bims-mascan 2026-07-05 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–07–05
fourteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Systematic optimization and benchmarking of synchro-PASEF for high-throughput phosphoproteome profiling.
Large Language Model-Generated Dietary Metabolite Biomarker Database Drives Deep Annotation of the Human Diet Metabolome.
Characterization of O-glycoproteases for applications in mass spectrometry.
The MassBank contributions of the mFam collaboration.
From Method-Defined Signals to Reference Measurement Procedures: Two Decades of Mass Spectrometry-Based ProGRP Quantification.
Democratized single-cell proteomics resolves cell state heterogeneity in skin tumors.
Agentic AI for Structural Elucidation and Discovery of Drug Metabolites from Mass Spectrometry Data.
Comparison of Information-Dependent Acquisition and Sequential Window Acquisition of All Theoretical Mass Spectra for Untargeted Drug Testing on a Linear Ion Trap-Pulsing Quadrupole-Time of Flight Mass Spectrometer.
Recent advancements in sample pretreatment methods for glycoproteomics.
Steatosis shapes prognosis-defining liver metastasis heterogeneity in CRC.
Hierarchy of MS-Based Evidence.
High-Throughput, Ultralow-Input Proteomics Enabled by Narrow-Bore Open Tubular Liquid Chromatography.
Identification of Age-Associated Circulating Proteins and Lipids in 3800 Comorbidity-Enriched Older Adults from Japan-Based Cohorts Using Olink Assays and MRM Mass Spectrometry.
Targeting amino acid metabolism in hepatocellular carcinoma.

bioRxiv. 2026 Jun 27. pii: 2026.06.26.734570. [Epub ahead of print]

Systematic optimization and benchmarking of synchro-PASEF for high-throughput phosphoproteome profiling.

Dain R Brademan, Angelina A Mullarkey, Mia L Greeson, Sarah Szvetecz, Olga Vitek, Emily E Blythe, Ruth Hüttenhain.

High-throughput data-independent acquisition (DIA) workflows paired with short chromatographic separations are increasingly adopted for systems biology and clinical proteomics. However, narrower peak widths from rapid separations demand faster mass spectrometer cycle times to maintain quantitative depth and reproducibility. The synchro-PASEF acquisition mode on timsTOF mass spectrometers diagonally scans across ion mobility and m/z space, enabling efficient sampling of the precursor ion cloud with shortened cycle times. While synchro-PASEF has demonstrated competitive identification depth for global protein abundance samples compared to conventional dia-PASEF, its performance for phosphoproteomics-where the precursor ion cloud is characteristically broader and bimodally distributed-has not been evaluated. Here, we systematically optimized synchro-PASEF methods for phosphoproteomics and benchmarked performance against two dia-PASEF methods across three sub-hour separations. We found that synchro-PASEF performance depends critically on balancing diagonal window number, total isolation width, and gradient length, with longer gradients favoring more windows for selectivity and shorter gradients favoring fewer windows to preserve sampling frequency. An optimized configuration quantified over 19,000 localized phosphosites using a 23-minute separation. Retention time summation (RTsum) with a factor of 2 increased phosphopeptide identifications by 5-20% and reduced phosphosite-level coefficients of variation by up to 30% across all dia-PASEF and synchro-PASEF methods tested. Using β2-adrenergic receptor (B2AR) activation as a signaling model, we demonstrate that label-free DIA phosphoproteomics can be used to model phosphoproteomics dose-response relationships, showing that synchro-PASEF and dia-PASEF produce highly concordant phosphoproteomic responses, with comparable numbers of responding phosphosites, similar effect sizes, and nearly identical predicted protein kinase A (PKA) substrates downstream of the activated B2AR. While synchro-PASEF did not surpass optimized dia-PASEF in identification depth, its comparable biological performance and amenability to post-acquisition optimization through RTsum support its utility for high-throughput phosphoproteomics. This work provides a transferable framework for synchro-PASEF method optimization and demonstrates the broad utility of retention time summation for PASEF-based phosphoproteomics workflows.
Highlights: Systematic benchmarking of synchro-PASEF for typical phosphoproteomics workflows.RT summation improves IDs and quantitative precision for dia-PASEF and synchro-PASEFdia-PASEF and synchro-PASEF capture dose-response phosphosignaling with comparable performanceProvides transferable framework for high-throughput DIA method design.

DOI: https://doi.org/10.64898/2026.06.26.734570
Anal Chem. 2026 Jul 01.

Large Language Model-Generated Dietary Metabolite Biomarker Database Drives Deep Annotation of the Human Diet Metabolome.

Zijun Nie, Fujian Zheng, Dejun Hu, Zhenzhen Fu, Chongjiang Cao.

The annotation of dietary biomarkers is crucial for nutritional epidemiology. While untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a powerful analytical approach, the annotation of dietary biomarkers is hampered by the low specificity of existing public databases, which limits annotation coverage and accuracy. To address this limitation, we developed a novel database construction strategy and a dual-annotation workflow. We first employed an automated, large language model (LLM)-based text-mining pipeline to parse 7339 scientific articles and supplementary materials, creating the Dietary Metabolite Biomarker Database (DMBDB), which contains 4983 nonredundant biomarkers. The LLMs workflow demonstrated high performance, achieving an F1 score of 0.9269 for biomarker name recognition. Subsequently, two complementary annotation strategies were designed: (i) a specialized LC-MS database derived from DMBDB, incorporating predicted retention times and experimental MS/MS spectra for high-confidence matching, and (ii) a structure-guided molecular networking strategy (SGMNS) that uses DMBDB as background knowledge to annotate dietary biomarkers and their metabolites lacking spectral evidence. The framework was validated using untargeted LC-HRMS analysis of urine samples. LC-MS database directly annotated 566 metabolites, and the integration with SGMNS expanded the total number of annotations to 2078. The LLM-driven database construction combined with the dual-strategy annotation framework provides a powerful paradigm for achieving high-coverage and high-accuracy dietary metabolomics.

DOI: https://doi.org/10.1021/acs.analchem.6c01612
Methods Enzymol. 2026 ;pii: S0076-6879(26)00077-7. [Epub ahead of print]732 31-63

Characterization of O-glycoproteases for applications in mass spectrometry.

Alexandra D Steigmeyer, Joann Chongsaritsinsuk, Stacy A Malaker.

  This chapter describes practical workflows for the characterization and application of O-glycoproteases and mucinases in mass spectrometry-based analysis of mucin-domain glycoproteins. Dense O-glycosylation limits the effectiveness of conventional proteases, making specialized enzymes essential for generating informative peptides. We outline a stepwise strategy that begins with rapid gel-based assays to determine enzyme activity, glycan dependence, and sensitivity to sialylation. These qualitative experiments identify suitable substrates and guide optimization of enzyme to substrate ratios and digestion conditions. We then detail preparation of mucin samples for LC-MS/MS, including optional glycosidase treatment, controlled proteolysis, cleanup, and acquisition parameters that enable confident identification of glycopeptides. Particular emphasis is placed on interpreting fragmentation data and combining collisional and electron-based dissociation to determine peptide sequence and glycosite localization. Because automated searches remain error prone for O-glycopeptides, we provide guidelines for manual validation and for constructing cleavage motifs that incorporate both amino acid and glycan preferences. Together, these approaches establish a general framework for defining enzyme specificity and selecting appropriate tools for complex biological samples. Comprehensive characterization of mucin-degrading enzymes expands the analytical toolbox for studying mucin biology and improves the ability to map site-specific O-glycosylation with molecular precision.

Keywords:  O-glycoprotease; O-glycosylation; glycoproteomics; mass spectrometry; mucinase

DOI:  https://doi.org/10.1016/bs.mie.2026.03.004
Metabolomics. 2026 Jul 02. pii: 114. [Epub ahead of print]22(4):

The MassBank contributions of the mFam collaboration.

Anusha Ahlendorf, Asaph Aharoni, Khabat Vahabi, Tillmann G Fischer, Pierre-Marie Allard, Megan Augustin, Ulschan Bathe, David I Broadhurst, Corey Broeckling, Joerg Buescher, Adrian Covaci, Katyeny Manuela da Silva, Ric C H de Vos, Micha Gracianna Devi, Stefanie Döll, Maximilian Frey, Andrej Frolov, Emmanuel Gaquerel, Vasuk Gautam, Esteban Charria-Girón, Alain Goossens, Jeremy Grosjean, Maria Halabalaki, Elias Iturrospe, Kim Kultima, Stephanie Herman, Toni M Kutchan, Romain Larbat, René Meier, Eleni V Mikropoulou, Gregory Mouille, Luca Nicolotti, Nir Shahaf, François Perreau, Pierre Pétriacq, Michael Reichelt, Stacey N Reinke, Rani Robeyns, Alena Soboleva, Otmar Spring, Akshai Parakkal Sreenivasan, Alain Tissier, Jean Chrisologue Totozafy, Hiroshi Tsugawa, Josep Valls-Fonayet, Maria van de Lavoir, Justin J J van der Hooft, Fredd Vergara, David Wishart, Ludger A Wessjohann, Jean-Luc Wolfender, Jörg Ziegler, Gerd Ulrich Balcke, Steffen Neumann.

   INTRODUCTION: The analysis of metabolic profiles using high resolution mass spectrometry (MS) data provides deep insights into biological processes. In metabolomics, MS analysis generates a large number of features that represent metabolites. However, identifying specific metabolites from these features can be challenging. One of the major bottlenecks in the metabolomics field is the identification of MS features, which is a prerequisite for any biochemical interpretation. By identifying similarities and differences within a metabolite family (mFam), evaluating MS features at the metabolite family level can help assigning functional roles to individual MS features. These data can help interpreting metabolic pathways and processes within a biological system. For the assignment of metabolite families to MS features, it is important to have good quality, reliable, and comprehensive spectral libraries.
OBJECTIVE: We initiated a global effort to collect high-resolution MS/MS spectra of metabolites from labs working in different fields, including metabolomics of animals, microorganisms, and plants. The mFam-MS/MS collection delivers valuable training data to assign machine-readable classified information on the unknown metabolites.
RESULTS: The mFam collaboration used a standardized metadata template and has developed a globally curated MS/MS spectral library of 7,872 spectra with 2,126 unique metabolites. This library was compiled from 47 datasets contributed by 25 laboratories measured on 12 instrument types, including QTOF, Orbitrap, and Ion Mobility-QTOF systems. It comprises 4,646 spectra in positive mode and 3,226 in negative mode. This standardized resource significantly enhances metabolite identification capabilities, supports the development of machine learning-based annotation tools, and accelerates the discovery of novel metabolites. All spectra are available under the collective contributor label mFam in the MassBank system, including the web interface and the 2025.10 data release available at GitHub and Zenodo.

Keywords:  Data processing; FAIR data; Metabolomics; Open science; Reference spectra; Spectral libraries; Tandem mass spectrometry

DOI:  https://doi.org/10.1007/s11306-026-02480-y
J Proteome Res. 2026 Jul 02.

From Method-Defined Signals to Reference Measurement Procedures: Two Decades of Mass Spectrometry-Based ProGRP Quantification.

Donatella Coradduzza, Emanuela Azara, Anna La Salvia, Serenella Medici, Ciriaco Carru, Troy D Wood, Giuseppe Fanciulli.

  Pro-gastrin-releasing peptide (ProGRP) is a clinically established biomarker in small cell lung cancer (SCLC) and other neuroendocrine neoplasms (NENs). Despite widespread clinical use, ProGRP measurement is challenged by low circulating concentrations and proteoform heterogeneity arising from alternative precursor processing. Current immunoassay-based methods generate operationally defined signals anchored to antibody-epitope interactions rather than chemically explicit molecular quantities, limiting result comparability across platforms. Mass spectrometry (MS) addresses these limitations through sequence-defined quantification, with the capacity to resolve sequence-distinct isoforms and, at the intact-protein level, individual proteoforms. In this Perspective, we examine two decades of MS-based ProGRP quantification as a case study for quantitative proteomics and measurement science. We trace the evolution from single-quadrupole workflows to isotope dilution strategies enabling increasingly selective and metrologically robust measurement, showing that performance gains were driven primarily by selectivity engineering and internal standardization rather than by mass analyzer advances. This trajectory progressively exposes a fundamental comparability problem: immunoassays and MS-based methods do not measure the same quantities. Resolving this discordance requires an explicit metrological framework encompassing measurand definition, traceability architecture, and reference measurement procedures (RMPs) based on protein-level isotope dilution MS. ProGRP serves as an instructive model for harmonizing quantitative protein biomarker measurements in clinical proteomics.

Keywords:  LC-MS/MS; ProGRP; clinical proteomics; isotope dilution mass spectrometry; metrological traceability; neuroendocrine neoplasms; proteoform; quantitative proteomics; reference measurement procedure; small cell lung cancer

DOI:  https://doi.org/10.1021/acs.jproteome.6c00368
Life Sci Alliance. 2026 Sep;pii: e202603759. [Epub ahead of print]9(9):

Democratized single-cell proteomics resolves cell state heterogeneity in skin tumors.

Joseph Inns, Andrew Michael Frey, Weng Wi Ng, Matthias Trost, Neil Rajan.

Single-cell proteomics (SCP) reveals cellular heterogeneity and biological insights inaccessible to bulk analysis. Existing limitations are cost, sample loss during processing, and accessibility to state-of-the-art instrumentation. We describe a label-free SCP methodology in human tissue, combining FACS, oil-immersion cell handling, mass spectrometry, and neural-network-derived spectral libraries, which address these issues. We tested this methodology in a skin tumor syndrome, CYLD cutaneous syndrome (CCS), assessing tumor heterogeneity. Using a Bruker timsTOF HT platform, we quantified >4,000 proteins, averaging ∼700 per cell, through a cost-effective pipeline without specialised liquid handling infrastructure. By using preexisting bioinformatic tools from the scRNA-seq field, we implemented a robust analysis methodology, discriminating between macrophages, dendritic cells, and tumor keratinocytes, in an unbiased analysis of 419 CCS tumor cells. We validated the biological accuracy of cell annotations by cross referencing with each cell's FACS markers. Furthermore, we identified a novel CCS tumor-associated macrophage population, which carried a tumor microenvironment remodelling signature. Our findings demonstrate an accessible SCP technology capable of yielding novel biological discoveries in clinical tissue.

DOI: https://doi.org/10.26508/lsa.202603759
bioRxiv. 2026 Jun 26. pii: 2026.06.23.734138. [Epub ahead of print]

Agentic AI for Structural Elucidation and Discovery of Drug Metabolites from Mass Spectrometry Data.

Xianghu Wang, Abubaker Patan, Haoqi Nina Zhao, Vincent Charron-Lamoureux, Yourae Shin, Daniel Petras, Yuhui Hong, Benjamin P Bowen, Trent R Northen, Pieter C Dorrestein, Mingxun Wang.

The majority of chemical signals detected in public metabolomics repositories remain structurally undefined. Large language models (LLMs) are probabilistic systems whose capacity to generate outputs beyond their training data, which can cause hallucinations, makes them also potentially suited to hypothesize structures for molecules that have never been described. We aimed to build a system that could harness this LLM generative capacity combined with domain specific tools/framework to constrain hallucination and produce validated discoveries. We developed a GNPS2 agentic AI system that interprets LC-MS/MS data by integrating spectral alignment, molecular formula inference, rule-based structural enumeration, machine learning-based spectrum prediction, and translates natural language hypotheses from domain experts into dynamically generated analytical workflows. We demonstrate the annotation of unknown drug metabolites from public data guided by chemical hypotheses. The agent predicted, and we experimentally confirmed, a phosphorylated hydroxyzine, an acetaminophen-p-coumaric acid ester, and identified two new oxidative ibuprofen-carnitine conjugates from public repositories. These results demonstrate that LLM-driven agentic reasoning, when combined with domain expertise, can indeed generate experimentally testable structural hypotheses for previously uncharacterized metabolites leveraging pan repository data.

DOI: https://doi.org/10.64898/2026.06.23.734138
Clin Chem. 2026 Jul 01. pii: hvag079. [Epub ahead of print]

Comparison of Information-Dependent Acquisition and Sequential Window Acquisition of All Theoretical Mass Spectra for Untargeted Drug Testing on a Linear Ion Trap-Pulsing Quadrupole-Time of Flight Mass Spectrometer.

Morgan W Mann, Lilly Lim, Ruben Y Luo, Kara L Lynch.

BACKGROUND: Comprehensive drug testing (CDT) by liquid chromatography-high-resolution mass spectrometry (LC-HRMS) is a valuable method for evaluating clinical samples for unknown toxicological agents. However, the sensitivity of CDT methods is generally lower than targeted approaches. A novel MS technology, linear ion trap (LIT)-pulsing, has demonstrated improved sensitivity in proteomics and metabolomics applications, but its utility and interactions with different HRMS acquisition types, such as information-dependent acquisition (IDA) and sequential window acquisition of all theoretical fragment ion spectra (SWATH), have not been explored in small molecule analysis.
METHODS: CDT methods utilizing both IDA and SWATH acquisition were developed on a quadrupole time-of-flight (QTOF) instrument featuring LIT pulsing. Limits of detection (LODs) and process efficiencies were estimated for 150 toxicological agents. Additionally, 101 patient urine specimens were used to compare both methods against an established clinical CDT without LIT-pulsing. Selectivity was assessed using a targeted LC-HRMS method.
RESULTS: LIT-pulsed SWATH acquisition improved detection of toxicological agents compared to LIT-pulsed IDA, with a median 5-fold reduction to measured LODs and an average 5.9% reduction to matrix suppression. In authentic patient samples, LIT-pulsed IDA and SWATH acquisition identified 889 and 1083 toxicological agents compared to 835 using IDA without LIT-pulsing. The estimated selectivity of LIT-pulsed IDA was improved compared to conventional IDA acquisition, but SWATH acquisition demonstrated significant nonselectivity.
CONCLUSION: LIT-pulsing improves the sensitivity and selectivity of CDT methods. The largest improvements to sensitivity are observed using SWATH acquisition; however, these gains are accompanied by reduced selectivity. This highlights the need for careful validation of SWATH CDT methods to ensure high clinical performance.

DOI: https://doi.org/10.1093/clinchem/hvag079
Biophys Rep. 2026 Jun 30. 12(3): 175-192

Recent advancements in sample pretreatment methods for glycoproteomics.

Wei Zhang, Siyuan Kong, Weiqian Cao.

  Comprehensive glycoprotein analysis is essential for exploring the role of protein glycosylation in diverse biological processes and disease mechanisms. Yet it remains challenging due to the structural complexity and heterogeneity of glycans. Bottom-up glycoproteomics utilizing liquid chromatography-mass spectrometry (LC-MS)-based techniques has emerged as a powerful tool for in-depth protein glycosylation analysis. Sample pretreatment is the first and critical step that significantly influences subsequent chromatographic separation and MS analysis. This review provides an overview of the key steps in current sample pretreatment workflows for glycoproteomics, emphasizing recent advancements in sample preparation and enrichment strategies developed over the past decade. It highlights improvements in enrichment efficiency, compatibility with high-throughput analyses, and applications to biological samples, and also discusses the remaining challenges and future directions for these technologies.

Keywords:  Enrichment strategies; Glycoproteomics; High-throughput analysis; Sample pretreatment

DOI:  https://doi.org/10.52601/bpr.2025.240072
Nature. 2026 Jul 01.

Steatosis shapes prognosis-defining liver metastasis heterogeneity in CRC.

Yiming Peng-Winkler, Xiao-Zheng Liu, Sanne M L Verheul, Charlotte Girondel, Sebastian Igelmann, Sara Marie Rotter, Michail Doukas, Ming Liu, Anke Vandekeere, Mélanie Planque, Juan Fernández-García, Sébastien Tabariès, Marta Buetas-Arcas, Sandra Martínez-Martín, Florian Döbbe, Margherita Demicco, Ines Vermeire, Janine Theile, Johannes Ceuppens, Jonas Haesevoets, Emma Tobarra-López, Dorien Broekaert, Johannes Georg Bode, Tom Luedde, Peter Vermeulen, Daniele V F Tauriello, Raquel Perez-Lopez, Steve Stegen, Laura Soucek, Tiago De Oliveira, Lena-Christin Conradi, Peter M Siegel, Cornelis Verhoef, Sarah-Maria Fendt.

Patients with colorectal cancer (CRC) frequently develop liver metastases1-3. The prognosis of these patients is skewed by the histopathological heterogeneity of their liver metastases4,5. Patients with 'replacement' metastases have a 5-year overall survival of less than 44.2%, compared with 73.4% in patients with 'encapsulated' (previously known as desmoplastic) metastases5; yet there are currently no approved therapies targeting replacement liver metastases. Here we show that treatment-naive patients with CRC with liver steatosis have an increased occurrence of replacement metastases compared with patients without steatosis. Mechanistically, we find that steatosis-promoted fatty acid oxidation increases formation of replacement metastases by increasing MYC stability through acetylation. In turn, MYC activates proline synthesis, fuelling collagen production, enabling growth of replacement metastases. Targeting MYC, P5CS or COL1A1 suppresses the occurrence and growth of replacement metastases in patient-derived organoids, mouse or patient-derived xenograft models. Spatial metabolite and protein analyses of liver metastases from patients with CRC further support this mechanism. In conclusion, we provide a mechanistic understanding of the emergence of liver metastases with poor prognosis in treatment-naive patients with CRC, identifying potential targets for therapeutic intervention.

DOI: https://doi.org/10.1038/s41586-026-10686-2
J Proteome Res. 2026 Jun 30.

Hierarchy of MS-Based Evidence.

Charlotte Adams, Michael G Leeming, David Teschner, Andreas Hildebrandt, Sebastian A Leidel, Magnus Palmblad.

Scientific confidence relies on the integrity and verifiability of primary observations, yet increasing experimental scales and computational complexities challenge traditional mechanisms of trust. In proteomics, community standards emphasize the public deposition of raw mass spectrometry (MS) data to enable reanalysis and reproducibility. However, recent advances in software capable of simulating MS data raise the possibility that datasets may be altered or generated entirely in silico and presented as experimentally acquired data. Here, we argue that current standards rarely distinguish between raw data formats in terms of their provenance guarantees or susceptibility to modification. To address this emerging vulnerability, we propose a hierarchy of evidence for MS-based reporting, analogous to established evidence hierarchies in medicine, in which confidence in reported findings increases with the traceability and verifiable provenance of raw data files. With this framework, we aim to support reviewers and readers in assessing the robustness of published claims and to stimulate discussion on strengthening data integrity safeguards in proteomics.

DOI: https://doi.org/10.1021/acs.jproteome.6c00265
Anal Chem. 2026 Jul 02.

High-Throughput, Ultralow-Input Proteomics Enabled by Narrow-Bore Open Tubular Liquid Chromatography.

Piliang Xiang, Yumi Kwon, Ljiljana Paša-Tolić.

Picogram-scale proteomics faces significant challenges in balancing sensitivity and throughput, driven largely by the performance limitations of liquid chromatography (LC) separations. We addressed this challenge by implementing a low-loss sample injection method for narrow bore open tubular LC (nOTLC). This approach enabled the injection of ∼20% of the sample, marking a 295-fold improvement over the high-loss injection used in previous nOTLC publications. Our platform identified 3955 proteins on average from <40 pg of HeLa digest loaded on the column at a throughput of 261 samples per day (SPD). We further demonstrated the ability to increase throughput to a projected 720 SPD while still identifying an average of 2460 proteins from only ∼20 pg of HeLa digest. The narrow chromatographic peaks combined with ultralow flow rates provided excellent electrospray ionization efficiency resulting in enhanced sensitivity for limited samples. The optimized nOTLC-MS system effectively balances the throughput-sensitivity trade-off in picogram-scale proteomics, providing a transformative platform for large-scale exploration of cellular heterogeneity with unmatched speed and sensitivity.

DOI: https://doi.org/10.1021/acs.analchem.5c06985
J Proteome Res. 2026 Jul 03.

Identification of Age-Associated Circulating Proteins and Lipids in 3800 Comorbidity-Enriched Older Adults from Japan-Based Cohorts Using Olink Assays and MRM Mass Spectrometry.

Suzumi M Tokuoka, Fumie Hamano, Ayako Kobayashi, Tohru Natsume, Masaya Sugiyama, Koichi Matsuda, Yoshiya Oda.

  Aging entails complex physiological changes, yet large-scale evidence among older Japanese individuals, especially those with comorbidities, remains limited. We analyzed serum and plasma samples from approximately 3800 Japanese aged 40 years and older to identify age-associated proteins and lipids, focusing on reproducibility and robustness. Chemokines CXCL9 and CCL11 and phosphatidylcholines PC 31:0 and PC 32:0 were positively associated with age across five cohorts, whereas lysophosphatidylcholines LPC-LA and LPC-AA showed negative associations. These molecular relationships were consistently reproduced across serum and plasma matrices and replicated in independent cohorts. Cross-platform consistency was confirmed between Olink Target 96 (relative NPX) and Target 48 (absolute quantification), with direct validation in Cohort 2. To our knowledge, this is the largest study to demonstrate reproducibility of age-associated molecular biomarkers in a comorbidity-enriched Japanese population. The principal contribution is technical─defining a set of robust, cross-platform, cross-matrix biomarkers of aging in older adults. Unlike previous Western studies which focused on younger or healthier populations, this work establishes reproducibility and generalizability in real-world aging. These validated biomarkers provide a valuable reference for clinical and translational research, including risk stratification and biological age assessment in comorbidity-enriched settings.

Keywords:  CCL11; CXCL9; aging biomarkers; comorbidity-enriched cohorts; cross-matrix; cross-platform; lysophosphatidylcholines; multiomics; older Japanese

DOI:  https://doi.org/10.1021/acs.jproteome.5c01086
Trends Mol Med. 2026 Jul 02. pii: S1471-4914(26)00138-3. [Epub ahead of print]

Targeting amino acid metabolism in hepatocellular carcinoma.

Yibing Wu, Carmen Chak-Lui Wong.

  Hepatocellular carcinoma (HCC) remains lethal due to its high refractoriness to standard treatment, which is contributed to by factors including adaptive metabolic reprogramming of HCC cells and pre-existing liver diseases that compromise liver function. Abnormal tumor vasculature creates a nutrient-deprived microenvironment, intensifying competition between HCC cells and immune cells and impairing antitumor immunity. Among the complex metabolic network, amino acid (AA) metabolism emerges as a critical player and an attractive therapeutic target. This review first examines how dysregulated AA metabolism supports HCC hallmarks, including metabolic reprogramming and immune evasion. We discuss the translational potential of therapies targeting AA metabolism in HCC, ranging from pharmacologic inhibition to dietary AA intervention, which can be further integrated with existing HCC treatments to improve clinical outcomes.

Keywords:  amino acid metabolism; amino acid-targeted therapy; hepatocellular carcinoma; tumor microenvironment

DOI:  https://doi.org/10.1016/j.molmed.2026.06.002