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Intestinal metaplasia key molecules and UPP1 activation via Helicobacter pylori /NF-kB: drivers of malignant progression in gastric cancer.
Gastric cancer (GC) remains a significant global health challenge due to its high morbidity and mortality rates. The development of GC is a multi-hit process and the exploration of precancerous lesions is crucial. To elucidate the molecular and cellular dynamics underlying gastric carcinogenesis, we conducted an integrative single-cell RNA sequencing analysis of 26,028 high-quality cells from gastric antral mucosa biopsies across various stages, including non-atrophic gastritis, chronic atrophic gastritis, intestinal metaplasia, and early gastric cancer. By constructing a detailed single-cell atlas, we identified distinct epithelial cell subpopulations and their corresponding molecular signatures. We focused on the biological link between gastric epithelial cells and cancer cells. Notably, we observed that gland mucous cells acquired an intestinal-like stem cell phenotype during metaplasia, with MUC6, MUC2 and OLFM4 emerging as the specific markers for unique endocrine cells in early malignant lesions. Additionally, our analysis highlighted UPP1 as a key oncogene, with its expression progressively increasing from normal epithelial cells to malignant cells. UPP1 upregulation was shown to promote GC cell proliferation and migration, implicating it in the oncogenic process. Further, we explored the impact of Helicobacter pylori infection on gene expression, revealing that Helicobacter pylori infection upregulates UPP1 via the NF-κB pathway. Our cell-cell communication analysis underscored the significant role of the Macrophage migration inhibitory factor pathway in the tumor microenvironment, contributing to GC progression. Various key molecules involved in intestinal metaplasia, along with UPP1 and the Macrophage migration inhibitory factor pathway, collectively illustrate the multifaceted nature and complexity of gastric cancer evolution, highlighting the cumulative impacts that drive tumorigenesis.
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[Machine learning-based programmed cell death signature model for precise prediction of prognosis and treatment response in melanoma].
OBJECTIVES: The occurrence, metastasis, and drug resistance of melanoma pose major challenges to patient prognosis, and predictive models capable of accurately forecasting patient outcomes and guiding treatment are still lacking. This study aims to develop predictive models for melanoma prognosis and drug sensitivity based on mechanisms of programmed cell death (PCD). METHODS: Genes related to 19 PCD patterns were collected and integrated from gene set enrichment analysis (GSEA), the Kyoto Encyclopedia of Genes and Genomes (KEGG), relevant reviews, and published studies to establish a comprehensive PCD signature gene set. Transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) skin cutaneous melanoma (SKCM) cohort were obtained, and 3 untreated Gene Expression Omnibus (GEO) datasets (GSE65904, GSE19234, and GSE100797) were included as external validation cohorts. In addition, immunotherapy cohorts PRJEB23709, GSE136961, and GSE215222 were collected to validate the predictive value for immunotherapy. Single-cell transcriptomic data (GSE115978 and GSE215120) were processed using Seurat for quality control, normalization, dimensionality reduction, clustering, and cell annotation; spatial transcriptomic data were obtained from 10× Genomics and combined with Cottrazm for spatial partitioning and SpaCET deconvolution. Cell-cell communication was evaluated using CellChat to assess secreted signaling, extracellular matrix (ECM)-receptor interactions, and cell contact-mediated communication patterns. Based on the TCGA training set, a machine learning strategy comprising 10 algorithms and 101 combinations was used to construct PCD-related prognostic signatures, and the optimal model was selected using the average concordance index (C-index) across multiple cohorts. Differential analysis, GSEA, CIBERSORT, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) were further applied to evaluate immune infiltration, calculate T-cell receptor (TCR) clonal diversity, cytolytic activity, and T-cell effector gene expression profiles, and to explore the association between the PCD score (PCDS) and drug sensitivity. RESULTS: The activities of the 19 PCD pathways differed significantly between normal skin and SKCM, suggesting that dysregulation is involved in melanoma progression. Mutation analysis showed that titin (TTN) and mucin 16 (MUC16) had the highest mutation frequencies. After controlling for collinearity and correlation screening, 314 candidate genes were obtained. Among the 101 model combinations, StepCox (backward)+Ridge performed best (average C-index across 4 cohorts=0.677). The PCDS constructed based on this model stably stratified prognosis: The high-PCDS group showed significantly worse survival in TCGA and 3 external validation cohorts and remained an independent prognostic indicator after adjustment for age, sex, stage, Breslow thickness, Clark level, and ulceration. Immune analyses indicated that high PCDS was associated with immunosuppression: Immune scores were reduced; CD8+ T cells, activated memory CD4+ T cells, plasma cells, and M1 macrophages were decreased, whereas M2 macrophages were increased. PCDS was significantly negatively correlated with immune checkpoint molecules, TCR Shannon index, cytolytic activity score (CYT), and T-cell effector gene expression profile (T-GEP), and was highest in the immune-desert subtype. Spatial transcriptomic and single-cell results suggested spatial and cellular heterogeneity of PCDS: PCDS was lower at boundary regions and negatively correlated with macrophage proportions; myeloid cells had the lowest overall PCDS, macrophages exhibited the strongest communication with T/natural killer (NK) cells, and after immunotherapy, macrophages were enriched with enhanced human leukocyte antigen (HLA)-A/B/C-CD8a-related interactions. In clinical applications, PCDS demonstrated greater robustness compared with 100 previously reported prognostic signatures and complemented hot-cold tumor classification by identifying high-risk patients even among "hot tumors". Validation in immunotherapy cohorts showed that PCDS was negatively associated with therapeutic benefit; high PCDS was more likely to be associated with non-response and poorer prognosis. Drug analyses suggested that PCDS was associated with sensitivity to multiple drugs and could be used for potential treatment stratification. CONCLUSIONS: PCDS is a cross-cohort robust tool for predicting melanoma prognosis and immunotherapy benefit, reflecting the degree of immunosuppression in the tumor immune microenvironment and myeloid/macrophage-related immunoregulatory features, and provides a basis for individualized risk stratification and potential drug selection. This study provides an in-depth elucidation of the regulatory mechanisms of PCD in the tumor immune microenvironment and offers an important theoretical foundation for personalized treatment decision-making in melanoma patients.
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Low PTGDS Expression Facilitates HNSCC by Suppressing Programmed Cell Death and Reducing B Cell-Mediated Immune Responses.
Head and neck squamous cell carcinoma (HNSCC) constitutes a significant global disease burden. Evasion of programmed cell death (PCD) is a hallmark of HNSCC progression. Cancer-associated fibroblasts (CAFs) are a major cellular component of the HNSCC tumor microenvironment (TME). However, the mechanism by which CAFs contribute to PCD resistance remains poorly understood. This study included 24 single-cell sequencing samples, the Cancer Genome Atlas (TCGA) data, and spatial transcriptome data from HNSCC samples. Single-cell data were clustered using the Seurat package, and pseudotemporal trajectory analysis of CAF subsets was performed with Monocle2. The random forest algorithm was used to assess the prognostic importance of candidate genes. The ssGSEA and CIBERSORT algorithms were used to assess immune cell infiltration patterns in the TCGA samples. Gain-of-function assays, Western blotting, and immunohistochemistry were conducted to validate the biological functions of key targets. Here, we identified seven CAF subsets and clarified their potential differentiation directions. Thirty-two CAF-associated PCD regulators with prognostic significance were identified, among which, prostaglandin D2 synthase (PTGDS) and squalene epoxidase (SQLE) are the most important genes. PTGDS is significantly downregulated in HNSCC and associated with favorable prognosis. PTGDS overexpression significantly inhibited clonogenicity, proliferation, and invasion while promoting apoptosis in HNSCC cells (p < 0.05). In addition, PTGDS expression is significantly enriched in B cell-related pathways. Mechanistically, PTGDS overexpression increased chemokine (C-X-C motif) ligand 13 (CXCL13) expression and enhanced immune cell infiltration. These findings identified PTGDS as a central regulator linking CAF-mediated PCD resistance and B cell immune modulation in HNSCC, suggesting its potential as both a diagnostic biomarker and therapeutic target.
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Spatial multiomics decipher fibroblast-macrophage dynamics in systemic sclerosis.
OBJECTIVES: Stromal-immune crosstalk shapes the pathogenic microenvironment of systemic sclerosis (SSc), but the spatial regulatory networks underlying fibrogenesis remain poorly defined. We aimed to explore tissue organisation and cell coordination in SSc skin, providing spatiotemporal insights into disease mechanisms and bridging the gap between omics discovery and precision medicine. METHODS: We performed spatial transcriptomics on skin biopsies from 10 patients with diffuse cutaneous SSc and 4 healthy controls using the 10× Visium platform. These findings were confirmed using higher-resolution Stereo-seq transcriptomics, spatial proteomics, and single-cell RNA sequencing data from patients with SSc, SSc mouse models, and wound-healing reindeer models. In vivo and in vitro studies were conducted to validate the key regulatory pathways. RESULTS: Fourteen skin biopsies were analysed, revealing significant expansion of fibrotic niches enriched with fibroblasts and macrophages in SSc, correlating with clinical severity. We revealed disease-specific cell states of fibroblasts and macrophages and evaluated their spatial dependency and cell-cell communication. Stratification based on signature genes enabled the identification of patients with SSc with progressive disease and treatment-nonresponsive phenotype. ACKR3 (a CXCL12 decoy receptor) was selectively expressed in myofibroblast progenitors, which diminished during differentiation towards mature myofibroblast, potentially serving to regulate CXCL12/CXCR4-mediated proinflammatory macrophage recruitment. Inhibition of CXCR4 attenuated skin and lung fibrosis in experimental fibrosis mouse models. CONCLUSIONS: Our spatially resolved atlas uncovered dynamic fibroblast-macrophage interplay as a hallmark of fibrotic niche expansion. These findings offer spatiotemporal insights into disease mechanisms and pave the way for advanced mechanistic and therapeutic studies, bridging the gap between omics discovery and precision medicine.
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ARHGAP11A, a member of Rho GTPase activating protein family, as a prognostic biomarker linked to DNA damage response across pan-cancer.
BACKGROUND: The Rho GTPase-activating protein (RhoGAP) family represents a large and diverse group of proteins that act as key regulators of Rho GTPases, small GTP-binding proteins involved in cellular signaling. Tight regulation of Rho GTPase activity is essential for fundamental biological processes, including cell motility, contractility, growth, differentiation, and development. Despite their biological importance, the roles of RhoGAPs in cancer remain largely undefined. METHODS: Bioinformatics analysis was performed using data from The Cancer Genome Atlas (TCGA), covering over 10,000 samples across 33 cancer types. The role of ARHGAP11A in the DNA damage response was evaluated through single-cell sequencing, western blotting, and colony formation assays. RESULTS: Pan-cancer analysis of RhoGAP family genes identified ARHGAP11A as the most significantly overexpressed member in tumors compared to adjacent normal tissues. ARHGAP11A expression was found to be elevated in most cancer types and was associated with DNA repair activity. Furthermore, its expression positively correlated with tumor mutational burden (TMB), a recognized predictive biomarker for immunotherapy. Intriguingly, it also correlated with increased infiltration of regulatory T (Treg) cells, which are known to suppress anti-tumor immunity. Consistent with these observations, high ARHGAP11A expression was concurrently linked to poor patient survival outcomes across multiple cancers. We also observed a positive correlation between ARHGAP11A and CHK1, suggesting a functional collaboration. In vitro experiments further demonstrated that ARHGAP11A confers resistance to DNA damage induced by CHK1 inhibitors. CONCLUSION: ARHGAP11A is a promising prognostic marker in cancer, with potential therapeutic implications through targeting DNA repair pathways and modulating the tumor immune microenvironment.
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Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer.
BACKGROUND: Ovarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with copper metabolism (CMRGs) on the prognosis of OC, discovered various CM clusters, and built a risk model to evaluate patient prognosis, immunological features, and therapy response. METHODS: 15 CMRGs affecting the prognosis of OC patients were identified in The Cancer Genome Atlas (TCGA). Consensus Clustering was used to identify two CM clusters. lasso-cox methods were used to establish the copper metabolism-related gene prognostic signature (CMRGPS) based on differentially expressed genes in the two clusters. The GSE63885 cohort was used as an external validation cohort. Expression of CM risk score-associated genes was verified by single-cell sequencing and quantitative real-time PCR (qRT-PCR). Nomograms were used to visually depict the clinical value of CMRGPS. Differences in clinical traits, immune cell infiltration, and tumor mutational load (TMB) between risk groups were also extensively examined. Tumour Immune Dysfunction and Rejection (TIDE) and Immune Phenotype Score (IPS) were used to validate whether CMRGPS could predict response to immunotherapy in OC patients. RESULTS: In the TCGA and GSE63885 cohorts, we identified two CM clusters that differed significantly in terms of overall survival (OS) and tumor microenvironment. We then created a CMRGPS containing 11 genes to predict overall survival and confirmed its reliable predictive power for OC patients. The expression of CM risk score-related genes was validated by qRT-PCR. Patients with OC were divided into low-risk (LR) and high-risk (HR) groups based on the median CM risk score, with better survival in the LR group. The 5-year AUC value reached 0.74. Enrichment analysis showed that the LR group was associated with tumor immune-related pathways. The results of TIDE and IPS showed a better response to immunotherapy in the LR group. CONCLUSION: Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of patients with OC, offering new insights into individualized treatment.
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Whole genome methylation profiling of menstrual stem cells identifies novel biomarkers for endometriosis.
BACKGROUND: Endometriosis, despite its high prevalence, is underdiagnosed and poorly managed due to lack of clinically validated biomarkers and pathophysiological insight. Menstrual blood-derived stem cells have been implicated in disease pathogenesis, but their diagnostic potential remains unexplored. METHODS: We conducted a case-control clinical study in women (n = 42; 19 endometriosis, 23 controls). Menstrual blood samples were collected, and menstrual blood-derived stem cells were isolated for whole-genome DNA methylation sequencing. Differential methylation analysis was performed to identify disease-specific epigenetic biomarkers, and machine learning models were applied to evaluate the diagnostic performance of candidate markers. An external endometrial single-cell RNA sequencing atlas including endometriosis samples was employed to correlate RNA expression with the identified disease-specific methylation signature. RESULTS: Here we identify differentially methylated regions enriched in genes linked to hallmarks of endometriosis such as inflammation, tissue remodelling and development. These differentially methylated regions robustly distinguish cases from controls, independent of technical and clinical variables. Machine learning models trained and validated on these differentially methylated regions achieve high diagnostic performance (specificity 83%, sensitivity 79%). Integration with an independent single-cell RNA sequencing dataset shows that the differentially methylated regions may modulate gene expression, further supporting their biological relevance. CONCLUSIONS: These findings position menstrual blood-derived stem cell DNA methylation profiling as a promising, non-invasive approach for early endometriosis diagnosis and personalised care.
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iPSC-Derived Microglia as a Model to Study Inflammation in Idiopathic Parkinson's Disease.
Parkinson's disease (PD) is a neurodegenerative disease with unknown cause in the majority of patients, who are therefore considered "idiopathic" (IPD). PD predominantly affects dopaminergic neurons in the substantia nigra pars compacta (SNpc), yet the pathology is not limited to this cell type. Advancing age is considered the main risk factor for the development of IPD and greatly influences the function of microglia, the immune cells of the brain. With increasing age, microglia become dysfunctional and release pro-inflammatory factors into the extracellular space, which promote neuronal cell death. Accordingly, neuroinflammation has also been described as a feature of PD. So far, studies exploring inflammatory pathways in IPD patient samples have primarily focused on blood-derived immune cells or brain sections, but rarely investigated patient microglia in vitro. Accordingly, we decided to explore the contribution of microglia to IPD in a comparative manner using, both, iPSC-derived cultures and postmortem tissue. Our meta-analysis of published RNAseq datasets indicated an upregulation of IL10 and IL1B in nigral tissue from IPD patients. We observed increased expression levels of these cytokines in microglia compared to neurons using our single-cell midbrain atlas. Moreover, IL10 and IL1B were upregulated in IPD compared to control microglia. Next, to validate these findings in vitro, we generated IPD patient microglia from iPSCs using an established differentiation protocol. IPD microglia were more readily primed as indicated by elevated IL1B and IL10 gene expression and higher mRNA and protein levels of NLRP3 after LPS treatment. In addition, IPD microglia had higher phagocytic capacity under basal conditions-a phenotype that was further exacerbated upon stimulation with LPS, suggesting an aberrant microglial function. Our results demonstrate the significance of microglia as the key player in the neuroinflammation process in IPD. While our study highlights the importance of microglia-mediated inflammatory signaling in IPD, further investigations will be needed to explore particular disease mechanisms in these cells.
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Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.
To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, we developed an interpretability model for better understanding the survival prediction of cancer. To this end, the optimal features were identified, ten different machine learning models were utilized to predict mortality across various datasets. Then we have utilized the important features identified by those machines learning methods to construct a new model named NKECLR to forecast mortality of patient with cancer. To explicitly clarify the model's decision-making process and uncover novel findings, an interpretable technique incorporating machine learning and SHapley Additive exPlanations (SHAP), as well as LIME, has been employed, and four genes EPGN, PHF11, RBM34, and ZFP36 were identified from those machine learning(ML). The experimental analysis conducted on training and validation datasets demonstrated that the proposed model has a good performance com- pared to existing methods with AUC value 81.8%, and 79.3%, respectively. Moreover, when combined our NKECLR with PD-L1, PD-1, and CTLA-4 the AUC value was 83%0. Finally, these findings have been applied to comprehend the response of drugs and immunotherapy. Our research introduced an innovative predictive NKECLR model utilizing natural killer(NK) cell marker genes for cohorts with melanoma cancer. The NKECLR model can effectively predict the survival of melanoma cancer cohorts and treatment results, revealing distinct immune cell infiltration in the high-risk group.
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CXCL13 as a Prognostic Biomarker and Modulator of the Tumor Microenvironment in Colorectal Cancer.
OBJECTIVES: Immunotherapy yields limited results in patients with colorectal cancer (CRC), emphasizing the need for a deeper understanding of the immune landscape within the tumor microenvironment. Although the C-X-C motif chemokine ligand 13 (CXCL13) recruits B cells and promotes tertiary lymphoid structure (TLS) formation, its immune function and prognostic value in CRC remain unclear. This study investigated the impact of CXCL13 on patient outcomes and CRC immune landscape. METHODS: Four independent cohorts were recruited in this study. The Cancer Genome Atlas Program (TCGA) cohort evaluated survival differences as well as immune contexture associated with CXCL13 expression in CRC. Immunohistochemistry (IHC) in the Renji Hospital (RJ) cohort was used to validate CXCL13 and CD8 levels, while multiplex IHC assessed their spatial correlation. Two single-cell RNA sequencing (scRNA-seq) cohorts were used for evaluating the potential roles of CXCL13 and CXCL13+CD8+T cells in the immune context of CRC. RESULTS: Our study revealed a positive correlation between CXCL13 expression and improved survival among CRC patients. Elevated CXCL13 levels and CXCL13+CD8+T cells were linked to a favorable immune context that impeded tumor growth in CRC. Moreover, CXCL13 expression was more prevalent in microsatellite instability-high (MSI-H)/mismatch repair-deficient (MMRd) tumors, demonstrating its potential role in enhancing the response to anti-programmed death-1 ligand (PD-L1) treatment. CONCLUSIONS: CXCL13 plays a critical role in shaping a favorable immune tumor microenvironment in CRC. Further research should elucidate how CXCL13 modulates CD8+T cell function to improve antitumor immunity.