48P CAF-specific targeting uncovered by patient-derived models synergistically restrains pancreatic tumor progression and chemoresistance

Background: Prostate cancer (PCa) shows marked molecular heterogeneity, and current markers insufficiently distinguish indolent from aggressive or castrationresistant disease.Long non-coding RNAs (lncRNAs) regulate AR signaling, epithelialmesenchymal transition, and therapy resistance.This study profiles stage-specific lncRNA expression directly in human prostate tissue.Methods: Human prostate samples (n=120) were collected: BPH (20), Localized PCa (40), High-Risk PCa (Gleason 8; 30), and Metastatic/CRPC tissue (30).RNA-seq was used for discovery and qRT-PCR for validation.Differential expression was assessed with limma (FDR <0.05).Diagnostic accuracy was measured using ROC-AUC, and prognostic significance using Cox regression.Results: Clear stage-dependent lncRNA patterns emerged across human prostate tissue.In localized PCa compared with BPH, PCA3, SChLAP1, PCAT-1, and MALAT1 showed significant upregulation (logFC 1.8-3.1;p<0.001), and their combined four-lncRNA panel achieved an AUC of 0.93 for discriminating benign from malignant tissue.In high-risk localized disease (Gleason 8), HOTAIR, FR348383, and PCAT-14 demonstrated progressive elevation (logFC 2.0-2.9), with FR348383 alone identifying high-risk tumors with an AUC of 0.91.Metastatic and CRPC biopsies showed marked overexpression of H19, LINC00963, UCA1, and PCGEM1 (logFC 2.4-4.2), and UCA1 distinguished CRPC from localized PCa with an AUC of 0.95.Prognostically, elevated levels of HOTAIR, FR348383, and LINC00963 were associated with increased risk of biochemical recurrence (HR 2.5; 95% CI 1.6-3.8),while high UCA1 expression in CRPC biopsies predicted poorer overall survival (HR 2.2; p=0.004). Conclusions:Human prostate tissue profiling identifies robust stage-specific lncRNA signatures that enhance diagnostic precision and predict aggressive and castrationresistant disease.These findings support incorporation of lncRNA panels into tissuebased and liquid-biopsy workflows to improve clinical decision-making in PCa.