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Regulatory mechanisms of ALKBH5/CIITA axis in the synergistic modulation of hepatocellular carcinoma radiotherapy and immunotherapy

By: Fei Wang · Hui Hou · Hong Yang · Ting Dou · Zhenxia Wang · Hongdan Fu · Hong Li · Wei Zhang · Haiping Zhao · Hao Yang

11 March 2026 at 18:00

Genes Immun. 2026 Mar 10. doi: 10.1038/s41435-026-00382-6. Online ahead of print.

ABSTRACT

The prognosis for hepatocellular carcinoma remains grim. Combining radiotherapy with immune checkpoint blockade (ICB) has shown potential to enhance therapeutic outcomes, yet there is a pressing need for further advancements. Our previous research demonstrated that this combined approach suppresses ALKBH5 gene expression and increases m6A modification levels in hepatocellular carcinoma tissues. High-throughput sequencing and detailed molecular analysis revealed that inhibiting ALKBH5 amplifies CIITA m6A modifications post-therapy. This modulation triggers MHC II molecule expression in tumors, facilitating the presentation of tumor-associated antigens to CD4 + T lymphocytes and the recruitment of CD8 + T cells for an anti-tumor immune response. Building on these findings, we engineered a CIITA vector with a specific site mutation to confirm that the regulation of CIITA by the combined radiotherapy and immunotherapy is mediated through m6A methylation. Consequently, we established a comprehensive network involving ALKBH5, CIITA, MHC II, and CD4+ and CD8 + T cells. To elucidate the role and underlying molecular mechanisms of this combined therapy in reshaping the tumor immune microenvironment for hepatocellular carcinoma, we employed multi-omics approaches across in vitro, animal model, and clinical multi-dimensional studies, offering novel insights for enhancing treatment efficacy.

PMID:41807814 | DOI:10.1038/s41435-026-00382-6

Rel-MOSS: Towards Imbalanced Relational Deep Learning on Relational Databases

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

By: Jun Yin · Peng Huo · Bangguo Zhu · Hao Yan · Senzhang Wang · Shirui Pan · Chengqi Zhang

10 March 2026 at 12:00

arXiv:2603.07916v1 Announce Type: new Abstract: In recent advances, to enable a fully data-driven learning paradigm on relational databases (RDB), relational deep learning (RDL) is proposed to structure the RDB as a heterogeneous entity graph and adopt the graph neural network (GNN) as the predictive model. However, existing RDL methods neglect the imbalance problem of relational data in RDBs and risk under-representing the minority entities, leading to an unusable model in practice. In this work, we investigate, for the first time, class imbalance problem in RDB entity classification and design the relation-centric minority synthetic over-sampling GNN (Rel-MOSS), in order to fill a critical void in the current literature. Specifically, to mitigate the issue of minority-related information being submerged by majority counterparts, we design the relation-wise gating controller to modulate neighborhood messages from each individual relation type. Based on the relational-gated representations, we further propose the relation-guided minority synthesizer for over-sampling, which integrates the entity relational signatures to maintain relational consistency. Extensive experiments on 12 entity classification datasets provide compelling evidence for the superiority of Rel-MOSS, yielding an average improvement of up to 2.46% and 4.00% in terms of Balanced Accuracy and G-Mean, compared with SOTA RDL methods and classic methods for handling class imbalance.