Search Results (4)

Bone metastasis develops within a specialized marrow ecosystem where hematopoiesis, immune regulation, vascular trafficking, and skeletal remodeling intersect. Neutrophil-lineage cells occupy a unique position in this setting because they are generated, retained, mobilized, aged, and reprogrammed within the same bone marrow niches that disseminated tumor cells exploit for homing and survival. This review examines how neutrophils, tumor-associated neutrophils, immature neutrophils, low-density neutrophils, and PMN-MDSCs shape skeletal colonization. We discuss tumor-to-marrow signaling, CXCR2-dependent recruitment, CXCR4/CXCL12-mediated marrow retention, neutrophil–circulating tumor cell interactions, vascular arrest, dormancy escape, NET-mediated matrix remodeling, immune suppression, and effects on osteoclast–osteoblast coupling. Evidence is strongest in breast and prostate cancer models, where pathways such as CXCL5/CXCR2, CTNND1–CXCR4/CXCL12, PR3–RAGE, and DKK1–CKAP4–STAT6–CHI3L3 link neutrophil-lineage cells to skeletal progression and immunotherapy resistance. However, several mechanisms, including CTC–neutrophil clustering and NET-driven dormancy awakening, remain partly extrapolated from non-skeletal models. We therefore emphasize evidence hierarchy, methodological limitations, and therapeutic opportunities, arguing that selective reprogramming or functional inhibition of pro-metastatic neutrophil states may be more promising than indiscriminate neutrophil depletion in metastatic bone disease. A clearer understanding of these context-dependent neutrophil programs may help refine biomarker development and guide combination therapies for patients with skeletal metastases. Full article

Lung adenocarcinoma (LUAD) is characterized by substantial genetic heterogeneity, making it challenging to identify reliable biomarkers for diagnosis and treatment. Tumor mutational burden (TMB) is widely recognized as a predictive biomarker due to its association with immune response and treatment efficacy. In this study, we take a different approach by treating TMB as a response variable to uncover its genetic drivers using multiomics data. We conducted a thorough evaluation of recent feature selection methods through extensive simulations and identified three top-performing approaches: projection correlation screening (PC-Screen), distance correlation sure independence screening (DC-SIS), and Wasserstein distance-based screening (WD-Screen). Unlike traditional approaches that rely on simple statistical tests or dataset splitting for validation, we adopt a method-based validation strategy, selecting top-ranked features from each method and identifying consistently selected genes across all three. Using The Cancer Genome Atlas (TCGA) dataset, we integrated copy number alteration (CNA), mRNA expression, and DNA methylation data as predictors and applied our selected methods. In the two-platform analysis (mRNA + CNA), we identified 13 key genes, including both previously reported LUAD-associated genes (CCNG1, CKAP2L, HSD17B4, SHROOM1, TIGD6, and TMEM173) and novel candidates (DTWD2, FLJ33630, NME5, NUDT12, PCBD2, REEP5, and SLC22A5). Expanding to a three-platform analysis (mRNA + CNA + methylation) further refined our findings, with PCBD2 and TMEM173 emerging as the robust candidates. These results highlight the complexity of multiomics integration and the need for advanced feature selection techniques to uncover biologically meaningful patterns. Our multiomics strategy and robust selection approach provide insights into the genetic determinants of TMB, offering potential biomarkers for targeted LUAD therapies and demonstrating the power of Wasserstein distance-based feature selection in complex genomic analysis. Full article

Background: Ovarian cancer (OC) is a heterogeneous malignancy associated with a poor prognosis, necessitating robust biomarkers for risk stratification and therapy optimization. Cellular senescence-related genes (CSGs) are emerging as pivotal regulators of tumorigenesis and immune modulation, yet their prognostic and therapeutic implications in OC remain underexplored. Methods: We integrated RNA-sequencing data from TCGA-OV (n = 376), GTEx (n = 88), and GSE26712 (n = 185) to identify differentially expressed CSGs (DE-CSGs). Consensus clustering, Cox regression, LASSO-penalized modeling, and immune infiltration analyses were employed to define molecular subtypes, construct a prognostic risk score, and characterize tumor microenvironment (TME) dynamics. Drug sensitivity was evaluated using the Genomics of Drug Sensitivity in Cancer (GDSC)-derived chemotherapeutic response profiles. Results: Among 265 DE-CSGs, 31 were prognostic in OC, with frequent copy number variations (CNVs) in genes such as STAT1, FOXO1, and CCND1. Consensus clustering revealed two subtypes (C1/C2): C2 exhibited immune-rich TME, elevated checkpoint expression (PD-L1, CTLA4), and poorer survival. A 19-gene risk model stratified patients into high-/low-risk groups, validated in GSE26712 (AUC: 0.586–0.713). High-risk patients showed lower tumor mutation burden (TMB), immune dysfunction, and resistance to Docetaxel/Olaparib. Six hub genes (HMGB3, MITF, CKAP2, ME1, CTSD, STAT1) were independently predictive of survival. Conclusions: This study establishes CSGs as critical determinants of OC prognosis and immune evasion. The molecular subtypes and risk model provide actionable insights for personalized therapy, while identified therapeutic vulnerabilities highlight opportunities to overcome chemoresistance through senescence-targeted strategies. Full article

Despite advances in the diagnosis and treatment of the central nervous system malignancy glioma, overall survival remains poor. Cytoskeleton-associated protein 2-like (CKAP2L), which plays key roles in neural progenitor cell division, has also been linked to poor prognosis in lung cancer. In the present study, we investigated the role of CKAP2L in glioma. From bioinformatics analyses of datasets from The Cancer Gene Atlas and the Chinese Glioma Genome Atlas, we found that CKAP2L expression correlates with tumor grade and overall survival. Gene set enrichment analysis (GSEA) showed that MITOTIC_SPINDLE, G2M_CHECKPOINT, and E2F_TARGETS are crucially enriched phenotypes associated with high CKAP2L expression. Using U87MG, U118MG, and LNZ308 human glioma cells, we confirmed that CKAP2L knockdown with siCKAP2L inhibits glioma cell proliferation, migration, invasion, and epithelial-mesenchymal transition. Interestingly, CKAP2L knockdown also induced cell cycle arrest at G2/M phase, which is consistent with the GSEA finding. Finally, we observed that CKAP2L knockdown led to significant increases in miR-4496. Treating cells with exogenous miR-4496 mimicked the effect of CKAP2L knockdown, and the effects of CKAP2L knockdown could be suppressed by miR-4496 inhibition. These findings suggest that CKAP2L is a vital regulator of miR-4496 activity and that CKAP2L is a potentially useful prognostic marker in glioma. Full article