Multi-omics and high-spatial-resolution omics: deciphering complexity in neurological disorders

Next Generation Sequencing (NGS) and Cancer: New Steps Towards Personalized MedicineIn the era of precision oncology, the scenario of cancer diagnostics, prognostics, and therapeutics has been revolutionized by the advent of next-generation sequencing (NGS). The ability to investigate cancer genomes and transcriptomes at remarkable depth has enabled the identification of novel mutations, fusion events, gene expression profiles, and non-coding RNA (ncRNA) signatures that contribute to tumorigenesis and therapeutic resistance. This Research Topic, Next Generation Sequencing (NGS) and Cancer: New Steps Towards Personalized Medicine, has been conceived to explore how NGS technologies continue to drive forward the era of personalized oncology. This collection, including original research and case reports, reflects the diversity and translational potential of NGS across tumor types and clinical contexts. The contributions cover key areas of precision oncology, ranging from biomarker discovery and drug sensitivity profiling to clinical case studies, that highlight the practical utility of NGS in guiding targeted therapies. Several studies emphasize the growing role of integrative bioinformatics and machine learning in finding out actionable insights from complex datasets. For example, Cui et al. applied topological data analysis and deep learning to uncover molecular drivers and predict therapeutic targets in melanoma, exploiting immune gene regulatory networks constructed from NGS data. Their computational approach exemplifies how artificial intelligence can bridge network-based analysis with precision oncology. Prognostic stratification is another critical application of NGS. Xu et al. performed a comprehensive analysis of programmed cell death (PCD) modalities in cholangiocarcinoma, integrating immune microenvironment features and drug sensitivity profiles. Their multidimensional approach stratified patients into prognostic subgroups and highlighted pathways that could inform personalized treatment strategies for this rare and aggressive cancer. Collectively, the articles in this Research Topic exemplify how NGS is reshaping oncology across research and clinical practice, from molecular discovery to clinical decision-making. Several contributions explore the identification of novel fusion genes, regulatory RNAs, and resistance mechanisms, while others focus on integrating multi-omic data, refining prognostic models, or demonstrating NGS-guided therapeutic interventions. These studies highlight the critical need to integrate high-throughput sequencing data into clinical workflows, in order to optimize treatment strategies and improve patient outcomes. They also reveal the ongoing need for reliable bioinformatics pipelines, functional validation, and close interdisciplinary collaboration among researchers, oncologists, pathologists, and bioinformaticians. As NGS technologies continue to evolve, becoming faster, more affordable, and more accessible, they are increasingly positioned to serve as core components of routine oncology care. To fully realize their potential, this technological progress must be accompanied by collaborative efforts to standardize data interpretation, address ethical considerations around genomic information, and ensure equitable access to testing across all patient populations. This Research Topic reflects the rapid advancement in the integration of genomics into clinical practice, offering novel insights into the molecular drivers of cancer and illustrating how precision oncology is already transforming patient care.

Cholangiocarcinoma (CCA) is a biologically complex and aggressive malignancy of the bile ducts, with limited diagnostic and therapeutic options. Recent advances in omics technologies, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics, have significantly enhanced our understanding of CCA pathogenesis. These approaches have provided a comprehensive molecular landscape of the disease, thus facilitating the identification of biomarkers and therapeutic targets. Integrative analyses have revealed subtype-specific genetic alterations, such as FGFR2 fusions and IDH1/2 mutations, epigenetic modifications, and metabolic changes associated with disease progression and treatment respon se. Artificial intelligence (AI) and machine learning (ML) have further expanded the potential of multiomics by facilitating data integration, molecular subtyping, prognostic modeling, and early detection strategies. These computational tools support precision oncology by stratifying patients, predicting treatment efficacy, and guiding personalized treatment decisions. The incorporation of multiomics profiling into clinical workflows will improve diagnostic precision, optimize treatment outcomes, and reduce adverse effects. Despite these advances, challenges remain in standardizing analytical pipelines, validating findings across diverse populations, and implementing multiomics approaches in routine clinical settings. Continued interdisciplinary collaboration and technological innovation are essential to fully harness the translational value of AI-driven multiomics to improve outcomes for CCA patients.

Report on the Barbados Workshop

The medical biotechnology community has undertaken significant endeavors to gain a comprehensive understanding of SARS-CoV-2’s biology and pathogenesis mechanisms. Omics approaches and technologies have been widely employed in the fight against SARS-CoV-2. Since the onset of the virus outbreak, researchers have demonstrated how recent omics and bioinformatics technological advancements have contributed to the diagnosis, vaccine development, treatment, and control of disease transmission. Studies conducted since the outbreak have been collected and summarized, with a focus on bioinformatics approaches and their contribution to controlling this pandemic. Developments and advanced omics technology in connection to the COVID-19 pandemic have been analyzed. The multi-omics technology, which offers various strategies in identifying potential diagnostics, therapeutics, studies of variants of concern, and drug repurposing approaches, has been assessed. Pandemic response has seen the application of multi-omics and pan-genomics approaches, including genomics, metabolomics, transcriptomics, proteomics, epigenomics, clustered regularly interspaced short palindromic repeats (CRISPR) technology, host-pathogen interactions, artificial intelligence, and machine learning in various research areas. Additionally, bioinformatics and mathematical modeling have played a significant role in disease control. The use of smart technologies to control virus transmission and predict patients’ health conditions and treatment outcomes has also been crucial. Transcriptome analysis has emerged as a major application, contributing to the generation of new knowledge on viral sequences and intracellular signaling pathways that regulate viral infection and pathogenesis mechanisms. The sequencing of the virus has paved the way for the use of omics technologies and an integrative technique in combating the pandemic. In general, the advancement of omics technology during this pandemic has been fascinating and has contributed a significant role to the science of health biotechnology in general and omics and bioinformatics in particular.Keywords: bioinformatics, coronavirus, COVID-19, omics, SARS COV-2

Neurological diseases contribute significantly to disability and mortality, necessitating improved diagnostic and prognostic tools. Advances in molecular biomarkers at genomic, transcriptomic, epigenomic, and proteomic levels have facilitated early disease detection. Notably, neurofilament light chain (NfL) serves as a key biomarker of neurodegeneration, while liquid biopsy techniques enable non-invasive monitoring through exosomal tau, α-synuclein, and inflammatory markers. Artificial intelligence (AI) and multi-omics integration further enhance biomarker discovery, promoting precision medicine. A comprehensive literature review was conducted using PubMed, Scopus, and Web of Science to identify studies (2010-2024) on molecular biomarkers in neurodegenerative and neuroinflammatory disorders. Key findings on genomic mutations, transcriptomic signatures, epigenetic modifications, and protein-based biomarkers were analyzed. The findings highlight the potential of liquid biopsy and multi-omics approaches in improving diagnostic accuracy and therapeutic stratification. Genomic, transcriptomic, and proteomic markers demonstrate utility in early detection and disease monitoring. AI-driven analysis enhances biomarker discovery and clinical application. Despite advancements, challenges remain in biomarker validation, standardization, and clinical implementation. Large-scale longitudinal studies are essential to ensure reliability. AI-powered multi-omics analysis may accelerate biomarker application, ultimately improving patient outcomes in neurological diseases.

Background: Cytogenetics and cytogenomics have revolutionized cancer diagnosis by revealing the underlying genetic alterations that characterize malignancies. The identification of chromosomal rearrangements, mutations, and DNA dosage abnormalities in cancer cells has enhanced our understanding of cancer as a genomic disease, enabling the detection of residual disease and improving patient prognosis. Aim: This review aims to explore the application of cytogenetic and cytogenomic evaluation techniques in cancer diagnostics. Methods: Through a detailed examination of traditional cytogenetic methods like karyotyping and fluorescence in situ hybridization (FISH), along with advanced genomic tools such as next-generation sequencing (NGS) and single-cell technologies, the review highlights their respective contributions to precision oncology. Findings: It also discusses the challenges posed by tumor heterogeneity and the need for individualized treatment approaches. The integration of cytogenetic and cytogenomic techniques provides critical insights into cancer heterogeneity, clonal evolution, and the identification of therapeutic targets, facilitating early diagnosis, prognosis, and personalized treatment plans. Conclusion: The advancement of high-throughput technologies has further accelerated the discovery of novel biomarkers, enhancing the precision of cancer diagnosis and treatment strategies.

BackgroundEmerging artificial intelligence (AI) technologies have diverse applications in medicine. As AI tools advance towards clinical implementation, skills in how to use and interpret AI in a healthcare setting could become integral for physicians. This study examines undergraduate medical students’ perceptions of AI, educational opportunities about of AI in medicine, and the desired medium for AI curriculum delivery.MethodsA 32 question survey for undergraduate medical students was distributed from May–October 2021 to students to all 17 Canadian medical schools. The survey assessed the currently available learning opportunities about AI, the perceived need for learning opportunities about AI, and barriers to educating about AI in medicine. Interviews were conducted with participants to provide narrative context to survey responses. Likert scale survey questions were scored from 1 (disagree) to 5 (agree). Interview transcripts were analyzed using qualitative thematic analysis.ResultsWe received 486 responses from 17 of 17 medical schools (roughly 5% of Canadian undergraduate medical students). The mean age of respondents was 25.34, with 45% being in their first year of medical school, 27% in their 2nd year, 15% in their 3rd year, and 10% in their 4th year. Respondents agreed that AI applications in medicine would become common in the future (94% agree) and would improve medicine (84% agree Further, respondents agreed that they would need to use and understand AI during their medical careers (73% agree; 68% agree), and that AI should be formally taught in medical education (67% agree). In contrast, a significant number of participants indicated that they did not have any formal educational opportunities about AI (85% disagree) and that AI-related learning opportunities were inadequate (74% disagree). Interviews with 18 students were conducted. Emerging themes from the interviews were a lack of formal education opportunities and non-AI content taking priority in the curriculum.ConclusionA lack of educational opportunities about AI in medicine were identified across Canada in the participating students. As AI tools are currently progressing towards clinical implementation and there is currently a lack of educational opportunities about AI in medicine, AI should be considered for inclusion in formal medical curriculum.

<div>Abstract<p>Oncology drug efficacy is evaluated in mouse models by continuously monitoring tumor volumes, which can be mathematically described by growth kinetic models. Although past studies have investigated various growth models, their reliance on small datasets raises concerns about whether their findings are truly representative of tumor growth in diverse mouse models under different vehicle or drug treatments. In this study, we systematically evaluated six parametric models (exponential, exponential quadratic, monomolecular, logistic, Gompertz, and von Bertalanffy) and the semiparametric generalized additive model (GAM) on fitting tumor volume data from more than 30,000 mice in 930 experiments conducted in patient-derived xenografts, cell line–derived xenografts, and syngeneic models. We found that the exponential quadratic model is the best parametric model and can adequately model 87% studies, higher than other models including von Bertalanffy (82%) and Gompertz (80%) models; the latter is often considered the standard growth model. At the mouse group level, 7.5% of growth data could not be fit by any parametric model and were fitted by GAM. We show that endpoint gain integrated in time, a GAM-derived efficacy metric, is equivalent to exponential growth rate, a metric we previously proposed and conveniently calculated by simple algebra. Using five studies on paclitaxel, anti-PD1 antibody, cetuximab, irinotecan, and sorafenib, we showed that exponential and exponential quadratic models achieve similar performance in uncovering drug mechanism and biomarkers. We also compared exponential growth rate–based association analysis and exponential modeling approach in biomarker discovery and found that they complement each other. Modeling methods herein are implemented in an open-source R package freely available at <a href="https://github.com/hjzhou988/TuGroMix" target="_blank">https://github.com/hjzhou988/TuGroMix</a>.</p>Significance:<p>We present a general strategy for mathematically modeling tumor growth in mouse models using data from 30,000 mice and show that modeling and nonmodeling approaches are complementary in biomarker discovery and drug mechanism studies.</p></div>

BackgroundLysine crotonylation has been implicated in the pathogenesis of preeclampsia (PE). However, the underlying mechanisms remain unclear. This study aimed to identify crotonylation-related biomarkers in PE through bioinformatics analysis.MethodsPublicly available datasets, including GSE48424 and GSE149440, were utilized in this study. Candidate biomarkers were identified by intersecting differentially expressed genes (DEGs) from differential expression analysis with key module genes obtained through weighted gene co-expression network analysis (WGCNA). Biomarkers were further refined using expression analysis and machine learning techniques. Functional analysis, immune infiltration analysis, methylation modifications, and construction of molecular regulatory networks were employed to explore the potential mechanisms underlying the involvement of candidate biomarkers in PE pathogenesis. Additionally, associations between the biomarkers and common clinical predictive molecules were examined, and their diagnostic performance for PE was evaluated.ResultsA total of 323 candidate genes were identified by intersecting 3,353 DEGs with 402 key module genes. Expression analysis and machine learning algorithms pinpointed DPYD and PRDX3 as biomarkers. DPYD and PRDX3 were identified as lysine crotonylation-related biomarkers in PE. These genes were significantly downregulated in PE and associated with pathways such as olfactory signaling, neutrophil degranulation, and immune microenvironment dysregulation. Both biomarkers demonstrated excellent diagnostic efficacy (AUC > 0.85), outperforming traditional markers like VEGFA. Additionally, DPYD and PRDX3 are associated with oxidative stress and immune dysregulation, which may be the key mechanisms for the development of PE.ConclusionThis study identified DPYD and PRDX3 as lysine crotonylation-related biomarkers in PE, providing new insights for the diagnosis and management of the condition.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12884-025-08122-w.

Human saliva is a biological fluid with enormous diagnostic potential. Because saliva can be non-invasively collected, it provides an attractive alternative for blood, serum or plasma. It has been postulated that the blood concentrations of many components are reflected in saliva. Saliva harbors a wide array of proteins, which can be informative for the detection of diseases. Profiling the proteins in saliva over the course of disease progression could reveal potential biomarkers indicative of different stages of diseases, which may be useful in medical diagnostics. With advanced instrumentation and developed refined analytical techniques, proteomics is widely envisioned as a useful and powerful approach for salivary proteomic biomarker discovery. As proteomic technologies continue to mature, salivary proteomics have great potential for biomarker research and clinical applications. The progress and current status of salivary proteomics and its application in the biomarker discovery of oral and systematic diseases will be reviewed. The scientific and clinical challenges underlying this approach will also be discussed.

Background: Artificial intelligence (AI) is influencing pediatric dentistry by supporting diagnostic accuracy, optimizing treatment planning, and improving patient care, especially for children with special needs. Previous studies explored various aspects of AI in pediatric dentistry and special care dentistry, predominantly focusing on clinical implementation or technical advancements. However, no prior review has specifically addressed its application at the intersection of pediatric dentistry and special care dentistry, particularly with respect to ethical and environmental perspectives. Objective: This scoping review provides a comprehensive synthesis of AI technologies in pediatric dentistry with a dedicated focus on children with special health care needs. It aims to critically evaluate current applications and examine the clinical, ethical, and environmental implementation challenges unique to these populations. Methods: A structured literature search was conducted in PubMed, Scopus, and Web of Science from inception to August 2025, using predefined inclusion and exclusion criteria. Eligible studies investigated AI applications in pediatric dental care or special needs contexts. Studies were synthesized narratively according to thematic domains. Results: Sixty-five studies met the inclusion criteria. Thematic synthesis identified nine domains of AI application: (1) diagnostic imaging and caries detection, (2) three-dimensional imaging, (3) interceptive and preventive orthodontics, (4) chatbots and teledentistry, (5) decision support, patient engagement and predictive analytics, (6) pain assessment and discomfort monitoring, (7) behavior management, (8) behavior modeling, and (9) ethical considerations and challenges. The majority of studies were conducted in general pediatric populations, with relatively few specifically addressing children with special health care needs. Conclusions: AI in pediatric dentistry is most developed in diagnostic imaging and caries detection, while applications in teledentistry and predictive analytics remain emerging, and areas such as pain assessment, behavior management, and behavior modelling are still exploratory. Evidence for children with special health care needs is limited and seldom validated, highlighting the need for focused research in this group. Ethical deployment of AI in pediatric dentistry requires safeguarding data privacy, minimizing algorithmic bias, preventing overtreatment, and reducing the carbon footprint of cloud-based technologies.

Spatial omics allows for the simultaneous measurement of multiple “omics” layers (genomics, transcriptomics, proteomics, metabolomics). The analysis reveals significant advances in tumor microenvironment characterization, biomarker discovery, and clinical applications. Spatial Multiomics is particularly crucial in cancer research because tumors are highly heterogeneous, and the spatial relationships between cancer cells, immune cells, and stromal cells within the tumor microenvironment (TME) are critical for understanding disease progression, metastasis, and treatment response. The convergence of large-scale spatial omics data with artificial intelligence is powering the next revolution in oncology research. The clinical applications of spatial multi-omics are expanding rapidly, from patient stratification for precision oncology to the design of more efficient clinical trials. Developing interpretable models that can guide treatment decisions based on spatial patterns of gene and protein expression will be a key focus of future research.

Revolutionary progress in biomarker-driven treatment has enhanced survival and preserved quality of life for patients and has become a standard of care in cancer management today.[1] Over the last 2 decades, global efforts have been made toward improving the quality of tumor tissue retrieved for histopathologic and genomic analysis and biomarker discovery. The US Precision Medicine Initiative[2] was one such step forward and underscored the need for accurate representative tissue acquisition, emphasizing the need for new age biospecimen collection. The role for primary biopsy specimens has already been established to better understand tumor evolution and heterogeneity, and now, longitudinal biopsy specimens are being advocated in clinical practice and clinical trials.[3] However, nearly one-third of core biopsy specimens are unable to yield the appropriate tissue quality for histopathologic testing, next-generation sequencing (NGS), and novel biomarker discovery, especially immune biomarkers. Observations from the MERIT and the BATTLE lung trials underline these challenges and NCI-MATCH, TAPUR, and M-PACT, a few of the most ambitious precision trials with a biomarker backbone, have also identified tissue acquisition as a barrier to an ideal conduct of the precision methods for investigation.[4–6]Image-guided biopsy procedures yield higher quality tissue samples for analysis and have become a standard of care in the field of oncology.[7] Computed tomography or ultrasound-guided biopsy procedures are most commonly performed. To enhance tissue capture, more recently, newer radiologic tests, such as magnetic resonance imaging or positron-emission tomography/computed tomography–guided biopsy procedures, are being investigated and practiced at some centers.[7] In the current times when clinical practices and research strategies are based on longitudinal efforts at biomarker discovery, it becomes imperative to ensure adequate representative tissue collection that would yield material for routine tests as well as novel attempts at biomarker discovery.[8–10] Many such initiatives require a higher level of coordination between and within multidisciplinary teams, including medical oncology, surgical oncology, radiation oncology, interventional radiology, histopathology, molecular biology, and sometimes even anesthesiology. Such coordinated efforts could be challenging and make longitudinal efforts difficult and fraught with errors.The need for “next-generation biospecimens” brings to the fore the necessity for designing well-synchronized, coordinated efforts that would improve the quality and the reliability of biospecimen acquired, enhance feasibility, and reduce the scope for errors in these challenging situations.[8] The minimum representative tumor required for NGS to be performed is different for DNA and RNA extraction and requires different levels of understanding and coordination. Immune biomarkers and tumor microenvironment analysis are new age biomarkers being studied extensively over the last decade, and quality and integrity of tissue requirement for these are different from the routine.[10] Management of lung cancer has revolutionized and transformed the approach to advanced disease. Initial and longitudinal biopsy specimens are imperative and central to the management of lung cancer. Barriers to acquiring the right tissue and risks associated with tissue retrieval in lung cancer are widely described and nearly 30% samples are known to not meet quality standards.[11] Each new lung biopsy specimen could cost nearly $US10,000 to 14,000 and any complication could raise this cost to nearly US$35,000 to 40,000, underscoring the need to reduce errors and reduce the need for repeat biopsy procedures.[12]The original research article by Xu et al,[13] recently published in the Journal of Immunotherapy and Precision Oncology, presents results from implementation of a novel web-based lesion selection tool (LST) to improve acquisition of tumor biopsy specimens. Debut of the so-called Naing tool is an example of a coordinated effort to enhance the reliability and integrity of the tissue acquired for testing. In this analysis, they included a total of 145 patients; the LST was used in 88 patients and 57 patients served as controls. The authors studied the consistency of lesions biopsied in longitudinal samples, total number of cores obtained, and adequacy of tumor cellularity for NGS testing from those specimens. The investigators reported 100% consistency in the lesions biopsied with the LST group versus 75% in the control group. The chances of yielding the representative tissue specimen increased when at least five cores were collected per biopsy procedure.In summary, this analysis demonstrates that the novel LST platform was able to identify the representative tumor specimen more accurately in longitudinal sample collection, especially when at least five cores were collected per biopsy.[13] The enhanced accuracy reduces the need and risk of repeat biopsy procedures, enhances appropriate resource utilization, and improves patient care. In addition, radiotherapy can alter the tissue architecture and impair immune monitoring strategies. Using the Naing tool to predesignate different lesions for radiotherapy and for longitudinal sampling is a remarkable strategy that will likely pay rich dividends. Identifying lesions to follow consistently is invaluable for evaluating response to treatment in RECIST (response evaluation criteria in solid tumors) and immune-related RECIST (irRECIST) criteria. The Naing tool does just that and prevents inadvertent sampling of lesions followed for response.The most striking point about the research of Xu et al[13] is that one can biopsy the same site consistently at baseline, on treatment, and after treatment with longitudinal sampling. This will provide valuable information on dynamic changes in the tumor microenvironment. The Naing tool will facilitate more accurate serial monitoring of biomarkers for response or resistance, rendering this an invaluable tool in this era of NGS and biomarker development.However, the study was limited by a small sample size and further testing is required in a larger population to become a standard of care. Funding for the effort of involved staff for synchronized tissue acquisition techniques should be considered. In addition, because the LST method is novel, the feasibility and adaptability into the implementation of this method is still unknown and needs further validation. The lack of integration to the current existing electronic medical record system poses an additional limitation to adaptability for a novel technique like LST.Despite all the challenges seen with the implementation of a novel technique, it does stand clear that the LST method could be a value addition to the diagnostic methods and procurement of representative patient tissue for genomically driven cancer care. A well-synchronized method, like LST, with strategic integration is the need of the hour to elevate the level of tissue retrieval for the next generation of biospecimen procurement and novel biomarker discovery.

Artificial Intelligence and the Pathologist: Future Frenemies?

Inborn errors of immunity (IEIs) are a heterogeneous group of disorders, genetically diverse but phenotypically overlapping. Over 500 IEI-related genes have been identified. Next generation sequencing (NGS) enables the simultaneous evaluation of a large number of genes in multiple patients in parallel. Since 2017, in our setting, custom IEI panels have been useful mainly for patients displaying clinical phenotypes of immune dysregulation and autoinflammatory disorders. Our aim is to evaluate the performance of targeted NGS panels based on International Union of Immunological Societies (IUIS) gene categories by examining how many patients achieved a conclusive diagnosis. A retrospective analysis was performed on 361 index cases submitted to four chronologically successive targeted NGS custom panels. The first panel included 124 IUIS genes, most of them related to autoimmune and immune dysregulation disease categories. The second panel contained 143 IUIS genes, including defects of innate immunity. The third and fourth, large panels covering 314 and 325 IUIS genes, respectively, included/comprised the majority of IUIS disease categories, with focus on autoinflammatory, immune dysregulation, and intrinsic immunity disease categories. From a total of 361 samples studied, 264 were referred due to clinical suspicion of immune dysregulation, autoinflammatory disease, or defects in innate immunity. Twenty-three percent of them (61/264) achieved a confirmed or highly probable genetic etiology. According to the main IEI IUIS disease categories included in targeted panels, immune dysregulation represented 26% (32/123) of these diagnoses, autoinflammatory disorders 21% (18/85), and defects in innate immunity 19% (11/56). In the remaining patients studied, 42% (41/97) of them achieved a conclusive diagnosis in others IEI IUIS disease categories. The NGS approach demonstrated the expected IEI diagnostic rate. The strategy of designing panels oriented toward IEI disease categories that present the greatest variability in clinical presentations with overlapping phenotypes proved to be effective regarding achievement of conclusive diagnoses in our cohort.