❌

Normal view

The landscape of peripheral blood RNA modifications and its clinical implications for diagnosis of hepatocellular carcinoma

Cell Commun Signal. 2026 Sep 11;24(1):488. doi: 10.1186/s12964-026-03206-2.

ABSTRACT

BACKGROUND: While over 170 RNA modifications have been identified and implicated in various cancers, their role in hepatocellular carcinoma (HCC) progression is increasingly recognized. Despite this established relevance in tumor biology, the landscape of RNA modifications in the peripheral blood of HCC patients-and their potential diagnostic utility-remains largely unexplored.

METHODS: Peripheral blood samples from patients with HCC, liver cirrhosis (LC), and normal healthy (NH) controls were collected. The abundances of 55 RNA modifications were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to assess their diagnostic potential for HCC, particularly at early stages. Correlations among these modifications and their associations with clinical parameters were analyzed. Simultaneously, differentially expressed genes, including those encoding RNA-modifying enzymes, were screened in peripheral blood. The biological relevance of the identified signatures was subsequently validated using in vitro co-culture and in vivo syngeneic HCC mouse models.

RESULTS: Compared to the combined non-HCC group (NH and LC), the abundances of 11 RNA modifications were significantly altered in both overall and stage I HCC groups, with N2,N2-dimethylguanosine (m2,2G) emerging as a key component exhibiting the most pronounced dysregulation. A diagnostic model centered on an m2,2G-based modification panel achieved area under the curves (AUCs) of 0.901 and 0.891 for detecting HCC and stage I HCC, respectively, demonstrating promising diagnostic potential. Notably, the incorporation of two upregulated genes in peripheral blood-IFI27 and CCR2-significantly enhanced the model's performance, yielding improved AUCs of 0.972 and 0.968, respectively. Further analysis revealed distinct correlation patterns among RNA modifications, as well as between RNA modifications and clinical laboratory parameters, exhibiting both shared and HCC-specific features that suggest systemic reprogramming of RNA modification network in HCC. This biological relevance was confirmed by elevated m2,2G abundances in human lymphocytes co-cultured with HCC cells and blood from a syngeneic HCC mouse model.

CONCLUSIONS: This study systematically profiled peripheral blood RNA modifications and provided preliminary evidence supporting their potential as diagnostic biomarkers for HCC. By integrating key modifications (m2,2G, m2,2,7G, m6,6A) with two mRNA markers (IFI27 and CCR2), we developed a multi-omics signature that demonstrated promising diagnostic performance, particularly for early-stage HCC.

PMID:42732057 | PMC:PMC13570552 | DOI:10.1186/s12964-026-03206-2

The landscape of peripheral blood RNA modifications and its clinical implications for diagnosis of hepatocellular carcinoma

Cell Commun Signal. 2026 Sep 11;24(1):488. doi: 10.1186/s12964-026-03206-2.

ABSTRACT

BACKGROUND: While over 170 RNA modifications have been identified and implicated in various cancers, their role in hepatocellular carcinoma (HCC) progression is increasingly recognized. Despite this established relevance in tumor biology, the landscape of RNA modifications in the peripheral blood of HCC patients-and their potential diagnostic utility-remains largely unexplored.

METHODS: Peripheral blood samples from patients with HCC, liver cirrhosis (LC), and normal healthy (NH) controls were collected. The abundances of 55 RNA modifications were quantified using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to assess their diagnostic potential for HCC, particularly at early stages. Correlations among these modifications and their associations with clinical parameters were analyzed. Simultaneously, differentially expressed genes, including those encoding RNA-modifying enzymes, were screened in peripheral blood. The biological relevance of the identified signatures was subsequently validated using in vitro co-culture and in vivo syngeneic HCC mouse models.

RESULTS: Compared to the combined non-HCC group (NH and LC), the abundances of 11 RNA modifications were significantly altered in both overall and stage I HCC groups, with N2,N2-dimethylguanosine (m2,2G) emerging as a key component exhibiting the most pronounced dysregulation. A diagnostic model centered on an m2,2G-based modification panel achieved area under the curves (AUCs) of 0.901 and 0.891 for detecting HCC and stage I HCC, respectively, demonstrating promising diagnostic potential. Notably, the incorporation of two upregulated genes in peripheral blood-IFI27 and CCR2-significantly enhanced the model's performance, yielding improved AUCs of 0.972 and 0.968, respectively. Further analysis revealed distinct correlation patterns among RNA modifications, as well as between RNA modifications and clinical laboratory parameters, exhibiting both shared and HCC-specific features that suggest systemic reprogramming of RNA modification network in HCC. This biological relevance was confirmed by elevated m2,2G abundances in human lymphocytes co-cultured with HCC cells and blood from a syngeneic HCC mouse model.

CONCLUSIONS: This study systematically profiled peripheral blood RNA modifications and provided preliminary evidence supporting their potential as diagnostic biomarkers for HCC. By integrating key modifications (m2,2G, m2,2,7G, m6,6A) with two mRNA markers (IFI27 and CCR2), we developed a multi-omics signature that demonstrated promising diagnostic performance, particularly for early-stage HCC.

PMID:42732057 | PMC:PMC13570552 | DOI:10.1186/s12964-026-03206-2

Targeting peripheral 5-HT2AR enhances antitumor immunity in colorectal cancer

By selectively targeting peripheral 5-HT2AR without inducing psychedelic effects, a non-brain-penetrant agonist boosts antitumor CD8+ T cell immunity and improves immunotherapy responses in preclinical models of colorectal cancer.

AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration

arXiv:2605.20025v2 Announce Type: replace Abstract: Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail and inform the next attempt, and lessons accumulate across cycles. Existing autonomous research systems often model this process as a linear pipeline: they rely on single-agent reasoning, stop when execution fails, and do not carry experience across runs. We present AutoResearchClaw, a multi-agent autonomous research pipeline built on five mechanisms: structured multi-agent debate for hypothesis generation and result analysis, a self-healing executor with a \textsc{Pivot}/\textsc{Refine} decision loop that transforms failures into information, verifiable result reporting that prevents fabricated numbers and hallucinated citations, human-in-the-loop collaboration with seven intervention modes spanning full autonomy to step-by-step oversight, and cross-run evolution that converts past mistakes into future safeguards. On ARC-Bench, a 25-topic experiment-stage benchmark, AutoResearchClaw outperforms AI Scientist v2 by 54.7%. A human-in-the-loop ablation across seven intervention modes reveals that precise, targeted collaboration at high-leverage decision points consistently outperforms both full autonomy and exhaustive step-by-step oversight. We position AutoResearchClaw as a research amplifier that augments rather than replaces human scientific judgment. Code is available at https://github.com/aiming-lab/AutoResearchClaw.

BacktestBench: Benchmarking Large Language Models for Automated Quantitative Strategy Backtesting

arXiv:2605.17937v2 Announce Type: replace-cross Abstract: Quantitative backtesting is essential for evaluating trading strategies but remains hampered by high technical barriers and limited scalability. While Large Language Models (LLMs) offer a transformative path to automate this complex, interdisciplinary workflow through advanced code generation, tool usage, and agentic planning, the practical realization is significantly challenged by the current lack of a large-scale benchmark dedicated to automated quantitative backtesting, which hinders progress in this field. To bridge this critical gap, we introduce BacktestBench, the first large-scale benchmark for automated quantitative backtesting. Built from over 6 million real market records, it comprises 18,246 meticulously annotated question-answering pairs across four task categories: metrics calculation, ticker selection, strategy selection, and parameter confirmation. We also propose AutoBacktest, a robust multi-agent baseline that translates natural language strategies into reproducible backtests by coordinating a Summarizer for semantic factor extraction, a Retriever for validated SQL generation, and a Coder for Python backtesting implementation. Our evaluation on 23 mainstream LLMs, complemented by targeted ablations, identifies key factors that influence end-to-end performance and highlights the importance of grounded verification and standardized indicator representations.

Pan-neurodegeneration proteomics reveals disease subtypes and molecular signatures

A pan-neurodegeneration atlas built from multilayer, deep proteomics of 2,279 brain samples across 6 major diseases integrates whole proteome, detergent-insoluble proteome, and posttranslational modifications to enable intra- and inter-disease comparisons to reveal disease-specific subtypes and dysregulated pathways, while identifying shared changes such as GPNMB upregulation and NPTX2 downregulation.

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

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

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

  • ✇Nature Cancer
  • Harnessing foundation models for digital pathology without re-training Zhiping Xiao · Sheng Wang
    Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-025-01108-9Applications of digital pathology in clinical oncology have largely depended on the requirement for labeled data and model re-training. A study now presents PRET, a training-free framework with robust performance for pan-cancer diagnosis that adapts pathology foundation models to diverse tasks at inference stage, from screening and subtyping tasks to segmentation and metastasis detection tasks.
     

Harnessing foundation models for digital pathology without re-training

3 April 2026 at 08:00

Nature Cancer, Published online: 03 April 2026; doi:10.1038/s43018-025-01108-9

Applications of digital pathology in clinical oncology have largely depended on the requirement for labeled data and model re-training. A study now presents PRET, a training-free framework with robust performance for pan-cancer diagnosis that adapts pathology foundation models to diverse tasks at inference stage, from screening and subtyping tasks to segmentation and metastasis detection tasks.

Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills

arXiv:2512.16301v3 Announce Type: replace Abstract: Large language model (LLM) agents are moving beyond prompting alone. ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learning with verifiable rewards can improve reasoning and tool use, and OpenClaw highlights a newer direction in which agents accumulate persistent memory and reusable skills. Yet the research landscape remains fragmented across post-training, retrieval, memory, and skill systems. This survey studies these developments under a single notion of \emph{adaptation}: improving an agent, its tools, or their interaction after pretraining. We organize the field with a four-paradigm framework spanning agent adaptation and tool adaptation. On the agent side, A1 (tool-execution-signaled) and A2 (agent-output-signaled) improve the agent itself through supervised fine-tuning, preference optimization, and reinforcement learning with verifiable rewards. On the tool side, T1 (agent-agnostic) provides reusable pre-trained modules any agent can call, while T2 (agent-supervised) uses the agent's outputs to train memory systems, skill libraries, or lightweight subagents. Using this framework, we review post-training methods, adaptive memory architectures, and agent skills; compare their trade-offs in cost, flexibility, and generalization; and summarize evaluation practices across deep research, software development, computer use, and drug discovery. We conclude by outlining open problems in agent-tool co-adaptation, continual learning, safety, and efficient deployment.

ConEQsA: Concurrent and Asynchronous Embodied Questions Scheduling and Answering

arXiv:2509.11663v2 Announce Type: replace-cross Abstract: This paper formulates the Embodied Questions Answering (EQsA) problem, introduces a corresponding benchmark, and proposes an agentic system to tackle the problem. Classical Embodied Question Answering (EQA) is typically formulated as answering one single question by actively exploring a 3D environment. Real deployments, however, often demand handling multiple questions that may arrive asynchronously and carry different urgencies. We formalize this setting as Embodied Questions Answering (EQsA) and present ConEQsA, an agentic framework for concurrent, urgency-aware scheduling and answering. ConEQsA leverages shared group memory to reduce redundant exploration, and a priority-planning method to dynamically schedule questions. To evaluate the EQsA setting fairly, we contribute the Concurrent Asynchronous Embodied Questions (CAEQs) benchmark containing 40 indoor scenes and five questions per scene (200 in total), featuring asynchronous follow-up questions and human-annotated urgency labels. We further propose metrics for EQsA performance: Direct Answer Rate (DAR), and Normalized Urgency-Weighted Latency (NUWL), which serve as a fair evaluation protocol for EQsA. Empirical evaluations demonstrate that ConEQsA consistently outperforms strong sequential baselines, and show that urgency-aware, concurrent scheduling is key to making embodied agents responsive and efficient under realistic, multi-question workloads. Code is available on https://anonymous.4open.science/r/ConEQsA.

ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Intrinsic Adaptation

arXiv:2602.07883v2 Announce Type: replace Abstract: Agentic systems powered by Large Language Models (LLMs) have demonstrated remarkable potential in tackling complex, long-horizon tasks. However, their efficacy is fundamentally constrained by static configurations governing agent behaviors, which are fixed prior to execution and fail to adapt to evolving task dynamics. Existing approaches, relying on manual orchestration or heuristic-based patches, often struggle with poor generalization and fragmented optimization. To transcend these limitations, we propose ToolSelf, a novel paradigm enabling tool-driven runtime self-reconfiguration. By abstracting configuration updates as a callable tool, ToolSelf unifies task execution and self-adjustment into a single action space, achieving a phase transition from external rules to intrinsic parameters. Agents can thereby autonomously update their sub-goals and context based on task progression, and correspondingly adapt their strategy and toolbox, transforming from passive executors into dual managers of both task and self. We further devise Configuration-Aware Two-stage Training (CAT), combining rejection sampling fine-tuning with trajectory-level reinforcement learning to internalize this meta-capability. Extensive experiments across diverse benchmarks demonstrate that ToolSelf rivals specialized workflows while generalizing to novel tasks, achieving a 24.1% average performance gain and illuminating a path toward truly self-adaptive agents.
❌