Front Oncol. 2026 Aug 14;16:1918635. doi: 10.3389/fonc.2026.1918635. eCollection 2026.

ABSTRACT

OBJECTIVE: Hepatocellular carcinoma (HCC) is characterized by frequent recurrence, therapeutic resistance, and marked metabolic adaptability. Disulfidptosis is a recently described form of regulated cell death associated with glucose deprivation and disulfide stress. This study aimed to identify disulfidptosis-related genes associated with HCC progression and to investigate the potential biological role of SLC5A6.

METHODS: Single-cell RNA sequencing and TCGA-LIHC transcriptomic data were integrated. A literature-derived, non-directional disulfidptosis-related gene-set enrichment score was calculated using ssGSEA, and copy-number alterations were inferred using inferCNV. WGCNA, differential expression analysis, and the SLC-family gene list were integrated to identify candidate genes. Bayesian deconvolution, ESTIMATE, TIDE, and GSVA were used to evaluate tumor-microenvironment-related features and pathway signatures. The biological effects of SLC5A6 silencing were assessed using proliferation, migration, invasion, apoptosis, and xenograft assays. Glucose-deprivation-induced disulfide stress was further evaluated by measuring protein disulfide content, the NADP+/NADPH ratio, and FLNA and FLNB band patterns under non-reducing conditions.

RESULTS: Single-cell analysis showed that malignant hepatocytes with higher inferCNV-derived CNV scores exhibited greater enrichment of the disulfidptosis-related gene signature. Integration of glucose-deprivation-associated DEGs, WGCNA modules, and SLC-family genes identified SLC5A6 as a candidate disulfidptosis-related gene that was upregulated in HCC and associated with poor prognosis. Bayesian deconvolution, ESTIMATE, TIDE, and GSVA analyses linked elevated SLC5A6 expression to advanced disease, stromal and immunosuppressive cell enrichment, higher T-cell exclusion scores, and activation of Wnt/mTOR-related signaling signatures. In SLC7A11-high HCC cells, glucose deprivation increased protein disulfide content and the NADP+/NADPH ratio and altered non-reducing FLNA and FLNB band patterns, whereas these changes were partially attenuated by SLC5A6 silencing. Under conventional culture conditions, SLC5A6 silencing inhibited proliferation, migration, invasion, and xenograft growth and increased apoptosis.

CONCLUSION: SLC5A6 is a candidate disulfidptosis-related gene and prognostic biomarker associated with malignant progression in HCC. The findings suggest that SLC5A6 may participate in glucose-deprivation-induced disulfide stress, while its direct role in regulating disulfidptotic cell death remains to be established. Its associations with immune-exclusion-related features also require further functional validation.

PMID:42666251 | PMC:PMC13521847 | DOI:10.3389/fonc.2026.1918635

J Gastrointest Oncol. 2026 Apr 30;17(2):96. doi: 10.21037/jgo-2025-aw-848. Epub 2026 Mar 27.

ABSTRACT

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC) is characterized by high postoperative recurrence rates and poor long-term survival despite advances in surgical and systemic therapies. Accurate prediction of postoperative recurrence and survival risk is critical for individualized surveillance, adjuvant treatment selection, and precision management. With the rapid development of artificial intelligence (AI) and medical informatics, multimodal data-driven models integrating clinical, imaging, pathological, and omics information have emerged as a promising paradigm. This narrative review aims to systematically summarize recent advances in multimodal prediction models for postoperative recurrence and survival in HCC, compare modeling strategies and fusion approaches, and discuss current challenges and future directions for clinical translation.

METHODS: A narrative literature review was conducted by searching PubMed, Web of Science, and Google Scholar for studies published between 2020 and 2025. Articles focusing on postoperative recurrence or survival prediction in HCC using single-modal or multimodal data were included. Relevant studies were identified using keywords related to HCC, multimodal data, AI, machine learning, deep learning, recurrence, and prognosis.

KEY CONTENT AND FINDINGS: This review summarizes commonly used data modalities, including clinical variables, medical imaging, pathological features, and multi-omics data, and outlines their respective strengths and limitations. Conventional statistical models and AI-based approaches, including non-deep learning and deep learning algorithms, are compared. Particular emphasis is placed on multimodal fusion strategies at the feature level and decision level, with discussion of their methodological characteristics and suitable clinical scenarios. Overall, multimodal models consistently demonstrate superior predictive performance compared with single-modality approaches. However, key challenges remain, including data heterogeneity, limited interpretability of complex models, insufficient external validation, and the predominance of static baseline modeling.

CONCLUSIONS: Multimodal data-driven prediction models represent a promising strategy for improving postoperative risk stratification and personalized management in HCC. While current evidence highlights their potential advantages over traditional prognostic tools, broader clinical adoption is hindered by methodological limitations and a lack of standardized frameworks. Future research should focus on longitudinal multimodal modeling, multi-center prospective validation, and enhanced model interpretability to facilitate integration into clinical workflows and inform precision oncology-oriented decision-making.

PMID:42169935 | PMC:PMC13187996 | DOI:10.21037/jgo-2025-aw-848