Nerves are important pathological elements of the microenvironment of tumors, including those in pancreatic, colon and rectal, prostate, head and neck, and breast cancers. Recent studies have associated perineural invasion with tumor progression and poor outcomes. In turn, tumors drive the reprogramming of neurons to recruit new nerve fibers. Therefore, the crosstalk between nerves and tumors is the hot topic and trend in current cancer investigations. Herein, we reviewed recent studies presenting direct supporting evidences for a better understanding of nerve–tumor interactions.

The relationship between metabolism and cancer is a major focus of current research, with an increasing number of studies highlighting the significant role of various metabolites in tumor cells, such as lactate, acetic acid, lysine, serine, tryptophan, palmitic acid, succinate, etc. These metabolites are involved in numerous biological processes within tumor cells, including transcription, translation, post-translational modification (PTM) of proteins, cell cycle regulation, and metabolism, thereby modulating tumor proliferation, migration, and drug resistance. Metabolite-mediated PTMs of proteins undoubtedly play a vital role in tumor cells, affecting both histones and non-histone proteins, covering modifications such as lactylation, crotonylation, acetylation, palmitoylation, and succinylation. Therefore, this review aims to elaborate on the abnormal levels of some major metabolites, related metabolic pathways, and the latest protein acyl PTMs they mediate in tumor cells, providing new insights for diagnosis and therapy in the field of oncology.

PD-1 restrains effective killing of cancer cells by the immune system and is predominantly located on the surface of T cells or other immune cells. However, cancer cells also express PD-1 to varying degrees, which is commonly associated with a poor prognosis. Here, we investigated the regulation and function of PD-1 expression in prostate cancer (PCa) and revealed the impact on the tumor microenvironment. PD-1 expression in cancer cells positively correlated with Gleason grade and metastasis but negatively correlated with CD8+ T cell infiltration in PCa patients. PCa cells secreted PD-1 in exosomes that enhanced the activity of myeloid-derived suppressor cells (MDSCs) by activating JAK/STAT3 signaling. The activated MDSCs in turn reduced the infiltration of CD8+ T cells within the tumor, promoting tumor immune evasion. The ubiquitin-specific protease USP7 induced deubiquitination and elevated the abundance of PD-1 in PCa, and USP7 inhibition sensitized PCa tumors to anti-PD-1 antibody treatment. Given the modest efficacy of current immunotherapeutic approaches for PCa, strategies to inhibit the secretion of PD-1-bearing exosomes or USP7 function may emerge as promising immunostimulatory interventions for treating PCa.

Background: Only a few bladder cancer patients benefit from anti‐programmed cell death protein 1/programmed cell death ligand 1 immunotherapy. The cluster of differentiation 47 (CD47) plays an important role in tumor immune evasion. CD47 is a highly glycosylated protein, however, the mechanisms governing CD47 glycosylation and its potential role in immunosuppression are unclear. Therefore, this study aimed to evaluate the function of CD47 glycosylation in bladder cancer. Methods: Western blotting, immunohistochemistry, and flow cytometry were used to measure protein expression, protein‐protein interactions, and phagocytosis in bladder cancer. A murine model was employed to investigate the impact of mannosidase alpha class 1B member 1 (MAN1B1) modification of CD47 on anti‐phagocytosis in vivo. An ex vivo model, patient‐derived tumor‐like cell clusters, was used to examine the effect of targeting MAN1B1 on phagocytosis. Results: Our research identified that aberrant CD47 glycosylation was responsible for its immunosuppression. The glycosyltransferase MAN1B1 responsible for CD47 glycosylation was highly expressed in bladder cancer. Abnormal activation of extracellular signal‐regulated kinase (ERK) was significantly associated with MAN1B1 stability by regulating the interaction between MAN1B1 and the E3 ubiquitin ligase HMG‐CoA reductase degradation 1 (HRD1). Mechanistically, abnormally activated ERK stabilized MAN1B1, resulting in the glycosylation of CD47 and facilitating immune evasion by enhancing its interaction with signal‐regulatory protein alpha (SIRP‐α). In vitro and in vivo experiments demonstrated that MAN1B1 knockout weakened CD47‐mediated anti‐phagocytosis. MAN1B1 inhibitors promoted phagocytosis without causing anemia, offering a safe alternative to anti‐CD47 therapy. Conclusions: This comprehensive analysis uncovered that ERK activation stabilizes MAN1B1 by regulating the interaction between MAN1B1 and HRD1, facilitates immune evasion via CD47 glycosylation, and presents new potential targets and strategies for cancer immunotherapy that do not cause anemia.

Renal cell carcinoma (RCC) is a common malignancy, with patients frequently diagnosed at an advanced stage due to the absence of sufficiently sensitive detection technologies, significantly compromising patient survival and quality of life. Advances in cell-free DNA (cfDNA) methylation profiling using liquid biopsies offer a promising non-invasive diagnostic option, but robust biomarkers for early detection are current not available. This study aimed to identify methylation biomarkers for RCC and establish a DNA methylation signature-based prognostic model for this disease. High-throughput methylation sequencing was performed on peripheral blood samples obtained from 49 primarily Stage I RCC patients and 44 healthy controls. Comparative analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression methods were employed to identify RCC methylation signatures.Subsequently, methylation markers-based diagnostic and prognostic models for RCC were independently trained and validated using random forest and Cox regression methodologies, respectively. Comparative analysis revealed 864 differentially methylated CpG islands (DMCGIs), 96.3% of which were hypermethylated. Using a training set from The Cancer Genome Atlas (TCGA) dataset of 443 early-stage RCC tumors and matched normal tissues, we applied LASSO regression and identified 23 methylation signatures. We then constructed a random forest-based diagnostic model for early-stage RCC and validated the model using two independent datasets: a TCGA set of 460 RCC tumors and controls, and a blood sample set from our study of 15 RCC cases and 29 healthy controls. For Stage I RCC tissue, the model showed excellent discrimination (AUC-ROC: 0.999, sensitivity: 98.5%, specificity: 100%). Blood sample validation also yielded commendable results (AUC-ROC: 0.852, sensitivity: 73.9%, specificity: 89.7%). Further analysis using Cox regression identified 7 of the 23 DMCGIs as prognostic markers for RCC, allowing the development of a prognostic model with strong predictive power for 1-, 3-, and 5-year survival (AUC-ROC > 0.7). Our findings highlight the critical role of hypermethylation in RCC etiology and progression, and present these identified biomarkers as promising candidates for diagnostic and prognostic applications.

Introduction: The objective of this study is to investigate the predictive ability of abdominal fat features derived from computed tomography (CT) to predict early recurrence within a year following the initial transurethral resection of bladder tumor (TURBT) in patients with non-muscle-invasive bladder cancer (NMIBC). A predictive model is constructed in combination with clinical factors to aid in the evaluation of the risk of early recurrence among patients with NMIBC after initial TURBT. Methods: This retrospective study enrolled 325 NMIBC patients from three centers. Machine learning–based visceral adipose tissue (VAT) radiomics models (VAT-RM) and subcutaneous adipose tissue (SAT) radiomics models (SAT-RM) were constructed to identify patients with early recurrence. A combined model integrating VAT-RM and clinical factors was established. The predictive performance of each variable and model was analyzed using the area under the receiver operating characteristic curve (AUC). The net benefit of each variable and model was presented through decision curve analysis (DCA). The calibration was evaluated utilizing the Hosmer–Lemeshow test. Findings: The VAT-RM demonstrated satisfactory performance in the training cohort (AUC = 0.853; 95% CI: 0.768–0.937), test cohort 1 (AUC = 0.823; 95% CI: 0.730–0.916), and test cohort 2 (AUC = 0.808; 95% CI: 0.681–0.935). Across all cohorts, the AUC values of the VAT-RM were higher than those of the SAT-RM ( P < 0.001). The DCA curves further confirmed that the clinical net profit of the VAT-RM was superior to that of the SAT-RM. In multivariate logistic regression analysis, the VAT-RM emerged as the most significant independent predictor [odds ratio (OR) = 0.295; 95% CI: 0.141–0.508; P < 0.001]. The fusion model exhibited excellent AUC values of 0.938, 0.909, and 0.905 across three cohorts. The fusion model surpassed the traditional risk assessment frameworks in both predictive efficacy and clinical net benefit. Interpretation: VAT serves as a crucial factor in early postoperative recurrence in NMIBC patients. The VAT-RM can accurately identify high-risk patients with early postoperative recurrence, offering significant advantages over SAT-RM. The new predictive model constructed by integrating the VAT-RM and clinical factors exhibits excellent predictive performance, clinical net benefits, and calibration accuracy.