Advances in Chiral Metabolomic Profiling and Biomarker Discovery.

Chiral metabolomics entails the enantioselective measurement of the metabolome present in a biological system. Over recent years, it has garnered significant interest for its potential in discovering disease biomarkers and aiding clinical diagnostics. D-Amino acids and D-hydroxy acids, traditionally overlooked as unnatural, are now emerging as novel signaling molecules and potential biomarkers for a range of metabolic disorders, brain diseases, kidney disease, diabetes, and cancer. Despite their significance, simultaneous measurements of multiple classes of chiral metabolites in a biological system remain challenging. Hence, limited information is available regarding the metabolic pathways responsible for synthesizing D-amino/hydroxy acid and their associated pathophysiological mechanisms in various diseases. Capitalizing on recent advancements in sensitive analytical techniques, researchers have developed various targeted chiral metabolomic methods for the analysis of chiral biomarkers. Here, we highlight the pivotal role of chiral metabolic profiling studies in disease diagnosis, prognosis, and therapeutic interventions. Furthermore, we describe cutting-edge chromatographic and mass spectrometry methods that enable enantioselective analysis of chiral metabolites. These advanced techniques are instrumental in unraveling the complexities of disease biomarkers, contributing to the ongoing efforts in disease biomarker discovery.

Triple and quadruple optimization for feature selection in cancer biomarker discovery.

The proliferation of omics data has advanced cancer biomarker discovery but often falls short in external validation, mainly due to a narrow focus on prediction accuracy that neglects clinical utility and validation feasibility. We introduce three- and four-objective optimization strategies based on genetic algorithms to identify clinically actionable biomarkers in omics studies, addressing classification tasks aimed at distinguishing hard-to-differentiate cancer subtypes beyond histological analysis alone. Our hypothesis is that by optimizing more than one characteristic of cancer biomarkers, we may identify biomarkers that will enhance their success in external validation. Our objectives are to: (i) assess the biomarker panel's accuracy using a machine learning (ML) framework; (ii) ensure the biomarkers exhibit significant fold-changes across subtypes, thereby boosting the success rate of PCR or immunohistochemistry validations; (iii) select a concise set of biomarkers to simplify the validation process and reduce clinical costs; and (iv) identify biomarkers crucial for predicting overall survival, which plays a significant role in determining the prognostic value of cancer subtypes. We implemented and applied triple and quadruple optimization algorithms to renal carcinoma gene expression data from TCGA. The study targets kidney cancer subtypes that are difficult to distinguish through histopathology methods. Selected RNA-seq biomarkers were assessed against the gold standard method, which relies solely on clinical information, and in external microarray-based validation datasets. Notably, these biomarkers achieved over 0.8 of accuracy in external validations and added significant value to survival predictions, outperforming the use of clinical data alone with a superior c-index. The provided tool also helps explore the trade-off between objectives, offering multiple solutions for clinical evaluation before proceeding to costly validation or clinical trials.

A best practices framework for spatial biology studies in drug discovery and development: enabling successful cohort studies using digital spatial profiling.

The discovery of biomarkers, essential for successful drug development, is often hindered by the limited availability of tissue samples, typically obtained through core needle biopsies. Standard 'omics platforms can consume significant amounts of tissue, forcing scientist to trade off spatial context for high-plex assays, such as genome-wide assays. While bulk gene expression approaches and standard single-cell transcriptomics have been valuable in defining various molecular and cellular mechanisms, they do not retain spatial context. As such, they have limited power in resolving tissue heterogeneity and cell-cell interactions. Current spatial transcriptomics platforms offer limited transcriptome coverage and have low throughput, restricting the number of samples that can be analyzed daily or even weekly. While the Digital Spatial Profiling (DSP) method does not provide single-cell resolution, it presents a significant advancement by enabling scalable whole transcriptome and ultrahigh-plex protein analysis from distinct tissue compartments and structures using a single tissue slide. These capabilities overcome significant constraints in biomarker analysis in solid tissue specimens. These advancements in tissue profiling play a crucial role in deepening our understanding of disease biology and in identifying potential therapeutic targets and biomarkers. To enhance the use of spatial biology tools in drug discovery and development, the DSP Scientific Consortium has created best practices guidelines. These guidelines, built on digital spatial profiling data and expertise, offer a practical framework for designing spatial studies and using current and future spatial biology platforms. The aim is to improve tissue analysis in all research areas supporting drug discovery and development.

Comprehensive analysis of systemic, metabolic, and molecular changes following prospective change to low-carbohydrate diet in adults with type 2 diabetes mellitus in India.

Purpose: South Asians, especially Indians, face higher diabetes-related risks despite lower body mass index (BMI) compared with the White population. Limited research connects low-carbohydrate high-fat (LCHF)/ketogenic diets to metabolic changes in this group. Systematic studies are needed to assess the long-term effects of the diet, such as ocular health. Method: In this prospective, observational study, 465 candidates aged 25-75 years with type 2 diabetes included with institutional ethics approval. A total of 119 subjects were included in the final study assessment based on the availability of pathophysiological reports, tears, and blood samples collected at baseline, 3rd, and 6th months. Serum and tear samples were analyzed by an enzyme-linked lectinsorbent assay, to examine secreted soluble protein biomarkers, such as IL-1ß (interleukin 1 Beta), IL-6 (interleukin 6), IL-10 (interleukin 10), IL-17A (interleukin 17A), MMP-9 (matrix metalloproteinase 9), ICAM-1 (intercellular adhesion molecule 1), VEGF-A (vascular endothelial growth factor A), and TNF-α (tumor necrosis factor-alpha). A Wilcoxon test was performed for paired samples. Spearman's correlation was applied to test the strength and direction of the association between tear biomarkers and HbA1c. p-value of < 0.05 was considered significant. Results: After a 3- and 6-month LCHF intervention, fasting blood sugar decreased by 10% (Δ: -14 mg/dL; p < 0.0001) and 7% (Δ: -8 mg/dL; p < 0.0001), respectively. Glycated hemoglobin A1c levels decreased by 13% (Δ: -1%; p < 0.0001) and 9% (Δ: -0.6%; p < 0.0001). Triglycerides reduced by 22% (Δ: -27 mg/dL; p < 0.0001) and 14% (Δ: -19 mg/dL; p < 0.0001). Total cholesterol reduced by 5.4% (Δ: -10.5 mg/dL; p < 0.003) and 4% (Δ: -7 mg/dL; p < 0.03), while low-density lipoprotein decreased by 10% (Δ: -11.5 mg/dL; p < 0.003) and 9% (Δ: -11 mg/dL; p < 0.002). High-density lipoprotein increased by 11% (Δ: 5 mg/dL; p < 0.0001) and 17% (Δ: 8 mg/dL; p < 0.0001). At the first follow-up, tear proteins such as ICAM-1, IL-17A, and TNF-α decreased by 30% (Δ: -2,739 pg/mL; p < 0.01), 22% (Δ: -4.5 pg/mL; p < 0.02), and 34% (Δ: -0.9 pg/mL; p < 0.002), respectively. At the second follow-up, IL-1ß and TNF-α reduced by 41% (Δ: -2.4 pg/mL; p < 0.05) and 34% (Δ: -0.67 pg/mL; p < 0.02). Spearman's correlation between HbA1c and tear analytes was not statistically significant. Conclusion: The LCHF diet reduces the risk of hyperglycemia and dyslipidemia. Changes in tear fluid protein profiles were observed, but identifying promising candidate biomarkers requires validation in a larger cohort.

Preclinical Detection of Early Glomerular Injury in Children with Kidney Diseases-Independently of Usual Markers of Kidney Impairment and Inflammation.

Glomerular kidney diseases typically begin insidiously and can progress to end stage kidney failure. Early onset of therapy can slow down disease progression. Early diagnosis is required to ensure such timely therapy. The goal of our study was to evaluate protein biomarkers (BMs) for common nephropathies that have been described for children with Alport syndrome. Nineteen candidate BMs were determined by commercial ELISA in children with congenital anomalies of the kidneys and urogenital tract, inflammatory kidney injury, or diabetes mellitus. It is particularly essential to search for kidney disease BMs in children because they are a crucial target group that likely exhibits early disease stages and in which misleading diseases unrelated to the kidney are rare. Only minor differences in blood between affected individuals and controls were found. However, in urine, several biomarker candidates alone or in combination seemed to be promising indicators of renal injury in early disease stages. The BMs of highest sensitivity and specificity were collagen type XIII, hyaluronan-binding protein 2, and complement C4-binding protein. These proteins are unrelated to inflammation markers or to risk factors for and signs of renal failure. In conclusion, our study evaluated several strong candidates for screening for early stages of kidney diseases and can help to establish early nephroprotective regimens.

Machine learning-based analysis of Ebola virus' impact on gene expression in nonhuman primates.

Introduction: This study introduces the Supervised Magnitude-Altitude Scoring (SMAS) methodology, a novel machine learning-based approach for analyzing gene expression data from non-human primates (NHPs) infected with Ebola virus (EBOV). By focusing on host-pathogen interactions, this research aims to enhance the understanding and identification of critical biomarkers for Ebola infection. Methods: We utilized a comprehensive dataset of NanoString gene expression profiles from Ebola-infected NHPs. The SMAS system combines gene selection based on both statistical significance and expression changes. Employing linear classifiers such as logistic regression, the method facilitates precise differentiation between RT-qPCR positive and negative NHP samples. Results: The application of SMAS led to the identification of IFI6 and IFI27 as key biomarkers, which demonstrated perfect predictive performance with 100% accuracy and optimal Area Under the Curve (AUC) metrics in classifying various stages of Ebola infection. Additionally, genes including MX1, OAS1, and ISG15 were significantly upregulated, underscoring their vital roles in the immune response to EBOV. Discussion: Gene Ontology (GO) analysis further elucidated the involvement of these genes in critical biological processes and immune response pathways, reinforcing their significance in Ebola pathogenesis. Our findings highlight the efficacy of the SMAS methodology in revealing complex genetic interactions and response mechanisms, which are essential for advancing the development of diagnostic tools and therapeutic strategies. Conclusion: This study provides valuable insights into EBOV pathogenesis, demonstrating the potential of SMAS to enhance the precision of diagnostics and interventions for Ebola and other viral infections.

Artificial Intelligence in Head and Neck Cancer: Innovations, Applications, and Future Directions.

Artificial intelligence (AI) is revolutionizing head and neck cancer (HNC) care by providing innovative tools that enhance diagnostic accuracy and personalize treatment strategies. This review highlights the advancements in AI technologies, including deep learning and natural language processing, and their applications in HNC. The integration of AI with imaging techniques, genomics, and electronic health records is explored, emphasizing its role in early detection, biomarker discovery, and treatment planning. Despite noticeable progress, challenges such as data quality, algorithmic bias, and the need for interdisciplinary collaboration remain. Emerging innovations like explainable AI, AI-powered robotics, and real-time monitoring systems are poised to further advance the field. Addressing these challenges and fostering collaboration among AI experts, clinicians, and researchers is crucial for developing equitable and effective AI applications. The future of AI in HNC holds significant promise, offering potential breakthroughs in diagnostics, personalized therapies, and improved patient outcomes.

Mass Spectrometry-Based Top-Down Proteomics in Nanomedicine: Proteoform-Specific Measurement of Protein Corona.

Conventional mass spectrometry (MS)-based bottom-up proteomics (BUP) analysis of the protein corona [i.e., an evolving layer of biomolecules, mostly proteins, formed on the surface of nanoparticles (NPs) during their interactions with biomolecular fluids] enabled the nanomedicine community to partly identify the biological identity of NPs. Such an approach, however, fails to pinpoint the specific proteoforms─distinct molecular variants of proteins in the protein corona. The proteoform-level information could potentially advance the prediction of the biological fate and pharmacokinetics of nanomedicines. Recognizing this limitation, this study pioneers a robust and reproducible MS-based top-down proteomics (TDP) technique for characterizing proteoforms in the protein corona. Our TDP approach has successfully identified about 900 proteoforms in the protein corona of polystyrene NPs, ranging from 2 to 70 kDa, revealing proteoforms of 48 protein biomarkers with combinations of post-translational modifications, signal peptide cleavages, and/or truncations─details that BUP could not fully discern. This advancement in MS-based TDP offers a more advanced approach to characterize NP protein coronas, deepening our understanding of NPs' biological identities. We, therefore, propose using both TDP and BUP strategies to obtain more comprehensive information about the protein corona, which, in turn, can further enhance the diagnostic and therapeutic efficacy of nanomedicine technologies.

Advancements and Challenges in Personalized Therapy for BRAF-Mutant Melanoma: A Comprehensive Review.

Over the past several decades, advancements in the treatment of BRAF-mutant melanoma have led to the development of BRAF inhibitors, BRAF/MEK inhibitor combinations, anti-PD-1 therapy, and anti-CTLA4 therapy. Although these therapies have shown substantial efficacy in clinical trials, their sustained effectiveness is often challenged by the tumor microenvironment, which is a highly heterogeneous and complex milieu of immunosuppressive cells that affect tumor progression. The era of personalized medicine holds substantial promise for the tailoring of treatments to individual genetic profiles. However, tumor heterogeneity and immune evasion mechanisms contribute to the resistance to immunotherapy. Despite these challenges, tumor-infiltrating lymphocyte (TIL) therapy, as exemplified by lifileucel, has demonstrated notable efficacy against BRAF V600-mutant melanoma. Additionally, early response biomarkers, such as COX-2 and MMP2, along with FDG-PET imaging, offer the potential to improve personalized immunotherapy by predicting patient responses and determining the optimal treatment duration. Future efforts should focus on reducing the T-cell harvesting periods and costs associated with TIL therapy to enhance efficiency and accessibility.

Mass Spectrometry Advancements and Applications for Biomarker Discovery, Diagnostic Innovations, and Personalized Medicine.

Mass spectrometry (MS) has revolutionized clinical chemistry, offering unparalleled capabilities for biomolecule analysis. This review explores the growing significance of mass spectrometry (MS), particularly when coupled with liquid chromatography (LC), in identifying disease biomarkers and quantifying biomolecules for diagnostic and prognostic purposes. The unique advantages of MS in accurately identifying and quantifying diverse molecules have positioned it as a cornerstone in personalized-medicine advancement. MS-based technologies have transformed precision medicine, enabling a comprehensive understanding of disease mechanisms and patient-specific treatment responses. LC-MS has shown exceptional utility in analyzing complex biological matrices, while high-resolution MS has expanded analytical capabilities, allowing the detection of low-abundance molecules and the elucidation of complex biological pathways. The integration of MS with other techniques, such as ion mobility spectrometry, has opened new avenues for biomarker discovery and validation. As we progress toward precision medicine, MS-based technologies will be crucial in addressing the challenges of individualized patient care, driving innovations in disease diagnosis, prognosis, and treatment strategies.

Biomarker Discovery in Liver Disease Using Untargeted Metabolomics in Plasma and Saliva.

Chronic liver diseases, including non-alcoholic fatty liver disease (NAFLD), cirrhosis, and hepatocellular carcinoma (HCC), continue to be a global health burden with a rise in incidence and mortality, necessitating a need for the discovery of novel biomarkers for HCC detection. This study aimed to identify novel non-invasive biomarkers for these different liver disease states. We performed untargeted metabolomics in plasma (Healthy = 9, NAFLD = 14, Cirrhosis = 10, HCC = 34) and saliva samples (Healthy = 9, NAFLD = 14, Cirrhosis = 10, HCC = 22) to test for significant metabolite associations with each disease state. Additionally, we identified enriched biochemical pathways and analyzed correlations of metabolites between, and within, the two biofluids. We identified two salivary metabolites and 28 plasma metabolites significantly associated with at least one liver disease state. No metabolites were significantly correlated between biofluids, but we did identify numerous metabolites correlated within saliva and plasma, respectively. Pathway analysis revealed significant pathways enriched within plasma metabolites for several disease states. Our work provides a detailed analysis of the altered metabolome at various stages of liver disease while providing some context to altered pathways and relationships between metabolites.

Tick exposure biomarkers: A One Health approach to new tick surveillance tools.

The spread of tick-borne disease (TBD) is escalating globally, driven by climate change and socio-economic shifts, underlining the urgency to improve surveillance, diagnostics, and control strategies. Ticks can transmit a range of pathogens increasing the risk of transmission of human and veterinary diseases such as Lyme disease, tick-borne encephalitis, theileriosis, anaplasmosis, or Crimean-Congo hemorrhagic fever. Surveillance methods play a crucial role in monitoring the spread of tick-borne pathogens (TBP). However, there are shortcomings in the current surveillance methods regarding risks related to ticks. Human-tick encounters offer a novel metric for disease risk assessment, integrating human behavior into traditional surveillance models. However, to more reliably measure tick exposure, a molecular marker is needed. The identification of antibodies against arthropod salivary proteins as biomarkers for vector exposure represents a promising avenue for enhancing existing diagnostic and surveillance metrics. Here we explore how the use of tick saliva biomarkers targeting recombinant proteins and synthetic peptides could significantly improve the assessment of TBD transmission risk and the effectiveness of vector control measures. With focused efforts on creating a biomarker against tick exposure suitable for humans and domestic animals alike, tick surveillance, diagnosis and control would be more achievable and aid in reducing the mounting threat of TBP through a One Health lens.

Deep Learning-Based Clustering of OCT Images for Biomarker Discovery in Age-Related Macular Degeneration (PINNACLE Study Report 4).

Purpose: We introduce a deep learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). Design: Retrospective analysis of a large data set of retinal OCT images. Participants: A total of 3456 adults aged between 51 and 102 years whose OCT images were collected under the PINNACLE project. Methods: Our system proposes candidates for novel AMD imaging biomarkers in OCT. It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46 496 retinal OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct 2 parallel 1.5-hour semistructured interviews with 2 independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates. Main Outcome Measures: We checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD, and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late-stage wet and dry AMD against an established clinical grading system and a demographic baseline model. Results: Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognized as known biomarkers used in established grading systems, and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid, and thick from thin choroids, and, in simulation, outperformed clinically used grading systems in prognostic value. Conclusions: Using self-supervised deep learning, we were able to automatically propose AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep learning tools can accelerate the discovery of novel prognostic biomarkers. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Computational methods and biomarker discovery strategies for spatial proteomics: a review in immuno-oncology.

Advancements in imaging technologies have revolutionized our ability to deeply profile pathological tissue architectures, generating large volumes of imaging data with unparalleled spatial resolution. This type of data collection, namely, spatial proteomics, offers invaluable insights into various human diseases. Simultaneously, computational algorithms have evolved to manage the increasing dimensionality of spatial proteomics inherent in this progress. Numerous imaging-based computational frameworks, such as computational pathology, have been proposed for research and clinical applications. However, the development of these fields demands diverse domain expertise, creating barriers to their integration and further application. This review seeks to bridge this divide by presenting a comprehensive guideline. We consolidate prevailing computational methods and outline a roadmap from image processing to data-driven, statistics-informed biomarker discovery. Additionally, we explore future perspectives as the field moves toward interfacing with other quantitative domains, holding significant promise for precision care in immuno-oncology.

A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology.

The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.

TopMarker: Computational screening biomarkers of hepatocellular carcinoma from transcriptome and interactome based on differential network topological parameters.

Identifying diagnostic biomarkers for cancer is crucial in the field of personalized medicine. The available transcriptome and interactome provide unprecedented opportunities and challenges for biomarker screening. From a systematic perspective, network-based medicine methods provide alternative approaches to organizing the available high-throughput omics data for deciphering molecular interactions and their associations with phenotypic states. In this work, we propose a bioinformatics strategy named TopMarker for discovering diagnostic biomarkers by comparing the network topology differences in control and disease samples. Specifically, we build up gene-gene interaction networks in the two states of control and disease respectively. The network rewiring status across the two networks results in differential network topologies reflecting dynamics and changes in normal samples when compared with those in disease. Thus, we identify the potential biomarker genes with differential network topological parameters between the control and disease gene networks. For a proof-of-concept study, we introduce the computational pipeline of biomarker discovery in hepatocellular carcinoma (HCC). We prove the effectiveness of the proposed TopMarker method using these candidate biomarkers in classifying HCC samples and validate its signature capability across numerous independent datasets. We also compare the discriminant power of biomarker genes identified by TopMarker with those identified by other baseline methods. The higher classification performances and functional implications indicate the advantages of our proposed method for discovering biomarkers from differential network topology.

BEROLECMI: a novel prediction method to infer circRNA-miRNA interaction from the role definition of molecular attributes and biological networks.

Circular RNA (CircRNA)-microRNA (miRNA) interaction (CMI) is an important model for the regulation of biological processes by non-coding RNA (ncRNA), which provides a new perspective for the study of human complex diseases. However, the existing CMI prediction models mainly rely on the nearest neighbor structure in the biological network, ignoring the molecular network topology, so it is difficult to improve the prediction performance. In this paper, we proposed a new CMI prediction method, BEROLECMI, which uses molecular sequence attributes, molecular self-similarity, and biological network topology to define the specific role feature representation for molecules to infer the new CMI. BEROLECMI effectively makes up for the lack of network topology in the CMI prediction model and achieves the highest prediction performance in three commonly used data sets. In the case study, 14 of the 15 pairs of unknown CMIs were correctly predicted.

Biomarker discovery in hepatocellular carcinoma (HCC) for personalized treatment and enhanced prognosis.

Hepatocellular carcinoma (HCC) is a leading contributor to cancer-related deaths worldwide and presents significant challenges in diagnosis and treatment due to its heterogeneous nature. The discovery of biomarkers has become crucial in addressing these challenges, promising early detection, precise diagnosis, and personalized treatment plans. Key biomarkers, such as alpha fetoprotein (AFP) glypican 3 (GPC3) and des gamma carboxy prothrombin (DCP) have shown potential in improving clinical results. Progress in proteomic technologies, including next-generation sequencing (NGS), mass spectrometry, and liquid biopsies detecting circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), has deepened our understanding of HCC's molecular landscape. Immunological markers, like PD-L1 expression and tumor-infiltrating lymphocytes (TILs), also play a crucial role in guiding immunotherapy decisions. Despite these advancements, challenges remain in biomarker validation, standardization, integration into clinical practice, and cost-related barriers. Emerging technologies like single-cell sequencing and machine learning offer promising avenues for further exploration. Continued investment in research and collaboration among researchers, healthcare providers, and policymakers is vital to harness the potential of biomarkers fully, ultimately revolutionizing HCC management and improving patient outcomes through personalized treatment approaches.