Novel Gene Signatures as Prognostic Biomarkers for Predicting the Recurrence of Hepatocellular Carcinoma

Emerging Prognostic Markers in Patients Undergoing Liver Resection for Hepatocellular Carcinoma: A Narrative Review

In patients with hepatocellular carcinoma (HCC), liver resection is potentially curative. Nevertheless, post-operative recurrence is common, occurring in up to 70% of patients. Factors traditionally recognized to predict recurrence and survival after liver resection for HCC include pathologic factors (i.e., microvascular and capsular invasion) and an increase in alpha-fetoprotein level. During the past decade, many new markers have been reported to correlate with prognosis after resection of HCC: liquid biopsy markers, gene signatures, inflammation markers, and other biomarkers, including PIVKA-II, immune checkpoint molecules, and proteins in urinary exosomes. However, not all of these new markers are readily available in clinical practice, and their reproducibility is unclear. Liquid biopsy is a powerful and established tool for predicting long-term outcomes after resection of HCC; the main limitation of liquid biopsy is represented by the cost related to its technical implementation. Numerous patterns of genetic expression capable of predicting survival after curative-intent hepatectomy for HCC have been identified, but published findings regarding these markers are heterogenous. Inflammation markers in the form of prognostic nutritional index and different blood cell ratios seem more easily reproducible and more affordable on a large scale than other emerging markers. To select the most effective treatment for patients with HCC, it is crucial that the scientific community validate new predictive markers for recurrence and survival after resection that are reliable and widely reproducible. More reports from Western countries are necessary to corroborate the evidence.

From prediction to prevention: Machine learning revolutionizes hepatocellular carcinoma recurrence monitoring

In this editorial, we comment on the article by Zhang et al entitled Development of a machine learning-based model for predicting the risk of early postoperative recurrence of hepatocellular carcinoma. Hepatocellular carcinoma (HCC), which is characterized by high incidence and mortality rates, remains a major global health challenge primarily due to the critical issue of postoperative recurrence. Early recurrence, defined as recurrence that occurs within 2 years posttreatment, is linked to the hidden spread of the primary tumor and significantly impacts patient survival. Traditional predictive factors, including both patient- and treatment-related factors, have limited predictive ability with respect to HCC recurrence. The integration of machine learning algorithms is fueled by the exponential growth of computational power and has revolutionized HCC research. The study by Zhang et al demonstrated the use of a groundbreaking preoperative prediction model for early postoperative HCC recurrence. Chall-enges persist, including sample size constraints, issues with handling data, and the need for further validation and interpretability. This study emphasizes the need for collaborative efforts, multicenter studies and comparative analyses to validate and refine the model. Overcoming these challenges and exploring innovative approaches, such as multi-omics integration, will enhance personalized oncology care. This study marks a significant stride toward precise, effi-cient, and personalized oncology practices, thus offering hope for improved patient outcomes in the field of HCC treatment.

Identification of differentially methylated regions associated with both liver fibrosis and hepatocellular carcinoma

BACKGROUND

Liver fibrosis is a major risk factor for hepatocellular carcinoma (HCC). We have previously reported that differentially methylated regions (DMRs) are correlated with the fibrosis stages of metabolic dysfunction-associated steatotic liver disease (MASLD). In this study, the methylation levels of those DMRs in liver fibrosis and subsequent HCC were examined.

METHODS

The methylation levels of DMRs were investigated using alcoholic cirrhosis and HCC (GSE60753). The data of hepatitis C virus-infected cirrhosis and HCC (GSE60753), and two datasets (GSE56588 and GSE89852) were used for replication analyses. The transcriptional analyses were performed using GSE114564, GSE94660, and GSE142530.

RESULTS

Hypomethylated DMR and increased transcriptional level of zinc finger and BTB domain containing 38 (ZBTB38) were observed in HCC. Hypermethylated DMRs, and increased transcriptional levels of forkhead box K1 (FOXK1) and zinc finger CCCH-type containing 3 (ZC3H3) were observed in HCC. The methylation levels of DMR of kazrin, periplakin interacting protein (KAZN) and its expression levels were gradually decreased as cirrhosis progressed to HCC.

CONCLUSIONS

Changes in the methylation and transcriptional levels of ZBTB38, ZC3H3, FOXK1, and KAZN are important for the development of fibrosis and HCC; and are therefore potential therapeutic targets and diagnostic tools for cirrhosis and HCC.

Gene expression signatures of stepwise progression of Hepatocellular Carcinoma

The molecular pathogenesis of Hepatocellular Carcinoma (HCC) is a complex process progressing from premalignant stages to cancer in a stepwise manner. Mostly, HCC is detected at advanced stages, leading to high mortality rates. Hence, characterising the molecular underpinnings of HCC from normal to cancer state through precancerous state may help in early detection and improve its prognosis and treatment. In this work, we analysed the transcriptomic profile of tumour and premalignant samples from HCC or chronic liver disease patients, who had undergone either total or partial hepatectomy. The normal samples from patients with metastatic cancer/polycystic liver disease/ cholangiocarcinoma were also included. A gene co-expression network approach was applied to identify hierarchical changes: modules, pathways, and genes related to different trajectories of HCC and patient survival. Our analysis shows that the progression from premalignant conditions to tumour is accompanied by differences in the downregulation of genes associated with HNF4A activity and the immune system and upregulation of cell cycle genes, bringing about variability in patient outcomes. However, an increase in immune and cell cycle activity is observed in premalignant samples. Interestingly, co-expression modules and genes from premalignant stages are associated with survival. THBD, a classical marker for dendritic cells, is a predictor of survival at the premalignant stage. Further, genes linked to microtubules, kinetochores, and centromere are altered in both premalignant and tumour conditions and are associated with survival. Our analysis revealed a three-way molecular axis of liver function, immune pathways, and cell cycle driving HCC pathogenesis.

Inflammation-related prognostic markers in resected hepatocellular carcinoma

Hepatocellular carcinoma is usually detected late and therapeutic options are unsatisfactory. Despite marked progress in patient care, HCC remains among the deadliest cancers world-wide. While surgical resection remains a key option for early-stage HCC, the 5-year survival rates after surgical resection are limited. One reason for limited outcomes is the lack of reliable prognostic biomarkers to predict HCC recurrence. HCC prognosis has been shown to correlate with different systemic and pathological markers which are associated with patient survival and HCC recurrence. Liver inflammatory processes offer a large variety of systemic and pathological markers which may be exploited to improve the reliability of prognosis and decision making of liver surgeons and hepatologists. The following review aims to dissect the potential tools, targets and prognostic meaning of inflammatory markers in patients with resectable HCC. We analyze changes in circulant cellular populations and assess inflammatory biomarkers as a surrogate of impaired outcomes and provide an overview on predictive gene expression signatures including inflammatory transcriptional patterns, which are representative of poor survival in these patients.

An immune and epigenetics-related scoring model and drug candidate prediction for hepatic carcinogenesis via dynamic network biomarker analysis and connectivity mapping

Hepatocellular carcinoma (HCC) is a malignant tumor with high mortality. This study aimed to build a prognostic signature for HCC patients based on immune-related genes (IRGs) and epigenetics-related genes (EPGs). RNA-seq data from Gene Expression Omnibus were used for dynamic network biomarker (DNB) analysis to identify 56 candidate IRG-EPG-DNBs and their first-neighbor genes. These genes were screened using LASSO-Cox regression analysis to finally obtain five candidate genes-RNF2, YBX1, EZH2, CAD, and PSMD1-which constituted the prognostic signature panel. According to this panel, patients in The Cancer Genome Atlas and International Cancer Genome Consortium were divided into high- and low-risk groups. The prognosis, clinicopathological features, and immune cell infiltration significantly differed between the two risk groups. The prognostic ability of the signature panel and expression profiling were further validated using online databases. We used an independent cohort of patients to validate the expression profiles of the five genes using reverse transcription-PCR. CMap and CellMiner predicted four small molecule drug-protein pairs based on the five prognostic genes. Of them, two market drugs approved by the Food and Drug Administration (AT-13387 and KU-55933) have emerged as candidates for HCC study. This new signature panel may serve as a potential prognostic marker, engendering the possibility of novel personalized therapy with classification of HCC patients.

Tumor Endothelial Cells-Associated Integrin Alpha-6 as a Promising Biomarker for Early Detection and Prognosis of Hepatocellular Carcinoma

HCC remains a lethal cancer type, with early detection being critical for improved patient outcomes. This study introduces a comprehensive methodological approach to identify the ITGA6 gene as a potential blood marker for early HCC (eHCC) detection. We initially analyzed the GSE114564 dataset encompassing various stages of liver disease, identifying 972 differentially expressed genes in HCC. A refined analysis yielded 59 genes specifically differentially expressed in early HCC, including ITGA6. Subsequent validation in multiple datasets confirmed the consistent upregulation of ITGA6 in HCC. In addition, when analyzing progression-free survival (PFS) within the entire patient cohort and overall survival (OS) specifically among patients classified as tumor grade G1, the group of patients characterized by high expression levels of ITGA6 displayed an elevated risk ratio in relation to prognosis. Further analyses demonstrated the predominant expression of ITGA6 in TECs and its enrichment in angiogenesis-related pathways. Additionally, positive correlations were found between ITGA6 expression and pro-tumorigenic immune cells, but not with anti-tumorigenic immune cells. Our study elucidates the potential of ITGA6 as a blood-based marker for HCC early detection and diagnosis and its complex interplay with the tumor microenvironment. Further research may lead to novel strategies for HCC management and patient care.

Identification of key genes related to immune infiltration in cirrhosis via bioinformatics analysis

Cirrhosis is the most common subclass of liver disease worldwide and correlated to immune infiltration. However, the immune-related molecular mechanism underlying cirrhosis remains obscure. Two gene expression profiles GSE89377 and GSE139602 were investigated to identify differentially expressed genes (DEGs) related to cirrhosis. Enrichment analysis for DEGs was conducted. Next, the immune infiltration of DEGs was evaluated using CIBERSORT algorithm. The hub DEGs with tight connectivity were identified using the String and Cytoscape databases, and the expression difference of these hub genes between normal liver and cirrhosis samples was determined. Moreover, in order to evaluate the discriminatory ability of hub genes and obtained the area under the receiver operating characteristic curve values in the GSE89377 and GSE139602 datasets. Finally, the association between hub DEGs and immune cell infiltration was explored by Spearman method. Among the 299 DEGs attained, 136 were up-regulated and 163 were down-regulated. Then the enrichment function analysis of DEGs and CIBERSORT algorithm showed significant enrichment in immune and inflammatory responses. And four hub DEGs (ACTB, TAGLN, VIM, SOX9) were identified, which also showed a diagnostic value in the GSE89377 and GSE 139,602 datasets. Finally, the immune infiltration analysis indicated that, these hub DEGs were highly related to immune cells. This study revealed key DEGs involved in inflammatory immune responses of cirrhosis, which could be used as biomarkers for diagnosis or therapeutic targets of cirrhosis.

A novel transcription factor-based signature to predict prognosis and therapeutic response of hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common aggressive malignancies with increasing incidence worldwide. The oncogenic roles of transcription factors (TFs) were increasingly recognized in various cancers. This study aimed to develop a predicting signature based on TFs for the prognosis and treatment of HCC. Methods: Differentially expressed TFs were screened from data in the TCGA-LIHC and ICGC-LIRI-JP cohorts. Univariate and multivariate Cox regression analyses were applied to establish a TF-based prognostic signature. The receiver operating characteristic (ROC) curve was used to assess the predictive efficacy of the signature. Subsequently, correlations of the risk model with clinical features and treatment response in HCC were also analyzed. The TF target genes underwent Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, followed by protein-protein-interaction (PPI) analysis. Results: A total of 25 differentially expressed TFs were screened, 16 of which were related to the prognosis of HCC in the TCGA-LIHC cohort. A 2-TF risk signature, comprising high mobility group AT-hook protein 1 (HMGA1) and MAF BZIP transcription factor G (MAFG), was constructed and validated to negatively related to the overall survival (OS) of HCC. The ROC curve showed good predictive efficiencies of the risk score regarding 1-year, 2-year and 3-year OS (mostly AUC >0.60). Additionally, the risk score independently predicted OS for HCC patients both in the training cohort of TCGA-LIHC dataset (HR = 2.498, p = 0.007) and in the testing cohort of ICGC-LIRI-JP dataset (HR = 5.411, p < 0.001). The risk score was also positively correlated to progressive characteristics regarding tumor grade, TNM stage and tumor invasion. Patients with a high-risk score were more resistant to transarterial chemoembolization (TACE) treatment and agents of lapatinib and erlotinib, but sensitive to chemotherapeutics. Further enrichment and PPI analyses demonstrated that the 2-TF signature distinguished tumors into 2 clusters with proliferative and metabolic features, with the hub genes belonging to the former cluster. Conclusion: Our study identified a 2-TF prognostic signature that indicated tumor heterogeneity with different clinical features and treatment preference, which help optimal therapeutic strategy and improved survival for HCC patients.