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Received — 14 March 2026 ⏭

CircRNA-encoded RIPK1-98 protein drives lung adenocarcinoma progression

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Qi Sun · Runqiu Chi · Xiao Zhang · Tianxiang Chen · Xin Xu · Lifang Ma · Xiaoting Tian · Yayou Miao · Yongchun Yu · Rui Kang · Guido Kroemer · Yi Shi · Daolin Tang · Jiayi Wang
13 March 2026 at 18:00

Dev Cell. 2026 Mar 12:S1534-5807(26)00079-1. doi: 10.1016/j.devcel.2026.02.014. Online ahead of print.

ABSTRACT

Unexplored biological matter-including uncharacterized genetic elements, molecular entities, and microbial components-remains poorly understood. Here, we use integrated multi-omics approaches to identify and characterize previously unrecognized protein products encoded by circular RNAs (circRNAs) in human tissue specimens and to delineate their roles in the progression of lung adenocarcinoma (LUAD). The transcription of precursor mRNA by RNA polymerase Ⅱ subunit A (RPB1) is crucial for the biogenesis of these potential circRNA-encoded proteins. Functional and translational analyses link their expression to distinct pathological stages of LUAD in patients. The protein RIPK1-98, encoded by circRIPK1, was identified as functionally distinct from its parental gene product, receptor-interacting serine/threonine kinase 1 (RIPK1). RIPK1-98 modulates cyclin-dependent kinase 2 (CDK2)-dependent cell-cycle regulation, thereby facilitating tumor proliferation in cellular and animal models. Together, these findings suggest that RIPK1-98 serves as a biomarker for cell-cycle progression in LUAD and highlight its potential as a therapeutic target to counteract resistance to first-line treatments, such as osimertinib.

PMID:41825439 | DOI:10.1016/j.devcel.2026.02.014

CircRNA-encoded RIPK1-98 protein drives lung adenocarcinoma progression

✇Omics In Lung
By: Qi Sun · Runqiu Chi · Xiao Zhang · Tianxiang Chen · Xin Xu · Lifang Ma · Xiaoting Tian · Yayou Miao · Yongchun Yu · Rui Kang · Guido Kroemer · Yi Shi · Daolin Tang · Jiayi Wang
13 March 2026 at 18:00

Dev Cell. 2026 Mar 12:S1534-5807(26)00079-1. doi: 10.1016/j.devcel.2026.02.014. Online ahead of print.

ABSTRACT

Unexplored biological matter-including uncharacterized genetic elements, molecular entities, and microbial components-remains poorly understood. Here, we use integrated multi-omics approaches to identify and characterize previously unrecognized protein products encoded by circular RNAs (circRNAs) in human tissue specimens and to delineate their roles in the progression of lung adenocarcinoma (LUAD). The transcription of precursor mRNA by RNA polymerase Ⅱ subunit A (RPB1) is crucial for the biogenesis of these potential circRNA-encoded proteins. Functional and translational analyses link their expression to distinct pathological stages of LUAD in patients. The protein RIPK1-98, encoded by circRIPK1, was identified as functionally distinct from its parental gene product, receptor-interacting serine/threonine kinase 1 (RIPK1). RIPK1-98 modulates cyclin-dependent kinase 2 (CDK2)-dependent cell-cycle regulation, thereby facilitating tumor proliferation in cellular and animal models. Together, these findings suggest that RIPK1-98 serves as a biomarker for cell-cycle progression in LUAD and highlight its potential as a therapeutic target to counteract resistance to first-line treatments, such as osimertinib.

PMID:41825439 | DOI:10.1016/j.devcel.2026.02.014

Received — 11 March 2026 ⏭

HarmonyCell: Automating Single-Cell Perturbation Modeling under Semantic and Distribution Shifts

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wenxuan Huang · Mingyu Tsoi · Yanhao Huang · Xinjie Mao · Xue Xia · Hao Wu · Jiaqi Wei · Yuejin Yang · Lang Yu · Cheng Tan · Xiang Zhang · Zhangyang Gao · Siqi Sun
10 March 2026 at 12:00
arXiv:2603.01396v2 Announce Type: replace Abstract: Single-cell perturbation studies face dual heterogeneity bottlenecks: (i) semantic heterogeneity--identical biological concepts encoded under incompatible metadata schemas across datasets; and (ii) statistical heterogeneity--distribution shifts from biological variation demanding dataset-specific inductive biases. We propose HarmonyCell, an end-to-end agent framework resolving each challenge through a dedicated mechanism: an LLM-driven Semantic Unifier autonomously maps disparate metadata into a canonical interface without manual intervention; and an adaptive Monte Carlo Tree Search engine operates over a hierarchical action space to synthesize architectures with optimal statistical inductive biases for distribution shifts. Evaluated across diverse perturbation tasks under both semantic and distribution shifts, HarmonyCell achieves a 95% valid execution rate on heterogeneous input datasets (versus 0% for general agents) while matching or even exceeding expert-designed baselines in rigorous out-of-distribution evaluations. This dual-track orchestration enables scalable automatic virtual cell modeling without dataset-specific engineering.
Received — 25 February 2026 ⏭

Modeling Epidemiological Dynamics Under Adversarial Data and User Deception

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yiqi Su · Christo Kurisummoottil Thomas · Walid Saad · Bud Mishra · Naren Ramakrishnan
24 February 2026 at 13:00
arXiv:2602.20134v1 Announce Type: cross Abstract: Epidemiological models increasingly rely on self-reported behavioral data such as vaccination status, mask usage, and social distancing adherence to forecast disease transmission and assess the impact of non-pharmaceutical interventions (NPIs). While such data provide valuable real-time insights, they are often subject to strategic misreporting, driven by individual incentives to avoid penalties, access benefits, or express distrust in public health authorities. To account for such human behavior, in this paper, we introduce a game-theoretic framework that models the interaction between the population and a public health authority as a signaling game. Individuals (senders) choose how to report their behaviors, while the public health authority (receiver) updates their epidemiological model(s) based on potentially distorted signals. Focusing on deception around masking and vaccination, we characterize analytically game equilibrium outcomes and evaluate the degree to which deception can be tolerated while maintaining epidemic control through policy interventions. Our results show that separating equilibria-with minimal deception-drive infections to near zero over time. Remarkably, even under pervasive dishonesty in pooling equilibria, well-designed sender and receiver strategies can still maintain effective epidemic control. This work advances the understanding of adversarial data in epidemiology and offers tools for designing more robust public health models in the presence of strategic user behavior.
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