Exosomal PGAM1 promotes prostate cancer angiogenesis and metastasis by interacting with ACTG1.

Tumor-derived exosomes and their contents promote cancer metastasis. Phosphoglycerate mutase 1 (PGAM1) is involved in various cancer-related processes. Nevertheless, the underlying mechanism of exosomal PGAM1 in prostate cancer (PCa) metastasis remains unclear. In this study, we performed in vitro and in vivo to determine the functions of exosomal PGAM1 in the angiogenesis of patients with metastatic PCa. We performed Glutathione-S-transferase pulldown, co-immunoprecipitation, western blotting and gelatin degradation assays to determine the pathway mediating the effect of exosomal PGAM1 in PCa. Our results revealed a significant increase in exosomal PGAM1 levels in the plasma of patients with metastatic PCa compared to patients with non-metastatic PCa. Furthermore, PGAM1 was a key factor initiating PCa cell metastasis by promoting invadopodia formation and could be conveyed by exosomes from PCa cells to human umbilical vein endothelial cells (HUVECs). In addition, exosomal PGAM1 could bind to γ-actin (ACTG1), which promotes podosome formation and neovascular sprouting in HUVECs. In vivo results revealed exosomal PGAM1 enhanced lung metastasis in nude mice injected with PCa cells via the tail vein. In summary, exosomal PGAM1 promotes angiogenesis and could be used as a liquid biopsy marker for PCa metastasis.

Gut dysbiosis promotes prostate cancer progression and docetaxel resistance via activating NF-κB-IL6-STAT3 axis.

BACKGROUND: The gut microbiota is reportedly involved in the progression and chemoresistance of various human malignancies. However, the underlying mechanisms behind how it exerts some effect on prostate cancer, as an extra-intestinal tumor, in a contact-independent way remain elusive and deserve exploration. Antibiotic exposure, one of the various factors affecting the gut microbiota community and capable of causing gut dysbiosis, is associated with multiple disorders. This study aims to preliminarily clarify the link between gut dysbiosis and prostate cancer. RESULTS: First, we discovered that perturbing the gut microbiota by consuming broad-spectrum antibiotics in water promoted the growth of subcutaneous and orthotopic tumors in mice. Fecal microbiota transplantation could transmit the effect of antibiotic exposure on tumor growth. Then, 16S rRNA sequencing for mouse feces indicated that the relative abundance of Proteobacteria was significantly higher after antibiotic exposure. Meanwhile, intratumoral lipopolysaccharide (LPS) profoundly increased under the elevation of gut permeability. Both in vivo and in vitro experiments revealed that the NF-κB-IL6-STAT3 axis activated by intratumoral LPS facilitated prostate cancer proliferation and docetaxel chemoresistance. Finally, 16S rRNA sequencing of patients' fecal samples revealed that Proteobacteria was enriched in patients with metastatic prostate cancer and was positively correlated with plasma IL6 level, regional lymph node metastasis status, and distant metastasis status. The receiver operating characteristic (ROC) curves showed that the relative abundance of Proteobacteria had better performance than the prostate-specific antigen (PSA) level in predicting the probability of distant metastasis in prostate cancer (area under the ROC curve, 0.860; p < 0.001). CONCLUSION: Collectively, this research demonstrated that gut dysbiosis, characterized by the enrichment of Proteobacteria due to antibiotic exposure, resulted in the elevation of gut permeability and intratumoral LPS, promoting the development of prostate cancer via the NF-κB-IL6-STAT3 axis in mice. Considering findings from human patients, Proteobacteria might act as an intestinal biomarker for progressive prostate cancer. Video Abstract.

A five-pseudouridylation-associated-LncRNA classifier for primary prostate cancer prognosis prediction.

Background: Prostate cancer (PCa) is one of the most common cancers in males around the globe, and about one-third of patients with localized PCa will experience biochemical recurrence (BCR) after radical prostatectomy or radiation therapy. Reportedly, a proportion of patients with BCR had a poor prognosis. Cumulative studies have shown that RNA modifications participate in the cancer-related transcriptome, but the role of pseudouridylation occurring in lncRNAs in PCa remains opaque. Methods: Spearman correlation analysis and univariate Cox regression were utilized to determine pseudouridylation-related lncRNAs with prognostic value in PCa. Prognostic pseudouridylation-related lncRNAs were included in the LASSO (least absolute shrinkage and selection operator) regression algorithm to develop a predictive model. KM (Kaplan-Meier) survival analysis and ROC (receiver operating characteristic) curves were applied to validate the constructed model. A battery of biological cell assays was conducted to confirm the cancer-promoting effects of RP11-468E2.5 in the model. Results: A classifier containing five pseudouridine-related lncRNAs was developed to stratify PCa patients on BCR and named the "ψ-lnc score." KM survival analysis showed patients in the high ψ-lnc score group experienced BCR more than those in the low ψ-lnc score group. ROC curves demonstrated that ψ-lnc score outperformed other clinical indicators in BCR prediction. An external dataset, GSE54460, was utilized to validate the predictive model's efficacy and authenticity. A ceRNA (competitive endogenous RNA) network was constructed to explore the model's potential molecular functions and was annotated through GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analyses. RP11-468E2.5 was picked for further investigation, including pan-cancer analysis and experimental validation. Preliminarily, RP11-468E2.5 was confirmed as a tumor promoter. Conclusion: We provide some evidence that pseudouridylation in lncRNA played a role in the development of PCa and propose a novel prognostic classifier for clinical practice.

5-Methylcytosine RNA Methyltransferases-Related Long Non-coding RNA to Develop and Validate Biochemical Recurrence Signature in Prostate Cancer.

The effects of 5-methylcytosine in RNA (m5C) in various human cancers have been increasingly studied recently; however, the m5C regulator signature in prostate cancer (PCa) has not been well established yet. In this study, we identified and characterized a series of m5C-related long non-coding RNAs (lncRNAs) in PCa. Univariate Cox regression analysis and least absolute shrinkage and selector operation (LASSO) regression analysis were implemented to construct a m5C-related lncRNA prognostic signature. Consequently, a prognostic m5C-lnc model was established, including 17 lncRNAs: MAFG-AS1, AC012510.1, AC012065.3, AL117332.1, AC132192.2, AP001160.2, AC129510.1, AC084018.2, UBXN10-AS1, AC138956.2, ZNF32-AS2, AC017100.1, AC004943.2, SP2-AS1, Z93930.2, AP001486.2, and LINC01135. The high m5C-lnc score calculated by the model significantly relates to poor biochemical recurrence (BCR)-free survival (p < 0.0001). Receiver operating characteristic (ROC) curves and a decision curve analysis (DCA) further validated the accuracy of the prognostic model. Subsequently, a predictive nomogram combining the prognostic model with clinical features was created, and it exhibited promising predictive efficacy for BCR risk stratification. Next, the competing endogenous RNA (ceRNA) network and lncRNA-protein interaction network were established to explore the potential functions of these 17 lncRNAs mechanically. In addition, functional enrichment analysis revealed that these lncRNAs are involved in many cellular metabolic pathways. Lastly, M AFG-AS1 was selected for experimental validation; it was upregulated in PCa and probably promoted PCa proliferation and invasion in vitro. These results offer some insights into the m5C's effects on PCa and reveal a predictive model with the potential clinical value to improve the prognosis of patients with PCa.