Machine learning approaches for predicting high cost high need patient expenditures in health care

Neutrophil extracellular traps-related genes contribute to sepsis-associated acute kidney injury

BACKGROUND

Neutrophil extracellular traps (NETs) and oxidative stress (OS) may be involved in sepsis-associated acute kidney injury (SA-AKI). The aim of this study was to identify potential regulators which modulate NETs and OS in SA-AKI, and to find potential therapeutic agents.

METHODS AND MATERIALS

SA-AKI-related datasets GSE255281 and GSE225192 were downloaded from Gene Expression Omnibus. Molecular subtypes associated with NETs were identified by unsupervised clustering. The OS-related genes were obtained by weighted gene co-expression network analysis. Differentially expressed genes were screened by "limma" package in R. Least absolute shrinkage and selection operator algorithm was applied to identify the hub genes. Additionally, the biological functions of the hub genes were analyzed with single sample gene set enrichment analysis. NetworkAnalyst database was searched to screen the drugs targeting the hub targets. qRT-PCR was used to analyze the expression of key genes in the peripheral blood mononuclear cells (PBMCs) of the patients with SA-AKI and healthy controls. HK-2 cells and human umbilical vein endothelial cells (HUVECs) were induced by lipopolysaccharide (LPS) to construct a SA-AKI model, and the effects of estradiol and (+)-JQ1 on HK-2 cells and HUVECs were evaluated by CCK-8 assays, flow cytometry and OS indices.

RESULTS

Based on NETs-related genes, SA-AKI samples could be divide into two subgroups, and the differentially expressed genes between two subgroups were associated with OS. In silico analyses identified 13 hub targets. The expression of ECT2 and CHRDL1 in PBMCs of SA-AKI patients was significantly lower than that in control group, and the expressions of PTAFR, CSF3 and FOS were significantly higher. Estradiol and (+)-JQ1, which targeted more of the hub targets with good binding affinity, could increase the viability of HK-2 cells and HUVECs induced by LPS and inhibit apoptosis and OS.

CONCLUSION

Formation of NETs, contributes to OS and pathogenesis of SA-AKI. Estradiol and (+)-JQ1, targeting multiple regulators in the formation of NETs, may be potential therapeutic agents for the treatment of SA-AKI.

CLINICAL TRIAL NUMBER

Not applicable.

Transcriptomic Profiling of Hypoxia-Adaptive Responses in Tibetan Goat Fibroblasts

The Tibetan goat (Capra hircus) exhibits remarkable adaptations to high-altitude hypoxia, yet the molecular mechanisms remain unclear. This study integrates RNA-seq, WGCNA, and machine learning to explore gene-environment interactions (G × E) in hypoxia adaptation. Fibroblasts from the Tibetan goat and Yunling goat were cultured under hypoxic (1% O2) and normoxic (21% O2) conditions, respectively. This identified 68 breed-specific (G), 100 oxygen-responsive (E), and 620 interaction-driven (I) Differentially Expressed Genes (DEGs). The notably higher number of interaction-driven DEGs compared to other effects highlights transcriptional plasticity. We defined two gene sets: Environmental Stress Genes (n = 632, E ∪ I) and Genetic Adaptation Genes (n = 659, G ∪ I). The former were significantly enriched in pathways related to oxidative stress defense and metabolic adaptation, while the latter showed prominent enrichment in pathways associated with vascular remodeling and transcriptional regulation. CTNNB1 emerged as a key regulatory factor in both gene sets, interacting with CASP3 and MMP2 to form the core of the protein-protein interaction (PPI) network. Machine learning identified MAP3K5, TGFBR2, RSPO1 and ITGB5 as critical genes. WGCNA identified key modules in hypoxia adaptation, where FOXO3, HEXIM1, and PPARD promote the stabilization of HIF-1α and metabolic adaptation through the HIF-1 signaling pathway and glycolysis. These findings underscore the pivotal role of gene-environment interactions in hypoxic adaptation, offering novel perspectives for both livestock breeding programs and biomedical research initiatives.

TMEM132A: a novel susceptibility gene for lung adenocarcinoma combined with venous thromboembolism identified through comprehensive bioinformatic analysis

Background

Mounting evidence indicates that lung adenocarcinoma (LUAD) patients are at elevated risk for venous thromboembolism (VTE), presenting a major clinical challenge. This study utilized public databases to identify crosstalk genes (CGs) between LUAD and VTE, applied machine learning methods to discover shared diagnostic biomarkers, and explored their underlying mechanisms.

Methods

Disease-specific genes for VTE were extracted from comprehensive genomic databases (CTD, DisGeNET, GeneCards, OMIM), while transcriptomic profiles of LUAD and VTE cohorts were retrieved from GEO via GEOquery implementation. Molecular crosstalk analysis identified candidate genes through differential expression algorithms and disease-association metrics. Functional annotation employed GO and KEGG analyses to elucidate the biological significance of identified CGs. LASSO regression analysis of VTE and LUAD matrices yielded overlapping diagnostic biomarkers. Immune contexture was characterized via CIBERSORT deconvolution, followed by correlation analyses between hub genes and immune infiltration profiles. Hub genes expression was corroborated through independent cohort validation and serological quantification. Diagnostic utility was evaluated through receiver operating characteristic (ROC) curve and nomogram. Therapeutic potential was assessed via DSigDB-based drug sensitivity profiling.

Result

Through transcriptomic analysis, we identified 381 CGs, which demonstrated significant enrichment in inflammatory cascades, immunological processes, and coagulation pathways. LASSO regression analysis of LUAD and VTE cohorts revealed TIMP1 and TMEM132A as putative shared diagnostic biomarkers. TMEM132A exhibited significant correlation with immune cell infiltration patterns across both diseases, modulating the immune microenvironment. Validation cohorts and serological assessment confirmed elevated TMEM132A expression in LUAD and LUAD combined with VTE phenotypes. The diagnostic accuracy of TMEM132A was substantiated by ROC curves and nomogram analyses. Pharmacological sensitivity analysis indicated that TMEM132A may serve as a potential target for the therapeutic agents birabresib and abemaciclib.

Conclusion

TMEM132A demonstrates diagnostic utility as a predictive biomarker for VTE occurrence in LUAD, suggesting its potential role as a susceptibility gene in this patient cohort.

Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods

Rapidly increasing healthcare spending globally is significantly driven by high-need, high-cost (HNHC) patients, who account for the top 5% of annual healthcare costs but over half of total expenditures. The programs targeting existing HNHC patients have shown limited long-term impact, and research predicting HNHC pediatric patients in China is limited. There is an urgent need to establish a specific, valid, and reliable prediction model using machine-learning-based methods to identify potential HNHC pediatric patients and implement proactive interventions before high costs arise. This study used a 7-year retrospective cohort dataset from two administrative databases in Shanghai, covering pediatric patients under 18 years. The machine-learning-based models were developed to predict HNHC status using logistic regression, k-nearest neighbors (KNN), random forest (RF), multi-layer perceptron (MLP), and Naive Bayes. This study divided the data from 2021-2022 into 70:30 as a training set and a test set, with the internal class balancing approach of the Synthetic Minority Over-sampling Technique (SMOTE). A grid search strategy was employed with k-fold cross-validation to optimize hyperparameters. Model performance was assessed by 5 metrics: Receiver Operating Characteristic-Area Under Curve (ROC-AUC), accuracy, sensitivity, specificity, and F1 score. The external validation from 2022-2023 data and the internal validation using different train-test ratios (80:20 and 90:10) were used to assess the robustness of the trained models. Among the 91,882 hospitalized children included in 2021, significant differences were found in socioeconomics, disease, healthcare service utilization, previous healthcare expenditure, and hospital characteristics between the HNHC and non-HNHC groups. The hospitalization costs for HNHC pediatric patients accounted for over 35% of total spending. The MLP model demonstrated the highest predictive performance (ROC-AUC: 0.872), followed by RF (0.869), KNN (0.836), and naive Bayes (0.828). The most important predictive factors included length of stay, number of hospitalizations, previous HNHC status, age, and presence of Top 20 HNHC diseases. MLP showed robustness as the most efficient model in external validation (ROC-AUC: 0.843) and internal validation using different train-test ratios (ROC-AUC: 0.826 in 80:20 ratio; 0.807 in 90:10 ratio). Machine learning models, particularly MLP, effectively predict HNHC pediatric patients, providing a basis for early identification of HNHC and proactive healthcare interventions into clinical practice. This approach can also assist policymakers and payers in optimizing healthcare resource allocation, controlling healthcare costs, and improving patient outcomes.

Concomitant Procedures, Black Race, Male Sex, and General Anesthesia Show Fair Predictive Value for Prolonged Rotator Cuff Repair Operative Time: Analysis of the National Quality Improvement Program Database Using Machine Learning

PURPOSE

To develop machine learning models using the American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database to predict prolonged operative time (POT) for rotator cuff repair (RCR), as well as use the trained machine learning models, cross-referenced with traditional multivariate logistic regression (MLR), to determine the key perioperative variables that may predict POT for RCR.

METHODS

Data were obtained from a large national database (ACS-NSQIP) from 2021. Patients with unilateral RCR procedures were included. Demographic, preoperative, and operative variables were analyzed. An MLR model and various other machine learning techniques, including random forest (RF) and artificial neural network, were compared using area under the curve, calibration, Brier score, and decision curve analysis. Feature importance was identified from the overall best-performing model.

RESULTS

A total of 6,690 patients met inclusion criteria. The RF machine learning model had the highest area under the curve upon internal validation (0.706) and the lowest Brier score (0.15), outperforming the other models. The RF model also demonstrated strong performance upon assessment of the calibration curves (slope = 0.86, intercept = 0.08) and decision curve analysis. The model identified concomitant procedure, specifically labral repair and biceps tenodesis, as the most important variable for determining POT, followed by age <30 years, Black or African American race, male sex, and general anesthesia.

CONCLUSIONS

Despite the advanced machine learning models used in this study, the ACS-NSQIP data set was only able to fairly predict POT following RCR. The RF model identified concomitant procedures, specifically labral repair and biceps tenodesis, as the most important variables for determining POT. Additionally, demographic factors such as age <30 years, Black race, and general anesthesia were significant predictors. While male sex was identified as important in the RF model, the MLR model indicated that its predictive value is primarily in conjunction with specific procedures like biceps tenodesis and subacromial decompression.

LEVEL OF EVIDENCE

Level IV, retrospective comparative prognostic trial.

CDC6 as early biomarker for myocardial infarction with acute cellular senescence and repair mechanisms

We aimed to the exploration of genes related to the cell cycle and the mechanisms of cardiac cell repair and senescence post-MI. The sequencing dataset of myocardial infarction in mice (GSE161427) was downloaded from the Gene Expression Omnibus (GEO) database, yielding 894 upregulated differentially expressed genes (DEGsup). Additionally, 494 senescence-related genes (SRGs) were obtained from the CellAge database. The overlapping differentially expressed genes (DEupSGs) between these two sets were identified using the R software. WGCNA using the GSE59867 dataset revealed a highly related module to MI. The intersection of core genes from the purple module and DEupSGs yielded 12 genes. Through machine learning and PPI analysis, two target genes related to MI and cellular senescence were identified: CDC6 and PLK1. ROC curve analysis using the MI animal myocardial sample dataset GSE775 and the patient blood sample dataset GSE60993 indicated that CDC6 expression has high diagnostic value for MI and cellular senescence, with differential expression levels at various times post-infarction.These results show that CDC6 is specifically upregulated in the early stages of MI, and both in vivo and ex vivo model results are consistent with bioinformatics findings. Additionally, overexpression of CDC6 in the early oxygen-glucose deprivation (OGD) model in vitro increased the expression of genes mediating cardiac repair. Interestingly, when ABT263 was used to clear senescent cells, the expression of genes mediating repair in primary cardiac cells decreased, suggesting that acute ischemic hypoxia early on, CDC6-mediated acute cardiac cell senescence may promote early cardiac repair. This finding may provide new insights into the monitoring and assessment of cardiac cell senescence and repair in the early stages of MI.

Integrative bioinformatics analysis reveals STAT1, ORC2, and GTF2B as critical biomarkers in lupus nephritis with Monkeypox virus infection

The monkeypox virus (MPXV) is currently spreading rapidly around the world, but the mechanisms by which it interacts with lupus nephritis (LN) are unknown. The aim of this study was to investigate the role and mechanism of lupus nephritis combined with monkeypox virus infection. The data comes from GEO and GeneCards.Through Limma and Weighted Gene Co-expression Network Analysis (WGCNA) analysis, differential expression genes (DEGs) and module genes were identified, and KEGG and GO enrichment analysis was carried out.In addition, a protein-protein interaction (PPI) network was constructed and LASSO regression was used to screen genes related to senescence. The diagnostic effectiveness was evaluated using a Nomogram and the receiver operating characteristic (ROC) curve and verified using GSE99967.Immune infiltration and gene set enrichment analysis (GSEA) Were also included in the study.In the end, miRNet was used to construct a miRNA-mRNA-TF network and screen targeted drugs through DGIdb. 5707 DEGs were identified in the lupus nephritis and 737 in the monkeypox data. WGCNA and Lasso regression analyses screened for three important targets (STAT1, ORC2, and GTF2B) .Predictive modeling and ROC of STAT1, ORC2 and GTF2B by Nomogram showed good diagnostic value .Immune infiltration analysis showed immune cell disorders and related pathway activation.The miRNA-mRNA-TF network covers 516 miRNAs and 15 transcription factors, and enrichment analysis shows that it plays an important role in senescence and inflammation.Potential Target Drugs Screened Include Guttiferone K And Silicon Phthalocyanine 4. This study identifies STAT1, ORC2, and GTF2B as key factors in cellular senescence and immune dysregulation associated with lupus nephritis and monkeypox infection, suggesting they may serve as important predictive targets.

Identifying semaphorin 3C as a biomarker for sarcopenia and coronary artery disease via bioinformatics and machine learning

OBJECTIVE

Sarcopenia not only affects patients' quality of life but also may exacerbate the pathological processes of coronary artery disease (CAD). This study aimed to identify potential biomarkers to improve the combined diagnosis and treatment of sarcopenia and CAD.

METHODS

Datasets for sarcopenia and CAD were sourced from the Gene Expression Omnibus (GEO). Weighted gene co-expression network analysis (WGCNA) was used to identify key module genes. Functional enrichment analysis was conducted to explore biological significance. Three machine learning algorithms were applied to further determine candidate hub genes, including SVM-RFE, LASSO regression, and random forest (RF). Then, we generated receiver operating characteristic (ROC) curves to evaluate the diagnostic efficacy of the candidate genes. Moreover, mendelian randomization (MR) analysis was conducted based on GWAS summary data, along with sensitivity analysis to explore causal relationships.

RESULTS

WGCNA analysis identified 278 genes associated with sarcopenia and CAD. The results of the enrichment analysis indicated a complex interplay between RNA metabolism, signaling pathways, and cellular stress responses. Through machine learning methods and ROC curves, we identified the key gene semaphorin 3C (SEMA3C). MR analysis revealed that higher plasma levels of SEMA3C are associated with an increased risk of CAD (OR = 1.068, 95 % CI 1.012-1.128, P = 0.016) and low hand grip strength (HGS) (OR = 1.059, 95 % CI 1.010-1.110, P = 0.018) .

CONCLUSION

SEMA3C has been identified as a key gene for sarcopenia and CAD. This insight suggests that targeting SEMA3C may offer new therapeutic opportunities in related conditions.

A computational framework for detecting inter-tissue gene-expression coordination changes with aging

Aging is a complex and systematic biological process that involves multiple genes and biological pathways across different tissues. While existing studies focus on tissue-specific aging factors, the inter-tissue interplay between molecular pathways during aging remains insufficiently explored. To bridge this gap, we propose a novel computational framework to identify the effect of aging on the coordinated patterns of gene-expression across multiple tissues. Our framework includes (1) an adjusted multi-tissue weighted gene co-expression network analysis, (2) differential network connectivity analysis between age groups and (3) machine learning models, XGBoost and Random Forest (RF) fed by gene expression levels and lower-dimensional pathway score space, to identify unique key inter-tissue genes and biological pathways for classifying aging. We applied our approach to three representative tissues: Adipose-Subcutaneous, Muscle-Skeletal and Brain-Cortex. The RF model demonstrated the best performance in predicting age group (AUC < 88%) highlighting key genes involved in inter-tissue coordination processes in aging. We also identified the inter-tissue involvement of lipid metabolism, immune system, and cell communication pathways during aging and detected distinct aging pathways manifested between tissues. The proposed framework highlights the importance of inter-tissue coordination processes underlying aging and provides valuable insights into aging mechanisms which can further assist in the development of therapeutic strategies promoting healthy aging.

Exploring The Role of TOP2A in the Intersection of Pathogenic Mechanisms Between Rheumatoid Arthritis and Idiopathic Pulmonary Fibrosis Based on Bioinformatics

Background

Rheumatoid arthritis (RA) and idiopathic pulmonary fibrosis (IPF) share a common pathogenic mechanism, but the underlying mechanisms remain ambiguous. Our study aims at exploring the genetic-level pathogenic mechanism of these two diseases.

Methods

We carried out bioinformatics analysis on the GSE55235 and GSE213001 datasets. Machine learning was employed to identify candidate genes, which were further verified using the GSE92592 and GSE89408 datasets, as well as quantitative real-time PCR (qRT-PCR). The expression levels of TOP2A in RA and IPF in vitro models were confirmed using Western blotting and qRT-PCR. Furthermore, we explored the influence of TOP2A on the occurrence and development of RA and IPF by using the selective inhibitor PluriSIn #2 in an in vitro model. Finally, an in vivo model of RA and IPF was constructed to assess TOP2A expression levels via immunohistochemistry.

Results

Our bioinformatics analysis suggests a potential intersection in the pathogenic mechanisms of RA and IPF. We have identified 7 candidate genes: CXCL13, TOP2A, MMP13, MMP1, LY9, TENM4, and SEMA3E. Our findings reveal that the expression level of TOP2A is significantly elevated in both in vivo and in vitro models of RA and IPF. Additionally, our research indicates that PluriSIn #2 can effectively restrain inflammatory factors, extracellular matrix deposition, migration, invasion, the expression and nuclear uptake of p-smad2/3 protein in RA and IPF in vitro models.

Conclusion

There is a certain correlation between RA and IPF at the genetic level, and the molecular mechanisms of their pathogenesis overlap, which might be the reason for the progression of RA. Among the candidate genes we identified, TOP2A may influence the occurrence and development of RA and IPF through the TGF-β/Smad signal pathway. This could be beneficial to the study of the pathogenesis and treatment of RA and IPF.

A novel signature of cartilage aging-related immunophenotyping biomarkers in osteoarthritis

The objective of this study was to identify aging-related immunophenotypic biomarkers associated with osteoarthritis (OA) using advanced machine learning techniques. We employed a combination of lasso regression and random forest algorithms to analyze transcriptomic data obtained from OA patients. Differential expression analysis and functional enrichment analysis were conducted to identify aging-related differentially expressed genes (ag-DEGs) and annotate their biological functions. Furthermore, correlation analysis among hub genes and immune cell infiltration analysis were performed to understand the molecular phenotypes of OA. Our analysis identified 43 ag-DEGs enriched in immune-related biological processes and pathways. Lasso regression and random forest analysis narrowed down the gene pool to three hub genes: CACNA1A, FLT1 and KCNAB3. These genes exhibited differential expression between normal and OA groups and demonstrated high accuracy in distinguishing between them. Clustering analysis revealed two distinct molecular phenotypes of OA: an "immune-activated subgroup" and an "immune-suppressed subgroup." Experimental validation confirmed the expression patterns of hub genes. This study identified biomarkers associated with the aging-related immune phenotype in OA, shedding light on potential targets for immunotherapy and personalized medical treatments. Characterized by CACNA1A, FLT1, and KCNAB3, clustering analysis suggests that OA can be divided into two molecular phenotypes: an "immune-activated subgroup" and an "immune-suppressed subgroup." The findings may contribute to the development of novel therapeutic strategies aimed at modulating immune responses in OA patients, ultimately improving treatment outcomes and prognosis.

Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking, and machine learning strategies

The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previous research suggests that various chemicals, including plasticizers like acetyl tributyl citrate (ATBC), may adversely affect thyroid health, yet the precise mechanisms remain poorly understood. The objective of this study was to elucidate the complex effects of acetyl tributyl citrate (ATBC) on the thyroid gland and to clarify the potential molecular mechanisms by which environmental pollutants influence the disease process. Through an exhaustive exploration of databases such as ChEMBL, STITCH, and GEO, we identified a comprehensive list of 19 potential targets closely associated with ATBC and the thyroid gland. After rigorous screening using the STRING platform and Cytoscape software, we narrowed this list to 15 candidate targets, ultimately identifying five core targets: CBX5, HADHB, TRIM33, TP53, and CUL4A, utilizing three well-established machine learning methods. In-depth Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses conducted in the DAVID database revealed that the primary pathways through which ATBC affects the thyroid gland involve key signaling cascades, including the FoxO signaling pathway and metabolic pathways such as fatty acid metabolism. Furthermore, molecular docking simulations using Molecular Operating Environment software confirmed strong binding interactions between ATBC and these core targets, enhancing our understanding of their interactions. Overall, our findings provide a theoretical framework for comprehending the intricate molecular mechanisms underlying ATBC's effects on thyroid damage and pave the way for the development of preventive and therapeutic strategies against thyroid disorders caused by exposure to ATBC-containing plastics or overexposure to ATBC.

Identification of hub biomarkers and immune cell infiltrations participating in the pathogenesis of endometriosis

Endometriosis (EM) is a chronic disease that can cause pain and infertility in patients. As is well known, immune cell infiltrations (ICIs) play important roles in the pathogenesis of EM. However, the pathogenesis and biomarkers of EM that can be used in clinical practice and their relationship with ICIs still need to be elucidated. The gene expression datasets of EM and the healthy control were obtained from the Gene Expression Omnibus (GEO). To identify the central modules and explore the correlation between the gene network and EM, weighted gene co-expression network analysis (WGCNA) was executed. The hub genes were screened using machine learning. The qRT-PCR results showed that only CHMP4C and KAT2B differentially expressed in ectopic tissues compared to the normal. Subsequently, the samples were clustered based on the expression of CHMP4C and KAT2B. Depending on the differential expression genes of the two 2rG Clusters, the samples were divided into two gene Clusters. Significant differences in immune cell infiltrations were observed among the two 2rG Clusters and the two gene Clusters. Furthermore, varied immune checkpoint genes were shown to be correlated with EM. The qRT-PCR results showed that the two genes were significantly related to the ICI genes in EM. Hub genes CHMP4C and KAT2B are involved in the pathogenesis of EM by regulating ICI.

Machine-learning-based cost prediction models for inpatients with mental disorders in China

BACKGROUND

Mental disorders are increasingly prevalent, leading to increased medical expenditures. To refine the reimbursement of medical costs for inpatients with mental disorders by health insurance, an accurate prediction model is essential. Per-diem payment is a common internationally implemented payment method for medical insurance of inpatients with mental disorders, necessitating the exploration of advanced machine learning methods for predicting the average daily hospitalization costs (ADHC) based on the characteristics of inpatients with mental disorders.

METHODS

We used data including demographic information, clinical/functional characteristics, institutional features, and cost information of 5070 hospitalized patients with mental disorders in Jinhua, China, and employed six algorithms to predict ADHC. Performance of these six algorithms was evaluated through 5- old cross-validation combined with bootstrap method to select the most suitable algorithm and identify key factors influencing ADHC.

RESULTS

The random forest (RF) model demonstrated better performance (R-squared (R2) = 0.6417 (95% CI, 0.6236-0.6611), root-mean-square error (RMSE) = 0.2398 (95% CI, 0.2252-0.2553), mean-absolute error (MAE) = 0.1677 (95% CI, 0.1626-0.1735), mean-absolute-percentage error (MAPE) = 0.0295 (95% CI, 0.0287-0.0304)). According to feature importance ranking, models incorporating top 11 factors (> 0.01) demonstrated comparable performance to those encompassing all variables. Top four factors (> 0.05) were level of medical institution, age, functional classification, and cognitive classification. Notably, level of medical institutions was the most significant factor across all primary models. Higher medical institutions level, patients below 20 and above 75 years old, lower functional classification, and lower cognitive classification are associated with increased ADHC.

CONCLUSIONS

Machine learning algorithms, particularly RF algorithm, enhance accuracy of predicting ADHC for mental health patients. The findings of this study provide evidence for setting up more reasonable insurance payment standards for inpatients with mental disorders and support resource allocation in clinical practice.

PANoptosis-related genes: Molecular insights into immune dysregulation in ulcerative colitis

BACKGROUND AND AIM

Ulcerative colitis (UC) is a chronic inflammatory disease driven by immune dysregulation. PANoptosis, a novel form of programmed cell death, has been implicated in inflammatory diseases, but its specific role in UC remains unclear. This study aimed to identify PANoptosis-related genes (PRGs) that may contribute to immune dysregulation in UC.

METHODS

Using bioinformatics analysis of the GEO databases, we identified seven hub PRGs. Based on these genes, we developed a predictive model to differentiate UC patients from healthy controls, and evaluated its diagnostic performance using ROC curve analysis. We further conducted functional enrichment, GSVA, and immune infiltration analyses. Immunohistochemistry (IHC) was used to validate the expression of hub genes in UC patients.

RESULTS

The prediction model, based on the seven hub genes, exhibited diagnostic ability in discriminating UC patients from controls. Furthermore, these hub PRGs were found to be associated with immune cells, including dendritic cells, NK cells, macrophages, regulatory T cells (Tregs), and CD8+ T cells. They were also linked to key signaling pathways implicated in UC pathogenesis, such as IFNγ, TNFα, IL6-and JAK-STAT3, as well as hypoxia and apoptosis. Immunohistochemistry analysis validated the expression levels of hub PRGs in UC patients using paraffin sections of intestinal biopsy specimens.

CONCLUSIONS

This study identified PANoptosis-related genes with potential diagnostic value for UC and suggest that PANoptosis may contribute to the pathogenesis of UC by regulating specific immune cells and interacting with key signaling pathways. This highlights the potential importance of PANoptosis-related genes as therapeutic targets in UC management.

Identification of hub genes, diagnostic model, and immune infiltration in preeclampsia by integrated bioinformatics analysis and machine learning

PURPOSE

This study aimed to identify novel biomarkers for preeclampsia (PE) diagnosis by integrating Weighted Gene Co-expression Network Analysis (WGCNA) with machine learning techniques.

PATIENTS AND METHODS

We obtained the PE dataset GSE25906 from the gene expression omnibus (GEO) database. Analysis of differentially expressed genes (DEGs) and module genes with Limma and Weighted Gene Co-expression Network analysis (WGCNA). Candidate hub genes for PE were identified using machine learning. Subsequently, we used western-blotting (WB) and real-time fluorescence quantitative (qPCR) to verify the expression of F13A1 and SCCPDH in preeclampsia patients. Finally, we estimated the extent of immune cell infiltration in PE samples by employing the CIBERSORT algorithms.

RESULTS

Our findings revealed that F13A1 and SCCPDH were the hub genes of PE. The nomogram and two candidate hub genes had high diagnostic values (AUC: 0.90 and 0.88, respectively). The expression levels of F13A1 and SCCPDH were verified by WB and qPCR. CIBERSORT analysis confirmed that the PE group had a significantly larger proportion of plasma cells and activated dendritic cells and a lower portion of resting memory CD4 + T cells.

CONCLUSION

The study proposes F13A1 and SCCPDH as potential biomarkers for diagnosing PE and points to an improvement in early detection. Integration of WGCNA with machine learning could enhance biomarker discovery in complex conditions like PE and offer a path toward more precise and reliable diagnostic tools.

Identification and validation of the diagnostic biomarker MFAP5 for CAVD with type 2 diabetes by bioinformatics analysis

Introduction

Calcific aortic valve disease (CAVD) is increasingly prevalent among the aging population, and there is a notable lack of drug therapies. Consequently, identifying novel drug targets will be of utmost importance. Given that type 2 diabetes is an important risk factor for CAVD, we identified key genes associated with diabetes - related CAVD via various bioinformatics methods, which provide further potential molecular targets for CAVD with diabetes.

Methods

Three transcriptome datasets related to CAVD and two related to diabetes were retrieved from the Gene Expression Omnibus (GEO) database. To distinguish key genes, differential expression analysis with the "Limma" package and WGCNA was applied. Machine learning (ML) algorithms were employed to screen potential biomarkers. The receiver operating characteristic curve (ROC) and nomogram were then constructed. The CIBERSORT algorithm was utilized to investigate immune cell infiltration in CAVD. Lastly, the association between the hub genes and 22 types of infiltrating immune cells was evaluated.

Results

By intersecting the results of the "Limma" and WGCNA analyses, 727 and 190 CAVD - related genes identified from the GSE76717 and GSE153555 datasets were obtained. Then, through differential analysis and interaction, 619 genes shared by the two diabetes mellitus datasets were acquired. Next, we intersected the differential genes and module genes of CAVD with the differential genes of diabetes, and the obtained genes were used for subsequent analysis. ML algorithms and the PPI network yielded a total of 12 genes, 10 of which showed a higher diagnostic value. Immune cell infiltration analysis revealed that immune dysregulation was closely linked to CAVD progression. Experimentally, we have verified the gene expression differences of MFAP5, which has the potential to serve as a diagnostic biomarker for CAVD.

Conclusion

In this study, a multi-omics approach was used to identify 10 CAVD-related biomarkers (COL5A1, COL5A2, THBS2, MFAP5, BTG2, COL1A1, COL1A2, MXRA5, LUM, CD34) and to develop an exploratory risk model. Western blot (WB) and immunofluorescence experiments revealed that MFAP5 plays a crucial role in the progression of CAVD in the context of diabetes, offering new insights into the disease mechanism.

Predicting Early Hospital Discharge Following Revision Total Hip Arthroplasty: An Analysis of a Large National Database Using Machine Learning

BACKGROUND

Revision total hip arthroplasty (rTHA) was recently removed from the Medicare inpatient-only list. However, appropriate candidate selection for outpatient rTHA remains paramount. The purpose of this study was to evaluate the utility of a large national database using machine learning (ML) and traditional multivariable logistic regression (MLR) models in predicting early hospital discharge (EHD) (< 24 hours) following rTHA. Furthermore, this study aimed to use the trained ML models, cross-referenced with traditional MLR, to determine key perioperative variables predictive of EHD following rTHA.

METHODS

Data were obtained from a large national database from 2021. Patients who had unilateral rTHA procedures were included. Demographic, preoperative, and operative variables were analyzed as inputs for the models. An ML regression model and various ML techniques were used to predict EHD and were compared using the area under the curve, calibration, Brier score, and decision curve analysis. Feature importance was identified from the overall best-performing model. Of the 3,097 patients in this study, 866 (27.96%) underwent EHD.

RESULTS

The random forest model performed the best overall and identified aseptic surgical indication, operative time < three hours, absence of anemia (hematocrit < 40% in men and < 35% in women), neuraxial anesthesia type, White race, men, independent functional status, body mass index > 20, age < 75 years, and the presence of home support as factors predictive of EHD. Each of these variables was also significant in the MLR model.

CONCLUSIONS

Each ML model and MLR displayed good performance and identified clinically important variables for determining candidates for EHD following rTHA. Machine learning (ML) techniques such as random forest may allow clinicians to accurately risk stratify their patients preoperatively to optimize resources and improve patient outcomes.

Immune-related gene characterization and biological mechanisms in major depressive disorder revealed based on transcriptomics and network pharmacology

Background

Major depressive disorder (MDD) is a severe psychiatric disorder characterized by complex etiology, with genetic determinants that are not fully understood. The objective of this study was to investigate the pathogenesis of MDD and to explore its association with the immune system by identifying hub biomarkers using bioinformatics analyses and examining immune infiltrates in human autopsy samples.

Methods

Gene microarray data were obtained from the Gene Expression Omnibus (GEO) datasets GSE32280, GSE76826, GSE98793, and GSE39653. Our approach included differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis to identify hub genes associated with MDD. Subsequently, gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Cytoscape plugin CluGO, and Gene Set Enrichment Analysis (GSEA) were utilized to identify immune-related genes. The final selection of immune-related hub genes was determined through the least absolute shrinkage and selection operator (Lasso) regression analysis and PPI analysis. Immune cell infiltration in MDD patients was analyzed using CIBERSORT, and correlation analysis was performed between key immune cells and genes. The diagnostic accuracy of the identified hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, we conducted a study involving 10 MDD patients and 10 healthy controls (HCs) meeting specific criteria to assess the expression levels of these hub genes in their peripheral blood mononuclear cells (PBMCs). The Herbal Ingredient Target Database (HIT) was employed to screen for herbal components that target these genes, potentially identifying novel therapeutic agents.

Results

A total of 159 down-regulated and 51 up-regulated genes were identified for further analysis. WGCNA revealed 12 co-expression modules, with modules "darked", "darkurquoise" and "light yellow" showing significant positive associations with MDD. Functional enrichment pathway analysis indicated that these differential genes were associated with immune functions. Integration of differential and immune-related gene analysis identified 21 common genes. The Lasso algorithm confirmed 4 hub genes as potential biomarkers for MDD. GSEA analysis suggested that these genes may be involved in biological processes such as protein export, RNA degradation, and fc gamma r mediated cytotoxis. Pathway enrichment analysis identified three highly enriched immune-related pathways associated with the 4 hub genes. ROC curve analysis indicated that these hub genes possess good diagnostic value. Quantitative reverse transcription-polymerase chain reaction (RT-qPCR) demonstrated significant expression differences of these hub genes in PBMCs between MDD patients and HCs. Immune infiltration analysis revealed significant correlations between immune cells, including Mast cells resting, T cells CD8, NK cells resting, and Neutrophils, which were significantly correlated with the hub genes expression. HIT identified one herb target related to IL7R and 14 targets related to TLR2.

Conclusions

The study identified four immune-related hub genes (TLR2, RETN, HP, and IL7R) in MDD that may impact the diagnosis and treatment of the disorder. By leveraging the GEO database, our findings contribute to the understanding of the relationship between MDD and immunity, presenting potential therapeutic targets.

Identification of glycosyltransferase genes for diagnosis of T-cell mediated rejection and prediction of graft loss in kidney transplantation

BACKGROUND

Glycosylation is a complex and fundamental metabolic biosynthetic process orchestrated by multiple glycosyltransferases (GT) and glycosidases enzymes. Functions of GT have been extensively examined in multiple human diseases. Our study investigated the potential role of GT genes in T-cell mediated rejection (TCMR) and possible prediction of graft loss of kidney transplantation.

METHODS

We downloaded the microarray datasets and GT genes from the GEO and the HUGO Gene Nomenclature Committee (HGNC) databases, respectively. Differentially expressed GT genes (DE-GTGs) were obtained by differential expression and Venn analysis. A TCMR diagnostic model was developed based on the hub DE-GTGs using LASSO regression and XGboost machine learning algorithms. In addition, a predictive model for graft survival was constructed by univariate Cox and LASSO Cox regression analysis.

RESULTS

We have obtained 15 DE-GTGs. Both GO and KEGG analyses showed that the DE-GTGs were mainly involved in the glycoprotein biosynthetic process. The TCMR diagnostic model exhibited high diagnostic potential with generally highly correlated accuracies [aera under the curve (AUC) of 0.83]. The immune characteristics analysis revealed that higher levels of immune cell infiltration and immune responses were observed in the high-risk group than in the low-risk group. In particular, the Kaplan-Meier survival analysis revealed that renal grafts in the high-risk group have poor prognostic outcomes than the low-risk group. The predictive AUC values of 1-, 2- and 3-year graft survival were 0.76, 0.81, and 0.70, respectively.

CONCLUSION

Our results indicated that GT genes could be used for diagnosis of TCMR and prediction of graft loss in kidney transplantation. These results provide new perspectives and tools for diagnosing, treating and predicting kidney transplant-related diseases.

Exploring Pyroptosis-related Signature Genes and Potential Drugs in Ulcerative Colitis by Transcriptome Data and Animal Experimental Validation

Ulcerative colitis (UC) is an idiopathic, relapsing inflammatory disorder of the colonic mucosa. Pyroptosis contributes significantly to UC. However, the molecular mechanisms of UC remain unexplained. Herein, using transcriptome data and animal experimental validation, we sought to explore pyroptosis-related molecular mechanisms, signature genes, and potential drugs in UC. Gene profiles (GSE48959, GSE59071, GSE53306, and GSE94648) were selected from the Gene Expression Omnibus (GEO) database, which contained samples derived from patients with active and inactive UC, as well as health controls. Gene Set Enrichment Analysis (GSEA), Weighted Gene Co-expression Network Analysis (WGCNA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on microarrays to unravel the association between UC and pyroptosis. Then, differential expressed genes (DEGs) and pyroptosis-related DEGs were obtained by differential expression analyses and the public database. Subsequently, pyroptosis-related DEGs and their association with the immune infiltration landscape were analyzed using the CIBERSORT method. Besides, potential signature genes were selected by machine learning (ML) algorithms, and then validated by testing datasets which included samples of colonic mucosal tissue and peripheral blood. More importantly, the potential drug was screened based on this. And these signature genes and the drug effect were finally observed in the animal experiment. GSEA and KEGG enrichment analyses on key module genes derived from WGCNA revealed a close association between UC and pyroptosis. Then, a total of 20 pyroptosis-related DEGs of UC and 27 pyroptosis-related DEGs of active UC were screened. Next, 6 candidate genes (ZBP1, AIM2, IL1β, CASP1, TLR4, CASP11) in UC and 2 candidate genes (TLR4, CASP11) in active UC were respectively identified using the binary logistic regression (BLR), least absolute shrinkage and selection operator (LASSO), random forest (RF) analysis and artificial neural network (ANN), and these genes also showed high diagnostic specificity for UC in testing sets. Specially, TLR4 was elevated in UC and further elevated in active UC. The results of the drug screen revealed that six compounds (quercetin, cyclosporine, resveratrol, cisplatin, paclitaxel, rosiglitazone) could target TLR4, among which the effect of quercetin on intestinal pathology, pyroptosis and the expression of TLR4 in UC and active UC was further determined by the murine model. These findings demonstrated that pyroptosis may promote UC, and especially contributes to the activation of UC. Pyroptosis-related DEGs offer new ideas for the diagnosis of UC. Besides, quercetin was verified as an effective treatment for pyroptosis and intestinal inflammation. This study might enhance our comprehension on the pathogenic mechanism and diagnosis of UC and offer a treatment option for UC.

Exploring cuproptosis-related molecular clusters and immunological characterization in ischemic stroke through machine learning

Objective

Ischemic stroke (IS) is a significant health concern with high disability and fatality rates despite available treatments. Immune cells and cuproptosis are associated with the onset and progression of IS. Investigating the interaction between cuproptosis-related genes (CURGs) and immune cells in IS can provide a theoretical basis for IS treatment.

Methods

We obtained IS datasets from the Gene Expression Omnibus (GEO) and employed machine learning to identify CURGs. The diagnostic efficiency of the CURGs was evaluated using receiver operating characteristic (ROC) curves. KEGG and gene set enrichment analysis (GSEA) were also conducted to identify biologically relevant pathways associated with CURGs in IS patients. Single-cell analysis was used to confirm the expression of 19 CURGs, and pathway activity calculations were performed using the AUCell package. Additionally, a risk prediction model for IS patients was developed, and core modules and hub genes related to IS were identified using weighted gene coexpression network analysis (WGCNA). We classified IS patients using a method of consensus clustering.

Results

We established a precise diagnostic model for IS. Enrichment analysis revealed major pathways, including oxidative phosphorylation, the NF-kappa B signaling pathway, the apoptosis pathway, and the Wnt signaling pathway. At the single-cell level, compared to those in non-IS samples, 19 CURGs were primarily overexpressed in the immune cells of IS samples and exhibited high activity in natural killer cell-mediated cytotoxicity, steroid hormone biosynthesis, and oxidative phosphorylation. Two clusters were obtained through consensus clustering. Notably, immune cell types including B cells, plasma cells, and resting NK cells, varied between the two clusters. Furthermore, the red module and hub genes associated with IS were uncovered. The expression patterns of CURGs varied over time.

Conclusion

This study developed a precise diagnostic model for IS by identifying CURGs and evaluating their interaction with immune cells. Enrichment analyses revealed key pathways involved in IS, and single-cell analysis confirmed CURG overexpression in immune cells. A risk prediction model and core modules associated with IS were also identified.

Development, evaluation and comparison of machine learning algorithms for predicting in-hospital patient charges for congestive heart failure exacerbations, chronic obstructive pulmonary disease exacerbations and diabetic ketoacidosis

BACKGROUND

Hospitalizations for exacerbations of congestive heart failure (CHF), chronic obstructive pulmonary disease (COPD) and diabetic ketoacidosis (DKA) are costly in the United States. The purpose of this study was to predict in-hospital charges for each condition using machine learning (ML) models.

RESULTS

We conducted a retrospective cohort study on national discharge records of hospitalized adult patients from January 1st, 2016, to December 31st, 2019. We constructed six ML models (linear regression, ridge regression, support vector machine, random forest, gradient boosting and extreme gradient boosting) to predict total in-hospital cost for admission for each condition. Our models had good predictive performance, with testing R-squared values of 0.701-0.750 (mean of 0.713) for CHF; 0.694-0.724 (mean 0.709) for COPD; and 0.615-0.729 (mean 0.694) for DKA. We identified important key features driving costs, including patient age, length of stay, number of procedures, and elective/nonelective admission.

CONCLUSIONS

ML methods may be used to accurately predict costs and identify drivers of high cost for COPD exacerbations, CHF exacerbations and DKA. Overall, our findings may inform future studies that seek to decrease the underlying high patient costs for these conditions.

Identification of neutrophil extracellular trap-related biomarkers in non-alcoholic fatty liver disease through machine learning and single-cell analysis

Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. Simultaneously, the annual disease burden, particularly liver cirrhosis caused by NAFLD, has increased significantly. Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the pathogenesis of NAFLD. However, research into the specific roles of NETs-related genes in NAFLD is still a field requiring thorough investigation. Utilizing techniques like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using the training dataset GSE164760. This study identified 116 genes linked to NETs across single-cell and bulk transcriptomic analyses. These genes demonstrated enrichment in immune and metabolic pathways. Additionally, two NETs-related hub genes, PHLDA1 and ZFP36L2, were selected through machine learning for integration into a prognostic model. These hub genes play roles in inflammatory and metabolic processes. scRNA-seq results showed variations in cellular communication among cells with different expression patterns of these key genes. In conclusion, this study explored the molecular characteristics of NETs-associated genes in NAFLD. It identified two potential biomarkers and analyzed their roles in the hepatic microenvironment. These discoveries could aid in NAFLD diagnosis and management, with the ultimate goal of enhancing patient outcomes.

NOX1 triggers ferroptosis and ferritinophagy, contributes to Parkinson's disease

The progressive loss of dopaminergic neurons in the midbrain is the hallmark of Parkinson's disease (PD). A newly emerging form of lytic cell death, ferroptosis, has been implicated in PD. However, it remains unclear in terms of PD-associated ferroptosis underlying causative genes and effective therapeutic approaches. This research explored the underlying mechanism of ferroptosis-related genes in PD. Here, Firstly, we found NOX1 associated with ferroptosis differently in PD patients by bioinformatics analysis. In vitro and in vivo models of PD were constructed to explore the underlying mechanism. qPCR, Western blot analysis, immunohistochemistry, immunofluorescence, Ferro orange, and BODIPY C11 were utilized to analyze the levels of ferroptosis. Transcriptomics sequencing was to investigate the downstream pathway and the analysis of immunoprecipitation to validate the upstream factor. In conclusion, NOX1 upregulation and activation of ferroptosis-related neurodegeneration, therefore, might be useful as a clinical therapeutic agent.

Gandouling ameliorates liver injury in Wilson's disease through the inhibition of ferroptosis by regulating the HSF1/HSPB1 pathway

Ferroptosis, an iron-dependent form of cell death, plays a crucial role in the progression of liver injury in Wilson's disease (WD). Gandouling (GDL) has emerged as a potential therapeutic agent for preventing and treating liver injury in WD. However, the precise mechanisms by which GDL mitigates ferroptosis in WD liver injury remain unclear. In this study, we discovered that treating Toxic Milk (TX) mice with GDL effectively decreased liver copper content, corrected iron homeostasis imbalances, and lowered lipid peroxidation levels, thereby preventing ferroptosis and improving liver injury. Bioinformatics analysis and machine learning algorithms identified Hspb1 as a pivotal regulator of ferroptosis. GDL treatment significantly upregulated the expression of HSPB1 and its upstream regulatory factor HSF1, thereby activating the HSF1/HSPB1 pathway. Importantly, inhibition of this pathway by NXP800 reversed the protective effects of GDL on ferroptosis in the liver of TX mice. In conclusion, GDL shows promise in alleviating liver injury in WD by inhibiting ferroptosis through modulation of the HSF1/HSPB1 pathway, suggesting its potential as a novel therapeutic agent for treating liver ferroptosis in WD.

Characterization of RNA Processing Genes in Colon Cancer for Predicting Clinical Outcomes

Objective

Colon cancer is associated with multiple levels of molecular heterogeneity. RNA processing converts primary transcriptional RNA to mature RNA, which drives tumourigenesis and its maintenance. The characterisation of RNA processing genes in colon cancer urgently needs to be elucidated.

Methods

In this study, we obtained 1033 relevant samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to explore the heterogeneity of RNA processing phenotypes in colon cancer. Firstly, Unsupervised hierarchical cluster analysis detected 4 subtypes with specific clinical outcomes and biological features via analysis of 485 RNA processing genes. Next, we adopted the least absolute shrinkage and selection operator (LASSO) as well as Cox regression model with penalty to characterise RNA processing-related prognostic features.

Results

An RNA processing-related prognostic risk model based on 10 genes including FXR1, MFAP1, RBM17, SAGE1, SNRPA1, SRRM4, ADAD1, DDX52, ERI1, and EXOSC7 was identified finally. A composite prognostic nomogram was constructed by combining this feature with the remaining clinical variables including TNM, age, sex, and stage. Genetic variation, pathway activation, and immune heterogeneity with risk signatures were also analysed via bioinformatics methods. The outcomes indicated that the high-risk subgroup was associated with higher genomic instability, increased proliferative and cycle characteristics, decreased tumour killer CD8+ T cells and poorer clinical prognosis than the low-risk group.

Conclusion

This prognostic classifier based on RNA-edited genes facilitates stratification of colon cancer into specific subgroups according to TNM and clinical outcomes, genetic variation, pathway activation, and immune heterogeneity. It can be used for diagnosis, classification and targeted treatment strategies comparable to current standards in precision medicine. It provides a rationale for elucidation of the role of RNA editing genes and their clinical significance in colon cancer as prognostic markers.

Identification of potential diagnostic genes for atherosclerosis in women with polycystic ovary syndrome

Polycystic ovary syndrome (PCOS), which is the most prevalent endocrine disorder among women in their reproductive years, is linked to a higher occurrence and severity of atherosclerosis (AS). Nevertheless, the precise manner in which PCOS impacts the cardiovascular well-being of women remains ambiguous. The Gene Expression Omnibus database provided four PCOS datasets and two AS datasets for this study. Through the examination of genes originating from differentially expressed (DEGs) and critical modules utilizing functional enrichment analyses, weighted gene co-expression network (WGCNA), and machine learning algorithm, the research attempted to discover potential diagnostic genes. Additionally, the study investigated immune infiltration and conducted gene set enrichment analysis (GSEA) to examine the potential mechanism of the simultaneous occurrence of PCOS and AS. Two verification datasets and cell experiments were performed to assess biomarkers' reliability. The PCOS group identified 53 genes and AS group identified 175 genes by intersecting DEGs and key modules of WGCNA. Then, 18 genes from two groups were analyzed by machine learning algorithm. Death Associated Protein Kinase 1 (DAPK1) was recognized as an essential gene. Immune infiltration and single-gene GSEA results suggest that DAPK1 is associated with T cell-mediated immune responses. The mRNA expression of DAPK1 was upregulated in ox-LDL stimulated RAW264.7 cells and in granulosa cells. Our research discovered the close association between AS and PCOS, and identified DAPK1 as a crucial diagnostic biomarker for AS in PCOS.

Identification and experimental verification of immune-related hub genes in intervertebral disc degeneration

Background

Inflammation and immune factors are the core of intervertebral disc degeneration (IDD), but the immune environment and epigenetic regulation process of IDD remain unclear. This study aims to identify immune-related diagnostic candidate genes for IDD, and search for potential pathogenesis and therapeutic targets for IDD.

Methods

Gene expression datasets were obtained from the Gene Expression Omnibus (GEO). Differential expression immune genes (Imm-DEGs) were identified through weighted gene correlation network analysis (WGCNA) and linear models for microarray data analysis (Limma). LASSO algorithm was used to identify feature genes related to IDD, which were compared with core node genes in PPI network to obtain hub genes. Based on the coefficients of hub genes, a risk model was constructed, and the diagnostic value of hub genes was further evaluated through receiver operating characteristic (ROC) analysis. Xcell, an immunocyte analysis tool, was used to estimate the infiltration of immune cells. Finally, nucleus pulposus cells were co-cultured with macrophages to create an M1 macrophage immune inflammatory environment, and the changes of hub genes were verified.

Results

Combined with the results of WGCNA and Limma gene differential analysis, a total of 30 Imm-DEGs were identified. Imm-DEGs enriched in multiple pathways related to immunity and inflammation. LASSO algorithm identified 10 feature genes from Imm-DEGs that significantly affected IDD, and after comparison with core node genes in the PPI network of Imm-DEGs, 6 hub genes (NR1H3, SORT1, PTGDS, AGT, IRF1, TGFB2) were determined. Results of ROC curves and external dataset validation showed that the risk model constructed with the 6 hub genes had high diagnostic value for IDD. Immunocyte infiltration analysis showed the presence of various dysregulated immune cells in the degenerative nucleus pulposus tissue. In vitro experimental results showed that the gene expression of NR1H3, SORT1, PTGDS, IRF1, and TGFB2 in nucleus pulposus cells in the immune inflammatory environment was up-regulated, but the change of AGT was not significant.

Conclusions

The hub genes NR1H3, SORT1, PTGDS, IRF1, and TGFB2 can be used as immunorelated biomarkers for IDD, and may be potential targets for immune regulation therapy for IDD.

Identification of macrophage differentiation related genes and subtypes linking atherosclerosis plaque processing and metabolic syndrome via integrated bulk and single-cell sequence analysis

Metabolic syndrome(MS) is a separate risk factor for the advancement of atherosclerosis(AS) plaque but mechanism behind this remains unclear. There may be a significant role for the immune system in this process. This study aims to identify potential diagnostic genes in MS patients at a higher risk of developing and progressing to AS. Datasets were retrevied from gene expression omnibus(GEO) database and differentially expressed genes were identified. Hub genes, immune cell dysregulation and AS subtypes were identified using a conbination of muliple bioinformatic analysis, machine learning and consensus clustering. Diagnostic value of hub genes was estimated using a nomogram and ROC analysis. Finally, enrichment analysis, competing endogenous RNA(ceRNA) network, single-cell RNA(scRNA) sequencing analysis and drug-protein interaction prediction was constructed to identify the functional roles, potential regulators and distribution for hub genes. Four hub genes and two macrophage-related subtypes were identified. Their strong diagnostic value was validated and functional process were identified. ScRNA analysis identified the macrophage differentiation regulation function of F13A1. CeRNA network and drug-protein binding modes revealed the potential therapeutic method. Four immune-correlated hub genes(F13A1, MMRN1, SLCO2A1 and ZNF521) were identified with their diagnostic value being assesed, which F13A1 was found strong correlated with macrophage differentiation and could be potential diagnostic and therapeutic marker for AS progression in MS patients.

Identification of potential shared gene signatures between gastric cancer and type 2 diabetes: a data-driven analysis

Background

Gastric cancer (GC) and type 2 diabetes (T2D) contribute to each other, but the interaction mechanisms remain undiscovered. The goal of this research was to explore shared genes as well as crosstalk mechanisms between GC and T2D.

Methods

The Gene Expression Omnibus (GEO) database served as the source of the GC and T2D datasets. The differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA) were utilized to identify representative genes. In addition, overlapping genes between the representative genes of the two diseases were used for functional enrichment analysis and protein-protein interaction (PPI) network. Next, hub genes were filtered through two machine learning algorithms. Finally, external validation was undertaken with data from the Cancer Genome Atlas (TCGA) database.

Results

A total of 292 and 541 DEGs were obtained from the GC (GSE29272) and T2D (GSE164416) datasets, respectively. In addition, 2,704 and 336 module genes were identified in GC and T2D. Following their intersection, 104 crosstalk genes were identified. Enrichment analysis indicated that "ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were mutual pathways. Through the PPI network, 10 genes were identified as candidate hub genes. Machine learning further selected BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 as hub genes.

Conclusion

"ECM-receptor interaction," "AGE-RAGE signaling pathway in diabetic complications," "aging," and "cellular response to copper ion" were revealed as possible crosstalk mechanisms. BGN, VCAN, FN1, FBLN1, COL4A5, COL1A1, and COL6A3 were identified as shared genes and potential therapeutic targets for people suffering from GC and T2D.

Sinomenine treats rheumatoid arthritis by inhibiting MMP9 and inflammatory cytokines expression: bioinformatics analysis and experimental validation

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease marked by inflammatory cell infiltration and joint damage. The Chinese government has approved the prescription medication sinomenine (SIN), an effective anti-inflammation drug, for treating RA. This study evaluated the possible anti-inflammatory actions of SIN in RA based on bioinformatics analysis and experiments. Six microarray datasets were acquired from the gene expression omnibus (GEO) database. We used R software to identify differentially expressed genes (DEGs) and perform function evaluations. The CIBERSORT was used to calculate the abundance of 22 infiltrating immune cells. The weighted gene co-expression network analysis (WGCNA) was used to discover genes associated with M1 macrophages. Four public datasets were used to predict the genes of SIN. Following that, function enrichment analysis for hub genes was performed. The cytoHubba and least absolute shrinkage and selection operator (LASSO) were employed to select hub genes, and their diagnostic effectiveness was predicted using the receiver operator characteristic (ROC) curve. Molecular docking was undertaken to confirm the affinity between the SIN and hub gene. Furthermore, the therapeutic efficacy of SIN was validated in LPS-induced RAW264.7 cells line using Western blot and Enzyme-linked immunosorbent assay (ELISA). The matrix metalloproteinase 9 (MMP9) was identified as the hub M1 macrophages-related biomarker in RA using bioinformatic analysis and molecular docking. Our study indicated that MMP9 took part in IL-17 and TNF signaling pathways. Furthermore, we found that SIN suppresses the MMP9 protein overexpression and pro-inflammatory cytokines, including tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) in the LPS-induced RAW264.7 cell line. In conclusion, our work sheds new light on the pathophysiology of RA and identifies MMP9 as a possible RA key gene. In conclusion, the above findings demonstrate that SIN, from an emerging research perspective, might be a potential cost-effective anti-inflammatory medication for treating RA.

Identification of shared gene signatures and pathways for diagnosing osteoporosis with sarcopenia through integrated bioinformatics analysis and machine learning

BACKGROUND

Prior studies have suggested a potential relationship between osteoporosis and sarcopenia, both of which can present symptoms of compromised mobility. Additionally, fractures among the elderly are often considered a common outcome of both conditions. There is a strong correlation between fractures in the elderly population, decreased muscle mass, weakened muscle strength, heightened risk of falls, and diminished bone density. This study aimed to pinpoint crucial diagnostic candidate genes for osteoporosis patients with concomitant sarcopenia.

METHODS

Two osteoporosis datasets and one sarcopenia dataset were obtained from the Gene Expression Omnibus (GEO). Differential expression genes (DEGs) and module genes were identified using Limma and Weighted Gene Co-expression Network Analysis (WGCNA), followed by functional enrichment analysis, construction of protein-protein interaction (PPI) networks, and application of a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to determine candidate hub genes for diagnosing osteoporosis combined with sarcopenia. Receiver operating characteristic (ROC) curves and column line plots were generated.

RESULTS

The merged osteoporosis dataset comprised 2067 DEGs, with 424 module genes filtered in sarcopenia. The intersection of DEGs between osteoporosis and sarcopenia module genes consisted of 60 genes, primarily enriched in viral infection. Through construction of the PPI network, 30 node genes were filtered, and after machine learning, 7 candidate hub genes were selected for column line plot construction and diagnostic value assessment. Both the column line plots and all 7 candidate hub genes exhibited high diagnostic value (area under the curve ranging from 1.00 to 0.93).

CONCLUSION

We identified 7 candidate hub genes (PDP1, ALS2CL, VLDLR, PLEKHA6, PPP1CB, MOSPD2, METTL9) and constructed column line plots for osteoporosis combined with sarcopenia. This study provides reference for potential peripheral blood diagnostic candidate genes for sarcopenia in osteoporosis patients.

Analysis and experimental validation of necroptosis-related molecular classification, immune signature and feature genes in Alzheimer's disease

Necroptosis, a programmed cell death pathway, has been demonstrated to be activated in Alzheimer's disease (AD). However, the precise role of necroptosis and its correlation with immune cell infiltration in AD remains unclear. In this study, we conducted non-negative matrix factorization clustering analysis to identify three subtypes of AD based on necroptosis-relevant genes. Notably, these subtypes exhibited varying necroptosis scores, clinical characteristics and immune infiltration signatures. Cluster B, characterized by high necroptosis scores, showed higher immune cell infiltration and was associated with a more severe pathology, potentially representing a high-risk subgroup. To identify potential biomarkers for AD within cluster B, we employed two machine learning algorithms: the least absolute shrinkage and selection operator regression and Random Forest. Subsequently, we identified eight feature genes (CARTPT, KLHL35, NRN1, NT5DC3, PCYOX1L, RHOQ, SLC6A12, and SLC38A2) that were utilized to develop a diagnosis model with remarkable predictive capacity for AD. Moreover, we conducted validation using bulk RNA-seq, single-nucleus RNA-seq, and in vivo experiments to confirm the expression of these feature genes. In summary, our study identified a novel necroptosis-related subtype of AD and eight diagnostic biomarkers, explored the roles of necroptosis in AD progression and shed new light for the clinical diagnosis and treatment of this disease.

Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning

BACKGROUND

There is mounting evidence that asthma might exacerbate depression. We sought to examine candidates for diagnostic genes in patients suffering from asthma and depression.

METHODS

Microarray data were downloaded from the Gene Expression Omnibus(GEO) database and used to screen for differential expressed genes(DEGs) in the SA and MDD datasets. A weighted gene co-expression network analysis(WGCNA) was used to identify the co-expression modules of SA and MDD. The least absolute shrinkage and selection operatoes(LASSO) and support vector machine(SVM) were used to determine critical biomarkers. Immune cell infiltration analysis was used to investigate the correlation between immune cell infiltration and common biomarkers of SA and MDD. Finally, validation of these analytical results was accomplished via the use of both in vivo and in vitro studies.

RESULTS

The number of DEGs that were included in the MDD dataset was 5177, whereas the asthma dataset had 1634 DEGs. The intersection of DEGs for SA and MDD included 351 genes, the strongest positive modules of SA and MDD was 119 genes, which played a function in immunity. The intersection of DEGs and modular hub genes was 54, following the analysis using machine learning algorithms,three hub genes were identified and employed to formulate a nomogram and for the evaluation of diagnostic effectiveness, which demonstrated a significant diagnostic value (area under the curve from 0.646 to 0.979). Additionally, immunocyte disorder was identified by immune infiltration. In vitro studies have revealed that STK11IP deficiency aggravated the LPS/IFN-γinduced up-regulation in M1 macrophage activation.

CONCLUSION

Asthma and MDD pathophysiology may be associated with alterations in inflammatory processes and immune pathways. Additionally, STK11IP may serve as a diagnostic marker for individuals with the two conditions.

Identification of Diagnostic Genes of Aortic Stenosis That Progresses from Aortic Valve Sclerosis

Background

Aortic valve sclerosis (AVS) is a pathological state that can progress to aortic stenosis (AS), which is a high-mortality valvular disease. However, effective medical therapies are not available to prevent this progression. This study aimed to explore potential biomarkers of AVS-AS advancement.

Methods

A microarray dataset and an RNA-sequencing dataset were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened from AS and AVS samples. Functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning model construction were conducted to identify diagnostic genes. A receiver operating characteristic (ROC) curve was generated to evaluate diagnostic value. Immune cell infiltration was then used to analyze differences in immune cell proportion between tissues. Finally, immunohistochemistry was applied to further verify protein concentration of diagnostic factors.

Results

A total of 330 DEGs were identified, including 92 downregulated and 238 upregulated genes. The top 5% of DEGs (n = 17) were screened following construction of a PPI network. IL-7 and VCAM-1 were identified as the most significant candidate genes via least absolute shrinkage and selection operator (LASSO) regression. The diagnostic value of the model and each gene were above 0.75. Proportion of anti-inflammatory M2 macrophages was lower, but the fraction of pro-inflammatory gamma-delta T cells was elevated in AS samples. Finally, levels of IL-7 and VCAM-1 were validated to be higher in AS tissue than in AVS tissue using immunohistochemistry.

Conclusion

IL-7 and VCAM-1 were identified as biomarkers during the disease progression. This is the first study to analyze gene expression differences between AVS and AS and could open novel sights for future studies on alleviating or preventing the disease progression.

PANoptosis and Autophagy-Related Molecular Signature and Immune Landscape in Ulcerative Colitis: Integrated Analysis and Experimental Validation

Background

Ulcerative colitis (UC) is an autoimmune inflammatory disorder of the gastrointestinal tract. Programmed cell death (PCD), including PANoptosis and autophagy, plays roles in inflammation and immunity. This study aimed to investigate the molecular signature and immune landscape of the PANoptosis- and autophagy-related differentially expressed genes (DEGs) in UC.

Methods

Analyzing UC dataset GSE206285 yielded DEGs. Differentially expressed PANoptosis- and autophagy-related genes were identified using DEGs and relevant gene collections. Functional and pathway enrichment analyses were conducted. A protein-protein interaction (PPI) network was established to identify hub genes. TRRUST database predicted transcription factors (TFs), pivotal miRNAs, and drugs interacting with hub genes. Immune infiltration analysis, UC-associated single-cell sequencing data analysis, and construction of a competing endogenous RNA (ceRNA) network for hub genes were conducted. Machine learning identified key candidate genes, evaluated for diagnostic value via receiver operating characteristic (ROC) curves. A UC mice model verified expression of key candidate genes.

Results

Identifying ten PANoptosis-related hub DEGs and four autophagy-related hub DEGs associated them with cell chemotaxis, wound healing and positive MAPK cascade regulation. Immune infiltration analysis revealed increased immunocyte infiltration in UC patients, with hub genes closely linked to various immune cell infiltrations. Machine learning identified five key candidate genes, TIMP1, TIMP2, TIMP3, IL6, and CCL2, with strong diagnostic performance. At the single-cell level, these genes exhibited high expression in inflammatory fibroblasts (IAFs). They showed significant expression differences in the colon mucosa of both UC patients and UC mice model.

Conclusion

This study identified and validated novel molecular signatures associated with PANoptosis and autophagy in UC, potentially influencing immune dysregulation and wound healing, thus opening avenues for future research and therapeutic interventions.

Nomogram for predicting post-therapy recurrence in BCLC A/B hepatocellular carcinoma with Child-Pugh B cirrhosis

Introduction

This study conducts a retrospective analysis on patients with BCLC stage A/B hepatocellular carcinoma (HCC) accompanied by Child-Pugh B cirrhosis, who underwent transarterial chemoembolization (TACE) in combination with local ablation therapy. Our goal was to uncover risk factors contributing to post-treatment recurrence and to develop and validate an innovative 1-, 3-, and 5-year recurrence free survival (RFS) nomogram.

Methods

Data from 255 BCLC A/B HCC patients with Child-Pugh B cirrhosis treated at Beijing You'an Hospital (January 2014 - January 2020) were analyzed using random survival forest (RSF), LASSO regression, and multivariate Cox regression to identify independent risk factors for RFS. The prognostic nomogram was then constructed and validated, categorizing patients into low, intermediate, and high-risk groups, with RFS assessed using Kaplan-Meier curves.

Results

The nomogram, integrating the albumin/globulin ratio, gender, tumor number, and size, showcased robust predictive performance. Harrell's concordance index (C-index) values for the training and validation cohorts were 0.744 (95% CI: 0.703-0.785) and 0.724 (95% CI: 0.644-0.804), respectively. The area under the curve (AUC) values for 1-, 3-, and 5-year RFS in the two cohorts were also promising. Calibration curves highlighted the nomogram's reliability and decision curve analysis (DCA) confirmed its practical clinical benefits. Through meticulous patient stratification, we also revealed the nomogram's efficacy in distinguishing varying recurrence risks.

Conclusion

This study advances recurrence prediction in BCLC A/B HCC patients with Child-Pugh B cirrhosis following TACE combined with ablation. The established nomogram accurately predicts 1-, 3-, and 5-year RFS, facilitating timely identification of high-risk populations.

Identifying of immune-associated genes for assessing the obesity-associated risk to the offspring in maternal obesity: A bioinformatics and machine learning

BACKGROUND

Perinatal exposure to maternal obesity predisposes offspring to develop obesity later in life. Immune dysregulation in the hypothalamus, the brain center governing energy homeostasis, is pivotal in obesity development. This study aimed to identify key candidate genes associated with the risk of offspring obesity in maternal obesity.

METHODS

We obtained obesity-related datasets from the Gene Expression Omnibus (GEO) database. GSE135830 comprises gene expression data from the hypothalamus of mouse offspring in a maternal obesity model induced by a high-fat diet model (maternal high-fat diet (mHFD) group and maternal chow (mChow) group), while GSE127056 consists of hypothalamus microarray data from young adult mice with obesity (high-fat diet (HFD) and Chow groups). We identified differentially expressed genes (DEGs) and module genes using Limma and weighted gene co-expression network analysis (WGCNA), conducted functional enrichment analysis, and employed a machine learning algorithm (least absolute shrinkage and selection operator (LASSO) regression) to pinpoint candidate hub genes for diagnosing obesity-associated risk in offspring of maternal obesity. We constructed a nomogram receiver operating characteristic (ROC) curve to evaluate the diagnostic value. Additionally, we analyzed immune cell infiltration to investigate immune cell dysregulation in maternal obesity. Furthermore, we verified the expression of the candidate hub genes both in vivo and in vitro.

RESULTS

The GSE135830 dataset revealed 2868 DEGs between the mHFD offspring and the mChow group and 2627 WGCNA module genes related to maternal obesity. The overlap of DEGs and module genes in the offspring with maternal obesity in GSE135830 primarily enriched in neurodevelopment and immune regulation. In the GSE127056 dataset, 133 DEGs were identified in the hypothalamus of HFD-induced adult obese individuals. A total of 13 genes intersected between the GSE127056 adult obesity DEGs and the GSE135830 maternal obesity module genes that were primarily enriched in neurodevelopment and the immune response. Following machine learning, two candidate hub genes were chosen for nomogram construction. Diagnostic value evaluation by ROC analysis determined Sytl4 and Kncn2 as hub genes for maternal obesity in the offspring. A gene regulatory network with transcription factor-miRNA interactions was established. Dysregulated immune cells were observed in the hypothalamus of offspring with maternal obesity. Expression of Sytl4 and Kncn2 was validated in a mouse model of hypothalamic inflammation and a palmitic acid-stimulated microglial inflammation model.

CONCLUSION

Two candidate hub genes (Sytl4 and Kcnc2) were identified and a nomogram was developed to predict obesity risk in offspring with maternal obesity. These findings offer potential diagnostic candidate genes for identifying obesity-associated risks in the offspring of obese mothers.

Identification of immune-associated genes in vascular dementia by integrated bioinformatics and inflammatory infiltrates

OBJECTIVE

Dysregulation of the immune system plays a vital role in the pathological process of vascular dementia, and this study aims to spot critical biomarkers and immune infiltrations in vascular dementia employing a bioinformatics approach.

METHODS

We acquired gene expression profiles from the Gene Expression Database. The gene expression data were analyzed using the bioinformatics method to identify candidate immune-related central genes for the diagnosis of vascular dementia. and the diagnostic value of nomograms and Receiver Operating Characteristic (ROC) curves were evaluated. We also examined the role of the VaD hub genes. Using the database and potential therapeutic drugs, we predicted the miRNA and lncRNA controlling the Hub genes. Immune cell infiltration was initiated to examine immune cell dysregulation in vascular dementia.

RESULTS

1321 immune genes were included in the combined immune dataset, and 2816 DEGs were examined in GSE122063. Twenty potential genes were found using differential gene analysis and co-expression network analysis. PPI network design and functional enrichment analysis were also done using the immune system as the main subject. To create the nomogram for evaluating the diagnostic value, four potential core genes were chosen by machine learning. All four putative center genes and nomograms have a solid diagnostic value (AUC ranged from 0.81 to 0.92). Their high confidence level became unquestionable by validating each of the four biomarkers using a different dataset. According to GeneMANIA and GSEA enrichment investigations, the pathophysiology of VaD is strongly related to inflammatory responses, drug reactions, and central nervous system degeneration. The data and Hub genes were used to construct a ceRNA network that includes three miRNAs, 90 lncRNA, and potential VaD therapeutics. Immune cells with varying dysregulation were also found.

CONCLUSION

Using bioinformatic techniques, our research identified four immune-related candidate core genes (HMOX1, EBI3, CYBB, and CCR5). Our study confirms the role of these Hub genes in the onset and progression of VaD at the level of immune infiltration. It predicts potential RNA regulatory pathways control VaD progression, which may provide ideas for treating clinical disease.

A diagnostic model based on bioinformatics and machine learning to differentiate bipolar disorder from schizophrenia and major depressive disorder

Bipolar disorder (BD) showed the highest suicide rate of all psychiatric disorders, and its underlying causative genes and effective treatments remain unclear. During diagnosis, BD is often confused with schizophrenia (SC) and major depressive disorder (MDD), due to which patients may receive inadequate or inappropriate treatment, which is detrimental to their prognosis. This study aims to establish a diagnostic model to distinguish BD from SC and MDD in multiple public datasets through bioinformatics and machine learning and to provide new ideas for diagnosing BD in the future. Three brain tissue datasets containing BD, SC, and MDD were chosen from the Gene Expression Omnibus database (GEO), and two peripheral blood datasets were selected for validation. Linear Models for Microarray Data (Limma) analysis was carried out to identify differentially expressed genes (DEGs). Functional enrichment analysis and machine learning were utilized to identify. Least absolute shrinkage and selection operator (LASSO) regression was employed for identifying candidate immune-associated central genes, constructing protein-protein interaction networks (PPI), building artificial neural networks (ANN) for validation, and plotting receiver operating characteristic curve (ROC curve) for differentiating BD from SC and MDD and creating immune cell infiltration to study immune cell dysregulation in the three diseases. RBM10 was obtained as a candidate gene to distinguish BD from SC. Five candidate genes (LYPD1, HMBS, HEBP2, SETD3, and ECM2) were obtained to distinguish BD from MDD. The validation was performed by ANN, and ROC curves were plotted for diagnostic value assessment. The outcomes exhibited the prediction model to have a promising diagnostic value. In the immune infiltration analysis, Naive B, Resting NK, and Activated Mast Cells were found to be substantially different between BD and SC. Naive B and Memory B cells were prominently variant between BD and MDD. In this study, RBM10 was found as a candidate gene to distinguish BD from SC; LYPD1, HMBS, HEBP2, SETD3, and ECM2 serve as five candidate genes to distinguish BD from MDD. The results obtained from the ANN network showed that these candidate genes could perfectly distinguish BD from SC and MDD (76.923% and 81.538%, respectively).

Identification of Neutrophil Extracellular Trap-Related Gene Expression Signatures in Ischemia Reperfusion Injury During Lung Transplantation: A Transcriptome Analysis and Clinical Validation

Purpose

Ischemia reperfusion injury (IRI) unavoidably occurs during lung transplantation, further contributing to primary graft dysfunction (PGD). Neutrophils are the end effectors of IRI and activated neutrophils release neutrophil extracellular traps (NETs) to further amplify damage. Nevertheless, potential contributions of NETs in IRI remain incompletely understood. This study aimed to explore NET-related gene biomarkers in IRI during lung transplantation.

Methods

Differential expression analysis was applied to identify differentially expressed genes (DEGs) for IRI during lung transplantation based on matrix data (GSE145989, 127003) downloaded from GEO database. The CIBERSORT and weighted gene co-expression network analysis (WGCNA) algorithms were utilized to identify key modules associated with neutrophil infiltration. Moreover, the least absolute shrinkage and selection operator regression and random forest were applied to identify potential NET-associated hub genes. Subsequently, the screened hub genes underwent further validation of an external dataset (GSE18995) and nomogram model. Based on clinical peripheral blood samples, immunofluorescence staining and dsDNA quantification were used to assess NET formation, and ELISA was applied to validate the expression of hub genes.

Results

Thirty-eight genes resulted from the intersection between 586 DEGs and 75 brown module genes, primarily enriched in leukocyte migration and NETs formation. Subsequently, four candidate hub genes (FCAR, MMP9, PADI4, and S100A12) were screened out via machine learning algorithms. Validation using an external dataset and nomogram model achieved better predictive value. Substantial NETs formation was demonstrated in IRI, with more pronounced NETs observed in patients with PGD ≥ 2. PADI4, S100A12, and MMP9 were all confirmed to be up-regulated after reperfusion through ELISA, with higher levels of S100A12 in PGD ≥ 2 patients compared with non-PGD patients.

Conclusion

We identified three potential NET-related biomarkers for IRI that provide new insights into early detection and potential therapeutic targets of IRI and PGD after lung transplantation.

Urinary PART1 and PLA2R1 Could Potentially Serve as Diagnostic Markers for Diabetic Kidney Disease Patients

Background

Diabetic kidney disease (DKD) is a chronic renal disease which could eventually develop into renal failure. Though albuminuria and estimated glomerular filtration rate (eGFR) are helpful for the diagnosis of DKD, the lack of specific biomarkers reduces the efficiency of therapeutic interventions.

Methods

Based on bulk-seq of 56 urine samples collected at different time points (including 11 acquired from DKD patients and 11 from healthy controls), in corporation of scRNA-seq data of urine samples and snRNA-seq data of renal punctures from DKD patients (retrieved from NCBI GEO Omnibus), urine-kidney specific genes were identified by Multiple Biological Information methods.

Results

Forty urine-kidney specific genes/differentially expressed genes (DEGs) were identified to be highly related to kidney injury and proteinuria for the DKD patients. Most of these genes participate in regulating glucagon and apoptosis, among which, urinary PART1 (mainly derived from distal tubular cells) and PLA2R1 (podocyte cell surface marker) could be used together for the early diagnosis of DKD. Moreover, urinary PART1 was significantly associated with multiple clinical indicators, and remained stable over time in urine.

Conclusion

Urinary PART1 and PLA2R1 could be shed lights on the discovery and development of non-invasive diagnostic method for DKD, especially in early stages.

Identification of neutrophil extracellular traps and crosstalk genes linking inflammatory bowel disease and osteoporosis by integrated bioinformatics analysis and machine learning

Musculoskeletal deficits are among the most common extra-intestinal manifestations and complications of inflammatory bowel disease (IBD). This study aimed to identify crosstalk genes between IBD and osteoporosis (OP) and potential relationships between crosstalk and neutrophil extracellular traps (NETs)-related genes. Three common hub genes from different compared groups are actually the same, namely HDAC6, IL-8, and PPIF. ROC showed that the combined diagnostic value of HDAC6, IL-8, and PPIF was higher than each of the three key hub genes. Immune infiltration results showed that HDAC6 and IL-8 key genes negatively correlated with CD65 bright natural killer cells. USF1 was the common upstream TFs between HDAC6 and PPIF, and MYC was the common upstream TFs between IL-8 and PPIF in RegNetwork. Taken together, this study shows a linked mechanism between IBD and OP via NETs and crosstalk genes. These findings may show light on better diagnosis and treatment of IBD complicated with OP.

Potential diagnostic markers and biological mechanism for osteoarthritis with obesity based on bioinformatics analysis

Numerous observational studies have shown that obesity (OB) is a significant risk factor in the occurrence and progression of osteoarthritis (OA), but the underlying molecular mechanism between them remains unclear. The study aimed to identify the key genes and pathogeneses for OA with OB. We obtained two OA and two OB datasets from the gene expression omnibus (GEO) database. First, the identification of differentially expressed genes (DEGs), weighted gene co-expression network analysis (WGCNA), and machine learning algorithms were used to identify key genes for diagnosing OA with OB, and then the nomogram and receiver operating characteristic (ROC) curve were conducted to assess the diagnostic value of key genes. Second, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to explore the pathogenesis of OA with OB. Third, CIBERSORT was created to investigate immunocyte dysregulation in OA and OB. In this study, two genes (SOD2, ZNF24) were finally identified as key genes for OA with OB. These two key genes had high diagnostic values via nomogram and ROC curve calculation. Additionally, functional analysis emphasized that oxidative stress and inflammation response were shared pathogenesis of OB and AD. Finally, in OA and OB, immune infiltration analysis showed that SOD2 closely correlated to M2 macrophages, regulatory T cells, and CD8 T cells, and ZNF24 correlated to regulatory T cells. Overall, our findings might be new biomarkers or potential therapeutic targets for OA and OB comorbidity.

Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning

Background: There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis. Methods: We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF). We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs. Results: The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immune-related pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment. Conclusion: Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed.

Identification of MARK2, CCDC71, GATA2, and KLRC3 as candidate diagnostic genes and potential therapeutic targets for repeated implantation failure with antiphospholipid syndrome by integrated bioinformatics analysis and machine learning

Background

Antiphospholipid syndrome (APS) is a group of clinical syndromes of thrombosis or adverse pregnancy outcomes caused by antiphospholipid antibodies, which increase the incidence of in vitro fertilization failure in patients with infertility. However, the common mechanism of repeated implantation failure (RIF) with APS is unclear. This study aimed to search for potential diagnostic genes and potential therapeutic targets for RIF with APS.

Methods

To obtain differentially expressed genes (DEGs), we downloaded the APS and RIF datasets separately from the public Gene Expression Omnibus database and performed differential expression analysis. We then identified the common DEGs of APS and RIF. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed, and we then generated protein-protein interaction. Furthermore, immune infiltration was investigated by using the CIBERSORT algorithm on the APS and RIF datasets. LASSO regression analysis was used to screen for candidate diagnostic genes. To evaluate the diagnostic value, we developed a nomogram and validated it with receiver operating characteristic curves, then analyzed these genes in the Comparative Toxicogenomics Database. Finally, the Drug Gene Interaction Database was searched for potential therapeutic drugs, and the interactions between drugs, genes, and immune cells were depicted with a Sankey diagram.

Results

There were 11 common DEGs identified: four downregulated and seven upregulated. The common DEG analysis suggested that an imbalance of immune system-related cells and molecules may be a common feature in the pathophysiology of APS and RIF. Following validation, MARK2, CCDC71, GATA2, and KLRC3 were identified as candidate diagnostic genes. Finally, Acetaminophen and Fasudil were predicted as two candidate drugs.

Conclusion

Four immune-associated candidate diagnostic genes (MARK2, CCDC71, GATA2, and KLRC3) were identified, and a nomogram for RIF with APS diagnosis was developed. Our findings may aid in the investigation of potential biological mechanisms linking APS and RIF, as well as potential targets for diagnosis and treatment.

Identification and Experimental Validation of Parkinson's Disease with Major Depressive Disorder Common Genes

Parkinson's disease (PD) is the second most common neurodegenerative disease that affects about 10 million people worldwide. Non-motor and motor symptoms usually accompany PD. Major depressive disorder (MDD) is one of the non-motor manifestations of PD it remains unrecognized and undertreated effectively. MDD in PD has complicated pathophysiologies and remains unclear. The study aimed to explore the candidate genes and molecular mechanisms of PD with MDD. PD (GSE6613) and MDD (GSE98793) gene expression profiles were downloaded from Gene Expression Omnibus (GEO). Above all, the data of the two datasets were standardized separately, and differentially expressed genes (DEGs) were obtained by using the Limma package of R. Take the intersection of the two differential genes and remove the genes with inconsistent expression trends. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were investigated to explore the function of the common DEGs. Additionally, the construction of the protein-protein interaction (PPI) network was to search the hub genes, and then the least absolute shrinkage and selection operator (LASSO) regression was used to further identify the key genes. GSE99039 for PD and GSE201332 for MDD were performed to validate the hub genes by the violin plot and receiver operating characteristic (ROC) curve. Last but not least, immune cell dysregulation in PD was investigated by immune cell infiltration. As a result, a total of 45 common genes with the same trend. Functional analysis revealed that they were enriched in neutrophil degranulation, secretory granule membrane, and leukocyte activation. LASSO was performed on 8 candidate hub genes after CytoHubba filtered 14 node genes. Finally, AQP9, SPI1, and RPH3A were validated by GSE99039 and GSE201332. Additionally, the three genes were also detected by the qPCR in vivo model and all increased compared to the control. The co-occurrence of PD and MDD can be attributed to AQP9, SPI1, and RPH3A genes. Neutrophils and monocyte infiltration play important roles in the development of PD and MDD. Novel insights may be gained from the findings for the study of mechanisms.

Identification of co-diagnostic effect genes for aortic dissection and metabolic syndrome by multiple machine learning algorithms

Aortic dissection (AD) is a life-threatening condition in which the inner layer of the aorta tears. It has been reported that metabolic syndrome (MS) has a close linkage with aortic dissection. However, the inter-relational mechanisms between them were still unclear. This article explored the hub gene signatures and potential molecular mechanisms in AD and MS. We obtained five bulk RNA-seq datasets of AD, one single cell RNA-seq (scRNA-seq) dataset of ascending thoracic aortic aneurysm (ATAA), and one bulk RNA-seq dataset of MS from the gene expression omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and key modules via weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and machine learning algorithms (Random Forest and LASSO regression) were used to identify hub genes for diagnosing AD with MS. XGBoost further improved the diagnostic performance of the model. The receiver operating characteristic (ROC) and precision-recall (PR) curves were developed to assess the diagnostic value. Then, immune cell infiltration and metabolism-associated pathways analyses were created to investigate immune cell and metabolism-associated pathway dysregulation in AD and MS. Finally, the scRNA-seq dataset was performed to confirm the expression levels of identified hub genes. 406 common DEGs were identified between the merged AD and MS datasets. Functional enrichment analysis revealed these DEGs were enriched for applicable terms of metabolism, cellular processes, organismal systems, and human diseases. Besides, the positively related key modules of AD and MS were mainly enriched in transcription factor binding and inflammatory response. In contrast, the negatively related modules were significantly associated with adaptive immune response and regulation of nuclease activity. Through machine learning, nine genes with common diagnostic effects were found in AD and MS, including MAD2L2, IMP4, PRPF4, CHSY1, SLC20A1, SLC9A1, TIPRL, DPYD, and MAPKAPK2. In the training set, the AUC of the hub gene on RP and RR curves was 1. In the AD verification set, the AUC of the Hub gene on RP and RR curves were 0.946 and 0.955, respectively. In the MS set, the AUC of the Hub gene on RP and RR curves were 0.978 and 0.98, respectively. scRNA-seq analysis revealed that the SLC20A1 was found to be relevant in fatty acid metabolic pathways and expressed in endothelial cells. Our study revealed the common pathogenesis of AD and MS. These common pathways and hub genes might provide new ideas for further mechanism research.

Exploring the potential relationship between frozen shoulder and Dupuytren's disease through bioinformatics analysis and machine learning

Background

Frozen shoulder (FS) and Dupuytren's disease (DD) are two closely related diseases, but the mechanism of their interaction is unknown. Our study sought to elucidate the molecular mechanism of these two diseases through shared gene and protein interactions.

Methods

GSE75152 and GSE140731 data were downloaded from the Gene Expression Omnibus (GEO) database, and shared genes between FS and DD were selected by using R packages. Then, we used Cytoscape software and the STRING database to produce a protein-protein interaction (PPI) network. Important interaction networks and hub genes were selected through MCODE and cytoHubba algorithms. To explore the potential mechanisms of the development of the two diseases, the hub genes were further enriched by GO and KEGG analyses. We predicted the transcription factors (TFs) of hub genes with Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST). Moreover, we identified candidate genes for FS with DD with cytoHubba and machine learning algorithms. Finally, we analyzed the role of immunocyte infiltration in FS and constructed the relationship between candidate genes and immunocytes in FS.

Results

We identified a total of 321 shared genes. The results of GO and KEGG enrichment of shared genes showed that extracellular matrix and collagen fibril tissue play a certain role in the occurrence and development of disease. According to the importance of genes, we constructed the key PPI network of shared genes and the top 15 hub genes for FS with DD. Then, we predicted that five TFs are related to the hub genes and are highly expressed in the FS group. Machine learning results show that the candidate genes POSTN and COL11A1 may be key for FS with DD. Finally, immune cell infiltration revealed the disorder of immunocytes in FS patients, and expression of candidate genes can affect immunocyte infiltration.

Conclusion

We identified a PPI network, 15 hub genes, and two immune-related candidate genes (POSTN and COL11A1) using bioinformatics analysis and machine learning algorithms. These genes have the potential to serve as diagnostic genes for FS in DD patients. Furthermore, our study reveals disorder of immunocytes in FS.