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Received — 15 September 2026 ⏭

Toward Robust Personalized Alignment for LLMs: Mitigating Persona Drift in Multi-Turn Dialogue

✇cs.AI, q-bio.NC updates on arXiv.org
By: Youyuan Zhang · Siyuan Li · Fangming Liu · Jing Li
14 September 2026 at 12:00
arXiv:2609.12373v1 Announce Type: new Abstract: Persona drift remains a central challenge for personalized language models, as user profiles evolve over long interactions rather than remain permanently fixed. Models must therefore revise persistent persona states when preferences genuinely change, while avoiding updates driven by transient, ambiguous, or unresolved observations. We propose CORE, which separates turn-local evidence from persistent persona-state revision and selectively updates grounded user preferences through uncertainty-aware belief revision. We also introduce PERSIST, a held-out post-anchor benchmark for persona-state robustness under sequential interaction stress, covering ambiguity, conflict, and controlled social influence. Across ALOE, PersonaChat, and PERSIST, CORE improves personalized alignment and robustness, with complementary gains in normalized closed-slot state fidelity. Human evaluation and mechanistic controls further support explicit update control beyond stronger generation or persistent memory alone.

EvoRS: On-Policy Self-Evolution of Reward Systems for Open-Ended Reinforcement Learning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Weiyuan Li · Aili Chen · Xintao Wang · Yikai Zhang · Qingqing Dong · Jinghan Xu · Hongru Hou · Wenxuan Zhao · Chengkun Lang · Jun Gao · Yuanli Guo · Hongcheng Guo · Yanghua Xiao · Deqing Yang
14 September 2026 at 12:00
arXiv:2609.12459v1 Announce Type: new Abstract: Open-ended reinforcement learning often relies on rubric-based rewards for tasks without directly verifiable answers. Yet the policy and reward system form a dynamic feedback loop: as the policy optimizes the current reward, an initially useful reward system may become unreliable due to reward hacking or reduced response discriminability. The reward system should therefore evolve rather than remain fixed during training. Existing dynamic-rubric methods adapt evaluation criteria, but reward failures can also arise from scoring mechanisms or signal composition. We introduce EvoRS, a self-evolving RL framework that evolves the reward system from on-policy experience, representing it as an executable Reward-DAG. Specifically, an agentic designer updates this system from on-policy rollouts and reward traces to maintain train-time reliability. Across writing and roleplay, EvoRS achieves the best quality under all three judges, outperforming the policy by \(2.107\) and \(4.767\) points, respectively, while reducing reward hacking and coverage failures and preserving reward informativeness. Ablations confirm that a comprehensive fixed reward system cannot remain reliable in open-ended tasks and must evolve throughout training.

When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration

✇cs.AI, q-bio.NC updates on arXiv.org
By: AI Collaboration · Jiaying Wu · Caleb Ziems · Raymond Chan · Nancy F. Chen · Corlyss Chua · Gerard Chung · Jungpil Hahn · Wee Sun Lee · Zhengyuan Liu · Jamie Ng · Desmond C. Ong · Jeryl Ong · Da Ren Soon · Tianqi Song · Zhi-Xuan Tan · Sixing Tao · Emily Yang · Yajing Yang · Stella Xin Yin · Min-Yen Kan · Diyi Yang
14 September 2026 at 12:00
arXiv:2609.12482v1 Announce Type: new Abstract: We aim to characterise the value of artificial intelligence in the workplace. Current studies largely measure this value in terms of the current automation capabilities and public adoption of AI. However, such metrics ignore the greater impacts of human--agent collaboration in transforming the nature of work. To account for this, we must expand the scope of our analysis beyond atomised tasks of today, and instead focus on how AI can augment entire workflows of the future. To ground this analysis, we establish a precise definition of AI augmentation comprising six conditions, spanning durable net value, meaningful human control, accountability and recovery, and long-term human development through learning, career pathways, and job purpose. We elaborate on these conditions and apply the framework in a case study of AI-mediated social surveys. We conclude by outlining how organisations, researchers, and government leaders can use this framework to make sense of the future of work.

Circular RNA MCM3 recruits USP49 to stabilize PTBP1 and promote cisplatin resistance in cervical squamous cell carcinoma

✇Oncogene - Issue - nature.com science feeds
By: Chenchen Zhu · Ying Wei · Yuebo Li · Zhengzheng Chen · Ying Zhou · Hanyuan Liu
12 September 2026 at 08:00

Oncogene, Published online: 12 September 2026; doi:10.1038/s41388-026-03988-2

Circular RNA MCM3 recruits USP49 to stabilize PTBP1 and promote cisplatin resistance in cervical squamous cell carcinoma

CircRHBDD1(4,5) drives malignant progression of oral squamous cell carcinoma by enhancing DKK1 mRNA stability through facilitating m<sup>6</sup>A-dependent IGF2BP2 binding

✇Oncogene - Issue - nature.com science feeds
By: Daixi Ren · Jiarong Zhang · Mao Huang · Jie Wu · Mei Yang · Yixuan Liu · Pingjuan Xiang · Hongke Qu · Dan Wang · Junshang Ge · Qijia Yan · Lei Shi · Bo Xiang · Wei Xiong · Guiyuan Li · Chunmei Fan · Zhaoyang Zeng
11 September 2026 at 08:00

Oncogene, Published online: 11 September 2026; doi:10.1038/s41388-026-03986-4

CircRHBDD1(4,5) drives malignant progression of oral squamous cell carcinoma by enhancing DKK1 mRNA stability through facilitating m6A-dependent IGF2BP2 binding
Received — 10 September 2026 ⏭

FlowCPO: A Unified Divergence View of Preference Alignment for Flow Models

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yansen Han · Shengyi Liao · Peng Sun · Deyuan Liu · Yuanxing Zhang · Pengfei Wan · Tao Lin
10 September 2026 at 12:00
arXiv:2609.09905v1 Announce Type: cross Abstract: Preference alignment for flow and diffusion models now spans online reinforcement learning and offline preference optimization, but the relation between these methods remains unclear. In particular, existing forward-process alignment methods require fresh samples from the current model, while offline methods based on fixed preference pairs rely primarily on positive-only fine-tuning or DPO-style likelihood-ratio surrogates. We organize these approaches through a divergence-based framework and introduce FlowCPO, an offline forward-KL objective that uses both preferred and dispreferred samples without online rollouts. For linear interpolation, we show under explicit regularity conditions that the forward-KL objective is bounded by a contrastive flow matching loss, yielding a tractable surrogate on fixed data. We further show that this loss is nonnegative, whereas the signed regression loss of simplified FlowDPO can be unbounded below. In the in-domain setting, FlowCPO achieves higher mean GenEval and OCR scores than the evaluated baselines, reaching 0.84 and 0.87 versus 0.81 and 0.74 for FlowDPO at CFG 3.0. In the out-of-domain setting, the results are mixed, with the best GenEval result but lower reward scores than RFT on several metrics.

Targeting KRAS reprograms a Treg-dominant immunosuppressive microenvironment and sensitizes KRAS-mutant gastric adenocarcinoma to CTLA-4 immunotherapy

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaohuan Tang · Peiyao Yang · Xiaoyong Guo · Shuhua Wei · Xiaojing Cheng · Mengyuan Li · Yigang Bao · Sachiyo Nomura · Yu Sun · Longtao Huangfu · Xiaofang Xing · Yongning Jia · Fei Shan · Xiangyu Gao · Chuanhui Han · Ziyu Li
9 September 2026 at 18:00

Sci China Life Sci. 2026 Sep 3. doi: 10.1007/s11427-026-3438-4. Online ahead of print.

ABSTRACT

Oncogenic KRAS mutations define a distinct molecular subset of gastric adenocarcinoma (GA), yet their impact on the tumor immune microenvironment remains incompletely understood. In this study, we established a genetically faithful and immunocompetent KRASG12D-driven mouse model of GA, together with matched organoids and cell lines, to investigate how oncogenic KRAS shapes tumor-immune interactions. KRAS-mutant tumors consistently developed an immunosuppressive microenvironment characterized by enrichment of regulatory T cells (Tregs), accompanied by reduced cytotoxic lymphocyte infiltration and intrinsic resistance to PD-1 blockade. Although pharmacologic targeting of KRAS effectively suppressed tumor growth and increased immune cell infiltration, functional immune analyses revealed persistent Treg-mediated immunosuppression that limited effective antitumor immunity. Mechanistically, TGF-β signaling was required to maintain Treg dominance and suppress effector T cell function in KRAS-driven tumors. Importantly, disruption of this suppressive axis through combined KRAS inhibition and CTLA-4 blockade attenuated TGF-β activity, impaired Treg function, and enhanced antitumor immune responses in vivo. Collectively, these findings identify oncogenic KRAS as a key regulator of TGF-β-dependent immune suppression in GA and provide mechanistic insight into immune evasion within this molecular subtype.

PMID:42714795 | DOI:10.1007/s11427-026-3438-4

Foaming photopolymers as a high-resolution biomimetic printing platform

✇Nature - Issue - nature.com science feeds
By: Detao Qin · Xianghui Liu · Baian Kuang · Yuzhe Zhang · Masateru Ito · Tatsuya Katsuno · Yuan Liu · Jianduo Zhang · Claudia Rosa · Meifang Zhu · Mikihito Takenaka · Hiroshi Imahori · Ganesh N. Pandian · Taiki Yanagishima · Easan Sivaniah
9 September 2026 at 08:00

Nature, Published online: 09 September 2026; doi:10.1038/s41586-026-10968-9

Deep-foam photolithography uses light-controlled polymer foaming to create high-resolution, multifunctional microstructures with tunable optical, wetting and fluid-handling properties for advanced manufacturing applications.

Genomics and social practices at Mogou and other Gansu sites during prehistoric trans-Eurasian exchange

✇Cell
By: Han Shi · E. Andrew Bennett · Guoke Chen · Hui Wang · Tianyi Wang · Peng Cao · Ruilin Mao · Fan Bai · Qingyan Dai · Feng Liu · Xiaotian Feng · Yongan Wang · Chong Yao · Jing Wu · Wenjun Wang · Ming Zhang · Tianxiang Liu · Jingkun Ran · Yue-Chen Liu · Yuan Liu · Dan Ju · Chuyan Li · Hongwei Hou · Xinyi Liu · Qiaomei Fu
3 September 2026 at 08:00
Ancient DNA from 149 individuals at 11 sites in Gansu, China, dated to around 4,700–3,000 years ago, reveals human population history during early transcontinental exchanges of agriculture and technology, as well as contemporary social practices, at the large Mogou cemetery.

DHCR24<sup>+</sup> tumor epithelial cells drive cisplatin resistance in bladder cancer by enhancing cholesterol metabolism to activate lipid raft-associated MAPK signaling

✇Oncogene - Issue - nature.com science feeds
By: Yuanqiao Zhao · Zhuo Xing · Mengmeng Wang · Liangmin Fu · Ruilin Liu · Zexian Ding · Yuan Li · Yinhuai Wang · Bin Yan · Xueqiang Zhu
29 August 2026 at 08:00

Oncogene, Published online: 29 August 2026; doi:10.1038/s41388-026-03967-7

DHCR24+ tumor epithelial cells drive cisplatin resistance in bladder cancer by enhancing cholesterol metabolism to activate lipid raft-associated MAPK signaling
Received — 27 May 2026 ⏭

Test-Time Deep Thinking to Explore Implicit Rules

✇cs.AI, q-bio.NC updates on arXiv.org
By: Wentong Chen · Xin Cong · Zhong Zhang · Yaxi Lu · Siyuan Zhao · Yesai Wu · Qinyu Luo · Haotian Chen · Yankai Lin · Zhiyuan Liu · Maosong Sun
26 May 2026 at 12:00
arXiv:2605.24828v1 Announce Type: new Abstract: With the continuous advancement of Large Language Models (LLMs), intelligent agents are becoming increasingly vital. However, these agents often fail in environments governed by implicit rules--hidden constraints that cannot be observed directly and must be inferred through interaction. This causes agents to fall into repetitive trial-and-error loops, ultimately leading to task failure. To address this challenge, we propose Test-Time Exploration (TTExplore), a framework where a thinker component analyzes interaction history to infer these implicit rules and guide an actor. Effective exploration in this setting critically depends on the reasoning ability of the thinker. However, evaluating deep reasoning trajectories is inherently unstable and difficult, which poses a major obstacle to effective training. To overcome this issue, we introduce a novel and stable reinforcement learning pipeline. The core idea is to use accurate task-level scores as indirect rewards to bypass the difficulty of evaluating intermediate reasoning, and to retain only a single thinking node per trajectory to alleviate reward sparsity. Using this pipeline, we train a specialized 7B model, Exp-Thinker. Experiments on five text-based embodied tasks show that TTExplore equipped with Exp-Thinker improves baseline agent performance by an average of $14$-$19$ points, demonstrating the effectiveness of explicitly reasoning about implicit rules.

CITYREP: A Unified Benchmark for Urban Representations Across Cities, Tasks, and Modalities

✇cs.AI, q-bio.NC updates on arXiv.org
By: Junyuan Liu · Xinglei Wang · Zichao Zeng · Jiazhuang Feng · Quan Qin · Ilya Ilyankou · Guangsheng Dong · Tao Cheng
26 May 2026 at 12:00
arXiv:2605.26036v1 Announce Type: new Abstract: Urban representation learning encodes complex urban environments into general-purpose embeddings for diverse downstream tasks and emerging urban foundation models. However, current evaluations are limited, typically focusing on one or two cities and tasks and relying on random splits that introduce spatial leakage, leading to inflated performance and weak support for cross-location generalization and fair comparison. To address this, we propose CityRep, a unified benchmark that evaluates urban representations across data modalities, cities, and tasks using spatially structured splits. CityRep consists of three key components: (1) a spatial unit-agnostic evaluation framework that supports heterogeneous urban representations through a standardized alignment module; (2) a unified evaluation protocol using block-based spatial splits to mitigate spatial leakage and enable rigorous model comparison; and (3) an extensible multi-city, multi-task benchmark suite spanning 8 cities and 8 tasks across regression, classification, and distribution prediction. We evaluate 11 representative urban representation models. Results show that performance is highly sensitive to the split protocol, with random splits inflating scores and altering model rankings. We also observe substantial variability across cities and tasks, underscoring the need for generalization-aware evaluation. CityRep is released as a reproducible benchmark with datasets, evaluation pipelines, and diagnostic tools to facilitate fair comparison and support future research in urban representation learning towards urban foundation models.

Claw-Anything: Benchmarking Always-On Personal Assistants with Broader Access to User's Digital World

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yusong Lin · Xinyuan Liang · Haiyang Wang · Qipeng Gu · Siqi Cheng · Jiangui Chen · Shuzhe Wu · Feiyang Pan · Lue Fan · Sanyuan Zhao · Dandan Tu
26 May 2026 at 12:00
arXiv:2605.26086v1 Announce Type: new Abstract: Large language model agents are increasingly envisioned as always-on personal assistants with access to anything relevant in the user's digital world. Yet current systems operate over only narrow slices of that world, limiting context-sensitive reasoning and effective assistance. Existing benchmarks similarly provide only partial user state and therefore fail to capture performance in such a broad, always-on setting. To address this gap, we introduce Claw-Anything, a benchmark that expands agent context along three dimensions: long-horizon activity histories, interdependent backend services, and integrated GUI and CLI interaction across multiple devices. To instantiate this setting, we simulate months of user activity through multi-round event injection, producing complex world states and realistic noise, including irrelevant events and conflicting signals. Agents must reason over rich contextual environments while remaining robust to such noise. This expanded scope also enables the evaluation of proactive assistance, requiring agents to anticipate user needs and deliver timely recommendations. Experiments show that GPT-5.5 achieves only 34.5% pass@1, substantially below prior benchmarks, underscoring a gap between current agent capabilities and the demands of always-on personal assistance. Alongside the benchmark, we release an automated data-generation pipeline that yields 2,000 training environments and improves the base model by 23.7%, demonstrating its utility of scalable data infrastructure.

MuNet: A Mutualistic Network for Joint 3D Human Mesh Recovery and 3D Clothed Human Reconstruction from Single Images

✇cs.AI, q-bio.NC updates on arXiv.org
By: Yunqi Gao · Leyuan Liu · Yuhan Li · Changxin Gao · Jingying Chen
26 May 2026 at 12:00
arXiv:2605.25861v2 Announce Type: cross Abstract: 3D human mesh recovery and 3D clothed human reconstruction are inherently related, yet they have long been studied in isolation, thereby overlooking the potential gains of joint optimization. To overcome this limitation, we propose to address these two tasks within a unified framework, which allows their mutual dependencies to be effectively exploited. Building on this idea, we propose MuNet, a mutualistic network for joint 3D human mesh recovery and 3D clothed human reconstruction from single images. First, we adopt 2-manifold graphs as a unified representation for all 3D models, enabling consistent modeling across 3D human mesh recovery and clothed human reconstruction. Second, we design an end-to-end graph convolutional network that progressively deforms an initial graph into a 3D human mesh and refines it into a detailed 3D clothed human model. Third, we introduce a mutualistic mechanism that allows reciprocal interaction between the two tasks {during training}, where 3D human mesh recovery provides guidance for 3D clothed human reconstruction, and reconstruction feedback refines the 3D human mesh recovery. We extensively evaluate MuNet on six benchmark datasets for 3D human mesh recovery and 3D clothed human reconstruction, including Human3.6M, 3DPW, MPI-INF-3DHP, THuman2.0, CAPE, and RenderPeople. Experimental results demonstrate that MuNet achieves state-of-the-art performance on both tasks across all datasets. The code of MuNet is released for research purposes at https://github.com/starVisionTeam/MuNet.

Data Difficulty and the Generalization--Extrapolation Tradeoff in LLM Fine-Tuning

✇cs.AI, q-bio.NC updates on arXiv.org
By: Siyuan Liu · Tinghong Chen · Xinghan Li · Yifei Wang · Jingzhao Zhang
26 May 2026 at 12:00
arXiv:2605.12906v2 Announce Type: replace-cross Abstract: Data selection during supervised fine-tuning (SFT) can critically change the behavior of large language models (LLMs). Although existing work has studied the effect of selecting data based on heuristics such as perplexity, difficulty, or length, the reported findings are often inconsistent or context-dependent. In this work, we systematically study the role of data difficulty in fine-tuning from both empirical and theoretical perspectives, and find that there is no universally optimal difficulty level; rather, its effectiveness depends on the dataset size. We show that for a fixed data budget, there exists an optimal data difficulty for SFT, and that this optimal difficulty shifts toward harder data as the data budget increases. To explain this phenomenon, we conduct controlled synthetic experiments that reveal a simple underlying mechanism: the interplay between the (in-distribution) generalization gap and the extrapolation gap. We further support this mechanism through a theoretical analysis using PAC-Bayesian generalization bounds. Overall, our results clarify how data size and difficulty jointly affect the trade-off between generalization and extrapolation in SFT, providing guidance for difficulty-based data selection under certain model and data conditions.

TimeGuard: Channel-wise Pool Training for Backdoor Defense in Time Series Forecasting

✇cs.AI, q-bio.NC updates on arXiv.org
By: Quang Duc Nguyen · Siyuan Liang · Yiming Li · Fushuo Huo · Dacheng Tao
26 May 2026 at 12:00
arXiv:2605.22365v2 Announce Type: replace-cross Abstract: Time Series Forecasting (TSF) is highly vulnerable to backdoor attacks, yet effective defenses remain underexplored due to challenges arising from data entanglement and shifts in task formulation. To fill this gap, we conduct a systematic evaluation of thirteen representative backdoor defenses across the TSF life cycle and analyze their failure modes. Our results reveal two fundamental issues: (1) data entanglement induces channel-level signal dilution, rendering sample-filtering and trigger-synthesis defenses ineffective at localizing backdoors; and (2) task-formulation shift leads to training-loss degeneration, causing poisoned and clean windows to become indistinguishable at training stages. Based on these findings, we propose a training-time backdoor defense for TSF, termed TimeGuard. Our method adopts channel-wise pool training as the core paradigm and initializes a high-confidence pool using time-aware criteria to mitigate signal dilution. Moreover, we introduce distance-regularized loss selection to progressively expand the reliable pool during training and ease loss degeneration. Extensive experiments across multiple datasets, forecasting architectures, and TSF backdoor attacks demonstrate that TimeGuard substantially improves robustness, boosting $\mathrm{MAE}_\mathrm{P}$ by $1.96\times$ over the leading baseline, while preserving clean performance within 5% $\mathrm{MAE}_\mathrm{C}$.

FGFR1 Promotes Malignant Progression in Lung Squamous Cell Carcinoma Through Activation of Wnt/beta-Catenin Signaling

✇Omics In Lung
By: Xingyuan Li · Shuya Mu · Yinjia Sun · Yongfeng Yu
18 April 2026 at 18:00

Cancer Med. 2026 Apr;15(4):e71833. doi: 10.1002/cam4.71833.

ABSTRACT

OBJECTIVES: This study aims to elucidate the role of FGFR1 in activating the Wnt/β-catenin signaling pathway and the underlying mechanisms by which it promotes malignant progression in lung squamous cell carcinoma (LUSC). By integrating multi-omics analysis with functional experiments, the clinical heterogeneity of FGFR1 amplification, signaling crosstalk, and their regulatory networks governing tumor phenotypes were revealed.

METHODS: Using TCGA data (n = 490), we analyzed the relationship between FGFR1 copy number variation (CNV) and mRNA expression in LUSC, and validated the correlation with protein expression in a clinical cohort (n = 38). GSEA and single-gene GSEA were performed to identify signaling pathways associated with high FGFR1 expression. The interaction between FGFR1 and the Wnt/β-catenin pathway was investigated by immunohistochemistry, immunofluorescence, stable cell lines, Western blot, qPCR, and functional assays.

RESULTS: FGFR1 amplification correlated with increased mRNA and protein expression. The top 25% FGFR1 high-expression group enriched Wnt/β-catenin, PI3K-Akt, and cAMP pathways. Mechanistically, FGFR1 promoted β-catenin nuclear accumulation and enhanced β-catenin signaling through PKA-associated phosphorylation and Akt/GSK3β-related regulation of β-catenin stability, and these effects were attenuated by AKT inhibition. CTNNB1 knockdown significantly inhibited proliferation, migration, invasion, and tumor growth of LUSC cells.

CONCLUSIONS: Our findings indicate that FGFR1 activates Wnt/β-catenin signaling through coordinated regulation of β-catenin phosphorylation, stability, and subcellular localization, thereby promoting malignant progression in LUSC. These results provide a rationale for targeting the FGFR1-Wnt/β-catenin axis as a potential therapeutic strategy.

PMID:41998829 | DOI:10.1002/cam4.71833

Received — 13 April 2026 ⏭

Characterization and regulatory mechanism evaluation of C8orf33 in hepatocellular carcinoma through multiomics profiling

✇(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Wei Gao · Siyi Xing · Mingjiang Liu · Xing Xie · Mei Feng · Zhipeng Quan · Housheng Wang · Zhilong He · Qianxin Hu · Huizhao Su · Shuiping Yu · Zeyuan Li
11 April 2026 at 18:00

Discov Oncol. 2026 Apr 11. doi: 10.1007/s12672-026-04951-z. Online ahead of print.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality. Chromosome 8 open reading frame 33 (C8orf33) has been noted as a potential oncogenic factor in several cancers, but its biological roles and regulatory mechanism in HCC microenvironment remain unknown.

METHODS: We integrated bulk RNA sequencing, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics (ST) to characterize the expression landscape of C8orf33. We then performed C8orf33 loss-of-function studies in HCC cell lines, including in vitro phenotypic assays and subcutaneous xenografts.

RESULTS: C8orf33 was broadly overexpressed and associated with unfavorable prognosis across multiple Cancers. In HCC, higher C8orf33 aligned with advanced stage and shorter overall survival. C8orf33 knockdown reduced proliferation and migration, impaired tumorigenic capacity, and increased apoptosis. ScRNA-seq analyses identified a malignant population of Epi3 with high C8orf33 expression. Cell-cell communication analysis suggested that C8orf33-high Epi3 state was associated with an enriched MIF-CD74/CXCR4/CD44 signaling program toward macrophage populations with M2-like features. ST analyses further confirmed the colocalization of C8orf33 with malignant features in tumor cores. In Huh7 cells, C8orf33 knockdown was accompanied by reduced mRNA and protein levels of MIF and its receptor components. Consistently, xenografts derived from C8orf33-silenced cells showed lower expression of these MIF-axis components and reduced infiltration of CD163 and CD206-positive macrophages.

CONCLUSION: These results support a tumor-promoting association of C8orf33 in HCC and suggest a potential link to macrophage-associated immunomodulatory features, nominating C8orf33 as a candidate biomarker and therapeutic target.

PMID:41965457 | DOI:10.1007/s12672-026-04951-z

Characterization and regulatory mechanism evaluation of C8orf33 in hepatocellular carcinoma through multiomics profiling

✇Omics in Hepatocellular
By: Wei Gao · Siyi Xing · Mingjiang Liu · Xing Xie · Mei Feng · Zhipeng Quan · Housheng Wang · Zhilong He · Qianxin Hu · Huizhao Su · Shuiping Yu · Zeyuan Li
11 April 2026 at 18:00

Discov Oncol. 2026 Apr 11. doi: 10.1007/s12672-026-04951-z. Online ahead of print.

ABSTRACT

BACKGROUND: Hepatocellular carcinoma (HCC) is a major cause of cancer-related mortality. Chromosome 8 open reading frame 33 (C8orf33) has been noted as a potential oncogenic factor in several cancers, but its biological roles and regulatory mechanism in HCC microenvironment remain unknown.

METHODS: We integrated bulk RNA sequencing, single-cell RNA sequencing (scRNA-seq), and spatial transcriptomics (ST) to characterize the expression landscape of C8orf33. We then performed C8orf33 loss-of-function studies in HCC cell lines, including in vitro phenotypic assays and subcutaneous xenografts.

RESULTS: C8orf33 was broadly overexpressed and associated with unfavorable prognosis across multiple Cancers. In HCC, higher C8orf33 aligned with advanced stage and shorter overall survival. C8orf33 knockdown reduced proliferation and migration, impaired tumorigenic capacity, and increased apoptosis. ScRNA-seq analyses identified a malignant population of Epi3 with high C8orf33 expression. Cell-cell communication analysis suggested that C8orf33-high Epi3 state was associated with an enriched MIF-CD74/CXCR4/CD44 signaling program toward macrophage populations with M2-like features. ST analyses further confirmed the colocalization of C8orf33 with malignant features in tumor cores. In Huh7 cells, C8orf33 knockdown was accompanied by reduced mRNA and protein levels of MIF and its receptor components. Consistently, xenografts derived from C8orf33-silenced cells showed lower expression of these MIF-axis components and reduced infiltration of CD163 and CD206-positive macrophages.

CONCLUSION: These results support a tumor-promoting association of C8orf33 in HCC and suggest a potential link to macrophage-associated immunomodulatory features, nominating C8orf33 as a candidate biomarker and therapeutic target.

PMID:41965457 | DOI:10.1007/s12672-026-04951-z

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