Prostate cancer (PCa) is a common malignancy in men, and bone metastasis is a leading cause of mortality in advanced-stage PCa. This study aims to identify critical genes involved in PCa bone metastasis, exploring biomarkers for prognosis and precision treatment. Forkhead Box Q1 (FOXQ1) was identified as a potential key gene through screening of public databases, and was found to be markedly upregulated in bone metastatic PCa compared to primary PCa. FOXQ1 promotes PCa cell proliferation and metastasis while inhibiting apoptosis. Additionally, FOXQ1 recruits macrophages, promotes M2 polarization, and enhances osteoclast differentiation in the tumor microenvironment. Mechanistically, FOXQ1 activates the transcription of Glycosyltransferase 8 Domain Containing 2 (GLT8D2) by directly binding to its promoter, and GLT8D2 upregulates the expression of C-C Motif Chemokine Ligand 2 (CCL2) by enhancing its N-glycosylation, thereby promoting PCa bone metastasis. Collectively, these findings establish FOXQ1 as a key regulator of PCa bone metastasis through the GLT8D2/CCL2 axis, and suggest that targeting this pathway may hold therapeutic promise for bone metastatic PCa.

The proteomics plays a crucial role in identifying therapeutic targets. In this study, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) and Mendelian Randomization (MR) to identify potential protein biomarkers and therapeutic targets for prostate cancer (PCa). To select PCa-related genes, we constructed a WGCNA using genetic data from The Cancer Genome Atlas (502 cases and 52 controls). We sourced expression quantitative trait locus (eQTL) data from the eQTLGen consortium database and obtained outcome data from the MR Base database (3269 cases and 459,664 controls). Subsequently, MR and colocalization analyses were performed to validate the causal relationships of the candidate proteins. Additionally, we conducted immune infiltration analysis, gene set enrichment analysis, gene set variation analysis, drug sensitivity analysis, Nomogram model construction, transcriptional regulatory analysis, correlation analysis with PCa-regulated proteins, single-cell sequencing, and spatial transcriptomics analysis to detect specific cell types with enriched expression and consider potential therapeutic targets. The results of our study revealed that five genetically predicted proteins were associated with PCa risk. Decreased levels of one protein (thrombomodulin [THBD]) and increased levels of four proteins (dystonin [DST], interferon-alpha-inducible protein 27 like 2 [IFI27L2], oxysterol-binding protein-related protein 10 [OSBPL10], and protein phosphatase 1 regulatory inhibitor subunit 14A [PPP1R14A]) were found to be linked to increased PCa risk. These protein-encoding genes are distributed across different types of cells in PCa tissue, indicating their potential as therapeutic targets for PCa. Our study identified several protein biomarkers associated with PCa risk, providing new insights into the etiology of PCa and offering promising targets for the development of PCa screening biomarkers and therapeutic drugs.

Background:Laparoscopic adrenalectomy is the primary treatment for most adrenal tumors. However, gasless single-port retroperitoneal laparoscopic adrenalectomy (GL-SPRLA) is rarely documented, and its safety and effectiveness need further investigation. To address this, we introduced an improved GL-SPRLA technique using a novel peritoneal spreader (PerS) that eliminates the need for CO2 insufflation while maintaining the benefits of minimally invasive surgery. Methods:This single-center retrospective study compared GL-SPRLA (n=58) with conventional single-port retroperitoneal laparoscopic adrenalectomy (SPRLA) (n=106) for adrenal tumors (<4 cm) at Sun Yat-sen University Cancer Center [2021-2023]. All procedures were performed by the same surgeon via retroperitoneal approach. We systematically compared demographic characteristics, intraoperative parameters, postoperative outcomes, and follow-up results. Statistical analysis was performed with Student's t-test, χ2 test and multivariable regression adjusting for potential confounders. Results:All 58 GL-SPRLA procedures were successfully completed using a novel gasless device that replaces carbon dioxide. Compared to SPRLA, GL-SPRLA showed comparable operative time, complication rates, and postoperative hospital stay (P>0.05), but showed smaller perioperative changes in pCO2 and pH in a physiologic subcohort, along with a 12.7% cost reduction (P<0.001), and zero conversions to multi-port versus 14.2% in SPRLA (P=0.001). There were no Clavien-Dindo grade 3-4 complications in either group, and neither tumor recurrence nor metastasis occurred at 12 months. Conclusions:GL-SPRLA may be a feasible minimally invasive option for selected patients with small adrenal tumors, with potential advantages in avoiding CO2 insufflation and reducing hospitalization costs, and requires confirmation in prospective multicenter studies.

BackgroundProstate cancer (PC) is a leading cause of male cancer mortality, with bone metastasis (BM) being a frequent and debilitating complication. Despite therapeutic advancements, the molecular mechanisms underlying BM remain poorly understood. This study aims to bridge this gap by integrating bibliometric analysis with bioinformatics to provide a comprehensive overview of the academic trends and molecular profiles associated with prostate cancer bone metastasis (PCBM).MethodsWe conducted a bibliometric analysis to identify key contributors in PCBM research from 2004 to 2024 with advanced tools like BioBERT to mine gene and disease entities from the abstracts of relevant articles. Gene expression data from GSE32269 was analyzed to identify differentially expressed genes (DEGs), followed by enrichment analyses for biological functions and pathways.ResultsThe bibliometric review showed an increasing trend in research output, focusing on therapeutic strategies and biomarkers. Bioinformatics analysis revealed various DEGs, significantly enriched in immune response and cell adhesion pathways. Semantic relationship analysis highlighted potential shared pathways between genes and diseases, offering clues for novel immunotherapy targets.ConclusionBy integrating bibliometric analysis with bioinformatics, this study provides new insights into PCBM. Specifically, our findings emphasize the impact of reprogramming on immune cells and its role in reshaping the tumor microenvironment to support cancer cells’ evasion of immune surveillance and promotion of metastasis. These results suggest that targeting immune checkpoints and innovative combination therapies may be critical directions for improving outcomes in prostate cancer patients.

Background The incidence of adrenal incidentalomas (AIs) is increasing yearly. The early discovery of AIs is helpful to better manage adrenal diseases, especially subclinical primary aldosteronism, Cushing’s syndrome and pheochromocytoma.Methods In this multicenter retrospective study, a total of 778 patients from three different medical centers were assessed. The two-stage cascade network consisted of a 3D Res-Unet network for adrenal gland segmentation and a classifier for determining the presence of AIs. The segmentation network was mainly evaluated by the Dice similarity coefficient (DSC), and the classifier was evaluated by the area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity. The Delong test was used to compare the classification performance between the cascade network and manual segmentation.Results A total of 443 patients were randomly assigned in a 7:3 ratio, stratified sampling, to train and valid sets of the model development cohort, and 335 patients from the three centers were included in the test cohort. In the validation set, the AUC of the model for identifying left AI was 88.15%, and the AUC of the model for identifying right AI was 87.90%. There was no significant difference between model performance and manual segmentation of AIs (p > 0.05). In the test cohort, the cascade network achieved AUC of more than 80% and accuracy of more than 75% for both left and right adrenal glands.Conclusions The two-stage cascade network based on a deep learning algorithm can be used for automatic recognition of AIs in nonenhanced CT from different centers.

Prostate cancer (PCa) is a highly common type of malignancy and affects millions of men in the world since it is easy to recur or emerge therapy resistance. Therefore, it is urgent to find novel treatments for PCa patients. In the current study, we found that tegaserod maleate (TM), an FDA-approved agent, inhibited proliferation, colony formation, migration as well as invasion, caused the arrest of the cell cycle, and promoted apoptosis of PCa cells in vitro. In addition, TM suppressed the tumor growth in the cell-derived xenograft (CDX) mouse model in vivo. Mechanistically, TM exerted anti-tumor effects via downregulating GLI2, and its downtream targets, thus inhibiting the sonic hedgehog (SHH) signaling pathway. In brief, our findings demonstrated that TM effectively inhibited the activities of PCa cells by suppressing the SHH signaling pathway and provided a potential new agent for the treatment of PCa.

OBJECTIVES:To identify immunosuppressive neutrophil subsets in patients with prostate cancer (PCa) and construct a risk prediction model for prognosis and immunotherapy response of the patients based on these neutrophil subsets. METHODS:Single-cell and transcriptome data from PCa patients were collected from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Neutrophil subsets in PCa were identified through unsupervised clustering, and their biological functions and effects on immune regulation were analyzed by functional enrichment, cell interaction, and pseudo-time series analyses. Lasso-Cox regression was utilized to construct a prognostic risk model based on the immunosuppressive neutrophil subsets, and survival analysis and ROC curve analysis were used to compare the prognosis of PCa patients with high and low risks stratified using this model. The relationship of the prognostic risk model with PCa immune infiltration and immune response was evaluated using CIBERSORT and TIDE scores. RESULTS:PCa tissues showed a significantly greater proportion of infiltrating neutrophils than the adjacent normal tissues (P<0.05). PCa-associated neutrophils could be clustered into two independent cell subsets: Neu_1 and Neu_2. Neu_2 cells exhibited highly enriched immunoregulatory functions and were highly differentiated and mature, with upregulated immunosuppressive cytokines such as TGFB1, ITGB2, and LGALS3. Based on the genetic characteristics of Neu_2 cell subsets, the prognostic risk model was constructed. The patients in the high-risk group identified by the model had a shorter biochemical recurrence time (P<0.05) and a higher proportion of Tregs and M2-TAMs cell infiltration (P<0.05) with a higher risk of immune rejection and poorer immune response scores. CONCLUSIONS:PCa-associated neutrophils are highly heterogeneous. The prognostic risk model constructed based on the immunosuppressive neutrophil Neu_2 subset can effectively predict both the survival outcomes and immune response of PCa patients.

IntroductionTo investigate the molecular mechanisms underlying enzalutamide resistance in castration-resistant prostate cancer (CRPC) and explore potential therapeutic strategies to overcome resistance.MethodsWe conducted comprehensive bioinformatic analysis using LNCaP/enzalutamide-resistant cells to identify key pathways associated with resistance. Functional validation was performed through targeted inhibition of the elongation of very-long chain fatty acid protein 2 (ELOVL2), followed by assays to assess cancer cell proliferation and enzalutamide sensitivity. Mechanistic studies were conducted to evaluate the impact of ELOVL2 on the ubiquitin-proteasome system and AR signaling pathways.ResultsBioinformatic analysis revealed that activation of fatty acid metabolism, particularly through upregulation of ELOVL2, plays a critical role in driving enzalutamide resistance in PCa. Functional studies demonstrated that targeted inhibition of ELOVL2 significantly suppressed cancer cell proliferation and restored enzalutamide sensitivity in resistant cells. Mechanistically, ELOVL2 facilitates enzalutamide resistance by impairing the ubiquitin-proteasome system, leading to the subsequent activation of AR signaling pathways.DiscussionOur findings demonstrate that ELOVL2 drives enzalutamide resistance in CRPC by stabilizing AR through inhibition of ubiquitin-proteasome-mediated degradation. Targeting ELOVL2 represents a promising therapeutic strategy to overcome resistance in CRPC, with potential to improve clinical outcomes for patients.

Expansion microscopy enhances the microscopy resolution by physically expanding biological specimens and improves the visualization of structural and molecular details. Numerous expansion microscopy techniques and labeling methods have been developed over the past decade to cater to specific research needs. Nonetheless, a shared limitation among current protocols is the extensive sample processing time, particularly for challenging-to-expand biological specimens (e.g., formalin-fixed paraffin-embedded (FFPE) sections and large three-dimensional specimens). Here we present BOOST, a rapid and robust expansion microscopy workflow that leverages a series of microwave-accelerated expansion microscopy chemistry. Specifically, BOOST enables a single-step 10-fold expansion of cultured cells, tissue sections, and even the challenging-to-expand FFPE sections under 90 minutes. Notably, BOOST pioneers a 10-fold expansion of large millimeter-sized three-dimensional specimens, previously unattainable to the best of our knowledge. The workflow is also easily adaptable based on stable and common reagents, thus boosting the potential adoption of expansion microscopy for applications.

Background: Bone metastasis represents the most common and fatal stage of advanced prostate cancer (PCa) with the poor prognosis. This study aims to elucidate the mechanisms underlying the development and progression of bone-metastatic PCa and to identify potential therapeutic targets. Methods: Bioinformatics analysis and immunohistochemistry were employed to identify the key gene Collagen Triple Helix Repeat Containing 1 (CTHRC1) involved in PCa bone metastasis. In vivo models, including left cardiac ventricle and tibial injections, were utilized to investigate changes in bone metastasis mediated by CTHRC1 secreted by PCa. In addition, an in vitro co culture system of PCa cells with osteoclasts and osteoblasts was established to explore the role and potential mechanism of CTHRC1 in mediating osteoclast differentiation and promoting bone metastasis. Results: We utilize in vitro co-culture systems and in vivo mice models to demonstrate that CTHRC1 promotes osteoclast differentiation, leading to osteolytic change in PCa bone metastasis. Mechanistically, CTHRC1 binds to integrin beta 3 (ITGB3) on osteoclasts, activating the SRC/FAK/ERK1/2 signaling cascade to initiate the osteoclastogenesis. Furthermore, CTHRC1 cooperates with receptor activator of nuclear factor-κB ligand (RANKL) signaling to enhance osteoblast-mediated osteoclast differentiation by modulating the RANKL/osteoprotegerin system. Blocking CTHRC1 with anti-ITGB3 can reduce the progression of bone metastasis in mouse models, providing a new perspective for the treatment of PCa bone metastasis Conclusion: Our findings identify CTHRC1 as a novel mediator in the bone microenvironment, offering a potential therapeutic target for inhibiting the occurrence and progression of PCa bone metastasis.

The incidence and mortality rates of prostate cancer are increasing annually, indicating that it poses a serious threat to men's health. Previous studies have demonstrated that bone-metastatic tumor cells undergo four hallmark bone-metastatic events, including colonization, dormancy, reactivation and bone reconstruction. However, most advanced patients experience four stages of progression and undergo advanced bone reconstruction—the “vicious circle”—which is irreversible. These patients have a poor prognosis and can even die. Therefore, determining how various components in the tumor and bone microenvironments affect the progression and events of bone metastasis is crucial. Here, we integrate the latest mechanisms of phenotypic plasticity and microenvironment interactions, proposing potential therapeutic targets to disrupt the ‘vicious cycle’ in advanced bone metastasis. We look forward to providing direction and guidance for the future treatment of prostate cancer bone metastasis.

Background: The proteomics play a crucial role in identifying therapeutic targets. In this study, we utilized Weighted Gene Co-expression Network Analysis (WGCNA) and Mendelian Randomization (MR) to identify potential protein biomarkers and therapeutic targets for prostate cancer (PCa).Methods: To select PCa-related genes, we constructed a WGCNA using genetic data from The Cancer Genome Atlas (TCGA) (502 cases and 52 controls). We sourced Expression Quantitative Trait Locus (eQTL) data from the eQTLGen consortium database and obtained outcome data from the MR Base database. Subsequently, MR and colocalization analyses were performed to validate the causal relationships of the candidate proteins. Additionally, we conducted immune infiltration analysis, Gene Set Enrichment Analysis (GSEA), Gene Set Variation Analysis (GSVA), drug sensitivity analysis, Nomogram model construction, transcriptional regulatory analysis, correlation analysis with PCa-regulated proteins, and single-cell sequencing analysis to detect specific cell types with enriched expression and consider potential therapeutic targets.Results: The results of our study revealed that five genetically predicted proteins were associated with PCa risk. Decreased levels of one protein (THBD) and increased levels of four proteins (DST, IFI27L2, OSBL10, PPP1R14A) were found to be linked to increased PCa risk. These protein-encoding genes are distributed across different types of cells in PCa tissue, indicating their potential as therapeutic targets for PCa.Conclusion: Our study identified several protein biomarkers associated with PCa risk, providing new insights into the etiology of PCa and offering promising targets for the development of PCa screening biomarkers and therapeutic drugs.Funding: This work was supported by National Key Research and Development Program, China (2023YFE0204500)，the National Natural Science Foundation of China (No. 82272856, 82370099 and 82170053), the Guangdong Basic and Applied Basic Research Foundation (2022A1515010437), the President’s Foundation of the Third Affiliated Hospital of Southern Medical University (No.YM2021010).Declaration of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

BACKGROUND:The prognostic value of the distinction between microscopic (pT3a) and macroscopic (pT3b) perivesical fat invasions remains a subject of debate. To explore whether the pattern of perivesical fat invasion can serve as a prognostic factor to better subgroup T3 stage bladder cancer.MATERIALS AND METHODS:One hundred and forty-nine patients diagnosed with T3 stage bladder cancer at Sun Yat-sen University Cancer Center (SYSUCC) were selected for the experimental cohort in this study. Ninety-seven T3 stage bladder cancer patients with pathological slices at the Cancer Genome Atlas (TCGA) were selected as validation cohort in this study. The perivesical fat invasive pattern was examined with hematoxylin and eosin-stained pathological slides by two pathologists independently. Two different perivesical fat invasive patterns, fibrous-surrounded (FS) pattern, and nonfibrous-surrounded (NFS) pattern were assessed.RESULTS:Perivesical fat invasion pattern had a significant influence on overall survival in T3 stage bladder cancer. Compared to the NFS pattern, the FS pattern was related to a better prognosis in both the SYSUCC cohort and TCGA cohort. The patients with NFS pattern tumor who underwent cisplatin-based adjuvant chemotherapy experienced an obvious improvement compared to observation after radical cystectomy in overall survival in the SYSUCC cohort.CONCLUSION:The perivesical fat invasion pattern could predict prognosis and clinically different chemotherapeutic survival outcomes in patients with T3 stage bladder cancer after radical cystectomy.

Glycine decarboxylase (GLDC) is one of the core enzymes for glycine metabolism, and its biological roles in prostate cancer (PCa) are unclear. First, we found that GLDC plays a central role in glycolysis in 540 TCGA PCa patients. Subsequently, a metabolomic microarray showed that GLDC enhanced aerobic glycolysis in PCa cells, and GLDC and its enzyme activity enhanced glucose uptake, lactate production and lactate dehydrogenase (LDH) activity in PCa cells. Next, we found that GLDC was highly expressed in PCa, was directly regulated by hypoxia-inducible factor (HIF1-α) and regulated downstream LDHA expression. In addition, GLDC and its enzyme activity showed a strong ability to promote the migration and invasion of PCa both in vivo and in vitro. Furthermore, we found that the GLDC-high group had a higher TP53 mutation frequency, lower CD8+ T-cell infiltration, higher immune checkpoint expression, and higher immune exclusion scores than the GLDC-low group. Finally, the GLDC-based prognostic risk model by applying LASSO Cox regression also showed good predictive power for the clinical characteristics and survival in PCa patients. This evidence indicates that GLDC plays crucial roles in glycolytic metabolism, invasion and metastasis, and immune escape in PCa, and it is a potential therapeutic target for prostate cancer.

Targeted delivery of anti-tumor drugs and overcoming drug resistance in malignant tumor cells remain significant clinical challenges. However, there are only few effective methods to address these issues. Extracellular vesicles (EVs), actively secreted by cells, play a crucial role in intercellular information transmission and cargo transportation. Recent studies have demonstrated that engineered EVs can serve as drug delivery carriers and showed promising application prospects. Nevertheless, there is an urgent need for further improvements in the isolation and purification of EVs, surface modification techniques, drug assembly processes, and precise recognition of tumor cells for targeted drug delivery purposes. In this review, we summarize the applications of engineered EVs in cancer treatment and overcoming drug resistance, and current challenges associated with engineered EVs are also discussed. This review aims to provide new insights and potential directions for utilizing engineered EVs as targeted delivery systems for anti-tumor drugs and overcoming drug resistance in the near future.

广东省医学会泌尿外科学分会疑难病例多学科会诊（multidisciplinary treatment，MDT）由林天歆主任委员倡导并成功举办了十期。2023年7月5日下午由广东省医学会泌尿外科分会主办、南方医科大学第三附属医院承办的"广东省医学会泌尿外科疑难病例多学科会诊（第11期）"在广州白云宾馆会议中心举行。本次会议由广东医学会泌尿外科学分会副主任委员、南方医科大学第三附属医院泌尿外科主任刘存东教授作为主要负责人，邀请了广东省医学会泌尿外科学分会主任委员林天歆教授及泌尿外科分会所有副主任委员、多名省内泌尿外科学专家和南方医科大学第三附属医院MDT团队出席。本次MDT会议共讨论了5例疑难病例，包括中山大学附属第三医院狄金明教授团队1例、深圳市第二人民医院梅红兵教授团队1例、广州医科大学附属第一医院刘永达教授团队1例及南方医科大学第三附属医院刘存东教授团队2例，具体包括膀胱癌、前列腺癌、肾癌、罕见孤立性促肾上腺皮质激素（adrenocorticotropic hormone，ACTH）综合征等病种。与会的各省内知名泌尿外科专家及南方医科大学第三附属医院影像科周全教授、肿瘤科关小倩教授、病理科魏清柱教授、中山大学附属肿瘤医院放疗科何立儒教授等专家结合自身学科和临床经验对每个病例进行了专业而深入的解析并提出了诊疗建议，本次活动取得了预期的效果，现精选本期经典讨论病例一例进行报道。

Cyclophosphamide-induced testosterone deficiency (CPTD) during the treatment of cancers and autoimmune disorders severely influences the quality of life of patients. Currently, several guidelines recommend patients suffering from CPTD receive testosterone replacement therapy (TRT). However, TRT has many disadvantages underscoring the requirement for alternative, nontoxic treatment strategies. We previously reported bone marrow mesenchymal stem cells-derived exosomes (BMSCs-exos) could alleviate cyclophosphamide (CP)-induced spermatogenesis dysfunction, highlighting their role in the treatment of male reproductive disorders. Therefore, we further investigated whether BMSCs-exos affect autophagy and testosterone synthesis in Leydig cells (LCs). Here, we examined the effects and probed the molecular mechanisms of BMSCs-exos on CPTD in vivo and in vitro by detecting the expression levels of genes and proteins related to autophagy and testosterone synthesis. Furthermore, the testosterone concentration in serum and cell-conditioned medium, and the photophosphorylation protein levels of adenosine monophosphate-activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR) were measured. Our results suggest that BMSCs-exos could be absorbed by LCs through the blood–testis barrier in mice, promoting autophagy in LCs and improving the CP-induced low serum testosterone levels. BMSCs-exos inhibited cell death in CP-exposed LCs, regulated the AMPK-mTOR signaling pathway to promote autophagy in LCs, and then improved the low testosterone synthesis ability of CP-induced LCs. Moreover, the autophagy inhibitor, 3-methyladenine (3-MA), significantly reversed the therapeutic effects of BMSCs-exos. These findings suggest that BMSCs-exos promote LC autophagy by regulating the AMPK-mTOR signaling pathway, thereby ameliorating CPTD. This study provides novel evidence for the clinical improvement of CPTD using BMSCs-exos.

Background: Sphingosine kinase 1 (SPHK1) is a key enzyme that catalyzes the phosphorylation of sphingosine. Recent studies reported SPHK1 to be associated with renal cell carcinoma (RCC) progression by inducing targeted therapy resistance. However, the expression and the clinical significance of SPHK1 on RCC in those having received targeted therapy have not been elucidated. The present study explored the expression of SPHK1 in RCC tissues from targeted therapy recipients, the correlation of SPHK1 with clinicopathological parameters, and the effect of SPHK1 on RCC patient prognosis. Methods: Differential gene expression analysis of RCC treated with and without targeted therapy was performed. The correlations of SPHK1 expression with clinical parameters of RCC were examined. Gene set enrichment analysis (GSEA) was performed to clarify the potential role of SPHK1 associated with targeted therapy resistance. The value of SPHK1 as a diagnostic marker for RCC was also evaluated. The Kaplan-Meier method was applied to analyze the correlation between SPHK1 expression and patient survival rate by using the clinical data from patients with RCC. Results: Significant overexpression of SPHK1 was detected in RCC treated with targeted therapy. SPHK1 expression was closely correlated with RCC progression-related clinicopathological parameters. Therefore, elevated SPHK1 could effectively diagnose RCC and distinguish RCC with an advanced clinical stage and a high pathological grade. SPHK1 was associated with the stemness of RCC cells via the activation of the Wnt, Hedgehog, or Notch signaling pathways in targeted drug-treated or untreated RCC. Survival analysis of a large cohort of RCC samples indicated overexpression of SPHK1 to be inversely correlated with the overall and disease-free survival of patients with RCC. Conclusions: Our study indicated that SPHK1 associated with targeted therapy resistance could serve as a potential prognostic marker and a valuable biomarker of response to angiogenic agents in RCC.

Castration-resistant prostate cancer (CRPC), especially metastatic castration-resistant prostate cancer (mCRPC) is one of the most prevalent malignancies and main cause of cancer-related death among men in the world. In addition, it is very difficult for clinical treatment because of the natural or acquired drug resistance of CRPC. Mechanisms of drug resistance are extremely complicated and how to overcome it remains an urgent clinical problem to be solved. Thus, a comprehensive and thorough understanding for mechanisms of drug resistance in mCRPC is indispensable to develop novel and better therapeutic strategies. In this review, we aim to review new insight of the treatment of mCRPC and elucidate mechanisms governing resistance to new drugs: taxanes, androgen receptor signaling inhibitors (ARSIs) and poly (ADP-ribose) polymerase (PARP) inhibitors (PARPi). Most importantly, in order to improve efficacy of these drugs, strategies of overcoming drug resistance are also discussed based on their mechanisms respectively.

Background Plasma cells as an important component of immune microenvironment plays a crucial role in immune escape and are closely related to immune therapy response. However, its role for prostate cancer is rarely understood. In this study, we intend to investigate the value of a new plasma cell molecular subtype for predicting the biochemical recurrence, immune escape and immunotherapy response in prostate cancer. Methods Gene expression and clinicopathological data were collected from 481 prostate cancer patients in the Cancer Genome Atlas. Then, the immune characteristics of the patients were analyzed based on plasma cell infiltration fractions. The unsupervised clustering based machine learning algorithm was used to identify the molecular subtypes of the plasma cell. And the characteristic genes of plasma cell subtypes were screened out by three types of machine learning models to establish an artificial neural network for predicting plasma cell subtypes. Finally, the prediction artificial neural network of plasma cell infiltration subtypes was validated in an independent cohort of 449 prostate cancer patients from the Gene Expression Omnibus. Results The plasma cell fraction in prostate cancer was significantly decreased in tumors with high T stage, high Gleason score and lymph node metastasis. In addition, low plasma cell fraction patients had a higher risk of biochemical recurrence. Based on the differential genes of plasma cells, plasma cell infiltration status of PCa patients were divided into two independent molecular subtypes(subtype 1 and subtype 2). Subtype 1 tends to be immunosuppressive plasma cells infiltrating to the PCa region, with a higher likelihood of biochemical recurrence, more active immune microenvironment, and stronger immune escape potential, leading to a poor response to immunotherapy. Subsequently, 10 characteristic genes of plasma cell subtype were screened out by three machine learning algorithms. Finally, an artificial neural network was constructed by those 10 genes to predict the plasma cell subtype of new patients. This artificial neural network was validated in an independent validation set, and the similar results were gained. Conclusions Plasma cell infiltration subtypes could provide a potent prognostic predictor for prostate cancer and be an option for potential responders to prostate cancer immunotherapy.