Deciphering the role of zinc homeostasis in the tumor microenvironment and prognosis of prostate cancer.

BACKGROUND: Dysregulation of zinc homeostasis is widely recognized as a hallmark feature of prostate cancer (PCa) based on the compelling clinical and experimental evidence. Nevertheless, the implications of zinc dyshomeostasis in PCa remains largely unexplored. METHODS: In this research, the zinc homeostasis pattern subtype (ZHPS) was constructed according to the profile of zinc homeostasis genes. The identified subtypes were assessed for their immune functions, mutational landscapes, biological peculiarities and drug susceptibility. Subsequently, we developed the optimal signature, known as the zinc homeostasis-related risk score (ZHRRS), using the approach won out in multifariously machine learning algorithms. Eventually, clinical specimens, Bayesian network inference and single-cell sequencing were used to excavate the underlying mechanisms of MT1A in PCa. RESULTS: The zinc dyshomeostasis subgroup, ZHPS2, possessed a markedly worse prognosis than ZHPS1. Moreover, ZHPS2 demonstrated a more conspicuous genomic instability and better therapeutic responses to docetaxel and olaparib than ZHPS1. Compared with traditional clinicopathological characteristics and 35 published signatures, ZHRRS displayed a significantly improved accuracy in prognosis prediction. The diagnostic value of MT1A in PCa was substantiated through analysis of clinical samples. Additionally, we inferred and established the regulatory network of MT1A to elucidate its biological mechanisms. CONCLUSIONS: The ZHPS classifier and ZHRRS model hold great potential as clinical applications for improving outcomes of PCa patients.

Advancements in Uric Acid Stone Detection: Integrating Deep Learning with CT Imaging and Clinical Assessments in the Upper Urinary Tract.

INTRODUCTION: Among upper urinary tract stones, a significant proportion comprises uric acid stones. The aim of this study was to use machine learning techniques to analyze CT scans and blood and urine test data, with the aim of establishing multiple predictive models that can accurately identify uric acid stones. METHODS: We divided 276 patients with upper urinary tract stones into two groups: 48 with uric acid stones and 228 with other types, identified using Fourier-transform infrared spectroscopy. To distinguish the stone types, we created three types of deep learning models and extensively compared their classification performance. RESULTS: Among the three major types of models, considering accuracy, sensitivity, and recall, CLNC-LR, IMG-support vector machine (SVM), and FUS-SVM perform the best. The accuracy and F1 score for the three models were as follows: CLNC-LR (82.14%, 0.7813), IMG-SVM (89.29%, 0.89), and FUS-SVM (29.29%, 0.8818). The area under the curves for classes CLNC-LR, IMG-SVM, and FUS-SVM were 0.97, 0.96, and 0.99, respectively. CONCLUSION: This study shows the feasibility of utilizing deep learning to assess whether urinary tract stones are uric acid stones through CT scans, blood, and urine tests. It can serve as a supplementary tool for traditional stone composition analysis, offering decision support for urologists and enhancing the effectiveness of diagnosis and treatment.

Establishment and application of a nomogram diagram for predicting calcium oxalate stones in patients with urinary tract stones.

This retrospective study aims to examine the correlation between calcium oxalate (CaOx) stones and common clinical tests, as well as urine ionic composition. Additionally, we aim to develop and implement a personalized model to assess the accuracy and feasibility of using charts to predict calcium oxalate stones in patients with urinary tract stones. A retrospective analysis was conducted on data from 960 patients who underwent surgery for urinary stones at the First Affiliated Hospital of Soochow University from January 1, 2010, to December 31, 2022. Among these patients, 447 were selected for further analysis based on screening criteria. Multivariate logistic regression analysis was then performed to identify the best predictive features for calcium oxalate stones from the clinical data of the selected patients. A prediction model was developed using these features and presented in the form of a nomogram graph. The performance of the prediction model was assessed using the C-index, calibration curve, and decision curve, which evaluated its discriminative power, calibration, and clinical utility, respectively. The nomogram diagram prediction model developed in this study is effective in predicting calcium oxalate stones which is helpful in screening and early identification of high-risk patients with calcium oxalate urinary tract stones, and may be a guide for urologists in making clinical treatment decisions.

Glucose Transporter-1 Cooperating with AKT Signaling Promote Gastric Cancer Progression.

OBJECTIVE: High expression of GLUT1 has been observed in numerous solid cancers, facilitating glucose consumption for supporting tumor cell survival. The altered metabolic activity is regulated by series of signaling pathways, including AKT signaling that acts as a key role in glucose metabolism and shows close correlation with the malignant transformation. In this study, we aimed to elucidate the effect of GLUT1 on gastric cancer (GC) and to explore the relation between GLUT1 and AKT signaling. MATERIALS AND METHODS: GLUT1, p-AKT, and p-S6k1 expression were investigated by immunohistochemistry and semi-quantitative analysis in 57 paired-GC samples. The relationship of GLUT1 with clinical indexes in GC tissues was investigated. The effects of GLUT1 on the prognosis of GC patients and the underlying mechanism involved were studied by subgroup analysis. RESULTS: In GC tissues, an obvious increase in GLUT1 expression was observed when compared with that of normal tissues (P<0.001). Advanced clinicopathological factors (tumor size P=0.019, invasion depth P=0.002, lymph node metastasis P<0.001, differentiation P=0.024, neural invasion P=0.003, and TNM staging P=0.001) correlated with high GLUT1 levels. GLUT1 was an independent risk factor resulting in poor prognosis (P=0.002, HR=5.132). GLUT1 increased the activation ratio of p-AKT (P<0.01) and p-S6K1 (P<0.001) in GC. The expression of p-S6K1 and GLUT1 was positively correlated. (P=0.001, R=0.173). The survival probability of GC patients with GLUT1(+)/p-S6K1(+) was worse when compared to that of GLUT1(+)/p-S6K1(-) or GLUT1(-)/p-S6K1(+) (P<0.001). CONCLUSION: High expression of GLUT1 facilitated GC progression, leading to poor prognosis. Overexpression of GLUT1 activated AKT-S6K1 axis, resulting in adverse outcomes of GC. GLUT1 is novel indicator of GC prognosis and GLUT1 targeted metabolic treatment that has potential therapeutic value.