Diagnostic and Prognostic Value of Protein Post-translational Modifications in Hepatocellular Carcinoma

A novel hypoxia-induced lncRNA, SZT2-AS1, boosts HCC progression by mediating HIF heterodimerization and histone trimethylation under a hypoxic microenvironment

Hypoxic microenvironment plays a critical role in solid tumor growth, metastasis and angiogenesis. Hypoxia-inducible factors (HIFs), which are canonical transcription factors in response to hypoxia, are stabilized under hypoxia and coordinate the process of hypoxia-induced gene expression, leading to cancer progression. Increasing evidence has uncovered that long noncoding RNAs (lncRNAs), which are closely associated with cancer, play crucial roles in hypoxia-mediated HCC progression, while the mechanisms are largely unknown. Here, we identified SZT2-AS1 as a novel lncRNA in HCC, which was induced by hypoxia in a HIF-1-dependent manner and promoted HCC growth, metastasis and angiogenesis both in vitro and in vivo. And SZT2-AS1 also mediated the hypoxia-induced HCC progression. Clinical data indicated that SZT2-AS1 level was substantially increased in HCC and closely associated with poor clinical outcomes, acting as an independent prognostic predictor. Mechanistically, SZT2-AS1 recruited HIF-1α and HIF-1β to form the HIF-1 heterodimer, and it was required for the occupancy of HIF-1 to hypoxia response elements (HREs) and HIF target gene transcription. In addition, SZT2-AS1 was required for hypoxia-induced histone trimethylation (H3K4me3 and H3K36me3) at HREs. Through recruiting methyltransferase SMYD2, SZT2-AS1 promoted trimethylation of H3K4 and H3K36 in HCC cells. Taken together, our results uncovered a lncRNA-involved positive feedback mechanism under hypoxia and established the clinical value of SZT2-AS1 in prognosis and as a potential therapeutic target in HCC.

Progress in ubiquitination and hepatocellular carcinoma: a bibliometric analysis

BACKGROUND AND PURPOSE

Ubiquitination modifications can affect hepatocellular carcinoma (HCC) progression through various signaling pathways. However, no significant results have been observed regarding protein ubiquitination in HCC's therapeutic transformation. This study aimed to explore the research areas related to ubiquitination and HCC from a bibliometric perspective.

METHODS

Articles and reviews on HCC and ubiquitination published between 2000 and 2023 were obtained from the Web of Science Core Collection (WOSCC). CiteSpace, VOSviewer, and R-bibliometrix were used for the bibliometric and visualization analyses.

RESULTS

Altogether, 358 papers on ubiquitination and HCC were extracted from the WOSCC. Over 24 years, the number of publications has increased. Since the beginning of 2019, studies related to this topic have increased significantly, indicating that the role of ubiquitination modification in HCC is currently popular. China is the leading country in this field with the largest number of publications. The Chinese Academy of Sciences is one of the most influential institutions. Qiao, Yongxia, and Zhang Jie are highly productive authors with major achievements. The journal Cell Death & Disease had the highest number of publications, and the most highly cited journal was Oncogene. The highest citation burst intensity was Sung (2021). In the keyword strategy map, "cancer antigens" are popular keywords in HCC and ubiquitination research.

CONCLUSION

A comprehensive visual analysis of ubiquitination and HCC research was conducted using bibliometric methods, showing the publications and popular topics in this field over the past two decades, thus providing references for the future direction of ubiquitination and HCC research.

Machine learning methods for histopathological image analysis: Updates in 2024

The combination of artificial intelligence and digital pathology has emerged as a transformative force in healthcare and biomedical research. As an update to our 2018 review, this review presents comprehensive analysis of machine learning applications in histopathological image analysis, with focus on the developments since 2018. We highlight significant advances that have expanded the technical capabilities and practical applications of computational pathology. The review examines progress in addressing key challenges in the field as follows: processing of gigapixel whole slide images, insufficient labeled data, multidimensional analysis, domain shifts across institutions, and interpretability of machine learning models. We evaluate emerging trends, such as foundation models and multimodal integration, that are reshaping the field. Overall, our review highlights the potential of machine learning in enhancing both routine pathological analysis and scientific discovery in pathology. By providing this comprehensive overview, this review aims to guide researchers and clinicians in understanding the current state of the pathology image analysis field and its future trajectory.

MiR-424-5p suppresses tumor growth and progression by directly targeting CHEK1 and activating cell cycle pathway in Hepatocellular Carcinoma

Objectives

The aim of this study is to elucidate the functional mechanism of the miRNA-424-5p/CHEK1 pathway in hepatocellular carcinoma (HCC), thereby offering novel insights for the development of targeted therapeutic strategies for HCC.

Methods

We employed a combination of bioinformatics analysis and data from the GEO to construct a regulatory network between miRNA and mRNA. Real-time quantitative polymerase chain reaction (RT-qPCR) was utilized to assess the expression levels of miR-424-5p and CHEK1. Protein expression of CHEK1 was determined using Western blot analysis. The targeting relationship between miR-424-5p and CHEK1 was validated through a dual-luciferase reporter assay. Furthermore, the effects of miR-424-5p on HCC cell proliferation, migration, and invasion were evaluated using the Cell Counting Kit-8 assay, wound healing assay, and Transwell invasion assay, respectively. Apoptosis of HCC cells was measured by flow cytometry.

Results

Bioinformatics analysis revealed that miR-424-5p was significantly downregulated, while CHEK1 was upregulated respectively in GEO dataset. Furthermore, this inverse expression pattern was observed in both HCC tissues and cell lines. Specifically, downregulation of miR-424-5p was found to promote the proliferation, migration, and invasion of HCC cells, while also inhibiting their apoptosis. The dual-luciferase reporter assay confirmed a direct targeting relationship between miR-424-5p and CHEK1. Inhibition of miR-424-5p was shown to counteract the suppressive effects on HCC cell proliferation, migration, and invasion that result from CHEK1 silencing. Additionally, experimental verification indicated that the activation of the cell cycle pathway is implicated in the oncogenic function of miR-424-5p/CHEK1 in HCC.

Conclusions

The present study demonstrates that miR-424-5p exerts a suppressive effect on HCC cell proliferation, migration, and invasion by downregulating the expression of CHEK1. This finding may offer a theoretical foundation for improving the prognosis and developing novel therapeutic strategies for HCC patients.

Enhancing recognition and interpretation of functional phenotypic sequences through fine-tuning pre-trained genomic models

BACKGROUND

Decoding human genomic sequences requires comprehensive analysis of DNA sequence functionality. Through computational and experimental approaches, researchers have studied the genotype-phenotype relationship and generate important datasets that help unravel complicated genetic blueprints. Thus, the recently developed artificial intelligence methods can be used to interpret the functions of those DNA sequences.

METHODS

This study explores the use of deep learning, particularly pre-trained genomic models like DNA_bert_6 and human_gpt2-v1, in interpreting and representing human genome sequences. Initially, we meticulously constructed multiple datasets linking genotypes and phenotypes to fine-tune those models for precise DNA sequence classification. Additionally, we evaluate the influence of sequence length on classification results and analyze the impact of feature extraction in the hidden layers of our model using the HERV dataset. To enhance our understanding of phenotype-specific patterns recognized by the model, we perform enrichment, pathogenicity and conservation analyzes of specific motifs in the human endogenous retrovirus (HERV) sequence with high average local representation weight (ALRW) scores.

RESULTS

We have constructed multiple genotype-phenotype datasets displaying commendable classification performance in comparison with random genomic sequences, particularly in the HERV dataset, which achieved binary and multi-classification accuracies and F1 values exceeding 0.935 and 0.888, respectively. Notably, the fine-tuning of the HERV dataset not only improved our ability to identify and distinguish diverse information types within DNA sequences but also successfully identified specific motifs associated with neurological disorders and cancers in regions with high ALRW scores. Subsequent analysis of these motifs shed light on the adaptive responses of species to environmental pressures and their co-evolution with pathogens.

CONCLUSIONS

These findings highlight the potential of pre-trained genomic models in learning DNA sequence representations, particularly when utilizing the HERV dataset, and provide valuable insights for future research endeavors. This study represents an innovative strategy that combines pre-trained genomic model representations with classical methods for analyzing the functionality of genome sequences, thereby promoting cross-fertilization between genomics and artificial intelligence.

Early monitoring values of oncogenic signalling molecules for hepatocellular carcinoma

The prevention and early diagnosis of liver cancer remains a global medical challenge. During the malignant transformation of hepatocytes, a variety of oncogenic cellular signalling molecules, such as novel high mobility group-Box 3, angiopoietin-2, Golgi protein 73, glypican-3, Wnt3a (a signalling molecule in the Wnt/β-catenin pathway), and secretory clusterin, can be expressed and secreted into the blood. These signalling molecules are derived from different signalling pathways and may not only participate in the malignant transformation of hepatocytes but also become early diagnostic indicators of hepatocarcinogenesis or specific targeted molecules for hepatocellular carcinoma therapy. This article reviews recent progress in the study of several signalling molecules as sensitive biomarkers for monitoring hepatocarcinogenesis.

Early monitoring values of oncogenic signalling molecules for hepatocellular carcinoma

The prevention and early diagnosis of liver cancer remains a global medical challenge. During the malignant transformation of hepatocytes, a variety of oncogenic cellular signalling molecules, such as novel high mobility group-Box 3, angiopoietin-2, Golgi protein 73, glypican-3, Wnt3a (a signalling molecule in the Wnt/β-catenin pathway), and secretory clusterin, can be expressed and secreted into the blood. These signalling molecules are derived from different signalling pathways and may not only participate in the malignant transformation of hepatocytes but also become early diagnostic indicators of hepatocarcinogenesis or specific targeted molecules for hepatocellular carcinoma therapy. This article reviews recent progress in the study of several signalling molecules as sensitive biomarkers for monitoring hepatocarcinogenesis.

PRMT1 Integrates Immune Microenvironment and Fatty Acid Metabolism Response in Progression of Hepatocellular Carcinoma

Background

Protein arginine methyltransferase (PRMT) family members have important roles in cancer processes. However, its functions in the regulation of cancer immunotherapy of hepatocellular carcinoma (HCC) are incompletely understood. This study aimed to investigate the roles of PRMT1 in HCC.

Methods

Single-cell RNA sequencing (scRNA-seq) and clinicopathological data were obtained and used to explore the diagnostic and prognostic value, cellular functions and roles in immune microenvironment regulation of PRMT1 in HCC. The functions of PRMT1 were explored using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO), as well as gene set enrichment analysis (GSEA). TIMER and CIBERSORT were used to analyze the relationships between PRMT1 expression and immune cell infiltration. The STRING database was used to construct a protein-protein interaction (PPI) network.

Results

PRMT1 was aberrantly expressed in HCC, which high expression was associated with tumor progression, worse overall survival (OS) and disease-free survival (DFS) of patients with HCC. PRMT1 was also associated with immune cell infiltration. Moreover, it was specifically expressed in immune cells, including exhausted CD8 T cells, B cells, and mono/macro cells in patients with immunotherapy. The expression of immune checkpoints was significantly increased in the high-PRMT1 expression groups of HCC patients. Regarding biological mechanisms, cell viability, migration and invasion, and the expression of genes related to fatty acid metabolism were suppressed in PRMT1 knockdown HCC cells. Moreover, genes co-expressed with PRMT1 were involved in the fatty acid metabolic process and enriched in fatty and drug-induced liver disease.

Conclusion

Taken together, these results indicate that PRMT1 might exert its oncogenic effects via immune microenvironment regulation and fatty acid metabolism in HCC. Our finding will provide a foundation for further studies and indicate a potential clinical therapeutic target for liver cancer.