Identification of useful genes from multiple microarrays for ulcerative colitis diagnosis based on machine learning methods

OLFML3 Promotes IRG1 Mitochondrial Localization and Modulates Mitochondrial Function in Macrophages

Olfactomedin-like protein 3 (OLFML3), belonging to olfactomedin (OLF) protein family, has poorly defined functions. Recent studies have reported the functions of OLFML3 in anti-viral immunity and tumorigenesis. In this study, we investigated the roles of OLFML3 in macrophages. In LPS- or Pseudomonas aeruginosa-induced acute lung injury (ALI) mouse model, OLFML3 depletion exacerbated inflammatory response, leading to reduced survival. OLFML3 achieved the in vivo activity by regulating macrophage phagocytosis and migration. Mass spectrometry analysis revealed immunoresponsive gene 1 (IRG1) as an OLFML3-interacting protein. IRG1 is a mitochondrial decarboxylase that catalyzes the conversion of cis-aconitate to itaconate, a myeloid-borne mitochondrial metabolite with immunomodulatory activities. Further investigation showed that OLFML3 could prevent LPS-induced mitochondrial dysfunction in macrophages by maintaining the homeostasis of mitochondrial membrane potential (MMP), mitochondrial reactive oxygen species (mtROS) and itaconate-related metabolites. In-depth protein-protein interaction studies showed that OLFML3 could promote IRG1 mitochondrial localization via a mitochondrial transport protein, apoptosis inducing factor mitochondria associated 1 (AIFM1). In summary, our study showed that OLFML3 could facilitate IRG1 mitochondrial localization and prevent LPS-induced mitochondrial dysfunction in macrophages.

Paradigm Shift in Inflammatory Bowel Disease Management: Precision Medicine, Artificial Intelligence, and Emerging Therapies

Inflammatory bowel disease (IBD) management stands at the cusp of a transformative era, with recent breakthroughs heralding a paradigm shift in treatment strategies. Traditionally, IBD therapeutics revolved around immunosuppressants, but the landscape has evolved significantly. Recent approvals of etrasimod, upadacitinib, mirikizumab, and risankizumab have introduced novel mechanisms of action, offering renewed hope for IBD patients. These medications represent a departure from the status quo, breaking years of therapeutic stagnation. Precision medicine, involving Artificial Intelligence, is a pivotal aspect of this evolution, tailoring treatments based on genetic profiles, disease characteristics, and individual responses. This approach optimizes treatment efficacy, and paves the way for personalized care. Yet, the rising cost of IBD therapies, notably biologics, poses challenges, impacting healthcare budgets and patient access. Ongoing research strives to assess cost-effectiveness, guiding policy decisions to ensure equitable access to advanced treatments. Looking ahead, the future of IBD management holds great promise. Emerging therapies, precision medicine, and ongoing research into novel targets promise to reshape the IBD treatment landscape. As these advances continue to unfold, IBD patients can anticipate a brighter future, one marked by more effective, personalized, and accessible treatments.

Exploring the Kidney-Brain Crosstalk: Biomarkers for Early Detection of Kidney Injury-Related Alzheimer's Disease

Background

The phenomenon of "kidney-brain crosstalk" has stimulated scholarly inquiry into the correlations between kidney injury (KI) and Alzheimer's disease (AD). Nonetheless, the precise interactions and shared mechanisms between KI and AD have yet to be fully investigated. The primary goal of this study was to investigate the link between KI and AD, with a specific focus on identifying diagnostic biomarkers for KI-related AD.

Methods

The first step of the present study was to use Mendelian randomization (MR) analysis to investigate the link between KI and AD, followed by verification of in vivo and in vitro experiments. Subsequently, bioinformatics and machine learning techniques were used to identify biomarkers for KI-associated ferroptosis-related genes (FRGs) in AD, which were validated in following experiments. Moreover, the relationship between hub biomarkers and immune infiltration was assessed using CIBERSORT, and the potential drugs or small molecules associated with the core biomarkers were identified via the DGIdb database.

Results

MR analysis showed that KI may be a risk factor for AD. Experiments showed that the combination of D-galactose and aluminum chloride was found to induce both KI and AD, with ferroptosis emerging as a bridge to facilitate crosstalk between KI and AD. Besides, we identified EGFR and RELA have significant diagnostic value. These biomarkers are associated with NK_cells_resting and B_cells_memory and could be targeted for intervention in KI-related AD by treating gefitinib and plumbagin.

Conclusion

Our study elucidates that ferroptosis may be an important pathway for kidney-brain crosstalk. Notably, gefitinib and plumbagin may be therapeutic candidates for intervening in KI-associated AD by targeting EGFR and RELA.

Artificial intelligence and machine learning technologies in ulcerative colitis

Interest in artificial intelligence (AI) applications for ulcerative colitis (UC) has grown tremendously in recent years. In the past 5 years, there have been over 80 studies focused on machine learning (ML) tools to address a wide range of clinical problems in UC, including diagnosis, prognosis, identification of new UC biomarkers, monitoring of disease activity, and prediction of complications. AI classifiers such as random forest, support vector machines, neural networks, and logistic regression models have been used to model UC clinical outcomes using molecular (transcriptomic) and clinical (electronic health record and laboratory) datasets with relatively high performance (accuracy, sensitivity, and specificity). Application of ML algorithms such as computer vision, guided image filtering, and convolutional neural networks have also been utilized to analyze large and high-dimensional imaging datasets such as endoscopic, histologic, and radiological images for UC diagnosis and prediction of complications (post-surgical complications, colorectal cancer). Incorporation of these ML tools to guide and optimize UC clinical practice is promising but will require large, high-quality validation studies that overcome the risk of bias as well as consider cost-effectiveness compared to standard of care.

Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection

This study, utilizing high-throughput technologies and Machine Learning (ML), has identified gene biomarkers and molecular signatures in Inflammatory Bowel Disease (IBD). We could identify significant upregulated or downregulated genes in IBD patients by comparing gene expression levels in colonic specimens from 172 IBD patients and 22 healthy individuals using the GSE75214 microarray dataset. Our ML techniques and feature selection methods revealed six Differentially Expressed Gene (DEG) biomarkers (VWF, IL1RL1, DENND2B, MMP14, NAAA, and PANK1) with strong diagnostic potential for IBD. The Random Forest (RF) model demonstrated exceptional performance, with accuracy, F1-score, and AUC values exceeding 0.98. Our findings were rigorously validated with independent datasets (GSE36807 and GSE10616), further bolstering their credibility and showing favorable performance metrics (accuracy: 0.841, F1-score: 0.734, AUC: 0.887). Our functional annotation and pathway enrichment analysis provided insights into crucial pathways associated with these dysregulated genes. DENND2B and PANK1 were identified as novel IBD biomarkers, advancing our understanding of the disease. The validation in independent cohorts enhances the reliability of these findings and underscores their potential for early detection and personalized treatment of IBD. Further exploration of these genes is necessary to fully comprehend their roles in IBD pathogenesis and develop improved diagnostic tools and therapies. This study significantly contributes to IBD research with valuable insights, potentially greatly enhancing patient care.

HOXD10 regulates intestinal permeability and inhibits inflammation of dextran sulfate sodium-induced ulcerative colitis through the inactivation of the Rho/ROCK/MMPs axis

Ulcerative colitis (UC) has been identified as a severe inflammatory disease with significantly increased incidence across the world. The detailed role and mechanism of HOXD10 in UC remain unclear. In present study, we found that HOXD10 was lowly expressed in UC samples and was notably decreased by dextran sulfate sodium (DSS) administration. Overexpression of HOXD10 dramatically ameliorated DSS-induced UC symptoms, including the loss of weight, increased disease activity index values, and the shortened colon length. Additionally, terminal-deoxynucleoitidyl transferase mediated nick end labeling and immunohistochemistry staining assays showed that HOXD10 overexpression suppressed cell apoptosis and facilitated proliferation of colon tissues after DSS treatment. Moreover, HOXD10 overexpression obviously suppressed DSS-triggered inflammatory response by decreasing the expression level of TNF-α, IL-6, and IL-1β. Furthermore, overexpression of HOXD10 effectively restored the intestinal permeability, thereby alleviating DSS-induced intestinal barrier dysfunction. Mechanistic study demonstrated that HOXD10 significantly reduced the activities of Rho/ROCK/MMPs axis in colon tissues of mice with UC. In conclusion, this study revealed that HOXD10 might effectively improve DSS-induced UC symptoms by suppressing the activation of Rho/ROCK/MMPs pathway.

Polymorphisms in CLEC5A and CLEC7A genes modify risk for inflammatory bowel disease

BACKGROUND

Inflammatory bowel disease (IBD) seems to arise from an interplay between genetic and environmental factors. CLEC5A and CLEC7A genes code for 2 members of the C-type lectin receptor superfamily, which participate in the immune response against various pathogens, mediating inflammatory signaling. CLEC5A polymorphisms have been linked to the risk of Crohn's disease (CD), whereas CLEC7A has been implicated in fungal dysbiosis, chemically induced colitis in mice and undertreated ulcerative colitis (UC) in humans. This study aimed to explore how specific CLEC5A and CLEC7A polymorphisms contribute to the development of CD and UC.

METHODS

One hundred twelve CD patients, 94 UC patients and 164 sex- and age- matched healthy individuals were genotyped for the single nucleotide polymorphisms rs2078178 and rs16910631 of the CLEC7A gene, and rs1285933 of the CLEC5A gene.

RESULTS

The CLEC7A rs2078178 AA genotype was more frequent in UC patients compared to healthy individuals, The CLEC7A rs16910631 CT genotype was significantly associated with UC risk compared to healthy individuals, while there was no statistical correlation with CD. The CLEC5A rs1285933 GA genotype was found to be protective against UC and CD, and the AA genotype against CD. Carriers of the rs1285933 A allele appeared to have reduced susceptibility to CD, implying that the presence of the A allele could be protective against CD development.

CONCLUSIONS

This is the first study to correlate the CLEC5A rs1285933 polymorphism with the risk for UC. The rs2078178 AA genotype and the CLEC7A rs16910631 CT could be promising biomarkers for UC susceptibility.

Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients

BACKGROUND

Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results.

METHODS

A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML.

RESULTS

Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils.

CONCLUSION

AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.

Multiple-model machine learning identifies potential functional genes in dilated cardiomyopathy

Introduction

Machine learning (ML) has gained intensive popularity in various fields, such as disease diagnosis in healthcare. However, it has limitation for single algorithm to explore the diagnosing value of dilated cardiomyopathy (DCM). We aim to develop a novel overall normalized sum weight of multiple-model MLs to assess the diagnosing value in DCM.

Methods

Gene expression data were selected from previously published databases (six sets of eligible microarrays, 386 samples) with eligible criteria. Two sets of microarrays were used as training; the others were studied in the testing sets (ratio 5:1). Totally, we identified 20 differently expressed genes (DEGs) between DCM and control individuals (7 upregulated and 13 down-regulated).

Results

We developed six classification ML methods to identify potential candidate genes based on their overall weights. Three genes, serine proteinase inhibitor A3 (SERPINA3), frizzled-related proteins (FRPs) 3 (FRZB), and ficolin 3 (FCN3) were finally identified as the receiver operating characteristic (ROC). Interestingly, we found all three genes correlated considerably with plasma cells. Importantly, not only in training sets but also testing sets, the areas under the curve (AUCs) for SERPINA3, FRZB, and FCN3 were greater than 0.88. The ROC of SERPINA3 was significantly high (0.940 in training and 0.918 in testing sets), indicating it is a potentially functional gene in DCM. Especially, the plasma levels in DCM patients of SERPINA3, FCN, and FRZB were significant compared with healthy control.

Discussion

SERPINA3, FRZB, and FCN3 might be potential diagnosis targets for DCM, Further verification work could be implemented.

A Python Clustering Analysis Protocol of Genes Expression Data Sets

Gene expression and SNPs data hold great potential for a new understanding of disease prognosis, drug sensitivity, and toxicity evaluations. Cluster analysis is used to analyze data that do not contain any specific subgroups. The goal is to use the data itself to recognize meaningful and informative subgroups. In addition, cluster investigation helps data reduction purposes, exposes hidden patterns, and generates hypotheses regarding the relationship between genes and phenotypes. Cluster analysis could also be used to identify bio-markers and yield computational predictive models. The methods used to analyze microarrays data can profoundly influence the interpretation of the results. Therefore, a basic understanding of these computational tools is necessary for optimal experimental design and meaningful data analysis. This manuscript provides an analysis protocol to effectively analyze gene expression data sets through the K-means and DBSCAN algorithms. The general protocol enables analyzing omics data to identify subsets of features with low redundancy and high robustness, speeding up the identification of new bio-markers through pathway enrichment analysis. In addition, to demonstrate the effectiveness of our clustering analysis protocol, we analyze a real data set from the GEO database. Finally, the manuscript provides some best practice and tips to overcome some issues in the analysis of omics data sets through unsupervised learning.

Development of a machine learning-based risk prediction model for cerebral infarction and comparison with nomogram model

BACKGROUND

Development of a cerebral infarction (CI) risk prediction model by mining routine test big data with machine learning algorithms.

METHODS

Cohort 1 included 2017 CI patients and health checkers, and the optimal machine learning algorithms in Extreme gradient Boosting (XgBoost), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF) were selected to mine all routine test data of the enrolled subjects for screening CI model features. Cohort 2 included patients with CI and Non-CI from 2018 to 2020 to develop an early warning model for CI and was analyzed in subgroups with a cutoff of 50 years. Cohort 3 included CI patients versus Non-CI patients in 2021, and a nomogram models was developed for comparison with the machine learning model.

RESULTS

The optimal algorithm XgBoost was used to develop a CI risk prediction model CI-Lab8 containing eight characteristics of fibrinogen, age, glucose, mean erythrocyte hemoglobin concentration, albumin, neutrophil absolute value, activated partial thromboplastin time, and triglycerides. The model had an AUC of 0.823 in cohort 2, significantly higher than the FIB (AUC = 0.737), which ranked first in feature importance. CI-Lab8 also had higher diagnostic accuracy in CI patients <50 years of age (AUC = 0.800), slightly lower than in CI patients ≥50 years of age (AUC = 0.856). Receiver operating characteristic curve, calibration curve, and decision curve analysis in cohort 3 showed CI-Lab8 to be superior to nomogram.

CONCLUSION

In this study, the CI risk prediction model developed by XgBoost algorithm outperformed the nomogram model and had higher diagnostic accuracy for CI patients in both <50 and ≥50 years old, which may assist clinical assessment for CI.