Bioinformatics analysis reveals meaningful markers and outcome predictors in HBV-associated hepatocellular carcinoma

The role of Aurora kinase A in hepatocellular carcinoma: Unveiling the intriguing functions of a key but still underexplored factor in liver cancer

Aurora Kinase A (AURKA) plays a central role as a serine/threonine kinase in regulating cell cycle progression and mitotic functions. Over the years, extensive research has revealed the multifaceted roles of AURKA in cancer development and progression. AURKA's dysregulation is frequently observed in various human cancers, including hepatocellular carcinoma (HCC). Its overexpression in HCC has been associated with aggressive phenotypes and poor clinical outcomes. This review comprehensively explores the molecular mechanisms underlying AURKA expression in HCC and its functional implications in cell migration, invasion, epithelial-to-mesenchymal transition, metastasis, stemness, and drug resistance. This work focuses on the clinical significance of AURKA as a diagnostic and prognostic biomarker for HCC. High levels of AURKA expression have been correlated with shorter overall and disease-free survival in various cohorts, highlighting its potential utility as a sensitive prognostic indicator. Recent insights into AURKA's role in modulating the tumour microenvironment, particularly immune cell recruitment, may provide valuable information for personalized treatment strategies. AURKA's critical involvement in modulating cellular pathways and its overexpression in cancer makes it an attractive target for anticancer therapies. This review discusses the evidence about novel and selective AURKA inhibitors for more effective treatments for HCC.

Identification of hub genes for early detection of bone metastasis in breast cancer

BACKGROUND

Globally, among all women, the most frequently detected and diagnosed and the most lethal type of cancer is breast cancer (BC). In particular, bone is one of the most frequent distant metastases 24in breast cancer patients and bone metastasis arises in approximately 80% of advanced patients. Thus, we need to identify and validate early detection markers that can differentiate metastasis from non-metastasis breast cancers.

METHODS

GSE55715, GSE103357, and GSE146661 gene expression profiling data were downloaded from the GEO database. There was 14 breast cancer with bone metastasis samples and 8 breast cancer tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). The volcano plots, Venn diagrams, and annular heatmap were generated by using the ggplot2 package. By using the cluster Profiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, K-M survival and ROC curves were generated to validate hub gene expression.

RESULTS

By GO enrichment analysis, 143 DEGs were enriched in the following GO terms: extracellular structure organization, extracellular matrix organization, leukocyte migration class II protein complex, collagen tridermic protein complex, extracellular matrix structural constituent, growth factor binding, and platelet-derived growth factor binding. In the KEGG pathway enrichment analysis, DEGs were enriched in Staphylococcus aureus infection, Complement and coagulation cascades, and Asthma. By PPI network analysis, we selected the top 10 genes, including SLCO2B1, STAB1, SERPING1, HLA-DOA, AIF1, GIMAP4, C1orf162, HLA-DMB, ADAP2, and HAVCR2. By using TCGA and THPA databases, we validated 2 genes, SERPING1 and GIMAP4, that were related to the early detection of bone metastasis in BC.

CONCLUSIONS

2 abnormally expressed hub genes could play a pivotal role in the breast cancer with bone metastasis by affecting bone homeostasis imbalance in the bone microenvironment.

Identification of 5 Hub Genes Related to the Early Diagnosis, Tumour Stage, and Poor Outcomes of Hepatitis B Virus-Related Hepatocellular Carcinoma by Bioinformatics Analysis

BACKGROUND

The majority of primary liver cancers in adults worldwide are hepatocellular carcinomas (HCCs, or hepatomas). Thus, a deep understanding of the underlying mechanisms for the pathogenesis and carcinogenesis of HCC at the molecular level could facilitate the development of novel early diagnostic and therapeutic treatments to improve the approaches and prognosis for HCC patients. Our study elucidates the underlying molecular mechanisms of HBV-HCC development and progression and identifies important genes related to the early diagnosis, tumour stage, and poor outcomes of HCC.

METHODS

GSE55092 and GSE121248 gene expression profiling data were downloaded from the Gene Expression Omnibus (GEO) database. There were 119 HCC samples and 128 nontumour tissue samples. GEO2R was used to screen for differentially expressed genes (DEGs). Volcano plots and Venn diagrams were drawn by using the ggplot2 package in R. A heat map was generated by using Heatmapper. By using the clusterProfiler R package, KEGG and GO enrichment analyses of DEGs were conducted. Through PPI network construction using the STRING database, key hub genes were identified by cytoHubba. Finally, KM survival curves and ROC curves were generated to validate hub gene expression.

RESULTS

By GO enrichment analysis, 694 DEGs were enriched in the following GO terms: organic acid catabolic process, carboxylic acid catabolic process, carboxylic acid biosynthetic process, collagen-containing extracellular matrix, blood microparticle, condensed chromosome kinetochore, arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. In the KEGG pathway enrichment analysis, DEGs were enriched in arachidonic acid epoxygenase activity, arachidonic acid monooxygenase activity, and monooxygenase activity. By PPI network construction and analysis of hub genes, we selected the top 10 genes, including CDK1, CCNB2, CDC20, BUB1, BUB1B, CCNB1, NDC80, CENPF, MAD2L1, and NUF2. By using TCGA and THPA databases, we found five genes, CDK1, CDC20, CCNB1, CENPF, and MAD2L1, that were related to the early diagnosis, tumour stage, and poor outcomes of HBV-HCC.

CONCLUSIONS

Five abnormally expressed hub genes of HBV-HCC are informative for early diagnosis, tumour stage determination, and poor outcome prediction.

High SGO2 Expression Predicts Poor Overall Survival: A Potential Therapeutic Target for Hepatocellular Carcinoma

Shugoshin2 (SGO2) may participate in the occurrence and development of tumors by regulating abnormal cell cycle division, but its prognostic value in hepatocellular carcinoma (HCC) remains unclear. In this study, we accessed The Cancer Genome Atlas (TCGA) database to get the clinical data and gene expression profile of HCC. The expression of SGO2 in HCC tissues and nontumor tissues and the relationship between SGO2 expression, survival, and clinicopathological parameters were analyzed. The SGO2 expression level was significantly higher in HCC tissues than in nontumor tissues (p < 0.001). An analysis from the Oncomine and Gene Expression Profiling Interactive Analysis 2 (GEPIA2) databases also demonstrated that SGO2 was upregulated in HCC (all p < 0.001). A logistic regression analysis revealed that the high expression of SGO2 was significantly correlated with gender, tumor grade, pathological stage, T classification, and Eastern Cancer Oncology Group (ECOG) score (all p < 0.05). The overall survival (OS) of HCC patients with higher SGO2 expression was significantly poor (p < 0.001). A multivariate analysis showed that age and high expression of SGO2 were independent predictors of poor overall survival (all p < 0.05). Twelve signaling pathways were significantly enriched in samples with the high-SGO2 expression phenotype. Ten proteins and 34 genes were significantly correlated with SGO2. In conclusion, the expression of SGO2 is closely related to the survival of HCC. It may be used as a potential therapeutic target and prognostic marker of HCC.

Identification of key genes in hepatitis B associated hepatocellular carcinoma based on WGCNA

Chronic Infection of Hepatitis B virus (HBV) is one risk factor of hepatocellular carcinoma (HCC). Much effort has been made to research the process of HBV-associated HCC, but its molecular mechanisms of carcinogenesis remain vague. Here, weighted gene co-expression network analysis (WGCNA) was employed to explore the co-expressed modules and hub/key genes correlated to HBV-associated HCC. We found that genes of the most significant module related to HBV-associated HCC were enriched in DNA replication, p53 signaling pathway, cell cycle, and HTLV-1 infection associated pathway; these cellular pathways played critical roles in the initiation and development of HCC or viral infections. Furthermore, seven hub/key genes were identified based on the topological network analysis, and their roles in HCC were verified by expression and Kaplan-Meier survival analysis. Protein-protein interaction and KEGG pathway analysis suggested that these key genes may stimulate cellular proliferation to promote the HCC progression. This study provides new perspectives to the knowledge of the key pathways and genes in the carcinogenesis process of HBV-associated HCC, and our findings provided potential therapeutic targets and clues of the carcinogenesis of HBV-associated HCC.