A Novel Gene Signature Based on CDC20 and FCN3 for Prediction of Prognosis and Immune Features in Patients with Hepatocellular Carcinoma

NamiRNA-mediated high expression of KNSTRN correlates with poor prognosis and immune infiltration in hepatocellular carcinoma

Introduction

Mutations of kinetochore-localized astrin/sperm-associated antigen 5 (KNSTRN) can interfere with chromatid cohesion, increase aneuploidy in tumours, and enhance tumourigenesis. However, the role of the KNSTRN-binding protein in hepatocellular carcinoma (HCC) remains unclear.

Material and methods

Using The Cancer Genome Atlas databases, we investigated the potential oncogenic functions of KNSTRN in HCC along with R and various computational tools.

Results

Detailed results revealed that elevated expression of KNSTRN was considerably associated with poor overall survival (HR = 1.48, 95% CI: 1.05-2.09, p = 0.027) and progress-free interval (HR = 1.41, 95% CI: 1.05-1.89, p = 0.021) in HCC. Gene ontology/Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis showed that KNSTRN is closely related to organelle fission, chromosomal region, tubulin binding, and cell cycle signalling pathway. TIMER database analysis showed the correlations between KNSTRN expression and tumour-infiltrating immune cells, biomarkers of immune cells, and immune checkpoint expression. Moreover, the KNSTRN level was significantly positively associated with immunosuppressive cells in the tumour microenvironment, including regulatory T-cells, myeloid-derived suppressor cells, and cancer-associated fibrocytes. Finally, a possible nuclear activating miRNA (NamiRNA)-enhancer network of hsa-miR-107, which activates the KNSTRN expression in liver hepatocellular carcinoma, was constructed by correlation analysis.

Conclusions

NamiRNA-mediated upregulation of KNSTRN correlated with poor prognosis and tumour immune infiltration in HCC. KNSTRN could serve as an effective biomarker for the diagnosis and prognosis of HCC and support the development of novel therapeutic strategies.

Comprehensive Profiling and Therapeutic Insights into Differentially Expressed Genes in Hepatocellular Carcinoma

Background: Drug repurposing is a strategy that complements the conventional approach of developing new drugs. Hepatocellular carcinoma (HCC) is a highly prevalent type of liver cancer, necessitating an in-depth understanding of the underlying molecular alterations for improved treatment. Methods: We searched for a vast array of microarray experiments in addition to RNA-seq data. Through rigorous filtering processes, we have identified highly representative differentially expressed genes (DEGs) between tumor and non-tumor liver tissues and identified a distinct class of possible new candidate drugs. Results: Functional enrichment analysis revealed distinct biological processes associated with metal ions, including zinc, cadmium, and copper, potentially implicating chronic metal ion exposure in tumorigenesis. Conversely, up-regulated genes are associated with mitotic events and kinase activities, aligning with the relevance of kinases in HCC. To unravel the regulatory networks governing these DEGs, we employed topological analysis methods, identifying 25 hub genes and their regulatory transcription factors. In the pursuit of potential therapeutic options, we explored drug repurposing strategies based on computational approaches, analyzing their potential to reverse the expression patterns of key genes, including AURKA, CCNB1, CDK1, RRM2, and TOP2A. Potential therapeutic chemicals are alvocidib, AT-7519, kenpaullone, PHA-793887, JNJ-7706621, danusertibe, doxorubicin and analogues, mitoxantrone, podofilox, teniposide, and amonafide. Conclusion: This multi-omic study offers a comprehensive view of DEGs in HCC, shedding light on potential therapeutic targets and drug repurposing opportunities.

The potential role of CDC20 in tumorigenesis, cancer progression and therapy: A narrative review

The cell division cycle 20 homologue (CDC20) is known to regulate the cell cycle. Many studies have suggested that dysregulation of CDC20 is associated with various pathological processes in malignant solid tumors, including tumorigenesis, progression, chemoradiotherapy resistance, and poor prognosis, providing a biomarker for cancer diagnosis and prognosis. Some researchers have demonstrated that CDC20 also regulates apoptosis, immune microenvironment, and tumor angiogenesis. In this review, we have systematically summarized the biological functions of CDC20 in solid cancers. Furthermore, we briefly synthesized multiple medicines that inhibited CDC20. We anticipate that CDC20 will be a promising and effective biomarker and therapeutic target for the treatment of human cancer.

Identification of a distinct tumor endothelial cell-related gene expression signature associated with patient prognosis and immunotherapy response in multiple cancers

BACKGROUND

Tumor endothelial cells (TECs) play a significant role in regulating the tumor microenvironment, drug response, and immune cell activities in various cancers. However, the association between TEC gene expression signature and patient prognosis or therapeutic response remains poorly understood.

METHODS

We analyzed transcriptomics data of normal and tumor endothelial cells obtained from the GEO database to identify differentially expressed genes (DEGs) associated with TECs. We then compared these DEGs with those commonly found across five different tumor types from the TCGA database to determine their prognostic relevance. Using these genes, we constructed a prognostic risk model integrated with clinical features to develop a nomogram model, which we validated through biological experiments.

RESULTS

We identified 12 TEC-related prognostic genes across multiple tumor types, of which five genes were sufficient to construct a prognostic risk model with an AUC of 0.682. The risk scores effectively predicted patient prognosis and immunotherapeutic response. Our newly developed nomogram model provided more accurate prognostic estimates of cancer patients than the TNM staging method (AUC = 0.735) and was validated using external patient cohorts. Finally, RT-PCR and immunohistochemical analyses indicated that the expression of these 5 TEC-related prognostic genes was up-regulated in both patient-derived tumors and cancer cell lines, while depletion of the hub genes reduced cancer cell growth, migration and invasion, and enhanced their sensitivity to gemcitabine or cytarabine.

CONCLUSIONS

Our study discovered the first TEC-related gene expression signature that can be used to construct a prognostic risk model for guiding treatment options in multiple cancers.

Loss of ficolin-3 expression is associated with poor prognosis in patients with hepatocellular carcinoma

Background: Ficolin-3 (FCN3) is a well-known circulating pattern recognition molecule which plays a role in host immune responses to cancer via activation of the lectin complement pathway. Nevertheless, the clinical significance of FCN3 in patients with hepatocellular carcinoma (HCC) is unclear. Methods: Eighty-seven HCC patients who received hepatectomy at our hospital were included. Immunohistochemical staining was used to assess the FCN3 expression in both tumorous and non-tumorous tissues from the patients, who were classified into high and low expression groups. Differences in clinicopathological characteristics between the two groups were then analyzed. Results: Survival was significantly associated with FCN3 immunohistochemical score (p for trend = 0.048). Kaplan-Meier analysis revealed a higher overall survival rate in the patients with a high FCN3 expression than in those with a low FCN3 expression (p=0.031). A high FCN3 expression in tumor tissue was independently associated with better overall survival (p=0.042). However, multivariate analysis showed that FCN3 expression was not an independent risk factor for overall survival. Conclusion: Our findings suggest that FCN3 is significantly related to the prognosis of HCC. FCN3 may be a prognostic marker in patients with HCC.

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.