Least Absolute Shrinkage And Selection Operator Research Articles

Exploratory Study of Differentially Expressed Genes of Peripheral Blood Monocytes in Patients with Carotid Atherosclerosis.

The abundance of circulating monocytes is closely associated with the development of atherosclerosis in humans. This study aimed to further research into diagnostic biomarkers and targeted treatment of carotid atherosclerosis (CAS). We performed transcriptomics analysis through weighted gene co-expression network analysis (WGCNA) of monocytes from patients in public databases with and without CAS. Differentially expressed genes (DEGs) were screened by R package limma. Diagnostic molecules were derived by the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) algorithms. NetworkAnalyst, miRWalk, and StarBase databases assisted in the construction of diagnostic molecule regulatory networks. The DrugBank database predicted drugs targeting the diagnostic molecules. RT-PCR tested expression profiles. From 14,369 hub genes and 61 DEGs, six differentially expressed monocyte-related hub genes were significantly associated with immune cells, immune responses, monocytes, and lipid metabolism. LASSO and SVM-RFE yielded five genes for CAS prediction. RT-PCR of these genes showed HMGB1 was upregulated, and CCL3, CCL3L1, CCL4, and DUSP1 were down-regulated in CAS versus controls. Then, we constructed and visualized the regulatory networks of 9 transcription factors (TFs), which significantly related to 5 diagnostic molecules. About 11 miRNAs, 19 lncRNAs, and 39 edges centered on four diagnostic molecules (CCL3, CCL4, DUSP1, and HMGB1) were constructed and displayed. Eleven potential drugs were identified, including ibrutinib, CTI-01, roflumilast etc. Conclusion: A set of five biomarkers were identified for the diagnosis of CAS and for the study of potential therapeutic targets.

Sparse Group Penalties for bi-level variable selection.

Many data sets exhibit a natural group structure due to contextual similarities or high correlations of variables, such as lipid markers that are interrelated based on biochemical principles. Knowledge of such groupings can be used through bi-level selection methods to identify relevant feature groups and highlight their predictive members. One of the best known approaches of this kind combines the classical Least Absolute Shrinkage and Selection Operator (LASSO) with the Group LASSO, resulting in the Sparse Group LASSO. We propose the Sparse Group Penalty (SGP) framework, which allows for a flexible combination of different SGL-style shrinkage conditions. Analogous to SGL, we investigated the combination of the Smoothly Clipped Absolute Deviation (SCAD), the Minimax Concave Penalty (MCP) and the Exponential Penalty (EP) with their group versions, resulting in the Sparse Group SCAD, the Sparse Group MCP, and the novel Sparse Group EP (SGE). Those shrinkage operators provide refined control of the effect of group formation on the selection process through a tuning parameter. In simulation studies, SGPs were compared with other bi-level selection methods (Group Bridge, composite MCP, and Group Exponential LASSO) for variable and group selection evaluated with the Matthews correlation coefficient. We demonstrated the advantages of the new SGE in identifying parsimonious models, but also identified scenarios that highlight the limitations of the approach. The performance of the techniques was further investigated in a real-world use case for the selection of regulated lipids in a randomized clinical trial.

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

A Clinical Fusion Model Based on Radiomics Features and Deep Learning for Predicting CDKN2A/B Homozygous Deletion Status in IDH-mutant Diffuse Astrocytoma

Purpose: To construct a fusion model for predicting CDKN2A/B homozygous deletion status in patients with isocitrate dehydrogenase (IDH)-mutant diffuse astrocytoma by combining the radiomics features and deep learning (DL). Methods: A total of 200 IDH-mutant astrocytoma (103 CDKN2A/B homozygous deletion (HD) and 97 CDKN2A/B non-homozygous deletion (NHD)) patients were retrospectively enrolled in the training cohort (n = 140) and the external test cohort (n = 60) for the prediction of CDKN2A/B homozygous deletion status in patients with IDH-mutant astrocytoma. DL model was constructed by SE-Net model, radiomics features of different regions (edema, tumor and overall lesion) were extracted using Pyradiomics, and radiomics model was built by selecting 4 features in the edema region and 7 features in the tumor region by the least absolute shrinkage and selection operator (LASSO). Finally, a fusion model was jointly constructed by the DL model, radiomics model, and clinical features. The predictive performance of the 3 models was evaluated using calibration curves and decision curves, and compared with the fusion model. Results: Based on the results of the different models, we finally selected a fusion model consisting of DL model, radiomics model, and clinical features. The fusion model showed the best performance with an area under the curve (AUC) of 0.958 in the training cohort and 0.914 in the test cohort. Conclusions: The clinical fusion model based on radiomics features and DL features showed good performance in predicting CDKN2A/B homozygous deletion status in patients with IDH-mutant diffuse astrocytoma. Key Points: 1) Used DL and radiomics to non-invasively predict the CDKN2A/B homozygous deletion status. 2) The model can predict CDKN2A/B homozygous deletion status in IDH-mutant astrocytoma patients. 3) Our result improved classification accuracy and demonstrated better performance in the fusion model.

TMErisk score: a tumor microenvironment-based model for predicting prognosis and immunotherapy in patients with head and neck squamous cell carcinoma

Tumor microenvironment (TME) is closely associated with the progression and prognosis of head and neck squamous cell carcinoma (HNSCC). To investigate potential biomarkers for predicting therapeutic outcomes in HNSCC, we analyzed the immune and stromal status of HNSCC based on the genes associated with TME using the ESTIMATE algorithm. Immune and stromal genes were identified with differential gene expression and weighted gene co-expression network analysis (WGCNA). From these genes, 118 were initially selected through Cox univariate regression and then further input into least absolute shrinkage and selection operator (LASSO) regression analysis. As a result, 11 genes were screened out for the TME-related risk (TMErisk) score model which presented promising overall survival predictive potential. The TMErisk score was negatively associated with immune and stromal scores but positively associated with tumor purity. Individuals with high TMErisk scores exhibited decreased expression of most immune checkpoints and all human leukocyte antigen family genes, and reduced abundance of infiltrating immune cells. Divergent genes were mutated in HNSCC. In both high and low TMErisk score groups, the tumor protein P53 exhibited the highest mutation frequency. A higher TMErisk score was found to be associated with reduced overall survival probability and worse outcomes of immunotherapy. Therefore, the TMErisk score could serve as a valuable model for the outcome prediction of HNSCC in clinic.

Individualized radiology screening for newly diagnosed nasopharyngeal carcinoma

ObjectivesCurrent guidelines recommend universal PET/CT screening for metastases staging in newly diagnosed nasopharyngeal carcinoma (NPC) despite the low rate of synchronous distant metastasis (SDM). The study aims to achieve individualized screening recommendations of NPC based on the risk of SDM. Methods and materials18 pre-treatment peripheral blood indicators was retrospectively collected from 2271 primary NPC patients. A peripheral blood risk score (PBRS) was constructed by indicators associated with SDM on least absolute shrinkage and selection operator (LASSO) regression. The PBRS-based distant metastases (PBDM) model was developed from features selected by logistic regression analyses in the training cohort and then validated in the validation cohort. Receiver operator characteristic curve analysis, calibration curves, and decision curve analysis were applied to evaluate PBDM model performance. ResultsPre-treatment Epstein–Barr viral DNA copy number, percentage of total lymphocytes, serum lactate dehydrogenase level, and monocyte-to-lymphocyte ratio were most strongly associated with SDM in NPC and used to construct the PBRS. Sex (male), T stage (T3-4), N stage (N2-3), and PBRS (≥1.076) were identified as independent risk factors for SDM and applied in the PBDM model, which showed good performance. Through the model, patients in the training cohort were stratified into low-, medium-, and high-risk groups. Individualized screening recommendations were then developed for patients with differing risk levels. ConclusionThe PBDM model offers individualized recommendations for applying PET/CT for metastases staging in NPC, allowing more targeted screening of patients with greater risk of SDM compared with current recommendations.

Integrated transcriptomic and immunological profiling reveals new diagnostic and prognostic models for cutaneous melanoma

The mortality rate associated with cutaneous melanoma (SKCM) remains alarmingly high, highlighting the urgent need for a deeper understanding of its molecular underpinnings. In our study, we leveraged bulk transcriptome sequencing data from the SKCM cohort available in public databases such as TCGA and GEO. We utilized distinct datasets for training and validation purposes and also incorporated mutation and clinical data from TCGA, along with single-cell sequencing data from GEO. Through dimensionality reduction, we annotated cell subtypes within the single-cell data and analyzed the expression of tumor-related pathways across these subtypes. We identified differentially expressed genes (DEGs) in the training set, which were further refined using the Least Absolute Shrinkage and Selection Operator (LASSO) machine learning algorithm, employing tenfold cross-validation. This enabled the construction of a prognostic model, whose diagnostic efficacy we subsequently validated. We conducted Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on the DEGs, and performed immunological profiling on two risk groups to elucidate the relationship between model genes and the immune responses relevant to SKCM diagnosis, treatment, and prognosis. We also knocked down the GMR6 expression level in the melanoma cells and verified its effect on cancer through multiple experiments. The results indicate that the GMR6 gene plays a role in promoting the proliferation, invasion, and migration of cancer cells in human melanoma. Our findings offer novel insights and a theoretical framework that could enhance prognosis, treatment, and drug development strategies for SKCM, potentially leading to more precise therapeutic interventions.

Machine learning model for cardiovascular disease prediction in patients with chronic kidney disease

IntroductionCardiovascular disease (CVD) is the leading cause of death in patients with chronic kidney disease (CKD). This study aimed to develop CVD risk prediction models using machine learning to support clinical decision making and improve patient prognosis.MethodsElectronic medical records from patients with CKD at a single center from 2015 to 2020 were used to develop machine learning models for the prediction of CVD. Least absolute shrinkage and selection operator (LASSO) regression was used to select important features predicting the risk of developing CVD. Seven machine learning classification algorithms were used to build models, which were evaluated by receiver operating characteristic curves, accuracy, sensitivity, specificity, and F1-score, and Shapley Additive explanations was used to interpret the model results. CVD was defined as composite cardiovascular events including coronary heart disease (coronary artery disease, myocardial infarction, angina pectoris, and coronary artery revascularization), cerebrovascular disease (hemorrhagic stroke and ischemic stroke), deaths from all causes (cardiovascular deaths, non-cardiovascular deaths, unknown cause of death), congestive heart failure, and peripheral artery disease (aortic aneurysm, aortic or other peripheral arterial revascularization). A cardiovascular event was a composite outcome of multiple cardiovascular events, as determined by reviewing medical records.ResultsThis study included 8,894 patients with CKD, with a composite CVD event incidence of 25.9%; a total of 2,304 patients reached this outcome. LASSO regression identified eight important features for predicting the risk of CKD developing into CVD: age, history of hypertension, sex, antiplatelet drugs, high-density lipoprotein, sodium ions, 24-h urinary protein, and estimated glomerular filtration rate. The model developed using Extreme Gradient Boosting in the test set had an area under the curve of 0.89, outperforming the other models, indicating that it had the best CVD predictive performance.ConclusionThis study established a CVD risk prediction model for patients with CKD, based on routine clinical diagnostic and treatment data, with good predictive accuracy. This model is expected to provide a scientific basis for the management and treatment of patients with CKD.

Network structure indicators predict ecological robustness in food webs

AbstractFood web robustness is a critical aspect of ecosystem stability and has been extensively studied in ecology. However, the potential of machine learning techniques in predicting food web robustness and the identification of key network structure indicators have not been fully explored. We compared the suitability of different machine learning methods and assessed the relative importance of network structure indicators for predicting the robustness of food webs. We utilized a variety of food web datasets spanning different ecosystems to calculate network structure indicators, which include average distance (AD), betweenness centrality (BC), directional connectivity (C), closeness centrality (CC), diameter (D), degree centrality (DC), edge betweenness centrality (EBC), number of links (L), linkage density (LD), and number of nodes (N). We then compared the performance of machine learning methods, including artificial neural network (ANN), random forest (RF), least absolute shrinkage and selection operator (LASSO), and decision tree (DT), and evaluated the relative importance of network structure indicators on robustness predictions. The results demonstrate that the RF model has the best performance (MAE = 0.0178, RMSE = 0.0263, R2 = 0.9063). Meanwhile, the CC indicator has a significant impact in predicting robustness of food webs. It is suggested that both the RF model and the CC indicator should be considered seriously in predicting food web robustness. This research elucidates the differential outcomes when various machine learning methodologies and indicators are employed to predict the robustness of food webs. It significantly enhances our understanding by demonstrating the precise capability of machine learning models in forecasting the robustness of food webs.

Development and validation of a nomogram for predicting cognitive frailty in patients on maintenance haemodialysis.

This study aimed to construct a nomogram for predicting the risk of cognitive frailty in patients on maintenance haemodialysis. An explorative cross-sectional design was adopted. From April 2022 to July 2022, 496 participants were recruited from five haemodialysis centres in Qingdao, Shandong Province, China. Participants with cognitive frailty were screened by Frailty Phenotype scale and Mini-Mental State Examination. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression were utilized to determine predictors. The predictive performance of the nomogram was validated by calibration and discrimination. Decision curve analysis was used to assess clinical utility. Internal validation was implemented using 1000 bootstrap samples to mitigate overfitting. The prevalence of cognitive frailty was 17.5% (n = 87). Six risk predictors, namely health empowerment, alexithymia, age, educational level, marital status and dialysis vintage, were screened and used to develop a nomogram model. The nomogram had satisfactory discrimination and calibration, and decision curve analysis revealed considerable clinical utility. A nomogram incorporated with the six risk predictors was developed, and it exhibited excellent prediction performance. The nomogram may strengthen the effective screening of patients at high risk of cognitive frailty. This study established a tool for healthcare staff to predict cognitive frailty probability and identify risk factors in patients on maintenance haemodialysis. The nomogram can meet the needs of personalized care and precision medicine simultaneously. Data were collected from patients on maintenance haemodialysis by using questionnaire survey. STROBE checklist was used.

A Preoperative Prediction Model for Lymph Node Metastasis in Patients with Gastric Cancer Using a Machine Learning-based Ultrasomics Approach.

This study aimed to develop an ultrasomics model for predicting lymph node metastasis preoperative in patients with gastric cancer (GC). This study enrolled GC patients who underwent preoperative ultrasound examination. Manual segmentation of the region of interest (ROI) was performed by an experienced radiologist to extract radiomics features using the Pyradiomics software. The Z-score algorithm was used for feature normalization, followed by the Wilcoxon test to identify the most informative features. Linear prediction models were constructed using the least absolute shrinkage and selection operator (LASSO). The performance of the ultrasomics model was evaluated using the area under curve (AUC), sensitivity, specificity, and the corresponding 95% confidence intervals (CIs). A total of 464 GC patients (mean age: 60.4 years ±11.3 [SD]; 328 men [70.7%]) were analyzed, of whom 291 had lymph node metastasis. The patients were randomly assigned to either the training (n=324) or test (n=140) sets, using a 7:3 ratio. An ultrasomics model that consisted of 19 radiomics features was developed using Wilcoxon and LASSO algorithms in the training set. Our ultrasomics model showed moderate performance for lymph node metastasis prediction in both the training (AUC: 0.802, 95%CI: 0.752-0.851, P<0.001) and test sets (AUC: 0.802, 95%CI: 0.724-0.879, P<0.001). The calibration curve analysis indicated good agreement between the predicted probabilities of ultrasomics and actual lymph node metastasis status. Our study highlights the potential of a machine learning-based ultrasomics model in predicting lymph node metastasis in GC patients, offering implications for personalized therapy approaches.

Integrated analysis of tumor-associated macrophages and M2 macrophages in CRC: unraveling molecular heterogeneity and developing a novel risk signature.

Emerging investigations have increasingly highlighted the critical role of tumor-associated macrophages (TAMs) and M2 macrophages in cancer development, progression, and metastasis, marking them as potential targets in various cancer types. The main objective of this research is to discover new biomarkers associated with TAM-M2 macrophages in colorectal cancer (CRC) and to dissect the molecular heterogeneity of CRC by combining single-cell RNA sequencing and bulk RNA-seq data. By utilizing weighted gene co-expression network analysis (WGCNA), we acquired TAM-M2-associated genes by intersecting TAM marker genes obtained from scRNA-seq data with module genes of M2 macrophages derived from bulk RNA-seq data. We employed least absolute shrinkage and selection operator (LASSO) Cox analysis to select predictive biomarkers from these TAM-M2-related genes. Quantitative polymerase chain reaction (qPCR) was employed to validate the mRNA expression levels of the genes identified in the screening. This led to the development of the TAM-M2-related signature (TAMM2RS). We also conducted functional and immune landscape analyses of different risk groups. The combination of scRNA-seq and bulk RNA-seq analyses yielded 377 TAM-M2-related genes. DAPK1, NAGK, and TRAF1 emerged as key prognostic genes in CRC, which were identified through LASSO Cox analysis. Utilizing these genes, we constructed and validated the TAMM2RS, demonstrating its effectiveness in predicting survival in CRC patients. Our research offers a thorough investigation into the molecular mechanisms associated with TAM-M2 macrophages in CRC and unveils potential therapeutic targets, offering new insights for treatment strategies in colorectal cancer.

Radiomic features at Contrast-enhanced CT Predict Virus-driven Liver Fibrosis: A Multi-institutional Study.

Liver fibrosis is a major cause of morbidity and mortality among in chronic hepatitis patients. Radiomics, particularly of the spleen, may improve diagnostic accuracy and treatment strategies. External validations are necessary to ensure reliability and generalizability. In this retrospective study, we developed three radiomics models using contrast-enhanced CT scans from 167 patients with liver fibrosis (training group) between January 2020 and December 2021. Radiomic features were extracted from arterial venous, portal venous, and equilibrium phase images. Recursive feature selection random forest (RFS-RF) and the least absolute shrinkage and selection operator (LASSO) logistic regression were used for feature selection and dimensionality reduction. Performance was assessed by area under the curve, C-index, calibration plots and decision curve analysis. External validation was performed on 114 patients from two institutions. Twenty-five radiomic features were significantly associated with fibrosis stage, with 80% of the top 10 features originating from portal venous phase spleen images. The radiomics models showed good performance in the validation cohort (C-indices: 0.723-0.808) and excellent calibration. Decision curve analysis indicated clinical benefits, with machine learning-based radiomics models (RFR-score and SVMR-score) providing more significant advantages. Radiomic features offer significant benefits over existing serum indices for staging virus-driven liver fibrosis, underscoring the value of radiomics in enhancing diagnostic accuracy. Specifically, radiomics analysis of the spleen presents additional noninvasive options for assessing fibrosis, highlighting its potential in improving patient management and outcomes.

Predictive Modeling for Functional Outcomes After All-Inside Arthroscopic Anterior Talofibular Ligament Repair of Chronic Ankle Instability.

Arthroscopic anterior talofibular ligament repair (AATFLR) is a surgical strategy to treat chronic ankle instability (CAI) patients. This study identified risk factors that influenced the functional outcomes of AATFLR for CAI and developed prognostic nomogram for predicting functional outcomes in future AATFLR cases. Patients undergoing AATFLR from January 2016 to June 2022 with at least 10 months of follow-up were included in the study. The Karlsson Ankle Functional Score (KAFS) was evaluated preoperatively and at last follow-up visit. A total of 15 potential predictors including age, sex, body mass index, side affected, time from injury to surgery, sports-related injury, osteophyte, loose bodies, distal tibiofibular syndesmosis, ATFL avulsion fracture, Outerbridge classification of osteochondral lesions, postoperative immobilization method, ambulation time, walking time, and follow-up time, were recorded. We first used univariate binary logistic regression analysis to select the potential significant prognostic features, which were then subjected to the least absolute shrinkage and selection operator (LASSO) regression algorithm for final feature selection. A nomogram based on the regression model was developed to estimate the functional outcomes of patients. Models were validated internally using bootstrapping and externally by calculating their performance on a validation cohort. Overall, 200 ankles fit inclusion criteria. Of these 200, a total of 185 (92.5%) ankles were eligible and divided into development (n = 121) and validation (n = 64) cohorts. Four predictors were ultimately included in the prognostic nomogram model: age, sex, sports-related injury, and postoperative immobilization method. We found in our cohort that the significant predictors of poorer functional outcomes of AATFLR were postoperative immobilization with lower-leg cast, female sex, non-sports-related ankle sprain, and increasing age. Prognostic nomograms were created. Level III, retrospective cohort study.

Prediction model for asymptomatic carotid atherosclerosis using retinal microvascular intelligent analysis: A retrospective study

ImportanceEarly detection and timely diagnosis of asymptomatic carotid atherosclerosis significantly assist in the prevention of ischemic stroke for them. ObjectiveThis observational study aimed to develop and validate a novel prediction model to assist in the early diagnosis of carotid atherosclerosis based on new characteristic variables screened by retinal microvascular intelligence analysis. Main Outcome(s) and Method (s)The least absolute shrinkage and selection operator (LASSO) combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the prediction model. Receiver operating characteristic (ROC) curves and area under the curve (AUC), calibration plots and brier score (BS), and decision curve analysis (DCA) were used to evaluate the risk model's discrimination, calibration, and clinical applicability. ResultsAge, gender, diabetes mellitus (DM), drinking history, vascular branching angle, mean vascular diameter within 0.5–1.0 papillary diameter (PD), curvature tortuosity arteriole in the inferior region of the optic disc, and vascular density in the nasal region of the optic disc were identified as characteristic variables for carotid atherosclerosis with retinal microvascular intelligence analysis. The predictive nomogram model presented good discrimination with AUCs of 0.790 (0.774–0.806), and the calibration curve displayed high consistency between predicted and actual probability. The DCA demonstrated that this nomogram model led to net benefits in a threshold probability range of 20 %–94 % and could be adapted for clinical decision-making. The results of the 100-bootstrap resampling strategy for internal validation also show that the risk model is well discriminated with an AUC of 0.789 and excellent calibration. External validation showed good discrimination with AUCs of 0.703 (0.627 - 0.779) and good calibration, the risk threshold is 10 %–92 % in terms of DCA. Conclusions and RelevanceThe novel prediction model based on retinal microvascular intelligence analysis constructed in this study could be effective prognoses for predicting the risk of asymptomatic carotid atherosclerosis in a Chinese screening population.

Machine learning identifies factors most associated with seeking medical care for migraine: Results of the OVERCOME (US) study.

Utilize machine learning models to identify factors associated with seeking medical care for migraine. Migraine is a leading cause of disability worldwide, yet many people with migraine do not seek medical care. The web-based survey, ObserVational survey of the Epidemiology, tReatment and Care Of MigrainE (US), annually recruited demographically representative samples of the US adult population (2018-2020). Respondents with active migraine were identified via a validated diagnostic questionnaire and/or a self-reported medical diagnosis of migraine, and were then asked if they had consulted a healthcare professional for their headaches in the previous 12 months (i.e., "seeking care"). This included in-person/telephone/or e-visit at Primary Care, Specialty Care, or Emergency/Urgent Care locations. Supervised machine learning (Random Forest) and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms identified 13/54 sociodemographic and clinical factors most associated with seeking medical care for migraine. Random Forest models complex relationships (including interactions) between predictor variables and a response. LASSO is also an efficient feature selection algorithm. Linear models were used to determine the multivariable association of those factors with seeking care. Among 61,826 persons with migraine, the mean age was 41.7 years (±14.8) and 31,529/61,826 (51.0%) sought medical care for migraine in the previous 12 months. Of those seeking care for migraine, 23,106/31,529 (73.3%) were female, 21,320/31,529 (67.6%) were White, and 28,030/31,529 (88.9%) had health insurance. Severe interictal burden (assessed via the Migraine Interictal Burden Scale-4, MIBS-4) occurred in 52.8% (16,657/31,529) of those seeking care and in 23.1% (6991/30,297) of those not seeking care; similar patterns were observed for severe migraine-related disability (assessed via the Migraine Disability Assessment Scale, MIDAS) (36.7% [11,561/31,529] vs. 14.6% [4434/30,297]) and severe ictal cutaneous allodynia (assessed via the Allodynia Symptom Checklist, ASC-12) (21.0% [6614/31,529] vs. 7.4% [2230/30,297]). Severe interictal burden (vs. none, OR 2.64, 95% CI [2.5, 2.8]); severe migraine-related disability (vs. little/none, OR 2.2, 95% CI [2.0, 2.3]); and severe ictal allodynia (vs. none, OR 1.7, 95% CI [1.6, 1.8]) were strongly associated with seeking care for migraine. Seeking medical care for migraine is associated with higher interictal burden, disability, and allodynia. These findings could support interventions to promote care-seeking among people with migraine, encourage assessment of these factors during consultation, and prioritize these domains in selecting treatments and measuring their benefits.

Molecular characterization, clinical value, and cancer–immune interactions of genes related to disulfidptosis and ferroptosis in colorectal cancer

BackgroundThis research strived to construct a new signature utilizing disulfidptosis-related ferroptosis (SRF) genes to anticipate response to immunotherapy, prognosis, and drug sensitivity in individuals with colorectal cancer (CRC).MethodsThe data for RNA sequencing as well as corresponding clinical information of individuals with CRC, were extracted from The Cancer Genome Atlas (TCGA) dataset. SRF were constructed with the help of the random forest (RF), least absolute shrinkage and selection operator (LASSO), and stepwise regression algorithms. To validate the SRF model, we applied it to an external cohort, GSE38832. Prognosis, immunotherapy response, drug sensitivity, molecular functions of genes, and somatic mutations of genes were compared across the high- and low-risk groups (categories). Following this, all statistical analyses were conducted with the aid of the R (version 4.23) software and various packages of the Cytoscape (version 3.8.0) tool.ResultsSRF was developed based on five genes (ATG7, USP7, MMD, PLIN4, and THDC2). Both univariate and multivariate Cox regression analyses established SRF as an independent, prognosis-related risk factor. Individuals from the high-risk category had a more unfavorable prognosis, elevated tumor mutational burden (TMB), and significant immunosuppressive status. Hence, they might have better outcomes post-immunotherapy and might benefit from the administration of pazopanib, lapatinib, and sunitinib.ConclusionIn conclusion, SRF can act as a new biomarker for prognosis assessment. Moreover, it is also a good predictor of drug sensitivity and immunotherapy response in CRC but should undergo optimization before implementation in clinical settings.

#1347 Development and validation of a nomogram for predicting complete kidney function recovery after drug-induced acute interstitial nephritis

Abstract Background and Aims Drug-induced acute interstitial nephritis (DI-AIN) represents a common cause of acute kidney injury. Early withdrawal of the culprit drug along with corticosteroid therapy (CS) remains the mainstay of treatment. Despite this, up to 50% of patients may not fully recover kidney function after DI-AIN. The aims of this study were to develop and externally validate a predictive nomogram to assess the probability of complete recovery (CR) of kidney function at 6 months after treatment. Method Multicenter, retrospective, observational study in 13 nephrology departments belonging to the GLOSEN group. Patients with biopsy proven DI-AIN treated with CS between 1996-2023 were included. Other potential causes of AIN such as infections or systemic diseases were carefully ruled out. Dataset was randomly divided into training group (n = 164) and validation group (n = 60). The least absolute shrinkage and selection operator (LASSO) regression was used to screen the main predictors of CR (serum creatinine value &amp;lt;25% of the last value before DI-AIN), and used to build the nomogram. Accuracy of the nomogram was assessed by discrimination and risk calibration, in the training and validation sets. Results The study group comprised 224 patients with DI-AIN, of whom: 51 (31%) in the training group and 19 (32%) in the validation set, achieved CR at 6 months after treatment with CS. Median age was 70 (IQR 57–76), and 115 (51%) were males. The clinical characteristics were well balanced between the training and validation data sets. The selected variables by LASSO were age (under/above 65 years), gender, degree of interstitial fibrosis and time to CS initiation (under/above 7 days). Based on multivariable logistic regression model, a nomogram was developed for assessing the probability of CR (Fig. 1). The area under the curve (AUC) of the nomogram was 0.809 (95% CI 0.721–0.880) indicating a good discriminative power. Bootstrap self- sampling was performed 1000 times for validation of the nomogram model. The AUC was 0.837 (95 % CI: 0.705–0.931), indicating good predictive stability. Calibration plot revealed that the predicted outcomes aligned well with the observations. Decision curve analysis (DCA), and clinical impact curve analysis (CICA) suggested that the model had clinical benefit. Conclusion We constructed and validated a practical nomogram with good discrimination and calibration, to predict the probability of CR of kidney function at 6 months in DI-AIN treated with corticosteroids. This clinical tool can provide clinicians with an estimate of kidney prognosis and can potentially assist them in titrating the intensity and duration of corticosteroid therapy.

Identification of mitochondria-related biomarkers in childhood allergic asthma

BackgroundThe mechanism of mitochondria-related genes (MRGs) in childhood allergic asthma (CAS) was unclear. The aim of this study was to find new biomarkers related to MRGs in CAS.MethodsThis research utilized two CAS-related datasets (GSE40888 and GSE40732) and extracted 40 MRGs from the MitoCarta3.0 Database. Initially, differential expression analysis was performed on CAS and control samples in the GSE40888 dataset to obtain the differentially expressed genes (DEGs). Differentially expressed MRGs (DE-MRGs) were obtained by overlapping the DEGs and MRGs. Protein protein interactions (PPI) network of DE-MRGs was created and the top 10 genes in the degree ranking of Maximal Clique Centrality (MCC) algorithm were defined as feature genes. Hub genes were obtained from the intersection genes from the Least absolute shrinkage and selection operator (LASSO) and EXtreme Gradient Boosting (XGBoost) algorithms. Additionally, the expression validation was conducted, functional enrichment analysis, immune infiltration analysis were finished, and transcription factors (TFs)-miRNA-mRNA regulatory network was constructed.ResultsA total of 1505 DEGs were obtained from the GSE40888, and 44 DE-MRGs were obtained. A PPI network based on these 44 DE-MRGs was created and revealed strong interactions between ADCK5 and MFN1, BNIP3 and NBR1. Four hub genes (NDUFAF7, MTIF3, MRPS26, and NDUFAF1) were obtained by taking the intersection of genes from the LASSO and XGBoost algorithms based on 10 signature genes which obtained from PPI. In addition, hub genes-based alignment diagram showed good diagnostic performance. The results of Gene Set Enrichment Analysis (GSEA) suggested that hub genes were closely related to mismatch repair. The B cells naive cells were significantly expressed between CAS and control groups, and MTIF3 was most strongly negatively correlated with B cells naive. In addition, the expression of MTIF3 and MRPS26 may have influenced the inflammatory response in CAS patients by affecting mitochondria-related functions. The quantitative real-time polymerase chain reaction (qRT‒PCR) results showed that four hub genes were all down-regulated in the CAS samples.ConclusionNDUFAF7, MTIF3, MRPS26, and NDUFAF1 were identified as an MRGs-related biomarkers in CAS, which provides some reference for further research on CAS.

#2031 Exploring the utility of serum proteomics for outcome prediction of autosomal polycystic kidney disease (ADPKD)

Abstract Background and Aims ADPKD is an inherited disorder characterized by the development of cysts in both kidneys, leading to renal enlargement and a gradual decline in renal function. It is the leading cause of end stage renal disease (ESRD) with an estimated prevalence of 1:1000. The estimated glomerular filtration rate (eGFR) is the calculated estimation of kidney function by using different equations. The eGFR equation used in this study is Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI, 2009) equation, which includes serum creatinine level, age, and gender. eGFR is also frequently used for assessing overall health of ADPKD patients, classifying the disease stages, and determining appropriate therapeutic interventions. As in ADPKD, clinical symptoms manifest typically in later disease stages, early diagnosis is crucial to slow the disease progression and improve patients’ life quality. This study has two main objectives: (1) the identification of molecular and clinical parameters associated with the annual eGFR decline and (2) the development of a predictive model for future eGFR in patients with ADPKD using serum proteomics. This research aims to advance more effective strategies for the misdiagnosis, stratification, and treatment of ADPKD by combining proteomic and clinical parameters. Method Utilizing retrospective eGFR values (n = 7187) of patients with ADPKD (n = 1291) recruited from the AD(H)PKD patient cohort, the annual decline in eGFR (slope) was calculated. A minimum of three eGFR values and the absence of tolvaptan use (a medication that inhibits the mechanisms underlying eGFR decline) were inclusion criteria. In order to determine the slope, robust linear regression was applied. Prior to the analyses, patients whose slopes were clinically extreme (−10 &amp; &amp;gt;5 mL/min/1.73 m2 per year) were excluded. Total of 2469 eGFR values from 219 ADPKD patients were included in this study. To derive a feature set (FS) suitable for slope prediction, the least absolute shrinkage and selection operator (LASSO) was applied independently to two subsets of the serum proteome (SP) data: (i) SP alone and (ii) SP in conjunction with key clinical variables: age, gender, eGFR, and MAYO class (SPMC). Linear regression (LR) models were constructed using the acquired FSs individually (SP and SPMC), and cross-validation (CV) was incorporated to prevent overfitting. Following that, prediction models were generated and compared in terms of their ability to predict slopes and were visualized for an overview. Results Using feature selection, four features associated with slope were identified in both SP and SPMC data. Three of those features were proteomic-based and common to both feature sets (SC and SPMC). LR models exhibited stability in R2s in test sets of CV, indicating no sign of overfitting. Therefore, the LR slope prediction models were built with SP and SPMC feature sets by using all available data. The adjusted R2 of SP and SCMP LR models were 0.274 (R2 = 0.287, F(4,213) = 21.48, p = 6.74 × 10−15) and 0.301 (R2 = 0.317, F(5,208) = 19.31, p = 8.94 × 10−16). This step was followed by the slope prediction of the same patients (internal validation) to examine models’ prediction capacity by comparing the predicted to observed slopes. According to comparisons, our LR models predict closer to actual slope if slope is greater than −5 and lower than 0 mL/min/1.73 m2 per year. Most of the observed slopes were in between the specified thresholds, which explains why the model is working well. However, the deviations between predicted and the observed slopes increases as the slopes exceed this range. Conclusion Identified biomarkers could be used for how the disease will progress, however, fine-tuning of the models might be necessary. Even though there were patients with positive and extremely negative eGFR slopes, our model accurately predicts slopes in the previously specified range. These extreme cases might also be responsible for models’ reduced accuracy outside of that range. One possible solution could be to use weighted lasso to reduce the effect of “extreme” slopes on the models.