An integrated network pharmacology, molecular docking and experiment validation study to investigate the potential mechanism of Isobavachalcone in the treatment of osteoarthritis.

BACKGROUND: In many different plants, including Dorstenia and Psoralea corylifolia L., Isobavachalcone (IBC) is a naturally occurring flavonoid chemical having a range of biological actions, including anti-inflammatory, immunomodulatory, and anti-bacterial. The "Theory of Medicinal Properties" of the Tang Dynasty states that Psoralea corylifolia L. has the ability to alleviate discomfort in the knees and waist. One of the most widespread chronic illnesses, osteoarthritis (OA), is characterized by stiffness and discomfort in the joints. However, there hasn't been much research done on the effectiveness and underlying processes of IBC in the treatment of osteoarthritis. AIM OF THE STUDY: To investigate the potential efficacy and mechanism of IBC in treating osteoarthritis, we adopted an integrated strategy of network pharmacology, molecular docking and experiment assessment. MATERIALS AND METHODS: The purpose of this research was to determine the impact of IBC on OA and the underlying mechanisms. IBC and OA possible targets and processes were predicted using network pharmacology, including the relationship between IBC and OA intersection targets, Cytoscape protein-protein interaction (PPI) to obtain key potential targets, and GO and KEGG pathway enrichment analysis to reveal the probable mechanism of IBC on OA. Following that, in vitro tests were carried out to confirm the expected underlying processes. Finally, in vivo tests clarified IBC's therapeutic efficacy on OA. RESULTS: We anticipated and validated that the impact of IBC on osteoarthritis is mostly controlled by the PI3K-AKT-NF-κB signaling pathway by combining the findings of network pharmacology analysis, molecular docking and Experiment Validation. CONCLUSIONS: This study reveals the IBC has potential to delay OA development.

Glutamine metabolism in tumor metastasis: Genes, mechanisms and the therapeutic targets.

Cancer cells frequently change their metabolism from aerobic glycolysis to lipid metabolism and amino acid metabolism to adapt to the malignant biological behaviours of infinite proliferation and distant metastasis. The significance of metabolic substances and patterns in tumour cell metastasis is becoming increasingly prominent. Tumour metastasis involves a series of significant steps such as the shedding of cancer cells from a primary tumour, resistance to apoptosis, and colonisation of metastatic sites. However, the role of glutamine in these processes remains unclear. This review summarises the key enzymes and transporters involved in glutamine metabolism that are related to the pathogenesis of malignant tumour metastasis. We also list the roles of glutamine in resisting oxidative stress and promoting immune escape. Finally, the significance of targeting glutamine metabolism in inhibiting tumour metastasis was proposed, research in this field improving our understanding of amino acid metabolism rewiring and simultaneously bringing about new and exciting therapeutic prospects.

Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning.

This study aimed at establishing more accurate predictive models based on novel machine learning algorithms, with the overarching goal of providing clinicians with effective decision-making assistance. We retrospectively analyzed the breast cancer patients recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016. Multivariable logistic regression analyses were used to identify risk factors for bone metastases in breast cancer, whereas Cox proportional hazards regression analyses were used to identify prognostic factors for breast cancer with bone metastasis (BCBM). Based on the identified risk and prognostic factors, we developed diagnostic and prognostic models that incorporate six machine learning classifiers. We then used the area under the receiver operating characteristic (ROC) curve (AUC), learning curve, precision curve, calibration plot, and decision curve analysis to evaluate performance of the machine learning models. Univariable and multivariable logistic regression analyses showed that bone metastases were significantly associated with age, race, sex, grade, T stage, N stage, surgery, radiotherapy, chemotherapy, tumor size, brain metastasis, liver metastasis, lung metastasis, breast subtype, and PR. Univariate and multivariate Cox regression analyses revealed that age, race, marital status, grade, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, lung metastasis, breast subtype, ER, and PR were closely associated with the prognosis of BCBM. Among the six machine learning models, the XGBoost algorithm predicted the most accurate results (Diagnostic model AUC = 0.98; Prognostic model AUC = 0.88). According to the Shapley additive explanations (SHAP), the most critical feature of the diagnostic model was surgery, followed by N stage. Interestingly, surgery was also the most critical feature of prognostic model, followed by liver metastasis. Based on the XGBoost algorithm, we could effectively predict the diagnosis and survival of bone metastasis in breast cancer and provide targeted references for the treatment of BCBM patients.

Multi-omics analysis of MRPL-13 as a tumor-promoting marker from pan-cancer to lung adenocarcinoma.

BACKGROUND: As a member of the mitochondrial ribosomal protein family, mitochondrial ribosomal protein L13 (MRPL13) is responsible for synthesizing mitochondrial proteins in cells. Several studies have indicated that MRPL13 is associated with the proliferation cycle, migration ability, apoptosis and autophagy of cancer cells. However, a thorough examination of MRPL13 across cancers remains uncertain. Therefore, we tried to clarify the relationship between MRPL13 and pan-cancer, and verified it in lung adenocarcinoma by various methods. Finally, our research is expected to reveal new targets for pan-cancer treatment and improve the prognosis of cancer patients. METHODS: Using bioinformatics tools, we quantified the differential expression of MRPL13 between cancer tissues and corresponding or noncorresponding normal tissues across cancers. We also analyzed the relationships between MRPL13 expression levels and several factors, including diagnosis, prognosis, mutation, functional signaling pathways, immune infiltration, RNA modification, and the relationship with cuproptosis-related genes. Furthermore, we studied the relationship between the expression level of MRPL13 across cancers and the change in cancer functional status through single-cell data. In addition, quantitative experiments (PCR and Western blot) proved that the expression of MRPL13 was significantly different between LUAD and control samples. Finally, the effect of knocking out MRPL13 on cancer cells was compared by gene silencing experiments. In summary, we used a combination of bioinformatics and experimental applications to study the potential roles of MRPL13 in cancer. RESULTS: After conducting a multidimensional analysis, we found that the application of MRPL13 multigroup analysis can effectively improve the diagnostic efficiency of various cancers and predict the prognosis of cancer. Moreover, MRPL13 in pan-cancer is related to the cancer immune infiltration pattern, methylation level and cuproptosis-related genes. Furthermore, single-cell data analysis showed that the modules of metastasis, EMT, cell cycle, DNA repair, invasion, DNA damage and proliferation were positively correlated with the expression of MRPL13 in LUAD (Lung adenocarcinoma), while the modules of hypoxia and inflammation were negatively correlated. Moreover, through quantitative experiments, we observed higher expression of MRPL13 in cancer tissues at the RNA or protein level. Knockdown of MRPL13 in LUAD led to decreased cancer cell survival, delayed tumor division and migration, reduced invasion, and increased cancer cell apoptosis. CONCLUSIONS: Our study demonstrates the potential of using MRPL13 as a molecular biomarker for diagnosing and suggesting the prognosis of certain malignant tumors. Furthermore, our research shows that MRPL13 may be an effective therapeutic target for lung adenocarcinoma.

Clinical application of superselective transarterial embolization of renal tumors in zero ischaemia robotic-assisted laparoscopic partial nephrectomy.

Objective: To assess the feasibility and safety of zero ischaemia robotic-assisted laparoscopic partial nephrectomy (RALPN) after preoperative superselective transarterial embolization (STE) of T1 renal cancer. Methods: We retrospectively analyzed the data of 32 patients who underwent zero ischaemia RALPN after STE and 140 patients who received standard robot-assisted laparoscopic partial nephrectomy (S-RALPN). In addition, we selected 35 patients treated with off-clamp RALPN (O-RALPN) from September 2017 to March 2022 for comparison. STE was performed by the same interventional practitioner, and zero ischaemia laparoscopic partial nephrectomy (LPN) was carried out by experienced surgeon 1-12 hours after STE. The intraoperative data and postoperative complications were recorded. The postoperative renal function, routine urine test, urinary Computed Tomography (CT), and preoperative and postoperative glomerular filtration rate (GFR) data were analyzed. Results: All operations were completed successfully. There were no cases of conversion to opening and no deaths. The renal arterial trunk was not blocked. No blood transfusions were needed. The mean operation time was 91.5 ± 34.28 minutes. The mean blood loss was 58.59 ± 54.11 ml. No recurrence or metastasis occurred. Conclusion: For patients with renal tumors, STE of renal tumors in zero ischaemia RALPN can preserve more renal function, and it provides a safe and feasible surgical method.

LINC00482 sponged miR-2467-3p to promote bone metastasis of prostate cancer through activating Wnt/ß-catenin signaling pathway.

This study was designed to investigate the biological functions of LINC00482 in prostate cancer (PCa) with bone metastasis. TCGA dataset of PCa was applied for LINC00482 expression analysis and real time PCR was used to verify the expression level of LINC00482 in PCa tissues as well as PCa bone metastatic tissues. To detect the biological functions of LINC00482 in vitro, various assays were used including CCK-8, EdU, colony formation and transwell assays. The biological functions of LINC00482 were also identified in vivo by inoculating PCa cells into the left cardiac ventricle of mice, followed by evaluating the osteolytic lesions and osteolytic score. In addition, Starbase and Lncbase databases were applied for predicting the potential target miRNA of LINC00482, while TargetScan and Starbase databases were used for predicting the potential target of miRNA. The luciferase reporter assay was utilized to determine the interactions among these molecules and western blotting was employed to verified the targeted proteins. Results showed that high expression level of LINC00482 was observed in bone metastatic PCa tissues and associated with PCa progression. Silencing of LINC00482 inhibited cell proliferation, migration and invasion in PCa. Furthermore, LINC00482 was proved to act as a competing endogenous RNA by sponging miR-2467-3p to activate Wnt/ß-catenin signaling pathway, which may be a promising therapeutic target for PCa with bone metastasis.

Association of visceral and subcutaneous fat with bone mineral density in US adults: a cross-sectional study.

The relationship between the accumulation of fat in visceral or subcutaneous tissue and bone mineral density (BMD) remains unclear. Our primary objective in this study was to illuminate this relationship by conducting an investigation on a vast scale, encompassing a nationally representative population in the United States. A weighted multiple linear regression model was established to evaluate the relationship between visceral fat, subcutaneous fat, and BMD. Additionally, the exploration of the potential nonlinear relationship was conducted employing the methodology of smooth curve fitting. In order to determine potential inflection points, a two-stage linear regression model was utilized. A total of 10,455 participants between the ages of 20 and 59 were included in this study. Various weighted multiple linear regression models revealed a negative correlation between lumbar BMD and visceral mass index (VMI) and subcutaneous mass index (SMI). However, the association between VMI and lumbar BMD displayed a U-shaped pattern upon employing the smooth curve fitting, and the inflection point of 0.304 kg/m2was determined using a two-stage linear regression model. Our findings indicated a negative association between subcutaneous fat and BMD. A U-shaped relationship was observed between visceral fat and BMD.

Characterization of cuproptosis-related lncRNA landscape for predicting the prognosis and aiding immunotherapy in lung adenocarcinoma patients.

Cuproptosis is a newly discovered mechanism of regulated cell death, which serves as a novel target for cancer therapy. Long non-coding RNAs (lncRNAs) play an important role in the initiation and progression of cancer cells; however, the relationship between cuproptosis and lncRNAs in tumorigenesis and cancer treatment has not been well established in lung adenocarcinoma (LUAD). Thus, it is important to clarify and characterize the cuproptosis-related lncRNA landscape in LUAD. In this study, cuproptosis-related lncRNAs was screened by Pearson correlation analysis. Then, univariate, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression were conducted to identify 6 cuproptosis-related lncRNAs (AC090541.1, AC009226.1, NIFK-AS1, AC027097.2, AC026355.2, and AC106028.2) which were used to construct a cuproptosis-related lncRNA signature (CRLS). Multi-dimensional assessments including Kaplan-Meier analysis, receiver operating characteristics (ROC) curves, and principal component analysis (PCA) verified that the CRLS could reliably predict the prognosis and survival of LUAD patients. We further compared the immune cell infiltration, somatic mutation landscape, and functional enrichment pathways between the high and low CRLS groups. Patients with low CRLS scores had prolonged survival and were sensitive to immunotherapy, whereas patients with high CRLS scores might benefit better from chemotherapy. We further analyzed the individualized immunotherapeutic strategies and the candidate compounds for the potential clinical treatment. Moreover, the expression level of these 6 lncRNAs was examined experimentally in vitro by using quantitative real-time polymerase chain reaction (RT-qPCR). Additionally, one of the significantly differentially expressed lncRNAs, NIFK-AS1, was confirmed to suppress the proliferation and migration of LUAD by Cell Counting Kit-8 Assays (CCK-8), wound healing assay, and colony formation assays. Taken together, we established a CRLS that might be a promising tool for predicting the prognosis, guiding individualized treatment, and serving as a promising therapeutic target for patients with LUAD.

Bone metastasis risk and prognosis assessment models for kidney cancer based on machine learning.

Background: Bone metastasis is a common adverse event in kidney cancer, often resulting in poor survival. However, tools for predicting KCBM and assessing survival after KCBM have not performed well. Methods: The study uses machine learning to build models for assessing kidney cancer bone metastasis risk, prognosis, and performance evaluation. We selected 71,414 kidney cancer patients from SEER database between 2010 and 2016. Additionally, 963 patients with kidney cancer from an independent medical center were chosen to validate the performance. In the next step, eight different machine learning methods were applied to develop KCBM diagnosis and prognosis models while the risk factors were identified from univariate and multivariate logistic regression and the prognosis factors were analyzed through Kaplan-Meier survival curve and Cox proportional hazards regression. The performance of the models was compared with current models, including the logistic regression model and the AJCC TNM staging model, applying receiver operating characteristics, decision curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Results: Our prognosis model achieved an AUC of 0.8269 (95%CI: 0.8083-0.8425) in the internal validation cohort and 0.9123 (95%CI: 0.8979-0.9261) in the external validation cohort. In addition, we tested the performance of the extreme gradient boosting model through decision curve analysis curve, Precision-Recall curve, and Brier score and two models exhibited excellent performance. Conclusion: Our developed models can accurately predict the risk and prognosis of KCBM and contribute to helping improve decision-making.

Osteoporosis, fracture and survival: Application of machine learning in breast cancer prediction models.

The risk of osteoporosis in breast cancer patients is higher than that in healthy populations. The fracture and death rates increase after patients are diagnosed with osteoporosis. We aimed to develop machine learning-based models to predict the risk of osteoporosis as well as the relative fracture occurrence and prognosis. We selected 749 breast cancer patients from two independent Chinese centers and applied six different methods of machine learning to develop osteoporosis, fracture and survival risk assessment models. The performance of the models was compared with that of current models, such as FRAX, OSTA and TNM, by applying ROC, DCA curve analysis, and the calculation of accuracy and sensitivity in both internal and independent external cohorts. Three models were developed. The XGB model demonstrated the best discriminatory performance among the models. Internal and external validation revealed that the AUCs of the osteoporosis model were 0.86 and 0.87, compared with the FRAX model (0.84 and 0.72)/OSTA model (0.77 and 0.66), respectively. The fracture model had high AUCs in the internal and external cohorts of 0.93 and 0.92, which were higher than those of the FRAX model (0.89 and 0.86). The survival model was also assessed and showed high reliability via internal and external validation (AUC of 0.96 and 0.95), which was better than that of the TNM model (AUCs of 0.87 and 0.87). Our models offer a solid approach to help improve decision making.

Two Novel Nomograms Predicting the Risk and Prognosis of Pancreatic Cancer Patients With Lung Metastases: A Population-Based Study.

Background: Pancreatic cancer (PC) is one of the most common malignant types of cancer, with the lung being the frequent distant metastatic site. Currently, no population-based studies have been done on the risk and prognosis of pancreatic cancer with lung metastases (PCLM). As a result, we intend to create two novel nomograms to predict the risk and prognosis of PCLM. Methods: PC patients were selected from the Surveillance, Epidemiology, and End Results Program (SEER) database from 2010 to 2016. A multivariable logistic regression analysis was used to identify risk factors for PCLM at the time of diagnosis. The multivariate Cox regression analysis was carried out to assess PCLM patient's prognostic factors for overall survival (OS). Following that, we used area under curve (AUC), time-dependent receiver operating characteristics (ROC) curves, calibration plots, consistency index (C-index), time-dependent C-index, and decision curve analysis (DCA) to evaluate the effectiveness and accuracy of the two nomograms. Finally, we compared differences in survival outcomes using Kaplan-Meier curves. Results: A total of 803 (4.22%) out of 19,067 pathologically diagnosed PC patients with complete baseline information screened from SEER database had pulmonary metastasis at diagnosis. A multivariable logistic regression analysis revealed that age, histological subtype, primary site, N staging, surgery, radiotherapy, tumor size, bone metastasis, brain metastasis, and liver metastasis were risk factors for the occurrence of PCLM. According to multivariate Cox regression analysis, age, grade, tumor size, histological subtype, surgery, chemotherapy, liver metastasis, and bone metastasis were independent prognostic factors for PCLM patients' OS. Nomograms were constructed based on these factors to predict 6-, 12-, and 18-months OS of patients with PCLM. AUC, C-index, calibration curves, and DCA revealed that the two novel nomograms had good predictive power. Conclusion: We developed two reliable predictive models for clinical practice to assist clinicians in developing individualized treatment plans for patients.