Int J Biol Macromol. 2026 Sep 25:154618. doi: 10.1016/j.ijbiomac.2026.154618. Online ahead of print.

ABSTRACT

Inonotus obliquus is a medicinal fungus growing on birch bark. A neutral polysaccharide (IOP-W, M_w = 8.222 kDa) was isolated from its crude polysaccharides via sequential DEAE DE-52 cellulose column and Sephadex G-200 gel filtration column chromatography. IOP-W, composed primarily of galactose, glucose, and mannose, was structurally characterized by UV-Vis, FT-IR, GC-MS, and NMR as a glucan containing →4)-α-D-Glcp-(1→, →6)-β-D-Glcp-(1→, →3)-β-D-Glcp-(1→, →4,6)-α-D-Glcp-(1→, terminal α-D-Glcp, and β-D-Glcp reducing end. AFM confirmed its aggregated spherical morphology. IOP-W exerted antitumor activity against MIA PaCa-2 cells by modulating apoptosis and migration. Metabolomics revealed effects on amino acids, alkaloids, lipids, and nucleotides involving 20 pathways, while transcriptomic KEGG analysis showed regulation of MAPK, TNF, and p53 signaling. In vivo investigations employing small animal MRI technology have validated that tumor growth is significantly suppressed in animal models, accompanied by elevated spleen index and improved physiological parameters. Western blotting and immunohistochemistry revealed altered expression of Parp-1, p53, Bax/Bcl-2, p-ERK1/2, p-JNK1, NF-κB, vimentin, and MMP-9. These findings indicate that IOP-W inhibits pancreatic cancer via the p53/MAPK pathway, highlighting its potential as a fungal polysaccharide-based therapeutic candidate.

PMID:42790566 | DOI:10.1016/j.ijbiomac.2026.154618

Comb Chem High Throughput Screen. 2026 Mar 27. doi: 10.2174/0113862073445368260131002109. Online ahead of print.

ABSTRACT

INTRODUCTION/OBJECTIVE: Lung cancer is the leading cause of cancer-related mortality worldwide, and non-small cell lung cancer (NSCLC) accounts for the majority of cases. Alterations in metabolic activities play important roles in NSCLC development, wherein related genes and metabolites interact with each other, involving multiple forms.

METHODS: To comprehensively understand the pathogenic mechanisms and improve the performance of clinical early, precise diagnosis, this study proposed a data-driven approach for biomarker discovery based on U-centered distance correlation network (DCN) to investigate NSCLC metabolism-related reactions. In DCN, changes in molecular relationships during NSCLC initiation and progression are measured using the t-statistics of U-centered distance correlation for network construction, in which prospective warning signals representing NSCLC onset can be identified without human intervention. Additionally, the network construction criterion in DCN can precisely and effectively capture both linear and nonlinear molecular relationships in simple and biologically relevant manners.

RESULTS: DCN was successfully employed to analyze NSCLC metabolism-related metabolomics and genomics datasets. Statistical analyses confirmed that compared with other algorithms, the gene and metabolite biomarker panels identified by DCN provided more reliable diagnostic capabilities for clinical NSCLC detection. Biological analyses revealed that disturbed energy metabolism and lipid metabolism occurred during tumor cell proliferation and growth in NSCLC patients.

DISCUSSION: The gene ASPA and metabolite aspartic acid were significantly decreased in NSCLC samples, suggesting that the corresponding amino acid metabolic activities were intricately linked to NSCLC progression.

CONCLUSION: These findings demonstrated that DCN can further facilitate NSCLC studies to improve clinical outcomes in patients.

PMID:41937706 | DOI:10.2174/0113862073445368260131002109

Comb Chem High Throughput Screen. 2026 Mar 27. doi: 10.2174/0113862073445368260131002109. Online ahead of print.

ABSTRACT

INTRODUCTION/OBJECTIVE: Lung cancer is the leading cause of cancer-related mortality worldwide, and non-small cell lung cancer (NSCLC) accounts for the majority of cases. Alterations in metabolic activities play important roles in NSCLC development, wherein related genes and metabolites interact with each other, involving multiple forms.

METHODS: To comprehensively understand the pathogenic mechanisms and improve the performance of clinical early, precise diagnosis, this study proposed a data-driven approach for biomarker discovery based on U-centered distance correlation network (DCN) to investigate NSCLC metabolism-related reactions. In DCN, changes in molecular relationships during NSCLC initiation and progression are measured using the t-statistics of U-centered distance correlation for network construction, in which prospective warning signals representing NSCLC onset can be identified without human intervention. Additionally, the network construction criterion in DCN can precisely and effectively capture both linear and nonlinear molecular relationships in simple and biologically relevant manners.

RESULTS: DCN was successfully employed to analyze NSCLC metabolism-related metabolomics and genomics datasets. Statistical analyses confirmed that compared with other algorithms, the gene and metabolite biomarker panels identified by DCN provided more reliable diagnostic capabilities for clinical NSCLC detection. Biological analyses revealed that disturbed energy metabolism and lipid metabolism occurred during tumor cell proliferation and growth in NSCLC patients.

DISCUSSION: The gene ASPA and metabolite aspartic acid were significantly decreased in NSCLC samples, suggesting that the corresponding amino acid metabolic activities were intricately linked to NSCLC progression.

CONCLUSION: These findings demonstrated that DCN can further facilitate NSCLC studies to improve clinical outcomes in patients.

PMID:41937706 | DOI:10.2174/0113862073445368260131002109

World J Oncol. 2026 Mar 5;17(2):247-255. doi: 10.14740/wjon2689. eCollection 2026 Apr.

ABSTRACT

BACKGROUND: We aimed to evaluate the potential association between genetically predicted vanin-2 (VNN2) expression and lung adenocarcinoma (LUAD) risk, and to explore the immune cell subtype that may underlie this relationship.

METHODS: We integrated whole-blood expression quantitative trait loci (eQTL) data from eQTLGen, plasma protein quantitative trait loci (pQTL) data from deCODE, and LUAD genome-wide association study (GWAS) data from European-ancestry cohorts, together with differential expression analysis using GEPIA2, to identify candidate genes for subsequent single-cell eQTL (sc-eQTL) Mendelian randomization (MR) analysis. For the sc-eQTL analysis, VNN2-associated eQTLs from 14 immune cell types profiled in the OneK1K single-cell eQTL resource were tested for associations with LUAD risk.

RESULTS: Bulk-level MR analysis showed that genetically predicted increases in VNN2 expression and protein levels were significantly associated with a reduced risk of LUAD (eQTL-MR: odds ratio (OR) = 0.964, 95% confidence interval (95% CI), 0.934-0.995; P = 0.024; pQTL-MR: OR = 0.946, 95% CI, 0.921-0.970; P = 2.87 × 10^-5). Transcriptomic analyses confirmed significant downregulation of VNN2 in LUAD tumors compared with normal lung tissues. sc-eQTL MR identified the strongest association in resting natural killer (rNK) cells (OR = 0.896, 95% CI, 0.829-0.967; P = 0.005).

CONCLUSIONS: Multi-omics and sc-eQTL MR analyses indicated that genetically predicted increases in VNN2 expression were associated with a reduced risk of LUAD, with the most pronounced effect observed in rNK cells. These findings suggest a potential cell type-specific role of VNN2 in LUAD susceptibility and warrant further studies to validate its biological relevance and clinical implications.

PMID:41822323 | PMC:PMC12978397 | DOI:10.14740/wjon2689