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Received β€” 8 April 2026 ⏭

A Data-driven Approach for Biomarker Discovery based on U-centered Distance Correlation Network: Multi-omics Warning Signals for Non-small Cell Lung Cancer

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xin Huang Β· Mei Yan Β· Yang Zhou
6 April 2026 at 18:00

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

A Data-driven Approach for Biomarker Discovery based on U-centered Distance Correlation Network: Multi-omics Warning Signals for Non-small Cell Lung Cancer

βœ‡Omics In Lung
By: Xin Huang Β· Mei Yan Β· Yang Zhou
6 April 2026 at 18:00

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

Received β€” 14 March 2026 ⏭

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

βœ‡Omics In Lung
By: Zhi Chun Xue Β· Jing Lin Β· Jian Hui Wu Β· Zhi Wen Peng Β· Mei Yan Tang Β· Peng Liang Β· Hui Ling Chen Β· Gui Ju Fang Β· 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 Γ— 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

Received β€” 11 March 2026 ⏭

Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Jian Xu Β· Shian Du Β· Junmei Yang Β· Qianli Ma Β· Delu Zeng Β· John Paisley
10 March 2026 at 12:00
arXiv:2408.06710v3 Announce Type: replace-cross Abstract: Gaussian Process Latent Variable Models (GPLVMs) have become increasingly popular for unsupervised tasks such as dimensionality reduction and missing data recovery due to their flexibility and non-linear nature. An importance-weighted version of the Bayesian GPLVMs has been proposed to obtain a tighter variational bound. However, this version of the approach is primarily limited to analyzing simple data structures, as the generation of an effective proposal distribution can become quite challenging in high-dimensional spaces or with complex data sets. In this work, we propose an Annealed Importance Sampling (AIS) approach to address these issues. By transforming the posterior into a sequence of intermediate distributions using annealing, we combine the strengths of Sequential Monte Carlo samplers and VI to explore a wider range of posterior distributions and gradually approach the target distribution. We further propose an efficient algorithm by reparameterizing all variables in the evidence lower bound (ELBO). Experimental results on both toy and image datasets demonstrate that our method outperforms state-of-the-art methods in terms of tighter variational bounds, higher log-likelihoods, and more robust convergence.
Received β€” 25 February 2026 ⏭

Foundation and Large-Scale AI Models in Neuroscience: A Comprehensive Review

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Shihao Yang Β· Xiying Huang Β· Danilo Bernardo Β· Jun-En Ding Β· Andrew Michael Β· Jingmei Yang Β· Patrick Kwan Β· Ashish Raj Β· Feng Liu
24 February 2026 at 13:00
arXiv:2510.16658v2 Announce Type: replace Abstract: The development of large-scale artificial intelligence (AI) models is influencing neuroscience research by enabling end-to-end learning from raw brain signals and neural data. In this paper, we review applications of large-scale AI models across five major neuroscience domains: neuroimaging and data processing, brain-computer interfaces and neural decoding, clinical decision support and translational frameworks, and disease-specific applications across neurological and psychiatric disorders. These models show potential to address major computational neuroscience challenges, including multimodal neural data integration, spatiotemporal pattern interpretation, and the development of translational frameworks for clinical research. Moreover, the interaction between neuroscience and AI has become increasingly reciprocal, as biologically informed architectural constraints are now incorporated to develop more interpretable and computationally efficient models. This review highlights both the promise of such technologies and critical implementation considerations, with particular emphasis on rigorous evaluation frameworks, effective integration of domain knowledge, prospective clinical validation, and comprehensive ethical guidelines. Finally, a systematic listing of critical neuroscience datasets used to develop and evaluate large-scale AI models across diverse research applications is provided.
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