MAPRE1 promotes cell cycle progression of hepatocellular carcinoma cells by interacting with CDK2

Construction of a ceRNA regulatory network to explore potential pathogenesis mechanisms involved in human hepatocellular carcinoma

Worldwide, primary liver cancer is the third leading cause of cancer-related death. Hepatocellular carcinoma (HCC) accounts for the majority of primary liver cancers. Recent studies have shown that circular RNAs (circRNAs) that interact with microRNAs (miRNAs) are involved in the occurrence and development of various tumours. Transcriptional profile analysis was used to analyse expression of circRNAs in HCC in this study. The top ten upregulated circRNAs were selected and validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) in another 34 HCC patients. MiRNAs and mRNAs downstream of these circRNAs were explored through database analysis, and finally, the competitive endogenous RNA (ceRNA) networks were constructed for 5 selected circRNAs. We identified 9658 differentially expressed circRNAs by transcriptional profile analysis. QRT-PCR was performed to validate the top ten upregulated circRNAs, and five circRNAs were selected for further analysis. The miRNAs and mRNAs downstream of these five circRNAs were predicted to construct ceRNA network diagrams. Further analysis revealed five circRNA-miRNA-mRNA axes that correlate negatively with HCC prognosis. Numerous differentially expressed circRNAs exist in HCC, and they can regulate the biological behaviour of HCC through ceRNA networks. Bioinformatics analysis showed that ceRNA regulatory axes involved in HCC have high diagnostic and prognostic value and deserve further exploration.

Function of TRPC1 in modulating hepatocellular carcinoma progression

The liver is the main organ of metabolism in the human body, and it is easy to suffer from hepatitis, cirrhosis, liver cancer, and other diseases, the most serious of which is liver cancer. Worldwide, liver cancer is the most common and deadly malignant tumor, the third leading cause of cancer death in the world. Based on TCGA and ICGC databases, our research discovered the important role of TRPC1 in liver cancer through bioinformatics. The results showed that TRPC1 was over-expressed in hepatocellular carcinoma, and the higher the expression level of TRPC1, the worse the OS and the lower the survival rate. TRPC1 was a risk factor affecting the overall survival probability of hepatocellular carcinoma patients. By analyzing the function of the TRP family in liver cancer, TRPC1 might promote the occurrence of liver cancer by up-regulating common signal pathways in tumors such as tumor proliferation signature, and down-regulating important metabolic reactions such as retinol metabolism. In addition, TRPC1 could promote the development of liver cancer by up-regulating the expression of ABI2, MAPRE1, YEATS2, MTA3, TMEM237, MTMR2, CCDC6, AC069544.2, and NCBP2 genes. These results illustrate that TRPC1 is very valuable in the study of liver cancer.

A review on the role of cyclin dependent kinases in cancers

The Cyclin-dependent kinase (CDK) class of serine/threonine kinases has crucial roles in the regulation of cell cycle transition and is mainly involved in the pathogenesis of cancers. The expression of CDKs is controlled by a complex regulatory network comprised of genetic and epigenetic mechanisms, which are dysregulated during the progression of cancer. The abnormal activation of CDKs results in uncontrolled cancer cell proliferation and the induction of cancer stem cell characteristics. The levels of CDKs can be utilized to predict the prognosis and treatment response of cancer patients, and further understanding of the function and underlying mechanisms of CDKs in human tumors would pave the way for future cancer therapies that effectively target CDKs. Defects in the regulation of cell cycle and mutations in the genes coding cell-cycle regulatory proteins lead to unrestrained proliferation of cells leading to formation of tumors. A number of treatment modalities have been designed to combat dysregulation of cell cycle through affecting expression or activity of CDKs. However, effective application of these methods in the clinical settings requires recognition of the role of CDKs in the progression of each type of cancer, their partners, their interactions with signaling pathways and the effects of suppression of these kinases on malignant features. Thus, we designed this literature search to summarize these findings at cellular level, as well as in vivo and clinical levels.

miR-10a-5p inhibits chicken granulosa cells proliferation and Progesterone(P4) synthesis by targeting MAPRE1 to suppress CDK2

The proliferation and steroid hormone synthesis of granulosa cells (GCs) are essential for ovarian follicle growth and ovulation, which are necessary to support the normal function of the follicle. Numerous studies suggest that miRNAs play key roles in this process. In this study, we report a novel role for miR-10a-5p that inhibits ovarian GCs proliferation and progesterone (P4) synthesis in chicken. Specifically, we found that miR-10a-5p significantly decreased the P4 secretion by quantitative real-time PCR (qRT-PCR), enzyme-linked immunosorbent assay (ELISA), and western blot. Moreover, we observed that miR-10a-5p can inhibit the proliferation of chicken GCs through the investigation of cell proliferation gene expression, cell counting kit 8 (CCK-8), cell cycle progression, and 5-ethynyl-2'-deoxyuridine (EdU) assay. Then we screened a target gene MAPRE1 of miR-10a-5p, which can promote P4 synthesis and proliferation of GCs. To explore how miR-10a-5p affects cell cycle by MAPRE1, we investigated the interaction between MAPRE1 and cyclin-dependent kinase 2 (CDK2) by Co-Immunoprecipitation (Co-IP), and then we found that MAPRE1 can form a complex with CDK2. In addition, miR-10a-5p was found to inhibit CDK2 expression by repressing the expression of MAPRE1. Overall, our results indicate that miR-10a-5p regulates the proliferation and P4 synthesis of chicken GCs by targeting MAPRE1 to suppress CDK2.

Identification of KRBA1 as a Potential Prognostic Biomarker Associated with Immune Infiltration and m6A Modification in Hepatocellular Carcinoma

Purpose

Hepatocellular carcinoma (HCC) is a malignancy with high incidence, but its prognosis is not optimistic. KRBA1 is a member of the KRAB family and participates in the regulation of gene transcription. However, no studies have focused on the role of KRBA1 in HCC.

Patients and Methods

In this study, we first analyzed the expression of KRBA1 in HCC using TCGA and ICGC databases and validated by Immunohistochemistry in clinical HCC samples. The Wilcoxon rank-sum test was used to determine the relationship between KRBA1 expression and clinicopathological features. Subsequently, we used Kaplan-Meier online website analysis and Cox regression model to predict the prognostic value of KRBA1 in HCC patients. Furthermore, the functions of KRBA1 were identified by enrichment analysis. TIMER and GSCALite were used to investigate the relationship between KRBA1 expression in HCC and immune infiltration and drug targets, respectively. Finally, the relationship between KRBA1 expression and m6A modification in HCC was analyzed using the TCGA and ICGA datasets.

Results

The results showed that KRBA1 was upregulated in HCC and was associated with many clinicopathological features. High KRBA1 causes poor overall survival and may be an independent risk factor for HCC. KRBA1 tends to be hypermethylated and associated with poor prognosis in HCC compared with normal tissues. Enrichment analysis indicates that KRBA1 is associated with cell cycle and immune processes, and TIMER analysis shows that KRBA1 expression is associated with infiltration levels and immune characteristics of various immune cells. Silenced KRBA1 evidently reduced three chemokine expression in HCC cells. Drug sensitivity analysis showed that KRBA1 was sensitive to 39 drug small molecules. KRBA1 showed a strong positive correlation with five m6A related genes.

Conclusion

KRBA1 is a prognostic biomarker associated with HCC immunity and m6a modification, serving as an effective target for the diagnosis and treatment of HCC.

Identification of Epithelial Mesenchymal Transition-Related lncRNAs Associated with Prognosis and Tumor Immune Microenvironment of Hepatocellular Carcinoma

Purpose. The long noncoding RNAs (lncRNAs) play the important role in tumor occurrence and progression, and the epithelial to mesenchymal transition (EMT) is the critical process for tumor migration. However, the role of EMT-related lncRNA in hepatocellular carcinoma (HCC) has not been elucidated. Methods. In this study, we selected the EMT-related lncRNAs in HCC by using data from The Cancer Genome Atlas database (TCGA). Two prognostic models of the overall survival (OS) and relapse-free survival (RFS) were constructed and validated through Cox regression model, Kaplan-Meier analysis, and the receiver-operating characteristic (ROC) curves. The unsupervised clustering analysis was utilized to investigate the association between EMT-lncRNAs with tumor immune microenvironment. ESTIMATE algorithm and gene set enrichment analysis (GSEA) were used to estimate tumor microenvironment and associated KEGG pathways. Results. Two EMT-related lncRNA prognostic models of OS and RFS were constructed. Kaplan-Meier curves showed the dismal prognosis of OS and RFS in the group with high-risk score. The ROC curves and AUC values in two prognostic models indicated the discriminative value in the training set and validation set. Patients with HCC were clustered into two subgroups according the unsupervised clustering analysis. Lnc-CCNY-1 was selected as the key lncRNA. GSVA analysis showed that lnc-CCNY-1 was negatively associated with peroxisome proliferator-activated receptor (PPAR) signaling pathway and positively correlated with CELL cycle pathway. Conclusion. Two EMT-related lncRNA prognostic models of OS and RFS were constructed to discriminate patients and predict prognosis of HCC. EMT-related lncRNAs may play a role on prognosis of HCC by influencing the immune microenvironment. Lnc-CCNY-1 was selected as the key EMT-related lncRNA for further exploration.

Circular RNA SOX13 promotes malignant behavior and cisplatin resistance in non-small cell lung cancer through targeting microRNA-3194-3p/microtubule-associated protein RP/EB family member 1

Circular RNA (circRNA) presents an essential regulatory role in affecting the occurrence and acquired resistance in non-small cell lung cancer (NSCLC), but how circSOX13 impacts NSCLC is unclear. In this work it was found that compared with adjacent normal tissues, circSOX13 and the microtubule-associated protein RP/EB family member 1 (MAPRE1) were signally up-regulated in NSCLC while miR-3194-3p was signally lowered. Pulmonary function tests (PETs) revealed that knocking down circSOX13 or overexpressing miR-3194-3p inhibited NSCLC proliferation, invasion and migration but promoted its apoptosis. The promoting effect of overexpressing circSOX13 on NSCLC was reversed via knocking down MAPRE1. Additionally, knocking down circSOX13 reduced cisplatin resistance in NSCLC. Furthermore, circSOX13 mediated MAPRE1 expression via competitively binding miR-3194-3p to exert its tumorigenic impact. To conclude, this work clarified the carcinogenic impact of circSOX13-miR-3194-3p-MAPRE1 axis on NSCLC and DDP resistance. CircSOX13 can be a potential diagnostic marker and therapeutic target for NSCLC, thus providing a new insight for clinically reversing its acquired resistance.

Screening Hub Genes of Hepatocellular Carcinoma Based on Public Databases

Tumor recurrence and metastasis often occur in HCC patients after surgery, and the prognosis is not optimistic. Hence, searching effective biomarkers for prognosis of is of great importance. Firstly, HCC-related data was acquired from the TCGA and GEO databases. Based on GEO data, 256 differentially expressed genes (DEGs) were obtained firstly. Subsequently, to clarify function of DEGs, clusterProfiler package was used to conduct functional enrichment analyses on DEGs. Protein-protein interaction (PPI) network analysis screened 20 key genes. The key genes were filtered via GEPIA database, by which 11 hub genes (F9, CYP3A4, ASPM, AURKA, CDC20, CDCA5, NCAP, PRC1, PTTG1, TOP2A, and KIFC1) were screened out. Then, univariate Cox analysis was applied to construct a prognostic model, followed by a prediction performance validation. With the risk score calculated by the model and common clinical features, univariate and multivariate analyses were carried out to assess whether the prognostic model could be used independently for prognostic prediction. In conclusion, the current study screened HCC prognostic gene signature based on public databases.