The early detection of asthma based on blood gene expression

Impact of CYP3A5 Polymorphisms on Pediatric Asthma Outcomes

Genetic variation among inhaled corticosteroid (ICS)-metabolizing enzymes may affect asthma control, but evidence is limited. This study tested the hypothesis that single-nucleotide polymorphisms (SNPs) in Cytochrome P450 3A5 (CYP3A5) would affect asthma outcomes. Patients aged 2-18 years with persistent asthma were recruited to use the electronic AsthmaTracker (e-AT), a self-monitoring tool that records weekly asthma control, medication use, and asthma outcomes. A subset of patients provided saliva samples for SNP analysis and participated in a pharmacokinetic study. Multivariable regression analysis adjusted for age, sex, race, and ethnicity was used to evaluate the impact of CYP3A5 SNPs on asthma outcomes, including asthma control (measured using the asthma symptom tracker, a modified version of the asthma control test or ACT), exacerbations, and hospital admissions. Plasma corticosteroid and cortisol concentrations post-ICS dosing were also assayed using liquid chromatography-tandem mass spectrometry. Of the 751 patients using the e-AT, 166 (22.1%) provided saliva samples and 16 completed the PK study. The e-AT cohort was 65.1% male, and 89.6% White, 6.0% Native Hawaiian, 1.2% Black, 1.2% Native American, 1.8% of unknown race, and 15.7% Hispanic/Latino; the median age was 8.35 (IQR: 5.51-11.3) years. CYP3A5*3/*3 frequency was 75.8% in White subjects, 50% in Native Hawaiians and 76.9% in Hispanic/Latino subjects. Compared with CYP3A5*3/*3, the CYP3A5*1/*x genotype was associated with reduced weekly asthma control (OR: 0.98; 95% CI: 0.97-0.98; p < 0.001), increased exacerbations (OR: 6.43; 95% CI: 4.56-9.07; p < 0.001), and increased asthma hospitalizations (OR: 1.66; 95% CI: 1.43-1.93; p < 0.001); analysis of 3/*3, *1/*1 and *1/*3 separately showed an allelic copy effect. Finally, PK analysis post-ICS dosing suggested muted changes in cortisol concentrations for patients with the CYP3A5*3/*3 genotype, as opposed to an effect on ICS PK. Detection of CYP3A5*3/3, CYPA35*1/*3, and CYP3A5*1/*1 could impact inhaled steroid treatment strategies for asthma in the future.

Use of Electronic Nicotine Delivery Systems and Age of Asthma Onset Among US Adults and Youths

Importance

The association of use of electronic nicotine delivery systems (ENDS) with the age of asthma onset is unknown.

Objective

To explore the association of past 30-day ENDS use with the age of asthma onset in adults and youths who did not have asthma or chronic obstructive pulmonary disease and never used cigarettes.

Design, Setting, and Participants

This cohort study was a secondary analysis of waves 1 to 6 of the US nationally representative Population of Tobacco and Health Study (2013-2021). Eligible participants included adults (≥18 years) and youths (12-17 years) who did not have asthma or chronic obstructive pulmonary disease at the first wave of participation. Data analysis was conducted from September 2022 to April 2024.

Exposure

Past 30-day ENDS use at the first wave of participation in the study preceding the onset of asthma.

Main outcome and measures

Lower and upper age limits were estimated using the age reported at the first wave of participation and the number of weeks between follow-up waves until asthma was first reported or censored. The association of past 30-day ENDS use with the age of asthma onset was estimated using weighted interval-censoring Cox regression. The cumulative hazard function for the age of asthma onset was estimated using interval-censoring survival analysis.

Results

A total of 24 789 participants were included, with 7766 adults (4461 female [weighted percentage, 59.11%] and 3305 male [weighted percentage, 40.89%]), representing 80.0 million adults, and 17 023 youths (8514 female [weighted percentage, 50.60%] and 8496 male [weighted percentage 49.32%]), representing 33.9 million youths. By age 27 years, 6.2 per 1000 adults reported asthma incidence (hazard ratio [HR], 0.62%; 95% CI, 0.46%-0.75%). While controlling for covariates, there was a 252% increased risk of the onset of asthma at earlier ages for adults who used ENDS in the past 30 days vs adults who did not (adjusted HR, 3.52; 95% CI, 1.24-10.02). For youths, there was no association of ENDS use in the past 30 days with age of asthma onset (adjusted HR, 1.79; 95% CI, 0.67-4.77), which could be due to a lack of statistical power.

Conclusion and relevance

In this cohort study, past 30-day ENDS use among adults was associated with earlier ages of asthma onset. These findings suggest that prevention and cessation programs directed to adults who use ENDS are needed to educate the public, protect public health, prevent adverse health outcomes, and motivate users to stop. Furthermore, modifying symptom-screening asthma guidelines, resulting in earlier asthma detection and treatment, may reduce morbidity and mortality due to asthma.

A Roadmap for Using Causal Inference and Machine Learning to Personalize Asthma Medication Selection

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient's characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient's ICS response in the next year based on the patient's characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.

The Effect of Blood Clots on the Quality of RNA Extracted from PAXgene Blood RNA Tubes

Background: PAXgene® Blood RNA tubes are routinely used in clinical research and molecular biology applications to preserve the stability of RNA in whole blood. However, in practice, blood clots are occasionally observed after blood collection and are often ignored. Currently, there are few studies on whether blood clots affect the quality of RNA extracted from these tubes. Materials and Methods: Fifteen pairs of non-clot and clot PAXgene Blood RNA tube samples (n = 30) were collected to form two matched groups from 15 patients. According to the maximum diameter (d) of the blood clot observed visually at the time of sample reception, the clot groups were divided into a small-clot group (0 cm < d < 0.5 cm) and a large-clot group (d ≥ 0.5 cm). RNA was extracted by the PAXgene Blood RNA Kit. To analyze the quality of RNA, its yield and purity were assessed by spectrophotometry, and integrity was measured by microfluidic electrophoresis. An A260/280 ratio between 1.8 and 2.2 indicated purified RNA, and RNA integrity number (RIN) values ≥7.0 were considered to represent qualified integrity. Results: The median yields of RNA from the non-clot and clot groups were 3.84 (2.80-6.38) μg and 4.87 (2.77-8.30) μg, respectively. The median A260/280 ratios were 2.08 (2.06-2.09) and 2.09 (2.07-2.11), whereas the median A260/230 ratios were 1.77 (1.31-1.91) and 1.67 (1.21-1.94) in the two groups. In addition, the median RINs were 8.20 (8.00-8.40) and 7.20 (6.60-7.70), respectively. There were no significant differences in RNA yields, A260/280, or A260/230 between the two groups. However, the RIN value of the clot group was significantly lower compared with the non-clot group (p < 0.05), with RIN ≥7.0 found in all non-clot samples and 60% of clot samples (p < 0.05). Furthermore, in the clot groups, the small-clot samples had higher RIN values than large-clot samples (8.25 [7.75-8.75] vs. 6.90 [6.60-7.30], p < 0.001). Conclusions: The formation of large blood clots in PAXgene Blood RNA tubes will reduce the integrity of extracted RNA.

Automated detection of airflow obstructive diseases: A systematic review of the last decade (2013-2022)

BACKGROUND AND OBJECTIVE

Obstructive airway diseases, including asthma and Chronic Obstructive Pulmonary Disease (COPD), are two of the most common chronic respiratory health problems. Both of these conditions require health professional expertise in making a diagnosis. Hence, this process is time intensive for healthcare providers and the diagnostic quality is subject to intra- and inter- operator variability. In this study we investigate the role of automated detection of obstructive airway diseases to reduce cost and improve diagnostic quality.

METHODS

We investigated the existing body of evidence and applied Preferred Reporting Items for Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to search records in IEEE, Google scholar, and PubMed databases. We identified 65 papers that were published from 2013 to 2022 and these papers cover 67 different studies. The review process was structured according to the medical data that was used for disease detection. We identified six main categories, namely air flow, genetic, imaging, signals, and miscellaneous. For each of these categories, we report both disease detection methods and their performance.

RESULTS

We found that medical imaging was used in 14 of the reviewed studies as data for automated obstructive airway disease detection. Genetics and physiological signals were used in 13 studies. Medical records and air flow were used in 9 and 7 studies, respectively. Most papers were published in 2020 and we found three times more work on Machine Learning (ML) when compared to Deep Learning (DL). Statistical analysis shows that DL techniques achieve higher Accuracy (ACC) when compared to ML. Convolutional Neural Network (CNN) is the most common DL classifier and Support Vector Machine (SVM) is the most widely used ML classifier. During our review, we discovered only two publicly available asthma and COPD datasets. Most studies used private clinical datasets, so data size and data composition are inconsistent.

CONCLUSIONS

Our review results indicate that Artificial Intelligence (AI) can improve both decision quality and efficiency of health professionals during COPD and asthma diagnosis. However, we found several limitations in this review, such as a lack of dataset consistency, a limited dataset and remote monitoring was not sufficiently explored. We appeal to society to accept and trust computer aided airflow obstructive diseases diagnosis and we encourage health professionals to work closely with AI scientists to promote automated detection in clinical practice and hospital settings.

Silencing lncRNA CDKN2B-AS1 Alleviates Childhood Asthma Progression Through Inhibiting ZFP36 Promoter Methylation and Promoting NR4A1 Expression

LncRNA cyclin-dependent kinase inhibitor 2B antisense RNA 1 (CDKN2B-AS1) was found to be upregulated in plasma of patients with bronchial asthma. This study aimed to explore the roles and mechanisms of CDKN2B-AS1 in childhood asthma. We found that CDKN2B-AS1 was upregulated and zinc finger protein 36 (ZFP36) mRNA was downregulated in blood samples of children with asthma compared with healthy controls as measured by RT-qPCR. Human bronchial epithelial cell line BEAS-2B was treated with LPS to induce inflammation model. Small interfering RNA against CDKN2B-AS1 (si-CDKN2B-AS1) was transfected into LPS-treated BEAS-2B cells, and we observed that CDKN2B-AS1 silencing increased cell viability and inhibited apoptosis and inflammation cytokine levels in LPS-treated BEAS-2B cells. Methylation-specific PCR, ChIP, and RIP assays indicated that CDKN2B-AS1 inhibited ZFP36 expression by recruiting DNMT1 to promote ZFP36 promoter methylation. Co-immunoprecipitation (Co-IP) assay verified the interaction between ZFP36 and nuclear receptor subfamily 4 group A member 1 (NR4A1) proteins. Then rescue experiments revealed that ZFP36 knockdown reversed the effects of CDKN2B-AS1 silencing on BEAS-2B cell functions. ZFP36 overexpression facilitated apoptosis, inflammation, and p-p65 expression in BEAS-2B cells, while NR4A1 knockdown reversed these effects. Additionally, CDKN2B-AS1 silencing alleviated airway hyperresponsiveness and inflammation in ovalbumin (OVA)-induced asthma mice. In conclusion, silencing lncRNA CDKN2B-AS1 enhances BEAS-2B cell viability, reduces apoptosis and inflammation in vitro, and alleviated asthma symptoms in OVA-induced asthma mice in vivo through inhibiting ZFP36 promoter methylation and NR4A1-mediated NF-κB signaling pathway.

Shegan Mahuang Decoction May Reduce Airway Inflammation in Neutrophilic Asthmatic Mice by Improving the Mitochondrial Function of Bronchoalveolar Lavage Fluid Exosomes

Asthma is a common pulmonary disease mainly caused by the infiltration of neutrophils. There is a limit to the therapeutic effects of the available asthma drugs on neutrophilic asthma. Shegan Mahuang Decoction (SMD) is one of the representative traditional Chinese medicine (TCM) prescriptions for asthma, and it can effectively relieve the clinical symptoms of patients. However, the effect of SMD on the treatment of neutrophilic asthma remains unknown. In this study, a mouse model of neutrophilic asthma induced by lipopolysaccharide (LPS)/ovalbumin (OVA) was established, and the effect of a modified SMD prescription on the model was evaluated. After treatment, SMD was demonstrated to be therapeutically effective on asthmatic mice via airway resistance detection and lung pathology and was able to affect cytokine levels in vivo. Further experiments verified that SMD regulated the expression of mitochondrial function proteins in bronchoalveolar lavage fluid (BALF) exosomes. The results demonstrate that SMD confers a therapeutic effect on a neutrophilic asthma mouse model, and it may reduce neutrophil airway inflammation by regulating myeloid-derived regulatory cell (MDRC) function and airway exosome mitochondrial function.

Emerging Advances of Non-coding RNAs and Competitive Endogenous RNA Regulatory Networks in Asthma

Asthma is a chronic inflammatory disease characterized by airway remodeling and bronchial hyperresponsiveness. A variety of effector cells and cytokines jointly stimulate the occurrence of inflammatory response in asthma. Although the pathogenesis of asthma is not entirely clear, the possible roles of non-coding RNAs (ncRNAs) have been recently demonstrated. NcRNAs are non-protein-coding RNA molecules, such as circular RNAs (circRNAs), long non-coding RNAs (lncRNAs) and microRNAs (miRNAs), which are involved in the regulation of a variety of biological processes. Mounting studies have shown that ncRNAs play pivotal roles in the occurrence and progression of asthma via competing endogenous RNA (ceRNA) regulatory networks. However, the specific mechanism and clinical application of ncRNAs and ceRNA regulatory networks in asthma have not been fully elucidated, which are worthy of further investigation. This paper comprehensively summarized the current progress on the roles of miRNAs, lncRNAs, circRNAs, and ceRNA regulatory networks in asthma, which can provide a better understanding for the disease pathogenesis and is helpful for identifying novel biomarkers for asthma.

Proteome profiling reveals the efficacy and targets of sophocarpine against asthma

Sophocarpine (SPC) as a quinolizidine alkaloid displays powerful effects on inflammatory diseases through regulating multiple targets. Asthma is a complex heterogeneous and inflammatory disease with an increasing incidence worldwide. Here we established a mice asthma model and investigated the effect of SPC. Mice induced by ovalbumin (OVA) exhibits exacerbated Th1/Th2 immune imbalance and allergic lung inflammation. SPC treatment regulated Th1/Th2 cytokines production (IL-4, IL-5 and INF-γ) in BALF, reduced IgE level in serum, inhibited inflammatory cell infiltration, and improved the lung tissue pathology. Proteomic results showed that 5064 proteins in lung tissue were detected and among them 223 preliminary therapeutic targets of SPC were selected. Subsequently, excluding non-human genes, 109 targets with established crystal structures were harvested. Meanwhile, the molecular docking results showed that the binding energy of 87 targets with SPC was varied from -9.72 kcal/mol to 227.16 kcal/mol. Further, SPC suppressed arrb2, anxa1, myd88 and sphk1 expression and activated p-stat1. All of the five targets based on the screened results of proteomics and molecular docking are critical in allergic asthma. Thus, our data revealed that SPC alleviated bronchial asthma via targeting multi-targets.

Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure

Heart failure (HF) is a serious condition in which the support of blood pumped by the heart is insufficient to meet the demands of body at a normal cardiac filling pressure. Approximately 26 million patients worldwide are suffering from heart failure and about 17–45% of patients with heart failure die within 1-year, and the majority die within 5-years admitted to a hospital. The molecular mechanisms underlying the progression of heart failure have been poorly studied. We compared the gene expression profiles between patients with heart failure (n = 177) and without heart failure (n = 136) using multiple feature selection strategies and identified 38 HF signature genes. The support vector machine (SVM) classifier based on these 38 genes evaluated with leave-one-out cross validation (LOOCV) achieved great performance with sensitivity of 0.983 and specificity of 0.963. The network analysis suggested that the hub gene SMOC2 may play important roles in HF. Other genes, such as FCN3, HMGN2, and SERPINA3, also showed great promises. Our results can facilitate the early detection of heart failure and can reveal its molecular mechanisms.

The Methylation Pattern for Knee and Hip Osteoarthritis

Osteoarthritis is one of the most prevalent chronic joint diseases for middle-aged and elderly people. But in recent years, the number of young people suffering from the disease increases quickly. It is known that osteoarthritis is a common degenerative disease caused by the combination and interaction of many factors such as natural and environmental factors. DNA methylations reflect the effects of environmental factors. Several researches on DNA methylation at specific genes in OA cartilage indicated the great potential roles of DNA methylation in OA. To systematically investigate the methylation pattern in knee and hip osteoarthritis, we analyzed the methylation profiles in cartilage of 16 OA hip samples, 19 control hip samples and 62 OA knee samples. 12 discriminative methylation sites were identified using advanced minimal Redundancy Maximal Relevance (mRMR) and Incremental Feature Selection (IFS) methods. The SVM classifier of these 12 methylation sites from genes like MEIS1, GABRG3, RXRA, and EN1, can perfectly classify the OA hip samples, control hip samples and OA knee samples evaluated with LOOCV (Leave-One Out-Cross Validation). These 12 methylation sites can not only serve as biomarker, but also provide underlying mechanism of OA.

Investigation and Prediction of Human Interactome Based on Quantitative Features

Protein is one of the most significant components of all living creatures. All significant and essential biological structures and functions relies on proteins and their respective biological functions. However, proteins cannot perform their unique biological significance independently. They have to interact with each other to realize the complicated biological processes in all living creatures including human beings. In other words, proteins depend on interactions (protein-protein interactions) to realize their significant effects. Thus, the significance comparison and quantitative contribution of candidate PPI features must be determined urgently. According to previous studies, 258 physical and chemical characteristics of proteins have been reported and confirmed to definitively affect the interaction efficiency of the related proteins. Among such features, essential physiochemical features of proteins like stoichiometric balance, protein abundance, molecular weight and charge distribution have been validated to be quite significant and irreplaceable for protein-protein interactions (PPIs). Therefore, in this study, we, on one hand, presented a novel computational framework to identify the key factors affecting PPIs with Boruta feature selection (BFS), Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and on the other hand, built a quantitative decision-rule system to evaluate the potential PPIs under real conditions with random forest (RF) and RIPPER algorithms, thereby supplying several new insights into the detailed biological mechanisms of complicated PPIs. The main datasets and codes can be downloaded at https://github.com/xypan1232/Mass-PPI.

Toll-Like Receptor Characterization Correlates with Asthma and Is Predictive of Diagnosis

Toll-like receptors (TLRs) play crucial roles in the recognition of invading pathogens and the immune system. However, the effect of TLRs in asthma is still not fully known. This study was performed to better understand the role of TLR signatures in asthma. Blood samples from case-control studies (study 1: 348 asthmas and 39 normal controls and validation study 2: 411 asthmas and 87 normal controls) were enrolled. The single-sample gene set enrichment analysis method was performed to quantify the abundance of 21 TLR signatures. Gene ontology analysis and pathway function analysis were conducted for functional analysis, and a protein-protein interaction network was constructed. The area under the curve (AUC) value was used to assess the diagnostic capacity. In this study, TLR2/TLR3/TLR4 pathway, MyD88-dependent/independent TLR pathway, positive regulation of TLR4 pathway, and TLR binding signatures were significantly higher in asthma. Functional analysis showed that biological processes and pathways were still involved in TLR cascades and TLR signaling pathway. Eleven hub TLR-related genes were identified, and further validation demonstrated that the combination of TLR-related genes was a good diagnostic biomarker for asthma (AUC = 0.8). Our study provided more insight into the underlying immune mechanism of how TLR signatures affected asthma. The use of the easy-to-apply TLR-related genes might represent a promising blood-based biomarker for early detection of asthma.

Identification of Post-myocardial Infarction Blood Expression Signatures Using Multiple Feature Selection Strategies

Myocardial infarction (MI) is a type of serious heart attack in which the blood flow to the heart is suddenly interrupted, resulting in injury to the heart muscles due to a lack of oxygen supply. Although clinical diagnosis methods can be used to identify the occurrence of MI, using the changes of molecular markers or characteristic molecules in blood to characterize the early phase and later trend of MI will help us choose a more reasonable treatment plan. Previously, comparative transcriptome studies focused on finding differentially expressed genes between MI patients and healthy people. However, signature molecules altered in different phases of MI have not been well excavated. We developed a set of computational approaches integrating multiple machine learning algorithms, including Monte Carlo feature selection (MCFS), incremental feature selection (IFS), and support vector machine (SVM), to identify gene expression characteristics on different phases of MI. 134 genes were determined to serve as features for building optimal SVM classifiers to distinguish acute MI and post-MI. Subsequently, functional enrichment analyses followed by protein-protein interaction analysis on 134 genes identified several hub genes (IL1R1, TLR2, and TLR4) associated with progression of MI, which can be used as new diagnostic molecules for MI.

The Functional Effects of Key Driver KRAS Mutations on Gene Expression in Lung Cancer

Lung cancer is a common malignant cancer. Kirsten rat sarcoma oncogene (KRAS) mutations have been considered as a key driver for lung cancers. KRAS p.G12C mutations were most predominant in NSCLC which was comprised about 11–16% of lung adenocarcinomas (p.G12C accounts for 45–50% of mutant KRAS). But it is still not clear how the KRAS mutation triggers lung cancers. To study the molecular mechanisms of KRAS mutation in lung cancer. We analyzed the gene expression profiles of 156 KRAS mutation samples and other negative samples with two stage feature selection approach: (1) minimal Redundancy Maximal Relevance (mRMR) and (2) Incremental Feature Selection (IFS). At last, 41 predictive genes for KRAS mutation were identified and a KRAS mutation predictor was constructed. Its leave one out cross validation MCC was 0.879. Our results were helpful for understanding the roles of KRAS mutation in lung cancer.

Identifying Microbiota Signature and Functional Rules Associated With Bacterial Subtypes in Human Intestine

Gut microbiomes are integral microflora located in the human intestine with particular symbiosis. Among all microorganisms in the human intestine, bacteria are the most significant subgroup that contains many unique and functional species. The distribution patterns of bacteria in the human intestine not only reflect the different microenvironments in different sections of the intestine but also indicate that bacteria may have unique biological functions corresponding to their proper regions of the intestine. However, describing the functional differences between the bacterial subgroups and their distributions in different individuals is difficult using traditional computational approaches. Here, we first attempted to introduce four effective sets of bacterial features from independent databases. We then presented a novel computational approach to identify potential distinctive features among bacterial subgroups based on a systematic dataset on the gut microbiome from approximately 1,500 human gut bacterial strains. We also established a group of quantitative rules for explaining such distinctions. Results may reveal the microstructural characteristics of the intestinal flora and deepen our understanding on the regulatory role of bacterial subgroups in the human intestine.

Identifying Methylation Pattern and Genes Associated with Breast Cancer Subtypes

Breast cancer is regarded worldwide as a severe human disease. Various genetic variations, including hereditary and somatic mutations, contribute to the initiation and progression of this disease. The diagnostic parameters of breast cancer are not limited to the conventional protein content and can include newly discovered genetic variants and even genetic modification patterns such as methylation and microRNA. In addition, breast cancer detection extends to detailed breast cancer stratifications to provide subtype-specific indications for further personalized treatment. One genome-wide expression–methylation quantitative trait loci analysis confirmed that different breast cancer subtypes have various methylation patterns. However, recognizing clinically applied (methylation) biomarkers is difficult due to the large number of differentially methylated genes. In this study, we attempted to re-screen a small group of functional biomarkers for the identification and distinction of different breast cancer subtypes with advanced machine learning methods. The findings may contribute to biomarker identification for different breast cancer subtypes and provide a new perspective for differential pathogenesis in breast cancer subtypes.