Normal view

Received — 10 September 2026 ⏭

Pulmonary nodule
Protein glycosylation profiling in lung adenocarcinoma and precursor lesions: analysis of FFPE tissue sections Jinggang Cui · Yan Chen · Shuang Yue · Jianbo Deng · Wenqi Zhang · Shengye Wen · Lingbo Zhao · Rumeng Zhang · Shuang Yang · Junhong Jiang
Anal Bioanal Chem. 2026 Jul 27. doi: 10.1007/s00216-026-06702-z. Online ahead of print.ABSTRACTProtein glycosylation is a major post-translational modification that regulates tumor initiation and progression; however, its dynamic modeling during multistep evolution of lung adenocarcinoma (LUAD) remains poorly understood, particularly in clinically archived tissues. Here, we established an integrated multi-omics workflow combining global proteomes, N-glycans, and site-specific intact N-glycopepti
27 July 2026 at 18:00

Protein glycosylation profiling in lung adenocarcinoma and precursor lesions: analysis of FFPE tissue sections

By: Jinggang Cui · Yan Chen · Shuang Yue · Jianbo Deng · Wenqi Zhang · Shengye Wen · Lingbo Zhao · Rumeng Zhang · Shuang Yang · Junhong Jiang

27 July 2026 at 18:00

Anal Bioanal Chem. 2026 Jul 27. doi: 10.1007/s00216-026-06702-z. Online ahead of print.

ABSTRACT

Protein glycosylation is a major post-translational modification that regulates tumor initiation and progression; however, its dynamic modeling during multistep evolution of lung adenocarcinoma (LUAD) remains poorly understood, particularly in clinically archived tissues. Here, we established an integrated multi-omics workflow combining global proteomes, N-glycans, and site-specific intact N-glycopeptides to comprehensively characterize glycosylation in formalin-fixed paraffin-embedded (FFPE) specimens spanning four pathological stages of LUAD progression: inflammatory nodules (IN), atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC). Using optimized protein extraction, hydrophilic interaction liquid chromatography (HILIC)-based glycopeptide enrichment, and high-resolution LC-MS/MS, we achieved large-scale identification of proteins, N-glycans, and intact glycopeptides from archival clinical samples. Integrated analyses revealed progressive remodeling of site-specific N-glycosylation during malignant transformation, characterized by increased glycan branching, fucosylation, and sialylation during the transition from premalignant lesions to invasive cancer. Sialylated glycans reached their highest abundance in the premalignant AAH stage, whereas highly branched and fucosylated complex N-glycans predominated in invasive adenocarcinoma, indicating stage-dependent glycan remodeling throughout disease progression. Functional enrichment analyses linked these glycosylation alterations to extracellular matrix organization, neutrophil degranulation, and immune-associated pathways, while representative glycoproteins, including CEACAM6 and FGB, exhibited coordinated changes in protein abundance and site-specific glycoform micro-heterogeneity across pathological stages. Collectively, this study demonstrates the feasibility of deep glycoproteomic profiling using archived FFPE tissues and provides a comprehensive molecular atlas of glycosylation remodeling during LUAD progression. These findings establish a valuable resource for elucidating disease mechanisms and identifying stage-specific glycosylation biomarkers and potential glycan-targeted therapeutic candidates for early lung adenocarcinoma.

PMID:42509285 | DOI:10.1007/s00216-026-06702-z

Received — 26 March 2026 ⏭

cs.AI, q-bio.NC updates on arXiv.org
MemReward: Graph-Based Experience Memory for LLM Reward Prediction with Limited Labels Tianyang Luo · Tao Feng · Zhigang Hua · Yan Xie · Shuang Yang · Ge Liu · Jiaxuan You
arXiv:2603.19310v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have been driven by reinforcement-learning-based post-training, which requires multiple rollouts with rewards. However, obtaining ground truth labels for the calculation of rewards on a scale often requires expensive human labeling or time-consuming verification procedures. For instance, evaluating mathematical proofs demands expert review, and open-ended question answering lacks definitive
25 March 2026 at 12:00

MemReward: Graph-Based Experience Memory for LLM Reward Prediction with Limited Labels

cs.AI, q-bio.NC updates on arXiv.org

By: Tianyang Luo · Tao Feng · Zhigang Hua · Yan Xie · Shuang Yang · Ge Liu · Jiaxuan You

25 March 2026 at 12:00

arXiv:2603.19310v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have been driven by reinforcement-learning-based post-training, which requires multiple rollouts with rewards. However, obtaining ground truth labels for the calculation of rewards on a scale often requires expensive human labeling or time-consuming verification procedures. For instance, evaluating mathematical proofs demands expert review, and open-ended question answering lacks definitive ground truth. When ground truth labels are scarce, the effectiveness of reinforcement learning fine-tuning can be constrained. We introduce MemReward, a graph-based experience memory framework: an initial LLM policy generates rollouts for each query, each comprising a thinking process and a final answer, and these rollouts are stored as experience memory. Queries, thinking processes, and answers form nodes in a heterogeneous graph with similarity and structural edges; a GNN trained on labeled rollouts propagates rewards to unlabeled rollouts during online optimization. Experiments on Qwen2.5-3B and 1.5B in mathematics, question answering, and code generation demonstrate that MemReward, with only 20% labels, achieves 97.3% of Oracle performance on 3B and 96.6% on 1.5B, surpassing Oracle in out-of-domain tasks. Performance scales smoothly with label budget, reaching 99.4% of Oracle at 70% labels.