TRIM36 and CAMK2N2 regulate ferroptosis and antigen presentation in small cell lung cancer

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Yifan Cai · Shuang Zhu · Honglin Wang · Fang Huang · Zhongyuan Yin · Jun Nie

6 April 2026 at 18:00

iScience. 2026 Mar 11;29(4):115310. doi: 10.1016/j.isci.2026.115310. eCollection 2026 Apr 17.

ABSTRACT

Small cell lung cancer (SCLC) is a highly aggressive tumor with poor prognosis. Ferroptosis is closely linked to tumor antigen presentation: it affects antigen presentation efficiency via immunostimulatory signals, while CD8⁺ T cell activation induced by antigen presentation promotes tumor cell ferroptosis by secreting IFNγ. This study used multi-omics analyses and machine learning to screen key genes, verified by in vitro/in vivo experiments. TRIM36 and CAMK2N2 were significantly upregulated in SCLC, negatively correlating with patient survival, effector memory CD8⁺ T cell infiltration, and tumor MHC I expression. They suppress SCLC antigen presentation via ferroptosis-dependent/independent mechanisms, limiting T cell function. TRIM36 and CAMK2N2 are promising SCLC biomarkers and therapeutic targets, providing clues to unravel ferroptosis-antigen presentation associations in tumor cells and optimize immunotherapeutic strategies.

PMID:41940332 | PMC:PMC13049528 | DOI:10.1016/j.isci.2026.115310

TRIM36 and CAMK2N2 regulate ferroptosis and antigen presentation in small cell lung cancer

Omics In Lung

By: Yifan Cai · Shuang Zhu · Honglin Wang · Fang Huang · Zhongyuan Yin · Jun Nie

6 April 2026 at 18:00

iScience. 2026 Mar 11;29(4):115310. doi: 10.1016/j.isci.2026.115310. eCollection 2026 Apr 17.

ABSTRACT

Small cell lung cancer (SCLC) is a highly aggressive tumor with poor prognosis. Ferroptosis is closely linked to tumor antigen presentation: it affects antigen presentation efficiency via immunostimulatory signals, while CD8⁺ T cell activation induced by antigen presentation promotes tumor cell ferroptosis by secreting IFNγ. This study used multi-omics analyses and machine learning to screen key genes, verified by in vitro/in vivo experiments. TRIM36 and CAMK2N2 were significantly upregulated in SCLC, negatively correlating with patient survival, effector memory CD8⁺ T cell infiltration, and tumor MHC I expression. They suppress SCLC antigen presentation via ferroptosis-dependent/independent mechanisms, limiting T cell function. TRIM36 and CAMK2N2 are promising SCLC biomarkers and therapeutic targets, providing clues to unravel ferroptosis-antigen presentation associations in tumor cells and optimize immunotherapeutic strategies.

PMID:41940332 | PMC:PMC13049528 | DOI:10.1016/j.isci.2026.115310

WoVR: World Models as Reliable Simulators for Post-Training VLA Policies with RL

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

By: Zhennan Jiang · Shangqing Zhou · Yutong Jiang · Zefang Huang · Mingjie Wei · Yuhui Chen · Tianxing Zhou · Zhen Guo · Hao Lin · Quanlu Zhang · Yu Wang · Haoran Li · Chao Yu · Dongbin Zhao

17 February 2026 at 13:00

arXiv:2602.13977v1 Announce Type: cross Abstract: Reinforcement learning (RL) promises to unlock capabilities beyond imitation learning for Vision-Language-Action (VLA) models, but its requirement for massive real-world interaction prevents direct deployment on physical robots. Recent work attempts to use learned world models as simulators for policy optimization, yet closed-loop imagined rollouts inevitably suffer from hallucination and long-horizon error accumulation. Such errors do not merely degrade visual fidelity; they corrupt the optimization signal, encouraging policies to exploit model inaccuracies rather than genuine task progress. We propose WoVR, a reliable world-model-based reinforcement learning framework for post-training VLA policies. Instead of assuming a faithful world model, WoVR explicitly regulates how RL interacts with imperfect imagined dynamics. It improves rollout stability through a controllable action-conditioned video world model, reshapes imagined interaction to reduce effective error depth via Keyframe-Initialized Rollouts, and maintains policy-simulator alignment through World Model-Policy co-evolution. Extensive experiments on LIBERO benchmarks and real-world robotic manipulation demonstrate that WoVR enables stable long-horizon imagined rollouts and effective policy optimization, improving average LIBERO success from 39.95% to 69.2% (+29.3 points) and real-robot success from 61.7% to 91.7% (+30.0 points). These results show that learned world models can serve as practical simulators for reinforcement learning when hallucination is explicitly controlled.

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