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TIM-3/CD68 double-high expression in Glioma: Prognostic characteristics and potential therapeutic approaches.
BACKGROUND: Immunotherapy has revolutionized the treatment of various types of tumors, but there has been no breakthrough in the treatment of gliomas. The aim of this study is to discover valuable immunotherapy target in glioma, analyze its expression in glioma and the related microenvironment, explore potential immunotherapy strategies, and propose new possibilities for the treatment of gliomas. METHODS: Immunohistochemistry (IHC) and multiplex fluorescence immunohistochemistry (mIHC) were used to analyze the expression of common immune markers and checkpoints in 187 glioma patients from Sun Yat-sen University Caner Center (SYSUCC). Bioinformatics analysis was used to examine the expression of TIM-3 in different macrophages using the Chinese Glioma Genome Atlas (CGGA) single-cell sequencing database. The Kaplan-Meier curve was used to predict the prognostic value of samples with high TIM-3 and CD68 expression. The R package was used to analyze the somatic mutation status and the sensitivity of small molecule inhibitors in TIM-3/CD68 double-high expression samples. RESULTS: TIM-3 is a relatively highly expressed immune checkpoint in glioma. Unlike other tumors, TIM-3 is mainly expressed on macrophages in the glioma microenvironment. TIM-3/CD68 double-high expression suggests poor survival in glioma and may be a new upgrade marker in both IDH-mutant glioma and IDH-wildtype low-grade glioma (LGG) glioma (P < 0.01). Exploring the combination of TIM-3 inhibitors and p38 MAPK inhibitor may be a potential treatment direction for TIM-3/CD68 double high expression gliomas in the future. CONCLUSIONS: The combination of TIM-3 and CD68 holds significant importance as a potential target for both prognosis and therapeutic intervention in glioma.
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Identification of prognostic biomarkers and immunotherapy response predictors in lung adenocarcinoma: integrative Mendelian randomization and machine learning analysis.
BACKGROUND: Lung adenocarcinoma (LUAD), the most prevalent and clinically heterogeneous form of lung cancer, lacks robust biomarkers for prognosis and immunotherapy response prediction. This study aimed to identify key prognostic genes and develop a predictive framework to guide personalized treatment strategies. METHODS: We integrated summary data-based Mendelian randomization (SMR) with machine learning to identify LUAD-associated genes. RNA-sequencing and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed. A prognostic model was constructed with 101 combinations of 10 machine learning algorithms and validated via receiver operating characteristic (ROC) curve and concordance index analyses. Single-cell RNA-sequencing and spatial transcriptomic data were further examined to clarify the therapeutic implications of the identified genes. RESULTS: SMR identified 316 LUAD-associated genes, among which 33 were associated with prognosis according to univariate Cox regression. The optimal model combining random survival forest and ridge regression achieved superior predictive accuracy, with the area under the curves for 1-, 3-, and 5-year survival being 0.757, 0.707, and 0.686, respectively. High-risk patients exhibited distinct immune microenvironment suppression, reduced tumor mutation burden, and diminished immunotherapy responsiveness as compared to their low-risk counterparts. Single-cell and spatial transcriptomic analyses identified interactions between cancer cells and stromal cells as potential therapeutic targets. CONCLUSIONS: The integrative approach employed in this study identified prognostic biomarkers and immunotherapy response predictors for patients with LUAD, offering a machine learning-driven framework for risk stratification and precision oncology. The findings underscore the clinical utility of combining genetic epidemiology with advanced analytics to optimize therapeutic strategies.
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HPV Infection Drives CXCL8+ Neutrophil-Mediated Immune Suppression in Cervical Cancer.
Persistent HPV infection is a major risk factor for cervical cancer, but the immune mechanisms behind HPV-induced tumor progression are not fully understood. Neutrophils may play a key role in immune remodeling and evasion within the HPV-infected tumor microenvironment. We integrated 31 cervical samples (including HPV-infected and non-infected normal, tumor, and HSIL tissues) to construct a single-cell atlas of 141,785 cells. Neutrophil heterogeneity, cell-cell communication, and immunosuppressive programs were systematically analyzed. Transcriptomic findings were validated via immunohistochemistry, immunofluorescence, and Mendelian randomization (MR). HPV infection dramatically remodeled the immune landscape, inducing robust neutrophil and plasma cell infiltration while reducing T cell abundance. Neutrophils in HPV+ tissues were terminally differentiated and displayed a distinct CXCL8+ phenotype. These CXCL8+ neutrophils were identified as late-stage, transcriptionally defined low-plasticity state enriched with immunosuppressive genes (e.g., SPP1, VEGFA). They exhibited enhanced interactions with T cell subsets, particularly regulatory and cytotoxic T cells, via ligands like CXCL16 and APOE, suggesting direct immunosuppressive crosstalk. MR analysis indicated a causal role for CXCL8 in cervical cancer, and spatial protein assays confirmed elevated CXCL8 and its receptor CSF3R in HPV+ tumors. HPV infection expands immunosuppressive CXCL8+ neutrophils that modulate T cell activity and promote immune escape. CXCL8 is identified as a causal factor and potential therapeutic target in HPV-driven cervical cancer.
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A comparative transcriptomic analysis of mouse demyelination models and multiple sclerosis lesions.
Demyelinating diseases, including multiple sclerosis (MS), are characterized by loss of myelin and progressive neurodegeneration. It remains unclear if demyelination mouse models, such as cuprizone (CPZ) and lysophosphatidylcholine (LPC) elicit distinct responses or are comparable to human disease. Here, we integrate new and published single-cell transcriptomic datasets from CPZ- and LPC-induced demyelination and compare them with human MS data. We find that CPZ induces a distinct, stressed oligodendrocyte (OL) state, marked by Cdkn1a and Nupr1, that resembles phenotypes in MS lesions. The models converge on an immune responsive OL state expressing Socs3, B2m, and interferon-response genes during remyelination. Mouse microglia share a conserved activation program, although LPC drives a stronger, prolonged response. However, neither model captures the oligodendrocyte progenitor and microglial heterogeneity observed in MS. These results provide a cross-model, cross-species atlas of glial states and offer a framework to strategically leverage mouse models to study myelin injury and repair.
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Molecular heterogeneity of the non-human primate cochlea.
Unraveling the intricate composition and function of the cochlea is paramount to comprehending the mechanisms underlying sound perception and the pathogenesis of auditory disorders. The mammalian cochlea displays a highly organized structure, which contributes to the diversity and complexity of auditory processing. However, the cellular intricacies in non-human primates remain largely unexplored. In the present study, we employed high-throughput transcriptomic sequencing to profile over 36,701 nuclei across virtually all cochlear cell types in both juvenile and adult Macaca fascicularis at single-cell resolution. Our analysis unveiled remarkable heterogeneity both across and within cell types. Despite a largely conserved cellular composition of the cochlea between mouse and macaque species, glial cells exhibited substantial species-specific diversity, while hair cells and spiral ganglion neurons with specialized transcriptional programs were well-mapped onto their murine counterparts, underscoring the similarities that persist despite evolutionary divergence. Furthermore, we constructed a disease map associated with hearing loss, establishing this transcriptomic atlas of the macaque cochlea as an indispensable resource for future investigations in both human and non-human primates.
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Müller Glia Subtypes Define Neuro-Glial Associations and Spatial Morphogen Axes in the Zebrafish Retina.
Müller glia are instrumental macroglia of the vertebrate retina, once thought to be a homogeneous population. Now Müller glia are generally accepted as transcriptionally heterogeneous, and new evidence suggests functional diversity may exist in the way these cells respond to retinal injury. It remains unclear, however, whether this functional heterogeneity is limited to a transient phenotype that stems from injury or a fundamental feature of the healthy retina. Here, we investigate Müller glia heterogeneity in the uninjured zebrafish retina across development and adulthood using a comprehensive single-cell transcriptomic atlas of the 5 days post-fertilization (dpf) eye, validated in vivo and integrated with 9 dpf and adult datasets. We reveal three constitutive subpopulations of Müller glia that persist from early larval stages into adulthood: (1) a proliferative and immature population in both the peripheral and central retina; (2) a novel cohort of neuron-associated Müller glia that express coherent transcriptional programs specific to distinct neuronal subtypes, including retinal ganglion, amacrine, and horizontal cells; and (3) spatially distinct Müller glia subsets that appear to define a dorso-ventral axis of retinoic acid metabolism, bisected by a novel cyp26c1-expressing equatorial domain. Finally, cross-species analysis reveals that while neuron-associated programs are evolutionarily conserved in mammals, the spatial patterning of morphogens in adult retinae may be specific to the teleost lineage. Collectively, these findings provide robust evidence for intrinsic functional heterogeneity in the uninjured vertebrate retina, reframing Müller glia from a general support population to a specialized cellular network that actively maintains retinal geography and function.
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Histopathology-guided multimodal pathomics identifies a PLEKHA6-positive epithelial state associated with phosphoinositide metabolism in esophageal cancer.
Esophageal cancer is biologically heterogeneous, and conventional clinicopathological staging incompletely captures variation in patient outcomes. This study aimed to determine whether histopathology-derived features associated with phosphoinositide metabolism could support exploratory prognostic stratification and identify biologically interpretable epithelial states. Diagnostic whole-slide images and bulk transcriptomic data from The Cancer Genome Atlas Esophageal Carcinoma (TCGA-ESCA) cohort were integrated with single-cell RNA-sequencing data. Tissue-rich image tiles underwent manual quality control, deep features were extracted using an ImageNet-pretrained ResNet-50 model, and phosphoinositide metabolism activity was quantified by single-sample gene set enrichment analysis (ssGSEA). Associated image features were evaluated using machine-learning survival models, followed by clinicopathological adjustment and multimodal molecular characterization. The selected random survival forest plus gradient boosting machine model stratified overall survival in the training and internal validation cohorts, with concordance indices of 0.762 and 0.723, respectively. The standardized pathology-derived risk score remained associated with overall survival after adjustment for age, sex, histological subtype, and pathological stage (hazard ratio, 2.37; 95% confidence interval, 1.60-3.50). However, calibration was imperfect, and performance estimates may be optimistic because the internal validation cohort informed model selection. Most initial pathology-phosphoinositide metabolism associations were attenuated after adjustment for histological subtype. Multimodal analyses prioritized PLEKHA6 as a candidate pathology-associated epithelial-state marker rather than a subtype-independent prognostic biomarker. PLEKHA6-positive epithelial cells exhibited inflammatory and microenvironment-related transcriptional programs, predicted midkine- and macrophage migration inhibitory factor-related communication features, and stronger inferred copy-number variation-like signals. Higher tumor-level PLEKHA6 expression was also associated with distinct inferred immune, metabolic, and predicted drug-response profiles, although malignant-cell identity was not established. Because the analyzed tiles were not derived from pathologist-annotated malignant regions, the image signal represents composite diagnostic-slide tissue context. These findings identify an exploratory pathology-derived prognostic signal and a PLEKHA6-associated epithelial state in esophageal cancer that require independent, subtype-specific, pathological, and experimental validation.
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SARS-CoV-2 receptor is co-expressed with elements of the kinin-kallikrein, renin-angiotensin and coagulation systems in alveolar cells.
SARS-CoV-2, the pathogenic agent of COVID-19, employs angiotensin converting enzyme-2 (ACE2) as its cell entry receptor. Clinical data reveal that in severe COVID-19, SARS-CoV-2 infects the lung, leading to a frequently lethal triad of respiratory insufficiency, acute cardiovascular failure, and coagulopathy. Physiologically, ACE2 plays a role in the regulation of three systems that could potentially be involved in the pathogenesis of severe COVID-19: the kinin-kallikrein system, resulting in acute lung inflammatory edema; the renin-angiotensin system, promoting cardiovascular instability; and the coagulation system, leading to thromboembolism. Here we assembled a healthy human lung cell atlas meta-analysis with ~ 130,000 public single-cell transcriptomes and show that key elements of the bradykinin, angiotensin and coagulation systems are co-expressed with ACE2 in alveolar cells and associated with their differentiation dynamics, which could explain how changes in ACE2 promoted by SARS-CoV-2 cell entry result in the development of the three most severe clinical components of COVID-19.
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Prognostic and immunological roles of ammonia-induced cell death-related genes in non-small cell lung cancer.
BACKGROUND: Lung cancer remains the predominant cause of cancer-related mortality globally, with non-small cell lung cancer (NSCLC) comprising approximately 85% of cases, despite advancements in immunotherapy. This challenge is primarily due to the persistent dysfunction of effector T cells within the tumor microenvironment (TME). Ammonia-induced cell death (AICD), a newly identified form of programmed cell death, leads to the attrition of CD8⁺ T cells and contributes to immune suppression. However, the clinical significance of AICD in NSCLC has yet to be elucidated. In light of this context, the current study seeks to investigate AICD-related genes and their prognostic and immunological implications in NSCLC. METHODS: Transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts were integrated with single-cell RNA sequencing and functional assays. Glutamine metabolism–related genes were retrieved from GeneCards and intersected with NSCLC differentially expressed genes to identify candidate AICD regulators. Prognostic value was evaluated by Kaplan–Meier survival and ROC analyses. Single-cell and immune infiltration analyses were used to define gene expression across malignant and immune subsets. Finally, Jurkat T cells were exposed to ammonia stress to validate gene function in AICD. RESULTS: Four genes—SLC7A5, SLC2A1, CAV1, and SPP1—emerged as key regulators of AICD in NSCLC. These genes were aberrantly expressed in tumors, stratified patient survival, and correlated with immune checkpoint activity and TME immunosuppression. A gene signature derived from the four regulators effectively predicted overall survival and immune response. Single-cell analysis confirmed their distribution across malignant and immune subpopulations. Functional assays demonstrated that CAV1, SPP1, and SLC7A5 protected against ammonia-induced T cell death, whereas SLC2A1 increased susceptibility. CONCLUSION: This study establishes AICD as a clinically relevant metabolic cell death mechanism in NSCLC and identifies SLC7A5, SLC2A1, CAV1, and SPP1 as potential prognostic biomarkers and therapeutic targets. Targeting ammonia metabolism may offer new strategies to overcome immune resistance and enhance immunotherapy efficacy in NSCLC.
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Reciprocal regulation of hMENA and TGF-β signaling in cancer-associated fibroblasts promotes EMT, immunosuppression, poor prognosis, and ICT resistance in NSCLC.
BACKGROUND: Cancer-associated fibroblasts (CAFs) significantly impact cancer progression and CAF subtypes are key determinants of response to immune checkpoint therapy (ICT). The transforming growth factor-β (TGF-β) signaling is a main pathway in protumorigenic activity of CAFs and resistance to ICT. The actin cytoskeleton regulator hMENA plays crucial roles in epithelial-mesenchymal transition (EMT) and regulates pathways critical to antitumor immune response, such as interferon-I and Axl-GAS6. METHODS: We constructed a single-cell atlas of CAFs using integrated public dataset. Experimental data were obtained by biochemical and molecular approaches in CAFs from freshly explanted non-small cell lung cancer (NSCLC) tissues hMENA silenced and in tumor cell lines or peripheral blood mononuclear cells treated with CAF conditioned medium. Multiparametric flow cytometry was used to characterize T cells. A gene signature indicative of ICT response was developed by a machine learning model. RESULTS: Computational analysis indicates that hMENA is primarily overexpressed in a myofibroblast cluster enriched for a TGF-β-activated CAF signature. Experimentally, we showed that TGF-β1 treatment increases hMENA expression and, reciprocally, hMENA/hMENAΔv6 modulate TGF-β1/2 production and secretion and transforming growth factor-β type II receptor expression in CAFs. Functionally, hMENA contributes to TGF-β1-driven CAF phenotype, programmed death-ligand 1 (PD-L1) upregulation, extracellular matrix remodeling and secretion of immunosuppressive cytokines/chemokines. The hMENA-driven TGF-β secretion in CAFs promotes PD-L1 expression and EMT in cancer cells by activating TGF-β signaling. On the tumor cell side, hMENA expression sustains the TGF-β signaling and EMT mediated by hMENA-driven CAFs secretome. This immunosuppressive secretome favors regulatory T cell (Treg) abundance and reduces CD8+ and CD4+ T cell functionality. Finally, based on the hMENA and TGF-β enriched CAF subtype, we developed a 9-gene signature, which in combination with hMENA/hMENAΔv6 correlates with increased Treg abundance and poor prognosis in the Cancer Genome Atlas NSCLC and associates with ICT resistance in Stand Up To Cancer (SU2C) and in phase III clinical trial (OAK) (NCT02008227) datasets. CONCLUSIONS: Our findings indicate that hMENA overexpression in CAFs defines a myofibroblast-like subset predominantly driven by TGF-β signaling, which sustains TGF-β1-mediated crosstalk between cancer cells and CAFs and impairs T-cell functionality. In NSCLC tissues, hMENAhigh CAFs associate with TGF-β and regulatory T-cell signatures and correlate with poor patient prognosis and resistance to immune checkpoint therapies, supporting their role as key contributors to an immunosuppressive, ICT-refractory tumor microenvironment.