Skull metastasis is a poor prognostic factor for prostate cancer patients with bone metastasis: a retrospective study based on a Chinese population.

BACKGROUND: Skull is a relatively rare metastasis site for prostate cancer (PCa). There is no evidence regarding the prognostic indication of skull metastasis (SM) in PCa patients. In this study, we analyzed the prognostic value of SM for metastatic PCa patients receiving androgen deprivation therapy (ADT). METHODS: 107 consecutive patients were included from September 2008 to August 2021. All patients were administered with standard ADT. Abiraterone plus glucocorticoid and/or docetaxel chemotherapy were given after failure to castration-resistant prostate cancer. Clinical parameters and follow-up prognostic data were retrospectively analyzed. The association of clinical and pathological parameters with SM were analyzed. The progression-free survival (PFS) and overall survival (OS) were assessed using Kaplan-Meier analysis and Cox regression analyses. RESULTS: Patients with SM (n = 26) had significantly higher biopsy Gleason scores, higher clinical T stage, higher prostate-specific antigen level at diagnosis, and were more likely to have high-burden metastasis and lymph node metastasis, compared with those without SM (n = 81). They also showed significantly lower level of hemoglobin, albumin and serum calcium, along with higher level of alkaline phosphatase. SM was significantly associated with shorter medium PFS (9.4 vs. 18.3 months, p < 0.001) and OS (22.2 vs. 58.2 months, p < 0.001). Cox analysis demonstrated that SM was an independent risk factor for shorter PFS (hazard ratio 2.327 [1.429-3.789], p = 0.001) and shorter OS (hazard ratio 2.810 [1.615-4.899], p < 0.001). CONCLUSION: In this study, we found that SM was significantly correlated with more aggressive disease and indicated poor prognosis in PCa patients with bone metastasis. Our study may provide useful reference for the risk stratification of PCa patients.

Mapping spatial transcriptome with light-activated proximity-dependent RNA labeling.

RNA molecules are highly compartmentalized in eukaryotic cells, with their localizations intimately linked to their functions. Despite the importance of RNA targeting, our current knowledge of the spatial organization of the transcriptome has been limited by a lack of analytical tools. In this study, we develop a chemical biology approach to label RNAs in live cells with high spatial specificity. Our method, called CAP-seq, capitalizes on light-activated, proximity-dependent photo-oxidation of RNA nucleobases, which could be subsequently enriched via affinity purification and identified by high-throughput sequencing. Using this technique, we investigate the local transcriptomes that are proximal to various subcellular compartments, including the endoplasmic reticulum and mitochondria. We discover that messenger RNAs encoding for ribosomal proteins and oxidative phosphorylation pathway proteins are highly enriched at the outer mitochondrial membrane. Due to its specificity and ease of use, CAP-seq is a generally applicable technique to investigate the spatial transcriptome in many biological systems.

Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization.

Scene representation networks (SRNs) have been recently proposed for compression and visualization of scientific data. However, state-of-the-art SRNs do not adapt the allocation of available network parameters to the complex features found in scientific data, leading to a loss in reconstruction quality. We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN) and propose a domain decomposition training and inference technique for accelerated parallel training on multi-GPU systems. We also release an open-source neural volume rendering application that allows plug-and-play rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses multiple spatially adaptive feature grids that learn where to be placed within the domain to dynamically allocate more neural network resources where error is high in the volume, improving state-of-the-art reconstruction accuracy of SRNs for scientific data without requiring expensive octree refining, pruning, and traversal like previous adaptive models. In our domain decomposition approach for representing large-scale data, we train an set of APMGSRNs in parallel on separate bricks of the volume to reduce training time while avoiding overhead necessary for an out-of-core solution for volumes too large to fit in GPU memory. After training, the lightweight SRNs are used for realtime neural volume rendering in our open-source renderer, where arbitrary view angles and transfer functions can be explored. A copy of this paper, all code, all models used in our experiments, and all supplemental materials and videos are available at https://github.com/skywolf829/APMGSRN.

Regularized Multi-Decoder Ensemble for an Error-Aware Scene Representation Network.

Feature grid Scene Representation Networks (SRNs) have been applied to scientific data as compact functional surrogates for analysis and visualization. As SRNs are black-box lossy data representations, assessing the prediction quality is critical for scientific visualization applications to ensure that scientists can trust the information being visualized. Currently, existing architectures do not support inference time reconstruction quality assessment, as coordinate-level errors cannot be evaluated in the absence of ground truth data. By employing the uncertain neural network architecture in feature grid SRNs, we obtain prediction variances during inference time to facilitate confidence-aware data reconstruction. Specifically, we propose a parameter-efficient multi-decoder SRN (MDSRN) architecture consisting of a shared feature grid with multiple lightweight multi-layer perceptron decoders. MDSRN can generate a set of plausible predictions for a given input coordinate to compute the mean as the prediction of the multi-decoder ensemble and the variance as a confidence score. The coordinate-level variance can be rendered along with the data to inform the reconstruction quality, or be integrated into uncertainty-aware volume visualization algorithms. To prevent the misalignment between the quantified variance and the prediction quality, we propose a novel variance regularization loss for ensemble learning that promotes the Regularized multi-decoder SRN (RMDSRN) to obtain a more reliable variance that correlates closely to the true model error. We comprehensively evaluate the quality of variance quantification and data reconstruction of Monte Carlo Dropout (MCD), Mean Field Variational Inference (MFVI), Deep Ensemble (DE), and Predicting Variance (PV) in comparison with our proposed MDSRN and RMDSRN applied to state-of-the-art feature grid SRNs across diverse scalar field datasets. We demonstrate that RMDSRN attains the most accurate data reconstruction and competitive variance-error correlation among uncertain SRNs under the same neural network parameter budgets. Furthermore, we present an adaptation of uncertainty-aware volume rendering and shed light on the potential of incorporating uncertain predictions in improving the quality of volume rendering for uncertain SRNs. Through ablation studies on the regularization strength and decoder count, we show that MDSRN and RMDSRN are expected to perform sufficiently well with a default configuration without requiring customized hyperparameter settings for different datasets.

An end-to-end deep convolutional neural network-based dose engine for parotid gland cancer seed implant brachytherapy.

BACKGROUND: Seed implant brachytherapy (SIBT) is a promising treatment modality for parotid gland cancers (PGCs). However, the current clinical standard dose calculation method based on the American Association of Physicists in Medicine (AAPM) Task Group 43 (TG-43) Report oversimplifies patient anatomy as a homogeneous water phantom medium, leading to significant dose calculation errors due to heterogeneity surrounding the parotid gland. Monte Carlo Simulation (MCS) can yield accurate dose distributions but the long computation time hinders its wide application in clinical practice. PURPOSE: This paper aims to develop an end-to-end deep convolutional neural network-based dose engine (DCNN-DE) to achieve fast and accurate dose calculation for PGC SIBT. METHODS: A DCNN model was trained using the patient's CT images and TG-43-based dose maps as inputs, with the corresponding MCS-based dose maps as the ground truth. The DCNN model was enhanced based on our previously proposed model by incorporating attention gates (AGs) and large kernel convolutions. Training and evaluation of the model were performed using a dataset comprising 188 PGC I-125 SIBT patient cases, and its transferability was tested on an additional 16 non-PGC head and neck cancers (HNCs) I-125 SIBT patient cases. Comparison studies were conducted to validate the superiority of the enhanced model over the original one and compare their overall performance. RESULTS: On the PGC testing dataset, the DCNN-DE demonstrated the ability to generate accurate dose maps, with percentage absolute errors (PAEs) of 0.67% ± 0.47% for clinical target volume (CTV) D90 and 1.04% ± 1.33% for skin D0.1cc. The comparison studies revealed that incorporating AGs and large kernel convolutions resulted in 8.2% (p < 0.001) and 3.1% (p < 0.001) accuracy improvement, respectively, as measured by dose mean absolute error. On the non-PGC HNC dataset, the DCNN-DE exhibited good transferability, achieving a CTV D90 PAE of 1.88% ± 1.73%. The DCNN-DE can generate a dose map in less than 10 ms. CONCLUSIONS: We have developed and validated an end-to-end DCNN-DE for PGC SIBT. The proposed DCNN-DE enables fast and accurate dose calculation, making it suitable for application in the plan optimization and evaluation process of PGC SIBT.

Cancer-associated fibroblast exosomes promote prostate cancer metastasis through miR-500a-3p/FBXW7/HSF1 axis under hypoxic microenvironment.

Metastasis is the main cause of deaths in prostate cancer (PCa). However, the exact mechanisms underlying PCa metastasis are not fully understood. In this study, we discovered pronounced hypoxia in primary lesions of metastatic PCa(mPCa). The exosomes secreted by cancer-associated fibroblasts (CAFs) under hypoxic conditions significantly enhance PCa metastasis both in vitro and in vivo. Through miRNA sequencing and reverse transcription quantitative PCR (RT-qPCR), we found that hypoxia elevated miR-500a-3p levels in CAFs exosomes. Subsequent RT-qPCR, western blotting, and dual luciferase reporter assays identified F-box and WD repeat domain-containing 7(FBXW7) as a target of miR-500a-3p. In addition, immunohistochemistry revealed that FBXW7 expression decreased with the progression of PCa, while heat shock transcription factor 1(HSF1) expression increased. Introducing an FBXW7 plasmid into PCa cells reduced their metastatic potential and significantly lowered HSF1 expression. These findings suggest that CAFs exosomes drive PCa metastasis via the miR-500a-3p/FBXW7/HSF1 axis in a hypoxic microenvironment. Targeting either hypoxia or exosomal miR-500a-3p could be a promising strategy for PCa management.

Association between maximal urethral length preservation and postoperative continence after robot-assisted radical prostatectomy: a meta-analysis and systematic review.

ABSTRACT: Urinary incontinence is a common complication following robot-assisted radical prostatectomy (RARP). Urethral length has been identified as a factor affecting postoperative continence recovery. In this meta-analysis, we examined the association between use of the maximal urethral length preservation (MULP) technique and postoperative urinary continence in patients undergoing RARP. We conducted a comprehensive search of PubMed, Web of Science, Embase, and the Cochrane Library up to December 31, 2023. The quality of the literature was assessed using the Newcastle-Ottawa Scale. A random-effects meta-analysis was performed to synthesize data and calculate the odds ratio (OR) from eligible studies on continence and MULP. Six studies involving 1869 patients met the eligibility criteria. MULP was positively associated with both early continence (1 month after RARP; Z = 3.62, P = 0.003, OR = 3.10, 95% confidence interval [CI]: 1.68-5.73) and late continence (12 months after RARP; Z = 2.34, P = 0.019, OR = 2.10, 95% CI: 1.13-3.90). Oncological outcomes indicated that MULP did not increase the overall positive surgical margin rate or the positive surgical margin status at the prostate apex (both P > 0.05). In conclusion, the use of the MULP technique in RARP significantly improved both early and late postoperative continence outcomes without compromising oncological outcomes.

Automatic planning for head and neck seed implant brachytherapy based on deep convolutional neural network dose engine.

BACKGROUND: Seed implant brachytherapy (SIBT) is an effective treatment modality for head and neck (H&N) cancers; however, current clinical planning requires manual setting of needle paths and utilizes inaccurate dose calculation algorithms. PURPOSE: This study aims to develop an accurate and efficient deep convolutional neural network dose engine (DCNN-DE) and an automatic SIBT planning method for H&N SIBT. METHODS: A cohort of 25 H&N patients who received SIBT was utilized to develop and validate the methods. The DCNN-DE was developed based on 3D-unet model. It takes single seed dose distribution from a modified TG-43 method, the CT image and a novel inter-seed shadow map (ISSM) as inputs, and predicts the dose map of accuracy close to the one from Monte Carlo simulations (MCS). The ISSM was proposed to better handle inter-seed attenuation. The accuracy and efficacy of the DCNN-DE were validated by comparing with other methods taking MCS dose as reference. For SIBT planning, a novel strategy inspired by clinical practice was proposed to automatically generate parallel or non-parallel potential needle paths that avoid puncturing bone and critical organs. A heuristic-based optimization method was developed to optimize the seed positions to meet clinical prescription requirements. The proposed planning method was validated by re-planning the 25 cases and comparing with clinical plans. RESULTS: The absolute percentage error in the TG-43 calculation for CTV V100 and D90 was reduced from 5.4% and 13.2% to 0.4% and 1.1% with DCNN-DE, an accuracy improvement of 93% and 92%, respectively. The proposed planning method could automatically obtain a plan in 2.5 ± 1.5 min. The generated plans were judged clinically acceptable with dose distribution comparable with those of the clinical plans. CONCLUSIONS: The proposed method can generate clinically acceptable plans quickly with high accuracy in dose evaluation, and thus has a high potential for clinical use in SIBT.

Prognostic value of Controlling Nutritional Status score for postoperative complications and biochemical recurrence in prostate cancer patients undergoing laparoscopic radical prostatectomy.

Background: Controlling Nutritional Status (CONUT) score was used for screening the preoperative nutritional status. The correlation between the CONUT score and the prognosis of patients with prostate cancer (PCa) has yet to be elucidated. Herein, we analyzed the prognostic value of CONUT scores in patients with PCa who underwent laparoscopic radical prostatectomy. Materials and methods: Data of 244 patients were retrospectively evaluated. Perioperative variables and follow-up data were analyzed. The patients were categorized into 2 groups according to their preoperative CONUT scores. Postoperative complication and incontinence rates were also compared. The Kaplan-Meier method was used to estimate the median biochemical recurrence-free survival (BCRFS) between the 2 groups. Univariate and multivariate Cox regression analyses were performed to identify the potential prognostic factors for BCRFS. Results: Patients were categorized into the low-CONUT group (CONUT score <3, n = 207) and high-CONUT group (CONUT score ≥3, n = 37). The high-CONUT group had a higher overall complication rate (40.5% vs.19.3%, p = 0.004), a higher major complication rate (10.8% vs. 3.9%, p = 0.013), and longer postoperative length of stay (8 days vs. 7 days, p = 0.017). More fever, urinary infection, abdominal infection, scrotal edema, rash, and hemorrhagic events (all p values < 0.05) were observed in the high-CONUT group. A higher rate of urinary incontinence was observed in the high-CONUT group at 1 (34.4% vs. 13.2%, p = 0.030) and 3 months (24.1% vs. 8.2%, p = 0.023) postoperatively. The high-CONUT group had shorter medium BCRFS (23.8 months vs. 54.6 months, p = 0.029), and a CONUT score ≥3 was an independent risk factor for a shorter BCRFS (hazards ratio, 1.842; p = 0.026). Conclusions: The CONUT score is a useful predictive tool for higher postoperative complication rates and shorter BCRFS in patients with PCa who undergo laparoscopic radical prostatectomy.

Automatic planning for functional lung avoidance radiotherapy based on function-guided beam angle selection and plan optimization.

Objective.This study aims to develop a fully automatic planning framework for functional lung avoidance radiotherapy (AP-FLART).Approach.The AP-FLART integrates a dosimetric score-based beam angle selection method and a meta-optimization-based plan optimization method, both of which incorporate lung function information to guide dose redirection from high functional lung (HFL) to low functional lung (LFL). It is applicable to both contour-based FLART (cFLART) and voxel-based FLART (vFLART) optimization options. A cohort of 18 lung cancer patient cases underwent planning-CT and SPECT perfusion scans were collected. AP-FLART was applied to generate conventional RT (ConvRT), cFLART, and vFLART plans for all cases. We compared automatic against manual ConvRT plans as well as automatic ConvRT against FLART plans, to evaluate the effectiveness of AP-FLART. Ablation studies were performed to evaluate the contribution of function-guided beam angle selection and plan optimization to dose redirection.Main results.Automatic ConvRT plans generated by AP-FLART exhibited similar quality compared to manual counterparts. Furthermore, compared to automatic ConvRT plans, HFL mean dose,V20, andV5were significantly reduced by 1.13 Gy (p< .001), 2.01% (p< .001), and 6.66% (p< .001) respectively for cFLART plans. Besides, vFLART plans showed a decrease in lung functionally weighted mean dose by 0.64 Gy (p< .01),fV20by 0.90% (p= 0.099), andfV5by 5.07% (p< .01) respectively. Though inferior conformity was observed, all dose constraints were well satisfied. The ablation study results indicated that both function-guided beam angle selection and plan optimization significantly contributed to dose redirection.Significance.AP-FLART can effectively redirect doses from HFL to LFL without severely degrading conventional dose metrics, producing high-quality FLART plans. It has the potential to advance the research and clinical application of FLART by providing labor-free, consistent, and high-quality plans.

MRI-measured periprostatic adipose tissue volume as a prognostic predictor in prostate cancer patients undergoing laparoscopic radical prostatectomy.

In this study, we evaluated the association between the PPAT volume and the prognosis of PCa patients after LRP. We retrospectively analysed data of 189 PCa patients who underwent LRP in Beijing Chaoyang Hospital. Volumes of PPAT and prostate were measured by magnetic resonance imaging (MRI), and normalized PPAT volume was computed (PPAT volume divided by prostate volume). Patients were then stratified into the high-PPAT group (n = 95) and low-PPAT group (n = 94) by the median of normalized PPAT volume (73%). The high-PPAT group had significantly higher Gleason score (total score 8 or more, 39.0% vs. 4.3%, p < 0.001) and pathological stage (stage T3b, 28.4% vs. 13.8%, p = 0.048). No significant correlation between normalized PPAT volume and body mass index (ρ = -0.012, p = 0.872) was observed. Kaplan-Meier curve analysis showed the high-PPAT group had significantly shorter biochemical recurrence (BCR) interval (median progression-free survival time 15.9 months vs. 32.7 months, p = 0.001). Univiarate and multivariate Cox regression analyses showed high normalized PPAT volume (>73%) (hazard ratio 1.787 [1.075-3.156], p = 0.002) were independent risk factors for BCR post-operatively. In conclusion, MRI-measured PPAT volume is of significant prognostic value for PCa patients undergoing LRP.

CBCT-to-CT Synthesis for Cervical Cancer Adaptive Radiotherapy via U-Net-Based Model Hierarchically Trained with Hybrid Dataset.

PURPOSE: To develop a deep learning framework based on a hybrid dataset to enhance the quality of CBCT images and obtain accurate HU values. MATERIALS AND METHODS: A total of 228 cervical cancer patients treated in different LINACs were enrolled. We developed an encoder-decoder architecture with residual learning and skip connections. The model was hierarchically trained and validated on 5279 paired CBCT/planning CT images and tested on 1302 paired images. The mean absolute error (MAE), peak signal to noise ratio (PSNR), and structural similarity index (SSIM) were utilized to access the quality of the synthetic CT images generated by our model. RESULTS: The MAE between synthetic CT images generated by our model and planning CT was 10.93 HU, compared to 50.02 HU for the CBCT images. The PSNR increased from 27.79 dB to 33.91 dB, and the SSIM increased from 0.76 to 0.90. Compared with synthetic CT images generated by the convolution neural networks with residual blocks, our model had superior performance both in qualitative and quantitative aspects. CONCLUSIONS: Our model could synthesize CT images with enhanced image quality and accurate HU values. The synthetic CT images preserved the edges of tissues well, which is important for downstream tasks in adaptive radiotherapy.

MRI-measured adipose features as predictive factors for detection of prostate cancer in males undergoing systematic prostate biopsy: a retrospective study based on a Chinese population.

In this study, we retrospectively evaluated the data of 901 men undergoing ultrasonography-guided systematic prostate biopsy between March 2013 and May 2022. Adipose features, including periprostatic adipose tissue (PPAT) thickness and subcutaneous fat thickness, were measured using MRI before biopsy. Prediction models of all PCa and clinically significant PCa (csPCa) (Gleason score higher than 6) were established based on variables selected by multivariate logistic regression and prediction nomograms were constructed. Patients with PCa had higher PPAT thickness (4.64 [3.65-5.86] vs. 3.54 [2.49-4.51] mm, p < 0.001) and subcutaneous fat thickness (29.19 [23.05-35.95] vs. 27.90 [21.43-33.93] mm, p = 0.013) than those without PCa. Patients with csPCa had higher PPAT thickness (4.78 [3.80-5.88] vs. 4.52 [3.80-5.63] mm, p = 0.041) than those with non-csPCa. Adding adipose features to the prediction models significantly increased the area under the receiver operating characteristics curve for the prediction of all PCa (0.850 vs. 0.819, p < 0.001) and csPCa (0.827 vs. 0.798, p < 0.001). Based on MRI-measured adipose features and clinical parameters, we established two nomograms that were simple to use and could improve patient selection for prostate biopsy in Chinese population.

Does radiation therapy need more than two photon energies from Linac?

Purpose: Modern Linacs are equipped with multiple photon energies for radiation therapy, and proper energy is chosen for each case based on tumor characteristics and patient anatomy. The aim of this study is to investigate whether it is necessary to have more than two photons energies. Methods: The principle of photon energy synthesis is presented. It is shown that a photon beam of any intermediate energy (Esyn) can be synthesized from a linear combination of a low energy (Elow) and a high energy (Ehigh). The principle is validated on a wide range of scenarios: different intermediate photon energies on the same Linac; between Linacs from the same manufacturer or different manufacturers; open and wedge beams; and extensive photon energies available from published reference data. In addition, 3D dose distributions in water phantom are compared using Gamma analysis. The method is further demonstrated in clinical cases of various tumor sites and multiple treatment modalities. Experimental measurements are performed for IMRT plans and they are analyzed using the standard clinical protocol. Results: The synthesis coefficients vary with energy and field size. The root mean square error (RMSE) is within 1.1% for open and wedge fields. Excellent agreement was observed for British Journal of Radiology (BJR) data with an average RMSE of 0.11%. The 3D Gamma analysis shows a good match for all field sizes in the water phantom and all treatment modalities for the five clinical cases. The minimum gamma passing rate of 95.7% was achieved at 1%/1mm criteria for two measured dose distributions of IMRT plans. Conclusion: A Linac with two photon energies is capable of producing dosimetrically equivalent plans of any energy in-between through the photon energy synthesis, supporting the notion that there is no need to equip more than two photon energies on each Linac. This can significantly reduce the cost of equipment for radiation therapy.

Technical note: A fast and accurate analytical dose calculation algorithm for 125 I seed-loaded stent applications.

PURPOSE: The safety and clinical efficacy of 125 I seed-loaded stent for the treatment of portal vein tumor thrombosis (PVTT) have been shown. Accurate and fast dose calculation of the 125 I seeds with the presence of the stent is necessary for the plan optimization and evaluation. However, the dosimetric characteristics of the seed-loaded stents remain unclear and there is no fast dose calculation technique available. This paper aims to explore a fast and accurate analytical dose calculation method based on Monte Carlo (MC) dose calculation, which takes into account the effect of stent and tissue inhomogeneity. METHODS: A detailed model of the seed-loaded stent was developed using 3D modeling software and subsequently used in MC simulations to calculate the dose distribution around the stent. The dose perturbation caused by the presence of the stent was analyzed, and dose perturbation kernels (DPKs) were derived and stored for future use. Then, the dose calculation method from AAPM TG-43 was adapted by integrating the DPK and appropriate inhomogeneity correction factors (ICF) to calculate dose distributions analytically. To validate the proposed method, several comparisons were performed with other methods in water phantom and voxelized CT phantoms for three patients. RESULTS: The stent has a considerable dosimetric effect reducing the dose up to 47.2% for single-seed stent and 11.9%-16.1% for 16-seed stent. In a water phantom, dose distributions from MC simulations and TG-43-DP-ICF showed a good agreement with the relative error less than 3.3%. In voxelized CT phantoms, taking MC results as the reference, the relative errors of TG-43 method can be up to 33%, while those of TG-43-DP-ICF method were less than 5%. For a dose matrix with 256 × 256 × 46 grid (corresponding to a phantom of 17.2 × 17.2 × 11.5 cm3 ) for 16-seed-loaded stent, it only takes 17 s for TG-43-DP-ICF to compute, compared to 25 h for the full MC calculation. CONCLUSIONS: The combination of DPK and inhomogeneity corrections is an effective approach to handle both the presence of stent and tissue heterogeneity. Exhibiting good agreement with MC calculation and computational efficiency, the proposed TG-43-DP-ICF method is adequate for dose evaluation and optimization in seed-loaded stent implantation treatment planning.

Delivery of miR-224-5p by Exosomes from Cancer-Associated Fibroblasts Potentiates Progression of Clear Cell Renal Cell Carcinoma.

OBJECTIVES: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma. Cancer-associated fibroblasts (CAFs) as the primary components of cancer stroma can affect tumor progression by secreting exosomes, while exosomes are carriers for proteins, nucleic acids, and other agents that responsible for delivery of biological information. Given this, exosomes derived from CAFs are emerging as promising biomarkers in clinical cancer diagnosis. Nevertheless, their role in clear cell renal cell carcinoma (ccRCC) remains poorly understood. METHODS: Here, we separated fibroblasts from ccRCC tissue, extracted exosomes, observed their morphology, and detected the expression of exosome marker proteins including Hsp70, CD9, and CD63. In the meantime, we labeled exosomes and performed coculture experiment to verify the delivery of miR-224-5p from CAFs to 769-P cells with exosomes as a carrier, so as to clarify the effect of CAF-derived exosomes on ccRCC cell malignant behaviors, as well as to discuss how miR-224-5p involves in above regulation. RESULTS: Transmission electron microscopy was firstly applied, and it was noted that the exosomes we isolated were in normal range. Besides, Western blot also confirmed the presence of exosome marker proteins Hsp70, CD9, and CD63. Furthermore, coculture experiments were performed and the CAF-derived exosomes were observed to be able to facilitate the malignant behaviors of ccRCC cells, and the exosomal miR-224-5p could be internalized by ccRCC cells to participate in regulation of cell proliferation, migration, invasion, and apoptosis. CONCLUSION: To sum up, miR-224-5p can enter ccRCC cells via CAF-derived exosomes, in turn, promoting the malignant behaviors of ccRCC cells, which indicates that miR-224-5p has the potential severing as a therapeutic target for ccRCC.