Logistic Least Absolute Shrinkage And Selection Operator Research Articles

Development of a Practical Prediction Model for Adverse Neonatal Outcomes at the Start of the Second Stage of Labor.

To develop a prediction model for adverse neonatal outcomes using electronic fetal monitoring (EFM) interpretation data and other relevant clinical information known at the start of the second stage of labor. This was a retrospective cohort study of individuals who labored and delivered at two academic medical centers between July 2016 and June 2020. Individuals were included if they had a singleton gestation at term (more than 37 weeks of gestation), a vertex-presenting, nonanomalous fetus, and planned vaginal delivery and reached the start of the second stage of labor. The primary outcome was a composite of severe adverse neonatal outcomes. We developed and compared three modeling approaches to predict the primary outcome using factors related to EFM data (as interpreted and entered in structured data fields in the electronic health record by the bedside nurse), maternal comorbidities, and labor characteristics: traditional logistic regression, LASSO (least absolute shrinkage and selection operator), and extreme gradient boosting. Model discrimination and calibration were compared. Predicted probabilities were stratified into risk groups to facilitate clinical interpretation, and positive predictive values for adverse neonatal outcomes were calculated for each. A total of 22,454 patients were included: 14,820 in the training set and 7,634 in the test set. The composite adverse neonatal outcome occurred in 3.2% of deliveries. Of the three modeling methods compared, the logistic regression model had the highest discrimination (0.690, 95% CI, 0.656-0.724) and was well calibrated. When stratified into risk groups (no increased risk, higher risk, and highest risk), the rates of the composite adverse neonatal outcome were 2.6% (95% CI, 2.3-3.1%), 6.7% (95% CI, 4.6-9.6%), and 10.3% (95% CI, 7.6-13.8%), respectively. Factors with the strongest associations with the composite adverse neonatal outcome included the presence of meconium (adjusted odds ratio [aOR] 2.10, 95% CI, 1.68-2.62), fetal tachycardia within the 2 hours preceding the start of the second stage (aOR 1.94, 95% CI, 1.03-3.65), and number of prior deliveries (aOR 0.77, 95% CI, 0.60-0.99).

A Circadian Rhythm-related Signature to Predict Prognosis, Immunei Infiltration, and Drug Response in Breast Cancer.

Circadian rhythm genes (CRRGs) play essential roles in cancer occurrence and development. However, the prognostic significance of CRRGs in breast cancer (BC) has not been fully elucidated. Our study aimed to develop a prognostic gene signature based on CRRGs that can accurately and stably predict the prognosis of BC. The transcriptome data and clinical information for BC patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A consensus unsupervised clustering analysis was carried out to investigate the roles of CRRGs in BC. A CRRGs-related prognostic risk model was established by using logistic least absolute shrinkage and selection operator (LASSO) Cox regression and univariate Cox regression analyses. Kaplan-Meier (KM) curves analysis, time-dependent receptor operation characteristics (ROC) curves analysis, and nomogram were plotted to evaluate the predictive efficacy of the model. The relevance of risk score to the immune cell infiltration, tumor burden mutation (TMB), and therapeutic response was assessed. A risk model comprising six CRRGs (SLC44A4, SLC16A6, TPRG1, FABP7, GLYATL2, and FDCSP) was constructed and validated, demonstrating a good predictor of BC. The low-risk group displayed a higher number of immune activities and immune checkpoint expression and a lower burden of tumor mutation. Additionally, drug sensitivity analysis demonstrated the prognostic signature may serve as a potential chemosensitivity predictor. We established 6 CRRGs-related risk signatures for the prognosis of BC, which is of great value in predicting the prognosis of patients with BC and guiding the treatment for BC.

Uncovering waterlogging-responsive genes in cucumber through machine learning and differential gene correlation analysis

As climate change intensifies, the frequency and severity of waterlogging are expected to increase, necessitating a deeper understanding of the cucumber response to this stress. In this study, three public RNA-seq datasets (PRJNA799460, PRJNA844418, and PRJNA678740) comprising 36 samples were analyzed. Various feature selection algorithms including Uncertainty, Relief, SVM (Support Vector Machine), Correlation, and logistic least absolute shrinkage, and selection operator (LASSO) were performed to identify the most significant genes related to the waterlogging stress response. These feature selection techniques, which have different characteristics, were used to reduce the complexity of the data and thereby identify the most significant genes related to the waterlogging stress response. Uncertainty, Relief, SVM, Correlation, and LASSO identified 4, 4, 10, 21, and 13 genes, respectively. Differential gene correlation analysis (DGCA) focusing on the 36 selected genes identified changes in correlation patterns between the selected genes under waterlogged versus control conditions, providing deeper insights into the regulatory networks and interactions among the selected genes. DGCA revealed significant changes in the correlation of 13 genes between control and waterlogging conditions. Finally, we validated 13 genes using the Random Forest (RF) classifier, which achieved 100% accuracy and a 1.0 Area Under the Curve (AUC) score. The SHapley Additive exPlanations (SHAP) values clearly showed the significant impact of LOC101209599, LOC101217277, and LOC101216320 on the model’s predictive power. In addition, we employed the Boruta as a wrapper feature selection method to further validate our gene selection strategy. Eight of the 13 genes were common across the four feature weighting algorithms, LASSO, DGCA, and Boruta, underscoring the robustness and reliability of our gene selection strategy. Notably, the genes LOC101209599, LOC101217277, and LOC101216320 were among genes identified by multiple feature selection methods from different categories (filtering, wrapper, and embedded). Pathways associated with these specific genes play a pivotal role in regulating stress tolerance, root development, nutrient absorption, sugar metabolism, gene expression, protein degradation, and calcium signaling. These intricate regulatory mechanisms are crucial for cucumbers to adapt effectively to waterlogging conditions. These findings provide valuable insights for uncovering targets in breeding new cucumber varieties with enhanced stress tolerance.

A novel nomogram to predict the recurrence of hepatocellular carcinoma after liver transplantation using extended selection criteria

BackgroundLiver transplantations (LTs) with extended criteria have produced surgical results comparable to those obtained with traditional standards. However, it is not sufficient to predict hepatocellular carcinoma (HCC) recurrence after LT according to morphological criteria alone. The present study aimed to construct a nomogram for predicting HCC recurrence after LT using extended selection criteria. MethodsRetrospective data on patients with HCC, including pathology, serological markers and follow-up data, were collected from January 2015 to April 2020 at Huashan Hospital, Fudan University, Shanghai, China. Logistic least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analyses were performed to identify and construct the prognostic nomogram. Receiver operating characteristic (ROC) curves, Kaplan-Meier curves, decision curve analyses (DCAs), calibration diagrams, net reclassification indices (NRIs) and integrated discrimination improvement (IDI) values were used to assess the prognostic capacity of the nomogram. ResultsA total of 301 patients with HCC who underwent LT were enrolled in the study. The nomogram was constructed, and the ROC curve showed good performance in predicting survival in both the development set (2/3) and the validation set (1/3) (the area under the curve reached 0.748 and 0.716, respectively). According to the median value of the risk score, the patients were categorized into the high- and low-risk groups, which had significantly different recurrence-free survival (RFS) rates (P < 0.01). Compared with the Milan criteria and University of California San Francisco (UCSF) criteria, DCA revealed that the new nomogram model had the best net benefit in predicting 1-, 3- and 5-year RFS. The nomogram performed well for calibration, NRI and IDI improvement. ConclusionsThe nomogram, based on the Milan criteria and serological markers, showed good accuracy in predicting the recurrence of HCC after LT using extended selection criteria.

Development and Validation of a Novel Placental DNA Methylation Biomarker of Maternal Smoking during Pregnancy in the ECHO Program.

Maternal cigarette smoking during pregnancy (MSDP) is associated with numerous adverse health outcomes in infants and children with potential lifelong consequences. Negative effects of MSDP on placental DNA methylation (DNAm), placental structure, and function are well established. Our aim was to develop biomarkers of MSDP using DNAm measured in placentas (), collected as part of the Vitamin C to Decrease the Effects of Smoking in Pregnancy on Infant Lung Function double-blind, placebo-controlled randomized clinical trial conducted between 2012 and 2016. We also aimed to develop a digital polymerase chain reaction (PCR) assay for the top ranking cytosine-guanine dinucleotide (CpG) so that large numbers of samples can be screened for exposure at low cost. We compared the ability of four machine learning methods [logistic least absolute shrinkage and selection operator (LASSO) regression, logistic elastic net regression, random forest, and gradient boosting machine] to classify MSDP based on placental DNAm signatures. We developed separate models using the complete EPIC array dataset and on the subset of probes also found on the 450K array so that models exist for both platforms. For comparison, we developed a model using CpGs previously associated with MSDP in placenta. For each final model, we used model coefficients and normalized beta values to calculate placental smoking index (PSI) scores for each sample. Final models were validated in two external datasets: the Extremely Low Gestational Age Newborn observational study, ; and the Rhode Island Children's Health Study, . Logistic LASSO regression demonstrated the highest performance in cross-validation testing with the lowest number of input CpGs. Accuracy was greatest in external datasets when using models developed for the same platform. PSI scores in smokers only () were moderately correlated with maternal plasma cotinine levels. One CpG (cg27402634), with the largest coefficient in two models, was measured accurately by digital PCR compared with measurement by EPIC array (). To our knowledge, we have developed the first placental DNAm-based biomarkers of MSDP with broad utility to studies of prenatal disease origins. https://doi.org/10.1289/EHP13838.

Development of a risk assessment model for cardiac injury in patients newly diagnosed with acute myeloid leukemia based on a multicenter, real-world analysis in China

BackgroundStudies have revealed that acute myeloid leukemia (AML) patients are prone to combined cardiac injury. We aimed to identify hematological risk factors associated with cardiac injury in newly diagnosed AML patients before chemotherapy and develop a personalized predictive model.MethodsThe population baseline, blood test, electrocardiogram, echocardiograph, and genetic and cytogenetic data were collected from newly diagnosed AML patients. The data were subdivided into training and validation cohorts. The independent risk factors were explored by univariate and multivariate logistic regression analysis respectively, and data dimension reduction and variable selection were performed using the least absolute shrinkage and selection operator (LASSO) regression models. The nomogram was generated and the reliability and generalizability were verified by receiver operating characteristic (ROC) curves, the area under the curve (AUC) and calibration curves in an external validation cohort.ResultsFinally, 499 AML patients were included. After univariate logistic regression, LASSO regression and multivariate logistic regression analysis, abnormal NT-proBNP, NPM1 mutation, WBC, and RBC were independent risk factors for cardiac injury in AML patients (all P < 0.05). The nomogram was constructed based on the above four variables with high accuracy. The area under the curve was 0.742, 0.750, and 0.706 in the training, internal validation, and external validation cohort, respectively. The calibration curve indicated that the model has good testing capability. The Kaplan-Meier curve showed that the higher the risk of combined cardiac injury in AML patients, the lower their probability of survival.ConclusionsThis prediction nomogram identifies hematological risk factors associated with cardiac injury in newly diagnosed AML patients and can help hematologists identify the risk and provide precise treatment options.

Prediction Model for Postoperative Pressure Injury in Patients with Acute Type A Aortic Dissection.

To establish a risk assessment model to predict postoperative National Pressure Injury Advisory Panel stage 2 or higher pressure injury (PI) risk in patients undergoing acute type A aortic dissection surgery. This retrospective assessment included consecutive patients undergoing acute type A aortic dissection surgery in the authors' hospital from September 2017 to June 2021. The authors used LASSO (logistic least absolute shrinkage and selection operator) regression analysis to identify the most relevant variables associated with PI by running cyclic coordinate descent with 10-times cross-validation. The variables selected by LASSO regression analysis were subjected to multivariate logistic analysis. A calibration plot, receiver operating characteristic curve, and decision curve analysis were used to validate the model. There were 469 patients in the study, including 94 (27.5%) with postoperative PI. Ten variables were selected from LASSO regression: body mass index, diabetes, Marfan syndrome, stroke, preoperative skin moisture, hemoglobin, albumin, serum creatinine, platelet, and d-dimer. Four risk factors emerged after multivariate logistic regression: Marfan syndrome, preoperative skin moisture, albumin, and serum creatinine. The area under the receiver operating characteristic curve of the model was 0.765. The calibration plot and the decision curve analysis both suggested that the model was suitable for predicting postoperative PI. This study built an efficient predictive model that could help identify high-risk patients.

Diabetes Distress and Associated Factors Among Chinese Americans with Type 2 Diabetes in New York City.

The purpose of this study is to describe diabetes distress and related factors among Chinese Americans with type 2 diabetes in New York City (NYC). We conducted a secondary data analysis of the baseline data from three research studies conducted among community-dwelling Chinese American adults with type 2 diabetes. Diabetes Distress Scale (DDS) was used to measure sources of diabetes distress including emotional-, regimen-, interpersonal-, and physician-related distress. A score of 2 or greater indicates moderate diabetes distress or higher. Patient Health Questionnaire-2 (PHQ-2) was used to measure depressive symptoms. Participants' sociodemographic information was also collected. Descriptive statistics were used to describe diabetes distress, and logistic least absolute shrinkage and selection operator (LASSO) regression was used to examine factors associated with diabetes distress level. Data from 178 participants (mean age 63.55±13.56 years) were analyzed. Most participants were married (76.40%), had a high school degree or less (65.73%), had a household annual income < $25,000 (70.25%), and reported limited English proficiency (93.22%). About 25.84% reported moderate or higher overall distress. The most common sources of distress were emotional burden (29.78%), followed by regimen- (28.65%), interpersonal- (18.54%), and physician-related distress (14.04%). Participants who were younger, female, limited English proficient, and had elevated depressive symptoms were more likely to have higher diabetes distress. Diabetes distress is prevalent among Chinese immigrants with type 2 diabetes, especially emotional- and regimen-related distress. Given the known link between diabetes distress and poor glycemic control, it is critical to screen for diabetes distress at primary care clinics and incorporate psychological counseling in diabetes care in this underserved population.

Hsa_circRNA_405498 and hsa_circRNA_100033 Serve as Potential Biomarkers for Differential Diagnosis of Type 1 Diabetes.

The role of circular RNAs (circRNAs) in type 1 diabetes (T1D) is largely unknown. We aimed to identify some circRNAs as differential diagnostic biomarkers for T1D to distinguish between patients with latent autoimmune diabetes in adults (LADA) and type 2 diabetes (T2D). The circRNA expression profiles were determined by Arraystar human circRNA microarray in T1D compared to controls (n = 6 each). The differentially expressed circRNAs were validated by real-time quantitative polymerase chain reaction using a validation cohort with 20 T1D and 20 controls. The diagnostic performances of the candidate circRNAs and the clinical parameters were assessed using the logistic least absolute shrinkage and selection operator (LASSO) regression model in a larger cohort with 457 individuals, including patients with T1D, T2D, and LADA, and controls. We identified 110 differentially expressed circular transcripts (53 upregulated and 57 downregulated) in T1D patients compared with controls. Further analysis showed that the levels of hsa_circRNA_405498 and hsa_circRNA_100033 were significantly downregulated in T1D compared to controls (both P < .05). Moreover, the expression levels of these 2 circRNAs showed sequential downregulation from controls, patients with T2D, LADA, to T1D (P < .05). The area under the curve (AUC) of receiver operating characteristic plots in logistic LASSO regression model showed high diagnostic accuracy for combination model with the 2 circRNAs and some clinical parameters in distinguishing T1D from LADA (AUC = 0.915), T2D (AUC = 0.993), and controls (AUC = 0.992). Our study demonstrated that hsa_circRNA_405498 and hsa_circRNA_100033 are promising novel differential diagnostic biomarkers for T1D.

A nomogram model combining ultrasound-based radiomics features and clinicopathological factors to identify germline BRCA1/2 mutation in invasive breast cancer patients

ObjectiveBRCA1/2 status is a key to personalized therapy for invasive breast cancer patients. This study aimed to explore the association between ultrasound radiomics features and germline BRCA1/2 mutation in patients with invasive breast cancer. Materials and methodsIn this retrospective study, 100 lesions in 92 BRCA1/2-mutated patients and 390 lesions in 357 non-BRCA1/2-mutated patients were included and randomly assigned as training and validation datasets in a ratio of 7:3. Gray-scale ultrasound images of the largest plane of the lesions were used for feature extraction. Maximum relevance minimum redundancy (mRMR) algorithm and multivariate logistic least absolute shrinkage and selection operator (LASSO) regression were used to select features. The multivariate logistic regression method was used to construct predictive models based on clinicopathological factors, radiomics features, or a combination of them. ResultsIn the clinical model, age at first diagnosis, family history of BRCA1/2-related malignancies, HER2 status, and Ki-67 level were found to be independent predictors for BRCA1/2 mutation. In the radiomics model, 10 significant features were selected from the 1032 radiomics features extracted from US images. The AUCs of the radiomics model were not inferior to those of the clinical model in both training dataset [0.712 (95% CI, 0.647–0.776) vs 0.768 (95% CI, 0.704–0.835); p = 0.429] and validation dataset [0.705 (95% CI, 0.597–0.808) vs 0.723 (95% CI, 0.625–0.828); p = 0.820]. The AUCs of the nomogram model combining clinical and radiomics features were 0.804 (95% CI, 0.748–0.861) in the training dataset and 0.811 (95% CI, 0.724–0.894) in the validation dataset, which were proved significantly higher than those of the clinical model alone by DeLong’s test (p = 0.041; p = 0.007). To be noted, the negative predictive values (NPVs) of the nomogram model reached a favorable 0.93 in both datasets. ConclusionThis machine nomogram model combining ultrasound-based radiomics and clinical features exhibited a promising performance in identifying germline BRCA1/2 mutation in patients with invasive breast cancer and may help avoid unnecessary gene tests in clinical practice.

Machine learning-based prediction models for parathyroid carcinoma using pre-surgery cognitive function and clinical features

Patients with parathyroid carcinoma (PC) are often diagnosed postoperatively, due to incomplete resection during the initial surgery, resulting in poor outcomes. The aim of our study was to investigate the pre-surgery indicators of PC and try to develop a predictive model for PC utilizing machine learning. Evaluation of pre-surgery neuropsychological function and confirmation of pathology were carried out in 133 patients with primary hyperparathyroidism in Beijing Chaoyang Hospital from December 2019 to January 2023. Patients were randomly divided into a training cohort (n = 93) and a validating cohort (n = 40). Analysis of the clinical dataset, two machine learning including the extreme gradient boosting (XGBoost) and the least absolute shrinkage and selection operator (LASSO) regression were utilized to develop the prediction model for PC. Logistic regression analysis was also conducted for comparison. Significant differences in elevated parathyroid hormone and decreased serum phosphorus in PC compared to (BP). The lower score of MMSE and MOCA was observed in PC and a cutoff of MMSE < 24 was the optimal threshold to stratify PC from BP (area under the curve AUC 0.699 vs 0.625). The predicted probability of PC by machine learning was similar to the observed probability in the test set, whereas the logistic model tended to overpredict the possibility of PC. The XGBoost model attained a higher AUC than the logistic algorithms and LASSO models. (0.835 vs 0.683 vs 0.607). Preoperative cognitive function may be a probable predictor for PC. The cognitive function-based prediction model based on the XGBoost algorithm outperformed LASSO and logistic regression, providing valuable preoperative assistance to surgeons in clinical decision-making for patients suspected PC.

Risk factors and predictive model for acute kidney Injury Transition to acute kidney disease in patients following partial nephrectomy

PurposeAcute kidney disease (AKD) is believed to be involved in the transition from acute kidney injury (AKI) to chronic kidney disease in general populations, but little is understood about this possibility among kidney surgical populations. This study aimed to elucidate the incidence of AKD after partial nephrectomy and risk factors that promote the AKI to AKD transition.MethodsFrom January 2010 to January 2020, this study retrospectively collected a dataset of consecutive patients with renal masses undergoing partial nephrectomy in 4 urological centers. Cox proportional regression analyses were adopted to identify risk factors that promoted the AKI to AKD transition. To avoid overfitting, the results were then verified by logistic least absolute shrinkage and selection operator (LASSO) regression. A nomogram was then constructed and validated for AKI to AKD transition prediction.ResultsAKI and AKD occurred in 228 (21.4%) and 42 (3.9%) patients among a total of 1062 patients, respectively. In patients with AKI, multivariable Cox regression analysis and LASSO regression identified that age (HR 1.078, 1.029–1.112, p < 0.001), baseline eGFR (HR 1.015, 1.001–1.030, p < 0.001), RENAL score (HR1.612, 1.067–2.437, p = 0.023), ischemia time > 30 min (HR 7.284, 2.210–23.999, p = 0.001), and intraoperative blood loss > 300ml (HR 8.641, 2.751–27.171, p < 0.001) were risk factors for AKD transition. These five risk factors were then integrated into a nomogram. The nomogram showed excellent discrimination, calibration, and clinical net benefit ability.ConclusionAround 3.9% patients following partial nephrectomy would transit from AKI to AKD. Intraoperative blood loss and ischemia time need to be diminished to avoid on-going functional decline. Our nomogram can accurately predict the transition from AKI to AKD.

Diet-Related Risk Factors for Cervical Cancer: Data from National Health and Nutrition Examination Survey 1999–2018

Diverse dietary constituents, encompassing both macro- and micronutrient intakes, have established connections with various cancers, though their specific roles in cervical cancer remain unclear. This study explores dietary intake correlations among women aged 30 yrs and above diagnosed with cervical cancer (n = 215), contrasted with women without (n = 860). These populations were selected from the 1999–2018 cycle of the National Health and Nutrition Examination Survey. The research implemented the univariate analysis and the least absolute shrinkage and selection operator (LASSO) regression to estimate the association of 29 variables with cervical cancer, subsequently identifying the most pertinent variables linked to cervical cancer. Six covariates emerged as significantly associated with cervical cancer in univariate analyses (age, race, fiber, magnesium, caffeine, vitamin C) (p < 0.05). In LASSO regression, with the escalating penalty factor (λ), it was discerned that specific covariates, including age, race, fiber, and Vitamin C, consistently remained in the model. Univariate analysis and logistic LASSO regression findings suggested that diets deficient in fiber and vitamin C were related to cervical cancer.

Cuproptosis-related risk score predicts prognosis and characterizes the tumor microenvironment in colon adenocarcinoma.

Cuproptosis is a novel copper-dependent regulatory cell death (RCD), which is closely related to the occurrence and development of multiple cancers. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of colon adenocarcinoma (COAD) remains unclear. Transcriptome, somatic mutation, somatic copy number alteration and the corresponding clinicopathological data of COAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus database (GEO). Difference, survival and correlation analyses were conducted to evaluate the characteristics of CRGs in COAD patients. Consensus unsupervised clustering analysis of CRGs expression profile was used to classify patients into different cuproptosis molecular and gene subtypes. TME characteristics of different molecular subtypes were investigated by using Gene set variation analysis (GSVA) and single sample gene set enrichment analysis (ssGSEA). Next, CRG Risk scoring system was constructed by applying logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis and multivariate cox analysis. Real-time quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) were used to exam the expression of key Risk scoring genes. Our study indicated that CRGs had relatively common genetic and transcriptional variations in COAD tissues. We identified three cuproptosis molecular subtypes and three gene subtypes based on CRGs expression profile and prognostic differentially expressed genes (DEGs) expression profile, and found that changes in multilayer CRGs were closely related to the clinical characteristics, overall survival (OS), different signaling pathways, and immune cell infiltration of TME. CRG Risk scoring system was constructed according to the expression of 7 key cuproptosis-related risk genes (GLS, NOX1, HOXC6, TNNT1, GLS, HOXC6 and PLA2G12B). RT-qPCR and IHC indicated that the expression of GLS, NOX1, HOXC6, TNNT1 and PLA2G12B were up-regulated in tumor tissues, compared with those in normal tissues, and all of GLS, HOXC6, NOX1 and PLA2G12B were closely related with patient survival. In addition, high CRG risk scores were significantly associated with high microsatellite instability (MSI-H), tumor mutation burden (TMB), cancer stem cell (CSC) indices, stromal and immune scores in TME, drug susceptibility, as well as patient survival. Finally, a highly accurate nomogram was constructed to promote the clinical application of the CRG Risk scoring system. Our comprehensive analysis showed that CRGs were greatly associated with TME, clinicopathological characteristics, and prognosis of patient with COAD. These findings may promote our understanding of CRGs in COAD, providing new insights for physicians to predict prognosis and develop more precise and individualized therapy strategies.

Construction and validation of a risk prediction model for aromatase inhibitor-associated bone loss.

To establish a high-risk prediction model for aromatase inhibitor-associated bone loss (AIBL) in patients with hormone receptor-positive breast cancer. The study included breast cancer patients who received aromatase inhibitor (AI) treatment. Univariate analysis was performed to identify risk factors associated with AIBL. The dataset was randomly divided into a training set (70%) and a test set (30%). The identified risk factors were used to construct a prediction model using the eXtreme gradient boosting (XGBoost) machine learning method. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods were used for comparison. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the model in the test dataset. A total of 113 subjects were included in the study. Duration of breast cancer, duration of aromatase inhibitor therapy, hip fracture index, major osteoporotic fracture index, prolactin (PRL), and osteocalcin (OC) were found to be independent risk factors for AIBL (p < 0.05). The XGBoost model had a higher AUC compared to the logistic model and LASSO model (0.761 vs. 0.716, 0.691). The XGBoost model outperformed the logistic and LASSO models in predicting the occurrence of AIBL in patients with hormone receptor-positive breast cancer receiving aromatase inhibitors.

Investigating the change in gene expression profile of blood mononuclear cells post-laparoscopic sleeve gastrectomy in Chinese obese patients.

Laparoscopic sleeve gastrectomy (LSG) is a sustainable technique that effectively treats morbid obesity. However, the molecular mechanisms underlying the improvement of metabolic health following this process warrants more investigation. This study investigates LSG-related molecules and uses bulk RNA-sequencing high-throughput analysis to unravel their regulatory mechanisms. Peripheral blood mononuclear cells (PBMC) were collected from ten obese patients with BMI ≥ 32.5 kg/m2 in the Department of General Surgery of Kunming First People's Hospital. After LSG, patients were followed up for one month, and blood samples were retaken. Blood samples from ten patients before and after LSG and bulk RNA-Seq data were analyzed in this study. LSG-associated gene expression was detected by weighted gene coexpression network analysis (WGCNA) and differential analysis. Subsequently, essential signature genes were identified using logistic least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were utilized to reveal the potential functions of the target genes. Furthermore, the Pearson correlation of signature genes with leptin and lipocalin was also explored. Finally, we constructed a robust endogenous RNA (ceRNA) network based on miRWalk and starBase databases. We identified 18 overlapping genes from 91 hub genes, and 165 differentially expressed mRNAs (DE-mRNA), which were revealed to be significantly associated with immune cells, immune response, inflammatory response, lipid storage, and localization upon functional enrichment analysis. Three signature genes, IRF1, NFKBIA, and YRDC, were identified from the 18 overlapping genes by LASSO and SVM-REF algorithms. The logistic regression model based on the three signature genes highlighted how robustly they discriminated between samples. ssGSEA indicated these genes to be involved in lipid metabolism and degradation pathways. Moreover, leptin levels were significantly reduced in patients undergoing LSG, and NFKBIA significantly negatively correlated with leptin. Finally, we identified how the long non-coding RNA (lncRNA) ATP2B1-AS1 regulated the expression of the signature genes by competitively binding to six microRNAs (miRNAs), which were hsa-miR-6509-5p, hsa-miR-330-5P, hsa-miR-154-5P, hsa-miR-145-5P, hsa-miR4726-5P and hsa-miR-134-5P. This study identified three critical regulatory genes significantly differentiated between patients before and after LSG treatment and highlighted their potentially crucial role after bariatric surgery. This provides novel insights to increase our understanding of the underlying mechanisms of weight loss and associated metabolic improvement after bariatric surgery.

Derivation and validation of a risk prediction score for nonsteroidal anti-inflammatory drug-related serious gastrointestinal complications in the elderly.

Few studies have quantified the impact of risk factors on GI complications in elderly nonsteroidal anti-inflammatory drug (NSAID) users. This study aimed to develop and validate a risk prediction score for severe GI complications to identify high-risk elderly patients using NSAID. We used the following two Korean claims datasets: customized data with an enrolment period 2016-2017 for model development, and the sample data in 2019 for external validation. We conducted a nested case-control study for model development and validation. NSAID users were identified as the elderly (≥65 years) who received NSAIDs for more than 30 days. Serious GI complications were defined as hospitalizations or emergency department visits, with a main diagnosis of GI bleeding or perforation. We applied the logistic least absolute shrinkage and selection operator (LASSO) regression model for variable selection and model fitting. We identified 8176 cases and 81 760 controls with a 1:10 matched follow-up period in the derivation cohort. In the external validation cohort, we identified 372 cases from 254 551 patients. The risk predictors were high-dose NSAIDs, nonselective NSAID, complicated GI ulcer history, male sex, concomitant gastroprotective agents, relevant co-medications, severe renal disease and cirrhosis. Area under the receiver operating characteristic curve was 0.79 (95% confidence interval, 0.77-0.81) in the external validation dataset. The prediction model may be a useful tool for reducing the risk of serious GI complications by identifying high-risk elderly patients.

Development of a classification model and an immune-related network based on ferroptosis in periodontitis.

Periodontitis is an immunoinflammatory disease characterized by irreversible periodontal attachment loss and bone destruction. Ferroptosis is a kind of immunogenic cell death that depends on the participation of iron ions and is involved in various inflammatory and immune processes. However, information regarding the relationship between ferroptosis and immunomodulation processes in periodontitis is extremely limited. The purpose of this study was to investigate the correlation between ferroptosis and immune responses in periodontitis. Gene expression profiles of gingivae were collected from the Gene Expression Omnibus data portal. After detecting differentially expressed ferroptosis-related genes (FRGs), we used univariate logistic regression analysis followed by logistic least absolute shrinkage and selection operator (LASSO) regression to establish a ferroptosis-related classification model in an attempt to accurately distinguish periodontitis gingival tissues from healthy samples. The infiltration level of immunocytes in periodontitis was then assessed through single-sample gene-set enrichment analysis. Subsequently, we screened out immune-related genes by weighted correlation network analysis and protein-protein interaction (PPI) analysis and constructed an immune-related network based on FRGs and immune-related genes. A total of 24 differentially expressed FRGs were detected, and an 8-FRG combined signature constituted the classification model. The established model showed outstanding discriminating ability according to the results of receiver operating characteristic (ROC) curve analysis. In addition, the periodontitis samples had a higher degree of immunocyte infiltration. Activated B cells had the strongest positive correlation while macrophages had a strong negative correlation with certain FRGs, and we found that XBP1, ALOX5 and their interacting genes might be crucial genes in the immune-related network. The FRG-based classification model had a satisfactory determination ability, which could bring new insights into the pathogenesis of periodontitis. Those genes in the immune-related network, especially hub genes along with XBP1 and ALOX5, would have the potential to serve as promising targets of immunomodulatory treatments for periodontitis.

Glucose and lipid metabolism indexes and blood inflammatory biomarkers of patients with severe periodontitis: A cross-sectional study.

To investigate the relation of established glucose and lipid metabolism indexes and blood inflammatory biomarkers with severe periodontitis in systemically healthy patients. Systemically healthy Stage III/IV periodontitis patients (case group) (n=397), Stage II periodontitis patients (n=36), and periodontally healthy subjects (control group) (n=285) were recruited. A periodontal examination, complete blood cell examination, and blood biochemical examination were conducted for all participants. Full-mouth apical films were taken for the case group. Both the case and control groups were divided by age into younger (≤ 35 years) and elder subjects. Multiple logistic regression analysis and Pearson correlation analysis were conducted. A logistic least absolute shrinkage and selection operator (LASSO) model was constructed for the younger subgroups. Various glucose and lipid metabolism indexes and blood inflammatory biomarkers significantly differed between severe periodontitis patients and healthy controls, and the younger subgroups presented a greater degree of statistical differences than the elder ones. More pairs of periodontal parameters and blood indexes with significantly fair linear correlations were found in the younger patient subgroup. A logistic LASSO regression model containing eight blood indexes to assess a severe periodontitis outcome in younger subgroups showed satisfactory predictive ability. The present study revealed various glucose and lipid metabolism indexes and blood inflammatory biomarkers significantly differ between severe periodontitis patients and healthy controls, especially in the younger subgroups. A LASSO regression model could be a viable option to assess severe periodontitis risk for younger patients.

Identification of cuproptosis-related subtypes, construction of a prognosis model, and tumor microenvironment landscape in gastric cancer.

Cuproptosis is a novel identified regulated cell death (RCD), which is correlated with the development, treatment response and prognosis of cancer. However, the potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment (TME) of gastric cancer (GC) remains unknown. Transcriptome profiling, somatic mutation, somatic copy number alteration and clinical data of GC samples were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database to describe the alterations of CRGs from genetic and transcriptional fields. Differential, survival and univariate cox regression analyses of CRGs were carried out to investigate the role of CRGs in GC. Cuproptosis molecular subtypes were identified by using consensus unsupervised clustering analysis based on the expression profiles of CRGs, and further analyzed by GO and KEGG gene set variation analyses (GSVA). Genes in distinct molecular subtypes were also analyzed by GO and KEGG gene enrichment analyses (GSEA). Differentially expressed genes (DEGs) were screened out from distinct molecular subtypes and further analyzed by GO enrichment analysis and univariate cox regression analysis. Consensus clustering analysis of prognostic DEGs was performed to identify genomic subtypes. Next, patients were randomly categorized into the training and testing group at a ratio of 1:1. CRG Risk scoring system was constructed through logistic least absolute shrinkage and selection operator (LASSO) cox regression analysis, univariate and multivariate cox analyses in the training group and validated in the testing and combined groups. Real-time quantitative polymerase chain reaction (RT-qPCR) was used to evaluate the expression of key Risk scoring genes. Sensitivity and specificity of Risk scoring system were examined by using receiver operating characteristic (ROC) curves. pRRophetic package in R was used to investigate the therapeutic effects of drugs in high- and low- risk score group. Finally, the nomogram scoring system was developed to predict patients' survival through incorporating the clinicopathological features and CRG Risk score. Most CRGs were up-regulated in tumor tissues and showed a relatively high mutation frequency. Survival and univariate cox regression analysis revealed that LIAS and FDX1 were significantly associated with GC patients' survival. After consensus unsupervised clustering analysis, GC patients were classified into two cuproptosis molecular subtypes, which were significantly associated with clinical features (gender, age, grade and TNM stage), prognosis, metabolic related pathways and immune cell infiltration in TME of GC. GO enrichment analyses of 84 DEGs, obtained from distinct molecular subtypes, revealed that DEGs primarily enriched in the regulation of metabolism and intracellular/extracellular structure in GC. Univariate cox regression analysis of 84 DEGs further screened out 32 prognostic DEGs. According to the expression profiles of 32 prognostic DEGs, patients were re-classified into two gene subtypes, which were significantly associated with patients' age, grade, T and N stage, and survival of patients. Nest, the Risk score system was constructed with moderate sensitivity and specificity. A high CRG Risk score, characterized by decreased microsatellite instability-high (MSI-H), tumor mutation burden (TMB) and cancer stem cell (CSC) index, and high stromal and immune score in TME, indicated poor survival. Four of five key Risk scoring genes expression were dysregulated in tumor compared with normal samples. Moreover, CRG Risk score was greatly related with sensitivity of multiple drugs. Finally, we established a highly accurate nomogram for promoting the clinical applicability of the CRG Risk scoring system. Our comprehensive analysis of CRGs in GC demonstrated their potential roles in TME, clinicopathological features, and prognosis. These findings may improve our understanding of CRGs in GC and provide new perceptions for doctors to predict prognosis and develop more effective and personalized therapy strategies.