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Nanomaterial-assisted immunodiagnostic profiling and therapeutic targeting of hepatocellular carcinoma: from molecular biomarkers to clinical applications

Front Immunol. 2025 Oct 14;16:1668630. doi: 10.3389/fimmu.2025.1668630. eCollection 2025.

ABSTRACT

AIMS AND OBJECTIVES: This study aimed to identify immunologically relevant transcriptomic and proteomic biomarkers in hepatocellular carcinoma (HCC) and to characterize their B-cell epitopes for potential integration into nanomaterial-based biosensors and immunomodulatory platforms for early diagnosis and targeted therapy.

METHODS: We conducted a comprehensive multi-omics analysis by integrating transcriptomic (TCGA-LIHC) and proteomic data to identify differentially expressed genes (DEGs) in HCC. Protein-protein interaction networks and pathway enrichment were used to prioritize hub genes. Five candidate biomarkers, RFC2, HSP90AB1, YWHAZ, CYP2E1, and ADH4, were selected for qRT-PCR and serum ELISA validation in clinical cohorts comprising 85 HCC patients and 50 healthy controls. B-cell epitope prediction was performed using BepiPred 2.0 and validated through synthetic peptide-based ELISA in the same cohort to assess immunoreactivity. Diagnostic performance was evaluated using ROC curve analysis.

RESULTS: RFC2, HSP90AB1, and YWHAZ were significantly upregulated (|log2FC|>0.2) and showed high serological expression, whereas CYP2E1 and ADH4 were consistently downregulated. Predicted B-cell epitopes from RFC2, HSP90AB1, and YWHAZ exhibited strong immunoreactivity (AUC>0.84), indicating their diagnostic potential. Enrichment analysis revealed that upregulated DEGs were involved in cell cycle and mitotic progression, while downregulated genes were linked to immune suppression and metabolic dysfunction. These validated immunogenic epitopes offer promising anchors for nanomaterial-functionalized biosensors, such as gold nanoparticle-conjugated ELISA, graphene-based electrochemical platforms, and peptide-coated quantum dots, for ultrasensitive and multiplexed HCC detection.

CONCLUSION: By integrating transcriptomic and proteomic screening with epitope-level validation, we identified a novel panel of immunogenic biomarkers suitable for nanomaterial-enabled diagnostics in HCC. These findings support the translational potential of peptide-nano scaffold conjugates in developing minimally invasive, immune-responsive biosensing and therapeutic tools tailored for early-stage liver cancer management.

PMID:41164201 | PMC:PMC12558944 | DOI:10.3389/fimmu.2025.1668630

Using Large Language Models to Assess the Consistency of Randomized Controlled Trials on AI Interventions With CONSORT-AI: Cross-Sectional Survey

Background: Chatbots based on large language models (LLMs) have shown promise in evaluating the consistency of research. Previously, researchers used LLM to assess if randomized controlled trial (RCT) abstracts adhered to the CONSORT-Abstract guidelines. However, the consistency of artificial intelligence (AI) interventional RCTs align with the CONSORT-AI standards by LLMs remains unclear. Objective: The aim of this study is to identify the consistency of randomized controlled trials on AI interventions with CONSORT-AI using chatbots based on LLMs. Methods: This cross-sectional study employed six LLM models to assess the consistency of RCTs on AI interventions. The sample selection is based on articles published in JAMA Network Open, which included a total of 41 RCTs. All queries were submitted to LLMs through an API interface with a temperature setting of 0 to ensure deterministic responses. One researcher posed the questions to each model, while another independently verified the responses for validity before recording the results. The Overall Consistency Score (OCS), recall, inter-rater reliability and consistency of contents were analyzed. Results: We found gpt-4-0125-preview has the best average OCS (86.5%, 95%CI: 82.5%-90.5% and 81.6%, 95% CI: 77.6%-85.6%), followed by gpt-4-1106-preview(80.3%, 95%CI: 76.3%-84.3% and 78.0%, 95% CI: 74.0%-82.0%). The model with the worst average OCS is gpt-3.5-turbo-0125 (61.9%, 95%CI: 57.9%-65.9% and 63.0%, 95% CI: 59.0%-67.0%). Among the 11 unique items of CONSORT-AI, Item 2 (“State the inclusion and exclusion criteria at the level of the input data”) received the poorest overall evaluation across six models, with an average OCS of 48.8%. For other items, those with an average OCS greater than 80% across the six models included Items 1, 5, 8, and 9. Conclusions: GPT-4 variants demonstrate strong performance in assessing the consistency of RCTs with CONSORT-AI. Nonetheless, refining the prompts could enhance the precision and consistency of the outcomes. While AI tools like GPT-4 variants are valuable, they are not yet fully autonomous in addressing complex and nuanced tasks such as adherence to CONSORT-AI standards. Therefore, integrating AI with higher levels of human supervision and expertise will be crucial to ensuring more reliable and efficient evaluations, ultimately advancing the quality of medical research.

RMethyMD: An integrated platform for exploring RNA methylation in pan-cancer via a multiomics analysis

Cancer Lett. 2025 Jan 12;612:217462. doi: 10.1016/j.canlet.2025.217462. Online ahead of print.

ABSTRACT

A user-friendly integrated database, RMethyMD (http://www.tmliang.cn/rnamethy), was developed to provide a comprehensive analysis of methylation regulators aimed at facilitating the exploration of molecular features in tumorigenesis and clinical implications in cancer diagnosis and treatment via a multiomics approach. Subsequently, molecular landscapes and a robust constructed m6A-based prognostic model using coxBoost + RSF algorithms in lung cancer highlighted m6A as a suitable marker to guide therapeutic strategy. RMethyMD provides a comprehensive resource and multiomics analysis to explore m6A-based prognostic and clinical values, thereby contributing to aiding personalized cancer therapy.

PMID:39809358 | DOI:10.1016/j.canlet.2025.217462

KDM5B promotes SMAD4 loss-driven drug resistance through activating DLG1/YAP to induce lipid accumulation in pancreatic ductal adenocarcinoma

Cell Death Discovery, Published online: 24 May 2024; doi:10.1038/s41420-024-02020-4

KDM5B promotes SMAD4 loss-driven drug resistance through activating DLG1/YAP to induce lipid accumulation in pancreatic ductal adenocarcinoma

Genome-wide characterization of circulating metabolic biomarkers

Nature, Published online: 06 March 2024; doi:10.1038/s41586-024-07148-y

A meta-analysis of genome-wide association studies for 233 circulating metabolites from 33 cohorts reveals more than 400 loci and suggests probable causal genes, providing insights into metabolic pathways and disease aetiology.

Scientific discovery in the age of artificial intelligence

Nature, Published online: 02 August 2023; doi:10.1038/s41586-023-06221-2

The advances in artificial intelligence over the past decade are examined, with a discussion on how artificial intelligence systems can aid the scientific process and the central issues that remain despite advances.

ZNF655 accelerates progression of pancreatic cancer by promoting the binding of E2F1 and CDK1

Oncogenesis, Published online: 04 August 2022; doi:10.1038/s41389-022-00418-2

ZNF655 accelerates progression of pancreatic cancer by promoting the binding of E2F1 and CDK1
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