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: 4 942
Page 454 of 495-
Integrating Transcriptomic Data with Mechanistic Systems Pharmacology Models for Virtual Drug Combination Trials.
Monotherapy clinical trials with mutation-targeted kinase inhibitors, despite some success in other cancers, have yet to impact glioblastoma (GBM). Besides insufficient blood-brain barrier penetration, combinations are key to overcoming obstacles such as intratumoral heterogeneity, adaptive resistance, and the epistatic nature of tumor genomics that cause mutation-targeted therapies to fail. With now hundreds of potential drugs, exploring the combination space clinically and preclinically is daunting. We are building a simulation-based approach that integrates patient-specific data with a mechanistic computational model of pan-cancer driver pathways (receptor tyrosine kinases, RAS/RAF/ERK, PI3K/AKT/mTOR, cell cycle, apoptosis, and DNA damage) to prioritize drug combinations by their simulated effects on tumor cell proliferation and death. Here we illustrate a first step, tailoring the model to 14 GBM patients from The Cancer Genome Atlas defined by an mRNA-seq transcriptome, and then simulating responses to three promiscuous FDA-approved kinase inhibitors (bosutinib, ibrutinib, and cabozantinib) with evidence for blood-brain barrier penetration. The model captures binding of the drug to primary targets and off-targets based on published affinity data and simulates responses of 100 heterogeneous tumor cells within a patient. Single drugs are marginally effective or even counterproductive. Common copy number alterations (PTEN loss, EGFR amplification, and NF1 loss) have a negligible correlation with single-drug or combination efficacy, reinforcing the importance of postgenetic approaches that account for kinase inhibitor promiscuity to match drugs to patients. Drug combinations tend to be either cytostatic or cytotoxic, but seldom both, highlighting the need for considering targeted and nontargeted therapy. Although we focus on GBM, the approach is generally applicable.
-
Multi-omics characterization of the unsaturated fatty acid biosynthesis pathway in colon cancer.
The biosynthesis of unsaturated fatty acids is involved in the initiation and progression of colon adenocarcinoma (COAD). In this study, we aimed to investigate the multi-omics characteristics of unsaturated fatty acid biosynthesis-related genes and explore their prognostic value in colon cancer by analyzing the data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. An unsaturated fatty acid biosynthesis pathway related-genes enrichment score (BUFAS) was constructed utilizing the single sample gene set enrichment analysis (ssGSEA). We discovered that a high BUFAS was associated with longer overall survival (OS) in both the training and the validation sets. Multivariable analysis including the clinical characteristics further verified the independent prognostic value of the BUFAS in both the TCGA-COAD and the GSE39582 datasets. In addition, GSEA analysis revealed that BUFAS was positively associated with several signaling pathways, including MTORC1, peroxisome, and pathways related to fatty acid metabolism, while was negatively associated with other signaling pathways, such as hedgehog, NOTCH, and Wnt/beta-catenin pathway. Furthermore, in the COAD cell lines of the Genomics of Drug Sensitivity in Cancer (GDSC) database, we found that BUFAS was positively correlated with the drug sensitivities of cisplatin, gemcitabine, camptothecin, lapatinib, and afatinib, while was negatively correlated with that of ponatinib. Moreover, in the COAD single-cell transcriptomic dataset (GSE146771), the BUFAS varied among different cell types and was enriched in mast cells and fibroblasts. Taken together, the BUFAS we constructed could be used as an independent prognostic signature in predicting the OS and drug resistance of colon cancer. Unsaturated fatty acid biosynthesis pathway might serve as potential therapeutic targets for cancer treatment.
-
Characterization of the treatment-naive immune microenvironment in melanoma with BRAF mutation.
BACKGROUND: Patients with BRAF-mutant and wild-type melanoma have different response rates to immune checkpoint blockade therapy. However, the reasons for this remain unknown. To address this issue, we investigated the precise immune composition resulting from BRAF mutation in treatment-naive melanoma to determine whether this may be a driver for different response to immunotherapy. METHODS: In this study, we characterized the treatment-naive immune context in patients with BRAF-mutant and BRAF wild-type (BRAF-wt) melanoma using data from single-cell RNA sequencing, bulk RNA sequencing, flow cytometry and immunohistochemistry (IHC). RESULTS: In single-cell data, BRAF-mutant melanoma displayed a significantly reduced infiltration of CD8+ T cells and macrophages but also increased B cells, natural killer (NK) cells and NKT cells. We then validated this finding using bulk RNA-seq data from the skin cutaneous melanoma cohort in The Cancer Genome Atlas and deconvoluted the data using seven different algorithms. Interestingly, BRAF-mutant tumors had more CD4+ T cells than BRAF-wt samples in both primary and metastatic cohorts. In the metastatic cohort, BRAF-mutant melanoma demonstrated more B cells but less CD8+ T cell infiltration when compared with BRAF-wt samples. In addition, we further investigated the immune cell infiltrate using flow cytometry and multiplex IHC techniques. We confirmed that BRAF-mutant melanoma metastases were enriched for CD4+ T cells and B cells and had a co-existing decrease in CD8+ T cells. Furthermore, we then identified B cells were associated with a trend for improved survival (p=0.078) in the BRAF-mutant samples and Th2 cells were associated with prolonged survival in the BRAF-wt samples. CONCLUSIONS: In conclusion, treatment-naive BRAF-mutant melanoma has a distinct immune context compared with BRAF-wt melanoma, with significantly decreased CD8+ T cells and increased B cells and CD4+ T cells in the tumor microenvironment. These findings indicate that further mechanistic studies are warranted to reveal how this difference in immune context leads to improved outcome to combination immune checkpoint blockade in BRAF-mutant melanoma.
-
Spatiotemporal cellular map of the developing human reproductive tract.
The human reproductive tract is essential for species perpetuation and overall health. Its development involves complex processes of sex specification, tissue patterning and morphogenesis, the disruption of which can cause lifelong issues, including infertility1-5. Here we present an extensive single-cell and spatial multi-omic atlas of the human reproductive tract during prenatal development to provide insights beyond those that are possible with smaller-scale, organ-focused studies. We describe potential regulators of sexual dimorphism in reproductive organs and pinpoint previously unknown genes involved in Müllerian duct emergence and regression and urethral canalization of the penis. By combining histological features with gene expression and chromatin accessibility data, we define transcription factors and signalling events potentially involved in the regionalization of the Müllerian and Wolffian ducts. We also refine how the HOX code is established in distinct reproductive organs and reveal that the expression of thoracic HOX genes is increased in the rostral mesenchyme of the fallopian tube and epididymis. Our findings further indicate that epithelial regionalization of the fallopian tube and epididymis, which probably contribute to sperm maturation and capacitation, is established during development. By contrast, later events are necessary for regionalization of the uterocervical canal epithelium. Finally, on the basis of single-cell data and fetal-derived organoids, we show that the fetal uterine epithelium is vulnerable to oestrogen-mimicking endocrine disruptors. By mapping sex-specific reproductive tract regionalization and differentiation at the cellular level, our study provides valuable insights into causes and potential treatments of developmental reproductive disorders.
-
Role of CALCR expression in liver cancer: Implications for the immunotherapy response.
Liver hepatocellular carcinoma (LIHC) is a prevalent and lethal malignancy with a complex molecular landscape. Fibrosis and ferroptosis are implicated in LIHC progression, yet their roles remain to be elucidated. The present study investigated the expression and prognostic significance of calcitonin receptor (CALCR), a gene that intersects the pathways of fibrosis and ferroptosis, across LIHC and other types of cancer. Data were obtained from The Cancer Genome Atlas and the Molecular Signatures Database. LIHC patients were classified into two clusters based on fibrosis‑related gene expression using ConsensusClusterPlus. Single‑sample gene set enrichment analysis was employed to quantify fibrosis and ferroptosis levels. Correlation, survival and nomogram analyses were performed to assess the prognostic value of CALCR. Additionally, single‑cell RNA sequencing data from the Tumor Immune Single Cell Hub 2 (TISCH2) and pan‑cancer analyses of genomic heterogeneity features were incorporated. The present study also identified a putative regulatory role for CALCR in LIHC cell migration, proliferation and apoptosis. CALCR was identified as a significant prognostic marker for LIHC. Patients with high CALCR expression exhibited shortened overall survival (OS) and disease‑specific survival (DSS). Specifically, the hazard ratios (HRs) for OS and DSS were 1.76 [95% confidence interval (CI): 1.23=2.49) and 1.77 (95% CI: 1.13=2.78], respectively, with corresponding P‑values of 0.002 for OS and 0.013 for DSS. Analyses of immune cell infiltration revealed a more complex immune environment in patients with low CALCR expression, suggesting differential responses to immunotherapy. Furthermore, in HepG‑2 and HuH‑7 cells, small interfering (si)‑CALCR increased apoptosis while reducing proliferation and migration compared with si‑negative control. CALCR serves as a significant prognostic biomarker for LIHC, influencing both molecular pathways and the immune landscape. Its expression is associated with improved survival outcomes and distinct genomic features, positioning it as a potential therapeutic target and predictor of immunotherapy efficacy.
-
Comprehensive big data analysis reveals SLC10A3 as a potential biomarker in head and neck cancer.
Head and neck cancer (HNC) continues to pose a significant global health challenge, highlighting the urgent need for discovering new therapeutic targets. Recent studies highlight the role of solute carrier (SLC) proteins in cancer progression. This study investigates the expression and potential role of SLC10A3 in HNC, aiming to determine its clinical significance and therapeutic relevance. Publicly available datasets, including The Cancer Genome Atlas (TCGA), Clinical Proteomics Tumor Analysis Consortium (CPTAC), and Gene Expression Omnibus (GEO), were analyzed to assess SLC10A3 expression in head and neck squamous cell carcinoma (HNSCC). The prognostic relevance of SLC10A3 was assessed using Kaplan-Meier (KM) survival and Receiver Operating Characteristic (ROC) curve analysis. Correlation analysis within TCGA, CPTAC, and GEO datasets identified genes associated with SLC10A3 expression. Protein-protein docking studies were performed to predict potential interactions between SLC10A3 and identified protein coding genes. SLC10A3 was found to be significantly upregulated in HNSCC tumor samples compared to normal tissues across TCGA and CPTAC datasets. Increased SLC10A3 expression correlated with poor survival outcomes in TCGA-HNSCC patients. Correlation analysis identified 26 genes positively associated with SLC10A3, where BCAP31, IRAK1, and UBL4A showed consistent correlation across TCGA, CPTAC, and GEO datasets. Computational protein interaction modeling using docking and AI/ML-based Evolutionary Scale Modelling (ESM) framework revealed significant binding affinities between SLC10A3 and identified protein-coding genes, suggesting potential functional interactions. These findings establish SLC10A3 as a promising therapeutic target in HNC. Its consistent upregulation, association with poor prognosis, and potential interactions with key regulatory proteins highlight its relevance for future therapeutic strategies.
-
CD38+ Endothelial Remodeling Defines Spatially Diverse Vasculopathy Programs in Rapidly Advancing Oral Inflammation.
Oral inflammatory diseases affect nearly half of the global population. Among them, newly defined peri-implantitis and high-grade periodontitis represent rapidly advancing inflammatory disease types, marked by relatively rapid tissue destruction. Despite their prevalence, the cell mechanisms and spatial architecture driving this severity remain poorly understood. Focusing first on peri-implantitis versus low- and moderate-grade periodontitis, we applied microbial profiling, single-cell RNA sequencing (scRNA-seq), and spatial proteomics (sp-proteomics) to uncover shared pathogenic programs linked to accelerated niche breakdown. Furthermore, to preserve spatial fidelity, each tissue was anatomically orientated along the tooth- or implant-epithelial interface, analogous sites of disease origination. Laser capture microdissection followed by microbiome analysis of unique tissue compartments revealed reduced bacterial load and diversity in peri-implantitis stroma. We then expanded our version-1 Human Periodontal Atlas by integrating newly generated peri-implantitis scRNAseq data (36-total samples; 121395-cells), revealing widespread transcriptional alterations, including oxidative stress, hypoxic, and NAD+ metabolism-associated signatures, primarily in a subpopulation of TNFRSF6B +/ICAM1 + post-capillary venules. We then performed high-resolution sp-proteomics (15-total samples; 337260-cells) and analyzed VEC states and associated neighborhoods via AstroSuite using newly developed tri-wise spatial analysis. This revealed CD34+-VEC loss and CD38+-VEC expansion almost exclusively in peri-implantitis. We extended this analysis to high-grade periodontitis. Mucosal biopsies from four lesion-affected and four unaffected sites within the same individuals (1:1 matched; 8-samples; 225137-cells) again demonstrated spatially restricted CD38+-VEC remodeling exclusively in affected tissues, with similar vasculopathy front patterning. The findings nominate spatially distinct vasculopathy patterning as a hallmark of rapidly advancing oral inflammation and a targetable therapeutic axis.
-
A clinical prognostic model related to T cells based on machine learning for predicting the prognosis and immune response of ovarian cancer.
BACKGROUND: Ovarian cancer (OV) is regarded as one of the most lethal malignancies affecting the female reproductive system, with individuals diagnosed with OV often facing a dismal prognosis due to resistance to chemotherapy and the presence of an immunosuppressive environment. T cells serve as a crucial mediator for immune surveillance and cancer elimination. This study aims to analyze the mechanism of T cell-associated markers in OV and create a prognostic model for clinical use in enhancing outcomes for OV patients. METHODS: Based on the single-cell dataset GSE184880, this study used single-cell data analysis to identify characteristic T cell subsets. Analysis of high dimensional weighted gene co-expression network analysis (hdWGCNA) is utilized to identify crucial gene modules along with their corresponding hub genes. A grand total of 113 predictive models were formed utilizing ten distinct machine learning algorithms along with the combination of the cancer genome atlas (TCGA)-OV dataset and the GSE140082 dataset. The most dependable clinical prognostic model was created utilizing the leave one out cross validation (LOOCV) framework. The validation process for the models was achieved by conducting survival curve analysis and receiver operating characteristic (ROC) analysis. The relationship between risk scores and immune cells was explored through the utilization of the Cibersort algorithm. Additionally, an analysis of drug sensitivity was carried out to anticipate chemotherapy responses across various risk groups. The genes implicated in the model were authenticated utilizing qRT-PCR, cell viability experiments, and EdU assay. RESULTS: This study developed a clinical prognostic model that includes ten risk genes. The results obtained from the training set of the study indicate that patients classified in the low-risk group experience a significant survival advantage compared to those in the high-risk group. The ROC analysis demonstrates that the model holds significant clinical utility. These results were verified using an independent dataset, strengthening the model's precision and dependability. The risk assessment provided by the model also serves as an independent prognostic factor for OV patients. The study also unveiled a noteworthy relationship between the risk scores calculated by the model and various immune cells, suggesting that the model may potentially serve as a valuable tool in forecasting responses to both immune therapy and chemotherapy in ovarian cancer patients. Notably, experimental evidence suggests that PFN1, one of the genes included in the model, is upregulated in human OV cell lines and has the capacity to promote cancer progression in in vitro models. CONCLUSION: We have created an accurate and dependable clinical prognostic model for OV capable of predicting clinical outcomes and categorizing patients. This model effectively forecasts responses to both immune therapy and chemotherapy. By regulating the immune microenvironment and targeting the key gene PFN1, it may improve the prognosis for high-risk patients.
-
Single-Cell Sequencing Reveals the Heterogeneity of Glioma and Identifies IGFBP2 as A Potential Therapeutic Target.
Background: Glioma is among the most malignant brain tumors, and its heterogeneity contributes significantly to treatment failure. Comprehensive profiling of cellular and molecular heterogeneity across different glioma stages and recurrence states is crucial for understanding therapeutic resistance and identifying novel targets. Accordingly, this study sought to systematically characterize the cellular and molecular heterogeneity of glioma across different stages and recurrence states using single-cell RNA sequencing, and to identify prognostic subtypes and potential therapeutic targets. Methods: We integrated public single-cell RNA sequencing data from glioma specimens, including lower-grade glioma (LGG), glioblastoma (GBM), and paired primary and recurrent tumors. Using these datasets, we identified distinct cellular subpopulations and their molecular signatures. Based on these glioma cell subpopulations, we reclassified gliomas from The Cancer Genome Atlas (TCGA) database into molecular subtypes and constructed a prognostic model. The functional role of a key candidate gene, insulin-like growth factor binding protein 2 (IGFBP2), was validated using in vitro knockdown experiments in mouse and human tumor cells and in vivo therapeutic studies in murine models, including combination therapy with anti-programmed cell death protein 1 (anti-PD-1) immune checkpoint blockade. Results: This analysis revealed that T cells in GBM and recurrent samples were predominantly exhausted, characterized by upregulation of PD-1 and T-cell immunoglobulin and mucin-domain containing-3 (Tim3), while myeloid cells exhibited an immunosuppressive phenotype with elevated expression of macrophage migration inhibitory factor (MIF) and cluster of differentiation (CD)276. We identified 12 distinct glioma cell subpopulations with varying proliferative and hypoxic signatures. Based on these subpopulations, TCGA gliomas were reclassified into two major subtypes. One subtype, enriched with myeloid-derived suppressor cells (MDSCs) and regulatory T cells (Tregs), was associated with poorer patient prognosis. A prognostic model was successfully established using differentially expressed genes between the subtypes. Furthermore, IGFBP2 was highly expressed in glioma cells, and its expression negatively correlated with T cell infiltration. In vitro knockdown of IGFBP2 downregulated programmed death-ligand 1 (PD-L1) expression on tumor cells. In vivo, IGFBP2 knockdown significantly suppressed tumor growth (p < 0.01) and extended survival in tumor-bearing mice (p < 0.05), and its combination with anti-PD-1 therapy markedly enhanced antitumor efficacy. Conclusions: This study provides deeper insights into the cellular ecosystem and heterogeneity of glioma, linking specific cellular features to patient prognosis. We identify IGFBP2 may play a role in regulating immunosuppressive tumor microenvironment and a potential therapeutic target.
-
Developing a Metabolic-Associated Prognostic Index for Risk Stratification and Therapeutic Guidance in Stage I Lung Adenocarcinoma via Multiomics Analysis.
PURPOSE: Early-stage lung adenocarcinoma (LUAD) exhibits substantial clinical heterogeneity that is not fully explained by TNM staging, highlighting the need for biology-driven prognostic tools. Although metabolic reprogramming is an established cancer hallmark, its systematic prognostic significance in stage I LUAD remains unexplored. MATERIALS AND METHODS: We analyzed stage I LUAD samples from The Cancer Genome Atlas, Gene Expression Omnibus, and European Genome-phenome Archive databases. Weighted gene coexpression network analysis and differential expression analysis were conducted to identify metabolic genes associated with LUAD malignancy and prognosis. A metabolic-associated prognostic index (MAPI) was subsequently developed using machine-learning combinations. The performance of MAPI was evaluated from multiple biologic perspectives and at the single-cell level. Key gene functions were experimentally verified in vitro. RESULTS: MAPI robustly stratified patients into high- and low-risk groups with significantly divergent survival outcomes, outperformed conventional clinicopathologic features and previously published signatures, and emerged as an independent prognostic factor across all validation cohorts. The high-risk group was characterized by enhanced cancer stemness, genetic heterogeneity, metabolic reprogramming, immune exclusion, and reduced responsiveness to immunotherapy. We also pinpointed three potential therapeutic agents (paclitaxel, bortezomib, and vincristine) for high-risk patients. The single-cell RNA sequencing further validated the association between MAPI and malignant progression. Functional analyses demonstrated that knockdown of DEGS1 or PLOD1 significantly suppressed LUAD cell proliferation and migration. CONCLUSION: Our results establish MAPI as a biologically interpretable and clinically applicable tool for risk stratification and precision treatment decision making in patients with stage I LUAD.