Ye-Hui Chen

Recent studies suggest potential associations between gut microbiota (GM) and benign prostatic hyperplasia (BPH); however, the causal relationship remains uncertain. This study aims to explore the potential causal links between GM and BPH through Mendelian randomization (MR) analysis. A two‐sample MR analysis was conducted using data from genome‐wide association studies in accordance with the MR‐STROBE statement. The inverse variance–weighted (IVW) method was utilized as the primary estimator. Robustness was evaluated by heterogeneity testing, horizontal pleiotropy assessment, and leave‐one‐out analyses. Fecal samples from BPH patients and healthy controls underwent 16S rRNA sequencing to evaluate their association with lower urinary tract symptom (LUTS) severity. IVW analyses associated Phascolarctobacterium (OR = 1.286; 95% CI, 1.023–1.618; p = 0.031), Faecalibacterium (OR = 1.134; 95% CI, 1.008–1.275; p = 0.037), and Escherichia – Shigella (OR = 1.348; 95% CI, 1.121–1.621; p = 0.002) with higher BPH risk, while Lactobacillus (OR = 0.572; 95% CI, 0.412–0.793; p < 0.001) and Burkholderia (OR = 0.718; 95% CI, 0.565–0.912; p = 0.007) were protective. WM and MR‐Egger confirmed only Escherichia – Shigella as consistently associated. Reverse MR found no causal effect of BPH on these taxa, with no heterogeneity or pleiotropy detected. 16S rRNA sequencing showed greater Escherichia – Shigella abundance in BPH patients than controls. Multivariate analysis identified Escherichia – Shigella relative abundance as an independent predictor of severe LUTS (OR = 1.10; 95% CI, 1.01–1.21; p = 0.046). ROC analysis demonstrated its predictive value for LUTS severity (AUC = 0.777; 95% CI, 0.649–0.876; p < 0.001). Our study supports a potential causal role of GM in BPH, with Escherichia – Shigella emerging as a key predictor of LUTS severity. These findings may provide insights for future therapeutic interventions targeting microbial dynamics in BPH treatment.

M2-tumor-associated macrophages (TAMs) work as a promoter in the processes of bone metastases, chemotherapy resistance, and castration resistance in prostate cancer (PCa), but how M2-TAMs affect PCa has not been fully understood. In this study, we analyzed the proportion of tumor-infiltrating immune cells using the CIBERSORT algorithm, based on samples from the Cancer Genome Atlas database. Then we performed weighted gene co-expression network analysis to examine the modules concerning infiltrated M2-TAMs. Gene Ontology analysis and pathway enrichment analysis were performed for functional annotation and a protein–protein interaction network was constructed. The International Cancer Genomics Consortium cohort was used as a validation cohort. The red module showed the most correlation with M2-TAMs in PCa. Biological processes and pathways were mainly associated with the immune-related processes, as revealed by functional annotation. Four hub genes were screened: ACSL1, DLGAP5, KIF23 and NCAPG. Further validation showed that the four hub genes had a higher expression level in tumor tissues than that in normal tissues, and they were good prognosis biomarkers for PCa. In conclusion, these findings contribute to understanding the underlying molecular mechanisms of how M2-TAMs affect PCa, and looking for the potential biomarkers and therapeutic targets for PCa patients.

Expression of kinesin family member 18B (KIF18B), an ATPase with key roles in cell division, is deregulated in many cancers, but its involvement in prostate cancer (PCa) is unclear. Here, we investigated the expression and function of KIF18B in human PCa specimens and cell lines using bioinformatics analyses, immunohistochemical and immunofluorescence microscopy, and RT-qPCR and western blot analyses. KIF18B was overexpressed in PCa specimens compared with paracancerous tissues and was associated with poorer disease-free survival. In vitro, KIF18B knockdown in PCa cell lines promoted cell proliferation, migration, and invasion, and inhibited cell apoptosis, while KIF18B overexpression had the opposite effects. In a mouse xenograft model, KIF18B overexpression accelerated and promoted the growth of PCa tumors. Bioinformatics analysis of control and KIF18B-overexpressing PCa cells showed that genes involved in the PI3K–AKT–mTOR signaling pathway were significantly enriched among the differentially expressed genes. Consistent with this observation, we found that KIF18B overexpression activates the PI3K–AKT–mTOR signaling pathway in PCa cells both in vitro and in vivo. Collectively, our results suggest that KIF18B plays a crucial role in PCa via activation of the PI3K–AKT–mTOR signaling pathway, and raise the possibility that KIF18B could have utility as a novel biomarker for PCa.

Objective: To explore the risk factors for postoperatively pathological lymph node metastasis in patients with clinical T2N0M0 stage prostate cancer (PCa). Methods: We retrospectively analyzed clinicopathological data of 316 patients with clinical T2 stage PCa and preoperative negative lymph nodes [LN(−)] indicated by imaging (cT2N0M0) between January 2014 and May 2019. Multivariate logistic regression analysis was performed to determine risk factors for postoperatively pathological pLN(+) in patients with cT2N0M0 stage PCa. Spearman correlation analysis was used to explore the relationship between tumor burden and Prostate Imaging Reporting and Data System version 2 (PI-RADS v2) score. Results: A total of 45 patients (14.2%) were confirmed by postoperative pathology to have LN metastasis. Univariate analysis indicated that total prostate-specific antigen (tPSA), PI-RADS v2 score, postoperative Gleason grade group (GGG), intraductal carcinoma of the prostate (IDC-P), clinical T2 substaging, and postoperative pathological tumor burden were risk factors for pLN(+) in all patients. Multivariate analysis showed that tPSA and postoperative GGG were risk factors for pLN(+) in all patients. Univariate analysis revealed that tPSA, PIRADS v2 score, clinical T2 substaging, IDC-P, postoperative pathological tumor burden, and postoperative GGG were risk factors for pLN(+) in patients with GGG ≥ 3. Multivariate analysis suggested that tPSA, PI-RADS v2 score, clinical T2 substaging, postoperative pathological tumor burden, and GGG were risk factors for pLN (+) in patients with GGG ≥ 3. Spearman correlation analysis showed that PI-RADS v2 score was positively correlated with clinical T2 substaging and postoperative pathological tumor burden. Conclusion: There was a high risk of LN metastasis in patients with cT2 PCa if they had high preoperative tPSA or high postoperative GGG. Patients with cT2 PCa and GGG ≥ 3 had a high risk of LN metastasis if they had high PI-RADS v2 score, high preoperative clinical stage or high postoperative pathological tumor burden. PI-RADS v2 score predicted tumor burden well in patients with GGG ≥ 3.

Lymph node metastasis is one of the most important independent risk factors that can negatively affect the prognosis of prostate cancer (PCa); however, the exact mechanisms have not been well studied. This study aims to better understand the underlying mechanism of lymph node metastasis in PCa by bioinformatics analysis. We analysed a total of 367 PCa cases from the cancer genome atlas database and performed weighted gene co‐expression network analysis to explore some modules related to lymph node metastasis. Gene Ontology analysis and pathway enrichment analysis were conducted for functional annotation, and a protein‐protein interaction network was built. Samples from the International Cancer Genomics Consortium database were used as a validation set. The turquoise module showed the most relevance with lymph node metastasis. Functional annotation showed that biological processes and pathways were mainly related to activation of the processes of cell cycle and mitosis. Four hub genes were selected: CKAP2L, CDCA8, ERCC6L and ARPC1A. Further validation showed that the four hub genes well‐distinguished tumour and normal tissues, and they were good biomarkers for lymph node metastasis of PCa. In conclusion, the identified hub genes facilitate our knowledge of the underlying molecular mechanism for lymph node metastasis of PCa.

Background. Patients with brain metastases (BM) from renal cell carcinoma (RCC) were considered to experience a poor prognosis. However, there is little knowledge on the risk factors for BM from RCC at diagnosis. This study was aimed at exploring the risk factors for patients with BM from RCC and the interaction among these risk factors. Methods. A total of 38759 cases of RCC were identified from the Surveillance, Epidemiology, and End Results (SEER) database. Risk factors for BM from RCC were evaluated by univariate and multivariate logistic regression analyses. Interaction effect between age and tumor size was tested. Results. There was a significant difference in univariate analysis, including T stage, tumor size, grades III and IV, lymph node metastasis, bone metastasis, liver metastasis, lung metastasis, and surgery. There was a significant difference in multivariate analysis, including age, T stage, tumor size < 10 cm, grade IV, lymph node metastasis, bone metastasis, lung metastasis, and surgery. Patients older than 70 had 0.653‐fold lower risk of developing BM compared with those younger than 70. Patients with tumor size ≥ 4 cm and P = 0.0114) and model II analysis (P = 0.0248) but not in model I analysis (P = 0.1136). Although age significantly affected the risk of BM from RCC, impact of age on the risk of developing BM from RCC was limited to patients with tumor size ≥ 7 cm. Conclusion. Both tumor size and age were independent risk factors for brain metastases in patients with RCC. The impact of age on the risk of developing BM from RCC was limited to patients with tumor size ≥ 7 cm. Patients with a larger tumor size and younger age might have the higher risk of developing BM at diagnosis of RCC.