Development and Validation of a Clinic Machine Learning Classifier for the Prediction of Risk Stratifications of Prostate Cancer Bone Metastasis Progression to Castration Resistance.

Objective: To explore the predictive factors and predictive model construction for the progression of prostate cancer bone metastasis to castration resistance. Methods: Clinical data of 286 patients diagnosed with prostate cancer with bone metastasis, initially treated with endocrine therapy, and progressing to metastatic castration resistant prostate cancer (mCRPC) were collected. By comparing the differences in various factors between different groups with fast and slow occurrence of castration-resistant prostate cancer (CRPC). Kaplan-Meier survival analysis and COX multivariate risk proportional regression model were used to compare the differences in the time to progression to CRPC in different groups. The COX multivariate risk proportional regression model was used to evaluate the impact of candidate factors on the time to progression to CRPC and establish a predictive model. The accuracy of the model was then tested using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Results: The median time for 286 mCRPC patients to progress to CRPC was 17 (9.5-28.0) months. Multivariate analysis showed that the lowest value of PSA (PSA nadir), the time when PSA dropped to its lowest value (timePSA), and the number of BM, and LDH were independent risk factors for rapid progression to CRPC. Based on the four independent risk factors mentioned above, a prediction model was established, with the optimal prediction model being a random forest with area under curve (AUC) of 0.946[95% CI: 0.901-0.991] and 0.927[95% CI: 0.864-0.990] in the training and validation cohort, respectively. Conclusion: After endocrine therapy, the PSA nadir, timePSA, the number of BM, and LDH are the main risk factors for rapid progression to mCRPC in patients with prostate cancer bone metastases. Establishing a CRPC prediction model is helpful for early clinical intervention decision-making.

TRPA1 promotes the maturation of embryonic stem cell-derived cardiomyocytes by regulating mitochondrial biogenesis and dynamics.

BACKGROUND: Cardiomyocytes derived from pluripotent stem cells (PSC-CMs) have been widely accepted as a promising cell source for cardiac drug screening and heart regeneration therapies. However, unlike adult cardiomyocytes, the underdeveloped structure, the immature electrophysiological properties and metabolic phenotype of PSC-CMs limit their application. This project aimed to study the role of the transient receptor potential ankyrin 1 (TRPA1) channel in regulating the maturation of embryonic stem cell-derived cardiomyocytes (ESC-CMs). METHODS: The activity and expression of TRPA1 in ESC-CMs were modulated by pharmacological or molecular approaches. Knockdown or overexpression of genes was done by infection of cells with adenoviral vectors carrying the gene of interest as a gene delivery tool. Immunostaining followed by confocal microscopy was used to reveal cellular structure such as sarcomere. Staining of mitochondria was performed by MitoTracker staining followed by confocal microscopy. Calcium imaging was performed by fluo-4 staining followed by confocal microscopy. Electrophysiological measurement was performed by whole-cell patch clamping. Gene expression was measured at mRNA level by qPCR and at protein level by Western blot. Oxygen consumption rates were measured by a Seahorse Analyzer. RESULTS: TRPA1 was found to positively regulate the maturation of CMs. TRPA1 knockdown caused nascent cell structure, impaired Ca2+ handling and electrophysiological properties, and reduced metabolic capacity in ESC-CMs. The immaturity of ESC-CMs induced by TRPA1 knockdown was accompanied by reduced mitochondrial biogenesis and fusion. Mechanistically, we found that peroxisome proliferator-activated receptor gamma coactivator-1α (PGC-1α), the key transcriptional coactivator related to mitochondrial biogenesis and metabolism, was downregulated by TRPA1 knockdown. Interestingly, overexpression of PGC-1α ameliorated the halted maturation induced by TRPA1 knockdown. Notably, phosphorylated p38 MAPK was upregulated, while MAPK phosphatase-1 (MKP-1), a calcium-sensitive MAPK inhibitor, was downregulated in TRPA1 knockdown cells, suggesting that TRPA1 may regulate the maturation of ESC-CMs through MKP-1-p38 MAPK-PGC-1α pathway. CONCLUSIONS: Taken together, our study reveals the novel function of TRPA1 in promoting the maturation of CMs. As multiple stimuli have been known to activate TRPA1, and TRPA1-specific activators are also available, this study provides a novel and straightforward strategy for improving the maturation of PSC-CMs by activating TRPA1. Since a major limitation for the successful application of PSC-CMs for research and medicine lies in their immature phenotypes, the present study takes a big step closer to the practical use of PSC-CMs.

Integrative Analysis of Peripheral Blood Indices for the Renal Sinus Invasion Prediction of T1 Renal Cell Carcinoma: An Ensemble Study Using Machine Learning-Assisted Decision-Support Models.

PURPOSE: Renal sinus invasion is an attributive factor affecting the prognosis of renal cell carcinoma (RCC). This study aimed to construct a risk prediction model that could stratify patients with RCC and predict renal sinus invasion with the help of a machine learning (ML) algorithm. PATIENTS AND METHODS: We retrospectively recruited 1229 patients diagnosed with T1 stage RCC at the Baotou Cancer Hospital between November 2013 and August 2021. Iterative analysis was used to screen out predictors related to renal sinus invasion, after which ML-based models were developed to predict renal sinus invasion in patients with T1 stage RCC. The receiver operating characteristic curve (ROC), decision curve analysis (DCA), and clinical impact curve (CIC) were performed to evaluate the robustness and clinical practicability of each model. RESULTS: A total of 21 candidate variables were shortlisted for model building. Iterative analysis screened that neutrophil to albumin ratio (NAR), hemoglobin level * albumin level * lymphocyte count/platelet count ratio (HALP), prognostic nutrition index (PNI), body mass index*serum albumin/neutrophil-lymphocyte ratio (AKI), NAR, and fibrinogen (FIB) concentration (NARFIB), platelet to lymphocyte ratio (PLR), and R.E.N.A.L score was related to renal sinus invasion and contributed significantly to ML-based algorithm. The areas under the ROC curve (AUCs) of the random forest classifier (RFC) model, support vector machine (SVM), eXtreme gradient boosting (XGBoost), artificial neural network (ANN), and decision tree (DT) ranged from 0.797 to 0.924. The optimal risk probability of renal sinus invasion predicted was RFC (AUC = 0.924, 95% confidence interval [CI]: 0.414-1.434), which showed robust discrimination for identifying high-risk patients. CONCLUSION: We successfully develop practical models for renal sinus invasion prediction, particularly the RFC, which could contribute to early detection via integrating systemic inflammatory factors and nutritional parameters.

Rheinheimera lutimaris sp. nov., a marine bacterium isolated from coastal sediment.

A Gram-stain-negative, aerobic, rod-shaped bacterium, designated strain YQF-2T, was isolated from coastal sediment sampled in Jiangsu Province and characterized phylogenetically and phenotypically. Optimal bacterial growth occurred at 28 °C (range 4-38 °C) and pH 7 (pH 6-10). Phylogenetic analysis based on 16S rRNA gene sequences showed that strain YQF-2T was related to members of the genus Rheinheimera and shared the highest sequence identities with Rheinheimera pacifica KMM 1406T (98.6%), followed by Rheinheimera aestuarii H29T (98.4%), Rheinheimera japonica KMM 9513T (98.3%), Rheinheimera aquimaris SW-353T (98.3%), Rheinheimera hassiensis E48T (97.8%) and Rheinheimera muenzenbergensis E49T (97.7%). The 16S rRNA gene sequence identities between strain YQF-2T and other members of the genus Rheinheimera were below 97.2%. The digital DNA-DNA hybridization value between strain YQF-2T and R. pacifica KMM 1406T was 23.3±2.3%. The average nucleotide identity value between strain YQF-2T and R. pacifica KMM 1406T was 79.7%. The unique respiratory quinone was ubiquinone-8. Phosphatidylethanolamine and phosphatidylglycerol were identified as the major polar lipids. The strain had summed feature 3 (C16:1 ω7c and/or C16:1 ω6c), summed feature 8 (C18:1 ω7c and/or C18:1 ω6c), C16:0, C12:0 3-OH and iso-C17:0 3-OH as major fatty acids. The G+C content of the genomic DNA was 50.0 mol%. On the basis of phenotypic, genotypic and phylogenetic evidence, strain YQF-2T represents a novel species of the genus Rheinheimera, for which the name Rheinheimera lutimaris sp. nov. is proposed, with the type strain YQF-2T (=KCTC 72184T=MCCC 1K03663T).

Rheinheimera sediminis sp. nov., a marine bacterium isolated from coastal sediment.

A Gram-stain-negative, aerobic, rod-shaped bacterium, designated strain YQF-1T, was isolated from coastal sediment in Jiangsu Province (PR China) and characterized phylogenetically and phenotypically. Bacterial optimal growth occurred at 28 °C (range 4-40 °C) and pH 7 (range pH 6-11). Phylogenetic analysis based on 16S rRNA gene sequence indicated that YQF-1T was related to members of the genus Rheinheimera and shared the highest sequence identities with Rheinheimera mesophila DSM 29723T (98.5 %), followed by Rheinheimera tangshanensis DSM 19460T (98.4 %), Rheinheimera tilapiae Ruye-90T (97.9 %), Rheinheimera soli BD-d46T (97.9 %), Rheinheimera aquatica GR5T (97.4 %), Rheinheimera coerulea TAPG2T (97.3 %) and Rheinheimera texasensis A62-14BT (97.1 %). The 16S rRNA gene sequence identities between YQF-1T and other members of the genus Rheinheimera were below 97.0 %. The digital DNA-DNA hybridization value between YQF-1T and Rheinheimera mesophila DSM 29723T was 25.1±2.3 %. The average nucleotide identity (ANI) value between YQF-1T and Rheinheimera mesophila DSM 29723T was 81.4 %. The major respiratory quinone was Q-8. The major polar lipids were phosphatidylethanolamine, phosphatidylglycerol, phospholipid, two unidentified aminolipids and three unidentified lipids. The strain had summed feature 8 (C18 : 1 ω7c and/or C18 : 1 ω6c), C16 : 0, summed feature 3 (C16 : 1 ω7c and/or C16 : 1 ω6c), iso-C15 : 0, and anteiso-C17 : 1 ω9c as the major fatty acids. The G+C content of the genomic DNA was 46.2 mol%. On the basis of phenotypic, genotypic and phylogenetic evidence, strain YQF-1T represents a novel species of the genus Rheinheimera, for which the name Rheinheimera sediminis sp. nov. is proposed, with the type strain YQF-1T (=KCTC 72183T=MCCC 1K03646T).

TRPC3 Regulates the Proliferation and Apoptosis Resistance of Triple Negative Breast Cancer Cells through the TRPC3/RASA4/MAPK Pathway.

Currently, there is no effective molecular-based therapy for triple-negative breast cancer (TNBC). Canonical transient receptor potential isoform 3 (TRPC3) was previously shown to be upregulated in breast cancer biopsy tissues when compared to normal breast tissues. However, the biological role of TRPC3 in breast cancer still remains to be elucidated. In this study, subcellular fractionation followed by Western blot and immunocytochemistry showed that TRPC3 was over-expressed on the plasma membrane of TNBC line MDA-MB-231 when compared to an estrogen receptor-positive cell line MCF-7. TRPC3 blocker Pyr3 and dominant negative of TRPC3 attenuated proliferation, induced apoptosis and sensitized cell death to chemotherapeutic agents in MDA-MB-231 as measured by proliferation assays. Interestingly, Ras GTPase-activating protein 4 (RASA4), a Ca2+-promoted Ras-MAPK pathway suppressor, was found to be located on the plasma membrane of MDA-MB-231. Blocking TRPC3 decreased the amount of RASA4 located on the plasma membrane, with concomitant activation of MAPK pathways. Our results suggest that, in TNBC MDA-MB-231 cells, Ca2+ influx through TRPC3 channel sustains the presence of RASA4 on the plasma membrane where it inhibits the Ras-MAPK pathway, leading to proliferation and apoptosis resistance. Our study reveals the novel TRPC3-RASA4-MAPK signaling cascade in TNBC cells and suggests that TRPC3 may be exploited as a potential therapeutic target for TNBC.