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Found: 4 944
Page 383 of 495-
Pan-cancer Transcriptomic Predictors of Perineural Invasion Improve Occult Histopathologic Detection.
PURPOSE: Perineural invasion (PNI) is associated with aggressive tumor behavior, recurrence, and metastasis, and can influence the administration of adjuvant treatment. However, standard histopathologic examination has limited sensitivity in detecting PNI and does not provide insights into its mechanistic underpinnings. EXPERIMENTAL DESIGN: A multivariate Cox regression was performed to validate associations between PNI and survival in 2,029 patients across 12 cancer types. Differential expression and gene set enrichment analysis were used to learn PNI-associated programs. Machine learning models were applied to build a PNI gene expression classifier. A blinded re-review of hematoxylin and eosin (H&E) slides by a board-certified pathologist helped determine whether the classifier could improve occult histopathologic detection of PNI. RESULTS: PNI associated with both poor overall survival [HR, 1.73; 95% confidence interval (CI), 1.27-2.36; P < 0.001] and disease-free survival (HR, 1.79; 95% CI, 1.38-2.32; P < 0.001). Neural-like, prosurvival, and invasive programs were enriched in PNI-positive tumors (P adj < 0.001). Although PNI-associated features likely reflect in part the increased presence of nerves, many differentially expressed genes mapped specifically to malignant cells from single-cell atlases. A PNI gene expression classifier was derived using random forest and evaluated as a tool for occult histopathologic detection. On a blinded H&E re-review of sections initially described as PNI negative, more specimens were reannotated as PNI positive in the high classifier score cohort compared with the low-scoring cohort (P = 0.03, Fisher exact test). CONCLUSIONS: This study provides salient biological insights regarding PNI and demonstrates a role for gene expression classifiers to augment detection of histopathologic features.
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A TMED3-governed disulfidptosis-related diagnostic signature reveals tumor microenvironment remodeling in intrahepatic cholangiocarcinoma.
BACKGROUND: Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy with poor prognosis and limited treatment options. Disulfidptosis, a novel cell death pathway driven by disulfide bond accumulation, has emerged as a potential mechanism in cancer biology; however, its role in ICC remains unclear. METHODS: We integrated single-cell RNA sequencing (GSE138709) with bulk transcriptomic datasets (TCGA-CHOL, GSE107943, GSE32225) to systematically characterize the ICC cellular landscape. Analyses included CNV inference, stemness scoring, disulfidptosis activity assessment, and cell-cell communication profiling. A diagnostic model was constructed using LASSO-logistic regression with 10-fold cross-validation and validated in independent cohorts. TME characterization, survival analysis, and drug-target screening were also performed. Experimental validation included HPA immunohistochemistry, qRT-PCR, and functional assays following TMED3 knockdown. RESULTS: Seven major cell types were identified, with malignant cholangiocytes exhibiting high aneuploidy (74%), elevated stemness, upregulated disulfidptosis activity, and extensive communication via SPP1-CD44 and IGFBP3-TMEM219 networks. A five-gene signature (TMED3, TMEM184B, MAPK13, MFSD10, GRB7) demonstrated robust diagnostic performance. Survival analysis showed borderline prognostic value for TMED3 (adjusted HR = 2.37, P = 0.073), while TMEM184B emerged as an independent prognostic factor (adjusted HR = 4.79, P = 0.028). PPI and co-expression analyses established links between signature genes and disulfidptosis regulators. Functional experiments confirmed that TMED3 knockdown suppressed ICC cell proliferation, migration, and enhanced sensitivity to glucose deprivation-induced disulfidptosis. Network-based drug screening identified eight high-priority candidates for therapeutic repurposing. CONCLUSION: This study provides a comprehensive single-cell atlas of ICC, identifies TMED3 as a key regulator of a disulfidptosis-related diagnostic signature, and demonstrates its functional role in promoting ICC malignancy. The five-gene signature shows diagnostic and prognostic promise, and the drug screening offers preliminary leads for therapeutic repurposing, providing a foundation for precision diagnosis and targeted therapy in ICC.
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Expression, oncological and immunological characterizations of BZW1/2 in pancreatic adenocarcinoma.
Background: Despite the progress in early diagnosis and treatment, prognosis of pancreatic adenocarcinoma (PAAD) is still poor. Basic leucine zipper and W2 domain-containing protein 1 (BZW1) and protein 2 (BZW2) are attached to the basic leucine zipper (bZIP) superfamily. Recently, BZW1 was identified as an important role in glycolysis of PAAD. However, the comprehensive reports about BZW1/2 in PAAD are not sufficient. Methods: RNA-seq data in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were retrospectively analyzed. We explored the expression of BZW1/2 in PAAD tissues and the associations between BZW1/2 and prognosis. In addition, the potential roles of BZW1/2 in tumor microenvironment (TME) of PAAD were analyzed. Finally, clinicopathological data of 49 patients with PAAD in our institution were collected. Immunohistochemistry was used to determine the expression of BZW1/2 in PAAD samples. Results: BZW1 and BZW2 were upregulated in PAAD tissues compared to normal tissues (p < 0.05). The expression of BZW1/2 were not significantly correlated with gender, grade and stage of PAAD (p > 0.05). High expression of BZW2 was an independent predictor for poor prognosis of PAAD (HR 1.834, 95%CI 1.303-2.581, p = 0.001). And a nomogram to predict overall survival (OS) of PAAD was established with a C-index of 0.685. BZW1 and BZW2 expression were positively associated with T cell mediated immune response to tumor cell and Th2 cells in xCell database. Tumor Immune Single-Cell Hub (TISCH) analyses indicated that BZW1 and BZW2 were mainly expressed in B cells and malignant cells. External cohort furtherly validated that high expression of BZW1 and BZW2 were predictors for poor prognosis of PAAD. Conclusion: We found that BZW1 and BZW2 are highly expressed in malignant cells and B cells in the TME of PAAD. BZW2 is an independent predictor for OS of PAAD. BZW1 and BZW2 expression are positively associated with T cell mediated immune response to tumor cell and Th2 cells in PAAD.
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An Immune Gene Signature Stratifies Breast Cancer Prognosis Through iCAF-Driven Immunosuppressive Microenvironment.
Background/Objectives: Breast cancer is the leading cause of cancer-related mortality in women, highlighting the urgent need for robust prognostic tools to enable individualized risk stratification. Methods: Transcriptomic data from 1075 breast cancer and 113 adjacent normal tissues in The Cancer Genome Atlas (TCGA) were integrated with clinical information. Differential expression analysis identified 531 immune-related genes, which were further selected by univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression to construct a 13-gene prognostic signature. The model was validated in an independent cohort (n = 327). Tumor immune microenvironment and single-cell RNA sequencing data were analyzed to explore underlying biological differences. Results: The 13-gene signature effectively stratified patients into low- and high-risk groups with significantly different overall survival in both the TCGA cohort (log-rank p < 0.0001; C-index = 0.678; 5-year AUC = 0.72) and the validation cohort (log-rank p < 0.0001; C-index = 0.703; 3-year AUC = 0.81). Low-risk tumors exhibited an antitumor immune microenvironment enriched in CD8+ T cells, T follicular helper (Tfh) cells, and M1 macrophages, whereas high-risk tumors were dominated by immunosuppressive regulatory T cells and M2 macrophages (all p < 0.0001). Single-cell analysis revealed expansion of malignant epithelial cells and inflammatory cancer-associated fibroblasts (iCAFs) in high-risk tumors, with higher iCAF scores significantly associated with poorer survival (log-rank p = 0.00036). Conclusions: Collectively, this study delivers a rigorously validated 13-gene immune signature whose prognostic utility is rooted in distinct immune microenvironmental features, while unveiling iCAF-targeted therapeutic strategies as a promising intervention avenue.
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Causal intervention validation of gene regulatory signals in scGPT.
OBJECTIVE: Single-cell foundation models such as scGPT have been promoted as representations of gene regulation, but their advertised regulatory signal has been judged largely from attention weights, which are correlational. We ask a deliberately bounded question: whether direct interventions on scGPT gene tokens-causal with respect to the model's own computation-recover model-internal transcription-factor (TF)-target dependencies that align with curated references, whether that signal is robust, and whether it transfers to real perturbation responses. We separate these into two explicit validation axes and report both, including where the model-internal signal does not correspond to biological causality. METHODS: On Tabula Sapiens kidney, lung, and immune subsets, plus an external Krasnow lung atlas and three CRISPR perturbation datasets (Adamson, Dixit, Shifrut), we ablate or swap TF token values inside scGPT and quantify changes in target-token readouts. Axis 1 (reference alignment): intervention scores are evaluated against curated TRRUST and DoRothEA references and against attention and coexpression baselines, with new robustness studies over cell count (120-500), positive/negative pair count, four negative-sampling designs, and four readout strategies (mean, L2, cosine, max). We trace component-level circuits via activation patching, and benchmark against twelve GRN inference methods including a neural-network baseline on a matched substrate, additionally giving the classical methods larger cell budgets to test the equal-information question. Axis 2 (perturbation transfer): scores are evaluated against CRISPR perturbation-derived edges under balanced labels and AUROC. RESULTS: On Axis 1, lung shows reproducible enrichment that is stable across cell count and survives all four negative-sampling designs (permutation p improving from 0.07 to 0.03 as cells grow from 120 to 500); kidney enrichment is significant at small samples but does not survive scaling (permutation p rising to 0.16 at 500 cells); immune is at baseline. Richer readouts (L2, cosine) recover signal the scalar mean compresses away, particularly in kidney. On the matched benchmark scGPT leads classical and neural baselines at the shared 120-cell budget, but the lead narrows as the classical methods are given more cells, confirming this is a signal-efficiency result, not general superiority. On Axis 2, balanced evaluation of all three CRISPR datasets, including the TF-screen Dixit data, yields AUROC ≈ 0.50: the model-internal signal does not predict real perturbation responses. CONCLUSION: scGPT encodes tissue-conditional, intervention-sensitive regulatory structure that is aligned with literature-curated TF-target edges (robustly in lung) but is representational rather than biologically causal: it does not transfer to perturbation outcomes. The pipeline is a practical mechanistic-audit toolkit for biological foundation models, and the gap between reference alignment and perturbation transfer is a concrete cautionary result for using such models in regulatory inference.
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Multi-omics reveals key cell types and gene families regulating eggshell strength in chicken uteri.
Eggs represent an accessible and nutrient-dense source of high-quality animal protein, and decades of selective breeding have markedly elevated reproductive output in commercial laying hens. However, sustaining elevated productivity while improving eggshell integrity presents a critical challenge, as the molecular mechanisms of eggshell strength remain unclear. In this study, phenotypic assessment of eggshell strength was combined with single-cell transcriptomic profiling of the uterus from high- and low-strength groups, transcriptomic analysis of multiple tissues, and quantitative proteomic analysis of uterine fluid. Serum calcium and phosphorus levels did not differ significantly between groups. A single-cell atlas of the Rhode Island Red uterus was successfully generated for the first time, identifying nine distinct cell populations encompassing smooth muscle, epithelial, endothelial, and immune subsets. Integration of transcriptomic and proteomic datasets revealed that genes encoding collagens ( COL4A1/ 2, COL1A1/ 2, COL5A1, and COL6A1/ 2/ 3), solute carriers ( SLC4A4/ 7, SLC6A4, SLC9A2/ 9, and SLC38A2), ATPases ( ATP1A1, ATP1B1, ATP2B1/ 2, ATP2A2/ 3, and ATP2C1), calcium voltage-gated channels ( CACNB2, CACNA1C, and CACNA2D1), annexins ( ANXA5 and ANXA6), and integrins ( ITGB1 and ITGA9) were key molecular determinants associated with variation in eggshell strength. These genes were primarily enriched in signaling cascades involved in focal adhesion, actin cytoskeleton regulation, extracellular matrix (ECM)-receptor interactions, and calcium signaling. Notably, collagen family genes were predominantly localized to smooth muscle cells, consistent with the tissue remodeling and uterine inversion that occur during shell calcification, which may enhance spatial proximity between calcium ions and matrix proteins. These findings establish a multi-omics framework for understanding the uterine regulatory mechanisms underlying eggshell formation and offer a molecular foundation for breeding strategies aimed at prolonging laying cycles while preserving shell quality.
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Classification of Muscle-Invasive Bladder Cancer Based on Immunogenomic Profiling.
There is a significant heterogeneity in the immunotherapeutic responsiveness of each muscle-invasive bladder cancer (MIBC) patient. In our research, we aimed to identify a novel classification of MIBC based on immunogenomic profiling that may facilitate the reasonable stratification of prognosis and response to immunotherapy. The single-sample gene-set enrichment analysis (ssGSEA) was used to analyze the RNA-seq data of 29 important immune signatures from TCGA. Unsupervised hierarchical clustering was performed to identify an immunogenomic classification of MIBC. Then, we assessed the features of the classification in prognosis, immune infiltration, tumor-infiltration lymphocytes, HLA genes, and PD-L1 expression level. A total of 399 MIBC samples were included and three subtypes named Immunity_High, Immunity_Medium, and Immunity_Low were identified. The Immunity_High had a significant advantage in overall survival over the Immunity_Medium and Immunity_Low (p = 0.046 and p = 0.024). From Immunity_Low to Immunity_High, immune cell infiltration and stromal content showed an upward trend (p < 0.001). Meanwhile, Immunity_High was associated with a significantly higher proportion of TILs including dendritic cells resting, macrophages M1, mast cells resting, T cells CD4 memory activated, and T cells CD8+. And the expression levels of all HLA genes and PD-L1 of Immunity_High were the highest, consistent (p < 0.001). Two hundred ninety eight MIBC patients treated with immunotherapy from the IMvigor210 were included to form an independent validation cohort to verify the robustness of immunogenomic classification and the ability to predict the response to immunotherapy. This classification had potential clinical implications for predicting prognosis and immunotherapeutic responsiveness of MIBC patients.
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Unveiling anti-atherosclerotic targets of Perilla frutescens through a multi-scale computational framework integrating network pharmacology, single-cell analysis, machine learning, and molecular dynamics.
Medicinal plants have long served as important sources of therapeutic agents owing to their diverse bioactive constituents and multi-target pharmacological properties. In particular, plant-derived compounds have attracted increasing attention for the management of chronic inflammatory and metabolic diseases, including atherosclerosis. However, the molecular mechanisms by which medicinal plants modulate the cellular heterogeneity and intercellular communication networks within atherosclerotic plaques remain insufficiently understood. Despite the widespread implementation of lipid-lowering therapy, the persistence of residual inflammatory risk, driven by immunometabolic network dysregulation, remains a cardinal therapeutic challenge in atherosclerosis (AS) management. While Perilla frutescens exhibits well-documented anti-inflammatory properties, the precise molecular targeting within the atherosclerotic plaque microenvironment and the regulatory mechanisms governing intercellular communication networks remain poorly elucidated. To address this gap, we established a multi-scale integrative computational framework synergizing network pharmacology, human atherosclerotic plaque single-cell transcriptomic (scRNA-seq) profiling, and ensemble machine learning algorithms (LASSO and random forest) for systematic identification of robust therapeutic targets. Subsequently, molecular docking coupled with 100-ns all-atom molecular dynamics (MD) simulations validated the binding affinity and thermodynamic stability of drug-target complexes. The study successfully analyzed the cellular heterogeneity lineage of plaques and identified a core feature set of 10 genes including HIF1A, PPARG and ITGB1, which specifically mapped the differentiation trajectory of macrophages to foam cells. External validation in an independent cohort demonstrated superior diagnostic performance of this signature (AUC = 0.996). Cellular communication network dissection revealed the foam cell-driven SPP1-ITGB1 signaling axis as a pivotal conduit orchestrating inflammatory crosstalk. Molecular docking demonstrated pronounced binding affinity between luteolin, the principal bioactive constituent of Perilla frutescens, and ITGB1 (binding energy: - 8.9 kcal/mol). MD simulations further corroborated the efficacy of luteolin in stabilizing ITGB1 conformation via a "conformational-locking" mechanism (RMSD equilibration within 0.10-0.20 nm), thereby abrogating pathological cell adhesion signaling transduction. Collectively, this study provides a high-resolution molecular atlas of Perilla frutescens-mediated AS intervention, systematically elucidating the mechanistic paradigm whereby luteolin attenuates vascular inflammation through targeted disruption of the SPP1-ITGB1 communication axis. These findings underscore the therapeutic targeting of cell adhesion receptors as a translationally promising strategy for mitigating residual inflammatory risk in AS.
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Evidence for Multiple Independent Expansions of Fox Gene Families Within Flatworms.
Expansion and losses of gene families are important drivers of molecular evolution. A recent survey of Fox genes in flatworms revealed that this superfamily of multifunctional transcription factors, present in all animals, underwent extensive losses and expansions during platyhelminth evolution. In this paper, I analyzed Fox gene complement in four additional species of platyhelminths, that represent early-branching lineages in the flatworm phylogeny: catenulids (Stenostomum brevipharyngium and Stenostomum leucops) and macrostomorphs (Macrostomum hystrix and Macrostomum cliftonense). Phylogenetic analysis of Fox genes from this expanded set of species provided evidence for multiple independent expansions of Fox gene families within flatworms. Notably, FoxG, a panbilaterian brain-patterning gene, appears to be the least susceptible to duplication, while FoxJ1, a conserved ciliogenesis factor, has undergone extensive expansion in various flatworm lineages. Analysis of the single-cell atlas of S. brevipharyngium, combined with RNA in situ hybridization, elucidated the tissue-specific expression of the selected Fox genes: FoxG is expressed in the brain, three of the Fox genes (FoxN2/3-2, FoxO4 and FoxP1) are expressed in the pharyngeal cells of likely glandular function, while one of the FoxQD paralogs is specifically expressed in the protonephridium. Overall, the evolution of Fox genes in flatworms appears to be characterized by an early contraction of the gene complement, followed by lineage-specific expansions that have enabled the co-option of newly evolved paralogs into novel physiological and developmental functions.
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A comparative single-cell transcriptomic atlas for diverse populations of vertebrate hair cells.
Mechanosensitive hair cells vary widely in morphology and regenerative capacity across vertebrate organs and species. To investigate their underlying transcriptomic diversity, we integrated human, mouse, chicken, and zebrafish single-cell and single-nucleus RNA sequencing datasets and assembled a cross-species atlas of hair cells spanning organs, developmental stages, and species. Analysis of 29 hair cell populations, encompassing the major cochlear, vestibular, and lateral-line hair cell types, identified approximately 5,000 genes enriched in at least one hair cell population compared to supporting cells from the same organs. Unsupervised clustering of these hair cell-enriched (HCE) genes defined species-, organ-, and hair cell state-associated cohorts as well as broadly conserved hair cell-enriched programs. Using an AUC-based scoring framework, we further defined 884 pan hair cell-enriched (pan-HCE) genes with elevated expression in most developing and/or mature hair cell populations, including genes implicated in deafness, mechanotransduction, and synaptic transmission, along with genes not previously linked to hair cell function. Independent analysis of developing hair cells using the same metrics stratified pan-HCE genes based on when they are first enriched and identified an additional 97 genes that are transiently enriched. We used the pan- and developing HCE gene sets to assess transcriptional similarity between baseline hair cell states and hair cells produced during avian hair cell regeneration and in mouse cochlear organoids, as well as hair cell-like populations produced by fibroblast reprogramming. HCE gene sets with different developmental dynamics identified young vs. more mature hair cells when projected onto independent single-cell RNA sequencing datasets from developing zebrafish, mouse, and human. We provide a web-based resource of all HCE metrics and expression profiles, enabling future exploration of vertebrate hair cell gene expression across organs, species, and experimental contexts.