell analysis of both normal livers and liver cancers. Principal component analysis was firstly performed to delineate the cell lineages in liver tissues. Differential gene expression within major cell types were then analyzed between tumor and normal samples, thus resolved the cell type-specific molecular alterations in liver cancer development. Moreover, a comparison between liver cancer derived versus normal liver derived cell components revealed that two subpopulations of fibroblasts were exclusively expanded in liver cancer tissues. By further defining subpopulation-specific gene signatures, characterizing their spatial distribution in tumor tissues and investigating their clinical significance, we found that the SPARCL1 positive fibroblasts, representing a group of tumor vessel associated fibroblasts, were related to reduced vascular invasion and prolonged survival of liver cancer patients. Through establishing an in-vitro endothelial-to-mesenchymal transition model, we verified the conversion of the fetal liver sinusoidal endothelial cells into the fibroblast-like cells, demonstrating a possible endothelial cell origination of the SPARCL1 positive fibroblasts. Our study provides new insights into the cell atlas alteration, especially the expanded fibroblasts in liver cancers.