Integrative bioinformatics approach yields a novel gene expression risk model for prognosis and progression prediction in prostate cancer.

Prostate cancer (PCa), a prevalent malignancy among elderly males, exhibits a notable rate of advancement, even when subjected to conventional androgen deprivation therapy or chemotherapy. An effective progression prediction model would prove invaluable in identifying patients with a higher progression risk. Using bioinformatics strategies, we integrated diverse data sets of PCa to construct a novel risk model predicated on gene expression and progression-free survival (PFS). The accuracy of the model was assessed through validation using an independent data set. Eight genes were discerned as independent prognostic factors and included in the prediction model. Patients assigned to the high-risk cohort demonstrated a diminished PFS, and the areas under the curve of our model in the validation set for 1-year, 3-year, and 5-year PFS were 0.9325, 0.9041 and 0.9070, respectively. Additionally, through the application of single-cell RNA sequencing to two castration-related prostate cancer (CRPC) samples and two hormone-related prostate cancer (HSPC) samples, we discovered that luminal cells within CRPC exhibited an elevated risk score. Subsequent molecular biology experiments corroborated our findings, illustrating heightened SYK expression levels within tumour tissues and its contribution to cancer cell migration. We found that the knockdown of SYK could inhibit migration in PCa cells. Our progression-related risk model demonstrated the potential prognostic value of SYK and indicated its potential as a target for future diagnosis and treatment strategies in PCa management.

Loss of AR-regulated AFF3 contributes to prostate cancer progression and reduces ferroptosis sensitivity by downregulating ACSL4 based on single-cell sequencing analysis.

Prostate cancer (PCa) is one of the most common cancers affecting the health of men worldwide. Castration-resistant prostate cancer (CRPC), the advanced and refractory phase of prostate cancer, has multiple mechanisms of resistance to androgen deprivation therapy (ADT) such as AR mutations, aberrant androgen synthase, and abnormal expression of AR-related genes. Based on the research of the AR pathway, new drugs for the treatment of CRPC have been developed in clinical practice, such as Abiraterone and enzalutamide. However, many areas in this pathway are still worth exploring. In this study, single-cell sequencing analysis was utilized to scrutinize significant genes in the androgen receptor (AR) pathway related to CRPC. Our analysis of single-cell sequencing combined with bulk-cell sequencing revealed a substantial downregulation of AR-regulated AFF3 in CRPC. Overexpression of AFF3 restricted the proliferation and migration of prostate cancer cells whilst also increasing their sensitivity towards enzalutamide, while knockdown of AFF3 had the opposite effect. To elucidate the mechanism of tumor inhibition by AFF3, we applied GSVA and GSEA to investigate the metabolic pathways related to AFF3 and revealed that AFF3 had an impact on fatty acids metabolism and ferroptosis through the regulation of ACSL4 protein expression. Based on correlation analysis and flow cytometry, we can speculate that AFF3 can impact the sensitivity of the CRPC cell lines to the ferroptosis inducer (RSL3) by regulating ACSL4. Therefore, our findings may provide new insights into the mechanisms of drug resistance in CRPC, and AFF3 may serve as a novel prognostic biomarker in prostate cancer.

Circular RNA hsa_circ_0001610 promotes prostate cancer progression by sponging miR-1324 and upregulating PTK6.

Prostate cancer (PCa) incidence and cancer-related deaths are both high in the male population. Once castration-resistant prostate cancer (CRPC) has developed, PCa can be difficult to manage. Circular RNAs (circRNAs) play essential roles in the regulation of carcinogenesis and cancer progression. In CRPC, however, the potential molecular mechanisms and biological functions of circRNAs are yet to be defined. In this study, we conducted RNA sequencing on four hormone-sensitive prostate cancer (HSPC) tumor tissue samples and three CRPC samples. We recognized hsa_circ_0001610, a novel circRNA that was highly expressed in the cells and tissue of CRPC. We used quantitative real-time PCR (qRT-PCR) to evaluate hsa_circ_0001610 expression. We conducted in vivo and in vitro experiments and found that hsa_circ_0001610 overexpression caused PCa cells to proliferate and migrate and caused enzalutamide resistance. In contrast, the opposite results were found for hsa_circ_0001610 knockdown. We used Western blot, dual-luciferase reporter assays, RNA immunoprecipitation (RIP), qRT-PCR, and rescue experiments to reveal the underlying mechanisms of hsa_circ_0001610. Mechanistically, hsa_circ_0001610 acted as a molecular sponge for miR-1324 and thus reversed its inhibitory effect on its target gene PTK6. As a result, the PTK6 expression was enhanced, which accelerated PCa progression. The findings of this study confirmed that hsa_circ_0001610 drives the progression of PCa through the hsa_circ_0001610/miR-1324/PTK6 axis. Thus, hsa_circ_0001610 is potentially an effective therapeutic target and specific biomarker for advanced PCa.

Single-cell RNA sequencing reveals that HSD17B2 in cancer-associated fibroblasts promotes the development and progression of castration-resistant prostate cancer.

Castration-resistant prostate cancer (CRPC) responds poorly to existing therapy and appears as the lethal consequence of prostate cancer (PCa) progression. The tumour microenvironment (TME) has been thought to play a crucial role in CRPC progression. Here, we conducted single-cell RNA sequencing analysis on two CRPC and two hormone-sensitive prostate cancer (HSPC) samples to reveal potential leading roles in castration resistance. We described the single-cell transcriptional landscape of PCa. Higher cancer heterogeneity was explored in CRPC, with stronger cell cycling status and heavier copy number variant burden of luminal cells. Cancer-associated fibroblasts (CAFs), which are one of the most critical components of TME, demonstrated unique expression and cell-cell communication features in CRPC. A CAFs subtype with high expression of HSD17B2 in CRPC was identified with inflammatory features. HSD17B2 catalyses the conversion of testosterone and dihydrotestosterone to their less active forms, which was associated with steroid hormone metabolism in PCa tumour cells. However, the characteristics of HSD17B2 in PCa fibroblasts remained unknown. We found that HSD17B2 knockdown in CRPC-CAFs could inhibit migration, invasion, and castration resistance of PCa cells in vitro. Further study showed that HSD17B2 could regulate CAFs functions and promote PCa migration through the AR/ITGBL1 axis. Overall, our study revealed the important role of CAFs in the formation of CRPC. HSD17B2 in CAFs regulated AR activation and subsequent ITGBL1 secretion to promote the malignant behaviour of PCa cells. HSD17B2 in CAFs could serve as a promising therapeutic target for CRPC.

A novel prognostic model for prostate cancer based on androgen biosynthetic and catabolic pathways.

Prostate cancer (PCa) is one of the most common malignancies in males globally, and its pathogenesis is significantly related to androgen. As one of the important treatments for prostate cancer, androgen deprivation therapy (ADT) inhibits tumor proliferation by controlling androgen levels, either surgically or pharmacologically. However, patients treated with ADT inevitably develop biochemical recurrence and advance to castration-resistant prostate cancer which has been reported to be associated with androgen biosynthetic and catabolic pathways. Thus, gene expression profiles and clinical information of PCa patients were collected from TCGA, MSKCC, and GEO databases for consensus clustering based on androgen biosynthetic and catabolic pathways. Subsequently, a novel prognostic model containing 13 genes (AFF3, B4GALNT4, CD38, CHRNA2, CST2, ADGRF5, KLK14, LRRC31, MT1F, MT1G, SFTPA2, SLC7A4, TDRD1) was constructed by univariate cox regression, lasso regression, and multivariate cox regression. Patients were divided into two groups based on their risk scores: high risk (HS) and low risk (LS), and survival analysis was used to determine the difference in biochemical recurrence-free time between the two. The results were validated on the MSKCC dataset and the GEO dataset. Functional enrichment analysis revealed some pivotal pathways that may have an impact on the prognosis of patients including the CDK-RB-E2F axis, G2M checkpoint, and KRAS signaling. In addition, somatic mutation, immune infiltration, and drug sensitivity analyses were performed to further explore the characteristics of HS and LS groups. Besides, two potential therapeutic targets, BIRC5 and RHOC, were identified by us in prostate cancer. These results indicate that the prognostic model may serve as a predictive tool to guide clinical treatment and provide new insight into the basic research in prostate cancer.

An immune-related gene signature for the prognosis of human bladder cancer based on WGCNA.

The innovation of immunotherapy was a milestone in the treatment of bladder cancer (BLCA). However, the treatment benefits varied by individual thus promoting the investigation of the biomarker of the patients. Unfortunately, there were not many effective predictive models, which were desired by clinicians, for BLCA that can predict the prognosis and benefit of immunotherapy. We constructed a three genes prognosis prediction model termed RiskScore based on the result of weighted correlation network analysis (WGCNA) from The Cancer Genome Atlas (TCGA) cohort (n = 406). We then validated the prediction accuracy with three validation cohort(GSE13507 (n = 165), GSE48075(n = 73), GSE32894(n = 224)). We compared the differences in gene expression, immune relate function, and immune infiltration between two groups divided by RiskScore. We further discovered the potential drug target and suitable compounds for high-risk groups. Our results suggested that the low-risk group may be more potential for immunotherapy for they have higher B cell infiltration, higher expression of immune checkpoints(PDCD1, CTLA4), and much more active immune-related pathways(B cell and T cell receptor signaling pathway). The RiskScore showed a well predictive accuracy for the prognosis of BLCA. After Spearman analysis, we found the suitable drug target and compounds for the patients in the high-risk group. The model we constructed is able to predict the prognosis of BLCA patients with ease and accuracy. PLK1 and gefitinib may be utilized for further treatment of BLCA patients.