Emerging evidence indicates that aberrant cholesterol metabolism in macrophages plays a role in tumorigenesis, however, how specific metabolites released by macrophages influence immunosurveillance and along with their potential as therapeutic targets and prognostic biomarkers, remain unclear. We integrated 465 single-cell multi-omics and 1,318 bulk RNA-seq samples of colorectal cancer (CRC) using bioinformatics, single-cell metabolic flux modeling and machine learning to decipher cholesterol metabolic rewiring specific to tumor-associated macrophages (TAM), along with associated druggable targets and prognostic potential. We uncover that CYP27A1-mediated 26-hydroxycholesterol metabolism drives the formation of immunosuppressive SPP1 + APOE + TAM in CRC. Multi-omics and analysis (scATAC-seq, gene regulatory network, and spatial transcriptomics data) reveals that this subset activates SPI1, establishing a macrophage-Treg co-infiltration niche that inhibits CD8 + T-cell infiltration in large amounts of spatial transcriptomics data. Finally, we constructed a cholesterol metabolism-based gene signature derived from SPP1 + APOE + TAM and applied machine learning methods, achieving robust prognostic accuracy across four independent CRC cohorts. Collectively, our findings reveal CYP27A1 as a central regulator linking cholesterol metabolism to immune suppression, highlighting its therapeutic potential in tumor microenvironment-driven cancers and lipid-associated disorders.

Epilepsy is mainly characterized by spontaneous seizures caused by hyperactive neural circuits. To delineate the cell-type-specific mechanisms underlying neuronal hyperexcitability, we resolve the hyperexcitability of excitatory neurons across epileptic human brain trans-foci at single-cell resolution to identify the key drivers and potential diagnostic signatures. We constructed a comprehensive atlas encompassing 240,000 cells derived from the temporal cortex and hippocampus, detecting trans-regional cellular and molecular diversity. We further delineated dynamic trajectories, gene expression patterns, and functional reorganization across cell types. Using the LASSO and random forest algorithms, we prioritized the core genes and developed a logistic regression-based diagnostic model. Despite transregional cellular landscape conservation, major cell types varied in abundance. Detailed analysis delineated various excitatory neuron subtypes’ dynamic trajectories, intricate expression, and functional reorganization, with pronounced dysfunction in the posterior hippocampal and temporal cortex networks, indicating hyperactive pro-epileptic effects. Excitatory neurons exhibit an intrinsic ability to autonomously organize themselves into distinct, highly active modules, characterized by a high activation state during epileptogenesis, as illustrated by ten epilepsy-associated functions. Transcription circuits FOSL2/FOS/EGR3/EGR1 promote neuronal hyperexcitability. Integrating epilepsy bulk RNA-seq data, we identified 24 overlapping genes between differential genes and circuit targets. The LASSO and random forest algorithms prioritized three core genes (IL1B, SOCS6, and COL4A1). A logistic regression model based on these three genes showed variable performance, with an apparent AUC of 1.000 in the discovery cohort (GSE256068) and AUCs of 0.974 and 0.722 in and two validation cohorts, indicating the need for further validation. Our study establishes the FOSL2/FOS/EGR3/EGR1 circuit as a master regulator of pathological neuronal hyperactivity across epileptic foci, linking transcriptional activation to network dysfunction. Identifying overactive factors may represent a candidate molecular pathway for future therapeutic exploration against hyperexcitability.

Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, with its highly heterogeneous tumor microenvironment posing substantial challenges for precision diagnosis and therapy. To address this, we aim to construct a novel prognostic framework based on tumor-immune interactions. Through integrative analysis of single-cell RNA sequencing data from 30 TNBC samples (106,132 cells), we identify key tumor expression metaprograms and uncover their interaction with an immunosuppressive dendritic-cell subset, a process associated with the NECTIN1–NECTIN4 axis. Leveraging these interactions, we developed and validated two immunological prognostic models using multi-cohort transcriptomic data, including the stress response tumor cell and pDC_CLEC4C prognostic model (SPSM) and the immune response tumor cell and pDC_CLEC4C prognostic model (IPSM). These models effectively stratified TNBC patients into distinct risk groups, with the low-risk group characterized by an immunologically active microenvironment and elevated expression of immune checkpoint genes, suggesting a potential responsiveness to immunotherapy. Furthermore, we identified several potential therapeutic agents, including imatinib and bortezomib. Collectively, our dual-model framework provides a tool for risk stratification, offers translational insights for precision treatment, and presents new directions for understanding TNBC heterogeneity and therapeutic development.

T cell dysfunction (TCD) plays a critical role in cancer progression and significantly impacts patient outcomes. Despite its importance, the exact molecular mechanisms underlying TCD remain poorly understood. To address this, we constructed a comprehensive pan‐cancer landscape of TCD, with a particular focus on identifying miRNA biomarkers that define and predict TCD severity. Our analysis revealed six key miRNAs (miR‐203b, miR‐214, miR‐4772, miR‐141, miR‐200a, and miR‐200b) that were closely associated with varying degrees of TCD. These prognostic miRNAs not only exhibited distinct expression patterns across four identified TCD subtypes (from low to high TCD severity) but also demonstrated strong predictive performance in classifying patients with different levels of TCD. The identified miRNA signatures serve as reliable biomarkers for stratifying patients into high‐risk and low‐risk groups, with higher TCD levels correlating to poorer overall survival. In addition to miRNA biomarkers, we observed that patients with severe TCD exhibited increased infiltration of immune cells and macrophages and dysregulation of DNA methylation patterns. Patients with higher degrees of TCD displayed low methylation levels, which further contributed to the progression of T cell dysfunction. In summary, our study highlights the pivotal role of miRNA biomarkers in shaping the landscape of T cell dysfunction across cancers. These miRNAs serve as both prognostic indicators and predictive tools, enabling accurate classification of TCD severity and offering new avenues for therapeutic exploration and patient stratification in cancer immunotherapy.