Integrative analysis regarding the correlation between collagen-related genes and prostate cancer.

PURPOSE: Prostate cancer (PCa) is a common malignancy in men, with an escalating mortality rate attributed to Recurrence and metastasis. Recent studies have illuminated collagen's critical regulatory role within the tumor microenvironment, significantly influencing tumor progression. Accordingly, this investigation is dedicated to examining the relationship between genes linked to collagen and the prognosis of PCa, with the objective of uncovering any possible associations between them. METHODS: Gene expression data for individuals with prostate cancer were obtained from the TCGA repository. Collagen-related genes were identified, leading to the development of a risk score model associated with biochemical recurrence-free survival (BRFS). A prognostic nomogram integrating the risk score with essential clinical factors was crafted and evaluated for efficacy. The influence of key collagen-related genes on cellular behavior was confirmed through various assays, including CCK8, invasion, migration, cell cloning, and wound healing. Immunohistochemical detection was used to evaluate PLOD3 expression in prostate cancer tissue samples. RESULTS: Our study identified four key collagen-associated genes (PLOD3, COL1A1, MMP11, FMOD) as significant. Survival analysis revealed that low-risk groups, based on the risk scoring model, had significantly improved prognoses. The risk score was strongly associated with prostate cancer prognosis. Researchers then created a nomogram, which demonstrated robust predictive efficacy and substantial clinical applicability.Remarkably, the suppression of PLOD3 expression notably impeded the proliferation, invasion, migration, and colony formation capabilities of PCa cells. CONCLUSION: The risk score, derived from four collagen-associated genes, could potentially act as a precise prognostic indicator for BRFS of patients. Simultaneously, our research has identified potential therapeutic targets related to collagen. Notably, PLOD3 was differentially expressed in cancer and para-cancer tissues in clinical specimens and it also was validated through in vitro studies and shown to suppress PCa tumorigenesis following its silencing.

Utilizing Liquid-liquid phase separation-related lncRNAs to predict the prognosis and treatment response of PCa.

BACKGROUND: Studies have indicated a close association between genes linked to liquid-liquid phase separation (LLPS) and the progression of prostate cancer (PCa). However, the interplay among long non-coding RNAs (lncRNAs) linked to LLPS in PCa remains elusive. Therefore, we constructed a prediction model based on LLPS-related LncRNA in PCa to explore its relationship with the prognosis and drug treatment of PCa. METHODS: We obtained clinical and sequencing data from TCGA and LLPS genes from the Phase Separation Protein Database. By analyzing the differential expression of LLPS-related genes and lncRNAs in prostate cancer, and using Poisson correlation, we identified LLPS-related lncRNAs. Prognostic LLPS-lncRNAs were found through prognostic correlation analysis and included in a Cox model to compute regression coefficients. Patients were scored and divided into high- and low-risk groups. Independent prognostic factors were integrated into a prognostic nomogram with risk and Gleason scores. We also conducted drug sensitivity analyses, GSEA, and validated the impact of key lncRNAs through functional experiments. RESULTS: Our study identified five LLPS-associated lncRNAs that are of prognostic importance. And found notable disparities in biochemical recurrence rates and survival outcomes between these risk groups, with the low-risk cohort exhibiting superior prognostic indicators. Moreover, our prediction nomogram demonstrated robust predictive accuracy and significant clinical utility. Furthermore, our model exhibited promising capabilities in forecasting patient sensitivity to various conventional therapeutic drugs, thereby highlighting its potential in personalized treatment strategies. GSEA showed that these lncRNAs may influence PCa prognosis and sensitivity to therapeutic agents by affecting pathways such as cell cycle. Knockdown of AC009812.4 could inhibit the ability of PCa cells to proliferate, migrate and invade, and compare to paracancerous tissue, AC009812.4 in PCa tissue has significantly higher expression. CONCLUSION: Our research uncovers the prognostic significance of lncRNAs associated with LLPS in PCa and established a model exhibiting excellent predictive accuracy for prognosis. Those lncRNAs may influence progress of PCa as well as sensitivity to therapy drugs through pathways such as cell cycle.

Identification and validation of screening models for breast cancer with 3 serum miRNAs in an 11,349 samples mixed cohort.

PURPOSE: The study focuses on enhancing breast cancer (BC) prognosis through early detection, aiming to establish a non-invasive, clinically viable BC screening method using specific serum miRNA levels. METHODS: Involving 11,349 participants across BC, 11 other cancer types, and control groups, the study identified serum biomarkers through feature selection and developed two BC screening models using six machine learning algorithms. These models underwent evaluation across test, internal, and external validation sets, assessing performance metrics like accuracy, sensitivity, specificity, and the area under the curve (AUC). Subgroup analysis was conducted to test model stability. RESULTS: Based on the three serum miRNA biomarkers (miR-1307-3p, miR-5100, and miR-4745-5p), a BC screening model, SM4BC3miR model, was developed. This model achieved AUC performances of 0.986, 0.986, and 0.939 on the test, internal, and external sets, respectively. Furthermore, the SSM4BC model, utilizing ratio scores of miR-1307-3p/miR-5100 and miR-4745-5p/miR-5100, showed AUCs of 0.973, 0.980, and 0.953, respectively. Subgroup analyses underscored both models' robustness and stability. CONCLUSION: This research introduced the SM4BC3miR and SSM4BC models, leveraging three specific serum miRNA biomarkers for breast cancer screening. Demonstrating high accuracy and stability, these models present a promising approach for early detection of breast cancer. However, their practical application and effectiveness in clinical settings remain to be further validated.

An In Vivo Assessment of a Novel Temperature Control Flexible Ureteroscope System for Monitoring and Controlling Intrarenal Temperature During Flexible Ureteroscopy.

OBJECTIVE: To evaluate the efficacy of a novel temperature control flexible ureteroscope system in the precise monitoring and control of intrarenal temperature (IRT) during ureteroscopy. METHODS: We developed a novel temperature control flexible ureteroscope system (PT-Scope), including a temperature-monitoring ureteroscope and a irrigation-suction platform for temperature regulation. A porcine thermometry model was established to observe temperature changes under varying holmium laser powers (10, 20, 30 W) and irrigation rates (0, 20, 50 mL/min), utilizing percutaneous nephrostomy thermometry and PT-Scope measurements, with subsequent evaluation of temperature variations at different distances from the laser fiber tip. A porcine kidney stone model was established while porcine was randomly assigned to two groups: In the temperature control group, PT-Scope was connected to the irrigation-suction platform with temperature regulation, while in the nontemperature control group without temperature regulation. Comparative analysis was performed to evaluate differences in IRT between the two groups. RESULTS: Across various laser powers and irrigation rates, the temperature measurement capability of the PT-Scope was precise, demonstrating consistency with percutaneous nephrostomy temperature measurements. The temperature obtained from the PT-Scope reflect the temperature approximately 0.05 cm away from the fiber tip, whereas temperatures close to fiber tip were significantly higher. The peak temperature of the temperature control group vs nontemperature control group were 31.70 ± 2.609°C and 44.37 ± 3.318 °C, respectively (P < 0.01). The mean temperature of the temperature control group vs nontemperature control group was 27.40 ± 2.107 °C vs 35.9 ± 1.921 °C (P < 0.01). CONCLUSION: PT-Scope has demonstrated the capability to precisely monitor and control IRT within a safe threshold.

Clinical-Inspired Framework for Automatic Kidney Stone Recognition and Analysis on Transverse CT Images.

The stone recognition and analysis in CT images are significant for automatic kidney stone diagnosis. Although certain contributions have been made, existing methods overlook the promoting effect of clinical knowledge on model performance and clinical interpretation. Thus, it is attractive to establish methods for detecting and evaluating kidney stones originating from the practical diagnostic process. Inspired by this, a novel clinical-inspired framework is proposed to involve the diagnostic process of urologists for better analysis. The diagnostic process contains three main steps, the localization step, the identification step and the evaluation step. Three modules integrating the decision-making mode of urologists are designed to mimic the diagnosis process. The object attention module simulates the localization step to provide the position of kidneys by embedding weight feature factor and angle loss. The feature-driven discriminative module mimics the identification step to detect stones by extracting geometric and positional features. The analysis module based on the principle of clustering and graphic combination is a quantitative analysis strategy for simulating the evaluation step. This work constructed a clinical dataset collecting 27,885 transverse CT images with stones and/or clinical interference. Experiments on the dataset show that the object attention module outperforms the well-performing Yolov7 model by +1% mAP.5:.95, and the analysis module outperforms the well-performing AR-DBSCAN model and the formula method by +21.9% average cluster accuracy and -17.35% average error. Experiments demonstrate that the proposed framework is recently the most effective solution for recognizing and evaluating kidney stones.

Developing and validating a nomogram for penile cancer survival: A comprehensive study based on SEER and Chinese data.

OBJECTIVE: The primary aim of this study was to create a nomogram for predicting survival outcomes in penile cancer patients, utilizing data from the Surveillance, Epidemiology, and End Results (SEER) and a Chinese organization. METHODS: Our study involved a cohort of 5744 patients diagnosed with penile cancer from the SEER database, spanning from 2004 to 2019. In addition, 103 patients with penile cancer from Sun Yat-sen Memorial Hospital of Sun Yat-sen University were included during the same period. Based on the results of regression analysis, a nomogram is constructed and validated internally and externally. The predictive performance of the model was evaluated by concordance index (c-index), area under the curve, decision curve analysis, and calibration curve, in internal and external datasets. Finally, the prediction efficiency is compared with the TNM staging model. RESULTS: A total of 3154 penile patients were randomly divided into the training group and the internal validation group at a ratio of 2:1. Nine independent risk factors were identified, including age, race, marital status, tumor grade, histology, TNM stage, and the surgical approach. Based on these factors, a nomogram was constructed to predict OS. The nomogram demonstrated relatively better consistency, predictive accuracy, and clinical relevance, with a c-index over 0.73 (in the training cohort, the validation cohort, and externally validation cohort.) These evaluation indexes are far better than the TNM staging system. CONCLUSION: Penile cancer, often overlooked in research, has lacked detailed investigative focus and guidelines. This study stands as the first to validate penile cancer prognosis using extensive data from the SEER database, supplemented by data from our own institution. Our findings equip surgeons with an essential tool to predict the prognosis of penile cancer better suited than TNM, thereby enhancing clinical decision-making processes.

Elucidating the role of MICAL1 in pan-cancer using integrated bioinformatics and experimental approaches.

Molecule interacting with CasL 1 (MICAL1) is a crucial protein involved in cell motility, axon guidance, cytoskeletal dynamics, and gene transcription. This pan-cancer study analyzed MICAL1 across 33 cancer types using bioinformatics and experiments. Dysregulated expression, diagnostic potential, and prognostic value were assessed. Associations with tumor characteristics, immune factors, and drug sensitivity were explored. Enrichment analysis revealed MICAL1's involvement in metastasis, angiogenesis, metabolism, and immune pathways. Functional experiments demonstrated its impact on renal carcinoma cells. These findings position MICAL1 as a potential biomarker and therapeutic target in specific cancers, warranting further investigation into its role in cancer pathogenesis.

The predictive significance of chromobox family members in prostate cancer in humans.

PURPOSE: The Chromobox (CBX) family proteins are crucial elements of the epigenetic regulatory machinery and play a significant role in the development and advancement of cancer. Nevertheless, there is limited understanding regarding the role of CBXs in development or progression of prostate cancer (PCa). Our objective is to develop a unique prognostic model associated with CBXs to improve the accuracy of predicting outcomes of patients with PCa. METHODS: Data from TCGA and GEO databases were analyzed to assess differential expression, prognostic value, gene pathway enrichment, and immune cell infiltration. COX regression analysis was utilized to identify the independent prognostic factors that impact disease-free survival (DFS). The expression of CBX2 and FOXP3+ cells infiltration was verified by immunohistochemical staining of clinical tissue sections. In vitro proliferation, migration and invasion assay were conducted to examine the function of CBX2. RNA-seq was employed to examine the CBX2 related pathway enrichment. RESULTS: CBX2, CBX3, CBX4, and CBX8 were upregulated, while CBX6 and CBX7 were downregulated in PCa tissues. CBXs expression varied by stage and grade. Elevated expression of CBX1, CBX2, CBX3, CBX4 and CBX8 is correlated with poor outcome. CBX2 expression, T stage, and Gleason score were independent prognostic factors. The expression level of CBX2 in PCa tissues was significantly higher than that in adjacent normal tissues. More Treg infiltration was observed in the group with high CBX2 expression. CBX2 expression affected PCa cell growth, migration, and invasion. CONCLUSIONS: CBX2 is involved in the development and advancement of PCa, suggesting its potential as a reliable prognostic indicator for PCa patients.

Establishment of a prognostic risk prediction model incorporating disulfidptosis-related lncRNA for patients with prostate cancer.

PURPOSE: Prostate cancer (PCa) is one of the major tumor diseases that threaten men's health globally, and biochemical recurrence significantly impacts its prognosis. Disulfidptosis, a recently discovered cell death mechanism triggered by intracellular disulfide accumulation leading to membrane rupture, is a new area of research in the context of PCa. Currently, its impact on PCa remains largely unexplored. This study aims to investigate the correlation between long non-coding RNAs (lncRNAs) associated with disulfidptosis and the prognosis of PCa, seeking potential connections between the two. METHODS: Transcriptomic data for a PCa cohort were obtained from the Cancer Genome Atlas database. Disulfidptosis-related lncRNAs (DDRLs) were identified through differential expression and Pearson correlation analysis. DDRLs associated with biochemical recurrence-free survival (BRFS) were precisely identified using univariate Cox and LASSO regression, resulting in the development of a risk score model. Clinical factors linked to BRFS were determined through both univariate and multivariate Cox analyses. A prognostic nomogram combined the risk score with key clinical variables. Model performance was assessed using Receiver Operating Characteristic (ROC) curves, Decision Curve Analysis (DCA), and calibration curves. The functional impact of a critical DDRL was substantiated through assays involving CCK8, invasion, migration, and cell cloning. Additionally, immunohistochemical (IHC) staining for the disulfidptosis-related protein SLC7A11 was conducted. RESULTS: The prognostic signature included AC026401.3, SNHG4, SNHG25, and U73166.1 as key components. The derived risk score from these signatures stood as one of the independent prognostic factor for PCa patients, correlating with poorer BRFS in the high-risk group. By combining the risk score with clinical variables, a practical nomogram was created, accurately predicting BRFS of PCa patients. Notably, silencing AC026401.3 significantly hindered PCa cell proliferation, invasion, migration, and colony formation. IHC staining revealed elevated expression of the dithiosulfatide-related protein SLC7A11 in tumor tissue. CONCLUSIONS: A novel prognostic signature for PCa DDRLs, possessing commendable predictive power, has been constructed, simultaneously providing potential therapeutic targets associated with disulfidptosis, among which AC026401.3 has been validated in vitro and demonstrated inhibition of PCa tumorigenesis after its silencing.