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