NUSAP1, a novel stemness-related protein, promotes early recurrence of hepatocellular carcinoma

Proteo-genomic characterization of cirrhosis-associated liver cancers reveals potential subtypes and therapeutic targets

BACKGROUND

This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0.

METHODS

Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis.

RESULTS

The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis.

CONCLUSION

These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.

Unveiling NUSAP1 as a common gene signature linking chronic HBV infection and HBV-related HCC

BACKGROUND

Hepatitis B virus (HBV) is a significant contributor to the development of hepatocellular carcinoma (HCC). Chronic HBV infection (CHB) facilitates disease progression through various mechanisms. However, the specific factor responsible for the progression of HBV infection to HCC remains unresolved. This study aims to identify the hub gene linking CHB and HBV-related HCC through bioinformatic analysis and experimental verification.

METHODS

Differentially expressed genes (DEGs) were identified in datasets encompassing CHB and HBV-HCC patients from the GEO database. Enriched pathways were derived from GO and KEGG analysis. Hub genes were screened by protein-protein interaction (PPI) analysis and different modules in Cytoscape software. The significance of the selected hub gene in prognosis was further assessed in validated datasets. The effects of hub genes on cell growth and apoptosis were further determined in functional experiments.

RESULTS

The study revealed upregulation of NUSAP1 in CHBs and HBV-HCCs. High expression of NUSAP1 served as an independent predictor for poor prognosis of liver cancers. Functional experiments demonstrated that NUSAP1 promotes cell growth, influences cell cycle process, and protects cells from apoptosis in HepG2.2.15 cells.

CONCLUSION

NUSAP1 serves as a poor prognostic indicator for liver cancers, and potentially plays a crucial role in HBV-HCC progression by promoting proliferation and inhibiting apoptosis.

Comprehensive analysis of the potential pathogenesis of COVID-19 infection and liver cancer

BACKGROUND

A growing number of clinical examples suggest that coronavirus disease 2019 (COVID-19) appears to have an impact on the treatment of patients with liver cancer compared to the normal population, and the prevalence of COVID-19 is significantly higher in patients with liver cancer. However, this mechanism of action has not been clarified.

AIM

To investigate the disease relevance of COVID-19 in liver cancer.

METHODS

Gene sets for COVID-19 (GSE180226) and liver cancer (GSE87630) were obtained from the Gene Expression Omnibus database. After identifying the common differentially expressed genes (DEGs) of COVID-19 and liver cancer, functional enrichment analysis, protein-protein interaction network construction and screening and analysis of hub genes were performed. Subsequently, the validation of the differential expression of hub genes in the disease was performed and the regulatory network of transcription factors and hub genes was constructed.

RESULTS

Of 518 common DEGs were obtained by screening for functional analysis. Fifteen hub genes including aurora kinase B, cyclin B2, cell division cycle 20, cell division cycle associated 8, nucleolar and spindle associated protein 1, etc., were further identified from DEGs using the "cytoHubba" plugin. Functional enrichment analysis of hub genes showed that these hub genes are associated with P53 signalling pathway regulation, cell cycle and other functions, and they may serve as potential molecular markers for COVID-19 and liver cancer. Finally, we selected 10 of the hub genes for in vitro expression validation in liver cancer cells.

CONCLUSION

Our study reveals a common pathogenesis of liver cancer and COVID-19. These common pathways and key genes may provide new ideas for further mechanistic studies.

Quantitative association between gene expression and blood cell production of individual hematopoietic stem cells in mice

Individual hematopoietic stem cells (HSCs) produce different amounts of blood cells upon transplantation. Taking advantage of the intercellular variation, we developed an experimental and bioinformatic approach to evaluating the quantitative association between gene expression and blood cell production across individual HSCs. We found that most genes associated with blood production exhibit the association only at some levels of blood production. By mapping gene expression with blood production, we identified four distinct patterns of their quantitative association. Some genes consistently correlate with blood production over a range of levels or across all levels, and these genes are found to regulate lymphoid but not myeloid production. Other genes exhibit one or more clear peaks of association. Genes with overlapping peaks are found to be coexpressed in other tissues and share similar molecular functions and regulatory motifs. By dissecting intercellular variations, our findings revealed four quantitative association patterns that reflect distinct dose-response molecular mechanisms modulating the blood cell production of HSCs.

The tumor microenvironment in the postsurgical liver: Mechanisms and potential targets of postoperative recurrence in human hepatocellular carcinoma

Surgery remains to be the mainstay of treatment for hepatocellular carcinoma (HCC). Nonetheless, its therapeutic efficacy is significantly impaired by postoperative recurrence, which occurs in more than half of cases as a result of intrahepatic metastasis or de novo tumorigenesis. For decades, most therapeutic strategies on inhibiting postoperative HCC recurrence have been focused on the residual tumor cells but satisfying therapeutic outcomes are barely observed in the clinic. In recent years, a better understanding of tumor biology allows us to shift our focus from tumor cells toward the postoperative tumor microenvironment (TME), which is gradually identified to play a pivotal role in tumor recurrence. In this review, we describe various surgical stress and surgical perturbation on postoperative TME. Besides, we discuss how such alternations in TME give rise to postoperative recurrence of HCC. Based on its clinical significance, we additionally highlight the potential of the postoperative TME as a target for postoperative adjuvant therapeutics.

Contrast enhanced ultrasound combined with serology predicts hepatocellular carcinoma recurrence: a retrospective observation cohort study

Objectives

To construct a novel model based on contrast-enhanced ultrasound (CEUS) and serological biomarkers to predict the early recurrence (ER) of primary hepatocellular carcinoma within 2 years after hepatectomy.

Methods

A total of 466 patients who underwent CEUS and curative resection between 2016.1.1 and 2019.1.1 were retrospectively recruited from one institution. The training and testing cohorts comprised 326 and 140 patients, respectively. Data on general characteristics, CEUS Liver Imaging Reporting and Data System (LI-RADS) parameters, and serological were collected. Univariate analysis and multivariate Cox proportional hazards regression model were used to evaluate the independent prognostic factors for tumor recurrence, and the Contrast-enhanced Ultrasound Serological (CEUSS) model was constructed. Different models were compared using prediction error and time-dependent area under the receiver operating characteristic curve (AUC). The CEUSS model's performances in ER prediction were assessed.

Results

The baseline data of the training and testing cohorts were equal. LI-RADS category, α-fetoprotein level, tumor maximum diameter, total bilirubin level, starting time, iso-time, and enhancement pattern were independent hazards, and their hazards ratios were 1.417, 1.309, 1.133, 1.036, 0.883, 0.985, and 0.70, respectively. The AUCs of CEUSS, BCLC,TNM, and CNLC were 0.706, 0.641, 0.647, and 0.636, respectively, in the training cohort and 0.680, 0.583, 0.607, and 0.597, respectively, in the testing cohort. The prediction errors of CEUSS, BCLC, TNM, and CNLC were 0.202, 0.205, 0.205, and 0.200, respectively, in the training cohort and 0.204, 0.221, 0.219, and 0.211, respectively, in the testing cohort.

Conclusions

The CEUSS model can accurately and individually predict ER before surgery and may represent a new tool for individualized treatment.