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GPAT3 protects against lipid stress-induced ferroptosis in hepatocellular carcinoma: From multi-omics analysis to functional validation

Biochim Biophys Acta Mol Basis Dis. 2027 Jan;1873(1):168471. doi: 10.1016/j.bbadis.2026.168471. Epub 2026 Sep 24.

ABSTRACT

BACKGROUND: The global burden of metabolic-associated hepatocellular carcinoma (HCC) is increasing, with obesity emerging as a key causal factor. However, the molecular mechanisms linking lipid metabolic dysregulation to HCC progression and therapeutic vulnerability remain unclear.

METHODS: We analyzed Global Burden of Disease 2021 data to assess liver cancer burden attributable to metabolic risks from 1990 to 2021. Mendelian randomization was used to evaluate causal associations between metabolic traits and liver cancer risk. TCGA, GTEx, and GEO datasets were integrated to identify lipid stress-responsive regulators. Clinical relevance was assessed using public datasets and tissue microarray immunohistochemistry. Functional validation was performed in HCC cells and a high-fat diet-fed syngeneic mouse tumor model.

RESULTS: Liver cancer deaths and DALYs attributable to metabolic risks increased markedly from 1990 to 2021. Mendelian randomization showed that obesity-related traits, including BMI, waist circumference, and body fat percentage, were causally associated with liver cancer risk, whereas glycemic traits were not. Bioinformatics screening identified GPAT3 as a lipid metabolism regulator upregulated in HCC, induced by palmitic acid, associated with poor prognosis, and enriched in patients with higher BMI. Tissue microarray analysis confirmed increased GPAT3 protein expression in HCC and its association with higher BMI and GPX4 expression. GPAT3 depletion sensitized HCC cells to palmitic acid-induced ferroptosis, whereas Fer-1 rescue and GPAT3 overexpression supported its protective role. In vivo, FSG67 enhanced sorafenib-associated antitumor effects and increased tumor lipid peroxidation.

CONCLUSIONS: GPAT3 protects HCC cells from lipid stress-induced ferroptosis and represents a potential metabolic vulnerability in obesity-associated HCC.

PMID:42785105 | DOI:10.1016/j.bbadis.2026.168471

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GPAT3 protects against lipid stress-induced ferroptosis in hepatocellular carcinoma: From multi-omics analysis to functional validation

Biochim Biophys Acta Mol Basis Dis. 2027 Jan;1873(1):168471. doi: 10.1016/j.bbadis.2026.168471. Epub 2026 Sep 24.

ABSTRACT

BACKGROUND: The global burden of metabolic-associated hepatocellular carcinoma (HCC) is increasing, with obesity emerging as a key causal factor. However, the molecular mechanisms linking lipid metabolic dysregulation to HCC progression and therapeutic vulnerability remain unclear.

METHODS: We analyzed Global Burden of Disease 2021 data to assess liver cancer burden attributable to metabolic risks from 1990 to 2021. Mendelian randomization was used to evaluate causal associations between metabolic traits and liver cancer risk. TCGA, GTEx, and GEO datasets were integrated to identify lipid stress-responsive regulators. Clinical relevance was assessed using public datasets and tissue microarray immunohistochemistry. Functional validation was performed in HCC cells and a high-fat diet-fed syngeneic mouse tumor model.

RESULTS: Liver cancer deaths and DALYs attributable to metabolic risks increased markedly from 1990 to 2021. Mendelian randomization showed that obesity-related traits, including BMI, waist circumference, and body fat percentage, were causally associated with liver cancer risk, whereas glycemic traits were not. Bioinformatics screening identified GPAT3 as a lipid metabolism regulator upregulated in HCC, induced by palmitic acid, associated with poor prognosis, and enriched in patients with higher BMI. Tissue microarray analysis confirmed increased GPAT3 protein expression in HCC and its association with higher BMI and GPX4 expression. GPAT3 depletion sensitized HCC cells to palmitic acid-induced ferroptosis, whereas Fer-1 rescue and GPAT3 overexpression supported its protective role. In vivo, FSG67 enhanced sorafenib-associated antitumor effects and increased tumor lipid peroxidation.

CONCLUSIONS: GPAT3 protects HCC cells from lipid stress-induced ferroptosis and represents a potential metabolic vulnerability in obesity-associated HCC.

PMID:42785105 | DOI:10.1016/j.bbadis.2026.168471

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Multi-omic profiling reveals metabolic vulnerabilities in enzalutamide resistant prostate cancer

Cell Death Discovery, Published online: 09 September 2026; doi:10.1038/s41420-026-03332-3

Multi-omic profiling reveals metabolic vulnerabilities in enzalutamide resistant prostate cancer
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SoK: DARPA's AI Cyber Challenge (AIxCC): Competition Design, Architectures, and Lessons Learned

arXiv:2602.07666v3 Announce Type: replace-cross Abstract: DARPA's AI Cyber Challenge (AIxCC, 2023--2025) is the largest competition to date for building fully autonomous cyber reasoning systems (CRSs) that leverage recent advances in AI -- particularly large language models (LLMs) -- to discover and remediate vulnerabilities in real-world open-source software. This paper presents the first systematic analysis of AIxCC. Drawing on design documents, source code, execution traces, and discussions with organizers and competing teams, we examine the competition's structure and key design decisions, characterize the architectural approaches of finalist CRSs, and analyze competition results beyond the final scoreboard. Our analysis reveals the factors that truly drove CRS performance, identifies genuine technical advances achieved by teams, and exposes limitations that remain open for future research. We conclude with lessons for organizing future competitions and broader insights toward deploying autonomous CRSs in practice.
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Pulmonary-Intestinal Axis: Shared Genetic Basis and Mediating Factors Identified Through Multi-Omics Analysis

Int J Chron Obstruct Pulmon Dis. 2026 Apr 7;21:561645. doi: 10.2147/COPD.S561645. eCollection 2026.

ABSTRACT

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a systemic condition with comorbidities beyond the lung (eg, cardiovascular and metabolic disorders), and gastrointestinal (GI) disorders are also common. The shared genetic basis of COPD-GI comorbidity and its mediating factors remain unclear. We hypothesized that COPD and GI diseases share pleiotropic genetic architecture implicating lipid-metabolic pathways, with smoking mediating part of the association.

METHODS: We analyzed publicly available European-ancestry GWAS summary statistics for COPD (Global Biobank Meta-analysis Initiative), 15 GI diseases (FinnGen), and smoking phenotypes (UK Biobank). Genetic correlation was estimated using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL). Multi-trait analysis of GWAS (MTAG) boosted COPD discovery by leveraging genetically correlated GI traits. We integrated locus-to-gene mapping with multi-tissue expression quantitative trait loci (eQTL) and plasma protein quantitative trait loci (pQTL) evidence to prioritize shared loci, genes, and proteins. Bidirectional two-sample Mendelian randomization (MR) tested causal directions, and two-step mediation MR evaluated smoking.

RESULTS: COPD showed significant genetic correlation with nine GI diseases. We identified six comorbidity-associated loci (three with CADD > 12.37) and 13 unique candidate pleiotropic genes; APOE was supported by proteomic evidence. Enrichment analyses highlighted lipid-metabolism pathways. MR suggested COPD increases risk of gastroesophageal reflux disease (GERD), irritable bowel syndrome (IBS), acute appendicitis, and gastric ulcer, while diverticular disease showed reverse causality toward COPD. Smoking partially mediated the COPD effect on GERD, acute appendicitis, and gastric ulcer.

CONCLUSION: COPD and multiple GI disorders share a distributed pleiotropic genetic basis within the broader systemic comorbidity spectrum of COPD. Multi-omics evidence supports a genomic pulmonary-intestinal axis in which lipid metabolism and smoking-related mechanisms contribute to COPD and GI comorbidity, providing targets for risk stratification and potential intervention.

PMID:41978582 | PMC:PMC13070119 | DOI:10.2147/COPD.S561645

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SETDB2 induces abnormal SHP-1 splicing and promotes immunosuppression in hepatocellular carcinoma

Oncogene, Published online: 07 April 2026; doi:10.1038/s41388-026-03759-z

SETDB2 induces abnormal SHP-1 splicing and promotes immunosuppression in hepatocellular carcinoma
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OSS-CRS: Liberating AIxCC Cyber Reasoning Systems for Real-World Open-Source Security

arXiv:2603.08566v1 Announce Type: cross Abstract: DARPA's AI Cyber Challenge (AIxCC) showed that cyber reasoning systems (CRSs) can go beyond vulnerability discovery to autonomously confirm and patch bugs: seven teams built such systems and open-sourced them after the competition. Yet all seven open-sourced CRSs remain largely unusable outside their original teams, each bound to the competition cloud infrastructure that no longer exists. We present OSS-CRS, an open, locally deployable framework for running and combining CRS techniques against real-world open-source projects, with budget-aware resource management. We ported the first-place system (Atlantis) and discovered 10 previously unknown bugs (three of high severity) across 8 OSS-Fuzz projects. OSS-CRS is publicly available.
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Beyond One-Size-Fits-All: Adaptive Subgraph Denoising for Zero-Shot Graph Learning with Large Language Models

arXiv:2603.02938v1 Announce Type: cross Abstract: Graph-based tasks in the zero-shot setting remain a significant challenge due to data scarcity and the inability of traditional Graph Neural Networks (GNNs) to generalize to unseen domains or label spaces. While recent advancements have transitioned toward leveraging Large Language Models (LLMs) as predictors to enhance GNNs, these methods often suffer from cross-modal alignment issues. A recent paradigm (i.e., Graph-R1) overcomes the aforementioned architectural dependencies by adopting a purely text-based format and utilizing LLM-based graph reasoning, showing improved zero-shot generalization. However, it employs a task-agnostic, one-size-fits-all subgraph extraction strategy, which inevitably introduces significant structural noise--irrelevant neighbors and edges--that distorts the LLMs' receptive field and leads to suboptimal predictions. To address this limitation, we introduce GraphSSR, a novel framework designed for adaptive subgraph extraction and denoising in zero-shot LLM-based graph reasoning. Specifically, we propose the SSR pipeline, which dynamically tailors subgraph extraction to specific contexts through a "Sample-Select-Reason" process, enabling the model to autonomously filter out task-irrelevant neighbors and overcome the one-size-fits-all issue. To internalize this capability, we develop SSR-SFT, a data synthesis strategy that generates high-quality SSR-style graph reasoning traces for supervised fine-tuning of LLMs. Furthermore, we propose SSR-RL, a two-stage reinforcement learning framework that explicitly regulates sampling and selection operations within the proposed SSR pipeline designed for adaptive subgraph denoising. By incorporating Authenticity-Reinforced and Denoising-Reinforced RL, we guide the model to achieve accurate predictions using parsimonious, denoised subgraphs for reasoning.
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