Latest Research
All publications from the Cancer3.AI database, newest first.
Roles of FXR in Lipid Biochemistry within Hepatocellular Carcinoma Using MALDI MSI.
Bender KJ, et al
Researchers used matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) to map lipid distributions in liver tissue and investigate how the farnesoid X receptor (FXR), a key regulator of lipid metabolism, influences the biochemistry of hepatocellular carcinoma (HCC), one of the most lethal and prevalent forms of liver cancer. The study focused on phosphatidylcholine (PC) lipid species and revealed clear spatial and compositional differences between cancerous HCC regions and surrounding non-tumorous liver tissue. A specific lipid molecule, PC(38:4), was identified as a potential contributor to HCC biology, a hypothesis further supported by laboratory cell culture experiments using Hep3B liver cancer cells incubated with this lipid. These findings deepen our understanding of how disrupted lipid metabolism driven by FXR dysfunction shapes the tumor microenvironment in HCC. Identifying specific lipid signatures associated with liver cancer could open new avenues for diagnostic biomarker development and lipid-targeted therapeutic strategies.
Journal of the American Society for Mass Spectrometry
Source →Cost-effectiveness analysis of atezolizumab and bevacizumab as first-line systemic therapy in unresectable hepatocellular carcinoma in Malaysia.
Wong YF, et al
A new cost-effectiveness analysis published in the Journal of Medical Economics evaluated atezolizumab plus bevacizumab as a first-line treatment for unresectable hepatocellular carcinoma (advanced liver cancer) compared to sorafenib and lenvatinib within Malaysia's public healthcare system. Researchers used a partitioned survival model populated with local cost data and clinical trial evidence to project lifetime health outcomes and costs from the Malaysian Ministry of Health perspective. The analysis found that atezolizumab plus bevacizumab delivered the greatest number of quality-adjusted life years among all three treatments, while sorafenib was excluded because it was more expensive yet less effective than lenvatinib. Compared to lenvatinib, the combination immunotherapy yielded approximately 0.87 additional quality-adjusted life years at an incremental cost of around RM 44,863, producing an incremental cost-effectiveness ratio of RM 51,399 per quality-adjusted life year gained—less than one times Malaysia's GDP per capita and well within accepted thresholds. These findings provide strong economic evidence supporting expanded public funding and adoption of atezolizumab plus bevacizumab in Malaysia, offering liver cancer patients a more effective treatment option that represents good value for money within the national health system.
Journal of medical economics
Source →Amelioration of experimentally induced-arthritis by guaiazulene through targeting inflammatory and oxidative stress biomarkers in Wistar rat.
Saleem A, et al
Researchers investigated the anti-arthritic potential of guaiazulene (GA), a natural plant-derived compound found in chamomile and related species, using a Complete Freund's Adjuvant-induced rheumatoid arthritis model in Wistar rats. GA was administered orally for 21 days at doses of 20, 40, and 60 mg/kg, both as monotherapy and in combination with the established drug methotrexate (MTX). Treatment with GA, particularly in combination with methotrexate, significantly reduced joint swelling, pain, and clinical arthritis scores while restoring normal body weight and normalizing biochemical and hematological parameters in treated animals. The combination therapy also markedly decreased oxidative stress markers in liver and sciatic nerve tissues and favorably modulated the expression of key inflammatory mediators, including TNF-α, IL-6, NF-κB, COX-2, and IL-4, as quantified by qRT-PCR. These findings suggest that guaiazulene, especially when combined with methotrexate, may offer a promising complementary approach to managing rheumatoid arthritis, though rigorous safety and clinical trials in humans are required before therapeutic application.
Inflammopharmacology
Source →TCHP drives hepatocarcinogenesis through LLPS-mediated AURKA condensation and enables synergistic therapy.
Li J, et al
Researchers investigated the role of trichoplein (TCHP), a centrosomal protein, in the development and progression of liver cancer, specifically hepatocellular carcinoma and hepatoblastoma. The study found that TCHP is markedly overexpressed in liver tumor tissue compared to normal liver, and that higher TCHP levels correlate with significantly worse patient survival outcomes. Mechanistically, TCHP was shown to localize to centrosomes and drive the formation of biomolecular condensates with the kinase AURKA through a process called liquid-liquid phase separation, thereby boosting AURKA activity and ensuring cancer cells can complete faulty cell divisions that fuel tumor growth. When TCHP was depleted in experimental models, tumor cells accumulated severe mitotic errors and underwent extensive cell death, halting tumor growth in mice. Crucially, combining TCHP inhibition with alisertib, an approved AURKA inhibitor, produced a synergistic anti-tumor effect at lower drug doses, potentially reducing treatment-related toxicity for patients. These findings establish TCHP as a novel oncogenic driver and therapeutic target in liver cancer, with the TCHP-AURKA axis offering a promising avenue for combination therapy strategies.
Cell death & disease
Source →Fine-tuned lightweight language models for structured extraction of liver cancer imaging free-text report: a comparative analysis with existing large language models.
Luo Y, et al
This study investigated whether fine-tuned lightweight language models can effectively extract structured, clinically useful information from free-text liver cancer imaging reports, a task that traditionally demands considerable manual effort from radiologists and data managers. Researchers systematically compared the performance of these smaller, computationally efficient models against state-of-the-art large language models (LLMs) on the specific challenge of parsing and organizing radiology report content related to hepatic malignancies. The findings suggest that lightweight models, when fine-tuned on domain-specific data, can achieve performance competitive with much larger models while requiring far fewer computational resources, making them more practical for real-world clinical deployment. This matters because automating structured data extraction from radiology reports could reduce administrative burden, minimize transcription errors, and accelerate the availability of standardized clinical information for treatment planning and cancer registries. Ultimately, this research supports a path toward more scalable and cost-effective AI tools that can be integrated into hospital systems to improve workflow efficiency and support better outcomes for liver cancer patients.
BMC medical imaging
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