When surface-enhanced Raman spectroscopy meets complex biofluids: A new representation strategy for reliable and comprehensive characterization.

BACKGROUND: Surface-enhanced Raman spectroscopy (SERS) has gained increasing importance in molecular detection due to its high specificity and sensitivity. Complex biofluids (e.g., cell lysates and serums) typically contain large numbers of different bio-molecules with various concentrations, making it extremely challenging to be reliably and comprehensively characterized via conventional single SERS spectra due to uncontrollable electromagnetic hot spots and irregular molecular motions. The traditional approach of directly reading out the single SERS spectra or calculating the average of multiple spectra is less likely to take advantage of the full information of complex biofluid systems. RESULTS: Herein, we propose to construct a spectral set with unordered multiple SERS spectra as a novel representation strategy to characterize full molecular information of complex biofluids. This new SERS representation not only contains details from each single spectra but captures the temporal/spatial distribution characteristics. To address the ordering-independent property of traditional chemometric methods (e.g., the Euclidean distance and the Pearson correlation coefficient), we introduce Wasserstein distance (WD) to quantitatively and comprehensively assess the quality of spectral sets on biofluids. WD performs its superiority for the quantitative assessment of the spectral sets. Additionally, WD benefits from its independence of the ordering of spectra in a spectral set, which is undesirable for traditional chemometric methods. With experiments on cell lysates and human serums, we successfully achieve the verification for the reproducibility between parallel samples, the uniformity at different positions in the same sample, the repeatability from multiple tests at one location of the same sample, and the cardinality effect of the spectral set. SERS spectral sets also manage to distinguish different classes of human serums and achieve higher accuracy than the traditional prostate-specific antigen in prostate cancer classification. SIGNIFICANCE: The proposed SERS spectral set is a robust representation approach in accessing full information of biological samples compared to relying on a single or averaged spectra in terms of reproducibility, uniformity, repeatability, and cardinality effect. The application of WD further demonstrates the effectiveness and robustness of spectral sets in characterizing complex biofluid samples, which extends and consolidates the role of SERS.

SERSomes for metabolic phenotyping and prostate cancer diagnosis.

Molecular phenotypic variations in metabolites offer the promise of rapid profiling of physiological and pathological states for diagnosis, monitoring, and prognosis. Since present methods are expensive, time-consuming, and still not sensitive enough, there is an urgent need for approaches that can interrogate complex biological fluids at a system-wide level. Here, we introduce hyperspectral surface-enhanced Raman spectroscopy (SERS) to profile microliters of biofluidic metabolite extraction in 15 min with a spectral set, SERSome, that can be used to describe the structures and functions of various molecules produced in the biofluid at a specific time via SERS characteristics. The metabolite differences of various biofluids, including cell culture medium and human serum, are successfully profiled, showing a diagnosis accuracy of 80.8% on the internal test set and 73% on the external validation set for prostate cancer, discovering potential biomarkers, and predicting the tissue-level pathological aggressiveness. SERSomes offer a promising methodology for metabolic phenotyping.

A Prospective Randomized Trial of Neoadjuvant Chemohormonal Therapy vs Hormonal Therapy in Locally Advanced Prostate Cancer Treated by Radical Prostatectomy.

PURPOSE: Benefits of docetaxel-based neoadjuvant chemohormonal therapy (NCHT) before radical prostatectomy (RP) remain largely unknown. We explored whether docetaxel-based NCHT would bring pathological benefits and improve biochemical progression-free survival (bPFS) over neoadjuvant hormonal therapy (NHT) in locally advanced prostate cancer. MATERIALS AND METHODS: A randomized trial was designed recruiting 141 locally advanced, high-risk prostate cancer patients who were randomly assigned at the ratio of 2:1 to the NCHT group (75 mg/m2 body surface area every 3 weeks plus androgen deprivation therapy for 6 cycles) and the NHT group (androgen deprivation therapy for 24 weeks). The primary end point was 3-year bPFS. Secondary end points were pathological response including pathological downstaging and minimal residual disease rates. RESULTS: The NCHT group showed significant benefits in 3-year bPFS compared to the NHT group (29% vs 9.5%, P = .002). At a median follow-up of 53 months, the NCHT group achieved a significantly longer median bPFS time than the NHT group (17 months vs 14 months). No significant differences were found between the 2 groups in pathological downstaging and minimal residual disease rates. CONCLUSIONS: NCHT plus RP achieved significant bPFS benefits when compared with NHT plus RP in high-risk, locally advanced prostate cancer. A larger cohort with longer follow-up duration is essential in further investigation.

Harnessing artificial intelligence for prostate cancer management.

Prostate cancer (PCa) is a common malignancy in males. The pathology review of PCa is crucial for clinical decision-making, but traditional pathology review is labor intensive and subjective to some extent. Digital pathology and whole-slide imaging enable the application of artificial intelligence (AI) in pathology. This review highlights the success of AI in detecting and grading PCa, predicting patient outcomes, and identifying molecular subtypes. We propose that AI-based methods could collaborate with pathologists to reduce workload and assist clinicians in formulating treatment recommendations. We also introduce the general process and challenges in developing AI pathology models for PCa. Importantly, we summarize publicly available datasets and open-source codes to facilitate the utilization of existing data and the comparison of the performance of different models to improve future studies.

[Diagnosis of pulmonary hypertension associated with congenital heart disease based on statistical features of the second heart sound].

Aiming at the problems of obscure clinical auscultation features of pulmonary hypertension associated with congenital heart disease and the complexity of existing machine-aided diagnostic algorithms, an algorithm based on the statistical characteristics of the high-frequency components of the second heart sound signal is proposed. Firstly, an endpoint detection adaptive segmentation method is employed to extract the second heart sounds. Subsequently, the high-frequency component of the heart sound is decomposed using the discrete wavelet transform. Statistical features including the Hurst exponent, Lempel-Ziv information and sample entropy are extracted from this component. Finally, the extracted features are utilized to train an extreme gradient boosting algorithm (XGBoost) classifier, which achieves an accuracy of 80.45% in triple classification. Notably, this method eliminates the need for a noise reduction algorithm, allows for swift feature extraction, and achieves effective multi-classification using only three features. It is promising for early screening of pulmonary hypertension associated with congenital heart disease.

[Research on bark-frequency spectral coefficients heart sound classification algorithm based on multiple window time-frequency reassignment].

The multi-window time-frequency reassignment helps to improve the time-frequency resolution of bark-frequency spectral coefficient (BFSC) analysis of heart sounds. For this purpose, a new heart sound classification algorithm combining feature extraction based on multi-window time-frequency reassignment BFSC with deep learning was proposed in this paper. Firstly, the randomly intercepted heart sound segments are preprocessed with amplitude normalization, the heart sounds were framed and time-frequency rearrangement based on short-time Fourier transforms were computed using multiple orthogonal windows. A smooth spectrum estimate is calculated by arithmetic averaging each of the obtained independent spectra. Finally, the BFSC of reassignment spectrum is extracted as a feature by the Bark filter bank. In this paper, convolutional network and recurrent neural network are used as classifiers for model comparison and performance evaluation of the extracted features. Eventually, the multi-window time-frequency rearrangement improved BFSC method extracts more discriminative features, with a binary classification accuracy of 0.936, a sensitivity of 0.946, and a specificity of 0.922. These results present that the algorithm proposed in this paper does not need to segment the heart sounds and randomly intercepts the heart sound segments, which greatly simplifies the computational process and is expected to be used for screening of congenital heart disease.

Identifying High Gleason Score Prostate Cancer by Prostate Fluid Metabolic Fingerprint-Based Multi-Modal Recognition.

Prostate cancer (PCa) is the second most common cancer in males worldwide. The Gleason scoring system, which classifies the pathological growth pattern of cancer, is considered one of the most important prognostic factors for PCa. Compared to indolent PCa, PCa with high Gleason score (h-GS PCa, GS ≥ 8) has greater clinical significance due to its high aggressiveness and poor prognosis. It is crucial to establish a rapid, non-invasive diagnostic modality to decipher patients with h-GS PCa as early as possible. In this study, ferric nanoparticle-assisted laser desorption/ionization mass spectrometry (FeNPALDI-MS) to extract prostate fluid metabolic fingerprint (PSF-MF) is employed and combined with the clinical features of patients, such as prostate-specific antigen (PSA), to establish a multi-modal diagnosis assisted by machine learning. This approach yields an impressive area under the curve (AUC) of 0.87 to diagnose patients with h-GS, surpassing the results of single-modal diagnosis using only PSF-MF or PSA, respectively. Additionally, using various screening methods, six key metabolites that exhibit greater diagnostic efficacy (AUC = 0.96) are identified. These findings also provide insights into related metabolic pathways, which may provide valuable information for further elucidation of the pathological mechanisms underlying h-GS PCa.

Precise diagnosis and risk stratification of prostate cancer by comprehensive serum metabolic fingerprints: a prediction model study.

OBJECTIVES: Prostate cancer (PCa) is one of the most common malignancies in men worldwide and has caused increasing clinical morbidity and mortality, making timely diagnosis and accurate staging crucial. The authors introduced a novel approach based on mass spectrometry for precise diagnosis and stratification of PCa to facilitate clinical decision-making. METHODS: Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry analysis of trace blood samples was combined with machine learning algorithms to construct diagnostic and stratification models. A total of 367 subjects, comprising 181 with PCa and 186 with non-PCa were enrolled. Additional 60 subjects, comprising 30 with PCa and 30 with non-PCa were enrolled as an external cohort for validation. Subsequent metabolomic analysis was carried out using Autoflex MALDI-TOF, and the mass spectra were introduced into various algorithms to construct different models. RESULTS: Serum metabolic fingerprints were successfully obtained from 181 patients with PCa and 186 patients with non-PCa. The diagnostic model based on the eight signals demonstrated a remarkable area under curve of 100% and was validated in the external cohort with the area under curve of 87.3%. Fifteen signals were selected for enrichment analysis, revealing the potential metabolic pathways that facilitate tumorigenesis. Furthermore, the stage prediction model with an overall accuracy of 85.9% precisely classified subjects with localized disease and those with metastasis. The risk stratification model, with an overall accuracy of 89.6%, precisely classified the subjects as low-risk and high-risk. CONCLUSIONS: Our study facilitated the timely diagnosis and risk stratification of PCa and provided new insights into the underlying mechanisms of metabolic alterations in PCa.

Extracellular vesicles related gene HSPH1 exerts anti-tumor effects in prostate cancer via promoting the stress response of CD8 + T cells.

BACKGROUND: T cell stress response state (TSTR), as a novel immune concept previous studies have proposed, has not yet been explored in prostate cancer (PC). As a type of cellular efflux, exosomes play important roles in the occurrence and development of PC. METHOD: Here, we conducted a combined analysis on extracellular vesicle related genes (EVRGs) in PC using data from single-cell RNA (scRNA), spatial transcriptome (ST), and bulk RNA sequencing. RESULT: Preliminary findings have revealed that heat shock protein family H (Hsp110) member 1 (HSPH1) possesses two identities, one being EVRGs and the other being a member of the heat shock protein family involved in TSTR, which may promote the differentiation of conventional T cells towards Th1 or Th2 cells through the pathway of IL2-MYC-IL2RA, thereby promoting the increase of CD8 + T cells in the tumor area, especially in the invasive zone, and inhibiting the invasion of PCs. We also notice the negative response of HSPH1 + CD8 + T cell related genes in immune checkpoint blockade (ICB). Western blot (WB) and droplet digital Polymerase Chain Reaction (ddPCR) demonstrated that the mRNA and protein levels of HSPH1 in EVs of PCs were significantly higher than those in adjacent tissues. CONCLUSION: Results above indicate the potential of HSPH1 as a critical therapeutic target in PC.

Clinical report and genetic analysis of a novel variant in ZMIZ1 causing neurodevelopmental disorder with dysmorphic factors and distal skeletal anomalies in a Chinese family.

BACKGROUND: Neurodevelopmental disorder with dysmorphic factors and distal skeletal anomalies (NEDDFSA) is a rare and phenotypically variable disorder. The zinc finger MIZ-type containing 1 gene (ZMIZ1) is a causative gene of NEDDFSA that encodes a protein inhibitor of the activated STAT-like family transcriptional regulator. Given the rarity of reported NEDDFSA cases, new phenotypes and genotypes of this disorder are still being discovered. OBJECTIVE: This study describes the phenotype characteristics of a Chinese NEDDFSA family caused by a novel ZMIZ1 variant. METHODS: We reviewed the clinical phenotype of a Chinese patient with NEDDFSA and performed whole-exome sequencing (WES) of the patient's family. We simulated the potential biological harmfulness of the mutant protein. Plasmids were constructed and used for western blot and immunofluorescence assays to analyze protein expression levels. RESULTS: The patient was a 6-month-old male infant who exhibited dysmorphic facial features, neurodevelopmental abnormalities, congenital heart disease, and previously unreported genitourinary system anomalies. WES revealed a non-frameshift deletion variant in ZMIZ1 (NM_020338.4: c.858_875del, p.Val288_Ala293del), resulting in a structural alteration in the protein's alanine-rich domain. Western blot and immunofluorescence assays indicated a significant decrease in the expression level of the mutant ZMIZ1 protein compared to the wild-type protein. CONCLUSION: The clinical manifestations of this patient may be associated with the ZMIZ1 variant, and the structural alteration in the alanine-rich domain of the ZMIZ1 protein may contribute to a more complex disease phenotype. These results expand the genotype-phenotype correlation of ZMIZ1.

Advances of hafnium based nanomaterials for cancer theranostics.

Hafnium-based nanomaterials (Hf-NMs) have attracted the interest of numerous biomedical researchers by their unique properties. Recent years have witnessed significant advancements in the field of Hafnium-based nanomaterials, particularly in the context of cancer diagnosis and treatment. However, research in this area, especially concerning the clinical application of Hafnium-based nanomaterials, has not been thoroughly reviewed. This review will cover: 1) Classification and synthesis of Hafnium-based nanomaterials including Hafnium oxide nanomaterials, Hafnium Metal-Organic Frameworks/nanoscale coordination polymers (MOFs/NCPs); 2) Hafnium-based nanomaterials act as contrast enhancement agent for cancer imaging, and hafnium-based nanomaterials used for diagnosis in cancer liquid biopsy; 3) hafnium-based nanomaterials for cancer therapy, including hafnium-based nanomaterials for radiotherapy, hafnium-based nanomaterials for photodynamic therapy, hafnium-based nanomaterials for various combined therapy; and 4) Translation, toxicity, and safety for Hf-NMs in human and preclinical animal models. More attention will be given to the clinical translation of Hf-NMs in cancer.

[Heart sound classification algorithm based on time-frequency combination feature and adaptive fuzzy neural network].

Feature extraction methods and classifier selection are two critical steps in heart sound classification. To capture the pathological features of heart sound signals, this paper introduces a feature extraction method that combines mel-frequency cepstral coefficients (MFCC) and power spectral density (PSD). Unlike conventional classifiers, the adaptive neuro-fuzzy inference system (ANFIS) was chosen as the classifier for this study. In terms of experimental design, we compared different PSDs across various time intervals and frequency ranges, selecting the characteristics with the most effective classification outcomes. We compared four statistical properties, including mean PSD, standard deviation PSD, variance PSD, and median PSD. Through experimental comparisons, we found that combining the features of median PSD and MFCC with heart sound systolic period of 100-300 Hz yielded the best results. The accuracy, precision, sensitivity, specificity, and F1 score were determined to be 96.50%, 99.27%, 93.35%, 99.60%, and 96.35%, respectively. These results demonstrate the algorithm's significant potential for aiding in the diagnosis of congenital heart disease.

The ELAVL3/MYCN positive feedback loop provides a therapeutic target for neuroendocrine prostate cancer.

Neuroendocrine prostate cancer is a rapidly progressive and lethal disease characterized by early visceral metastasis, poor prognosis, and limited treatment options. Uncovering the oncogenic mechanisms could lead to the discovery of potential therapeutic avenues. Here, we demonstrate that the RNA-binding protein ELAVL3 is specifically upregulated in neuroendocrine prostate cancer and that overexpression of ELAVL3 alone is sufficient to induce the neuroendocrine phenotype in prostate adenocarcinoma. Mechanistically, ELAVL3 is transcriptionally regulated by MYCN and subsequently binds to and stabilizes MYCN and RICTOR mRNA. Moreover, ELAVL3 is shown to be released in extracellular vesicles and induce neuroendocrine differentiation of adenocarcinoma cells via an intercellular mechanism. Pharmacological inhibition of ELAVL3 with pyrvinium pamoate, an FDA-approved drug, effectively suppresses tumor growth, reduces metastatic risk, and improves survival in neuroendocrine prostate cancer mouse models. Our results identify ELAVL3 as a critical regulator of neuroendocrine differentiation in prostate cancer and propose a drug repurposing strategy for targeted therapies.

Time-course transcriptome analysis of lungs from mice infected with inhaled aerosolized Stenotrophomonas maltophilia.

Background: Stenotrophomonas maltophilia (SMA) has emerged as an important pathogen capable of causing an opportunistic and nosocomial infection. We performed RNA sequencing (RNA-seq) of lung tissues from mice with pulmonary SMA infection over time via aerosolized intratracheal inhalation to investigate transcription profile changes in SMA-infected lungs. Methods: A mouse model of acute lethal SMA pneumonia was established in this study using aerosolized intratracheal inhalation, laying the groundwork for future SMA research. RNA-seq was then used to create a transcriptional profile of the lungs of the model mice at 0, 4, 12, 24, 48, and 72 hours post-infection (hpi). Mfuzz time clustering, weighted gene coexpression network analysis (WGCNA), and Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse) were used to analyze RNA-seq data. Results: A gradual change in the lung transcriptional profile was observed, which was consistent with the expected disease progression. At 4 hpi, the expression of genes related to the acute phase inflammatory response increased, as predicted abundance of innate immune cells. At this stage, an increased demand for energy was also observed, including an increase in the expression of genes involved in circulation, muscle function and mitochondrial respiratory chain function. The expression of genes associated with endoplasmic reticulum stress (ERS) and autophagy increased at 24 hpi. Unlike the number of natural killer (NK) cells following most bacterial lung infections, the abundance of NK cells decreased following infection with SMA. The expression levels of Cxcl10, Cd14, Gbp5, Cxcr2, Tnip1, Zc3h12a, Egr1, Sell and Gbp2 were high and previously unreported in SMA pneumonia, and they may be important targets for future studies. Conclusions: To our knowledge, this is the first study to investigate the pulmonary transcriptional response to SMA infection. The findings shed light on the molecular mechanisms underlying the pathogenesis of SMA pneumonia, which may aid in the development of therapies to reduce the occurrence of SMA pulmonary infection.

Transcriptomic signature defines two subtypes of locally advanced PCa with distinct neoadjuvant therapy benefits.

Background: Patients with locally advanced prostate cancer (LAPCa) received docetaxel-based neoadjuvant chemo-hormonal therapy (NCHT) had better clinical outcomes after surgery compared to neoadjuvant hormonal therapy (NHT) groups, but not all patients experienced favorable clinical outcomes with NCHT, raising the necessity for potential biomarker assessment. The transcriptomic profiling offers a unique opportunity to interrogate the accurate response to NCHT and NHT treatment and to identify the predictive biomarkers for neoadjuvant therapy. Methods: The whole transcriptomic profiling was performed on baseline biopsies and surgical tissue specimens from 64 patients with LAPCa at Renji Hospital between 2014 and 2018. Biochemical progression-free survival (bPFS)-based gene-by-treatment interaction effects were used to identify predictive biomarkers for guiding treatment selection. Results: Comparing the transcriptome profiling of pre- and post-treatment LAPCa specimens, NHT and NCHT shared 1917 up- and 670 down-regulated DEGs at least 2-fold. Pathway enrichment analysis showed up-regulated pathways in response to NHT and NCHT were both enriched in cytokine receptor interaction pathways, and down-regulated pathways in response to NCHT were enriched in cell cycle pathways. By comprehensive transcriptome profiling of 64 baseline specimens, ten predictive markers were identified. We integrated them into the signature to evaluate the relative benefits of neoadjuvant therapy, which categorizes patients into two subgroups with relative bPFS benefits from either NHCT or NHT. In the high-score (≥ -95.798) group (n = 37), NCHT treatment led to significantly longer bPFS (P< 0.0001), with a clear and early separation of the Kaplan-Meier curves. In the low-score (< -95.798) group (n = 27), NHT also led to significantly longer bPFS (P=0.0025). Conclusions: In this study, we proposed the first predictive transcriptomic signature might potentially guide the effective selection of neoadjuvant therapy in LAPCa and might provide precise guidance toward future personalized adjuvant therapy. Trial registration: The study was approved by the Ethics Committee of Renji Hospital affiliated to Shanghai Jiao Tong University (Ky2019-087).

Early serial circulating tumor DNA sequencing predicts the efficacy of chemohormonal therapy in patients with metastatic hormone-sensitive prostate cancer.

Chemohormonal therapy is a standard treatment for metastatic hormone-sensitive prostate cancer (mHSPC); however, there are no biomarkers to guide clinical decisions regarding therapeutic options. We aimed to evaluate the clinical utility of serial circulating tumor DNA (ctDNA) sequencing in early prediction of the efficacy of chemohormonal therapy in patients with mHSPC. We conducted a retrospective observational study of 66 patients with mHSPC receiving chemohormonal therapy who underwent serial targeted gene-panel ctDNA sequencing. Peripheral blood samples were collected before treatment and after one cycle of chemotherapy. Kaplan-Meier and log-rank analyses were used to analyze the association between ctDNA status and disease progression-free survival. Serial changes in the ctDNA fraction and genetic alterations were also observed. After one cycle of chemotherapy, 23 (34.8%) patients displayed elevated ctDNA levels, whereas the other patients (65.2%, n = 43) did not. The median time to castration resistance in the group with reduced ctDNA levels was significantly longer than that in the group with increased ctDNA levels (17.70 vs. 8.43 months [mo], p < 0.001). Interestingly, patients with de novo alterations in homologous recombination pathway genes after treatment experienced a shorter time to castration resistance than that experienced by the remaining patients (8.02 vs. 13.20 mo, p = 0.011). The increased ctDNA levels or de novo alterations detected in homologous recombination pathway genes are a harbinger of disease progression. Early serial ctDNA sequencing could aid clinicians in making accurate treatment decisions.

Baseline PSMA-PET/CT as a predictor of PSA persistence following radical prostatectomy in high-risk nonmetastatic prostate cancer patients receiving neoadjuvant therapy.

BACKGROUND: The precise staging and proper management of high-risk prostate cancer (PCa) continues to be a challenge. We aimed to demonstrate the prognostic value of baseline prostate-specific membrane antigen-ligand positron emission tomography/computed tomography (PSMA-PET/CT) in high-risk, nonmetastatic PCa patients who received neoadjuvant hormonal or chemohormonal treatment followed by radical prostatectomy (RP). METHODS: We performed retrospective analyses of 70 patients with high-risk, nonmetastatic PCa confirmed by biopsy between 2018 and 2021. All patients underwent neoadjuvant therapy followed by RP and pelvic lymph node dissection (PLND); PSMA-PET/CT was performed before initiation of neoadjuvant therapy. Acquired image information and clinical characteristics/outcomes were examined for possible associations. RESULTS: Among 70 high-risk PCa patients, median age was 69 years old and median prostate specific antigen (PSA) at presentation was 58.5 ng/mL. Thirty (42.9%) patients had uptake of the PSMA tracer only in the primary PCa lesions and 40 (57.1%) patients had PSMA-positive lesions in regional or distant sites. Sixteen (32%) localized PCa patients defined by pre-PET magnetic resonance imaging were found to have locally advanced PCa based on PSMA-PET/CT. Fifteen (30%) localized PCa patients and 7 (35%) locally advanced PCa patients were upstaged to metastatic PCa. The sensitivity and specificity of PSMA-PET/CT for the detection of lymph node involvement were 90.9% and 69.5%, respectively, with a positive prediction value of 35.7% and negative prediction value of 97.6%. The diagnostic accuracy was 72.9%. Univariate analysis showed upstaging, tumor stage, and metastasis location based on PSMA-PET/CT are predictors to PSA persistence after surgery, while multivariate logistic regression analysis showed only the tumor stage based on PSMA-PET/CT remained an independent predictor of the outcome. CONCLUSIONS: This study further highlights the accuracy and necessity of PSMA-PET/CT in newly diagnosed, high-risk, nonmetastatic PCa patients.

Identification of cancer-associated fibroblasts subtypes in prostate cancer.

Introduction: Cancer-associated fibroblasts (CAFs) are one of the most abundant cell types in tumor microenvironment. However, the phenotypic and functional heterogeneities among CAFs have not been sufficiently investigated in prostate cancer. Methods: We obtained and analyzed the single-cell RNA-sequencing data from 26 hormone-sensitive prostate cancer samples and 8 castration-resistant prostate cancer samples, along with the analysis of bulk-sequencing datasets. Furthermore, we performed multicolor immunofluorescence staining to verify the findings from the data analysis. Results: We identified two major CAFs subtypes with distinct molecular characteristics and biological functions in prostate cancer microenvironment, namely αSMA+ CAV1+ CAFs-C0 and FN1+ FAP+ CAFs-C1. Another single-cell RNA-sequencing dataset containing 7 bone metastatic prostate cancer samples demonstrated that osteoblasts in the bone metastatic lesions comprised two subtypes with molecular characteristics and biological functions similar to CAFs-C0 and CAFs-C1 in the primary tumor sites. In addition, we discovered a transcriptional factor regulatory network depending on CAFs-C1. CAFs-C1, but not CAFs-C0, was associated with castration resistance and poor prognosis. We also found that CAFs-C1 signature was involved in treatment resistance to immune checkpoint inhibitors. Discussion: In summary, our results identified the presence of heterogeneous CAFs subtypes in prostate cancer microenvironment and the potential of specific CAFs subtype as therapeutic target for castration-resistant prostate cancer.