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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.
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The expression of PD-1 ligands in the immune microenvironment was altered in TTF-1-negative lung adenocarcinoma.
Thyroid transcription factor-1 (TTF-1) is a lineage-specific marker for lung adenocarcinoma (LUAD), whereas the relatively minor subset of TTF-1-negative LUADs shows a poor prognosis and a limited response to therapy. However, its relationship with the tumor immune microenvironment remains poorly defined. How TTF-1 expression affects the immune context in LUAD was investigated, focusing on tumor-associated macrophages (TAMs) and T-cell infiltration. Immunohistochemical (IHC) analysis of 226 LUAD specimens showed that TTF-1-negative tumors were associated with epidermal growth factor receptor wild-type status, advanced stage, and worse progression-free and cancer-specific survivals. Notably, PD-L1 (programmed death-ligand 1) and PD-L2 expression in TAMs, but not in cancer cells, was significantly reduced in TTF-1-negative tumors. Public single-cell RNA sequencing data confirmed downregulation of CD274 (PD-L1) in TAMs from tumors with low expression of TTF-1-related genes. In contrast, PDCD1LG2 (PD-L2) expression showed less consistent patterns. On IHC analysis, infiltration of CD8+ and CD4+ T cells was modestly lower in TTF-1-negative tumors, accompanied by decreased HLA class I and II expressions. Transcriptomic analysis of The Cancer Genome Atlas LUAD cohort further showed lower interferon gamma (IFN-γ) signaling and decreased T cell-inflamed gene signatures in the low TTF-1-negative tumors. These findings suggest that TTF-1-negative LUAD exhibits more immune-suppressive features, with a relatively reduced antitumor immune response characterized by decreased T-cell infiltration and INF-γ signaling, which are related to PD-L1 and PD-L2 expressions in TAMs.
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Construction of chronic inflammation and mitochondrial energy metabolism-associated predictive and therapeutic models for lung adenocarcinoma patients.
BACKGROUND: Lung adenocarcinoma (LUAD) remains the most common subtype of non–small cell lung cancer with poor survival. Dysregulation of mitochondrial energy metabolism (MEM) can be considered as major driver for LUAD pathogenesis among Chronic obstructive pulmonary disease (COPD) patients. Hence, deeper understanding of MEM carcinogenic role for LUAD patients can provide novel insights into LUAD pathogenesis for COPD patients. METHODS: By employing integrative bioinformatic approaches, such as Limma and WGCNA in LUAD bulk profile (GSE32863), we first identified COPD and MEM (CM)-associated DEGs for LUAD patients, and these DEGs can divide LUAD patients from TCGA-LUAD cohort into 2 risk groups. Next, Lasso-cox regression and multi-variate cox regression identified CM-associated predictive model and hub gene for LUAD patients both in TCGA-LUAD cohort and GSE13213. Indeed, hub gene molecular and immune characters were estimated at LUAD bulk level (TCGA-LUAD cohort) and single-cell level (GSE203360). Besides, drug sensitivity and molecular docking analysis confirmed potential therapeutic agent targeting hub gene for the treatment of LUAD. RESULTS: 8 CM-associated gene signatures can divide LUAD patients into 2 molecular subgroups and guide the prognostic model construction for LUAD patients. TPI1 can be considered as CM-associated hub gene involved in LUAD progression. 17-AAG can be considered as drug reproposing framework for the treatment of LUAD. CONCLUSION: This study first highlighted the predictive and therapeutic potentials of CM, and highlighted TPI1 pathogenic role for LUAD patients.
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The activator protein-1 complex governs a vascular degenerative transcriptional programme in smooth muscle cells to trigger aortic dissection and rupture.
BACKGROUND AND AIMS: Stanford type A aortic dissection (AD) is a degenerative aortic remodelling disease marked by an exceedingly high mortality without effective pharmacologic therapies. Smooth muscle cells (SMCs) lining tunica media adopt a range of states, and their transformation from contractile to synthetic phenotypes fundamentally triggers AD. However, the underlying pathomechanisms governing this population shift and subsequent AD, particularly at distinct disease temporal stages, remain elusive. METHODS: Ascending aortas from nine patients undergoing ascending aorta replacement and five individuals undergoing heart transplantation were subjected to single-cell RNA sequencing. The pathogenic targets governing the phenotypic switch of SMCs were identified by trajectory inference, functional scoring, single-cell regulatory network inference and clustering, regulon, and interactome analyses and confirmed using human ascending aortas, primary SMCs, and a β-aminopropionitrile monofumarate-induced AD model. RESULTS: The transcriptional profiles of 93 397 cells revealed a dynamic temporal-specific phenotypic transition and marked elevation of the activator protein-1 (AP-1) complex, actively enabling synthetic SMC expansion. Mechanistically, tumour necrosis factor signalling enhanced AP-1 transcriptional activity by dampening mitochondrial oxidative phosphorylation (OXPHOS). Targeting this axis with the OXPHOS enhancer coenzyme Q10 or AP-1-specific inhibitor T-5224 impedes phenotypic transition and aortic degeneration while improving survival by 42.88% (58.3%-83.3% for coenzyme Q10 treatment), 150.15% (33.3%-83.3% for 2-week T-5224), and 175.38% (33.3%-91.7% for 3-week T-5224) in the β-aminopropionitrile monofumarate-induced AD model. CONCLUSIONS: This cross-sectional compendium of cellular atlas of human ascending aortas during AD progression provides previously unappreciated insights into a transcriptional programme permitting aortic degeneration, highlighting a translational proof of concept for an anti-remodelling intervention as an attractive strategy to manage temporal-specific AD by modulating the tumour necrosis factor-OXPHOS-AP-1 axis.