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Multi-Omics and Single-Cell Mendelian Randomization Reveal a Potential Role of VNN2 in Lung Adenocarcinoma in Resting Natural Killer Cells

By: Zhi Chun Xue · Jing Lin · Jian Hui Wu · Zhi Wen Peng · Mei Yan Tang · Peng Liang · Hui Ling Chen · Gui Ju Fang · Qing Xue

13 March 2026 at 18:00

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

FreeKV: Boosting KV Cache Retrieval for Efficient LLM Inference

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

By: Guangda Liu · Chengwei Li · Zhenyu Ning · Jing Lin · Yiwu Yao · Danning Ke · Minyi Guo · Jieru Zhao

10 March 2026 at 12:00

arXiv:2505.13109v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are widely deployed with rapidly expanding context windows to support increasingly demanding applications. However, long contexts pose significant deployment challenges, primarily due to the KV cache whose size grows proportionally with context length. While KV cache compression methods have been proposed to address this issue, KV dropping methods incur considerable accuracy loss, and KV retrieval methods suffer from significant efficiency bottlenecks. We propose FreeKV, a training-free algorithm-system co-optimization framework to enhance KV retrieval efficiency while preserving accuracy. On the algorithm side, FreeKV introduces speculative retrieval to shift the KV selection and recall processes out of the critical path, combined with fine-grained correction to ensure accuracy. On the system side, FreeKV employs hybrid KV layouts across CPU and GPU memory to eliminate fragmented data transfers, and leverages double-buffered streamed recall to further improve efficiency, enabling effective overlap with computation, full latency hiding, and practical speedups from speculative recall. Experiments demonstrate that FreeKV achieves near-lossless accuracy across various scenarios and models, delivering up to a 13$\times$ speedup compared to SOTA KV retrieval methods. Code is available at https://github.com/sjtu-zhao-lab/FreeKV.