Personalised prediction of maintenance dialysis initiation in patients with chronic kidney disease stages 3-5: a multicentre study using the machine learning approach.

BACKGROUND: Optimal timing for initiating maintenance dialysis in patients with chronic kidney disease (CKD) stages 3-5 is challenging. This study aimed to develop and validate a machine learning (ML) model for early personalised prediction of maintenance dialysis initiation within 1-year and 3-year timeframes among patients with CKD stages 3-5. METHODS: Retrospective electronic health record data from the Taipei Medical University clinical research database were used. Newly diagnosed patients with CKD stages 3-5 between 2008 and 2017 were identified. The observation period spanned from the diagnosis of CKD stages 3-5 until the maintenance dialysis initiation or a maximum follow-up of 3 years. Predictive models were developed using patient demographics, comorbidities, laboratory data and medications. The dataset was divided into training and testing sets to ensure robust model performance. Model evaluation metrics, including area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score, were employed. RESULTS: A total of 6123 and 5279 patients were included for 1 year and 3 years of the model development. The artificial neural network demonstrated better performance in predicting maintenance dialysis initiation within 1 year and 3 years, with AUC values of 0.96 and 0.92, respectively. Important features such as baseline estimated glomerular filtration rate and albuminuria significantly contributed to the predictive model. CONCLUSION: This study demonstrates the efficacy of an ML approach in developing a highly predictive model for estimating the timing of maintenance dialysis initiation in patients with CKD stages 3-5. These findings have important implications for personalised treatment strategies, enabling improved clinical decision-making and potentially enhancing patient outcomes.

Ensemble Machine Learning for Predicting 90-Day Outcomes and Analyzing Risk Factors in Acute Kidney Injury Requiring Dialysis.

Purpose: Our objectives were to (1) employ ensemble machine learning algorithms utilizing real-world clinical data to predict 90-day prognosis, including dialysis dependence and mortality, following the first hospitalized dialysis and (2) identify the significant factors associated with overall outcomes. Patients and Methods: We identified hospitalized patients with Acute kidney injury requiring dialysis (AKI-D) from a dataset of the Taipei Medical University Clinical Research Database (TMUCRD) from January 2008 to December 2020. The extracted data comprise demographics, comorbidities, medications, and laboratory parameters. Ensemble machine learning models were developed utilizing real-world clinical data through the Google Cloud Platform. Results: The Study Analyzed 1080 Patients in the Dialysis-Dependent Module, Out of Which 616 Received Regular Dialysis After 90 Days. Our Ensemble Model, Consisting of 25 Feedforward Neural Network Models, Demonstrated the Best Performance with an Auroc of 0.846. We Identified the Baseline Creatinine Value, Assessed at Least 90 Days Before the Initial Dialysis, as the Most Crucial Factor. We selected 2358 patients, 984 of whom were deceased after 90 days, for the survival module. The ensemble model, comprising 15 feedforward neural network models and 10 gradient-boosted decision tree models, achieved superior performance with an AUROC of 0.865. The pre-dialysis creatinine value, tested within 90 days prior to the initial dialysis, was identified as the most significant factor. Conclusion: Ensemble machine learning models outperform logistic regression models in predicting outcomes of AKI-D, compared to existing literature. Our study, which includes a large sample size from three different hospitals, supports the significance of the creatinine value tested before the first hospitalized dialysis in determining overall prognosis. Healthcare providers could benefit from utilizing our validated prediction model to improve clinical decision-making and enhance patient care for the high-risk population.

Specific patterns and potential risk factors to predict 3-year risk of death among non-cancer patients with advanced chronic kidney disease by machine learning.

Chronic kidney disease (CKD) is a major public health concern. But there are limited machine learning studies on non-cancer patients with advanced CKD, and the results of machine learning studies on cancer patients with CKD may not apply directly on non-cancer patients. We aimed to conduct a comprehensive investigation of risk factors for a 3-year risk of death among non-cancer advanced CKD patients with an estimated glomerular filtration rate < 60.0 mL/min/1.73m2 by several machine learning algorithms. In this retrospective cohort study, we collected data from in-hospital and emergency care patients from 2 hospitals in Taiwan from 2009 to 2019, including their international classification of disease at admission and laboratory data from the hospital's electronic medical records (EMRs). Several machine learning algorithms were used to analyze the potential impact and degree of influence of each factor on mortality and survival. Data from 2 hospitals in northern Taiwan were collected with 6565 enrolled patients. After data cleaning, 26 risk factors and approximately 3887 advanced CKD patients from Shuang Ho Hospital were used as the training set. The validation set contained 2299 patients from Taipei Medical University Hospital. Predictive variables, such as albumin, PT-INR, and age, were the top 3 significant risk factors with paramount influence on mortality prediction. In the receiver operating characteristic curve, the random forest had the highest values for accuracy above 0.80. MLP, and Adaboost had better performance on sensitivity and F1-score compared to other methods. Additionally, SVM with linear kernel function had the highest specificity of 0.9983, while its sensitivity and F1-score were poor. Logistic regression had the best performance, with an area under the curve of 0.8527. Evaluating Taiwanese advanced CKD patients' EMRs could provide physicians with a good approximation of the patients' 3-year risk of death by machine learning algorithms.

Downregulation of SIRT1 and GADD45G genes and left atrial fibrosis induced by right ventricular dependent pacing in a complete atrioventricular block pig model.

The molecular and genetic mechanisms underlying left atrial (LA) enlargement and atrial fibrosis following right ventricular (RV) dependent pacing remain unclear. Our objective was to investigate genetic expressions in the LA of pigs subjected to RV pacing for atrioventricular block (AVB), as well as to identify the differential gene expressions affected by biventricular (BiV) pacing. We established an AVB pig model and divided the subjects into three groups: a sham control group, an RV pacing group, and a BiV pacing group. Differential expression genes (DEGs) analyses conducted through next-generation sequencing (NGS) and enrichment analyses were employed to identify genes with altered expression in the LA myocardium. The RV pacing group showed a significant increase in extracellular fibrosis in the LA myocardium compared to the control group. NGS analysis revealed suppressed expression of the sirtuin signaling pathway in the RV pacing group. Among the DEGs within this pathway, GADD45G was found to be downregulated in the RV pacing group and upregulated in the BiV pacing group. Remarkably, the BiV pacing group exhibited elevated levels of GADD45G protein. In our study, we observed significant downregulation of SIRT1 and GADD45G genes, which are associated with the sirtuin signaling pathway, in the LA myocardium of the RV pacing group when compared to the control group. Moreover, these genes, which were downregulated in the RV pacing group, displayed a noteworthy upregulation in the BiV pacing group when compared to the RV pacing group.

Assessing Non-Specific Neck Pain through Pose Estimation from Images Based on Ensemble Learning.

BACKGROUND: Mobile phones, laptops, and computers have become an indispensable part of our lives in recent years. Workers may have an incorrect posture when using a computer for a prolonged period of time. Using these products with an incorrect posture can lead to neck pain. However, there are limited data on postures in real-life situations. METHODS: In this study, we used a common camera to record images of subjects carrying out three different tasks (a typing task, a gaming task, and a video-watching task) on a computer. Different artificial intelligence (AI)-based pose estimation approaches were applied to analyze the head's yaw, pitch, and roll and coordinate information of the eyes, nose, neck, and shoulders in the images. We used machine learning models such as random forest, XGBoost, logistic regression, and ensemble learning to build a model to predict whether a subject had neck pain by analyzing their posture when using the computer. RESULTS: After feature selection and adjustment of the predictive models, nested cross-validation was applied to evaluate the models and fine-tune the hyperparameters. Finally, the ensemble learning approach was utilized to construct a model via bagging, which achieved a performance with 87% accuracy, 92% precision, 80.3% recall, 95.5% specificity, and an AUROC of 0.878. CONCLUSIONS: We developed a predictive model for the identification of non-specific neck pain using 2D video images without the need for costly devices, advanced environment settings, or extra sensors. This method could provide an effective way for clinically evaluating poor posture during real-world computer usage scenarios.

Exploring the Potential Performance of Fibroscan for Predicting and Evaluating Metabolic Syndrome using a Feature Selected Strategy of Machine Learning.

Metabolic syndrome (MetS) includes several conditions that can increase an individual's predisposition to high-risk cardiovascular events, morbidity, and mortality. Non-alcoholic fatty liver disease (NAFLD) is a predominant cause of cirrhosis, which is a global indicator of liver transplantation and is considered the hepatic manifestation of MetS. FibroScan® provides an accurate and non-invasive method for assessing liver steatosis and fibrosis in patients with NAFLD, via a controlled attenuation parameter (CAP) and liver stiffness measurement (LSM or E) scores and has been widely used in current clinical practice. Several machine learning (ML) models with a recursive feature elimination (RFE) algorithm were applied to evaluate the importance of the CAP score. Analysis by ANOVA revealed that five symptoms at different CAP and E score levels were significant. All eight ML models had accuracy scores > 0.9, while treebags and random forest had the best kappa values (0.6439 and 0.6533, respectively). The CAP score was the most important variable in the seven ML models. Machine learning models with RFE demonstrated that using the CAP score to identify patients with MetS may be feasible. Thus, a combination of CAP scores and other significant biomarkers could be used for early detection in predicting MetS.

The downregulation of miR-509-3p expression by collagen type XI alpha 1-regulated hypermethylation facilitates cancer progression and chemoresistance via the DNA methyltransferase 1/Small ubiquitin-like modifier-3 axis in ovarian cancer cells.

BACKGROUND: MicroRNAs are a group of small non-coding RNAs that are involved in development and diseases such as cancer. Previously, we demonstrated that miR-335 is crucial for preventing collagen type XI alpha 1 (COL11A1)-mediated epithelial ovarian cancer (EOC) progression and chemoresistance. Here, we examined the role of miR-509-3p in EOC. METHODS: The patients with EOC who underwent primary cytoreductive surgery and postoperative platinum-based chemotherapy were recruited. Their clinic-pathologic characteristics were collected, and disease-related survivals were determined. The COL11A1 and miR-509-3p mRNA expression levels of 161 ovarian tumors were determined by real-time reverse transcription-polymerase chain reaction. Additionally, miR-509-3p hypermethylation was evaluated by sequencing in these tumors. The A2780CP70 and OVCAR-8 cells transfected with miR-509-3p mimic, while the A2780 and OVCAR-3 cells transfected with miR-509-3p inhibitor. The A2780CP70 cells transfected with a small interference RNA of COL11A1, and the A2780 cells transfected with a COL11A1 expression plasmid. Site-directed mutagenesis, luciferase, and chromatin immunoprecipitation assays were performed in this study. RESULTS: Low miR-509-3p levels were correlated with disease progression, a poor survival, and high COL11A1 expression levels. In vivo studies reinforced these findings and indicated that the occurrence of invasive EOC cell phenotypes and resistance to cisplatin are decreased by miR-509-3p. The miR-509-3p promoter region (p278) is important for miR-509-3p transcription regulation via methylation. The miR-509-3p hypermethylation frequency was significantly higher in EOC tumors with a low miR-509-3p expression than in those with a high miR-509-3p expression. The patients with miR-509-3p hypermethylation had a significantly shorter overall survival (OS) than those without miR-509-3p hypermethylation. Mechanistic studies further indicated that miR-509-3p transcription was downregulated by COL11A1 through a DNA methyltransferase 1 (DNMT1) stability increase. Moreover, miR-509-3p targets small ubiquitin-like modifier (SUMO)-3 to regulate EOC cell growth, invasiveness, and chemosensitivity. CONCLUSION: The miR-509-3p/DNMT1/SUMO-3 axis may be an ovarian cancer treatment target.

Effects of osteoporosis medications on bone fracture in patients with chronic kidney disease.

PURPOSE OF THE STUDY: The risk of bone fracture is high in patients with chronic kidney disease (CKD), and aggressive treatment to reduce fragility fracture risk is the major strategy. However, the outcomes of osteoporosis medications in patients with CKD remain unclear. STUDY DESIGN: Patients with stage 3-5 CKD during 2011-2019 were enrolled. Patients were divided into two groups based on receiving osteoporosis medications (bisphosphonates, raloxifene, teriparatide or denosumab) or not. Two groups were matched at a 1:1 ratio by using propensity scores. The outcomes of interest were bone fractures, cardiovascular (CV) events and all-cause mortality. Cox proportional hazard regression models were applied to identify the risk factors. Additional stratified analyses by cumulative dose, treatment length and menopause condition were performed. RESULTS AND CONCLUSIONS: 67 650 patients were included. After propensity score matching, 1654 patients were included in the study and control group, respectively. The mean age was 70.2±12.4 years, and 32.0% of patients were men. After a mean follow-up of 3.9 years, the incidence rates of bone fracture, CV events and all-cause mortality were 2.0, 1.7 and 6.5 per 1000 person-months, respectively. Multivariate analysis results showed that osteoporosis medications reduced the risk of CV events (HR, 0.35; 95% CI, 0.18 to 0.71; p = 0.004), but did not alleviate the risks of bone fracture (HR, 1.48; 95% CI, 0.73 to 2.98; p = 0.28) and all-cause mortality (HR, 0.93; 95% CI, 0.67 to 1.28; p = 0.65). Stratified analysis showed that bisphosphonates users have most benefits in the reduction of CV events (HR, 0.26; 95% CI, 0.11 to 0.64; p = 0.003). In conclusion, osteoporosis medications did not reduce the risk of bone fractures, or mortality, but improved CV outcomes in patients with CKD.

Machine Learning-Based Prediction of Atrial Fibrillation Risk Using Electronic Medical Records in Older Aged Patients.

Atrial fibrillation (AF) is an independent risk factor that increases the risk of stroke 5-fold. The purpose of our study was to develop a 1-year new-onset AF predictive model by machine learning based on 3-year medical information without electrocardiograms in our database to identify AF risk in older aged patients. We developed the predictive model according to the Taipei Medical University clinical research database electronic medical records, including diagnostic codes, medications, and laboratory data. Decision tree, support vector machine, logistic regression, and random forest algorithms were chosen for the analysis. A total of 2,138 participants (1,028 women [48.1%]; mean [standard deviation] age 78.8 [6.8] years) with AF and 8,552 random controls (after the matching process) without AF (4,112 women [48.1%]; mean [standard deviation] age 78.8 [6.8] years) were included in the model. The 1-year new-onset AF risk prediction model based on the random forest algorithm using medication and diagnostic information, along with specific laboratory data, attained an area under the receiver operating characteristic of 0.74, whereas the specificity was 98.7%. Machine learning-based model focusing on the older aged patients could offer acceptable discrimination in differentiating the risk of incident AF in the next year. In conclusion, a targeted screening approach using multidimensional informatics in the electronic medical records could result in a clinical choice with efficacy for prediction of the incident AF risk in older aged patients.

The Downregulation of miR-509-3p Expression by Collagen Type XI Alpha 1-Regulated Hypermethylation Facilitates Cancer Progression and Chemoresistance via the DNA Methyltransferase 1/Small Ubiquitin-like Modifier-3 Axis in Ovarian Cancer Cells.

Background MicroRNAs are a group of small non-coding RNAs that are involved in development and diseases such as cancer. Previously, we demonstrated that miR-335 is crucial for preventing collagen type XI alpha 1 (COL11A1)-mediated epithelial ovarian cancer (EOC) progression and chemoresistance. Here, we examined the role of miR-509-3p in EOC. Methods The patients with EOC who underwent primary cytoreductive surgery and postoperative platinum-based chemotherapy were recruited. Their clinic-pathologic characteristics were collected, and disease-related survivals were determined. The COL11A1 and miR-509-3p mRNA expression levels of 161 ovarian tumors were determined by real-time reverse transcription-polymerase chain reaction. Additionally, miR-509-3p hypermethylation was evaluated by sequencing in these tumors. The A2780CP70 and OVCAR-8 cells transfected with miR-509-3p mimic, while the A2780 and OVCAR-3 cells transfected with miR-509-3p inhibitor. The A2780CP70 cells transfected with a small interference RNA of COL11A1, and the A2780 cells transfected with a COL11A1 expression plasmid. Site-directed mutagenesis, luciferase, and chromatin immunoprecipitation assays were performed in this study. Results Low miR-509-3p levels were correlated with disease progression, a poor survival, and high COL11A1 expression levels. In vivo studies reinforced these findings and indicated that the occurrence of invasive EOC cell phenotypes and resistance to cisplatin are decreased by miR-509-3p. The miR-509-3p promoter region (p278) is important for miR-509-3p transcription regulation via methylation. The miR-509-3p hypermethylation frequency was significantly higher in EOC tumors with a low miR-509-3p expression than in those with a high miR-509-3p expression. The patients with miR-509-3p hypermethylation had a significantly shorter overall survival (OS) than those without miR-509-3p hypermethylation. Mechanistic studies further indicated that miR-509-3p transcription was downregulated by COL11A1 through a DNA methyltransferase 1 (DNMT1) phosphorylation and stability increase. Moreover, miR-509-3p targets small ubiquitin-like modifier (SUMO)-3 to regulate EOC cell growth, invasiveness, and chemosensitivity. Conclusion The miR-509-3p/DNMT1/SUMO-3 axis may be an ovarian cancer treatment target.

Microbiota dysbiosis in odontogenic rhinosinusitis and its association with anaerobic bacteria.

Odontogenic rhinosinusitis is a subtype of rhinosinusitis associated with dental infection or dental procedures and has special bacteriologic features. Previous research on the bacteriologic features of odontogenic rhinosinusitis has mainly used culture-dependent methods. The variation of microbiota between odontogenic and nonodontogenic rhinosinusitis as well as the interplay between the involved bacteria have not been explored. Therefore, we enrolled eight odontogenic rhinosinusitis cases and twenty nonodontogenic rhinosinusitis cases to analyze bacterial microbiota through 16S rRNA sequencing. Significant differences were revealed by the Shannon diversity index (Wilcoxon test p = 0.0003) and PERMANOVA test based on weighted UniFrac distance (Wilcoxon test p = 0.001) between odontogenic and nonodontogenic samples. Anaerobic bacteria such as Porphyromonas, Fusobacterium, and Prevotella were significantly dominant in the odontogenic rhinosinusitis group. Remarkably, a correlation between different bacteria was also revealed by Pearson's correlation. Staphylococcus was highly positively associated with Corynebacterium, whereas Fusobacterium was highly negatively correlated with Prophyromonas. According to our results, the microbiota in odontogenic rhinosinusitis, predominantly anaerobic bacteria, was significantly different from that in nonodontogenic rhinosinusitis, and the interplay between specific bacteria may a major cause of this subtype of rhinosinusitis.

Development and Validation of Novel Deep-Learning Models Using Multiple Data Types for Lung Cancer Survival.

A well-established lung-cancer-survival-prediction model that relies on multiple data types, multiple novel machine-learning algorithms, and external testing is absent in the literature. This study aims to address this gap and determine the critical factors of lung cancer survival. We selected non-small-cell lung cancer patients from a retrospective dataset of the Taipei Medical University Clinical Research Database and Taiwan Cancer Registry between January 2008 and December 2018. All patients were monitored from the index date of cancer diagnosis until the event of death. Variables, including demographics, comorbidities, medications, laboratories, and patient gene tests, were used. Nine machine-learning algorithms with various modes were used. The performance of the algorithms was measured by the area under the receiver operating characteristic curve (AUC). In total, 3714 patients were included. The best performance of the artificial neural network (ANN) model was achieved when integrating all variables with the AUC, accuracy, precision, recall, and F1-score of 0.89, 0.82, 0.91, 0.75, and 0.65, respectively. The most important features were cancer stage, cancer size, age of diagnosis, smoking, drinking status, EGFR gene, and body mass index. Overall, the ANN model improved predictive performance when integrating different data types.

Aminoglycosides use has a risk of acute kidney injury in patients without prior chronic kidney disease.

The outcome of acute kidney injury (AKI) as a result of aminoglycosides (AGs) use remains uncertain in patients without prior chronic kidney disease (CKD). Therefore, we explored the outcomes of AGs use on AKI episodes associated with renal recovery and progress in patients without prior CKD in Taiwan. This was a retrospective cohort study by using the Taipei Medical University Research Database from January 2008 to December 2019. 43,259 individuals without CKD who had received parenteral AGs were enrolled. The exposed and unexposed groups underwent propensity score matching for age, gender, patients in intensive care unit/emergency admission, and covariates, except serum hemoglobin and albumin levels. We identified an exposed group of 40,547 patients who used AGs (median age, 54.4 years; 44.3% male) and an unexposed group of 40,547 patients without AG use (median age, 55.7 years; 45.5% male). There was the risk for AKI stage 1 (adjusted hazard ratio [HR] 1.34; 95% confidence interval [CI] 1.00-1.79; p = 0.05) in patients that used AGs in comparison with the control subjects. Moreover, patients using AGs were significantly associated neither with the progression to acute kidney disease (AKD) stages nor with the progression to end-stage renal disease (ESRD) on dialysis. Further analyzed, there was an increased risk of AKI episodes for serum albumin levels less than 3.0 g/dL and hemoglobin levels less than 11.6 g/dL. Among patients without prior CKD, AGs-used individuals were associated with AKI risks, especially those at relatively low albumin (< 3.0 g/dL) or low hemoglobin (< 11.6 g/dL). That could raise awareness of AGs prescription in those patients in clinical practice.

High Inferior Vena Cava Diameter with High Left Ventricular End Systolic Diameter as a Risk Factor for Major Adverse Cardiovascular Events, Cardiovascular and Overall Mortality among Chronic Hemodialysis Patients.

Background: Little is known about the association of inferior vena cava diameter (IVCD) and left ventricular end-systolic diameter (LVESD) with mortality in patients undergoing hemodialysis (HD). Methods: The single medical center observational cohort study enrolled 241 adult chronic HD patients from 1 October 2018 to 31 December 2018. Echocardiography results of IVCD and LVESD prior to dialysis were retrieved and patients were divided into high IVCD and low IVCD groups. Patients who received HD via a tunneled cuffed catheter were excluded. Study outcomes included all-cause mortality, cardiovascular mortality, and major adverse cardiovascular events (MACE). Subgroup analyses of HD patients with high and low LVESD were also performed. Results: The incidence of all-cause mortality, cardiovascular mortality, and MACE were higher in chronic HD patients with high IVCD (p < 0.01). High IVCD patients had significantly greater all-cause mortality, cardiovascular mortality, and MACE (log-rank test; p < 0.05). High IVCD patients are also associated with an increased risk of all-cause mortality and MACE relative to low IVCD patients (aHRs, 2.88 and 3.42; 95% CIs, 1.06−7.86 and 1.73−6.77, respectively; all p < 0.05). In the subgroup analysis of patients with high or low LVESD, the high IVCD remained a significant risk factor for all-cause mortality and MACE, and the HR is especially high in the high LVESD group. Conclusions: Dilated IVCD is a risk factor for all-cause mortality and MACE in chronic HD patients. In addition, these patients with high LVESD also have a significantly higher HR of all-cause mortality and MACE.

Contact Laxative Use and the Risk of Arteriovenous Fistula Maturation Failure in Patients Undergoing Hemodialysis: A Multi-Center Cohort Study.

Laxatives are commonly prescribed for constipation management; however, they are recognized as an independent factor associated with cardiovascular diseases. Arteriovenous fistula (AVF) is the closest to the ideal model of hemodialysis (HD) vascular access and part of the cardiovascular system. Our study aims to explore the association of contact laxative use with AVF maturation outcomes in patients undergoing HD. We conducted a multi-center cohort study of 480 contact laxative users and 472 non-users who had undergone initial AVF creation. All patients were followed until the outcomes of AVF maturation were confirmed. Multivariable logistic regression models were performed to evaluate the risk of AVF maturation failure imposed by laxatives. Here, we found that patients who used contact laxatives were significantly associated with an increased risk of AVF maturation failure compared to non-users (adjusted odds ratio, 1.64; p = 0.003). Notably, the risk of AVF maturation failure increased when increasing their average daily doses and cumulative treatment days. In conclusion, our study found a significant dose- and duration-dependent relationship between contact laxative use and an increased risk of AVF maturation failure. Thus, laxatives should be prescribed with caution in this population. Further studies are needed to validate these observations and investigate the potential mechanisms.

Hypermethylation of TMEM240 predicts poor hormone therapy response and disease progression in breast cancer.

BACKGROUND: Approximately 25% of patients with early-stage breast cancer experience cancer progression throughout the disease course. Alterations in TMEM240 in breast cancer were identified and investigated to monitor treatment response and disease progression. METHODS: Circulating methylated TMEM240 in the plasma of breast cancer patients was used to monitor treatment response and disease progression. The Cancer Genome Atlas (TCGA) data in Western countries and Illumina methylation arrays in Taiwanese breast cancer patients were used to identify novel hypermethylated CpG sites and genes related to poor hormone therapy response. Quantitative methylation-specific PCR (QMSP), real-time reverse transcription PCR, and immunohistochemical analyses were performed to measure DNA methylation and mRNA and protein expression levels in 394 samples from Taiwanese and Korean breast cancer patients. TMEM240 gene manipulation, viability, migration assays, RNA-seq, and MetaCore were performed to determine its biological functions and relationship to hormone drug treatment response in breast cancer cells. RESULTS: Aberrant methylated TMEM240 was identified in breast cancer patients with poor hormone therapy response using genome-wide methylation analysis in the Taiwan and TCGA breast cancer cohorts. A cell model showed that TMEM240, which is localized to the cell membrane and cytoplasm, represses breast cancer cell proliferation and migration and regulates the expression levels of enzymes involved in estrone and estradiol metabolism. TMEM240 protein expression was observed in normal breast tissues but was not detected in 88.2% (67/76) of breast tumors and in 90.0% (9/10) of metastatic tumors from breast cancer patients. QMSP revealed that in 54.5% (55/101) of Taiwanese breast cancer patients, the methylation level of TMEM240 was at least twofold higher in tumor tissues than in matched normal breast tissues. Patients with hypermethylation of TMEM240 had poor 10-year overall survival (p = 0.003) and poor treatment response, especially hormone therapy response (p < 0.001). Circulating methylated TMEM240 dramatically and gradually decreased and then diminished in patients without disease progression, whereas it returned and its levels in plasma rose again in patients with disease progression. Prediction of disease progression based on circulating methylated TMEM240 was found to have 87.5% sensitivity, 93.1% specificity, and 90.2% accuracy. CONCLUSIONS: Hypermethylation of TMEM240 is a potential biomarker for treatment response and disease progression monitoring in breast cancer.

Galectin-1 orchestrates an inflammatory tumor-stroma crosstalk in hepatoma by enhancing TNFR1 protein stability and signaling in carcinoma-associated fibroblasts.

Most cases of hepatocellular carcinoma (HCC) arise with the fibrotic microenvironment where hepatic stellate cells (HSCs) and carcinoma-associated fibroblasts (CAFs) are critical components in HCC progression. Therefore, CAF normalization could be a feasible therapy for HCC. Galectin-1 (Gal-1), a ß-galactoside-binding lectin, is critical for HSC activation and liver fibrosis. However, few studies has evaluated the pathological role of Gal-1 in HCC stroma and its role in hepatic CAF is unclear. Here we showed that Gal-1 mainly expressed in HCC stroma, but not cancer cells. High expression of Gal-1 is correlated with CAF markers and poor prognoses of HCC patients. In co-culture systems, targeting Gal-1 in CAFs or HSCs, using small hairpin (sh)RNAs or an therapeutic inhibitor (LLS30), downregulated plasminogen activator inhibitor-2 (PAI-2) production which suppressed cancer stem-like cell properties and invasion ability of HCC in a paracrine manner. The Gal-1-targeting effect was mediated by increased a disintegrin and metalloprotease 17 (ADAM17)-dependent TNF-receptor 1 (TNFR1) shedding/cleavage which inhibited the TNF-α → JNK → c-Jun/ATF2 signaling axis of pro-inflammatory gene transcription. Silencing Gal-1 in CAFs inhibited CAF-augmented HCC progression and reprogrammed the CAF-mediated inflammatory responses in a co-injection xenograft model. Taken together, the findings uncover a crucial role of Gal-1 in CAFs that orchestrates an inflammatory CSC niche supporting HCC progression and demonstrate that targeting Gal-1 could be a potential therapy for fibrosis-related HCC.

Relationship between metformin use and lactic acidosis in advanced chronic kidney disease: The REMIND-TMU study.

BACKGROUND: Evidence of metformin-associated lactic acidosis (MALA) in advanced chronic kidney disease (CKD) has been limited due to high mortality rate but rare incidence rate. The mechanism of increased MALA in advanced CKD is mainly based on the hypothesis of decreased drug elimination, which might also be confounded by increased comorbidities as CKD progresses. The goal of the study is to analyze the incidence and associated factors of lactic acidosis between metformin user and non-user with advanced CKD. METHODS: This study used a three million population-based, propensity score-matched cohort from 2008 to 2016. The primary outcome was laboratory-defined lactic acidosis. Relationships between the probability of lactic acidosis and various estimated glomerular filtration rate (eGFR) values in advanced CKD patients were also presented in regression analysis. RESULTS: Adults with type 2 diabetes whose eGFR was <30 mL/min/1.73 m2 were enrolled in this study. After the process of propensity score matching, 7707 patients were divided into metformin and non-metformin groups. In linear regression model, metformin significantly increased the risk of lactic acidosis (p=0.0204) as the eGFR declined in advanced CKD over a mean follow up of over 600 days even after confounder adjustment with age, sex and comorbidities. CONCLUSIONS: Metformin was associated with a significant increased risk of laboratory-defined lactic acidosis (p=0.0204) even after adjusting confounder such as age, sex and underlying comorbidities. This "REMIND" study reminds us that metformin-associated lactic acidosis is mainly caused by decreased drug renal elimination other than underlying comorbidities in advanced CKD patients.

Comparative survival analysis of platinum-based adjuvant chemotherapy for early-stage squamous cell carcinoma and adenocarcinoma of the lung.

BACKGROUND AND PURPOSE: Although cytotoxic platinum-based adjuvant chemotherapy (pACT) has been recommended for patients with completely resected early-stage (ES) non-small-cell lung cancer (ES-NSCLC), therapeutic regimens for NSCLC have evolved in the past two decades. The study was aimed to examine the effectiveness of postoperative pACT for resected ES-NSCLC patients with squamous cell carcinoma (SCC) or adenocarcinoma (ADC) according to real-world data. METHODS AND PATIENTS: Inverse probability treatment weighting (IPTW) was used to adjust baseline characteristics between the group receiving pACT and those not receiving any treatment (observation, OBS) within 3 months after curative surgery. Cox regression models were used to compare overall survival (OS) and treatment failure-free survival (TFS) between the groups. RESULTS: Of 31,208 patients with ES-NSCLC, 4700 undergoing complete tumor resection were eligible, with a mean follow-up period of 4.5 years. The pACT (n = 2347) and OBS (n = 2353) groups were well-balanced after IPTW. The median OS differed between the pACT and OBS groups (77.2 vs. 75.5 months, adjusted hazard ratio [aHR] = 0.87, 95% confidence interval [CI] = 0.79-0.95, p = 0.003), and the 5-year survival rates were 58.2% and 55.3%, respectively (p < 0.001). In the SCC group, pACT was superior to OBS in OS (75.0 vs. 57.4 months, aHR = 0.74, 95% CI = 0.62-0.88, p = 0.001) and TFS (32.7 vs. 21.8 months, aHR = 0.74, 95% CI = 0.63-0.86, p < 0.001). Both OS and TFS did not differ between two groups in those with ADC. CONCLUSION: Real-world data indicated that pACT confers a survival benefit for resected ES-NSCLC patients with SCC but not ADC, which needs to be verified by a large sample of randomized controlled studies.