Liver cancer manifests as a profoundly heterogeneous malignancy, posing significant challenges in terms of both therapeutic intervention and prognostic evaluation. Given that the liver is the largest metabolic organ, a prognostic risk model grounded in single-cell transcriptome analysis and a metabolic perspective can facilitate precise prevention and treatment strategies for liver cancer. Hence, we identified 11 cell types in a scRNA-seq profile comprising 105,829 cells and found that the metabolic activity of malignant cells increased significantly. Subsequently, a prognostic risk model incorporating tumor heterogeneity, cell interactions, tumor cell metabolism, and differentially expressed genes was established based on eight genes; this model can accurately distinguish the survival outcomes of liver cancer patients and predict the response to immunotherapy. Analyzing the immune status and drug sensitivity of the high- and low-risk groups identified by the model revealed that the high-risk group had more active immune cell status and greater expression of immune checkpoints, indicating potential risks associated with liver cancer-targeted drugs. In summary, this study provides direct evidence for the stratification and precise treatment of liver cancer patients, and is an important step in establishing reliable predictors of treatment efficacy in liver cancer patients.

Cancer neuroscience highlights the critical role of neural signaling in tumors, yet a pan-cancer understanding of neuroregulatory dysregulation is lacking. We systematically characterized 130 neurotransmitter receptor (NTR) genes across 9,125 tumors from 33 cancer types. Our analysis revealed heterogeneous NTR mutations, with 17 cancer types showing elevated rates. Notably, amplification of the muscarinic receptor gene CHRM2 exhibited mutual exclusivity with the clinically actionable gene ERBB2, suggesting its potential as a therapeutic target. NTR expression was widely dysregulated and associated with altered DNA methylation, microRNA (miRNA) expression, and patient prognosis. Unsupervised clustering identified five recurrent neuroregulatory subtypes with distinct clinical and molecular features across cancers. Using low-grade glioma and liver cancer as examples, we validated that the S4 and S1 subtypes were consistently correlated with aggressive disease and poor outcomes in these two cancers, respectively. This study establishes a foundational framework for advancing cancer neuroscience and targeting neuroregulatory signaling in cancer therapy.

Background: The nervous system is increasingly recognized to play a critical role in tumor initiation and progression. Central to this complex relationship are the interactions between neurotransmitters secreted by neurons and their receptors (neurotransmitter receptors, NTRs) expressed on cancer cells, which activate multiple intracellular signaling pathways. However, the spectrum of NTR dysregulation and its association with biochemical recurrence (BCR) in prostate cancer (PCa) has not been explored. Therefore, the aim of this study was to fill this gap. Methods: We systematically characterized the expression profiles of 130 NTR genes by integrating bulk and single-cell transcriptomic data. Consistently dysregulated NTR (cdNTR) genes were identified and used to construct a PCa signature (PCaSig) using elastic-net regression. The robustness of PCaSig was evaluated across three independent cohorts. In addition, the associations of PCaSig with clinicopathological characteristics, genomic alterations, tumor immune-related characteristics, and biological pathways were comprehensively investigated. Results: Thirteen cdNTR genes with strong cell-type specificity, particularly in luminal epithelial cells, were identified. PCaSig robustly stratified patients into distinct BCR risk groups across multiple independent cohorts and remained an independent predictor after adjustment for clinicopathological factors. High PCaSig scores were associated with aggressive clinicopathological features, elevated tumor mutation burden (TMB), suppression of neurotransmitter-related signaling, and activation of cell-cycle and immune-related pathways. Notably, PCaSig refined prognostic stratification regardless of TMB status and was associated with distinct immune-related characteristics, including immune checkpoint expression and immune cell infiltration. Incorporation of PCaSig into a clinical nomogram significantly improved prognostic accuracy and clinical net benefit. Conclusions: These findings establish NTR dysregulation as a previously underappreciated dimension of PCa and support PCaSig as a clinically relevant tool for personalized management.