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A novel adrenomedullin receptor signaling-based prognostic model predicts immunosuppressive microenvironment and informs therapeutic stratification in hepatocellular carcinoma: A retrospective bioinformatics study.
The adrenomedullin receptor signaling pathway plays a crucial role in tumor progression, yet its comprehensive implication in hepatocellular carcinoma (HCC) remains underexplored. This study aimed to develop and validate a multi-gene prognostic signature based on adrenomedullin receptor signaling-related genes (ARGs) and to elucidate its associations with clinicopathological features, immune microenvironment, drug sensitivity, and somatic mutations in HCC. Transcriptomic and clinical data of HCC patients were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases. Pan-cancer analysis was performed to evaluate the expression and prognostic value of ARGs. A prognostic model was constructed using 116 machine learning algorithm combinations and evaluated by C-index and receiver operating characteristic analysis. The risk score (RS) derived from the model was further correlated with clinical characteristics, immune cell infiltration, drug sensitivity, somatic mutations, and pathway activities. A nomogram was established for clinical applicability. Single-cell RNA sequencing data were analyzed to delineate the cellular heterogeneity of ARG expression within the tumor microenvironment. ADM and RAMP3 were significantly associated with HCC prognosis. The optimal model, built with Random Survival Forest, demonstrated robust predictive performance in both the Cancer Genome Atlas (HR = 9.51, P < .001) and GSE14520 (HR = 2.24, 95% CI: 1.46-3.43, P < .001) cohorts. High-risk patients exhibited advanced T stage, higher histological grade, and poorer overall survival. Computational immune profiling suggested decreased immune cell infiltration and altered immune checkpoint gene expression in the high-risk group. In silico drug sensitivity analysis predicted increased susceptibility to certain targeted agents (e.g., erlotinib) in high-risk patients, though these findings are hypothesis-generating and require experimental validation. Furthermore, RS was weakly correlated with tumor mutational burden (R = 0.16, P = .004), though TP53 mutation frequency did not differ significantly between risk groups after covariate adjustment. The nomogram integrating RS demonstrated favorable predictive accuracy for 1-, 3-, and 5-year survival. Single-cell analysis revealed predominant expression of ARGs in tumor endothelial cells. We developed and validated an ARG-based prognostic signature that effectively stratifies HCC patients into distinct risk subgroups. This model may serve as a hypothesis-generating framework for individualized prognosis prediction and for identifying potential therapeutic vulnerabilities in HCC that warrant prospective investigation.
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Identification of RING finger protein-related biomarkers in sepsis based on explainable machine learning: A comprehensive analysis.
OBJECTIVE: To systematically identify RING finger protein (RNF) biomarkers with diagnostic value in sepsis and to explore their potential as therapeutic targets. METHODS: Sepsis peripheral blood transcriptome datasets from Gene Expression Omnibus (GEO) were integrated. Sepsis-associated RNF genes were identified via differential expression analysis and weighted gene co-expression network analysis (WGCNA). Feature genes were selected using LASSO, SVM-RFE, and SHAP. Summary-data-based Mendelian randomization (SMR) assessed causality, and diagnostic performance was validated by receiver operating characteristic (ROC) curves in three independent cohorts. Single-cell RNA sequencing resolved cellular localization and perturbations. DrugReflector deep learning predicted repurposable drugs, molecular docking evaluated binding affinity, and PheWAS assessed target safety. RESULTS: A total of 13 sepsis-related RNF genes were identified, and three feature genes (ZFP36L2, RNF125, RNF175) were further selected. SMR showed that only elevated RNF175 expression significantly increased sepsis risk (OR = 1.267, 95% CI 1.002-1.603, P = 0.048). The diagnostic AUCs of RNF175 were 0.931, 0.776, and 0.835 in the training and two validation cohorts, respectively. Single-cell atlas revealed predominant RNF175 expression in T cells with significant upregulation in sepsis (P < 0.001). Deep learning predicted tadalafil as the top candidate, with a molecular docking binding energy of -9.072 kcal/mol for RNF175. PheWAS found no significant phenotype associations, indicating low off-target risk. CONCLUSIONS: RNF175 is an early sepsis biomarker with both diagnostic value and causal relevance, potentially acting through T-cell modulation. Tadalafil may be a pathway-targeted repurposable drug. This study provides new perspectives for precision medicine in sepsis.
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Study on the expression level of folate receptor γ in thyroid cancer and its association with clinicopathological characteristics.
Despite generally favorable outcomes, identifying reliable biomarkers remains crucial for managing aggressive subtypes of thyroid cancer. The present study aimed to investigate the association between folate receptor γ (FOLR3) expression and the clinicopathological features of thyroid cancer, and to explore its clinical relevance using bioinformatics and experimental approaches, ultimately assessing its potential value as a diagnostic and therapeutic biomarker. Differential expression analysis of RNA-sequencing data from The Cancer Genome Atlas database was performed to identify the differentially expressed gene FOLR3, followed by standard single-cell analysis. Clinicopathological data from 293 patients with thyroid cancer were collected to investigate pathological characteristics and FOLR3 expression, which was further validated using immunohistochemistry. The functional effects of FOLR3 on the malignant behavior of thyroid cancer cells were assessed using colony formation, Cell Counting Kit-8, scratch wound healing and Transwell invasion assays. FOLR3 expression was significantly higher in normal thyroid tissue compared with cancer tissues (P<0.001). FOLR3 expression level was associated with capsular invasion, perineural invasion, striated muscle invasion and pT stage (all P<0.05). Functional experiments demonstrated that FOLR3 suppressed the proliferation, migration and invasion of TPC-1 cells (all P<0.05). In conclusion, FOLR3 expression is generally low or absent in thyroid cancer, particularly in cases demonstrating pronounced local infiltration, indicating its potential as a diagnostic biomarker for the evaluation of tumor invasiveness. Patients with high FOLR3 expression exhibit a higher survival rate, suggesting its potential prognostic value. FOLR3 inhibits the progression of thyroid cancer by inhibiting the STAT3/MAPK signaling pathways and reprogramming folate metabolism, highlighting its potential as a therapeutic target for thyroid cancer in the future.
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CancerOmicsStudio (CoS): a web server for integrative and interpretable analysis of multi-omics cancer data.
MOTIVATION: Large-scale omics resources, including The Cancer Genome Atlas, Genomics of Drug Sensitivity in Cancer, and the Cancer Dependency Map, have become essential for cancer research. However, these datasets are distributed across different platforms, formats and analysis frameworks, which limits their practical use by researchers without extensive computational expertise. RESULTS: We developed CancerOmicsStudio (CoS), a web server for integrative and interpretable analysis of multi-omics cancer data across 33 cancer types. CoS provides five major modules: CosAI, Traditional Analysis, Drug Sensitivity, CRISPR Dependency and Single-Cell Tumor Microenvironment. The Traditional Analysis module supports expression comparison, diagnostic evaluation, survival analysis, enrichment analysis and gene correlation. The Drug Sensitivity and CRISPR Dependency modules enable systematic evaluation of gene-drug response associations and gene essentiality in cancer cell lines. The Single-Cell Tumor Microenvironment module supports tumor microenvironment analysis at single-cell resolution. In total, approximately 1.23 million results have been precomputed to enable rapid retrieval. CosAI further allows users to submit natural-language queries and obtain results through a Real-time Analysis as Retrieval framework, with responses summarized by a lightweight language model. AVAILABILITY AND IMPLEMENTATION: CancerOmicsStudio is freely available at Zenodo (doi: 10.5281/zenodo.18744990) and https://cos.wanglab.bio.
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A single-nucleus transcriptomic atlas of medium spiny neurons in the rat nucleus accumbens.
Neural processing of rewarding stimuli involves several distinct regions, including the nucleus accumbens (NAc). The majority of NAc neurons are GABAergic projection neurons known as medium spiny neurons (MSNs). MSNs are broadly defined by dopamine receptor expression, but evidence suggests that a wider array of subtypes exist. To study MSN heterogeneity, we analyzed single-nucleus RNA sequencing data from the largest available rat NAc dataset. Analysis of 48,040 NAc MSN nuclei identified major populations belonging to the striosome and matrix compartments. Integration with mouse and human data indicated consistency across species and disease-relevance scoring using genome-wide association study results revealed potentially differential roles for MSN populations in substance use disorders. Additional high-resolution clustering identified 34 transcriptomically distinct subtypes of MSNs definable by a limited number of marker genes. Together, these data demonstrate the diversity of MSNs in the NAc and provide a basis for more targeted genetic manipulation of specific populations.
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Self-identified African ancestry and pathomics-derived tumor-infiltrating lymphocyte scores in colon and rectal adenocarcinoma whole-slide images: an exploratory multicohort whole-slide image study.
INTRODUCTION: AI assisted computed higher tumor infiltrating lymphocyte% (TIL%) scores from hematoxylin and eosin (H&E) stained whole slide images (WSI) may serve as a useful biomarker for stratifying patients for treatment with immune checkpoint inhibitors. METHODS: To test the hypothesis that self-identified African ancestry (AA) vs. European ancestry (EA) was associated with higher TIL% scores, WSI were assembled from three diverse cohorts of formalin-fixed paraffin embedded (FFPE) colon and rectal adenocarcinoma (COAD-READ) tumor tissues: 1.) 242 AA vs. EA WSI downloaded from The Cancer Genome Atlas (TCGA)-COAD-READ database; 2.) 97 independent US self-identified AA vs. EA WSI assembled from three US medical centers; 3.) 48 (of 51) Nigerian WSI assembled from a single Nigerian medical center. There were 33 self-identified AA WSI suitable for analysis in the TCGA cohort and 49 self-identified AA WSI in the US cohort. For the TCGA cohort, 241 whole transcriptome RNA-sequence data were generated from parallel frozen tumor samples collected alongside the FFPE samples. For the US cohort, 87 RNA-seq enriched by exome capture data were generated from the same FFPE blocks as WSI. RESULTS: No difference in median TIL% scores was detected between the combined TCGA and US AA cohorts and the Nigerian cohort. Exploratory multiple regression analysis of the combined TCGA and US cohorts revealed that AA had a higher TIL% score (Estimated difference = 3.01%, 95% CI (0.54%, 5.49%), P-value = 0.0170), while controlling for MMR/MSI status and other covariates. However, this result needs to be interpreted with caution because of differences between the TCGA and US cohorts with respect to representation of self-identified AA. After adjustment for cohort in the model, the association of AA with a higher TIL% score was no longer significant (Estimated difference = 2.05%, 95% CI(-0.59%, 4.70%), P-value = 0.1277). Moderate correlations were detected in the TCGA and US cohorts analyzed separately between computed TIL% scores and CIBERSORTx estimates of lymphocyte and T-cell abundance. Moderate correlations were also detected between TIL% scores and CXCL10 and CCL5 gene expression values. CONCLUSIONS: These preliminary results underscore the need for expanding the representation of minority populations in publicly accessible datasets.
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A Drosophila glial cell atlas reveals a mismatch between transcriptional and morphological diversity.
Morphology is a defining feature of neuronal identity. Like neurons, glia display diverse morphologies, both across and within glial classes, but are also known to be morphologically plastic. Here, we explored the relationship between glial morphology and transcriptional signature using the Drosophila central nervous system (CNS), where glia are categorised into 5 main classes (outer and inner surface glia, cortex glia, ensheathing glia, and astrocytes), which show within-class morphological diversity. We analysed and validated single-cell RNA sequencing data of Drosophila glia in 2 well-characterised tissues from distinct developmental stages, containing distinct circuit types: the embryonic ventral nerve cord (VNC) (motor) and the adult optic lobes (sensory). Our analysis identified a new morphologically and transcriptionally distinct surface glial population in the VNC. However, many glial morphological categories could not be distinguished transcriptionally, and indeed, embryonic and adult astrocytes were transcriptionally analogous despite differences in developmental stage and circuit type. While we did detect extensive within-class transcriptomic diversity for optic lobe glia, this could be explained entirely by glial residence in the most superficial neuropil (lamina) and an associated enrichment for immune-related gene expression. In summary, we generated a single-cell transcriptomic atlas of glia in Drosophila, and our extensive in vivo validation revealed that glia exhibit more diversity at the morphological level than was detectable at the transcriptional level. This atlas will serve as a resource for the community to probe glial diversity and function.
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Single-cell RNA sequencing reveals the effects of capsaicin in the treatment of sepsis-induced liver injury.
Sepsis is a difficult-to-treat systemic condition in which liver dysfunction acts as both regulator and target. However, the dynamic response of diverse intrahepatic cells to sepsis remains poorly characterized. Capsaicin (CAP), a multifunctional chemical derived from chilli peppers, has recently been shown to potentially possess anti-inflammatory effects, which is also one of the main approaches for drug discovery against sepsis. We performed single-cell RNA transcriptome sequencing on 86,830 intrahepatic cells isolated from normal mice, cecal ligation and puncture-induced sepsis model mice and CAP-treated mice. The transcriptional atlas of these cells revealed dynamic changes in hepatocytes, macrophages, neutrophils, and endothelial cells in response to sepsis. Among the extensive crosstalk across these major subtypes, KC_Cxcl10 shared strong potential interaction with other cells when responding to sepsis. CAP mitigated the severity of inflammation by partly reversing these pathophysiologic processes. Specific cell subpopulations in the liver act collectively to escalate inflammation, ultimately causing liver dysfunction. CAP displays its health-promoting function by ameliorating liver dysfunction induced by sepsis. Our study provides valuable insights into the pathophysiology of sepsis and suggestions for future therapeutic gain.
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A Cori cycle-related gene signature predicts prognosis, immune microenvironment, and drug response in breast cancer.
BACKGROUND: Breast cancer remains the most common malignancy among women worldwide. Metabolic reprogramming, particularly involving the Cori cycle, plays a crucial role in tumor progression and therapy resistance. However, the prognostic and immunological implications of Cori cycle-related genes (CCRGs) in breast cancer remain underexplored. This study aimed to integrate multi-omics data and machine learning to construct a CCRG-based prognostic signature and evaluate its predictive performance and clinical relevance in breast cancer. METHODS: We integrated multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A machine learning approach was employed to construct a prognostic signature based on CCRGs. The model was validated across independent cohorts. Immune infiltration, drug sensitivity, somatic mutations, and functional enrichment analyses were performed to elucidate the biological and clinical relevance of the signature. Single-cell and pan-cancer analyses were conducted to assess gene expression and functional associations at cellular and cross-cancer levels. RESULTS: A three-gene signature (HK1, PGK1, PGAM1) was identified and used to stratify patients into high- and low-risk groups with distinct survival outcomes. The risk score (RS) was significantly associated with advanced clinicopathological features, immunosuppressive microenvironments, and altered drug sensitivity. Enrichment analyses revealed activation of glycolysis, cell cycle, and PI3K-AKT pathways in high-risk patients. The signature also correlated with tumor mutational burden (TMB) and specific mutational patterns. Pan-cancer analysis confirmed the broad relevance of CCRGs across multiple cancer types. CONCLUSIONS: We developed and validated a robust CCRG signature that effectively predicts prognosis, immune contexture, and therapeutic response in breast cancer. This signature offers novel insights into metabolic immunosuppression and provides a potential tool for risk stratification and personalized treatment strategies.
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The emerging landscape of spatial profiling technologies.
Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.