Overexpression of CLEC18B Associates With the Proliferation, Migration, and Prognosis of Glioblastoma

Gene Expression Profiling Regulated by lncRNA H19 Using Bioinformatic Analyses in Glioma Cell Lines

BACKGROUND/AIM

Glioma, the most common type of primary brain tumor, is characterized by high malignancy, recurrence, and mortality. Long non-coding RNA (lncRNA) H19 is a potential biomarker for glioma diagnosis and treatment due to its overexpression in human glioma tissues and its involvement in cell division and metastasis regulation. This study aimed to identify potential therapeutic targets involved in glioma development by analyzing gene expression profiles regulated by H19.

MATERIALS AND METHODS

To elucidate the role of H19 in A172 and U87MG glioma cell lines, cell counting, colony formation, and wound healing assays were conducted. RNA-seq data analysis and bioinformatics analyses were performed to reveal the molecular interactions of H19.

RESULTS

Cell-based experiments showed that elevated H19 levels were related to cancer cell survival, proliferation, and migration. Bioinformatics analyses identified 2,084 differentially expressed genes (DEGs) influenced by H19 which are involved in cancer progression. Specifically, ANXA5, CLEC18B, RAB42, CXCL8, OASL, USP18, and CDCP1 were positively correlated with H19 expression, while CSDC2 and FOXO4 were negatively correlated. These DEGs were predicted to function as oncogenes or tumor suppressors in gliomas, in association with H19.

CONCLUSION

These findings highlight H19 and its associated genes as potential diagnostic and therapeutic targets for gliomas, emphasizing their clinical significance in patient survival.

CLEC19A overexpression inhibits tumor cell proliferation/migration and promotes apoptosis concomitant suppression of PI3K/AKT/NF-κB signaling pathway in glioblastoma multiforme

BACKGROUND

GBM is the most frequent malignant primary brain tumor in humans. The CLEC19A is a member of the C-type lectin family, which has a high expression in brain tissue. Herein, we sought to carry out an in-depth analysis to pinpoint the role of CLEC19A expression in GBM.

METHODS

To determine the localization of CLEC19A, this protein was detected using Western blot, Immunocytochemistry/Immunofluorescence, and confocal microscopy imaging. CLEC19A expression in glioma cells and tissues was evaluated by qRT-PCR. Cell viability, proliferation, migration, and apoptosis were examined through MTT assay, CFSE assay, colony formation, wound healing assay, transwell test, and flow cytometry respectively after CLEC19A overexpression. The effect of CLEC19A overexpression on the PI3K/AKT/NF-κB signaling pathway was investigated using Western blot. An in vivo experiment substantiated the in vitro results using the glioblastoma rat models.

RESULTS

Our in-silico analysis using TCGA data and measuring CLEC19A expression level by qRT-PCR determined significantly lower expression of CLEC19A in human glioma tissues compared to healthy brain tissues. By employment of ICC/IF, confocal microscopy imaging, and Western blot we could show that CLEC19A is plausibly a secreted protein. Results obtained from several in vitro readouts showed that CLEC19A overexpression in U87 and C6 glioma cell lines is associated with the inhibition of cell proliferation, viability, and migration. Further, qRT-PCR and Western blot analysis showed CLEC19A overexpression could reduce the expression levels of PI3K, VEGFα, MMP2, and NF-κB and increase PTEN, TIMP3, RECK, and PDCD4 expression levels in glioma cell lines. Furthermore, flow cytometry results revealed that CLEC19A overexpression was associated with significant cell cycle arrest and promotion of apoptosis in glioma cell lines. Interestingly, using a glioma rat model we could substantiate that CLEC19A overexpression suppresses glioma tumor growth.

CONCLUSIONS

To our knowledge, this is the first report providing in-silico, molecular, cellular, and in vivo evidences on the role of CLEC19A as a putative tumor suppressor gene in GBM. These results enhance our understanding of the role of CLEC19A in glioma and warrant further exploration of CLEC19A as a potential therapeutic target for GBM.

Identification of m6A-Related lncRNA to Predict the Prognosis of Patients with Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death worldwide. In the past decades, HCC treatment has achieved great progress; however, the overall prognosis remains poor. Therefore, it is the need of the hour to identify new prognostic biomarkers which can advance our understanding related to the underlying molecular mechanism of adverse prognosis and apply them to clinical work in prognosis prediction. In the present study, data of 576 HCC patients and 292 normal control cases from TCGA and ICGC databases were enrolled to our bioinformatic analysis. SNHG1 and SNHG3 were identified as overlapping genes in TCGA and ICGC databases using Pearson correlation analysis and univariate Cox regression analysis. Further, we used the median of the SNHG1 and SNHG3 expression values as the cutoff values to define the HCC patient groups with high or low expression level. The subsequent analysis revealed that abnormal high expression of SNHG1 or SNHG3 affected the immune infiltration patterns and the crosstalk among immune cells. Moreover, high expression of SNHG1 or SNHG3 resulted in drug resistant to AKT inhibitor VII, bexarotene, bicalutamide, dasatinib, erlotinib, and gefitinib. In addition, lower tumor neoantigen burden was observed in high SNHG1 or SNHG3 group. Further, we found significant relation between the aberrant upregulation of SNHG1 and SNHG3 in tumor grade and stage. We established a nomogram to systematically predict the 5- and 8-year overall survival of liver cancer patients with good accuracy. Finally, the in vitro assays suggest that SNHG1 and SNHG3 promote the proliferative, migratory, and invasive abilities of HCC cells.

WBSCR22 confers cell survival and predicts poor prognosis in glioma

Human WBSCR22 is involved in cancer proliferation, invasion and metastasis; however, its function in glioma remains unexplored. In our research, we aimed to investigate the role of WBSCR22 in the development of glioma and its possible molecular mechanisms. Using bioinformatic analysis of public datasets, we determined that WBSCR22 overexpression in glioma specimens was correlated with an unfavorable patient prognosis. Our results revealed that WBSCR22 was highly expressed in glioma cell lines. The loss of WBSCR22 inhibited the growth, invasion and migration of glioma cells, while WBSCR22 overexpression produced the opposite effects. Moreover, we found that WBSCR22 downregulation reduced the phosphorylation of Akt and GSK3β and decreased the levels of β-catenin and CyclinD1 in glioma cells. The opposite effects were observed when WBSCR22 was overexpressed. Additionally, we verified with a dual-luciferase reporter assay that WBSCR22 was a direct target of miR-146b-5p. Furthermore, overexpression of miR-146b-5p suppressed WBSCR22 mRNA and protein expression. Notably, the restoration of WBSCR22 expression remarkably reversed the effects of miR-146b-5p overexpression on cell survival, apoptosis and the cell cycle in glioma cells. Collectively, our findings revealed a tumor-promoting role for WBSCR22 in glioma cells, thus providing molecular evidence for WBSCR22 as a novel therapeutic target in glioma.

High expression levels of CLEC4M indicate poor prognosis in patients with hepatocellular carcinoma

The identification of novel and accurate biomarkers is important to improve the prognosis of patients with hepatocellular carcinoma (HCC). C-Type lectin domain family 4 member M (CLEC4M) is involved in the progression of numerous cancer types. However, the clinical significance of CLEC4M in HCC is yet to be elucidated. The aim of the present study is to evaluate the involvement of CLEC4M in HCC progression. The expression level of CLEC4M was determined in tumor, and their corresponding adjacent non-tumor tissues derived from 88 patients with HCC, using immunohistochemistry, western blot and reverse transcription-quantitative PCR. The correlation between CLEC4M expression and certain clinicopathological characteristics was retrospectively analyzed. The results suggested that CLEC4M was specifically labeled in sinusoidal endothelial cells, in both HCC and non-tumor tissues. Moreover, the expression of CLEC4M in tumor tissues was significantly lower than that in non-tumor tissues (P<0.0001), which indicated its potential as a biomarker of the development of HCC. Subsequently, correlation analysis suggested that the relatively higher CLEC4M expression in HCC tissues was significantly associated with increased microvascular invasion (P=0.008), larger tumor size (P=0.018), absence of tumor encapsulation (P<0.0001) and lower tumor differentiation (P=0.019). Notably, patients with high CLEC4M expression levels in their tumor tissues experienced more frequent recurrence and shorter overall survival (OS) times compared with the low-expression group. Furthermore, CLEC4M expression in tumor tissues was identified as an independent and significant risk factor for recurrence-free survival and OS. The results of the present study suggest that CLEC4M may be a valuable biomarker for the prognosis of the patients with HCC, postoperatively.

Identification of seven-gene signature for prediction of lung squamous cell carcinoma

Background and aim: Lung squamous cell carcinoma (LUSC), is a pathological subtype of lung cancer, accounting for 30% of the lung cancers. A reliable model was constructed, based on the whole gene expression profiles, to predict the prognosis of patients with LUSC. Methods: The RNA-Seq data of LUSC was downloaded from the TCGA database, and differentially expressed genes (p<0.05, |log2fold change| >1) were screened out. By univariate and multivariate Cox regression analysis, we identified seven prognosis-related genes. Then, we established a risk score staging system to predict the prognosis of patients with LUSC. Compared with other clinical parameters, the risk score was an independent prognostic factor and had a better performance in predicting prognosis. Finally, GSEA analysis was carried out to determine the enrichment pathway significantly. The risk score models were established by Cox proportional hazard regression analysis; the ROC curve was applied to test the performance of risk score model. All the statistical analysis was accomplished by R packages. Results: In this study, a model was constructed to predict prognosis, which contains seven genes: CSRNP1, CLEC18B, MIR27A, AC130456.4, DEFA6, ARL14EPL, and ZFP42. Based on the model, the risk score of each patient was calculated with LUSC (hazard ratio [HR]=2.673, 95% CI=1.871-3.525). It was found that the risk score can distinguish high-risk and low-risk groups in prognosis of LUSC patients, independently. Furthermore, the model was validated by ROC curves in the testing dataset and the whole dataset. Lastly, by gene set enrichment analysis (GSEA), we showed the main enrichment pathways were DNA damage stimulus, DNA repair, and DNA replication. It was suggested that the risk score may provide a new and reliable method for prognosis prediction. Conclusion: The results of this study suggested that the risk score based on seven-genes could indicate a promising and independent prognostic biomarker for LUSC patients.