Clinical significance of LARGE1 in progression of liver cancer and the underlying mechanism

Advances and challenges in molecular understanding, early detection, and targeted treatment of liver cancer

In this review, we explore the application of next-generation sequencing in liver cancer research, highlighting its potential in modern oncology. Liver cancer, particularly hepatocellular carcinoma, is driven by a complex interplay of genetic, epigenetic, and environmental factors. Key genetic alterations, such as mutations in TERT, TP53, and CTNNB1, alongside epigenetic modifications such as DNA methylation and histone remodeling, disrupt regulatory pathways and promote tumorigenesis. Environmental factors, including viral infections, alcohol consumption, and metabolic disorders such as nonalcoholic fatty liver disease, enhance hepatocarcinogenesis. The tumor microenvironment plays a pivotal role in liver cancer progression and therapy resistance, with immune cell infiltration, fibrosis, and angiogenesis supporting cancer cell survival. Advances in immune checkpoint inhibitors and chimeric antigen receptor T-cell therapies have shown potential, but the unique immunosuppressive milieu in liver cancer presents challenges. Dysregulation in pathways such as Wnt/β-catenin underscores the need for targeted therapeutic strategies. Next-generation sequencing is accelerating the identification of genetic and epigenetic alterations, enabling more precise diagnosis and personalized treatment plans. A deeper understanding of these molecular mechanisms is essential for advancing early detection and developing effective therapies against liver cancer.

Liver Cancer Diagnosis: Enhanced Deep Maxout Model with Improved Feature Set

This work proposed a liver cancer classification scheme that includes Preprocessing, Feature extraction, and classification stages. The source images are pre-processed using Gaussian filtering. For segmentation, this work proposes a LUV transformation-based adaptive thresholding-based segmentation process. After the segmentation, certain features are extracted that include multi-texon based features, Improved Local Ternary Pattern (LTP-based features), and GLCM features during this phase. In the Classification phase, an improved Deep Maxout model is proposed for liver cancer detection. The adopted scheme is evaluated over other schemes based on various metrics. While the learning rate is 60%, an improved deep maxout model achieved a higher F-measure value (0.94) for classifying liver cancer; however, the previous method like Support Vector Machine (SVM), Random Forest (RF), Recurrent Neural Network (RNN), Long Short Term Memory (LSTM), K-Nearest Neighbor (KNN), Deep maxout, Convolutional Neural Network (CNN), and DL model holds less F-measure value. An improved deep maxout model achieved minimal False Positive Rate (FPR), and False Negative Rate (FNR) values with the best outcomes compared to other existing models for liver cancer classification.

Involvement of abnormal dystroglycan expression and matriglycan levels in cancer pathogenesis

Dystroglycan (DG) is a glycoprotein composed of two subunits that remain non-covalently bound at the plasma membrane: α-DG, which is extracellular and heavily O-mannosyl glycosylated, and β-DG, an integral transmembrane polypeptide. α-DG is involved in the maintenance of tissue integrity and function in the adult, providing an O-glycosylation-dependent link for cells to their extracellular matrix. β-DG in turn contacts the cytoskeleton via dystrophin and participates in a variety of pathways transmitting extracellular signals to the nucleus. Increasing evidence exists of a pivotal role of DG in the modulation of normal cellular proliferation. In this context, deficiencies in DG glycosylation levels, in particular those affecting the so-called matriglycan structure, have been found in an ample variety of human tumors and cancer-derived cell lines. This occurs together with an underexpression of the DAG1 mRNA and/or its α-DG (core) polypeptide product or, more frequently, with a downregulation of β-DG protein levels. These changes are in general accompanied in tumor cells by a low expression of genes involved in the last steps of the α-DG O-mannosyl glycosylation pathway, namely POMT1/2, POMGNT2, CRPPA, B4GAT1 and LARGE1/2. On the other hand, a series of other genes acting earlier in this pathway are overexpressed in tumor cells, namely DOLK, DPM1/2/3, POMGNT1, B3GALNT2, POMK and FKTN, hence exerting instead a pro-oncogenic role. Finally, downregulation of β-DG, altered β-DG processing and/or impaired β-DG nuclear levels are increasingly found in human tumors and cell lines. It follows that DG itself, particular genes/proteins involved in its glycosylation and/or their interactors in the cell could be useful as biomarkers of certain types of human cancer, and/or as molecular targets of new therapies addressing these neoplasms.

A novel bioavailable curcumin-galactomannan complex modulates the genes responsible for the development of chronic diseases in mice: A RNA sequence analysis

BACKGROUND

Chronic diseases or non-communicable diseases are a major burden worldwide due to the lack of highly efficacious treatment modalities and the serious side effects associated with the available therapies.

PURPOSE/STUDY DESIGN

A novel self-emulsifying formulation of curcumin with fenugreek galactomannan hydrogel scaffold as a water-dispersible non-covalent curcumin-galactomannan molecular complex (curcumagalactomannosides, CGM) has shown better bioavailability than curcumin and can be used for the prevention and treatment of chronic diseases. However, the exact potential of this formulation has not been studied, which would pave the way for its use for the prevention and treatment of multiple chronic diseases.

METHODS

The whole transcriptome analysis (RNAseq) was used to identify differentially expressed genes (DEGs) in the liver tissues of mice treated with LPS to investigate the potential of CGM on the prevention and treatment of chronic diseases. Expression analysis using DESeq2 package, GO, and pathway analysis of the differentially expressed transcripts was performed using UniProtKB and KEGG-KAAS server.

RESULTS

The results showed that 559 genes differentially expressed between the liver tissue of control mice and CGM treated mice (100 mg/kg b.wt. for 14 days), with adjusted p-value below 0.05, of which 318 genes were significantly upregulated and 241 were downregulated. Further analysis showed that 33 genes which were upregulated (log2FC > 8) in the disease conditions were significantly downregulated, and 32 genes which were downregulated (log2FC < -8) in the disease conditions were significantly upregulated after the treatment with CGM.

CONCLUSION

Overall, our study showed CGM has high potential in the prevention and treatment of multiple chronic diseases.