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Received — 7 January 2026 ⏭ Favourites

The role of PCMT1 in prognosis tumor immune microenvironment and therapeutic responses across cancers

✇Omics In Lung
By: Bo Wang · Sijia Huang · Ruizhen Ren · Ruiqian Yao · Erwen Kou · Haixia Zhao · Hao Zhu · Mengyu Zhang · Liangzhe Wang · Yuanjie Zhu
6 January 2026 at 19:00

Discov Oncol. 2026 Jan 5. doi: 10.1007/s12672-025-04366-2. Online ahead of print.

ABSTRACT

BACKGROUND: Emerging evidence highlights the overexpression of Protein-L-isoaspartate (D-aspartate) O-methyltransferase (PCMT1) in multiple malignancies. However, its pan-cancer prognostic significance, tumor immune microenvironment (TIME) interactions, and therapeutic implications remain underexplored.

METHODS: Multi-omics data were integrated from UCSC Xena, GTEx, UALCAN, and published cohorts. PCMT1 expression patterns were systematically analyzed across 33 cancer types. Associations between PCMT1 and clinical outcomes, immune infiltration, immune checkpoint genes (ICGs), tumor mutation burden (TMB), microsatellite instability (MSI), and drug sensitivity were evaluated using bioinformatics pipelines.

RESULTS: Our pan-cancer analysis revealed differential expression patterns of PCMT1 across various malignancies, with significant upregulation in 20 cancer types and downregulation in 3 cancer types. Notably, PCMT1 overexpression was predominantly observed in epithelial-origin tumors, such as ACC (adrenocortical carcinoma), BRCA (breast invasive carcinoma), COAD (colon adenocarcinoma), and LUAD (lung adenocarcinoma). Survival analysis demonstrated that elevated PCMT1 expression was significantly correlated with unfavorable prognosis in multiple epithelial tumors, particularly in BRCA, esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), liver hepatocellular carcinoma (LIHC), and mesothelioma (MESO). Furthermore, comprehensive analysis identified significant associations between PCMT1 expression and various tumor microenvironment features, including immune scores, six distinct immune cell types, four immunosuppressive cell populations, cancer-associated fibroblasts (CAFs)-related markers, and immunosuppressive factors. PCMT1 expression also showed significant correlations with tumor mutation burden (TMB), microsatellite instability (MSI), DNA stemness score (DNAss), and RNA stemness score (RNAss). Particularly noteworthy was the strong positive correlation between PCMT1 expression and CAFs infiltration, along with their associated factors. These findings were further validated in independent immunotherapy cohorts, where PCMT1 consistently demonstrated immunosuppressive characteristics.

CONCLUSION: Multi-omics analysis suggests that PCMT1 may serve as a potential prognostic biomarker and a novel immunotherapy target for pan-cancer.

PMID:41491065 | DOI:10.1007/s12672-025-04366-2

Digital Twin AI: Opportunities and Challenges from Large Language Models to World Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Rong Zhou · Dongping Chen · Zihan Jia · Yao Su · Yixin Liu · Yiwen Lu · Dongwei Shi · Yue Huang · Tianyang Xu · Yi Pan · Xinliang Li · Yohannes Abate · Qingyu Chen · Zhengzhong Tu · Yu Yang · Yu Zhang · Qingsong Wen · Gengchen Mai · Sunyang Fu · Jiachen Li · Xuyu Wang · Ziran Wang · Jing Huang · Tianming Liu · Yong Chen · Lichao Sun · Lifang He
6 January 2026 at 13:00
arXiv:2601.01321v1 Announce Type: new Abstract: Digital twins, as precise digital representations of physical systems, have evolved from passive simulation tools into intelligent and autonomous entities through the integration of artificial intelligence technologies. This paper presents a unified four-stage framework that systematically characterizes AI integration across the digital twin lifecycle, spanning modeling, mirroring, intervention, and autonomous management. By synthesizing existing technologies and practices, we distill a unified four-stage framework that systematically characterizes how AI methodologies are embedded across the digital twin lifecycle: (1) modeling the physical twin through physics-based and physics-informed AI approaches, (2) mirroring the physical system into a digital twin with real-time synchronization, (3) intervening in the physical twin through predictive modeling, anomaly detection, and optimization strategies, and (4) achieving autonomous management through large language models, foundation models, and intelligent agents. We analyze the synergy between physics-based modeling and data-driven learning, highlighting the shift from traditional numerical solvers to physics-informed and foundation models for physical systems. Furthermore, we examine how generative AI technologies, including large language models and generative world models, transform digital twins into proactive and self-improving cognitive systems capable of reasoning, communication, and creative scenario generation. Through a cross-domain review spanning eleven application domains, including healthcare, aerospace, smart manufacturing, robotics, and smart cities, we identify common challenges related to scalability, explainability, and trustworthiness, and outline directions for responsible AI-driven digital twin systems.
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