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Received — 16 January 2026 ⏭ Favourites

Molecular features of early- vs. late-onset gastric cancer: a systematic review and meta-analysis

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xinmei Lin · Wenhan Shi · Zitong Zhou · Degang Li · Qiuyuan Yao · Yu Sun
15 January 2026 at 19:00

BMC Cancer. 2026 Jan 14. doi: 10.1186/s12885-026-15567-5. Online ahead of print.

ABSTRACT

BACKGROUND: Early-onset gastric cancer (EOGC), diagnosed before age 50, is characterized by distinct clinicopathological features, though its molecular landscape remains poorly defined.

METHODS: A systematic literature search of PubMed, Embase, and Web of Science identified studies comparing molecular characteristics of EOGC and late-onset gastric cancer (LOGC). Meta-analyses assessed differences in The Cancer Genome Atlas (TCGA) molecular subtypes, gene mutations, therapeutic biomarkers, and serum tumor markers. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated; heterogeneity was assessed using the I2 statistic.

RESULTS: EOGC was associated with a higher prevalence of the genomically stable (GS) subtype (OR = 1.71, 95% CI: 1.37-2.12) and a lower prevalence of the chromosomal instability (CIN) subtype (OR = 0.62, 95% CI: 0.50-0.77). CDH1 mutations were more frequent in EOGC (OR = 3.44, 95% CI: 2.85-4.16), while HER2 expression (OR = 0.54, 95% CI: 0.43-0.67), dMMR/MSI-H status (OR = 0.25, 95% CI: 0.12-0.53), and p53 expression (OR = 0.56, 95% CI: 0.39-0.82) were significantly lower. Serum markers including CEA and CA19-9 were also less frequently elevated in EOGC.

CONCLUSION: EOGC represents a biologically distinct subset of gastric cancer with unique genomic and immunological features. These findings support age-specific diagnostic approaches and emphasize the value of multiomic strategies to uncover the mechanisms driving early-onset disease.

PMID:41535782 | DOI:10.1186/s12885-026-15567-5

Received — 7 January 2026 ⏭ Favourites

Deployability-Centric Infrastructure-as-Code Generation: Fail, Learn, Refine, and Succeed through LLM-Empowered DevOps Simulation

✇cs.AI, q-bio.NC updates on arXiv.org
By: Tianyi Zhang · Shidong Pan · Zejun Zhang · Zhenchang Xing · Xiaoyu Sun
6 January 2026 at 13:00
arXiv:2506.05623v2 Announce Type: replace-cross Abstract: Infrastructure-as-Code (IaC) generation holds significant promise for automating cloud infrastructure provisioning. Recent advances in Large Language Models (LLMs) present a promising opportunity to democratize IaC development by generating deployable infrastructure templates from natural language descriptions. However, current evaluation focuses on syntactic correctness while ignoring deployability, the critical measure of the utility of IaC configuration files. Six state-of-the-art LLMs performed poorly on deployability, achieving only 20.8$\sim$30.2% deployment success rate on the first attempt. In this paper, we construct DPIaC-Eval, the first deployability-centric IaC template benchmark consisting of 153 real-world scenarios cross 58 unique services. Also, we propose an LLM-based deployability-centric framework, dubbed IaCGen, that uses iterative feedback mechanism encompassing format verification, syntax checking, and live deployment stages, thereby closely mirroring the real DevOps workflows. Results show that IaCGen can make 54.6$\sim$91.6% generated IaC templates from all evaluated models deployable in the first 10 iterations. Additionally, human-in-the-loop feedback that provide direct guidance for the deployability errors, can further boost the performance to over 90% passItr@25 on all evaluated LLMs. Furthermore, we explore the trustworthiness of the generated IaC templates on user intent alignment and security compliance. The poor performance (25.2% user requirement coverage and 8.4% security compliance rate) indicates a critical need for continued research in this domain.
Received — 24 July 2025 ⏭ Favourites

High-Sensitive Spatial Proteomics for Pancreatic Cancer Progression Analysis

✇(Multiomics OR Omics) AND (Pancreatic)
By: Jongmin Woo · Zhenyu Sun · Yingwei Hu · Trung Alvin Hoàng · Decapite Christine · Smith Katelyn · Singhi Aatur · Randall E Brand · Daniel W Chan · Qing Kay Li · Ralph H Hruban · Hui Zhang
14 July 2025 at 18:00

bioRxiv [Preprint]. 2025 May 5:2025.05.01.651678. doi: 10.1101/2025.05.01.651678.

ABSTRACT

Pancreatic cancer remains as one of the most challenging malignancies to diagnose and treat due to the late development of symptoms and limited early diagnostic options. Intraductal papillary mucinous neoplasms (IPMNs) are non-invasive precursors to invasive pancreatic ductal adenocarcinoma (PDAC)and an understanding of the changes in patterns of protein expression that accompany the progression from normal ductal (ND) cell, to IPMN to PDAC may provide avenues for improved earlier detection. In this study, we present an optimized spatial tissue proteomics workflow, termed SP-Max (Spatial Proteomics Optimized for Maximum Sensitivity and Reproducibility in Minimal Sample), designed to maximize protein recovery and quantification from limited laser micro dissected (LMD) samples. Our workflow enabled the identification of more than 6,000 proteins and the quantification of over 5,200 protein groups from FFPE tissue contours of pancreatic tissues. Comparative analyses across ND, IPMN, and PDAC revealed critical molecular differences in protein pathways and potential markers of progression. SP-Max provides a systematic, reproducible approach that significantly enhances our ability to study precancerous lesions and cancer progression in pancreatic tissues at unprecedented resolution.

PMID:40654937 | PMC:PMC12247709 | DOI:10.1101/2025.05.01.651678

Received — 24 December 2024 ⏭ Favourites
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