Precision Medicine Research Articles

Promise and Peril of a Genotype-First Approach to Mendelian Cardiovascular Disease.

Precision medicine, which among other aspects includes an individual's genomic data in diagnosis and management, has become the standard-of-care for Mendelian cardiovascular disease (CVD). However, early identification and management of asymptomatic patients with potentially lethal and manageable Mendelian CVD through screening, which is the promise of precision health, remains an unsolved challenge. The reduced costs of genomic sequencing have enabled the creation of biobanks containing in-depth genetic and health information, which have facilitated the understanding of genetic variation, penetrance, and expressivity, moving us closer to the genotype-first screening of asymptomatic individuals for Mendelian CVD. This approach could transform health care by diagnostic refinement and facilitating prevention or therapeutic interventions. Yet, potential benefits must be weighed against the potential risks, which include evolving variant pathogenicity assertion or identification of variants with low disease penetrance; costly, stressful, and inappropriate diagnostic evaluations; negative psychological impact; disqualification for employment or of competitive sports; and denial of insurance. Furthermore, the natural history of Mendelian CVD is often unpredictable, making identification of those who will benefit from preventive measures a priority. Currently, there is insufficient evidence that population-based genetic screening for Mendelian CVD can reduce adverse outcomes at a reasonable cost to an extent that outweighs the harms of true-positive and false-positive results. Besides technical, clinical, and financial burdens, ethical and legal aspects pose unprecedented challenges. This review highlights key developments in the field of genotype-first approaches to Mendelian CVD and summarizes challenges with potential solutions that can pave the way for implementing this approach for clinical care.

Form Equals Function: Influence of Coacervate Architecture on Drug Delivery Applications.

Complex coacervates, formed through electrostatic interactions between oppositely charged polymers, present a versatile platform for drug delivery, providing rapid assembly, selective encapsulation, and responsiveness to environmental stimuli. The architecture and properties of coacervates can be tuned by controlling structural and environmental design factors, which significantly impact the stability and delivery efficiency of the drugs. While environmental design factors such as salt, pH, and temperature play a crucial role in coacervate formation, structural design factors such as polymer concentration, polymer structure, mixing ratio, and chain length serve as the core framework that shapes coacervate architecture. These elements modulate the phase behavior and material properties of coacervates, allowing for a highly tunable system. In this review, we primarily analyze how these structural design factors contribute to the formation of diverse coacervate architecture, ranging from bulk coacervates to polyion complex micelles, vesicles, and cross-linked gels, though environmental design factors are considered. We then examine the effectiveness of these architectures in enhancing the delivery and efficacy of drugs across various administration routes, such as noninvasive (e.g., oral and transdermal) and invasive delivery. This review aims to provide foundational insights into the design of advanced drug delivery systems by examining how the origin and chemical structure of polymers influence coacervate architecture, which in turn defines their material properties. We then explore how the architecture can be tailored to optimize drug delivery for specific administration routes. This approach leverages the intrinsic properties derived from the coacervate architecture to enable targeted, controlled, and efficient drug release, ultimately enhancing therapeutic outcomes in precision medicine.

Digital phenotyping from wearables using AI characterizes psychiatric disorders and identifies genetic associations.

Psychiatric disorders are complex and influenced by both genetic and environmental factors. However, studying the full spectrum of these disorders is hindered by practical limitations on measuring human behavior. This highlights the need for novel technologies that can measure behavioral changes at an intermediate level between diagnosis and genotype. Wearable devices are a promising tool in precision medicine, since they can record physiological measurements over time in response to environmental stimuli and do so at low cost and minimal invasiveness. Here we analyzed wearable and genetic data from a cohort of the Adolescent Brain Cognitive Development study. We generated >250 wearable-derived features and used them as intermediate phenotypes in an interpretable AI modeling framework to assign risk scores and classify adolescents with psychiatric disorders. Our model identifies key physiological processes and leverages their temporal patterns to achieve a higher performance than has been previously possible. To investigate how these physiological processes relate to the underlying genetic architecture of psychiatric disorders, we also utilized these intermediate phenotypes in univariate and multivariate GWAS. We identified a total of 29 significant genetic loci and 52 psychiatric-associated genes, including ELFN1 and ADORA3. These results show that wearable-derived continuous features enable a more precise representation of psychiatric disorders and exhibit greater detection power compared to categorical diagnostic labels. In summary, we demonstrate how consumer wearable technology can facilitate dimensional approaches in precision psychiatry and uncover etiological linkages between behavior and genetics.

Editorial: Precision oncology in the era of CRISPR-Cas9 technology

Editorial: Precision oncology in the era of CRISPR-Cas9 technology

Amyotrophic Lateral Sclerosis: Insights and New Prospects in Disease Pathophysiology, Biomarkers and Therapies.

Amyotrophic Lateral Sclerosis (ALS) is a severe neurodegenerative disorder marked by the gradual loss of motor neurons, leading to significant disability and eventual death. Despite ongoing research, there are still limited treatment options, underscoring the need for a deeper understanding of the disease's complex mechanisms and the identification of new therapeutic targets. This review provides a thorough examination of ALS, covering its epidemiology, pathology, and clinical features. It investigates the key molecular mechanisms, such as protein aggregation, neuroinflammation, oxidative stress, and excitotoxicity that contribute to motor neuron degeneration. The role of biomarkers is highlighted for their importance in early diagnosis and disease monitoring. Additionally, the review explores emerging therapeutic approaches, including inhibitors of protein aggregation, neuroinflammation modulators, antioxidant therapies, gene therapy, and stem cell-based treatments. The advantages and challenges of these strategies are discussed, with an emphasis on the potential for precision medicine to tailor treatments to individual patient needs. Overall, this review aims to provide a comprehensive overview of the current state of ALS research and suggest future directions for developing effective therapies.

Innovative Biomedical Interventions for Cardiovascular Risk Reduction and Health Promotion: A Review Study

Cardiovascular diseases (CVD) remain the leading cause of mortality worldwide, with increasing prevalence in both developed and developing regions. The complexity of CVD necessitates innovative approaches that combine biomedical research with clinical practice for effective prevention, diagnosis, and treatment. This chapter explores cutting-edge biomedical interventions to reduce cardiovascular risk and promote heart health. It focuses on recent advancements in biomarker identification, gene therapy, nanotechnology, and regenerative medicine, offering new strategies for early detection and targeted treatment of cardiovascular conditions. Additionally, the chapter highlights the role of precision medicine and personalized healthcare approaches in managing individual risk factors, including hypertension, dyslipidemia, and diabetes, while addressing lifestyle influences like nutrition and physical activity. Public health initiatives are also discussed, examining how biomedical research has contributed to the development of population-level interventions, such as community-based screening programs and digital health tools for monitoring cardiovascular health. By integrating biomedical innovation with preventive healthcare strategies, this chapter provides a comprehensive overview of the future directions in cardiovascular risk reduction, underscoring the importance of a multi-disciplinary approach for enhancing patient outcomes and promoting long-term cardiovascular wellness.

Revisiting ecological fallacy: are single-case experimental study designs even more relevant in the era of precision medicine?

Revisiting ecological fallacy: are single-case experimental study designs even more relevant in the era of precision medicine?

Advances in clinical research on pharmacological management of chemotherapy-induced constipation in gastrointestinal tumor: A perspective.

Gastrointestinal tumors, including those of the stomach, colon, rectum, and esophagus, present significant global health challenges. Chemotherapy, essential for treating these cancers, often causes constipation, adversely affecting patients' quality of life. This study examines the mechanisms behind chemotherapy-induced constipation, such as the direct impact of chemotherapeutic drugs on intestinal function, reduced fluid intake, decreased physical activity, opioid use, and psychological stress. While traditional treatments like stimulant and osmotic laxatives are commonly used, emerging therapies such as 5-HT4 receptor agonists and probiotics show promise. Traditional Chinese medicine offers additional strategies with herbal remedies and dietary adjustments. Future research should prioritize precision medicine, combining pharmacological and non-pharmacological approaches, and developing innovative therapeutics utilizing biologics and nanotechnology. Ongoing research is crucial for improving chemotherapy-induced constipation management, aiming to enhance treatment outcomes and the quality of life for chemotherapy patients with gastrointestinal tumors.

From Genomics to Metabolomics: Molecular Insights into Osteoporosis for Enhanced Diagnostic and Therapeutic Approaches.

Osteoporosis (OP) is a prevalent skeletal disorder characterized by decreased bone mineral density (BMD) and increased fracture risk. The advancements in omics technologies-genomics, transcriptomics, proteomics, and metabolomics-have provided significant insights into the molecular mechanisms driving OP. These technologies offer critical perspectives on genetic predispositions, gene expression regulation, protein signatures, and metabolic alterations, enabling the identification of novel biomarkers for diagnosis and therapeutic targets. This review underscores the potential of these multi-omics approaches to bridge the gap between basic research and clinical applications, paving the way for precision medicine in OP management. By integrating these technologies, researchers can contribute to improved diagnostics, preventative strategies, and treatments for patients suffering from OP and related conditions.

The role of NOP58 in prostate cancer progression through SUMOylation regulation and drug response

BackgroundProstate cancer is one of the leading causes of cancer-related deaths in men. Its molecular pathogenesis is closely linked to various genetic and epigenetic alterations, including posttranslational modifications like SUMOylation. Identifying biomarkers that predict outcomes and specific therapeutic targets depends on a comprehensive understanding of these processes. With growing interest in SUMOylation as a mechanism affecting prostate cancer-related genes, this study aimed to investigate the central role of SUMOylation in prostate cancer prognostics, focusing on the significance of NOP58.MethodsWe conducted a comprehensive bioinformatics analysis, integrating differential expression analysis, survival analysis, gene set enrichment analysis (GSEA), and single-cell transcriptomic analyses using data from The Cancer Genome Atlas (TCGA). Key genes were identified through intersections of Venn diagrams, Boralta algorithm signatures, and machine learning models. These signaling mechanisms were validated through experimental studies, including immunohistochemical staining and gene ontology analyses.ResultsThe dual-gene molecular subtype analysis with SUMO1, SUMO2, and XPO1 genes revealed significant differences in survival outcomes across molecular subtypes, further emphasizing the potential impact of NOP58 on SUMOylation, a key post-translational modification, in prostate cancer. NOP58 overexpression was strongly associated with shorter overall survival (OS), progression-free interval (PFI), and disease-specific death in prostate cancer patients. Immunohistochemical analysis confirmed that NOP58 was significantly overexpressed in prostate cancer tissues compared to normal tissues. ROC curve analysis demonstrated that NOP58 could distinguish prostate cancer from control samples with high diagnostic accuracy. Gene Ontology analysis, along with GSVA and GSEA, suggested that NOP58 may be involved in cell cycle regulation and DNA repair pathways. Moreover, NOP58 knockdown led to increased BCL2 expression and decreased Ki67 levels, promoting apoptosis and inhibiting cell proliferation. Colony formation assays further showed that NOP58 knockdown inhibited, while its overexpression promoted, colony formation, highlighting the critical role of NOP58 in prostate cancer cell growth and survival. Additionally, NOP58 was linked to drug responses, including Methotrexate, Rapamycin, Sorafenib, and Vorinostat.ConclusionNOP58 is a key regulator of prostate cancer progression through its mediation of the SUMOylation pathway. Its expression level serves as a reliable prognostic biomarker and an actionable therapeutic target, advancing precision medicine for prostate cancer. Targeting NOP58 may enhance therapeutic efficacy and improve outcomes in oncology.

Applications of Novel Microscale and Nanoscale Materials for Theranostics: From Design to Clinical Translation.

The growing global prevalence of chronic diseases has highlighted the limitations of conventional drug delivery methods, which often suffer from non-specific distribution, systemic toxicity, and poor bioavailability. Microscale and nanoscale materials have emerged as innovative solutions, offering enhanced targeting, controlled release, and the convergence of therapeutic and diagnostic functions, referred to as theranostics. This review explores the design principles, mechanisms of action, and clinical applications of various novel micro- and nanomaterials in diseases such as cancer, cardiovascular disorders, and infectious diseases. These materials enable real-time monitoring of therapeutic responses and facilitate precision medicine approaches. Additionally, this paper addresses the significant challenges hindering clinical translation, including biocompatibility, potential toxicity, and regulatory issues. Ongoing clinical trials demonstrate the potential of nanomaterials in theranostic applications, but further research is needed to overcome the barriers to widespread clinical adoption. This work aims to contribute to the acceleration of integrating nanomedicine into clinical practice, ultimately enhancing the efficacy and safety of therapeutic interventions.

Interpretable Dynamic Directed Graph Convolutional Network for Multi-Relational Prediction of Missense Mutation and Drug Response.

Tumor heterogeneity presents a significant challenge in predicting drug responses, especially as missense mutations within the same gene can lead to varied outcomes such as drug resistance, enhanced sensitivity, or therapeutic ineffectiveness. These complex relationships highlight the need for advanced analytical approaches in oncology. Due to their powerful ability to handle heterogeneous data, graph convolutional networks (GCNs) represent a promising approach for predicting drug responses. However, simple bipartite graphs cannot accurately capture the complex relationships involved in missense mutation and drug response. Furthermore, Deep learning models for drug response are often considered "black boxes", and their interpretability remains a widely discussed issue. To address these challenges, we propose an Interpretable Dynamic Directed Graph Convolutional Network (IDDGCN) framework, which incorporates four key features: (1) the use of directed graphs to differentiate between sensitivity and resistance relationships, (2) the dynamic updating of node weights based on node-specific interactions, (3) the exploration of associations between different mutations within the same gene and drug response, and (4) the enhancement of interpretability models through the integration of a weighted mechanism that accounts for the biological significance, alongside a ground truth construction method to evaluate prediction transparency. The experimental results demonstrate that IDDGCN outperforms existing state-of-the-art models, exhibiting excellent predictive power. Both qualitative and quantitative evaluations of its interpretability further highlight its ability to explain predictions, offering a fresh perspective for precision oncology and targeted drug development.

Exploring bias risks in artificial intelligence and targeted medicines manufacturing

BackgroundThough artificial intelligence holds great value for healthcare, it may also amplify health inequalities through risks of bias. In this paper, we explore bias risks in targeted medicines manufacturing. Targeted medicines manufacturing refers to the act of making medicines targeted to individual patients or to subpopulations of patients within a general group, which can be achieved, for example, by means of cell and gene therapies. These manufacturing processes are increasingly reliant on digitalised systems which can be controlled by artificial intelligence algorithms. Whether and how bias might turn up in the process, however, is uncertain due to the novelty of the development.MethodsExamining stakeholder views across bioethics, precision medicine, and artificial intelligence, we document a range of opinions from eleven semi-structured interviews about the possibility of bias in AI-driven targeted therapies manufacturing.ResultFindings show that bias can emerge in upstream (research and development) and downstream (medicine production) processes when manufacturing targeted medicines. However, interviewees emphasized that downstream processes, particularly those not relying on patient or population data, may have lower bias risks. The study also identified a spectrum of bias meanings ranging from negative and ambivalent to positive and productive. Notably, some participants highlighted the potential for certain biases to have productive moral value in correcting health inequalities. This idea of “corrective bias” problematizes the conventional understanding of bias as primarily a negative concept defined by systematic error or unfair outcomes and suggests potential value in capitalizing on biases to help address health inequalities. Our analysis also indicates, however, that the concept of “corrective bias” requires further critical reflection before they can be used to this end.

Hydrogels Innovation: A Review on Recent Development, Characterization, and Applications

Hydrogels are special materials that can hold a large amount of water and form 3D networks. In the past few years, there have been exciting improvements in hydrogel technology, bringing new ideas to many different areas. By trying out new materials like smart hydrogels that can respond to different conditions, provided new ways to deliver medicine precisely, build tissues, and create wearable gadgets. Among the most significant developments is the creation of smart hydrogels, which can react dynamically to different environmental stimuli. With their ability to release therapeutic chemicals under regulated conditions in response to particular physiological cues, these intelligent materials have enormous potential for the administration of precision medicine. These kinds of customized drug delivery systems have the power to completely change how treatments are administered by reducing adverse effects and increasing therapeutic efficacy. Hydrogels are also useful in tissue engineering, where they are used as scaffolds to create biological tissues that function. Hydrogel-based tissue constructions, which imitate the extracellular matrix, offer a favorable microenvironment for cell proliferation and differentiation, promoting the healing of injured or ill tissues. With its enormous potential in regenerative medicine, this revolutionary strategy offers hope for the treatment of ailments including organ failure. this article gives a thorough look at the recent developments in hydrogels, characterization techniques, and the new application of hydrogels in various fields of science. In summary, new developments in hydrogel technology have opened up a plethora of opportunities in a variety of scientific fields. The adaptable properties of hydrogels continue to spur innovation in a variety of fields, including wearable technology, tissue engineering, and precision medicine. These applications offer revolutionary answers to urgent social issues.

Unique and Shared Molecular Mechanisms of Alcoholic and Non-Alcoholic Liver Cirrhosis Identified Through Transcriptomics Data Integration.

Liver cirrhosis is a severe chronic disease that results from various etiological factors and leads to substantial morbidity and mortality. Alcoholic cirrhosis (AC) and non-AC (NAC) arise from prolonged and excessive consumption of alcohol and metabolic syndromes, respectively. Precise molecular mechanisms of AC and NAC are yet to be comprehensively understood for diagnostics and therapeutic advances to materialize. This study reports novel findings to this end by utilizing high-throughput RNA sequencing and microarray data from the Gene Expression Omnibus (GEO). We performed a meta-analysis of transcriptomics data to identify the differentially expressed genes specific to AC and NAC. Functional enrichment and protein-protein interaction network analyses uncovered novel hub genes and transcription factors (TFs) critical to AC and NAC. We found that AC is primarily driven by metabolic dysregulation and oxidative stress, with key TFs such as RELA, NFKB1, and STAT3. NAC was characterized by fibrosis and tissue remodeling associated with metabolic dysfunction, with TFs including USF1, MYCN, and HIF1A. Key hub genes such as ESR1, JUN, FOS, and PKM in AC, and CD8A, MAPK3, CCND1, and CXCR4 in NAC were identified, along with their associated TFs, pointing to potential therapeutic targets. Our results underscore the unique and shared molecular mechanisms that underlie AC and NAC and inform the efforts toward precision medicine and improved patient outcomes in liver cirrhosis.

Biomimetic nanoparticles with red blood cell membranes for enhanced photothermal and immunotherapy for tumors.

The alarming escalation in cancer incidence and mortality has thrust into spotlight the quest for groundbreaking therapeutic strategies. Our research delves into the potential of RDIR780, a novel class of biomimetic nanoparticles cloaked in red blood cell membranes, to significantly enhance their in vivo persistence and therapeutic potency. Through an exhaustive suite of experiments, we have charted the therapeutic horizons of RDIR780 in the realms of tumor photothermal synergistic immunotherapy and targeted drug delivery. Preliminary in vitro cellular assays have revealed that RDIR780 not only achieves remarkable uptake by tumor cells but also triggers swift tumor cell death under the influence of laser irradiation. Subsequent in vivo fluorescence imaging studies have corroborated the nanoparticles' propensity for tumor-specific accumulation, thereby bolstering the case for precision medicine. The results of the precise imaging techniques of therapeutic trials conducted on mice with implanted tumors have underscored the profound impact of RDIR780 when synergized with an anti-PD-L1 antibody. This synergistic approach has shown to fairly eradicate tumor growth, marking a significant stride in the battle against cancer. This pioneering endeavor not only lays down a formidable groundwork for the evolution of long-circulating photothermal therapeutic nanoparticles but also heralds a new era of transformative clinical interventions.

Exome Sequence Data of Eight SLC Transporters Reveal That SLC22A1 and SLC22A3 Variants Alter Metformin Pharmacokinetics and Glycemic Control.

Background: Type 2 diabetes (T2D) is one of the leading causes of mortality and is a public health challenge worldwide. Metformin is the first-choice treatment for T2D; its pharmacokinetics (PK) is facilitated by members of the solute carrier (SLC) superfamily of transporters, it is not metabolized, and it is excreted by the kidney. Although interindividual variability in metformin pharmacokinetics is documented in the Mexican population, its pharmacogenomics is still underexplored. We aimed to identify variants in metformin SLC transporter genes associated with metformin PK and response in Mexican patients. Methods: Using exome data from 2217 Mexican adults, we identified 86 biallelic SNVs in the eight known genes encoding SLC transporters, with a minor allele frequency ≥ 1%, which were analyzed in an inadequate glycemic control (IGC) association study in T2D metformin treated patients. Metformin PK was evaluated in a pediatric cohort and the effect of associated SNVs was correlated. Results: Functional annotation classified two SNVs as pathogenic. The association study revealed two blocks associated with IGC. These haplotypes comprise rs622591, rs4646272, rs4646273, and rs4646276 in SLC22A1; and rs1810126 and rs668871 in SLC22A3. PK profiles revealed that homozygotes of the SLC22A1 haplotype reached lower plasma metformin concentrations 2 h post administration than the other groups. Conclusions: Our findings highlight the potential of pharmacogenomics studies to enhance precision medicine, which may involve dosage adjustments or the exploration of alternative therapeutic options. These hold significant implications for public health, particularly in populations with a high susceptibility to develop metabolic diseases, such as Latin Americans.

Chronic myelomonocytic leukemia: molecular pathogenesis and therapeutic innovations.

Chronic myelomonocytic leukemia (CMML) is an aggressive clonal stem cell disorder categorized amongst myelodysplastic/myeloproliferative (MDS/MPN) overlap neoplasms. While sharing features with both MDS and MPN, CMML has distinct molecular and clinical profiles. The presence of CMML-specific prognostic models, response criteria, and dedicated clinical trials underscores a unique and complex biology. Agerelated changes affecting the bone marrow microenvironment, immune responses, and the intricate balance between epigenetic deregulation and proinflammatory signaling are characteristic of this disease, collectively posing significant scientific and clinical challenges in management. CMML is an ageing-related, clinically heterogeneous neoplasm with limited approved therapeutic options, representing an area of unmet medical need. This review offers a comprehensive analysis of the current understanding of the molecular mechanisms driving CMML evolution and its clinical manifestations within the ever-evolving landscape of precision medicine. In light of the most recent molecular discoveries, we highlight the pitfalls of existing therapies and underscore promising investigational agents. Many of the biological findings discussed are shared across a spectrum of acute and chronic myeloid neoplasms, as well as clonal hematopoiesis, broadening the scope of this review.

Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships.

The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. However, few tools exist to support the development and evaluation of predictive models in the context of neuroimaging or lesion-behavior research, creating an obstacle to the widespread adoption of predictive modeling approaches in the field. Further, existing tools for lesion-behavior analysis are often unable to accommodate categorical outcome variables and often impose restrictions on the predictor data. Researchers therefore often must use different software packages and analytical approaches depending on whether they are addressing a classification vs. regression problem and on whether their predictor data correspond to binary lesion images, continuous lesion-network images, connectivity matrices, or other data modalities. To address these limitations, we have developed a MATLAB software toolkit that supports both inferential and predictive modeling frameworks, accommodates both classification and regression problems, and does not impose restrictions on the modality of the predictor data. The toolkit features both a graphical user interface and scripting interface, includes implementations of multiple mass-univariate, multivariate, and machine learning models, features built-in and customizable routines for hyper-parameter optimization, cross-validation, model stacking, and significance testing, and automatically generates text-based descriptions of key methodological details and modeling results to improve reproducibility and minimize errors in the reporting of methods and results. Here, we provide an overview and discussion of the toolkit features and demonstrate its functionality by applying it to the question of how expressive and receptive language impairments relate to lesion location, structural disconnection, and functional network disruption in a large sample of patients with left hemispheric brain lesions. We find that impairments in expressive vs. receptive language are most strongly associated with left lateral prefrontal and left posterior temporal/parietal damage, respectively. We also find that impairments in expressive vs. receptive language are associated with partially overlapping patterns of fronto-temporal structural disconnection, and that the associated functional networks are also similar. Importantly, we find that lesion location and lesion-derived network measures are highly predictive of both types of impairment, with predictions from models trained on these measures explaining ~30-40% of the variance on average when applied to data from patients not used to train the models. We have made the toolkit publicly available, and we have included a comprehensive set of tutorial notebooks to support new users in applying the toolkit in their studies.

Autism Spectrum Disorder and Atypical Brain Connectivity: Novel Insights from Brain Connectivity-Associated Genes by Combining Random Forest and Support Vector Machine Algorithm.

It is estimated that approximately one in every 100 children is diagnosed with autism spectrum disorder (ASD) around the globe. Currently, there are no curative pharmacological treatments for ASD. Discoveries on key molecular mechanisms of ASD are essential for precision medicine strategies. Considering that atypical brain connectivity patterns have been observed in individuals with ASD, this study examined the brain connectivity-associated genes and their putatively distinct expression patterns in brain samples from individuals diagnosed with ASD and using an iterative strategy based on random forest and support vector machine algorithms. We discovered a potential gene signature capable of differentiating ASD from control samples with a 92% accuracy. This gene signature comprised 14 brain connectivity-associated genes exhibiting enrichment in synapse-related terms. Of these genes, 11 were previously associated with ASD in varying degrees of evidence. Notably, NFKBIA, WNT10B, and IFT22 genes were identified as ASD-related for the first time in this study. Subsequent clustering analysis revealed the existence of two distinct ASD subtypes based on our gene signature. The expression levels of signature genes have the potential to influence brain connectivity patterns, potentially contributing to the manifestation of ASD. Further studies on the omics of ASD are called for so as to elucidate the molecular basis of ASD and for diagnostic and therapeutic innovations. Finally, we underscore that advances in ASD research can benefit from integrative bioinformatics and data science approaches.