Accurate biomarker identification is imperative for effective cancer diagnosis and therapeutic strategies. This study introduces the Voting-Based Enhanced Binary Ebola Optimization Search Algorithm (VBEOSA), an innovative ensemble-based approach combining binary optimization and the Ebola optimization search algorithm. VBEOSA harnesses the collective power of the state-of-the-art classification models through soft voting. Moreover, our research applies VBEOSA to an extensive lung cancer gene expression dataset obtained from TCGA, following essential preprocessing steps including outlier detection and removal, data normalization, and filtration. VBEOSA aids in feature selection, leading to the discovery of key hub genes closely associated with lung cancer, validated through comprehensive protein–protein interaction analysis. Notably, our investigation reveals ten significant hub genes—ADRB2, ACTB, ARRB2, GNGT2, ADRB1, ACTG1, ACACA, ATP5A1, ADCY9, and ADRA1B—each demonstrating substantial involvement in the domain of lung cancer. Furthermore, our pathway analysis sheds light on the prominence of strategic pathways such as salivary secretion and the calcium signaling pathway, providing invaluable insights into the intricate molecular mechanisms underpinning lung cancer. We also utilize the weighted gene co-expression network analysis (WGCNA) method to identify gene modules exhibiting strong correlations with clinical attributes associated with lung cancer. Our findings underscore the efficacy of VBEOSA in feature selection and offer profound insights into the multifaceted molecular landscape of lung cancer. Finally, we are confident that this research has the potential to improve diagnostic capabilities and further enrich our understanding of the disease, thus setting the stage for future advancements in the clinical management of lung cancer. The VBEOSA source codes is publicly available at  It is broadly categorized into two types: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC). The SCLC is an aggressive type of lung cancer that spreads rapidly. However, the NSCLC is considered as the most frequently diagnosed form of lung cancer, representing approximately 85% of all cases. It is known to grow and metastasize at a slower rate than SCLC4. The most prevalent risk factor for developing lung cancer is smoking, but exposure to other environmental hazards, including asbestos, radon and air pollution can also contribute to an elevated risk. Common symptoms associated with lung cancer are persistent chest pain, cough, shortness of breath, inexplicable weight loss, and fatigue5. Lung cancer is responsible for approximately 350 deaths per day, which is nearly 2.5 times higher than the number of deaths caused by colorectal cancer (CRC), the second leading cause of cancer deaths.Lung cancer begins in the lung tissues and can potentially metastasize to various parts of the body6. The early identification of cancer significantly increases the chances of survival. Accurate determination of the specific type of cancer is crucial for administering appropriate treatment to the patient. Conventional techniques that involve examining various biopsy samples under a microscope are both time-consuming and not cost-effective in advanced cases, and there is a risk of obtaining false negative outcomes7. Observable physical traits of cells and tissues, including their size, shape, and arrangement, are known as morphological characteristics.In 2023, cigarette smoking directly contributes to approximately 103,000 out of 127,070 lung cancer deaths (81%), with an additional 3560 deaths caused by second-hand smoke. If classified separately, the remaining balance of approximately 20,500 deaths not caused by smoking would rank as the eighth leading cause of cancer deaths among both genders9.The traditional method of classifying cancer relied primarily on these characteristics. However, multiple studies have revealed the significant limitations of this approach. For example, similar morphological characteristics among some cancer types make it challenging to differentiate between them. Moreover, interpreting these characteristics is subjective, and there is a risk of experts' bias in tumor identification. These drawbacks can result in misdiagnosis and inadequate treatment outcomes for patients. Consequently, researchers have sought alternative methods such as gene expression data obtained from microarrays to overcome these limitations. This approach provides a more objective and comprehensive understanding of cancer at the molecular level and has shown significant potential in enhancing cancer classification and treatment10. However, gene expression data present several challenges for analysis; these include noise and high dimensionality. The number of features or genes can significantly exceed the number of samples , leading to a potential lack of statistical power. Class imbalance is also a common issue because this can negatively affect the performance of classification models. Moreover, only a small subset of genes may be informative for a particular disease, rendering the majority of genes irrelevant11.The mechanism through which the genetic information contained in a gene is utilized to generate a functional product is known as gene expression. This is indicative of the biochemical processes within tissues, cells, other organism’s genetic characteristics and can, therefore, play a fundamental role in the early detection of cancer. Ribonucleic acid (RNA)-sequencing and deoxyribonucleic acid (DNA) microarrays technologies that allow for measuring the expression levels of genes in a sample and produce valuable and high dimensional data for computational analysis13. There are different types of feature selection techniques. The first category is the wrapper approach, which assesses the worth of features by measuring the model’s performance using a machine learning technique. The second category, known as the filter approach, assesses the statistical properties and relevance of features without using a machine learning classifier. It avoids the necessity of training a machine learning model and cross-validation steps required by wrapper-based methods. The filter approach includes techniques like meta-heuristic algorithms, recursive feature elimination, and sequential feature selection. However, compared to the wrapper method, the filter approach is generally more efficient but may exhibit lower accuracy. The third category of feature selection methods is the embedded method, which integrates feature selection directly into the learning process. This approach treats feature selection as an inherent component and includes techniques such as decision tree-based methods and L1 regularization as notable examples15. The last type is Hybrid-approaches that combine with filter and wrapper method to gain one model.Reducing data dimensionality is an effective solution for handling gene expression data. Feature selection techniques are commonly used to tackle this problem by selecting a minimal set of features that effectively represent the entire feature space while preserving essential information from the data. This approach reduces model training time while potentially improving classification accuracy16. Metaheuristic algorithms can be categorized into two methods: neighborhood-based and population-based17. The population-based method explores global optimal features by simultaneously considering multiple points. Population-based algorithms such as differential evolution (DE)18, ant colony optimization (ACO)19, particle swarm optimization (PSO)20, and genetic algorithms (GA)21 fall into this category. The neighborhood-based search algorithm focuses on exploring local optimal features by examining a single point at a time. Simulated annealing (SA)18 and Tabu search (TS)22 are examples of neighborhood-based algorithms.In recent times, metaheuristic algorithms have been effectively employed in conjunction with various feature selection methods and these have demonstrated successful solutions for various optimization problems, outperforming exact methodsWe proposed a novel model called Voting-Based Enhanced Binary Ebola Optimization Search Algorithm (VBEOSA). It improved the Binary Ebola Optimization Search Algorithm (BEOSA) by combining six classification algorithms using voting model based on lung cancer gene expression dataset. In the context of gene expression data, voting in the BEOSA algorithm provides an additional benefit. Gene expression data analysis often involves high-dimensional datasets with complex relationships. By incorporating multiple classification models and utilizing voting, the BEOSA algorithm can effectively capture the intricate patterns and variability present in gene expression data. The combination of diverse models helps to uncover different aspects of gene expression profiles and improves the interpretation and understanding of gene behavior. This enables more accurate identification of relevant genes and enhances the potential for discovering meaningful biological insights. Therefore, voting in the BEOSA algorithm not only brings diversity, improves accuracy, and increases robustness but also enables better analysis and interpretation of gene expression data.The paper makes two significant contributions to the field. Firstly, it introduces a novel approach called VBEOSA (Voting Binary Ebola Optimization Search Algorithm), which combines the BEOSA algorithm with a voting model. This integration enhances the feature selection and classification process by leveraging the collective decision-making capabilities of multiple classification models. By applying VBEOSA to gene expression data, which is known for its complexity and high dimensionality, the analysis and interpretation of gene expression profiles are improved, leading to the identification of relevant genes, and providing insights into biological processes. The aim is to achieve an optimal subset of features that maximizes classification models performance while minimizing the number of selected features. This innovative approach improves gene expression analysis and classification accuracy through the integration of the BEOSA algorithm and the voting model. Secondly, the study contributes to the field by leveraging RNASeq gene expression data to identify the differentially expressed genes (DEGs) and discover new biomarkers or hub genes.The DEGs are further used and analyzed to construct a protein–protein interaction (PPI) network using the STRING database and Cytoscape software. The PPI network captures both direct and indirect interactions. In direct interactions proteins are closely bound together for specific functions. Indirect interactions are known as functional associations. Computational methods and knowledge transfer between organisms are utilized to predict these interactions, incorporating information from primary databases. Additionally, the study conducts Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) analyses using the Enricher web tool to extract meaningful insights from the DEGs. In addition, we used the weighted gene co-expression network analysis (WGCNA) to identify gene modules that showed strong correlations with clinical characteristics. Additionally, we identified key genes within these selected modules based on their highest connectivity within the respective module. By integrating these approaches, the study aims to identify potential biomarkers and gain a deeper understanding of the biological processes associated with the analyzed gene expression data.23 proposed an efficient hybrid filter-wrapper metaheuristic-based gene selection method for high-dimensional datasets. They used different datasets including arcene, colon, prostate1, lung, dfiuse large b-cell lymphoma dataset (DLBCL), Dorothea, Central Nervous System dataset (CNS), prostate, breast, and leukemia. The method combined the strengths of filter and wrapper approaches to select informative genes and improve classification accuracy. It utilized metaheuristic algorithms to search for optimal subsets of genes. The hybrid approach enhanced the efficiency and effectiveness of gene selection, making it suitable for high-dimensional datasets. The experimental results of the proposed algorithm demonstrated its superior accuracy, surpassing similar methods with an average of 93.34%.The literature contains a wide range of feature selection approaches that make use of metaheuristic optimization methods specifically for gene expression data. Pirgazi et al.24. The method employed a customized similarity measure based on fuzzy rough set theory to assess attribute relevance and redundancy. By incorporating information gain and dependency degree, the approach effectively identified the most informative and non-redundant attributes for accurate lung cancer classification. The experimental evaluations using the random forest classifier on gene expression datasets for leukemia, lung, and ovarian cancer yielded accuracies of 86.11%, 81.94%, and 92.89% respectively. A hybrid machine learning framework which combined a nature-inspired cuckoo search (CS) algorithm with genetic algorithm (GA) and artificial bee colony (ABC) was developed by Rabia Musheer Aziz25. The framework utilized independent component analysis (ICA) in the preprocessing stage to extract important genes from the dataset. The proposed gene selection algorithms, along with leave-one-out cross-validation (LOOCV) and Naive Bayes (NB) classifier, aimed to identify a small set of informative genes for optimal classification accuracy. The framework's performance was assessed on six benchmark gene expression datasets. Experimental results demonstrated that the ICA and CS-based hybrid algorithm with NB classifier outperformed previously published feature selection methods for the NB classifier.A novel approach for attribute selection in lung cancer microarray gene expression data analysis was introduced by Arunkumar and Ramakrishnan26 proposed a novel hybrid binary optimization approach for effective feature selection in high-dimensional datasets. Their approach included a subpopulation selective mechanism that dynamically assigned individuals to a 2-level optimization process. The level-1 method involved mutating population items and then reassigning them to a level-2 optimizer. The selective mechanism determined the subpopulation assigned to the level-2 optimizer based on the exploration and exploitation phase of the level-1 optimizer. Nested transfer (NT) functions were designed and their influence on the level-1 optimizer was investigated. The binary Ebola optimization search algorithm (BEOSA) was used for the level-1 mutation, while the firefly (FFA) and simulated annealing (SA) algorithms were investigated for the level-2 optimizer. The resulting hybrid methods were named HBEOSA-FFA and HBEOSA-SA. Their corresponding variants HBEOSA-SA-NT and HBEOSA-FFA-NT were examined without applying NT. Experimental tests were conducted on high-dimensional datasets to address the challenge of feature selection. The results demonstrated classification accuracies of 0.995 for HBEOSA-FFA on large-scale datasets, 0.967 for HBEOSA-FFA-NT on medium-scale datasets, and 0.953 for HBEOSA-FFA on small-scale datasets.Oyelade et al.27 introduced a novel feature selection model called binary Ebola optimization search algorithm (BEOSA). Their proposed model incorporated V-shape and S-shape transfer functions to guide the mutation process in the exploitation and exploration phases. A representation of the binary search space and the mapping from continuous to discrete space were illustrated. The fitness and cost functions used in the algorithm were mathematically formulated. The performance of this method was evaluated on 22 benchmark datasets. The results indicated that the SVM and KNN algorithms performed effectively in conjunction with BEOSA and BIEOSA. The SVM achieved a classification accuracy of 0.845, while the KNN achieved a higher accuracy of 0.935. Bai et al.28 introduced a novel approach which combined multi-objective optimization for feature selection and classifier design (JMO-FSCD). Their proposed method incorporated a neural network as the classifier and employed a non-iterative algorithm for training, ensuring efficient performance and rapid learning. To optimize both feature selection and classifier simultaneously, a new coding scheme was devised. To validate the effectiveness of the proposed approach, they compared its performance with six state-of-the-art FS algorithms. Experimental results on thirty-five benchmark datasets demonstrated the superior performance of JMO-FSCD. Yongbin et al.12 presented a hybrid feature selection method called HFSIA to address the challenge of feature reduction in high-dimensional data. The proposed model effectively combined the filter method with a metaheuristic-based search strategy. To enhance the search performance of the algorithm they incorporated a Cauchy mutation operator and a lethal mutation mechanism with adaptive adjustment factors. The performance of HFSIA was evaluated through experimental comparisons on 22 high-dimensional benchmark datasets, where it was compared against 23 state-of-the-art feature selection methods. The results indicated that HFSIA achieved a computational cost that was comparable to 5 classical feature selection methods.Akinola et al.29 presented a survey that conducted a thorough examination of hybrid feature selection algorithms used in the analysis of microarray gene expression data for cancer classification. The main objective was to integrate diverse feature selection techniques to identify relevant genes that significantly contribute to accurate cancer classification. Different hybrid models, including combinations of filters, wrappers and embedded methods, were discussed and compared in terms of their limitations, advantages, and performance characteristics. More so, the survey offers a comprehensive overview of hybrid feature selection methods in the analysis of microarray gene expression data for cancer classification. Alhenawi et al.30 presented a systematic review centered on the utilization of feature selection models in the analysis of gene expression microarray data for cancer classification. The primary objective was to conduct a thorough and comprehensive analysis of the various feature selection techniques employed in this context. It explored and compared different approaches, including, wrapper, filter and embedded methods. Furthermore, it indicated that the research direction of presented hybrid feature selection algorithms had the highest percentage of 34.9%, suggesting it as the most compelling area of study. Other research directions had lower percentages ranging from 13.6% to 3%. This information serves as a valuable guide for researchers in selecting the most competitive research direction. It considered six key perspectives: methods employed, classifiers used, datasets utilized, range of dataset dimensions, performance metrics evaluated, and the results achieved. A comprehensive overview of hybrid feature selection techniques for analyzing gene expression microarray data in breast cancer was proposed by Mohd et al.31. Their work focused more on combining metaheuristic algorithms with feature selection methods to identify the most informative and relevant genes for breast cancer classification. Various metaheuristic approaches such as genetic algorithms, particle swarm optimization, ant colony optimization, and simulated annealing were discussed, highlighting their advantages, limitations, and applications in breast cancer research. Overall, this review provided researchers with valuable insights into the current state-of-the-art hybrid feature selection approaches for breast cancer gene expression microarray data, enabling a comprehensive understanding of the field's advancements.Almugren32 developed a novel computational technique to identify informative genes for early cancer diagnosis. Through the application of three methods , maximum neighborhood component (MCN) and node degree), eight common hub genes were identified: ASPM, CDK1, KIF11, TOP2A, AURKB, CCNB2, CENPE, and CCNA2. Enrichment analysis revealed their involvement in various pathways, including focal adhesion, ECM-receptor interaction, melanoma and prostate cancer pathways. Kaplan–Meier survival analysis demonstrated the potential of these hub genes as prognostic and diagnostic biomarkers for breast cancer. Dhirachaikulpanich et al.33 utilized microarray and RNASeq data integration to identify age-related macular degeneration (AMD) associated pathways and differentially expressed genes. Their findings revealed two novel pathways: the neuroactive-ligand receptor interaction pathway, and the extracellular matrix (ECM) receptor interaction pathway, which exhibited high enrichment in DEGs related to AMD. Additionally, a protein–protein interaction network analysis identified HDAC1 and CDK1 as central hub genes, involved in regulating cell proliferation and differentiation processes. Hozhabri et al.34 conducted an integration analysis of four microarray gene expression datasets related to colorectal cancer (CRC) obtained from the GEO database. They performed differential expression analysis, as well as enrichment analyses for Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The results revealed that the regulation of cell proliferation, bicarbonate transport, Wnt and IL-17 signaling pathways, and nitrogen metabolism were among the most significantly associated pathways with the identified differentially expressed genes.Elbashir et al.35 employed a comprehensive approach that involved the identification of overlapping genes between DEGs and WGCNA, leading to subsequent GO and KEGG analyses. This method allowed them to identify hub genes, which were then subjected to survival analysis. Interestingly, among the ten hub genes, only SPP1 demonstrated a significant impact on lung cancer survival. The authors further delved into the analysis of SPP1, predicting associated miRNAs and lncRNAs, which were subsequently utilized for a rigorous survival analysis. In a related study, Niemira et al.36 leveraged WGCNA to explore molecular networks associated with a range of clinical traits, including tumor size, SUVmax, BMI, smoking status, recurrence-free survival, and disease-free survival. Their findings highlighted the significance of a more profound investigation into the identified genes and pathways, particularly those linked to the tumor microenvironment and mechanisms related to immune evasion in adenocarcinoma (ADC) and squamous cell carcinoma (SCC). Furthermore, they constructed a protein–protein interaction network of the DEGs using Cytoscape software, leading to the identification of key hub genes, such as MYC, CXCL1, CD44, MMP1, and CXCL12. Nisar et al.37 conducted an integration analysis of microarray and RNASeq gene expression data in the context of pancreatic cancer (PaCa) to unearth differentially expressed genes. Their approach included a protein–protein interaction (PPI) network analysis and pathway investigations. Their results shed light on the significance of hub genes, including ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFb1 in PaCa. Moreover, their analysis revealed two critical pathways, namely the ECM-receptor interaction and focal adhesion pathways, which play crucial roles in the development and progression of PaCa. For a concise overview of related work in the literature, please refer to Table Luo et al.https://portal.gdc.cancer.gov/) The GDCquery function, available in the TCGAbiolinks library, was employed to query the data41. The lung cancer dataset consisted of 1208 clinical samples and 14,895 genes or features. Among these, there were 113 paracancerous normal tissues and 1095 tumor samples. Due to the presence of noise and numerous other features, various pre-processing steps namely, outlier removal, normalization, and filtration were implemented to obtain clean data that specifically contributed to lung cancer detection. To identify outlier samples, we calculated the array-array intensity correlation (AAIC), which measures the Spearman correlation between samples39. Using a cutoff value of 0.6, samples exceeding this threshold were considered outliers and removed from the analysis. Normalization was applied to the gene expression data to ensure the accuracy of expression levels and eliminate biases in the analysis. The TCGAanalyze-Normalization function from the TCGAbiolinks library was employed for this purpose. Subsequently, filtration was performed by selecting genes with mean expression values above a cutoff value of 0.25, resulting in a reduction in the number of genes39. As a result of these pre-processing steps, the dataset consisted of 1208 clinical samples and 14,895 genes. Figure We utilized the R software to analyze the lung cancer gene expression data obtained from the Cancer Genome Atlas (TCGA) repository , decision tree (DT), k-nearest neighbors (KNN), random forest (RF), multi-layer perceptron (MLP), and Gaussian Naïve Bayes (GNB). The VBEOSA (Voting Binary Ebola Optimization Search Algorithm) model is an enhanced version of the BEOSA algorithm that incorporates a voting mechanism to improve feature selection and classification performance. It leverages the collective decision-making capabilities of multiple classification models. The VBEOSA model follows a similar iterative process as the BEOSA algorithm, but with the addition of a voting step. Initially, a population of binary strings is initialized, representing potential subsets of features. The fitness of each binary string is evaluated by applying multiple classification models to the corresponding feature subset. This evaluation is based on performance metrics.Following the completion of the pre-processing step, the pre-processed dataset was utilized as input for the VBEOSA model to identify the top 50 genes that exhibited high significance. A recent metaheuristic algorithm BEOSA44. Soft voting was employed to combine the classification models in this study. Soft voting involves averaging the class probabilities predicted by each classification model to make the final prediction. By considering the aggregated probabilities, the soft voting approach leverages the strengths and expertise of each individual model, leading to improved classification accuracy and more robust predictions. The voting mechanism is then applied to determine the overall fitness of each binary string. This can involve a majority voting scheme, where each classification model's prediction contributes to the final decision, or weighted voting that assigns more weight to certain models based on their performance. The binary strings with higher fitness, determined by the voting mechanism, are selected for the next generation. Genetic operators like crossover and mutation are applied to the selected binary strings to generate offspring, promoting diversity and exploration. The newly generated offspring replace some of the original binary strings in the population. This process continues for a specified number of iterations or until a termination criterion is met.Ensemble-based classifiers are meta-classifiers that combine multiple machine learning classifiers for classification tasks. They utilize either hard voting, which involves selecting the majority prediction from the individual classifiers, or soft voting, which averages the class probabilities predicted by each classifier. Hard voting relies on the majority vote to make the final predictionThe VBEOSA model combines the predictions from multiple classification models using the voting mechanism, which allows for collective decision-making. By integrating the voting mechanism into the BEOSA algorithm, the VBEOSA model enhances feature selection accuracy and robustness. It improves the identification of relevant features by considering the consensus among multiple classification models. The selected features can be used for subsequent analysis or classification tasks. In summary, the VBEOSA model provides an innovative approach to feature selection by leveraging the power of multiple classification models through a voting mechanism. It enhances the analysis and interpretation of gene expression data by selecting informative features and improving classification accuracy. The algorithm listing 1 represents the pseudocode for VBEOSA algorithmic design steps.The algorithm was presented through the flowchart outlined in this subsection. First, we introduce the algorithmic formalization, as shown in Algorithm listing 1. The algorithm requires input values for To facilitate the discussion of the proposed VBEOSA method, we provide a summary of the mathematical models used in the techniques. The population initialization of EOSA is represented by Eq. , as follIn the context of the optimization problem, rand represents a randomly generated real number, while L and U denote the lower and upper bounds, respectively. Equation  describe46 provide detailed calculations for the allocation of individuals to compartments Q (Quarantine), S (Susceptible)), R (Recovered), V (Vaccinated), I (Infected), H , and D (Dead).ReferencesBEOSA introduced four transformation functions to locate infected individuals in the discrete space. These functions are categorized as S-functions and V-functions, with two functions belonging to each category. Equations  and 4)  4) definnd, Eqs.  and 6)  \documenThe purpose of applying these transform functions is to facilitate the conversion of feature positions within an individual to either 0 or 1. These functions also enhance the likelihood of altering the original composition of the individual, making it a potential solution for feature selection problems. This concept is demonstrated through Eqs.  and 6)..6). The The calculation of the fitness and cost functions in this paper depends heavily on the classification accuracy achieved by various classifiers when applied to a selected part of the dataset. Moreover, the main goal of the study is to analyze the effect of different widely used classifiers on solving the feature selection problems such as K-nearest-neighbor (KNN), random forest (RF), multi-layer perceptron (MLP), decision tree (DT), support vector machine (SVM), Gaussian Naive Bayes (GNB), and soft voting. These classifiers were explored to assess their efficacy in addressing the feature selection challenge. Based on this approach, the number of features selected for a random individual, denoted as sing Eq. . In thisThis equation essentially sums up the count of feature positions with a value of 1 in In order to assess the effectiveness of our model, we conducted a comprehensive evaluation using a range of performance metrics, which encompass classification accuracy, precision, recall, F1-score, and the area under the curve (AUC). In Eqs. –12), th, th12), http://geneontology.org) offers structured and computable information about gene and gene product functions. Established in 1998, GO has gained significant recognition in the life sciences field and is constantly evolving in terms of its content, encompassing both increased quantity and improved quality47. The GO is responsible for identifying the biological process, molecular function, and cellular location associated with an organism's genes. It comprises two main components: the GO annotation and the ontology. The ontology is structured as a hierarchical tree of concepts called GO terms. The GO annotation refers to the list of all annotated genes that are associated with ontology terms, providing descriptions for these genes48.Biological functions common to cells or organisms with a well-defined nucleus are determined by a considerable portion of genes. If the biological function of a shared protein is known, it can be transferred from one organism to another. The Gene Ontology resource (GO)  Network Analysis is performed using the STRING biological database, which integrates information from diverse sources to predict functional interactions among proteins54. Furthermore, we applied WGCNA to the selected genes to explore the intricate expression patterns that existed among them. Genes displaying strong interrelationships within the network were thoughtfully grouped into distinct clusters, effectively giving rise to specific modules. These modules represent assemblies of genes exhibiting highly correlated expression patterns within an unsigned co-expression network. To facilitate their differentiation, genes with similar expression patterns were visually distinguished by the assignment of unique colors, as elaborated in references56.We utilized the WGCNA package in R to establish co-expressed gene modules and, notably, identified a significant gene module with the most robust correlation to lung cancer, as documented in Refs.In our experiments we utilized VBEOSA with specific parameter settings. The values assigned to the parameters entclass1pt{minimaentclass1pt{minimaIn our particular scenario, the gene expression data related to lung cancer exhibits notable noise and a significant imbalance in the distribution of tumors to the paracancerous normal tissues, resulting in one class having considerably more instances than the other. Despite these inherent challenges, our proposed model demonstrated robust overall performance when compared to other algorithms. To evaluate the model's effectiveness in handling gene expression data with common imbalance issues, we employed various classification metrics, including precision, recall, F1-score, and AUC. Furthermore, we implemented a voting approach as a meta-classifier, utilizing DT, GNB, KNN, MLP, RF, and SVM as our selected base learners. The outputs generated by these base learners were aggregated and passed to the meta-classifier, enhancing the model's ability to make final predictions and, in turn, improving its capacity to generalize effectively.The GO term analysis was conducted to investigate the functional characteristics and biological processes associated with the common differentially expressed genes (DEGs). By performing this analysis, we aimed to gain insights into the functional roles and molecular functions of the DEGs in the studied phenomenon. The analysis helps to identify enriched Gene Ontology (GO) terms that provide information about the biological processes, molecular functions, and cellular components related to the DEGs. This analysis enhances our understanding of the underlying biological mechanisms and pathways involved in the observed gene expression changes. We used Fisher's exact test to rank the genes based on their p-values, indicating the probability of each gene belonging to one of the GO term categories.The analysis of enriched GO terms in the biological process category reveals that our DEGs are significantly enriched and related to Vasodilation , Adenylate Cyclase Activating Adrenergic Receptor Signaling Pathway , Positive Regulation Of Adenylate Cyclase Activity , Positive Regulation Of Protein Kinase A Signaling , Adrenergic Receptor Signaling Pathway , Activation Of Adenylate Cyclase Activity , Desensitization Of G Protein-Coupled Receptor Signaling Pathway , Regulation Of Circadian Sleep/Wake Cycle, Sleep , Positive Regulation Of Necroptotic Process , and Positive Regulation Of Programmed Necrotic Cell Death  , Gap Junction , Pseudopodium , PRC1 Complex , Desmosome , Connexin Complex , Ficolin-1-Rich Granule , Endocytic Vesicle Lumen , Ficolin-1-Rich Granule Lumen , and Actin Filament  , Alcohol Dehydrogenase Activity, Zinc-Dependent , RNA Polymerase III Type 3 Promoter Sequence-Specific DNA Binding , RNA Polymerase III Cis-Regulatory Region Sequence-Specific DNA Binding , Alcohol Dehydrogenase (NAD +) Activity , Acting On Carbon–Nitrogen (But Not Peptide) Bonds, In Linear Amides , Water Channel Activity , Adrenergic Receptor Binding , CoA Hydrolase Activity , and Water Transmembrane Transporter Activity  , we conducted further investigations by exploring them in a protein–protein interaction (PPI) network. The PPI network was constructed using the STRING database and Cytoscape application, initially a first-order network was created, which resulted in a large network consisting of 49 nodes and 32 edges  to address the challenges of feature selection and classification in lung cancer. The algorithm combines the power of binary optimization and Ebola optimization search and leverages popular classification models, including Support Vector Machines (SVM), Decision Trees (DT), k-Nearest Neighbors (KNN), Random Forest (RF), Multi-Layer Perceptron (MLP), and Gaussian Naïve Bayes (GNB), through a soft voting mechanism to generate robust predictions. We applied VBEOSA to a lung cancer gene expression dataset obtained from the Cancer Genome Atlas (TCGA) repository. Before the feature selection process, we performed preprocessing steps to clean and prepare the dataset. This included outlier detection using array-array intensity correlation, normalization of gene expression data, and filtration based on mean expression values. The selected features were then used to identify hub genes related to lung cancer using protein–protein interaction (PPI) analysis methods.Through PPI analysis, we identified the top 10 hub genes associated with lung cancer using MCC, including ADRB2, ACTB, ARRB2, GNGT2, ADRB1, ACTG1, ACACA, ATP5A1, ADCY9, and ADRA1B. These hub genes are found to be significantly involved in lung cancer based on enrichment analysis. Pathway analysis reveals the most significant pathways associated with lung cancer, including Salivary secretion, Dilated cardiomyopathy, Renin secretion, Arrhythmogenic right ventricular cardiomyopathy, Vibrio cholerae infection, cGMP-PKG signaling pathway, Regulation of lipolysis in adipocytes, Vascular smooth muscle contraction, Calcium signaling pathway, and Circadian entrainment. Our results demonstrate the effectiveness of VBEOSA in selecting informative features and identifying key hub genes and pathways associated with lung cancer. This study contributes to a better understanding of the molecular mechanisms underlying lung cancer and provides insights into potential diagnostic and therapeutic targets.In this study, we harnessed the potential of the VBEOSA algorithm to identify 50 significant genes closely linked to lung cancer. Further exploration through protein–protein interaction (PPI) analysis led to the identification of a select group of 10 hub genes, ADRB2, ACTB, ARRB2, GNGT2, ADRB1, ACTG1, ACACA, ATP5A1, ADCY9, and ADRA1B, each of which plays a pivotal role in the context of lung cancer, as indicated by MCC analysis. Enrichment analysis provided strong confirmation of the substantial involvement of these hub genes in the disease. Notably, our research not only validated prior studies but also unearthed promising novel biomarkers for lung cancer. Pathway analysis shed light on several significant pathways, including Salivary secretion, Dilated cardiomyopathy, Renin secretion, among others, offering insights into the underlying molecular mechanisms of lung cancer. These findings bear significant implications for enhancing the diagnosis, prognosis, and the development of therapeutic strategies for lung cancer. In addition, our use of the WGCNA method revealed a distinct "gray module" displaying a particularly robust association with lung cancer, which was subsequently chosen for in-depth analysis.


Accurate, easily accessible and economically viable cancer diagnostic tools are pivotal in improving the abysmal 5% survival rate of pancreatic cancer.A novel, affordable, non‐invasive diagnostic method has been developed by combining measurement precision of infrared spectroscopy with classification using machine learning tools.Diagnosis accuracy as high as 90% has been achieved. The study investigated urine and blood from pancreas cancer patients and healthy volunteers, and significantly improved accuracy by focusing on sweet‐spots within blood plasma fractions containing molecules within a narrow range of molecular weights. The graphical table of contents depicts the flow of cancer information, data acquisition and diagnosis. Raman was deemed useful to allow deeper investigation into subcellular mechanisms.Amongst the vibrational spectroscopy instruments, the Fourier Transform Infrared (FTIR) spectroscopy offers an economically viable opportunity due to its low set up cost.Many of the investigations only demonstrate the potential of a particular method to produce a spectral biomarker between diseased and healthy samples on a relatively small sample set. In one exemplar study, FTIR was used on human blood serum and plasma looking at colorectal cancer.Another study on brain cancermolecular‐weight window would refer to the set of molecules whose molecular weights lie between an upper and lower cut‐off limits. This novel methodology identifies a sweet‐spot or the molecular‐weight window with high classification accuracy for a group of patients' diseases and discards molecules outside the window. Focusing on a narrow molecular‐weight window provides the precision and enables detection of the spectral changes due to a specific disease.A recent study by our research groupTo improve accuracy and reduce variability in the diagnosis of pancreatic cancer using blood and an affordable spectroscopy method.To investigate the suitability of noninvasively obtainable bio‐fluid, urine, for spectral diagnosis using this method.The goals of this research are as follows:22.1Figure . InsteadBlood plasma and urine samples were collected with ethical approval from the same cohort of patients from Morriston hospital, Swansea, UK between 2020 and 2022. IRAS ID: 252525. A full list of patients is given in Supplementary Information Table . For theFor the FTIR measurement, each fraction was diluted in a 1:24 ratio of sample to MilliQ ultrapure water before 500 μL being deposited on a 25 mm diameter Crystran CaF2 slide, ensuring the surface was covered to the edges and left to dry overnight for analysis. Each biofluid sample was first filtered through a 100 kDa filter, both filtrate (permeate) and concentrate (retentate) being collected, and the filtrate being moved on to further filtering using 50, 30, 10 and 3 kDa filters until six subsets of the plasma samples were produced. The subset windows 0–3, 3–10, 10–30, 30–50, 50–100, >100 kDa and whole plasma were all analysed for comparison. For the full cohort, only a 10 kDa filter was used, both whole and< 10 kDa plasma being analysed.2.2−1, and spectra were acquired for 5 s with 10 accumulations over a range of 750–4000 cm−1.FTIR spectra were acquired with a Perkin Elmer ‘Spectrum Two’ FTIR spectrometer used in transmission mode. Resolution was 4 cm2.3−1 fingerprint region of most interest and then pre‐processed with a background correction using the asymmetric least squares smoothing (ALSS) method.Spectra were trimmed to the 1000 data points in the 800–1800 cm2.4−1 range) and classified these samples with Support Vector Machine (SVM) classifiers. We used a linear SVM classifier with and without PCA. As we have more features than samples, which can sometimes lead to overfitting, we used PCA to reduce the number of dimensions in the original data. The results were validated with complete Leave‐One‐Out (LOO) cross‐validation. As the combination of PCA and SVM provided the best results, we also studied the cross‐validated accuracy of the classifier according to the number of principal components used. Representative spectra are given in S‐7 for comparison. Classification accuracy was also obtained from spectra for each of the different molecular weight fractions , which produced 70%–80% accuracy on the same patient samples ; software (lead); writing – original draft . Matthew Mortimer: Formal analysis ; investigation ; methodology ; validation . Bilal Al‐Sarireh: Conceptualization ; project administration ; resources ; writing – review and editing . Venkateswarlu Kanamarlapudi: Formal analysis ; investigation ; methodology ; writing – review and editing . Debdulal Roy: Conceptualization ; data curation ; formal analysis ; funding acquisition ; methodology ; project administration ; resources ; supervision ; writing – review and editing .There are no conflicts to declare.Ethical approval was obtained from The Wales Research Ethics Committee (REC) 7 and the SBU Joint Scientific Review Committee. Written informed consent was obtained from the participants.Data S1.Click here for additional data file.


Enhancers play a crucial role in gene regulation, and some active enhancers produce noncoding RNAs known as enhancer RNAs (eRNAs) bi-directionally. The most commonly used method for detecting eRNAs is CAGE-seq, but the instability of eRNAs in vivo leads to data noise in sequencing results. Unfortunately, there is currently a lack of research focused on the noise inherent in CAGE-seq data, and few approaches have been developed for predicting eRNAs. Bridging this gap and developing widely applicable eRNA prediction models is of utmost importance.In this study, we proposed a method to reduce false positives in the identification of eRNAs by adjusting the statistical distribution of expression levels. We also developed eRNA prediction models using joint gene expressions, DNA methylation, and histone modification. These models achieved impressive performance with an AUC value of approximately 0.95 for intra-cell prediction and 0.9 for cross-cell prediction.Our method effectively attenuates the noise generated by stochastic RNA production, resulting in more accurate detection of eRNAs. Furthermore, our eRNA prediction model exhibited significant accuracy in both intra-cell and cross-cell validation, highlighting its robustness and potential application in various cellular contexts.The online version contains supplementary material available at 10.1186/s12859-023-05547-y. Enhancers play a critical role in controlling gene transcription by interacting with cis-acting DNA regulatory regions. Unlike proximal regulatory elements, enhancers are located at distal positions from target genes –4. Over Regarding the database of enhancers, in the FANTOM5 enhancer atlas, 65,339 candidate eRNA enhancers were identified by detecting eRNA expression through cap analysis of gene expression (CAGE) sequencing across 1829 cell lines, providing coverage of enhancers in the majority of human cell types and tissues –15. In aRecently, a plethora of computational methods have emerged for the identification of enhancers. As Table In fact, previous studies analyzing CAGE-seq data have struggled to effectively account for the inherent stochasticity in RNA production, resulting in noisy measurements of eRNA expression.In this study, we have introduced novel statistical methods and prediction models that effectively filter out the noise associated with eRNA expression. Using the FANTOM5 database, we first fitted the data distribution of eRNA expression obtained from CAGE-seq using maximum likelihood estimation to identify highly reliable and effective eRNA expression. Subsequently, we developed a user-friendly eRNA prediction model that exhibits reduced reliance on specific omics data, thereby enabling researchers to predict the eRNA with greater accuracy.eRNAs are expressed at relatively low levels, so using the CAGE-seq method to detecte eRNA is susceptible to random transcriptional noise . In FANTIn previous studies, the determination of eRNA production by enhancers was often based on a threshold, typically considering TPM values greater than 0 as indicative of eRNA expression. However, in this study, we took a different approach by fitting the data distribution and estimating its parameters. This allowed us to capture both the distribution of noise signals and the true expression of eRNA more accurately. Using this method, we identified a total of 11,584 and 6939 true eRNAs in GM12878 and HepG2 cells. By employing this approach, we were able to reduce false positives in eRNA identification, improving the reliability of our results. EP300, an important marker of active enhancers, was used in our analysis. We separately calculated the overlap ratio of EP300 with the regions identified as noisy and true eRNA, based on the designated cutoff. In GM12878, we observed overlap ratios of 0.0573 and 0.268 for EP300 with the regions identified as noise and true eRNA, respectively. Similarly, in HepG2, the overlap ratios were 0.0159 and 0.125 for EP300 with noise regions and true eRNA regions. These overlap ratios indicate a significant difference between the two categories. By fitting the data distribution and estimating its parameters, we were able to preliminarily eliminate the noise associated with eRNA.It is crucial to get more accurate and reliable samples when building an accurate model. To enhance the accuracy of the model, we relied on additional omics data profiles to obtain more precise eRNA information. In this study, we utilized DNase hypersensitivity sites as indicators of chromatin states. Enhancers that have the ability to generate eRNA are generally located in open chromatin regions. By incorporating DNase-chip peak data, we established a more stringent approach for defining candidate regions. This allowed us to identify more accurate positive and negative samples. Figure Next, the DNA methylation level and the distribution of H3K27ac for the three groups of samples were calculated. As Fig. The Random Forest (RF) and Extreme Gradient Boosting (XGBoost) are high-performing machine-learning algorithms and can better perform imbalanced multiclassification. We employed algorithms to predict eRNA. They were evaluated on the independent test set using different combinations of omics features in the same cell. As shown in Fig. To improve the accuracy of the model, histone modification signals were incorporated as additional features. When both H3K27ac and DNA methylation were used as features, the AUC values of RF in GM12878, K562, and HepG2 were 0.9374, 0.9630 and 0.9564, respectively. The AUC values of XGBoost model were consistently higher than 0.943 across all three cell types. It was observed that using H3K27ac as a feature yielded better performance compared to using H3K9ac. Furthermore, the inclusion of gene expression as a feature significantly improved the predictive capabilities of the model. The combination of gene expression, DNA methylation, and H3K27ac as features led to a substantial increase in the predicted AUC of the RF model, exceeding 0.961. These results highlight the robustness of using omics data, such as H3K27ac and DNA methylation, for accurate prediction of eRNA.To evaluate the performance of our supervised method across various cell types, we conducted cross-cell validation. The training data and testing data were extracted from different cell types. In three cells, 539 positive regions and 34,401 negative regions were shared , we found that only using DNA sequences is not sufficient for eRNA prediction. As shown in Table Utilizing the model trained within each individual cell type, we were able to predict eRNA regions across all three cell types, including those regions that were previously defined as fuzzy. This approach, which involved leveraging accurate data for model training and subsequently utilizing the trained model to predict fuzzy regions, effectively improved the identification of eRNA regions. By employing this strategy, we were able to achieve higher accuracy compared to solely relying on the variability of eRNA expression. The RF model, utilizing joint DNA methylation and H3K27ac as features, yielded high values for AUC, Sn, and Sp. This model, being characterized by these features, proved to be an effective tool for analyzing and identifying eRNA regions in all three cell lines.As Fig. In addition, we compared our results with those from ChromHMM annotation. As expected, in the three cells, 72.79%, 71.24%, and 51.74% of the positive examples we identified were annotated as enhancements in ChromHMM  low levels of DNA methylation , 32; (2)https://fantom.gsc.riken.jp/5/datafiles/latest/extra/CAGE_peaks/hg19.cage_peak_phase1and2combined_tpm.osc.txt.gz. Due to the extensive coverage of cell types, we proposed that the 65,407 potential enhancers in FANTOM5 likely encompass all regions with the capability of expressing eRNA in human cells. After converting the reference genome from hg19 to GRch38, the dataset contains 65,399 enhancer regions. In the eRNA expression data obtained from CAGE-seq experiments, we observed two distinct data distributions. As an example, we can consider the eRNA data of GM12878. All eRNA expression levels are quantified as TPM. Then, the TPM was logarithmically transformed and linearly amplified using the following formula:Our study utilized data from the FANTOM5 database, which includes 65,407 enhancers identified across 1829 cell types detecting by CAGE-seq. The download link is: To better visualize the level of eRNA expression, we converted TPM values to mentclass2pt{minimAccordingly, the larger values on the right side of the distribution correspond to effective eRNA expression signals originating from enhancers. Based on the detection technology principle, the eRNA expression distribution follows a negative binomial distribution. We assume that the lower data measurements The final data distribution of fusion is:To enhance the reliability of positive and negative samples during the construction of our algorithm, we included DNaseI data to represent the chromatin state. DNaseI data provide valuable insights into the accessibility of chromatin regions, indicating whether a particular genomic region is open and accessible for transcription factors and other regulatory elements. As shown in Fig. In this study, the negative samples outweigh the positive samples, leading to an imbalanced dataset. To tackle this issue, Random forests and the Extreme Gradient Boosting models were employed, as they can tolerate data imbalance. The 65,399 regions were extracted from FANTOM5. According to the expression of CAGE-seq and the coincidence of DNaseI, these regions were divided into three sets: PR (positive regions), FR (fuzzy regions) and NR (negative regions). For the intra-cell analysis, 80% of the regions were allocated for training the model, while the remaining 20% were set aside for independent testing. For the cross-cell analysis, we used data from cells not included in the model training process.For the random forest model, the number of binary tree variables (mtry) and the number of decision trees (ntree) were two hyperparameters that were fine-tuned by evaluating the out-of-bag error (OOB) one by one. For XGBoost, there were three hyperparameters were considered: nroundsi, max_depthi, etai. They were also selected by OOB. To determine the best values for these hyperparameters, fivefold cross-validation was utilized.To evaluate the performance of models trained using active enhancers and various feature combinations, we utilized the following metrics for evaluation: (1) area under the ROC curve (AUC), (2) sensitivity (Sn), (3) specificity (Sp), (4) Matthew’s correlation coefficient (MCC), (5) F1-scoreAdditional file 1. The overlap of positive and negative samples across all three cells. The overlap ratio of positive and negative samples across three types of cells varies greatly. A large number of negative samples are shared among the three types of cells. But positive samples are rarely shared. In three cells, 539 positive regions and 34401 negative regions were shared. Shared negative regions account for 74.66% of all negative regions, while the shared proportion of positive cases is only 4.75%.Additional file 2. The datasets used in the study for extracting input features comes from ENCODE. The files were downloaded from the ENCODE database according to 'EncodeFile_ID'.Additional file 3. The importance of features. The importance of 32 features measured by Gini index in Random Forest model and XGBoost model.


Studies have shown that blood platelets contain tumour-specific mRNA profiles tumour-educated platelets (TEPs). Here, we aim to train a TEP-based breast cancer detection classifier.Platelet mRNA was sequenced from 266 women with stage I–IV breast cancer and 212 female controls from 6 hospitals. A particle swarm optimised support vector machine (PSO-SVM) and an elastic net-based classifier (EN) were trained on 71% of the study population. Classifier performance was evaluated in the remainder (29%) of the population, followed by validation in an independent set (37 cases and 36 controls). Potential confounding was assessed in post hoc analyses.Both classifiers reached an area under the curve (AUC) of 0.85 upon internal validation. Reproducibility in the independent validation set was poor with an AUC of 0.55 and 0.54 for the PSO-SVM and EN classifier, respectively. Post hoc analyses indicated that 19% of the variance in gene expression was associated with hospital. Genes related to platelet activity were differentially expressed between hospitals.We could not validate two TEP-based breast cancer classifiers in an independent validation cohort. The TEP protocol is sensitive to within-protocol variation and revision might be necessary before TEPs can be reconsidered for breast cancer detection. The introduction of mammographic screening has enabled detection of breast cancer in asymptomatic women, both in the general population, as well as in women with increased risk of breast cancer who are screened from a young age onwards. Breast cancer screening has increased early detection rates, thereby improving treatment opportunities and decreasing morbidity and mortality , 2. HoweImplementation of blood-based markers may not only improve the performance of breast cancer screening but also decrease the costs and increase compliance to the screening programme due to its low burden. Currently, a plethora of blood-based biomarkers is being explored for various applications in breast cancer. Most efforts focus on circulating tumour DNA and circulating tumour cells in the context of treatment response monitoring. However, these biomarkers may be less suited for breast cancer screening, because of their low levels in blood of patients with early stage disease and the heterogeneous nature of breast tumours –10.Previously, differences have been shown between the mRNA content from blood platelets of cancer patients and healthy controls , 12. AsiIn this multicentre study, the objective was to develop a classification algorithm on TEP mRNA profiles to distinguish patients with breast cancer from healthy controls. In six centres, blood samples were collected and platelets were isolated according to the published protocol . SubsequIn total, blood was collected for 553 women of 18 years or older , Amsterdam, The Netherlands, the Academic Medical Center, Amsterdam The Netherlands, the Utrecht Medical Center, Utrecht, The Netherlands, the Medical University of Vienna, Vienna, Austria, and Massachusetts General Hospital, Boston, USA. For the single-centre external validation set, peripheral whole blood was drawn by venipuncture from breast cancer patients and asymptomatic female controls at the Netherlands Cancer Institute (NKI-AVL), Amsterdam, The Netherlands. Breast cancer patients were diagnosed by clinical, radiological and pathological examination. Blood of breast cancer patients was collected before start of or during treatment. Asymptomatic controls were self-reported to be free of disease at the moment of blood collection, and were not subjected to additional tests confirming the absence of cancer or other disease. No power calculation was used to determine the number of samples required for algorithm development and validation. Samples for the multicentre set were collected and processed similarly and simultaneously according to a previously published protocol . Samplesg centrifugation step, after which the platelets were pelleted by a 20-min 360 × g centrifugation step. Next, 9/10th of the PRP was removed carefully to reduce the risk of contamination of the platelet preparation with nucleated cells.Whole blood was collected in 6- or 10 mL EDTA-coated Vacutainer tubes and were processed within 12  or 48 h  using standardised protocols as described previously . PlatelePlatelet pellets were suspended in RNA (Life Technologies) and after overnight incubation at 4 °C frozen at −80 °C. All RNA isolations and sequencing was performed at the VU University Medical Center, Amsterdam, The Netherlands. For RNA isolation, frozen platelets were thawed on ice and total RNA was isolated using the mirVana miRNA isolation kit . Platelet RNA was eluted in 30 µL elution buffer. Quality was assessed using the RNA 6000 Picochip . Platelet RNA samples with a RIN value >7 and/or distinctive rRNA curves were considered for subsequent analyses.To obtain sufficient platelet cDNA for robust RNA-seq library preparation, the samples were subjected to cDNA synthesis and amplification using the SMARTer Ultra Low RNA Kit for Illumina Sequencing v3 . Prior to amplification, all samples were diluted to ~500 pg/µL total RNA and again the quality was assessed using the Bioanalyzer Picochip. For samples with a stock yield below 400 pg/µL, a volume of two or more microlitres of total RNA  was used as input for the SMARTer amplification. Quality control of amplified cDNA was measured using the Bioanalyzer 2100 with DNA High Sensitivity chip (Agilent). All SMARTer cDNA synthesis and amplifications were performed together with a negative control, which was required to be negative by Bioanalyzer analysis. Samples with detectable fragments in the 300–7500 bp region were selected for further processing. All amplified platelet cDNA was first subjected to nucleic acid shearing by sonication (Covaris Inc) and subsequently labelled with single index barcodes for Illumina sequencing using the TruSeq Nano DNA Sample Prep Kit . To account for the low platelet cDNA input concentration, all bead clean-up steps were performed using a 15-min bead-cDNA binding step and a 12-cycle enrichment PCR. All other steps were according to the manufacturer’s protocol. Labelled platelet DNA library quality and quantity were measured using the DNA 7500 chip or DNA High Sensitivity chip (Agilent). High-quality samples with product sizes between 300 and 500 bp were pooled (12–19 samples per pool) in equimolar concentrations for shallow thromboSeq and submitted for 100 bp Single Read sequencing on the Illumina Hiseq 2500 and 4000 platform using version 4 sequencing reagents.6 intron-spanning reads in total after sequencing, we again sequenced the original TruSeq preparation of the sample and merged the read counts generated from the two individual FASTQ-files after HTSeq count summarisation. RNAs encoded on the mitochondrial DNA were excluded from downstream analyses. All subsequent statistical and analytical analyses were performed in R (version 4.0.3). Initial QC and filtering operations were performed according to the previously published pipeline . BrieflFor the elastic net, PSO-SVM, and some downstream analyses, TMM normalisation from the edgeR package , 18 was https://github.com/MyronBest/thromboSeq_source_code. Briefly, the count matrix was normalised using an approach based on the remove unwanted variation (RUV) method, proposed by Risso et al. [t test: (a) the p value between RUVg variable and case–control status must exceed 0.01, and (b) the p value between RUVg variable and the potentially confounding variable must be less than 0.01.In addition to TMM, the PSO-SVM and some downstream analyses utilised RUV factor correction, described in detail in Best et al.  and implo et al. . The RUVFemale breast cancer cases were stratified by stage, followed by random allocation to training, evaluation and internal validation subsets using a 40%–30%–30% schema. For the training and evaluation subsets, a proportionally sized number of age-matched female controls was selected randomly . Age mathttps://github.com/MyronBest/thromboSeq_source_code and is described in detail in Best et al. [The PSO-SVM was trained and evaluated using publicly available code t et al. , 16. BriThe elastic net was developed using publicly available code and standard methodologies from the caret package in R. The model was produced using the glmnet engine (method = “glmnet”) and ROC as the optimisation metric. Ten-fold cross-validation was employed to optimise the regularisation penalty lambda and the mixing parameter alpha. As the elastic net does not require a separate “evaluation” data partition, “training” and “evaluation” samples were combined to train the elastic net. To reduce the potential impact of sampling artefacts on classifier performance, we additionally trained a second elastic net with leave-one-out cross-validation (LOOCV). This experiment was not possible for the PSO-SVM due to the extremely long runtimes (estimated >1 year) for the PSO-SVM in an LOOCV setting.Classifier performance on validation sets for both the PSO-SVM and the elastic net was assessed using the R package pROC and ggplot2. Confidence intervals for the ROCs were calculated using the Delong method. Additional performance metrics were extracted via caret::confusionMatrix.t-SNE visualisations were generated via the R package Rtsne. All t-SNE visualisations were performed on TMM-cpm-log2 corrected counts as described above. Where indicated, some t-SNE plots depicted counts normalised by RUV-correction prior to further normalisation steps.All differential expression tests were performed using the edgeR package in R. TMM normalisation was applied beforehand, using unmodified edgeR methodology. Significance was determined using a quasi-likelihood test. For pairwise comparisons between hospitals, the design formula was ~ age + group + hosp, where “group” indicates case–control status. Additional comparisons were subsetted to produce count matrices containing either cases only, or controls only. These comparisons were made using the design formula ~age + hosp.A comparison of the efficacy of batch correction methods was performed between the ComBat method from the R package sva, and RUV-based correction, described above. With given batch factors , RUV was used as a batch correction method in addition to a normalisation method. This was not intended as an exhaustive exploration of all available batch correction methods, but rather as a comparison between RUV-based correction, which was implemented in the previous PSO-SVM-based TEP-cancer publications , 16, andThe variance partition analysis was performed using the eponymous R package (variancePartition) . VariancWe utilised blood platelets from 266 patients with invasive breast cancer and 212 healthy controls from six different centres to develop and assess performance of a breast cancer classifier based on RNA-sequencing data. The study design is visualised in Fig. n = 106, 40.8%), evaluation  and internal validation  subsets. For the training and evaluation subsets, a proportionally sized number of age-matched controls was selected randomly, while the remainder of the controls was assigned to the internal validation set. Descriptive statistics for the total multicentre study population and for each of the three subsets are presented in Table Breast cancer cases were stratified by stage, followed by random allocation to the training n = 06, 40.8%Due to extensive computing time , it is not feasible to test more than a few randomly assigned data partitions with the PSO-SVM. To assess the impact of sample partitioning on performance, and to provide a baseline performance against which to measure the PSO-SVM, an alternative classifier was trained using an elastic net regression approach (EN). Elastic nets are less computationally intensive and are therefore well suited to nested cross-validation.For optimal comparison with the PSO-SVM, the EN was trained by 10-fold cross validation using the same data partitions as the PSO-SVM. Since the EN does not require an evaluation set, the training and evaluation sets of the PSO-SVM were combined for training of the EN. Performance of both classifiers in the internal validation set was high, with an area under the curve (AUC) of 0.85 (95% CI 0.79–0.92) for the PSO-SVM and 0.85 (95% CI 0.78–0.91) for the EN classifier  approach, during which a single sample is randomly assigned to be the “test” sample, while the remainder of the dataset is used for training. This process is repeated until every sample has been “left out”, after which performance can be calculated. The LOOCV- EN classifier also performed well, with an AUC of 0.83 (95% CI: 0.79–0.87). Taken together, the PSO-SVM performed well, but not better than the baseline established by the EN classifier Table .Table 2Pp < 0.001; Fig. Although efforts were made to ensure the study design was balanced regarding case–control distribution from participating centres, it was impossible to achieve a completely balanced design due to limited sample availability. Analysis of the multicentre dataset demonstrated a strong correlation between hospital of origin and case–control status . Platelet activation was the top upregulated term in MGH vs NKI  conducted at the NKI after the multicentre study. These samples were collected, processed and sequenced according to the same protocol as applied in the multicentre study. Isolation of mRNA, sequencing and classification was performed blinded, and case–control status was predicted by the PSO-SVM and EN classifiers described in the previous section. Classification labels were subsequently linked to true class labels by an independent third party. Descriptive statistics of the study population are provided in Table Finally, we applied the NKI-only EN classifier to the external validation dataset. Since all samples in the training and validation sets are from the same centre, this analysis should be free of confounding by hospital of origin. Unfortunately, performance on the external validation dataset remained poor, with an AUC of 0.62  and the same study did show enhanced platelet activation related gene expression in controls vs cases, which was not discussed in depth [First, we found that gene expression in platelets was heavily influenced by hospital of origin, despite all hospitals using the same platelet-processing protocol, and despite technicians at the NKI-AVL being trained in platelet isolation by technicians of the VUMC. Specifically, platelet activation related genes were differentially expressed between the three largest contributing hospitals, independent of case–control status. This may possibly be explained by differences in processing allowed for within the protocol, such as the time allowed between blood withdrawal and platelet isolation. According to the protocol, whole blood can be stored at room temperature for up to 48 h before platelet isolation . Howeverin depth . Since din depth . AlthougTo bypass the confounding effect of hospital we trained and tested a classifier solely on samples from a single hospital, which did not result in improved classifier performance. This suggests that batch effects are not only relevant between hospitals, but also within hospitals, indicating that the protocol is highly sensitive to subtle variations in execution other than the variables mentioned earlier. Moreover, batch correction as implemented in the classifier training portion of the protocol, proved unsuccessful in removing hospital-related confounding. In future studies these batch-related issues may in part be addressed by centralised sample processing. However, sample transportation time and temperature may influence TEP gene expression results, which is another concern. In addition, before TEP-based biomarkers can be implemented clinically, showing analytical validity of the protocol by reproducing study results in other centres is mandatory.Given the poor performance of the classifiers in the external validation set, and the lack of a leukodepletion step in the TEP protocol underlying our study, we investigated sample quality in post-hoc analyses. While the samples in the external validation set showed higher CD3 subunit expression than the samples in the original set, indicative of leucocyte contamination, no correlation was found with incorrect classification of the samples. Previous multi-centre studies based on the current TEP protocol have resulted in highly accurate classifiers without a leukodepletion step, but expression of leucocyte-specific markers was not reported , 13–15. The majority of the gene expression signal in our study was attributable to residual factors, which might be explained by variables such as use of medication or presence of other systemic disease. Our study contains some individuals with breast cancer who had started treatment at the time of blood withdrawal. While previous studies have shown that the TEP profile seems to normalise after tumour resection, impact of systemic treatment on platelet gene expression has not been investigated . Given tIn our study, the top genes that were selected in the PSO-SVM and EN classifiers did not correspond to the top genes that were associated with case–control status, indicating that there is no substantial signal in the data that can be utilised for reliable classifier training. In addition, the top classifier features do not represent known breast cancer biology, which raises uncertainty on their utility as biomarkers. The lack of a breast cancer signal may in part be explained by the small amounts of TEP mRNA subjected to PCR, which may give rise to PCR artefacts. In addition, only reads that span the splice junction were used for feature selection, which left out the majority of mRNA sequencing information that might be useful for classification.Our findings may have impact on the interpretation of previously published papers on TEP RNA based classifiers. A pan-cancer detection algorithm that was published in 2015 showed a detection accuracy of 96% using an internal validation subset, and correct tumour classification over six tumour types of 71% . SimilarIn conclusion, we were unable to successfully validate a TEP RNA based breast cancer detection classifier in a single-centre, independent, blinded study. Both elastic net and particle swarm-based classifiers performed poorly. The gene expression profile was severely influenced by hospital of origin and other factors unrelated to case–control status, suggesting that the wet lab protocol is highly sensitive to within-protocol variations in execution. Therefore, thorough revision of the protocol is necessary before TEP RNA based classifiers can be reconsidered for breast cancer detection in the future.Data SupplementSupplemental Table 1Supplemental Tables 2-6Supplemental Figures