Mendelian Randomization Analysis of the Relationship between Neurotransmitter-related Genes and Cancer: Insights from Multi-omics Data

By: Quan Yuan · Yuli Xi · Hao Yu · Rongjie Ye · Neng Wang · Ge Yu · Ming Niu

21 May 2026 at 18:00

Curr Top Med Chem. 2026 May 18. doi: 10.2174/0115680266436608260406113212. Online ahead of print.

ABSTRACT

INTRODUCTION: Epidemiological studies indicate a potential link between mental disorders and cancer; however, the role of neurotransmitter-related genes (NRGs) in carcinogenesis remains unclear. In this study, we employed Mendelian randomization utilizing multi-omics data to investigate the causal effects and mechanisms of NRGs in cancer.

METHODS: We assessed the causal relationships between ten mental disorders and fourteen cancer types. NRGs were sourced from GeneCards, and transcriptome data for breast cancer (BC) were obtained from the Gene Expression Omnibus (GEO). Summary-data-based Mendelian Randomization (SMR) integrated genome-wide association study (GWAS) data with expression quantitative trait loci (eQTLs), DNA methylation QTLs (mQTLs), intestinal eQTLs, and fecal microbiota QTLs (mbQTLs). Colocalization analyses were conducted to explore the relationships between host genes and gut microbiota, with sensitivity assessments performed using two additional Mendelian randomization methods.

RESULTS: Mendelian randomization confirmed a causal association between mental disorders and BC. A meta-analysis of five BC datasets identified 821 differentially expressed genes (DEGs) among 829 non-redundant genes. SMR highlighted KRTCAP2 as a potential causal gene in blood, and cg24674445 as a significant methylation site. The expression of KRTCAP2 was found to be inversely correlated with BC, while methylation at cg24674445 downregulated KRTCAP2, suggesting that cg24674445 may promote BC progression.

DISCUSSION: This study advances beyond established epidemiological correlations by providing genetically validated evidence for a causal link between mental disorders and breast cancer. Its primary significance lies in delineating a plausible biological pathway-epigenetic regulation of neurotransmitter-related genes-that may mechanistically elucidate this connection. By integrating multi-omics data, we transition from mere association to a testable model of disease etiology, where genetic predispositions to mental illness and cancer converge upon shared regulatory mechanisms within the genome.

CONCLUSION: Multi-omics Mendelian randomization demonstrates that DNA methylation modulates the association between neurotransmitter-related genes and breast cancer.

PMID:42163732 | DOI:10.2174/0115680266436608260406113212

MindCube: Spatial Mental Modeling from Limited Views

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

By: Qineng Wang · Baiqiao Yin · Pingyue Zhang · Jianshu Zhang · Kangrui Wang · Zihan Wang · Jieyu Zhang · Keshigeyan Chandrasegaran · Han Liu · Ranjay Krishna · Saining Xie · Jiajun Wu · Li Fei-Fei · Manling Li

1 April 2026 at 12:00

arXiv:2506.21458v2 Announce Type: replace Abstract: Can Vision-Language Models (VLMs) imagine the full scene from just a few views, like humans do? Humans form spatial mental models naturally, internal representations of unseen space, to reason about layout, perspective, and motion. Our MindCube benchmark with 21,154 questions across 3,268 images exposes this critical gap, where existing VLMs exhibit near-random performance. Using MindCube, we systematically evaluate how well VLMs build robust spatial mental models through representing positions (cognitive mapping), orientations (perspective-taking), and dynamics (mental simulation for "what-if" movements). We then explore three approaches to help approximate spatial mental models in VLMs, focusing on incorporating unseen intermediate views, natural language reasoning chains, and cognitive maps. The significant improvement comes from a synergistic approach, "map-then-reason", that jointly trains the model to first generate a cognitive map and then reason upon it. By training models to reason over these internal maps, we boosted accuracy from 37.8% to 57.8% (+20.0%). Adding reinforcement learning pushed performance even further to 61.3% (+23.5%). Our key insight is that such scaffolding of spatial mental models, actively constructing and utilizing internal structured spatial representations with flexible reasoning processes, significantly improves understanding of unobservable space.

ODESteer: A Unified ODE-Based Steering Framework for LLM Alignment

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

By: Hongjue Zhao · Haosen Sun · Jiangtao Kong · Xiaochang Li · Qineng Wang · Liwei Jiang · Qi Zhu · Tarek Abdelzaher · Yejin Choi · Manling Li · Huajie Shao

24 February 2026 at 13:00

arXiv:2602.17560v2 Announce Type: replace Abstract: Activation steering, or representation engineering, offers a lightweight approach to align large language models (LLMs) by manipulating their internal activations at inference time. However, current methods suffer from two key limitations: (i) the lack of a unified theoretical framework for guiding the design of steering directions, and (ii) an over-reliance on one-step steering that fail to capture complex patterns of activation distributions. In this work, we propose a unified ordinary differential equations (ODEs)-based theoretical framework for activation steering in LLM alignment. We show that conventional activation addition can be interpreted as a first-order approximation to the solution of an ODE. Based on this ODE perspective, identifying a steering direction becomes equivalent to designing a barrier function from control theory. Derived from this framework, we introduce ODESteer, a kind of ODE-based steering guided by barrier functions, which shows empirical advancement in LLM alignment. ODESteer identifies steering directions by defining the barrier function as the log-density ratio between positive and negative activations, and employs it to construct an ODE for multi-step and adaptive steering. Compared to state-of-the-art activation steering methods, ODESteer achieves consistent empirical improvements on diverse LLM alignment benchmarks, a notable $5.7\%$ improvement over TruthfulQA, $2.5\%$ over UltraFeedback, and $2.4\%$ over RealToxicityPrompts. Our work establishes a principled new view of activation steering in LLM alignment by unifying its theoretical foundations via ODEs, and validating it empirically through the proposed ODESteer method.

Reading view