Polygenic hazard score models for the prediction of Alzheimer's free survival using the lasso for Cox's proportional hazards model.

The prediction of the susceptibility of an individual to a certain disease is an important and timely research area. An established technique is to estimate the risk of an individual with the help of an integrated risk model, that is, a polygenic risk score with added epidemiological covariates. However, integrated risk models do not capture any time dependence, and may provide a point estimate of the relative risk with respect to a reference population. The aim of this work is twofold. First, we explore and advocate the idea of predicting the time-dependent hazard and survival (defined as disease-free time) of an individual for the onset of a disease. This provides a practitioner with a much more differentiated view of absolute survival as a function of time. Second, to compute the time-dependent risk of an individual, we use published methodology to fit a Cox's proportional hazard model to data from a genetic SNP study of time to Alzheimer's disease (AD) onset, using the lasso to incorporate further epidemiological variables such as sex, APOE (apolipoprotein E, a genetic risk factor for AD) status, 10 leading principal components, and selected genomic loci. We apply the lasso for Cox's proportional hazards to a data set of 6792 AD patients (composed of 4102 cases and 2690 controls) and 87 covariates. We demonstrate that fitting a lasso model for Cox's proportional hazards allows one to obtain more accurate survival curves than with state-of-the-art (likelihood-based) methods. Moreover, the methodology allows one to obtain personalized survival curves for a patient, thus giving a much more differentiated view of the expected progression of a disease than the view offered by integrated risk models. The runtime to compute personalized survival curves is under a minute for the entire data set of AD patients, thus enabling it to handle datasets with 60,000-100,000 subjects in less than 1 h.

Population-Specific gene expression profiles in prostate cancer: insights from Weighted Gene Co-expression Network Analysis (WGCNA).

This study investigates the genetic factors contributing to the disparity in prostate cancer incidence and progression among African American men (AAM) compared to European American men (EAM). The research focuses on employing Weighted Gene Co-expression Network Analysis (WGCNA) on public microarray data obtained from prostate cancer patients. The study employed WGCNA to identify clusters of genes with correlated expression patterns, which were then analyzed for their connection to population backgrounds. Additionally, pathway enrichment analysis was conducted to understand the significance of the identified gene modules in prostate cancer pathways. The Least Absolute Shrinkage and Selection Operator (LASSO) and Correlation-based Feature Selection (CFS) methods were utilized for selection of biomarker genes. The results revealed 353 differentially expressed genes (DEGs) between AAM and EAM. Six significant gene expression modules were identified through WGCNA, showing varying degrees of correlation with prostate cancer. LASSO and CFS methods pinpointed critical genes, as well as six common genes between both approaches, which are indicative of their vital role in the disease. The XGBoost classifier validated these findings, achieving satisfactory prediction accuracy. Genes such as APRT, CCL2, BEX2, MGC26963, and PLAU were identified as key genes significantly associated with cancer progression. In conclusion, the research underlines the importance of incorporating AAM and EAM population diversity in genomic studies, particularly in cancer research. In addition, the study highlights the effectiveness of integrating machine learning techniques with gene expression analysis as a robust methodology for identifying critical genes in cancer research.

LASSO-Based Machine Learning Algorithm for Prediction of PICS Associated with Sepsis.

Introduction: This study aims to establish a comprehensive, multi-level approach for tackling tropical diseases by proactively anticipating and managing Persistent Inflammation, Immunosuppression, and Catabolism Syndrome (PICS) within the initial 14 days of Intensive Care Unit (ICU) admission. The primary objective is to amalgamate a diverse array of indicators and pathogenic microbial data to pinpoint pivotal predictive variables, enabling effective intervention specifically tailored to the context of tropical diseases. Methods: A focused analysis was conducted on 1733 patients admitted to the ICU between December 2016 and July 2019. Utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) regression, disease severity and laboratory indices were scrutinized. The identified variables served as the foundation for constructing a predictive model designed to forecast the occurrence of PICS. Results: Among the subjects, 13.79% met the diagnostic criteria for PICS, correlating with a mortality rate of 38.08%. Key variables, including red-cell distribution width coefficient of variation (RDW-CV), hemofiltration (HF), mechanical ventilation (MV), Norepinephrine (NE), lactic acidosis, and multiple-drug resistant bacteria (MDR) infection, were identified through LASSO regression. The resulting predictive model exhibited a robust performance with an Area Under the Curve (AUC) of 0.828, an accuracy of 0.862, and a specificity of 0.977. Subsequent validation in an independent cohort yielded an AUC of 0.848. Discussion: The acquisition of RDW-CV, HF requirement, MV requirement, NE requirement, lactic acidosis, and MDR upon ICU admission emerges as a pivotal factor for prognosticating PICS onset in the context of tropical diseases. This study highlights the potential for significant improvements in clinical outcomes through the implementation of timely and targeted interventions tailored specifically to the challenges posed by tropical diseases.

SABO-ILSTSVR: a genomic prediction method based on improved least squares twin support vector regression.

In modern breeding practices, genomic prediction (GP) uses high-density single nucleotide polymorphisms (SNPs) markers to predict genomic estimated breeding values (GEBVs) for crucial phenotypes, thereby speeding up selection breeding process and shortening generation intervals. However, due to the characteristic of genotype data typically having far fewer sample numbers than SNPs markers, overfitting commonly arise during model training. To address this, the present study builds upon the Least Squares Twin Support Vector Regression (LSTSVR) model by incorporating a Lasso regularization term named ILSTSVR. Because of the complexity of parameter tuning for different datasets, subtraction average based optimizer (SABO) is further introduced to optimize ILSTSVR, and then obtain the GP model named SABO-ILSTSVR. Experiments conducted on four different crop datasets demonstrate that SABO-ILSTSVR outperforms or is equivalent in efficiency to widely-used genomic prediction methods. Source codes and data are available at: https://github.com/MLBreeding/SABO-ILSTSVR.

Integrated analysis of genes shared between type 2 diabetes mellitus and osteoporosis.

Background: The relationship between type 2 diabetes mellitus (T2DM) and osteoporosis (OP) has been widely recognized in recent years, but the mechanism of interaction remains unknown. The aim of this study was to investigate the genetic features and signaling pathways that are shared between T2DM and OP. Methods: We analyzed the GSE76894 and GSE76895 datasets for T2DM and GSE56815 and GSE7429 for OP from the Gene Expression Omnibus (GEO) database to identify shared genes in T2DM and OP, and we constructed coexpression networks based on weighted gene coexpression network analysis (WGCNA). Shared genes were then further analyzed for functional pathway enrichment. We selected the best common biomarkers using the least absolute shrinkage and selection operator (LASSO) algorithm and validated the common biomarkers, followed by RT-PCR, immunofluorescence, Western blotting, and enzyme-linked immunosorbent assay (ELISA) to validate the expression of these hub genes in T2DM and OP mouse models and patients. Results: We found 8,506 and 2,030 DEGs in T2DM and OP, respectively. Four modules were identified as significant for T2DM and OP using WGCNA. A total of 19 genes overlapped with the strongest positive and negative modules of T2DM and OP. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed these genes may be involved in pantothenate and CoA biosynthesis and the glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate and renin-angiotensin system signaling pathway. The LASSO algorithm calculates the six optimal common biomarkers. RT-PCR results show that LTB, TPBG, and VNN1 were upregulated in T2DM and OP. Immunofluorescence and Western blot show that VNN1 is upregulated in the pancreas and bones of T2DM model mice and osteoporosis model mice. Similarly, the level of VNN1 in the sera of patients with T2DM, OP, and T2DM and OP was higher than that in the healthy group. Conclusion: Based on the WGCNA and LASSO algorithms, we identified genes and pathways that were shared between T2DM and OP. Both pantothenate and CoA biosynthesis and the glycosaminoglycan biosynthesis-chondroitin sulfate/dermatan sulfate and renin-angiotensin systems may be associated with the pathogenesis of T2DM and OP. Moreover, VNN1 may be a potential diagnostic marker for patients with T2DM complicated by OP. This study provides a new perspective for the systematic study of possible mechanisms of combined OP and T2DM.

Identifying and evaluating a disulfidptosis-related gene signature to predict prognosis in colorectal adenocarcinoma patients.

Disulfidptosis, a regulated form of cell death, has been recently reported in cancers characterized by high SLC7A11 expression, including invasive breast carcinoma, lung adenocarcinoma, and hepatocellular carcinoma. However, its role in colon adenocarcinoma (COAD) has been infrequently discussed. In this study, we developed and validated a prognostic model based on 20 disulfidptosis-related genes (DRGs) using LASSO and Cox regression analyses. The robustness and practicality of this model were assessed via a nomogram. Subsequent correlation and enrichment analysis revealed a relationship between the risk score, several critical cancer-related biological processes, immune cell infiltration, and the expression of oncogenes and cell senescence-related genes. POU4F1, a significant component of our model, might function as an oncogene due to its upregulation in COAD tumors and its positive correlation with oncogene expression. In vitro assays demonstrated that POU4F1 knockdown noticeably decreased cell proliferation and migration but increased cell senescence in COAD cells. We further investigated the regulatory role of the DRG in disulfidptosis by culturing cells in a glucose-deprived medium. In summary, our research revealed and confirmed a DRG-based risk prediction model for COAD patients and verified the role of POU4F1 in promoting cell proliferation, migration, and disulfidptosis.

Identification of a combined hypoxia and lactate metabolism prognostic signature in lung adenocarcinoma : Author.

BACKGROUND: In the tumor microenvironment (TME), a bidirectional relationship exists between hypoxia and lactate metabolism, with each component exerting a reciprocal influence on the other, forming an inextricable link. The aim of the present investigation was to develop a prognostic model by amalgamating genes associated with hypoxia and lactate metabolism. This model is intended to serve as a tool for predicting patient outcomes, including survival rates, the status of the immune microenvironment, and responsiveness to therapy in patients with lung adenocarcinoma (LUAD). METHODS: Transcriptomic sequencing data and patient clinical information specific to LUAD were obtained from comprehensive repositories of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A compendium of genes implicated in hypoxia and lactate metabolism was assembled from an array of accessible datasets. Univariate and multivariate Cox regression analyses were employed. Additional investigative procedures, including tumor mutational load (TMB), microsatellite instability (MSI), functional enrichment assessments and the ESTIMATE, CIBERSORT, and TIDE algorithms, were used to evaluate drug sensitivity and predict the efficacy of immune-based therapies. RESULTS: A novel prognostic signature comprising five lactate and hypoxia-related genes (LHRGs), PKFP, SLC2A1, BCAN, CDKN3, and ANLN, was established. This model demonstrated that LUAD patients with elevated LHRG-related risk scores exhibited significantly reduced survival rates. Both univariate and multivariate Cox analyses confirmed that the risk score was a robust prognostic indicator of overall survival. Immunophenotyping revealed increased infiltration of memory CD4 + T cells, dendritic cells and NK cells in patients classified within the high-risk category compared to their low-risk counterparts. Higher probability of mutations in lung adenocarcinoma driver genes in high-risk groups, and the MSI was associated with the risk-score. Functional enrichment analyses indicated a predominance of cell cycle-related pathways in the high-risk group, whereas metabolic pathways were more prevalent in the low-risk group. Moreover, drug sensitivity analyses revealed increased sensitivity to a variety of drugs in the high-risk group, especially inhibitors of the PI3K-AKT, EGFR, and ELK pathways. CONCLUSIONS: This prognostic model integrates lactate metabolism and hypoxia parameters, offering predictive insights regarding survival, immune cell infiltration and functionality, as well as therapeutic responsiveness in LUAD patients. This model may facilitate personalized treatment strategies, tailoring interventions to the unique molecular profile of each patient's disease.

Estimating risk of loneliness in adulthood using survey-based prediction models: A cohort study.

It is widely accepted that loneliness is associated with health problems, but less is known about the predictors of loneliness. In this study, we constructed a model to predict individual risk of loneliness during adulthood. Data were from the prospective population-based FinHealth cohort study with 3444 participants (mean age 55.5 years, 53.4% women) who responded to a 81-item self-administered questionnaire and reported not to be lonely at baseline in 2017. The outcome was self-reported loneliness at follow-up in 2020. Predictive models were constructed using bootstrap enhanced LASSO regression (bolasso). The C-index from the final model including 11 predictors from the best bolasso -models varied between 0.65 (95% CI 0.61 to 0.70) and 0.71 (95% CI 0.67 to 0.75) the pooled C -index being 0.68 (95% CI 0.61 to 0.75). Although survey-based individualised prediction models for loneliness achieved a reasonable C-index, their predictive value was limited. High detection rates were associated with high false positive rates, while lower false positive rates were associated with low detection rates. These findings suggest that incident loneliness during adulthood. may be difficult to predict with standard survey data.

Serum biomarkers REG1A and REG3A combined with the traditional CEA represent a novel nomogram for the screening and risk stratification of colorectal cancer.

BACKGROUND: To develop and validate a serum protein nomogram for colorectal cancer (CRC) screening. METHODS: The serum protein characteristics were extracted from an independent sample containing 30 colorectal cancer and 12 polyp tissues along with their paired samples, and different serum protein expression profiles were validated using RNA microarrays. The prediction model was developed in a training cohort that included 1345 patients clinicopathologically confirmed CRC and 518 normal participants, and data were gathered from November 2011 to January 2017. The lasso logistic regression model was employed for features selection and serum nomogram building. An internal validation cohort containing 576 CRC patients and 222 normal participants was assessed. RESULTS: Serum signatures containing 27 secreted proteins were significantly differentially expressed in polyps and CRC compared to paired normal tissue, and REG family proteins were selected as potential predictors. The C-index of the nomogram1 (based on Lasso logistic regression model) which contains REG1A, REG3A, CEA and age was 0.913 (95% CI, 0.899 to 0.928) and was well calibrated. Addition of CA199 to the nomogram failed to show incremental prognostic value, as shown in nomogram2 (based on logistic regression model). Application of the nomogram1 in the independent validation cohort had similar discrimination (C-index, 0.912 [95% CI, 0.890 to 0.934]) and good calibration. The decision curve (DCA) and clinical impact curve (ICI) analysis demonstrated that nomogram1 was clinically useful. CONCLUSIONS: This study presents a serum nomogram that included REG1A, REG3A, CEA and age, which can be convenient for screening of colorectal cancer.

Development and validation of a nomogram predictive model for cognitive impairment in cerebral small vessel disease: a comprehensive retrospective analysis.

Background: Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes. Objective: The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD. Methods: The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model's validity was assessed through calibration curves and decision curve analysis (DCA). Results: A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model's reliability, and DCA highlighted its clinical utility. The model's sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set. Conclusion: This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model's high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.

Establishment of a Mitochondrial Metabolism-Related Diagnostic Model in Schizophrenia Based on LASSO Algorithm.

OBJECTIVE: Schizophrenia is a common mental disorder, and mitochondrial function represents a potential therapeutic target for psychiatric diseases. The role of mitochondrial metabolism-related genes (MRGs) in the diagnosis of schizophrenia remains unknown. This study aimed to identify candidate genes that may influence the diagnosis and treatment of schizophrenia based on MRGs. METHODS: Three schizophrenia datasets were obtained from the Gene Expression Omnibus database. MRGs were collected from relevant literature. The differentially expressed genes between normal samples and schizophrenia samples were screened using the limma package. Venn analysis was performed to identify differentially expressed MRGs (DEMRGs) in schizophrenia. Based on the STRING database, hub genes in DEMRGs were identified using the MCODE algorithm in Cytoscape. A diagnostic model containing hub genes was constructed using LASSO regression and logistic regression analysis. The relationship between hub genes and drug sensitivity was explored using the DSigDB database. An interaction network between miRNA-transcription factor (TF)-hub genes was created using the Network-Analyst website. RESULTS: A total of 1,234 MRGs, 172 DEMRGs, and 6 hub genes with good diagnostic performance were identified. Ten potential candidate drugs (rifampicin, fulvestrant, pentadecafluorooctanoic acid, etc.) were selected. Thirty-four miRNAs targeting genes in the diagnostic model (ANGPTL4, CPT2, GLUD1, MED1, and MED20), as well as 137 TFs, were identified. CONCLUSION: Six potential candidate genes showed promising diagnostic significance. rifampicin, fulvestrant, and pentadecafluorooctanoic acid were potential drugs for future research in the treatment of schizophrenia. These findings provided valuable evidence for the understanding of schizophrenia pathogenesis, diagnosis, and drug treatment.

Predicting ineffective thrombolysis in acute ischemic stroke with clinical and biochemical markers.

**Ischemic stroke remains a leading cause of morbidity and mortality globally. Despite the advances in thrombolytic therapy, notably recombinant tissue plasminogen activator (rtPA), patient outcomes are highly variable. This study aims to introduce a novel predictive model, the Acute Stroke Thrombolysis Non-Responder Prediction Model (ASTN-RPM), to identify patients unlikely to benefit from rtPA within the critical early recovery window. We conducted a retrospective cohort study at Baoding No.1 Central Hospital including 709 adult patients diagnosed with acute ischemic stroke and treated with intravenous alteplase within the therapeutic time window. The ASTN-RPM was developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression technique, incorporating a wide range of biomarkers and clinical parameters. Model performance was evaluated using Receiver Operating Characteristic (ROC) curves, calibration plots, and Decision Curve Analysis (DCA). ASTN-RPM effectively identified patients at high risk of poor response to thrombolysis, with an AUC of 0.909 in the training set and 0.872 in the validation set, indicating high sensitivity and specificity. Key predictors included posterior circulation stroke, high admission NIHSS scores, extended door to needle time, and certain laboratory parameters like homocysteine levels. The ASTN-RPM stands as a potential tool for refining clinical decision-making in ischemic stroke management. By anticipating thrombolytic non-response, clinicians can personalize treatment strategies, possibly improving patient outcomes and reducing the burden of ineffective interventions. Future studies are needed for external validation and to explore the incorporation of emerging biomarkers and imaging data.

Development and validation of a LASSO prediction model for cisplatin induced nephrotoxicity: a case-control study in China.

BACKGROUND: Early identification of high-risk individuals with cisplatin-induced nephrotoxicity (CIN) is crucial for avoiding CIN and improving prognosis. In this study, we developed and validated a CIN prediction model based on general clinical data, laboratory indications, and genetic features of lung cancer patients before chemotherapy. METHODS: We retrospectively included 696 lung cancer patients using platinum chemotherapy regimens from June 2019 to June 2021 as the traing set to construct a predictive model using Absolute shrinkage and selection operator (LASSO) regression, cross validation, and Akaike's information criterion (AIC) to select important variables. We prospectively selected 283 independent lung cancer patients from July 2021 to December 2022 as the test set to evaluate the model's performance. RESULTS: The prediction model showed good discrimination and calibration, with AUCs of 0.9217 and 0.8288, sensitivity of 79.89% and 45.07%, specificity of 94.48% and 94.81%, in the training and test sets respectively. Clinical decision curve analysis suggested that the model has value for clinical use when the risk threshold ranges between 0.1 and 0.9. Precision-Recall (PR) curve shown in recall interval from 0.5 to 0.75: precision gradually declines with increasing Recall, up to 0.9. CONCLUSIONS: Predictive models based on laboratory and demographic variables can serve as a beneficial complementary tool for identifying high-risk populations with CIN.

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

BACKGROUND: Liver 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. METHODS: Retrospective 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. RESULTS: A 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. CONCLUSIONS: The nomogram, based on the Milan criteria and serological markers, showed good accuracy in predicting the recurrence of HCC after LT using extended selection criteria.

Identification and verification of circRNA biomarkers for coronary artery disease based on WGCNA and the LASSO algorithm.

BACKGROUND: The role of circular RNAs (circRNAs) as biomarkers of coronary artery disease (CAD) remains poorly explored. This study aimed to identify and validate potential circulating circRNAs as biomarkers for the diagnosis of CAD. METHODS: The expression profile of circRNAs associated with CAD was obtained from Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operation (LASSO) were employed to identify CAD-related hub circRNAs. The expression levels of these hub circRNAs were validated using qRT-PCR in blood samples from 100 CAD patients and 100 controls. The diagnostic performance of these circRNAs was evaluated through logistic regression analysis, receiver operator characteristic (ROC) analysis, integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Functional enrichment analyses were performed to predict the possible mechanisms of circRNAs in CAD. RESULTS: A total of ten CAD-related hub circRNAs were identified through WGCNA and LASSO analysis. Among them, hsa_circ_0069972 and hsa_circ_0021509 were highly expressed in blood samples of CAD patients, and they were identified as independent predictors after adjustment for relevant confounders. The area under the ROC curve for hsa_circ_0069972 and hsa_circ_0021509 was 0.760 and 0.717, respectively. The classification of patients was improved with the incorporation of circRNAs into the clinical model composed of conventional cardiovascular risk factors, showing an IDI of 0.131 and NRI of 0.170 for hsa_circ_0069972, and an IDI of 0.111 and NRI of 0.150 for hsa_circ_0021509. Functional enrichment analyses revealed that the hsa_circ_0069972-miRNA-mRNA network was enriched in TGF-ß、FoxO and Hippo signaling pathways, while the hsa_circ_0021509-miRNA-mRNA network was enriched in PI3K/Akt and MAPK signaling pathways. CONCLUSION: Hsa_circ_0069972 and hsa_circ_0021509 were identified by integrated analysis, and they are highly expressed in CAD patients. They may serve as novel biomarkers for CAD.

Elucidating the multifaceted roles of GPR146 in non-specific orbital inflammation: a concerted analytical approach through the prisms of bioinformatics and machine learning.

Background: Non-specific Orbital Inflammation (NSOI) is a chronic idiopathic condition marked by extensive polymorphic lymphoid infiltration in the orbital area. The integration of metabolic and immune pathways suggests potential therapeutic roles for C-peptide and G protein-coupled receptor 146 (GPR146) in diabetes and its sequelae. However, the specific mechanisms through which GPR146 modulates immune responses remain poorly understood. Furthermore, the utility of GPR146 as a diagnostic or prognostic marker for NSOI has not been conclusively demonstrated. Methods: We adopted a comprehensive analytical strategy, merging differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) datasets GSE58331 and GSE105149 with immune-related genes from the ImmPort database. Our methodology combined LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) for feature selection, followed by Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to explore gene sets co-expressed with GPR146, identifying a significant enrichment in immune-related pathways. The tumor microenvironment's immune composition was quantified using the CIBERSORT algorithm and the ESTIMATE method, which confirmed a positive correlation between GPR146 expression and immune cell infiltration. Validation of GPR146 expression was performed using the GSE58331 dataset. Results: Analysis identified 113 DEGs associated with GPR146, with a significant subset showing distinct expression patterns. Using LASSO and SVM-RFE, we pinpointed 15 key hub genes. Functionally, these genes and GPR146 were predominantly linked to receptor ligand activity, immune receptor activity, and cytokine-mediated signaling. Specific immune cells, such as memory B cells, M2 macrophages, resting mast cells, monocytes, activated NK cells, plasma cells, and CD8+ T cells, were positively associated with GPR146 expression. In contrast, M0 macrophages, naive B cells, M1 macrophages, activated mast cells, activated memory CD4+ T cells, naive CD4+ T cells, and gamma delta T cells showed inverse correlations. Notably, our findings underscore the potential diagnostic relevance of GPR146 in distinguishing NSOI. Conclusion: Our study elucidates the immunological signatures associated with GPR146 in the context of NSOI, highlighting its prognostic and diagnostic potential. These insights pave the way for GPR146 to be a novel biomarker for monitoring the progression of NSOI, providing a foundation for future therapeutic strategies targeting immune-metabolic pathways.

The roles of IRF8 in nonspecific orbital inflammation: an integrated analysis by bioinformatics and machine learning.

BACKGROUND: Nonspecific Orbital Inflammation (NSOI) represents a persistent and idiopathic proliferative inflammatory disorder, characterized by polymorphous lymphoid infiltration within the orbit. The transcription factor Interferon Regulatory Factor 8 (IRF8), integral to the IRF protein family, was initially identified as a pivotal element for the commitment and differentiation of myeloid cell lineage. Serving as a central regulator of innate immune receptor signaling, IRF8 orchestrates a myriad of functions in hematopoietic cell development. However, the intricate mechanisms underlying IRF8 production remain to be elucidated, and its potential role as a biomarker for NSOI is yet to be resolved. METHODS: IRF8 was extracted from the intersection analysis of common DEGs of GSE58331 and GSE105149 from the GEO and immune- related gene lists in the ImmPort database using The Lasso regression and SVM-RFE analysis. We performed GSEA and GSVA with gene sets coexpressed with IRF8, and observed that gene sets positively related to IRF8 were enriched in immune-related pathways. To further explore the correlation between IRF8 and immune-related biological process, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate TME characteristics of each sample and confirmed that high IRF8 expression might give rise to high immune cell infiltration. Finally, the GSE58331 was utilized to confirm the levels of expression of IRF8. RESULTS: Among the 314 differentially expressed genes (DEGs), some DEGs were found to be significantly different. With LASSO and SVM-RFE algorithms, we obtained 15 hub genes. For biological function analysis in IRF8, leukocyte mediated immunity, leukocyte cell-cell adhesion, negative regulation of immune system process were emphasized. B cells naive, Macrophages M0, Macrophages M1, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD4 naive, and T cells gamma delta were shown to be positively associated with IRF8. While, Mast cells resting, Monocytes, NK cells activated, Plasma cells, T cells CD8, and T cells regulatory (Tregs) were shown to be negatively linked with IRF8. The diagnostic ability of the IRF8 in differentiating NSOI exhibited a good value. CONCLUSIONS: This study discovered IRF8 that are linked to NSOI. IRF8 shed light on potential new biomarkers for NSOI and tracking its progression.

Flow cytometry-based peripheral blood analysis as an easily friendly tool for prognostic monitoring of acute ischemic stroke: a multicenter study.

Background and objective: Acute ischemic stroke (AIS) is a leading cause of mortality, severe neurological and long-term disability world-wide. Blood-based indicators may provide valuable information on identified prognostic factors. However, currently, there is still a lack of peripheral blood indicators for the prognosis of AIS. We aimed to identify the most promising prognostic indicators and establish prognostic models for AIS. Methods: 484 subjects enrolled from four centers were analyzed immunophenotypic indicators of peripheral blood by flow cytometry. Least absolute shrinkage and selection operator (LASSO) regression was applied to minimize the potential collinearity and over-fitting of variables measured from the same subject and over-fitting of variables. Univariate and multivariable Cox survival analysis of differences between and within cohorts was performed by log-rank test. The areas under the receiving operating characteristic (ROC) curves were used to evaluate the selection accuracy of immunophenotypic indicators in identifying AIS subjects with survival risk. The prognostic model was constructed using a multivariate Cox model, consisting of 402 subjects as a training cohort and 82 subjects as a testing cohort. Results: In the prospective study, 7 immunophenotypic indicators of distinct significance were screened out of 72 peripheral blood immunophenotypic indicators by LASSO. In multivariate cox regression, CTL (%) [HR: 1.18, 95% CI: 1.03-1.33], monocytes/µl [HR: 1.13, 95% CI: 1.05-1.21], non-classical monocytes/µl [HR: 1.09, 95% CI: 1.02-1.16] and CD56high NK cells/µl [HR: 1.13, 95% CI: 1.05-1.21] were detected to decrease the survival probability of AIS, while Tregs/µl [HR:0.97, 95% CI: 0.95-0.99, p=0.004], BM/µl [HR:0.90, 95% CI: 0.85-0.95, p=0.023] and CD16+NK cells/µl [HR:0.93, 95% CI: 0.88-0.98, p=0.034] may have the protective effect. As for indicators' discriminative ability, the AUC for CD56highNK cells/µl attained the highest of 0.912. In stratification analysis, the survival probability for AIS subjects with a higher level of Tregs/µl, BM/µl, CD16+NK cells/µl, or lower levels of CD56highNK cells/µl, CTL (%), non-classical monocytes/µl, Monocytes/µl were more likely to survive after AIS. The multivariate Cox model showed an area under the curve (AUC) of 0.805, 0.781 and 0.819 and 0.961, 0.924 and 0.982 in the training and testing cohort, respectively. Conclusion: Our study identified 7 immunophenotypic indicators in peripheral blood may have great clinical significance in monitoring the prognosis of AIS and provide a convenient and valuable predictive model for AIS.

Identifying and assessing a prognostic model based on disulfidptosis-related genes: implications for immune microenvironment and tumor biology in lung adenocarcinoma.

Introduction: Lung cancer, with the highest global mortality rate among cancers, presents a grim prognosis, often diagnosed at an advanced stage in nearly 70% of cases. Recent research has unveiled a novel mechanism of cell death termed disulfidptosis, which is facilitated by glucose scarcity and the protein SLC7A11. Methods: Utilizing the least absolute shrinkage and selection operator (LASSO) regression analysis combined with Cox regression analysis, we constructed a prognostic model focusing on disulfidptosis-related genes. Nomograms, correlation analyses, and enrichment analyses were employed to assess the significance of this model. Among the genes incorporated into the model, CHRNA5 was selected for further investigation regarding its role in LUAD cells. Biological functions of CHRNA5 were assessed using EdU, transwell, and CCK-8 assays. Results: The efficacy of the model was validated through internal testing and an external validation set, with further evaluation of its robustness and clinical applicability using a nomogram. Subsequent correlation analyses revealed associations between the risk score and infiltration of various cancer types, as well as oncogene expression. Enrichment analysis also identified associations between the risk score and pivotal biological processes and KEGG pathways. Our findings underscore the significant impact of CHRNA5 on LUAD cell proliferation, migration, and disulfidptosis. Conclusion: This study successfully developed and validated a robust prognostic model centered on disulfidptosis-related genes, providing a foundation for predicting prognosis in LUAD patients.

Effects of exposure to multiple metallic elements in the first trimester of pregnancy on the risk of preterm birth.

Exposure to certain heavy metals has been demonstrated to be associated with a higher risk of preterm birth (PTB). However, studies focused on the effects of other metal mixtures were limited. A nested case‒control study enrolling 94 PTB cases and 282 controls was conducted. Metallic elements were detected in maternal plasma collected in the first trimester using inductively coupled plasma‒mass spectrometry. The effect of maternal exposure on the risk of PTB was investigated using logistic regression, least absolute shrinkage and selection operator, restricted cubic spline (RCS), quantile g computation (QGC) and Bayesian kernel machine regression (BKMR). Vanadium (V) and arsenic (As) were positively associated with PTB risk in the logistic model, and V remains positively associated in the multi-exposure logistic model. QGC analysis determined V (69.42%) and nickel (Ni) (70.30%) as the maximum positive and negative contributors to the PTB risk, respectively. BKMR models further demonstrated a positive relationship between the exposure levels of the mixtures and PTB risk, and V was identified as the most important independent variable among the elements. RCS analysis showed an inverted U-shape effect of V and gestational age, and plasma V more than 2.18 µg/L was considered a risk factor for shortened gestation length. Exposure to metallic elements mixtures consisting of V, As, cobalt, Ni, chromium and manganese in the first trimester was associated with an increased risk of PTB, and V was considered the most important factor in the mixtures in promoting the incidence of PTB.