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The widespread use of immune checkpoint inhibitors (ICIs) has revolutionised treatment of multiple cancer types. However, selecting patients who may benefit from ICI remains challenging. Artificial intelligence (AI) approaches allow exploitation of high-dimension oncological data in research and development of precision immuno-oncology. We conducted a systematic literature review of peer-reviewed original articles studying the ICI efficacy prediction in cancer patients across five data modalities: genomics (including genomics, transcriptomics, and epigenomics), radiomics, digital pathology (pathomics), and real-world and multimodality data. A total of 90 studies were included in this systematic review, with 80% published in 2021-2022. Among them, 37 studies included genomic, 20 radiomic, 8 pathomic, 20 real-world, and 5 multimodal data. Standard machine learning (ML) methods were used in 72% of studies, deep learning (DL) methods in 22%, and both in 6%. The most frequently studied cancer type was non-small-cell lung cancer (36%), followed by melanoma (16%), while 25% included pan-cancer studies. No prospective study design incorporated AI-based methodologies from the outset; rather, all implemented AI as a post hoc analysis. Novel biomarkers for ICI in radiomics and pathomics were identified using AI approaches, and molecular biomarkers have expanded past genomics into transcriptomics and epigenomics. Finally, complex algorithms and new types of AI-based markers, such as meta-biomarkers, are emerging by integrating multimodal/multi-omics data. AI-based methods have expanded the horizon for biomarker discovery, demonstrating the power of integrating multimodal data from existing datasets to discover new meta-biomarkers. While most of the included studies showed promise for AI-based prediction of benefit from immunotherapy, none provided high-level evidence for immediate practice change. A priori planned prospective trial designs are needed to cover all lifecycle steps of these software biomarkers, from development and validation to integration into clinical practice. • ML and DL (AI techniques) have been used to analyse big genomic and proteomic data in immuno-oncology. • This systematic review highlights the potential of AI in identifying new biomarkers that can personalise immunotherapy. • AI algorithms are promising in optimising treatment selection, and improving overall patient outcomes. • The article emphasises the need for further research and validation of the AI algorithm to switch from research to clinic. • Trustworthy explainable AI can be of great utility by providing explanations of the algorithms and discovery of biomarkers. [ABSTRACT FROM AUTHOR]

Psoriasis is an immune-mediated inflammatory skin disease. Psoriasis severity evaluation is important for clinicians in the assessment of disease severity and subsequent clinical decision making. However, no objective biomarker is available for accurately evaluating disease severity in psoriasis. To define and compare biomarkers of disease severity and progression in psoriatic skin. We performed proteome profiling to study the proteins circulating in the serum from patients with psoriasis, psoriatic arthritis and ankylosing spondylitis, and transcriptome sequencing to investigate the gene expression in skin from the same cohort. We then used machine learning approaches to evaluate different biomarker candidates across several independent cohorts. In order to reveal the cell-type specificity of different biomarkers, we also analyzed a single-cell dataset of skin samples. In-situ staining was applied for the validation of biomarker expression. We identified that the peptidase inhibitor 3 (PI3) was significantly correlated with the corresponding local skin gene expression, and was associated with disease severity. We applied machine learning methods to confirm that PI3 was an effective psoriasis classifier, Finally, we validated PI3 as psoriasis biomarker using in-situ staining and public datasets. Single-cell data and in-situ staining indicated that PI3 was specifically highly expressed in keratinocytes from psoriatic lesions. Our results suggest that PI3 may be a psoriasis-specific biomarker for disease severity and hyper-keratinization. • PI3 is highly expressed in psoriatic skin, compared to non-involved skin or healthy skin. • Our data reveals the pathogenic relationship between skin transcriptome and serum proteome from same donors. • Single-cell data and in-situ staining indicated PI3 was specifically expressed by keratinocytes found in psoriatic lesions. • PI3 can be used as a liquid biopsy-based biomarker of disease severity and hyper-keratinization progress in psoriasis. [ABSTRACT FROM AUTHOR]

Purpose: The availability of validated biomarkers to assess radiation exposure and to assist in developing medical countermeasures remains an unmet need.Methods and Materials: We used a cobalt-60 γ-irradiated nonhuman primate (NHP) model to delineate a multiomics-based serum probability index of radiation exposure. Both male and female NHPs were irradiated with different doses ranging from 6.0 to 8.5 Gy, with 0.5 Gy increments between doses. We leveraged high-resolution mass spectrometry for analysis of metabolites, lipids, and proteins at 1, 2, and 6 days postirradiation in NHP serum.Results: A logistic regression model was implemented to develop a 4-analyte panel to stratify irradiated NHPs from unirradiated with high accuracy that was agnostic for all doses of γ-rays tested in the study, up to 6 days after exposure. This panel was comprised of Serpin family A9, acetylcarnitine, glycerophosphocholine (16:0/22:6), and suberylglycine, which showed 2- to 4-fold elevation in serum abundance upon irradiation in NHPs and can potentially be translated as a molecular diagnostic for human use after larger validation studies.Conclusions: Taken together, this study, for the first time, demonstrates the utility of a combinatorial molecular characterization approach using an NHP model for developing minimally invasive assays from small volumes of blood that can be effectively used for radiation exposure assessments. [ABSTRACT FROM AUTHOR]

Psoraleae Fructus (Bu Gu Zhi) is the fruit of Psoralea corylifolia L. (PCL) and has been used for centuries in traditional Chinese medicine formulas to treat osteoporosis (OP). A new drug called "BX" has been developed from PCL, but its mechanism for treating OP is not yet fully understood. To explore the mechanism of action of BX in the treatment of ovariectomy-induced OP based function-oriented multi-omics analysis of gut microbiota (GM) and metabolites. C57BL/6 mice were bilaterally ovariectomized to replicate the OP model. The therapeutic efficacy of BX was evaluated by bone parameters (BMD, BV/TV, Tb.N, Tb.Sp), hematoxylin and eosin (H&E) staining results, and determination of bone formation markers procollagen type Ⅰ amino-terminal peptide (PⅠNP) and bone-specific alkaline phosphatase (BALP). Serum and fecal metabolomics and high-throughput 16S rDNA sequencing were performed to evaluate effects on endogenous metabolites and GM. In addition, an enzyme-based functional correlation algorithm (EBFC) algorithm was used to investigate functional correlations between GM and metabolites. BX improved OP in OVX mice by increasing BMD, BV/TV, serum PⅠNP, BALP, and improving Tb.N and Tb.Sp. A total of 59 differential metabolites were identified, and 9 metabolic pathways, including arachidonic acid metabolism, glycerophospholipid metabolism, purine metabolism, and tryptophan metabolism, were found to be involved in the progression of OP. EBFC analysis results revealed that the enzymes related to purine and tryptophan metabolism, which are from Lachnospiraceae_NK4A136_group , Blautia , Rs-E47_termite_group , UCG-009, and Clostridia_UCG-014 , were identified as the intrinsic link between GM and metabolites. The regulation of GM and restoration of metabolic disorders may be the mechanisms of action of BX in alleviating OP. This research provides insights into the function-oriented mechanism discovery of traditional Chinese medicine in the treatment of OP. [Display omitted] • BX exhibited therapeutic effects in the treatment of osteoporosis (OP). • BX modulated the gut microbiota (GM) composition and metabolism. • Functional correlations between GM and differential metabolites were discovered. • BX regulated purine and tryptophan metabolism-related GM and its metabolites. [ABSTRACT FROM AUTHOR]

Linderae Radix (Lindera aggregata (Sims) Kosterm) is a traditional Chinese medicine known for its capability to regulate qi and relieve pain, particularly in the context of gastrointestinal disorders. While our previous research has demonstrated the efficacy of the Linderae Radix water extract (LRWE) in the treatment of diarrhea-predominant irritable bowel syndrome (IBS-D), the precise mechanisms remain elusive. This study aims to provide a comprehensive understanding of the therapeutic effects of LRWE on IBS-D through multi-omics techniques. 16 S rRNA gene sequencing combined with LC-MS metabolomics was employed to investigate the effect of LRWE on the gut microbiota and metabolites of IBS-D rats. Spearman correlation analysis was performed on the gut microbiota and metabolites. LRWE administration significantly ameliorated IBS-D rats' symptoms, including diarrhea, visceral hypersensitivity, and low-grade intestinal inflammation. Gut microbiota analysis revealed that LRWE influenced the diversity of the gut microbiota in IBS-D rats by significantly reducing the relative abundance of Patescibacteria and Candidatus Saccharimonas, while increasing the relative abundance of Jeotgalicoccus. Serum metabolomic analysis identified 16 differential metabolites, associated with LRWE's positive effects on IBS-D symptoms, focusing on glyoxylate and dicarboxylic acid metabolism, and cysteine and methionine metabolism. Spearman analysis demonstrated a strong correlation between cecal microbiota composition and serum metabolite levels. This study elucidates that LRWE plays a crucial role in the comprehensive therapeutic approach to IBS-D by restoring the relative abundance of gut microbiota and addressing the disturbed metabolism of endogenous biomarkers. The identified bacteria and metabolites present potential therapeutic targets for IBS-D. [Display omitted] [ABSTRACT FROM AUTHOR]

• Multi-omics technologies enable comprehensive characterization of colorectal cancer (CRC). • Spatial omics provide insights into the tumor microenvironment and cellular interactions in CRC. • Artificial intelligence facilitates the identification of CRC biomarkers and molecular subtypes. • Integrating multi-omics and artificial intelligence advances understanding of CRC biology and personalized therapeutic strategies. Colorectal cancer (CRC) is a leading cause of cancer-related deaths. Recent advancements in genomic technologies and analytical approaches have revolutionized CRC research, enabling precision medicine. This review highlights the integration of multi-omics, spatial omics, and artificial intelligence (AI) in advancing precision medicine for CRC. Multi-omics approaches have uncovered molecular mechanisms driving CRC progression, while spatial omics have provided insights into the spatial heterogeneity of gene expression in CRC tissues. AI techniques have been utilized to analyze complex datasets, identify new treatment targets, and enhance diagnosis and prognosis. Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC. The future lies in integrating these technologies to provide deeper insights and enable personalized therapies for CRC patients. [ABSTRACT FROM AUTHOR]

• Global Male Infertility Surge: Male infertility is rising globally, indicating potential links to genitourinary cancers. • Early Cancer Warning: Male infertility serves as an early biomarker for cancer, urging deeper exploration of contributing factors. • Limited Semen Biomarkers: Current biomarkers tied to semen characteristics and cancer risk are limited but gaining research attention. • Multiomics for Novel Insights: Multiomics approaches are emerging tools, crucial for identifying novel biomarkers in male infertility and cancer risk. In recent years, there has been a global increase in cases of male infertility. There are about 30 million cases of male infertility worldwide and male reproductive health is showing rapid decline in last few decades. It is now recognized as a potential risk factor for developing certain types of cancer, particularly genitourinary malignancies like testicular and prostate cancer. Male infertility is considered a potential indicator of overall health and an early biomarker for cancer. Cases of unexplained male factor infertility have high levels of oxidative stress and oxidative DNA damage and this induces both denovo germ line mutations and epimutations due to build up of 8-hydroxy 2 deoxygunaosine abase which is highly mutagenic and also induces hypomethylation and genomic instability. Consequently, there is growing evidence to explore the various factors contributing to an increased cancer risk. Currently, the available prognostic and predictive biomarkers associated with semen characteristics and cancer risk are limited but gaining significant attention in clinical research for the diagnosis and treatment of elevated cancer risk in the individual and in offspring. The male germ cell being transcriptionally and translationally inert has a highly truncated repair mechanism and has minimal antioxidants and thus most vulnerable to oxidative injury due to environmental factors and unhealthy lifestyle and social habits. Therefore, advancing our understanding requires a thorough evaluation of the pathophysiologic mechanisms at the DNA, RNA, protein, and metabolite levels to identify key biomarkers that may underlie the pathogenesis of male infertility and associated cancer. Advanced methodologies such as genomics, epigenetics, proteomics, transcriptomics, and metabolomics stand at the forefront of cutting-edge approaches for discovering novel biomarkers, spanning from infertility to associated cancer types. Henceforth, in this review, we aim to assess the role and potential of recently identified predictive and prognostic biomarkers, offering insights into the success of assisted reproductive technologies, causes of azoospermia and idiopathic infertility, the impact of integrated holistic approach and lifestyle modifications, and the monitoring of cancer susceptibility, initiation and progression. Comprehending these biomarkers is crucial for providing comprehensive counselling to infertile men and cancer patients, along with their families. [ABSTRACT FROM AUTHOR]

Sophorae tonkinensis Radix et Rhizoma (STR) is an extensively applied traditional Chinese medicine (TCM) in southwest China. However, its clinical application is relatively limited due to its hepatotoxicity effects. To understand the material foundation and liver injury mechanism of STR. Chemical compositions in STR and its prototypes in mice were profiled by ultra-performance liquid chromatography coupled quadrupole-time of flight mass spectrometry (UPLC-Q/TOF MS). STR-induced liver injury (SILI) was comprehensively evaluated by STR-treated mice mode. The histopathologic and biochemical analyses were performed to evaluate liver injury levels. Subsequently, network pharmacology and multi-omics were used to analyze the potential mechanism of SILI in vivo. And the target genes were further verified by Western blot. A total of 152 compounds were identified or tentatively characterized in STR, including 29 alkaloids, 21 organic acids, 75 flavonoids, 1 quinone, and 26 other types. Among them, 19 components were presented in STR-medicated serum. The histopathologic and biochemical analysis revealed that hepatic injury occurred after 4 weeks of intragastric administration of STR. Network pharmacology analysis revealed that IL6, TNF, STAT3, etc. were the main core targets, and the bile secretion might play a key role in SILI. The metabolic pathways such as taurine and hypotaurine metabolism, purine metabolism, and vitamin B6 metabolism were identified in the STR exposed groups. Among them, taurine, hypotaurine, hypoxanthine, pyridoxal, and 4-pyridoxate were selected based on their high impact value and potential biological function in the process of liver injury post STR treatment. The mechanism and material foundation of SILI were revealed and profiled by a multi-omics strategy combined with network pharmacology and chemical profiling. Meanwhile, new insights were taken into understand the pathological mechanism of SILI. [Display omitted] • A total of 152 compounds were identified in STR by UPLC-Q/TOF MS. • Active components in STR-medicated serum were identified. • Multi-omics approaches were conducted to reveal the mechanism of SILI. • Taurine, hypotaurine, and pyridoxal might be the potential biomarkers in SILI. [ABSTRACT FROM AUTHOR]

• MDD has substantial changes in the structure and function of gut microbiota. • MDD exhibited decreased amino acids and bile acids and increased lipids in blood. • The changes of blood immune cell subtypes of MDD tend to promote inflammation. • MDD could be divided into two immune subtypes, and one is correlated with relapse. • We revealed integrative discriminative signatures for distinguishing MDD from HC. Major depressive disorder (MDD) involves systemic changes in peripheral blood and gut microbiota, but the current understanding is incomplete. Herein, we conducted a multi-omics analysis of fecal and blood samples obtained from an observational cohort including MDD patients (n = 99) and healthy control (HC, n = 50). 16S rRNA sequencing of gut microbiota showed structural alterations in MDD, as characterized by increased Enterococcus. Metagenomics sequencing of gut microbiota showed substantial functional alterations including upregulation in the superpathway of the glyoxylate cycle and fatty acid degradation and downregulation in various metabolic pathways in MDD. Plasma metabolomics revealed decreased amino acids and bile acids, together with increased sphingolipids and cholesterol esters in MDD. Notably, metabolites involved in arginine and proline metabolism were decreased while sphingolipid metabolic pathway were increased. Mass cytometry analysis of blood immune cell subtypes showed rises in proinflammatory immune subsets and declines in anti-inflammatory immune subsets in MDD. Furthermore, our findings revealed disease severity-related factors of MDD. Interestingly, we classified MDD into two immune subtypes that were highly correlated with disease relapse. Moreover, we established discriminative signatures that differentiate MDD from HC. These findings contribute to a comprehensive understanding of the MDD pathogenesis and provide valuable resources for the discovery of biomarkers. [ABSTRACT FROM AUTHOR]

• Omics tools can accurately and efficiently describe and identify food markers in the field of safety and authenticity. • Omics can detect proteins, metabolites or DNA in samples through high-throughput detection mode. • The application of omics technology in food safety testing has expanded the scope of food safety testing and brought greater development to food safety testing technology. • The characteristics, advantages, relationship and processes of different omics in food research are highlighted. One of the main goals of food science is to ensure the high quality and safety of food. The inspection technology for known hazards has matured, and the identification of unknown and potential food safety hazards, as well as the identification of their composition and origin, is a challenge faced by food safety. Food safety and authenticity require multi-omics methods to support the implementation of qualitative discrimination to precise quantitative analysis, from targeted screening to non-target detection, and from multi component to full component analysis to address these challenges. The present review aims to provide characterizations, advantages, the latest progress, and prospects of using omics (including genomics, proteomics, and metabonomics) in food safety and authenticity. Multi omics strategies used to detect and verify different standard biomarkers of food will contribute to understanding the basic relationship between raw materials, processing, foods, nutrition, food safety, and human health. [ABSTRACT FROM AUTHOR]

Neoadjuvant chemoradiotherapy (NCRT) followed by surgery has been established as the standard treatment strategy for operable locally advanced esophageal cancer (EC). However, achieving pathologic complete response (pCR) or near pCR to NCRT is significantly associated with a considerable improvement in survival outcomes, while pCR patients may help organ preservation for patients by active surveillance to avoid planned surgery. Thus, there is an urgent need for improved biomarkers to predict EC chemoradiation response in research and clinical settings. Advances in multiple high-throughput technologies such as next-generation sequencing have facilitated the discovery of novel predictive biomarkers, specifically based on multi-omics data, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra. The application of multi-omics data has shown the benefits in improving the understanding of underlying mechanisms of NCRT sensitivity/resistance in EC. Particularly, the prominent development of artificial intelligence (AI) has introduced a new direction in cancer research. The integration of multi-omics data has significantly advanced our knowledge of the disease and enabled the identification of valuable biomarkers for predicting treatment response from diverse dimension levels, especially with rapid advances in biotechnological and AI methodologies. Herein, we summarize the current status of research on the use of multi-omics technologies in predicting NCRT response for EC patients. Current limitations, challenges, and future perspectives of these multi-omics platforms will be addressed to assist in experimental designs and clinical use for further integrated analysis. [Display omitted] [ABSTRACT FROM AUTHOR]

Traditionally, biosensors are designed to detect one specific analyte. Nevertheless, disease progression is regulated in a highly interactive way by different classes of biomolecules like proteins and nucleic acids. Therefore, a more comprehensive analysis of biomarkers from a single sample is of utmost importance to further improve both, the accuracy of diagnosis as well as the therapeutic success. This review summarizes fundamentals like biorecognition and sensing strategies for the simultaneous detection of proteins and nucleic acids and discusses challenges related to multianalyte biosensor development. We present an overview of the current state of biosensors for the combined detection of protein and nucleic acid biomarkers associated with widespread diseases, among them cancer and infectious diseases. Furthermore, we outline the multianalyte analysis in the rapidly evolving field of single-cell multiomics, to stress its significance for the future discovery and validation of biomarkers. Finally, we provide a critical perspective on the performance and translation potential of multianalyte biosensors for medical diagnostics. [Display omitted] [ABSTRACT FROM AUTHOR]

The availability of a large amount of multiomics data enables data-driven discovery studies on cancers. High-throughput data on mutations, gene/protein expression, immune scores (tumor-infiltrating cells), drug screening, and RNAi (shRNAs and CRISPRs) screening are major integrated components of patient samples and cell line datasets. Improvements in data access and user interfaces make it easy for general scientists to carry out their data mining practices on integrated multiomics data platforms without computational expertise. Here, we summarize the extent of data integration and functionality of several portals and software that provide integrated multiomics data mining platforms for all cancer studies. Recent progress includes programming interfaces (APIs) for customized data mining. Precalculated datasets assist noncomputational users in quickly browsing data associations. Furthermore, stand-alone software provides fast calculations and smart functions, guiding optimal sampling and filtering options for the easy discovery of significant data associations. These efforts improve the utility of cancer omics big data for noncomputational users at all levels of cancer research. In the present review, we aim to provide analytical information guiding general scientists to find and utilize data mining tools for their research. [ABSTRACT FROM AUTHOR]

Accurate determination of kiwifruit maturity in the first half of the harvest window is crucial in predicting storage risk, since early-harvested fruit are more susceptible to disorders and rots during extended cold storage. Maturity estimations are usually based on soluble solids content (SSC), but SSC alone is unreliable at early harvest times, necessitating the need for more precise markers. To address this, we performed multi-omics on harvested, yellow-fleshed kiwifruit at four chronological ages based on days after anthesis, namely early harvest (H1), early-mid (H2), late-mid (H3) and late harvest (H4), across two seasons from the same orchard. Analysis of phenotype, transcriptome, metabolome, proteome, hormone concentrations and enzyme activity profiles identified discriminating features within the datasets that separated harvest times, particularly in the first half of the harvest window. Strong seasonal variability was observed. The most reliable variates for maturity estimation were found in the transcriptome, and two transcripts with sharply decreasing abundance during early harvest (Aquaporin TIP4 - 1 and MYB10) and three transcripts with sharply increasing abundance during later harvest (β-Amylase 3.2 , Pectinesterase inhibitor protein and α-Terpineol synthase) with relative seasonal stability were identified. In a different season we used these five potential biomarkers in combination with SSC as a predictive tool. This allowed precise assessment of the maturity stage of fruit in the first half of the harvest window from 12 orchards in four geographical regions, and accurate prediction of their storage outcome. • Early-season kiwifruit are more susceptible to storage disorders and rots. • Measuring SSC alone often gives insufficient information about maturity. • MixOmics was used to identify at-harvest biomarkers of fruit maturity. • TIP4-1, MYB10, β-Amylase 3.2 and PMEi were identified as best biomarkers. • Application of these biomarkers may assist in predicting storage performance. [ABSTRACT FROM AUTHOR]

Abstract: The high incidence and mortality of colorectal cancer (CRC) and the lack of adequate diagnostic molecules have led to poor treatment outcomes for colorectal cancer, making it particularly important to develop methods to obtain molecular with significant diagnostic effects. Here, we proposed a whole and part study strategy (early-stage colorectal cancer as "part" and colorectal cancer as "whole") to identify specific and co-pathways of change in early-stage and colorectal cancers and to discover the determinants of colorectal cancer development. Metabolite biomarkers discovered in plasma may not necessarily reflect the pathological status of tumor tissue. To explore the determinant biomarkers associated with plasma and tumor tissue in the CRC progression, multi-omics were performed on three phases of biomarker discovery studies (discovery, identification and validation) including 128 plasma metabolomes and 84 tissue transcriptomes. Importantly, we observe that the metabolic levels of oleic acid and FA (18:2) in patients with colorectal cancer were much higher than in healthy people. Finally, biofunctional verification confirmed that oleic acid and FA (18:2) can promote the growth of colorectal cancer tumor cells and be used as plasma biomarkers for early-stage colorectal cancer. We propose a novel research strategy to discover co-pathways and important biomarkers that may be targeted for a potential role in early colorectal cancer, and our work provides a promising tool for the clinical diagnosis of colorectal cancer. [Display omitted] • A "whole part" strategy based on multi-omics analysis is proposed. • Specific and co-pathways of change in ECRC and CRC were identified. • Oleic acid and FA18:2 promote the tumor genesis of ECRC in vivo. [ABSTRACT FROM AUTHOR]

The increasingly prominent role played by precision, equitable and sustainable diagnosis, particularly of prevalent and highly heterogeneous diseases, based on the determination and characterization of biomarkers of cellular origin in liquid biopsy, such as whole cells, their secretomes and the exosomes they release, is unquestionable. Electrochemical biosensing and biosensors, mainly those involving affinity reactions, have emerged in recent years as competitive analytical alternatives to address these challenging targets versus other existing strategies in terms of simplicity, cost and compatibility with multiplexed or multi-omics point-of-care determinations. This review article aims to put on the table in a concise, accurate and updated way the fundamentals, versatility and opportunities provided by state-of-the-art electrochemical biosensing (2018–2022) for the determination of these cellular-derived biomarkers. A more critical and personal view of the challenges to be faced in the shorter term and where future research and advances are expected to go is also given. • Role of biomarkers of cellular origin in personalized, equitable and sustainable diagnosis. • Unique features provided by modern electrochemical biosensing for cellular biomarkers determination. • Challenges to be faced in the shorter term and future research and developments. [ABSTRACT FROM AUTHOR]

The diagnosis of glioma is mainly based on imaging methods that do not distinguish between stage and subtype prior to histopathological analysis. Patients with gliomas are generally diagnosed in the symptomatic stage of the disease. Additionally, healing scar tissue may be mistakenly identified based on magnetic resonance imaging (MRI) as a false positive tumor recurrence in postoperative patients. Current knowledge of molecular alterations underlying gliomagenesis and identification of tumoral biomarkers allow for their use as discriminators of the state of the organism. Moreover, a multiomics approach provides the greatest spectrum and the ability to track physiological changes and can serve as a minimally invasive method for diagnosing asymptomatic gliomas, preceding surgery and allowing for the initiation of prophylactic treatment. It is important to create a vast biomarker library for adults and pediatric patients due to their metabolic differences. This review focuses on the most promising proteomic, metabolomic and lipidomic glioma biomarkers, their pathways, the interactions, and correlations that can be considered characteristic of tumor grade or specific subtype. [ABSTRACT FROM AUTHOR]

• Biomarkers of aging are a valuable tool to understand the aging process. • Intrinsic capacity may be characterized by the multi-omics technologies. • Biomarkers reflect at several molecular levels an individual's state of health. • Aging process is not linked to the chronological age. • Age-related diseases may be addressed by the biomarkers of aging. The aging process has been linked to the occurrence of chronic diseases and functional impairments, including cancer, sarcopenia, frailty, metabolic, cardiovascular, and neurodegenerative diseases. Nonetheless, aging is highly variable and heterogeneous and represents a challenge for its characterization. In this sense, intrinsic capacity (IC) stands as a novel perspective by the World Health Organization, which integrates the individual wellbeing, environment, and risk factors to understand aging. However, there is a lack of quantitative and qualitative attributes to define it objectively. Therefore, in this review we attempt to summarize the most relevant and promising biomarkers described in clinical studies at date over different molecular levels, including epigenomics, transcriptomics, proteomics, metabolomics, and the microbiome. To aid gerontologists, geriatricians, and biomedical researchers to understand the aging process through the IC. Aging biomarkers reflect the physiological state of individuals and the underlying mechanisms related to homeostatic changes throughout an individual lifespan; they demonstrated that aging could be measured independently of time (that may explain its heterogeneity) and to be helpful to predict age-related syndromes and mortality. In summary, we highlight the areas of opportunity and gaps of knowledge that must be addressed to fully integrate biomedical findings into clinically useful tools and interventions. [ABSTRACT FROM AUTHOR]