CKAP2L, as an Independent Risk Factor, Closely Related to the Prognosis of Glioma

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods

Gliomas are primary malignant brain tumors with poor survival and high resistance to available treatments. Improving the molecular understanding of glioma and disclosing novel biomarkers of tumor development and progression could help to find novel targeted therapies for this type of cancer. Public databases such as The Cancer Genome Atlas (TCGA) provide an invaluable source of molecular information on cancer tissues. Machine learning tools show promise in dealing with the high dimension of omics data and extracting relevant information from it. In this work, network inference and clustering methods, namely Joint Graphical lasso and Robust Sparse K-means Clustering, were applied to RNA-sequencing data from TCGA glioma patients to identify shared and distinct gene networks among different types of glioma (glioblastoma, astrocytoma, and oligodendroglioma) and disclose new patient groups and the relevant genes behind groups' separation. The results obtained suggest that astrocytoma and oligodendroglioma have more similarities compared with glioblastoma, highlighting the molecular differences between glioblastoma and the others glioma subtypes. After a comprehensive literature search on the relevant genes pointed our from our analysis, we identified potential candidates for biomarkers of glioma. Further molecular validation of these genes is encouraged to understand their potential role in diagnosis and in the design of novel therapies.

Identification of Prognostic Factors in Cholangiocarcinoma Based on Integrated ceRNA Network Analysis

This study is aimed at screening prognostic biomarkers in cholangiocarcinoma (CHOL) based on competitive endogenous RNA (ceRNA) regulatory network analysis. Microarray data for lncRNAs, mRNA, and miRNAs were downloaded from the GEO and TCGA databases. Differentially expressed RNAs (DERs) were identified in CHOL and normal liver tissue samples. WGCNA was used to identify disease-related gene modules. By integrating the information from the starBase and DIANA-LncBasev2 databases, we constructed a ceRNA network. Survival analysis was performed, and a prognostic gene-based prognostic score (PS) model was generated. The correlation between gene expression and immune cell infiltration or immune-related feature genes was analyzed using TIMER. Finally, real-time quantitative PCR (RT-qPCR) was used to verify the expression of the 10 DERs with independent prognosis. A large cohort of DERs was identified in the CHOL and control samples. The ceRNA network consisted of 6 lncRNAs, 2 miRNAs, 90 mRNAs, and 98 nodes. Ten genes were identified as prognosis-related genes, and a ten-gene signature PS model was constructed, which exhibited a good prognosis predictive ability for risk assessment of CHOL patients (AUC value = 0.975). Four genes, ELF4, AGXT, ABCG2, and LDHD, were associated with immune cell infiltration and closely correlated with immune-related feature genes (CD14, CD163, CD33, etc.) in CHOL. Additionally, the consistency rate of the RT-qPCR results and bioinformatics analysis was 80%, implying a relatively high reliability of the bioinformatic analysis results. Our findings suggest that the ten-signature gene PS model has significant prognostic predictive value for patients with CHOL. These four immune-related DERs are involved in the progression of CHOL and may be useful prognostic biomarkers for CHOLs.

CKAP2L Promotes Esophageal Squamous Cell Carcinoma Progression and Drug-Resistance by Modulating Cell Cycle

Esophageal squamous cell carcinoma (ESCC) is one of the most common types of cancer and the leading cause of cancer-related mortality worldwide, especially in Asia. In this study, the gene CKAP2L was selected by GEO, TCGA, and GTEx database analysis. The high expression of CKAP2L is related to the occurrence and development of ESCC. In addition, CKAP2L knockdown can inhibit the growth and migration of ESCC cells, while CKAP2L overexpression has the opposite effect. Furthermore, in vivo experiments indicated that down-regulation of CKAP2L can inhibit the tumorigenesis of ESCC cells. KEGG pathway analysis and the STRING database explored the relationship between cell cycle and CKAP2L and verified that depletion of CKAP2L markedly arrested cell cycle in the G2/M phase. Meanwhile, CKAP2L knockdown increased the sensitivity of ESCC cells to flavopiridol, the first CDK inhibitor to be tested in clinical trials, leading to an observable reduction in cell proliferation and an increase in cellular apoptosis. In brief, we identified CKAP2L as a tumor promoter, potential prognostic indicator, and therapeutic target of ESCC, which may play a role in regulating cell cycle progression.

CDC6 is a prognostic biomarker and correlated with immune infiltrates in glioma

BACKGROUND

Cell division cycle 6 (CDC6) has been proven to be associated with the initiation and progression of human multiple tumors. However, it's role in glioma, which is ranked as one of the common primary malignant tumor in the central nervous system and is associated with high morbidity and mortality, is unclear.

METHODS

In this study, we explored CDC6 gene expression level in pan-cancer. Furthermore, we focused on the relationships between CDC6 expression, its prognostic value, potential biological functions, and immune infiltrates in glioma patients. We also performed vitro experiments to assess the effect of CDC6 expression on proliferative, apoptotic, migrant and invasive abilities of glioma cells.

RESULTS

As a result, CDC6 expression was upregulated in multiple types of cancer, including glioma. Moreover, high expression of CDC6 was significantly associated with age, IDH status, 1p/19q codeletion status, WHO grade and histological type in glioma (all p < 0.05). Meanwhile, high CDC6 expression was associated with poor overall survival (OS) in glioma patients, especially in different clinical subgroups. Furthermore, a univariate Cox analysis showed that high CDC6 expression was correlated with poor OS in glioma patients. Functional enrichment analysis indicated that CDC6 was mainly involved in pathways related to DNA transcription and cytokine activity, and Gene Set Enrichment Analysis (GSEA) revealed that MAPK pathway, P53 pathway and NF-κB pathway in cancer were differentially enriched in glioma patients with high CDC6 expression. Single-sample gene set enrichment analysis (ssGSEA) showed CDC6 expression in glioma was positively correlated with Th2 cells, Macrophages and Eosinophils, and negative correlations with plasmacytoid dendritic cells, CD8 T cells and NK CD56bright cells, suggesting its role in regulating tumor immunity. Finally, CCK8 assay, flow cytometry and transwell assays showed that silencing CDC6 could significantly inhibit proliferation, migration, invasion, and promoted apoptosis of U87 cells and U251 cells (p < 0.05).

CONCLUSION

In conclusion, high CDC6 expression may serve as a promising biomarker for prognosis and correlated with immune infiltrates, presenting to be a potential immune therapy target in glioma.