Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography

      Integrative proteogenomic profiling of high-risk prostate cancer samples from Chinese patients indicates metabolic vulnerabilities and diagnostic biomarkers

      Disease prediction with multi-omics and biomarkers empowers case–control genetic discoveries in the UK Biobank

      Integrated landscape of plasma metabolism and proteome of patients with post-traumatic deep vein thrombosis

     Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer’s disease

      Identification of microenvironment features associated with primary resistance to anti-PD-1/PD-L1 + antiangiogenesis in gastric cancer through spatial transcriptomics and plasma proteomics

      Single-cell and spatial proteo-transcriptomic profiling reveals immune infiltration heterogeneity associated with neuroendocrine features in small cell lung cancer

      Chemoproteomic profiling unveils binding and functional diversity of endogenous proteins that interact with endogenous triplex DNA

      Multi-pass, single-molecule nanopore reading of long protein strands

      Metabolic labeling based methylome profiling enables functional dissection of histidine methylation in C3H1 zinc fingers

     Trem2-expressing multinucleated giant macrophages are a biomarker of good prognosis in head and neck squamous cell carcinoma

The widespread use of low-dose computed tomography (LDCT) in lung cancer screening has markedly increased the detection rate of pulmonary nodules. Accurately assessing the malignant risk of these nodules, however, remains a major challenge. The team led by Li Weimin at West China Hospital, Sichuan University, proposed C-Lung-RADS, a classification-driven Chinese lung nodule reporting and data system, developed through analysis of a large health check-up cohort of 45,064 cases. C-Lung-RADS adopts a stepwise approach: first, nodules are assigned a preliminary risk category based on density and size; second, imaging and clinical data are integrated to precisely diagnose high-risk nodules; and finally, follow-up data are incorporated to build a multimodal fusion model that confirms extremely high-risk nodules and provides individualized follow-up plans and decision recommendations tailored to each level of malignant risk. The system aims to ensure the efficient allocation of medical resources, preventing overtreatment of low-risk patients and the missed or incorrect diagnosis of extremely high-risk patients, thereby providing an evidence-based foundation for the management of pulmonary nodules in China.

Prostate cancer (PCa) shows pronounced geographic and ethnic differences, reflected both in cancer genome alterations and in patterns of disease progression. The team of Qin Jun at the CAS Key Laboratory of Tumor Microenvironment, together with the teams of Tan Minjia at the Shanghai Institute of Materia Medica, Xue Wei at Renji Hospital affiliated to Shanghai Jiao Tong University, and Li Jing at Shanghai Jiao Tong University, performed comprehensive proteogenomic characterization of localized high-risk PCa in 125 Chinese male patients, using paired tumor and adjacent normal tissues, with the aim of identifying potential biomarkers, revealing key oncogenic events and defining molecular subtypes associated with poor prognosis. The study found that GOLM1 can serve as a non-invasive serum biomarker for accurate PCa diagnosis within the PSA gray zone. In addition, phosphoproteomic analysis showed that phosphorylation of FOXA1-S331 plays a key role in regulating the FOXA1–AR-dependent chromatin landscape and promoting PCa progression. The study also identified three distinct proteomic subtypes, among which the metabolically active "immune desert" subtype (S-III) was closely associated with poor prognosis, pointing to a role for BCAT2-driven metabolic pathways in PCa.

Disease prediction with multi-omics and biomarkers empowers case–control genetic discoveries in the UK Biobank

The emergence of biobank-scale datasets offers new opportunities to discover novel biomarkers and develop algorithms for predicting human disease. A research team from the United Kingdom introduced MILTON, an integrated machine learning framework that combines multi-omics data and biomarkers to predict the incidence of 3,213 diseases in the UK Biobank. Analyzing 484,000 genome sequencing samples and 46,000 plasma proteomic data points, MILTON successfully predicted disease cases that had not yet been diagnosed at recruitment and identified 182 new gene–disease associations in gene–disease association analyses. Compared with conventional polygenic risk scores (PRS), MILTON performed better for most diseases; notably, for 52 diseases, incorporating proteomic data improved prediction accuracy (AUC) by more than 0.1.

Deep vein thrombosis (DVT) is a leading cause of high morbidity and mortality after trauma. The team of Professor Guo Yan at Xi'an Jiaotong University integrated metabolomic and proteomic data to investigate the metabolic and hematological characteristics of patients with post-traumatic deep vein thrombosis (pt-DVT). Analyzing 680 trauma patients, they identified 28 metabolites and two clusters of clinical parameters associated with pt-DVT, and used machine learning algorithms to build a nine-metabolite prediction model that effectively identifies pt-DVT patients. Further proteomic analysis identified 214 proteins associated with pt-DVT and revealed that upregulation of the glycolysis/gluconeogenesis–TCA cycle axis may promote thrombosis by raising reactive oxygen species (ROS) levels in erythrocytes, indicating that this metabolic pathway may represent a potential therapeutic target. Through large-scale metabolomic and proteomic analysis, this study reveals the metabolic abnormalities of pt-DVT and develops an early diagnostic tool with promising clinical application.

Integrating amyloid and tau imaging with proteomics and genomics in Alzheimer’s disease

Alzheimer's disease (AD) is the most common neurodegenerative disease, characterized by the aggregation of amyloid-β (Aβ) and tau in the brain. Therapies targeting Aβ have achieved breakthrough progress, bringing new hope for the management of AD. Yet to modify and ultimately prevent AD effectively, a better understanding of the biology of Aβ and tau in AD is still needed. Advances in high-throughput omics technologies—including genomics, proteomics and transcriptomics—combined with positron emission tomography (PET) molecular imaging of Aβ and tau, allow us to discover and modulate the biological pathways driving Aβ and tau aggregation and spread in living humans. The field of multi-omics and PET research on Aβ and tau in AD is evolving rapidly. A research team from Sweden reviews the latest progress in this area, covering both biological insights and the implications of integrated omics–molecular imaging studies for future clinical application.

Antiangiogenic agents exert considerable immunomodulatory effects within the tumor microenvironment, which supports the rationale for the combined clinical development of vascular endothelial growth factor and immune checkpoint inhibitors in advanced gastric cancer (AGC). A research team at the Institut Bergonié in France reported the results of the phase II REGOMUNE study. The combination of regorafenib, a multityrosine kinase inhibitor, with avelumab, a PD-L1-targeting monoclonal antibody, produced deep and durable responses in 19% of patients, while many patients derived no significant benefit. Comprehensive biomarker analysis revealed that the tumor microenvironment of patients with resistance contained significantly more M2 macrophages, and that overexpression of the S100A10 protein in tumor cells was also associated with resistance. Plasma proteomic results showed that elevated circulating cytokines including CSF-1, IL-4, IL-8 and TWEAK correlated with poor prognosis. These data indicate that the immunosuppressive activity of macrophages and overexpression of S100A10 are key factors affecting treatment efficacy, providing important clues for overcoming resistance. This study establishes a valuable foundation for elucidating—and potentially circumventing—resistance mechanisms in the current AGC treatment paradigm, underscoring the importance of tumor microenvironment dynamics and immune modulation.

Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine malignancy characterized by a cold tumor immune microenvironment (TIME), limited efficacy of immunotherapy and low survival. The spatiotemporal heterogeneity significantly associated with anti-tumor immunity in SCLC has not yet been systematically studied. The teams of Chen Ming at Zhejiang Cancer Hospital and Fan Yun at the Chinese Academy of Sciences, together with Zhang Jianjun at the University of Texas and Song Hai at Zhejiang University, performed ultra-high-plex digital spatial profiling and single-cell RNA sequencing on tissue samples from 44 untreated SCLC patients, revealing the complex relationship between immune infiltration heterogeneity and neuroendocrine features. Integrating data on more than 18,000 genes and 60 key immune proteins, the study systematically charted the spatial proteo-transcriptomic atlas of SCLC. The results show marked spatial heterogeneity within SCLC tumor tissue: tumor nests contain mainly innate immune cells and functionally impaired B cells, suggesting that monocytes/macrophages may play an important immunomodulatory role. The RE1-silencing transcription factor (REST) was found to be associated with low neuroendocrine features, active anti-tumor immune responses and longer survival, indicating that REST may serve as a novel biomarker for SCLC. Overall, this study proposes REST as a potential prognostic biomarker and highlights the importance of spatial and single-cell omics for a deeper understanding of the SCLC immune microenvironment.

Chemoproteomic profiling unveils binding and functional diversity of endogenous proteins that interact with endogenous triplex DNA

Triplex DNA structures form when a third DNA strand wraps around the major groove of double-stranded DNA; they act as important molecular regulators but can also pose a potential threat to the genome. The regulatory network governing triplex DNA dynamics, however, remains poorly resolved. Researcher Chen Jiayu at Nanjing University used chemoproteomic profiling in living cells to reveal the proteins that interact with triplex DNA and the scope of their functions. The team developed photoBQQ, a highly specific triplex DNA chemical probe, and performed quantitative chemoproteomic analysis in living cells. In HeLa and A549 cell lines, a chemoproteomic approach was used to specifically capture and—through quantitative mass spectrometry—identify 78 high-confidence triplex DNA-interacting proteins, laying a solid foundation for understanding the dynamic regulatory mechanisms of triplex DNA.

Multi-pass, single-molecule nanopore reading of long protein strands

Protein diversity plays a vital role in biological processes and is of critical importance in disease diagnosis and therapeutic development. Yet in comprehensively resolving protein sequences, existing technologies face numerous limitations: insufficient detection sensitivity, a narrow dynamic range, low analytical throughput and costly instrumentation. To address this, the team of Professor Jeff Nivala at the University of Washington proposed an innovative solution—a protein sequencing technology based on nanopores and the unfoldase ClpX (PASTOR). Using ClpX, proteins are threaded through the CsgG nanopore in a controlled manner, enabling successful sequencing of protein strands containing several hundred amino acids. This technology can detect single amino acid changes and identify various post-translational modifications along the protein strand. To further improve sequencing accuracy, the team demonstrated the ability to repeatedly read the same protein molecule and explored the possibility of protein barcode sequencing. In addition, they built a biophysical model to simulate in advance the raw nanopore signals produced by the volume and charge of amino acid residues, helping to strengthen the understanding and interpretation of signal data. These results reveal the considerable potential of PASTOR technology for identifying protein sequences and their post-translational modifications at the single-molecule level, laying a solid foundation for future proteomics research and protein barcoding technology. The technology is expected to have important impact in large-scale protein function analysis and precision medicine, particularly in the screening of disease biomarkers and the identification of therapeutic targets.

Metabolic labeling based methylome profiling enables functional dissection of histidine methylation in C3H1 zinc fingers

Protein methylation is an important post-translational modification occurring on a variety of amino acid residues. Current proteomic technologies are inefficient at detecting methylation on residues other than arginine and lysine, limiting our understanding of the functions of rare protein methylation events. The team of Ye Mingliang at the Dalian Institute of Chemical Physics, Chinese Academy of Sciences, together with the teams of Cheng Hong at the Center for Excellence in Molecular Cell Science and Wang Zefeng and Li Guohui at the Shanghai Institute of Nutrition and Health, jointly developed a new metabolic labeling-based technology for comprehensive analysis of protein methylation. Because the technology does not require prespecifying a particular methylation type, it can identify multiple types of methylation simultaneously, providing a powerful new tool for investigating the regulatory mechanisms of protein methylation.

Patients with head and neck squamous cell carcinoma (HNSCC) often have poor outcomes owing to suboptimal risk management and treatment strategies, yet introducing new prognostic biomarkers into clinical practice remains challenging. A research team from Singapore reported the presence of multinucleated giant cells (MGCs) in HNSCC patients and their relationship to prognosis in untreated patients and those receiving preoperative chemotherapy. MGC density increased in tumors after preoperative treatment, indicating that these cells play a role in the anti-tumor response. To translate MGC density into a clinical prognostic marker, the team developed a deep learning model that automatically quantifies MGC density on routinely stained pathology slide images. Finally, spatial transcriptomic and proteomic approaches were used to characterize the MGC-associated tumor microenvironment, revealing an increase in central memory CD4 T cells. The study defines an MGC-specific signature resembling TREM2-expressing mononuclear tumor-associated macrophages, cells that co-localize within the keratin tumor niche.