A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

Effects of ER-phagy regulatory genes on the microenvironment of hepatocellular carcinoma: a comprehensive analysis

The relationships between gene regulatory functions and hepatocellular carcinoma (HCC) occurrence and progression are constantly being clarified. However, tumour microenvironment complexity has hindered the classification of the role of genes. A comprehensive analysis to further clarify gene functions could provide additional benefits to HCC patients. In the present study, we combined single-cell sequencing data, Mendelian randomization, and bioinformatics analysis for comprehensive analysis. After the study was completed we found that T cell, dendritic cell (DC), macrophage and monocyte contents and the interaction between immune cells in the HCC microenvironment differed between the microvascular invasion-positive (MVI +) and microvascular invasion-negative (MVI-) groups. Mendelian randomization analysis indicated that causal relationships between several endoplasmic reticulum autophagy (ER-phagy) genes and T cell, DC, macrophage and monocyte contents. Single-cell sequencing data were used to validate the association of these genes with immune cells in the microenvironment. Based on the above results, we preliminarily elucidated the potential role of ER autophagy in the HCC microenvironment. Furthermore, a prognostic model was constructed using these causal association genes, which could accurately predict the prognosis and survival of HCC patients.

The molecular characteristics of DNA damage and repair related to P53 mutation for predicting the recurrence and immunotherapy response in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related deaths globally, owing to its high recurrence rate of 50 to 70% within five years. Despite known associations of certain DNA damage and repair (DDR) genes with tumor recurrence and drug resistance, a comprehensive understanding of DDR pathways' role in predicting HCC recurrence and therapeutic responses remains elusive. Addressing this gap could offer significant advancements in prognostic and therapeutic strategies for HCC. This study used 769 RNA sequencing samples from public datasets and 53 samples from Xiangya Hospital for DDR model training and validation. It came out that DDR pathways were significantly enriched in samples with P53 mutations. Next, among the 173 combinations of algorithms and parameters, CoxBoost + RSF, Lasso [fold = 10] + RSF, and Lasso [fold = 50] + RSF demonstrated the best performance. The average AUC values of 1 to 5 years and the average concordance index (C-index) value were around 0.7. The risk scores were increased in tumors with recurrence, P53 mutation, and higher TNM stages. High-risk groups, characterized by enriched DDR pathways, exhibited lower CD8 + T cell infiltration and poorer responses to immunotherapy using atezolizumab and bevacizumab, emphasizing the potential of DDR signatures as valuable prognostic and therapeutic biomarkers. In conclusion, the DDR signatures associated with P53 mutations can predict recurrence and therapeutic response in HCC, highlighting their potential as prognostic and therapeutic biomarkers.

Recent findings regarding the synergistic effects of emodin and its analogs with other bioactive compounds: Insights into new mechanisms

CONTEXT

Emodin is a natural bioactive ingredient mainly extracted from traditional Chinese herbs. Increasing lines of evidence suggest that emodin and its analogs exert notable synergistic pharmacological effects with other bioactive compounds.

OBJECTIVE

This review provides an overview of the pharmacological activity of emodin and its analogs in combination with other physiologically active substances, describes the related molecular mechanisms, and discusses future prospects in this field.

METHODS

Information from multiple scientific databases, such as PubMed, the China Knowledge Resource Integrated Database from the China National Knowledge Infrastructure (CNKI), the Web of Science, Google Scholar, and Baidu Scholar, was collected between January 2006 and August 2022. The subject terms used in the literature search were emodin, pharmaceutical activities, analogs, aloe emodin, rhein, and synergistic effects.

RESULTS

The comprehensive literature analysis suggested that combinations of emodin or its analogs with other bioactive compounds exert notable synergistic anticancer, anti-inflammatory, and antimicrobial effects and that such combinations improve glucose and lipid metabolism and central nervous system diseases.

DISCUSSION AND CONCLUSIONS

Further assessments of the dose-effect relationship and the differences in the efficacy of emodin or its analogs with other bioactive compounds among various modes of administration are needed, and a drug safety evaluation of these combinations needs to be carefully performed. Future studies should also focus on determining the optimal drug combinations for specific diseases.

The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

Bioinformatics analysis and experimental validation of a novel autophagy-related signature relevant to immune infiltration for recurrence prediction after curative hepatectomy

Hepatocellular carcinoma (HCC) remains imposing an enormous economic and healthcare burden worldwide. In this present study, we constructed and validated a novel autophagy-related gene signature to predict the recurrence of HCC patients. A total of 29 autophagy-related differentially expressed genes were identified. A five-gene signature (CLN3, HGF, TRIM22, SNRPD1, and SNRPE) was constructed for HCC recurrence prediction. Patients in high-risk groups exhibited a significantly poor prognosis compared with low-risk patients both in the training set (GSE14520 dataset) and the validation set (TCGA and GSE76427 dataset). Multivariate cox regression analysis demonstrated that the 5-gene signature was an independent risk factor for recurrence-free survival (RFS) in HCC patients. The nomograms incorporating 5-gene signature and clinical prognostic risk factors were able to effectively predict RFS. KEGG and GSEA analysis revealed that the high-risk group was enriched with multiple oncology characteristics and invasive-related pathways. Besides, the high-risk group had a higher level of immune cells and higher levels of immune checkpoint-related gene expression in the tumor microenvironment, suggesting that they might be more likely to benefit from immunotherapy. Finally, the immunohistochemistry and cell experiments confirmed the role of SNRPE, the most significant gene in the gene signature. SNRPE was significantly overexpressed in HCC. After SNRPE knockdown, the proliferation, migration and invasion ability of the HepG2 cell line were significantly inhibited. Our study established a novel five-gene signature and nomogram to predict RFS of HCC, which may help in clinical decision-making for individual treatment.

Identification of a novel lipid metabolism-related gene signature for predicting colorectal cancer survival

Colorectal cancer (CRC) is a common malignant tumor worldwide. Lipid metabolism is a prerequisite for the growth, proliferation and invasion of cancer cells. However, the lipid metabolism-related gene signature and its underlying molecular mechanisms remain unclear. The aim of this study was to establish a lipid metabolism signature risk model for survival prediction in CRC and to investigate the effect of gene signature on the immune microenvironment. Lipid metabolism-mediated genes (LMGs) were obtained from the Molecular Signatures Database. The consensus molecular subtypes were established using "ConsensusClusterPlus" based on LMGs and the cancer genome atlas (TCGA) data. The risk model was established using univariate and multivariate Cox regression with TCGA database and independently validated in the international cancer genome consortium (ICGC) datasets. Immune infiltration in the risk model was developed using CIBERSORT and xCell analyses. A total of 267 differentially expressed genes (DEGs) were identified between subtype 1 and subtype 2 from consensus molecular subtypes, including 153 upregulated DEGs and 114 downregulated DEGs. 21 DEGs associated with overall survival (OS) were selected using univariate Cox regression analysis. Furthermore, a prognostic risk model was constructed using the risk coefficients and gene expression of eleven-gene signature. Patients with a high-risk score had poorer OS compared with patients in the low-risk score group (p = 3.36e-07) in the TCGA cohort and the validationdatasets (p = 4.03e-05). Analysis of immune infiltration identified multiple T cells were associated with better prognosis in the low-risk group, including Th2 cells (p = 0.0208), regulatory T cells (p = 0.0425), and gammadelta T cells (p = 0.0112). A nomogram integrating the risk model and clinical characteristics was further developed to predict the prognosis of patients with CRC. In conclusion, our study revealed that the expression of lipid-metabolism genes were correlated with the immune microenvironment. The eleven-gene signature might be useful for prediction the prognosis of CRC patients.