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Biomedical subjects

Jun Lu

Publications and source records attributed to Jun Lu.

4 recordsLinked to original sources

The metastatic spectrum in functional and non-functional NENs: mechanistic insights from multi-omics.

Neuroendocrine neoplasms (NENs) are biologically heterogeneous tumors in which differentiation/grade and hormonal functionality are intersecting but non-equivalent axes. This review focuses on functional and non-functional well-differentiated neuroendocrine tumors (NETs), principally gastroenteropancreatic and pancreatic NETs, and critically evaluates how site, lineage, stage, tumor burden, genomic and epigenetic alterations, immune-stromal remodeling, metabolic adaptation, microbiome-associated signals, and treatment pressure converge on metastasis and recurrence. Apparent outcome differences by functionality are inconsistent after clinicopathological adjustment: non-functional presentation is often enriched for delayed diagnosis and adverse features, whereas functional subtypes range from typically indolent insulinomas to clinically aggressive hormone-producing tumors. We reconcile these observations through a layered model in which lineage-defining alterations and chromatin/telomere programs establish cellular state; signaling and metabolic plasticity enable stress adaptation; and hypoxia, angiogenesis, immune cells, fibroblasts, extracellular matrix, and therapy create selective niches for dissemination and relapse. We also define computational strategies for heterogeneous multi-omics integration and a staged biomarker-validation pathway. Evidence remains dominated by pancreatic NETs, and causal support is weakest for microbiome-functionality relationships and several proposed cross-omic links. A spectrum-based framework is therefore most useful when it generates testable, site- and grade-specific hypotheses rather than treating functionality as an isolated prognostic variable.

Humans

Mapping the covalent cysteine interactome of Ebselen reveals high-sensitivity target engagement and redox proteome remodeling.

Ebselen is a covalent organoselenium compound with broad pharmacological activity, yet its cellular cysteine targets and downstream proteomic consequences remain incompletely defined. Here, we integrated competitive gel-based activity-based protein profiling, reactivity-dependent tandem orthogonal proteolysis-activity-based protein profiling, and TMT-based quantitative proteomics to map Ebselen-induced cysteine engagement and proteome remodeling in living cancer cells. Ebselen exhibited dose-dependent cytotoxicity and markedly perturbed intracellular thiol-redox balance, as reflected by glutathione depletion and altered reactive oxygen species-associated fluorescence readouts. Competitive gel-based profiling confirmed concentration-dependent engagement of protein cysteine residues in live cells. Quantitative rdTOP-ABPP further identified hundreds of dose-responsive cysteine sites in HeLa and HepG2 cells and revealed a preference for cysteine microenvironments enriched with basic residues. Cross-cell-line comparison highlighted CDK5 Cys53, SMU1 Cys298, and RPSA2 Cys163 as conserved covalent nodes, among which CDK5 Cys53 showed high sensitivity to Ebselen treatment, a finding validated by competitive labeling and MS-based site assignment. Global TMT proteomics revealed extensive remodeling of redox-related and cell-survival-associated pathways, including compensatory upregulation of selenoproteins such as TXNRD1 and GPX family members. Together, these results define a chemical proteomic atlas of Ebselen-cysteine interactions and provide a framework for understanding and optimizing covalent organoselenium therapeutics.

Humans

Machine learning-based integration develops a novel lysosome-related prognostic signature associated with prognosis and immune infiltration landscape in acute myeloid leukemia.

BACKGROUND: Lysosomes are essential for intracellular degradation and recycling, and changes in their function significantly contribute to tumor growth. Nonetheless, the exact role of lysosome-related genes (LRGs) in the pathogenesis of acute myeloid leukemia (AML) is still inadequately comprehended. METHODS: Differentially expressed LRGs (DE-LRGs) between AML and control groups were identified using AML-related data extracted from the Gene Expression Omnibus (GEO). The LRGs-related prognostic genes were identified and the risk model was established using univariate COX regression analysis and machine learning algorithms, based on the data obtained from The Cancer Genome Atlas (TCGA). Subsequently, we performed comprehensive analyses regarding clinical features, functional pathways, immune microenvironment, and chemotherapeutic drugs sensitivity between the high- and low-risk groups. Reverse transcription Quantitative polymerase chain reaction (RT-qPCR) and western blot were adopted to validate the expression of prognostic genes in human bone marrow-derived cell line HS-27 A and human AML cell line MOLM-13. RESULTS: Through comprehensive analysis, a risk model was developed utilizing ten LRGs (ATP6V0E2, CALCRL, TMEM165, GZMB, HCK, TCIRG1, CD1D, GPRASP1, ABCA1, and NAGA), and this model was further validated using GEO datasets. Significant differences in clinical characteristics, functional pathways, immune microenvironment characteristics, and chemotherapeutic drug sensitivity were observed between the two risk groups In vitro validation experiment illustrated that the expression trends of ATP6V0E2, TMEM165, and ABCA1 were consistent with our bioinformatics analysis. CONCLUSION: Our study demonstrates that lysosome-associated signature might forecast the prognosis of AML patients and offer guidance for subsequent immunotherapy and chemotherapy strategies.

Acute myeloid leukemia

Multi-omics dynamic profiling reveals predictive biomarkers for first-line immunochemotherapy in extensive-stage small-cell lung cancer.

BACKGROUND: Extensive-stage small-cell lung cancer (ES-SCLC) is associated with a poor prognosis. Although first-line immunochemotherapy improves clinical outcomes, robust prognostic biomarkers for this treatment modality remain unavailable. The aim of this study was to identify non-invasive, easily accessible, and dynamically monitored biomarkers of ES-SCLC by machine learning integrating serum metabolomics, lipidomics, and proteomics at multiple time points. METHODS: A total of 816 serum samples were collected from ES-SCLC patients receiving first-line immunotherapy combined with chemotherapy or first-line chemotherapy for metabolomics, lipidomics, and proteomics analysis. The immunochemotherapy cohort was randomly divided into training and validation subsets at a 6:4 ratio. Biomarkers were identified using machine learning algorithms, and their prognostic significance was evaluated through receiver operating characteristic (ROC) analysis, Kaplan–Meier survival analysis, and multivariate Cox regression. Potential metabolic pathways and mechanisms were further explored via integrated multi-omic analysis. RESULTS: The immunochemotherapy exhibited a prolonged median progression-free survival (PFS) and higher objective response rate (ORR) compared to the chemotherapy group. A total of 5 serum metabolites (uric acid, L-aspartate-semialdehyde, dimethisterone, xanthine, L-cysteine), 6 lipids (Cer d18:1/26:0, Cer d18:2/25:0, SM d18:1/20:1, SM d17:1/25:1, DG O-18:1_16:0, PS 18:0_24:0), and 3 proteins (ACIN1, ACSL4, PHGDH) were identified and constructed into independent prognostic models. Among patients receiving immunochemotherapy, those categorized as low-risk based on the model demonstrated significantly longer PFS compared with those in the high-risk group. These prognostic signatures also retained predictive value in patients who underwent second-line treatment with anlotinib plus immunochemotherapy. Integrated analysis revealed that glycine, serine, and threonine metabolism was the commonly enriched pathway across all three omics layers. Notably, PHGDH (protein), L-aspartate-semialdehyde and L-cysteine (metabolites), and PS (18:0_24:0) (lipid), key elements in this pathway, were all incorporated in the predictive model. In addition, models of the composition of these substances after one cycle of treatment can still predict the prognosis of patients. CONCLUSION: In this study, we constructed and validated a set of non-invasive, dynamically monitorable prognostic models (containing 5 metabolites, 6 lipids, and 3 proteins) using machine learning by integrating multiple time point data from the serum metabolome, lipid panel, and proteome to accurately distinguish the prognostic risk of patients with ES-SCLC receiving immunochemotherapy. PFS was significantly prolonged in patients in the low-risk group, and this model remains predictive in the subsequent second-line treatment with anlotinib in combination with immunochemotherapy. Glycine-serine-threonine metabolic pathway may be the key mechanism, of which PHGDH, L-aspartate semialdehyde, L-cysteine and PS (18:0_24:0) are the core predictors. This study provides the first multi-omics dynamic prognostic tool for ES-SCLC immunochemotherapy and reveals potential therapeutic targets.

Humans