Variable Selection in Bayesian Multiple Instance Regression using Shotgun Stochastic Search.

In multiple instance learning (MIL), a bag represents a sample that has a set of instances, each of which is described by a vector of explanatory variables, but the entire bag only has one label/response. Though many methods for MIL have been developed to date, few have paid attention to interpretability of models and results. The proposed Bayesian regression model stands on two levels of hierarchy, which transparently show how explanatory variables explain and instances contribute to bag responses. Moreover, two selection problems are simultaneously addressed; the instance selection to find out the instances in each bag responsible for the bag response, and the variable selection to search for the important covariates. To explore a joint discrete space of indicator variables created for selection of both explanatory variables and instances, the shotgun stochastic search algorithm is modified to fit in the MIL context. Also, the proposed model offers a natural and rigorous way to quantify uncertainty in coefficient estimation and outcome prediction, which many modern MIL applications call for. The simulation study shows the proposed regression model can select variables and instances with high performance (AUC greater than 0.86), thus predicting responses well. The proposed method is applied to the musk data for prediction of binding strengths (labels) between molecules (bags) with different conformations (instances) and target receptors. It outperforms all existing methods, and can identify variables relevant in modeling responses.

Molecular and Metabolic Phenotyping of Hepatocellular Carcinoma for Biomarker Discovery: A Meta-Analysis.

Identifying and translating hepatocellular carcinoma (HCC) biomarkers from bench to bedside using mass spectrometry-based metabolomics and lipidomics is hampered by inconsistent findings. Here, we investigated HCC at systemic and metabolism-centric multiomics levels by conducting a meta-analysis of quantitative evidence from 68 cohorts. Blood transcript biomarkers linked to the HCC metabolic phenotype were externally validated and prioritized. In the studies under investigation, about 600 metabolites were reported as putative HCC-associated biomarkers; 39, 20, and 10 metabolites and 52, 12, and 12 lipids were reported in three or more studies in HCC vs. Control, HCC vs. liver cirrhosis (LC), and LC vs. Control groups, respectively. Amino acids, fatty acids (increased 18:1), bile acids, and lysophosphatidylcholine were the most frequently reported biomarkers in HCC. BAX and RAC1 showed a good correlation and were associated with poor prognosis. Our study proposes robust HCC biomarkers across diverse cohorts using a data-driven knowledge-based approach that is versatile and affordable for studying other diseases.

Alterations of lipid-related genes during anti-tuberculosis treatment: insights into host immune responses and potential transcriptional biomarkers.

Background: The optimal diagnosis and treatment of tuberculosis (TB) are challenging due to underdiagnosis and inadequate treatment monitoring. Lipid-related genes are crucial components of the host immune response in TB. However, their dynamic expression and potential usefulness for monitoring response to anti-TB treatment are unclear. Methodology: In the present study, we used a targeted, knowledge-based approach to investigate the expression of lipid-related genes during anti-TB treatment and their potential use as biomarkers of treatment response. Results and discussion: The expression levels of 10 genes (ARPC5, ACSL4, PLD4, LIPA, CHMP2B, RAB5A, GABARAPL2, PLA2G4A, MBOAT2, and MBOAT1) were significantly altered during standard anti-TB treatment. We evaluated the potential usefulness of this 10-lipid-gene signature for TB diagnosis and treatment monitoring in various clinical scenarios across multiple populations. We also compared this signature with other transcriptomic signatures. The 10-lipid-gene signature could distinguish patients with TB from those with latent tuberculosis infection and non-TB controls (area under the receiver operating characteristic curve > 0.7 for most cases); it could also be useful for monitoring response to anti-TB treatment. Although the performance of the new signature was not better than that of previous signatures (i.e., RISK6, Sambarey10, Long10), our results suggest the usefulness of metabolism-centric biomarkers. Conclusions: Lipid-related genes play significant roles in TB pathophysiology and host immune responses. Furthermore, transcriptomic signatures related to the immune response and lipid-related gene may be useful for TB diagnosis and treatment monitoring.

Genome-wide kinase-MAM interactome screening reveals the role of CK2A1 in MAM Ca2+ dynamics linked to DEE66.

The endoplasmic reticulum (ER) and mitochondria form a unique subcellular compartment called mitochondria-associated ER membranes (MAMs). Disruption of MAMs impairs Ca2+ homeostasis, triggering pleiotropic effects in the neuronal system. Genome-wide kinase-MAM interactome screening identifies casein kinase 2 alpha 1 (CK2A1) as a regulator of composition and Ca2+ transport of MAMs. CK2A1-mediated phosphorylation of PACS2 at Ser207/208/213 facilitates MAM localization of the CK2A1-PACS2-PKD2 complex, regulating PKD2-dependent mitochondrial Ca2+ influx. We further reveal that mutations of PACS2 (E209K and E211K) associated with developmental and epileptic encephalopathy-66 (DEE66) impair MAM integrity through the disturbance of PACS2 phosphorylation at Ser207/208/213. This, in turn, causes the reduction of mitochondrial Ca2+ uptake and the dramatic increase of the cytosolic Ca2+ level, thereby, inducing neurotransmitter release at the axon boutons of glutamatergic neurons. In conclusion, our findings suggest a molecular mechanism that MAM alterations induced by pathological PACS2 mutations modulate Ca2+-dependent neurotransmitter release.

Multiple instance neural networks based on sparse attention for cancer detection using T-cell receptor sequences.

Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in tumor biology. However, the one-to-many correspondence between a patient and multiple TCR sequences hinders researchers from simply adopting classical statistical/machine learning methods. There were recent attempts to model this type of data in the context of multiple instance learning (MIL). Despite the novel application of MIL to cancer detection using TCR sequences and the demonstrated adequate performance in several tumor types, there is still room for improvement, especially for certain cancer types. Furthermore, explainable neural network models are not fully investigated for this application. In this article, we propose multiple instance neural networks based on sparse attention (MINN-SA) to enhance the performance in cancer detection and explainability. The sparse attention structure drops out uninformative instances in each bag, achieving both interpretability and better predictive performance in combination with the skip connection. Our experiments show that MINN-SA yields the highest area under the ROC curve scores on average measured across 10 different types of cancers, compared to existing MIL approaches. Moreover, we observe from the estimated attentions that MINN-SA can identify the TCRs that are specific for tumor antigens in the same T cell repertoire.

An overview of heavy-tail extensions of multivariate Gaussian distribution and their relations.

Many extensions of the multivariate normal distribution to heavy-tailed distributions are proposed in the literature, which includes scale Gaussian mixture distribution, elliptical distribution, generalized elliptical distribution and transelliptical distribution. The inferences for each family of distributions are well studied. However, extensions are overlapped or similar to each other, and it is hard to differentiate one extension from the other. For this reason, in practice, researchers simply pick one of many extensions and apply it to the analysis. In this paper, to enlighten practitioners who should conduct statistical procedures not based on their preferences but based on how data look like, we comparatively review various extensions and their estimators. Also, we fully investigate the inclusion and exclusion relations of different extensions by Venn diagrams and examples. Moreover, in the numerical study, we illustrate visual differences of the extensions by bivariate plots and analyze different scatter matrix estimators based on the microarray data.

Netie: inferring the evolution of neoantigen-T cell interactions in tumors.

Neoantigens are the key targets of antitumor immune responses from cytotoxic T cells and play a critical role in affecting tumor progressions and immunotherapy treatment responses. However, little is known about how the interaction between neoantigens and T cells ultimately affects the evolution of cancerous masses. Here, we develop a hierarchical Bayesian model, named neoantigen-T cell interaction estimation (netie) to infer the history of neoantigen-CD8+ T cell interactions in tumors. Netie was systematically validated and applied to examine the molecular patterns of 3,219 tumors, compiled from a panel of 18 cancer types. We showed that tumors with an increase in immune selection pressure over time are associated with T cells that have an activation-related expression signature. We also identified a subset of exhausted cytotoxic T cells postimmunotherapy associated with tumor clones that newly arise after treatment. These analyses demonstrate how netie enables the interrogation of the relationship between individual neoantigen repertoires and the tumor molecular profiles. We found that a T cell inflammation gene expression profile (TIGEP) is more predictive of patient outcomes in the tumors with an increase in immune pressure over time, which reveals a curious synergy between T cells and neoantigen distributions. Overall, we provide a new tool that is capable of revealing the imprints left by neoantigens during each tumor's developmental process and of predicting how tumors will progress under further pressure of the host's immune system.

Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis.

Despite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O-) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865-0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.

Metabolic and Cardiovascular Benefits of Apple and Apple-Derived Products: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Background: Quantitative evidence of the metabolic and cardiovascular effects of apples (Malus domestica) is lacking in interventional studies. This study aimed to summarize the available evidence of the beneficial effects of apples and apple-derived products (ADPs) on metabolic and cardiovascular markers. Methods: Peer-reviewed randomized controlled trials (RCTs) were identified from four databases on May 3, 2021 and regularly updated until the end of May 2021. Demographic characteristics, intervention types, and evaluation parameters were extracted. A meta-analysis on the mean difference of change scores was conducted on commonly presented outcomes in the RCTs. Results: The metabolic and cardiovascular effects of diverse regimens, including whole apple, apple extract, and apple juice, were examined in 18 eligible RCTs. Nine common evaluation outcomes were eventually introduced to the meta-analysis, including total cholesterol (TC), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglyceride, glucose, insulin, C-reactive protein, and systolic/diastolic blood pressures. The levels of TC (-2.69 mg/dL; 95% CI: -5.43, 0.04 mg/dL) and LDL (-2.80 mg/dL; 95% CI: -5.78, 0.17 mg/dL) showed a non-significant decreasing tendency after at least a week of apple consumption. Further subgroup analysis, particularly, a comparison with placebo as a control, showed a significant reduction in TC and LDL levels. When stratified by the baseline level, subjects with high TC and LDL level were shown to have more benefits from the apple intake. Intriguingly, apple and ADPs significantly reduced HDL levels to a small extent (-1.04 mg/dL; 95% CI: -1.79, -0.29 mg/dL). The other markers were mostly unaffected by the intervention. Conclusion: Our investigation revealed that apples could improve blood cholesterol levels. Systematic Review Registration: [https://www.crd.york.ac.uk/prospero/], identifier [CRD42020215977].

Molecular perturbations in pulmonary tuberculosis patients identified by pathway-level analysis of plasma metabolic features.

Insight into the metabolic biosignature of tuberculosis (TB) may inform clinical care, reduce adverse effects, and facilitate metabolism-informed therapeutic development. However, studies often yield inconsistent findings regarding the metabolic profiles of TB. Herein, we conducted an untargeted metabolomics study using plasma from 63 Korean TB patients and 50 controls. Metabolic features were integrated with the data of another cohort from China (35 TB patients and 35 controls) for a global functional meta-analysis. Specifically, all features were matched to a known biological network to identify potential endogenous metabolites. Next, a pathway-level gene set enrichment analysis-based analysis was conducted for each study and the resulting p-values from the pathways of two studies were combined. The meta-analysis revealed both known metabolic alterations and novel processes. For instance, retinol metabolism and cholecalciferol metabolism, which are associated with TB risk and outcome, were altered in plasma from TB patients; proinflammatory lipid mediators were significantly enriched. Furthermore, metabolic processes linked to the innate immune responses and possible interactions between the host and the bacillus showed altered signals. In conclusion, our proof-of-concept study indicated that a pathway-level meta-analysis directly from metabolic features enables accurate interpretation of TB molecular profiles.

A 10-gene biosignature of tuberculosis treatment monitoring and treatment outcome prediction.

The clinical utility of blood transcriptomic biosignatures for the treatment monitoring and outcome prediction of tuberculosis (TB) remains limited. In this study, we aimed to discover and validate biomarkers for pulmonary TB treatment monitoring and outcome prediction based on kinetic responses of gene expression during treatment. In particular, differentially expressed genes (DEGs) were identified by time-series comparison. Subsequently, DEGs with the monotonic expression alterations during the treatment were selected. Ten consistently down-regulated genes (CD274, KIF1B, IL15, TLR1, TLR5, FCGR1A, GBP1, NOD2, GBP2, EGF) exhibited significant potential in treatment monitoring, demonstrated via biological and technical validation. Additionally, the biosignature showed potential in predicting the cured versus relapsed patients. Furthermore, the biosignature could be utilized for TB diagnosis, latent tuberculosis infection/active TB differential diagnosis, and risk of progression to active TB. Benchmarking analysis of the 10-gene biosignature with other biosignatures showed equivalent performance in tested data sets. In conclusion, we established a 10-gene transcriptomic biosignature that represents the kinetic responses of TB treatment. Subsequent studies are warranted to validate, refine and translate the biosignature into a precise assay to assist clinical decisions in a broad spectrum of TB management.

Effects of Oats (Avena sativa L.) on Inflammation: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Background: Oat and its compounds have been found to have anti-inflammatory effects. Through this systematic review and meta-analysis, we aimed to determine an evidence-based link between oat consumption and inflammatory markers. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. By the end of April 2021, we included randomized controlled trials (RCTs) that investigated the anti-inflammatory effect of oat and oat-related products through screening PubMed, Embase, Web of Science, ClinicalTrial.gov, and CENTRAL. Meta-analysis was conducted with a random-effect model on the standardized mean difference (SMD) of the change scores of inflammatory markers, including C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and interleukin-8 (IL-8). Subgroup analyses were conducted to stratify confounding variables. The risk of bias was evaluated using the Cochrane risk of bias tool and Grading of Recommendations, Assessment, Development and Evaluation (GRADE) was applied to report the quality of evidence. This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42021245844). Results: Systematic screening of five databases yielded 4,119 studies, of which 23 RCTs were finally selected. For the four systemic inflammatory markers analyzed, no significant alterations were found after oat consumption. However, oat intake was found to significantly decrease CRP levels in subjects with one or more health complications (SMD: -0.18; 95% CI: -0.36, 0.00; P = 0.05; I 2 = 10%). Furthermore, IL-6 levels were significantly decreased in subjects with dyslipidemia (SMD = -0.34; 95% CI: -0.59, -0.10; P = 0.006; I 2 = 0%). These beneficial effects might be attributed to the effects of avenanthramide and ß-glucan. Conclusions: Overall evidence supporting the alleviation of inflammatory response by oat intake was poor, calling for future studies including a larger sample size to confirm the findings.

Effects of ß-Cryptoxanthin on Improvement in Osteoporosis Risk: A Systematic Review and Meta-Analysis of Observational Studies.

Many studies have analyzed the effects of ß-cryptoxanthin (BCX) on osteoporosis and bone health. This systematic review and meta-analysis aimed at providing quantitative evidence for the effects of BCX on osteoporosis. Publications were selected and retrieved from three databases and carefully screened to evaluate their eligibility. Data from the final 15 eligible studies were extracted and uniformly summarized. Among the 15 studies, seven including 100,496 individuals provided information for the meta-analysis. A random effects model was applied to integrate the odds ratio (OR) to compare the risk of osteoporosis and osteoporosis-related complications between the groups with high and low intake of BCX. A high intake of BCX was significantly correlated with a reduced risk of osteoporosis (OR = 0.79, 95% confidence interval (CI) 0.70-0.90, p = 0.0002). The results remained significant when patients were stratified into male and female subgroups as well as Western and Asian cohorts. A high intake of BCX was also negatively associated with the incidence of hip fracture (OR = 0.71, 95% CI 0.54-0.94, p = 0.02). The results indicate that BCX intake potentially reduces the risk of osteoporosis and hip fracture. Further longitudinal studies are needed to validate the causality of current findings.

Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data.

Single-cell lineage tracing provides crucial insights into the fates of individual cells. Single-cell RNA sequencing (scRNA-seq) is commonly applied in modern biomedical research, but genetics-based lineage tracing for scRNA-seq data is still unexplored. Variant calling from scRNA-seq data uniquely suffers from "expressional drop-outs," including low expression and allelic bias in gene expression, which presents significant obstacles for lineage reconstruction. We introduce SClineager, which infers accurate evolutionary lineages from scRNA-seq data by borrowing information from related cells to overcome expressional drop-outs. We systematically validate SClineager and show that genetics-based lineage tracing is applicable for single-cell-sequencing studies of both tumor and non-tumor tissues using SClineager. Overall, our work provides a powerful tool that can be applied to scRNA-seq data to decipher the lineage histories of cells and that could address a missing opportunity to reveal valuable information from the large amounts of existing scRNA-seq data.

Isolation and Metabolic Assessment of Cancer Cell Mitochondria.

Mitochondrial metabolism plays an essential role in various biological processes of cancer cells. Herein, we established an experimental procedure for the metabolic assessment of mitochondria in cancer cells. We examined procedures for mitochondrial isolation coupled with various mitochondrial extraction buffers in three major cancer cell lines (PANC1, A549, and MDA-MB-231) and identified a potentially optimal and generalized approach. The purity of the mitochondrial fraction isolated by the selected protocol was verified using specific protein markers of cellular components, and the ultrastructure of the isolated mitochondria was also analyzed by transmission electron microscopy. The isolation procedure, involving a bead beater for cell lysis, a modified sucrose buffer, and differential centrifugation, appeared to be a suitable method for the extraction of mitochondria from cancer cells. Electron micrographs indicated an intact two-layer membrane and inner structures of mitochondria isolated by this procedure. Metabolomic and lipidomic analyses were conducted to examine the metabolic phenotypes of the mitochondria-enriched fractions and associated bulk cancer cells. A total of 44 metabolites, including malate and succinate, occurred at significantly higher levels in the mitochondrial fractions, whereas 51 metabolites, including citrate, oxaloacetate, and fumarate of the Krebs cycle and the oncometabolites glutamine and glutamate, were reduced in mitochondria compared to that in the corresponding bulk cells of PANC1. Similar patterns were observed in mitochondria and bulk cells of MDA-MB-231 and A549 cell lines. A clear difference between the lipid profiles of bulk PANC1, MDA-MB-231, and A549 and corresponding mitochondrial fractions of these cell lines was detected by principal component analysis. In conclusion, we developed an experimental procedure for a large-scale metabolic assessment for suborganelle metabolic profiling and multiple omics data integration in cancer cells with broad applications.

Research Quality-Based Multivariate Modeling for Comparison of the Pharmacological Effects of Black and Red Ginseng.

Black ginseng has various pharmacological activities, but only few studies have compared its pharmacological effects with those of red ginseng. We conducted an integrative systematic literature evaluation and developed a non-inferiority test based on the multivariate modeling approach to compare the pharmacological effects of red ginseng and black ginseng. We searched reported studies on the pharmaceutical effects and composition of ginsenosides and assigned numeric scores using nonlinear principal component analysis, based on discretization measures for the included publications. Downstream weighted linear regression models were constructed to study the eight major biological activities that are generally known to be exhibited by red ginseng. Our statistical model, based on available ordinal information gathered from previous literature, helped in comparing the overlapping effects of black ginseng. Black ginseng showed antioxidant effects comparable to those of red ginseng; however, this variant was inferior to red ginseng in enhancing immunity, relieving fatigue, alleviating depression/anxiety, decreasing body fat, and reducing blood pressure. We have showed a cost-efficient method to indirectly evaluate the biological effects of ginseng products using data from published articles. This method can also be used to compare the nutritional and medicinal value of herbal medicines that share similar compositions of bioactive compounds.

Bayesian multiple instance regression for modeling immunogenic neoantigens.

The relationship between tumor immune responses and tumor neoantigens is one of the most fundamental and unsolved questions in tumor immunology, and is the key to understanding the inefficiency of immunotherapy observed in many cancer patients. However, the properties of neoantigens that can elicit immune responses remain unclear. This biological problem can be represented and solved under a multiple instance learning framework, which seeks to model multiple instances (neoantigens) within each bag (patient specimen) with the continuous response (T cell infiltration) observed for each bag. To this end, we develop a Bayesian multiple instance regression method, named BMIR, using a Gaussian distribution to address continuous responses and latent binary variables to model primary instances in bags. By means of such Bayesian modeling, BMIR can learn a function for predicting the bag-level responses and for identifying the primary instances within bags, as well as give access to Bayesian statistical inference, which are elusive in existing works. We demonstrate the superiority of BMIR over previously proposed optimization-based methods for multiple instance regression through simulation and real data analyses. Our method is implemented in R package entitled "BayesianMIR" and is available at https://github.com/inmybrain/BayesianMIR.

Clustering of longitudinal interval-valued data via mixture distribution under covariance separability.

We consider the clustering of repeatedly measured 'min-max' type interval-valued data. We read the data as matrix variate data and assume the covariance matrix is separable for the model-based clustering (M-clustering). The use of a separable covariance matrix introduces several advantages in M-clustering, which include fewer samples required for a valid procedure. In addition, the numerical study shows that this structured matrix allows us to find the correct number of clusters more accurately compared to other commonly assumed covariance matrices. We apply the M-clustering with various covariance structures to clustering the longitudinal blood pressure data from the National Heart, Lung, and Blood Institute Growth and Health Study (NGHS).

An Integrative Data Mining and Omics-Based Translational Model for the Identification and Validation of Oncogenic Biomarkers of Pancreatic Cancer.

Substantial alterations at the multi-omics level of pancreatic cancer (PC) impede the possibility to diagnose and treat patients in early stages. Herein, we conducted an integrative omics-based translational analysis, utilizing next-generation sequencing, transcriptome meta-analysis, and immunohistochemistry, combined with statistical learning, to validate multiplex biomarker candidates for the diagnosis, prognosis, and management of PC. Experiment-based validation was conducted and supportive evidence for the essentiality of the candidates in PC were found at gene expression or protein level by practical biochemical methods. Remarkably, the random forests (RF) model exhibited an excellent diagnostic performance and LAMC2, ANXA2, ADAM9, and APLP2 greatly influenced its decisions. An explanation approach for the RF model was successfully constructed. Moreover, protein expression of LAMC2, ANXA2, ADAM9, and APLP2 was found correlated and significantly higher in PC patients in independent cohorts. Survival analysis revealed that patients with high expression of ADAM9 (Hazard ratio (HR)OS = 2.2, p-value < 0.001), ANXA2 (HROS = 2.1, p-value < 0.001), and LAMC2 (HRDFS = 1.8, p-value = 0.012) exhibited poorer survival rates. In conclusion, we successfully explore hidden biological insights from large-scale omics data and suggest that LAMC2, ANXA2, ADAM9, and APLP2 are robust biomarkers for early diagnosis, prognosis, and management for PC.

Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis.

Introducing novel biomarkers for accurately detecting and differentiating rheumatoid arthritis (RA) and osteoarthritis (OA) using clinical samples is essential. In the current study, we searched for a novel data-driven gene signature of synovial tissues to differentiate RA from OA patients. Fifty-three RA, 41 OA, and 25 normal microarray-based transcriptome samples were utilized. The area under the curve random forests (RF) variable importance measurement was applied to seek the most influential differential genes between RA and OA. Five algorithms including RF, k-nearest neighbors (kNN), support vector machines (SVM), naïve-Bayes, and a tree-based method were employed for the classification. We found a 16-gene signature that could effectively differentiate RA from OA, including TMOD1, POP7, SGCA, KLRD1, ALOX5, RAB22A, ANK3, PTPN3, GZMK, CLU, GZMB, FBXL7, TNFRSF4, IL32, MXRA7, and CD8A. The externally validated accuracy of the RF model was 0.96 (sensitivity = 1.00, specificity = 0.90). Likewise, the accuracy of kNN, SVM, naïve-Bayes, and decision tree was 0.96, 0.96, 0.96, and 0.91, respectively. Functional meta-analysis exhibited the differential pathological processes of RA and OA; suggested promising targets for further mechanistic and therapeutic studies. In conclusion, the proposed genetic signature combined with sophisticated classification methods may improve the diagnosis and management of RA patients.