The long non-coding RNA ASMTL-AS1 promotes hepatocellular carcinoma progression by sponging miR-1343-3p that suppresses LAMC1 (laminin subunit gamma 1)

Circ_0124208 Promotes the Progression of Hepatocellular Carcinoma by Regulating the miR-338-3p/LAMC1 Axis

Hundreds of circular RNAs (circRNAs) have been identified as key regulators in biological processes; however, only few of these circRNAs have been functionally described to participate in the development of hepatocellular carcinoma (HCC). The present study aimed to reveal the function and molecular mechanisms of circ_0124208 in HCC. Real-time quantitative PCR revealed the upregulation of circ_0124208 in HCC tissues and cells. Based on cell functional experiments, silencing circ_0124208 attenuated proliferation and migration, but boosted the apoptosis of Hep 3B and Huh7 cells in vitro. The in vivo experiment further validated the repression of tumor growth via circ_0124208 knockdown. RNA immunoprecipitation and dual-luciferase reporter assays showed that circ_0124208 sponged miR-338-3p and reduced its expression. miR-338-3p inhibition was found to partially reverse the tumor-suppressive effects caused by circ_0124208 in Hep 3B and Huh7 cells. Furthermore, miR-338-3p directly targeted laminin subunit gamma 1 (LAMC1). The malignancy of Hep 3B and Huh7 cell was decreased by LAMC1 knockdown, and this effect was mitigated by miR-338-3p suppression. Overall, circ_0124208 was demonstrated for the first time to play a crucial role as an oncogene in HCC, implying that it could be a useful biomarker for HCC diagnosis. Furthermore, the circ_0124208/miR-338-3p/LAMC1 axis can be used as a potential therapeutic target for HCC treatment.

Upregulation of hsa_circ_0002003 promotes hepatocellular carcinoma progression

BACKGROUND

Circular RNAs (circRNAs), which are involved in various human malignancies, have emerged as promising biomarkers. The present study aimed to investigate unique expression profiles of circRNAs in hepatocellular carcinoma (HCC) and identify novel biomarkers associated with HCC development and progression.

METHODS

CircRNA expression profiles of HCC tissues were jointly analyzed to identify differentially expressed circRNAs. Overexpression plasmid and siRNA targeting candidate circRNAs were used in functional assays in vitro. CircRNA-miRNA interactions were predicted using miRNAs expressed in the miRNA-seq dataset GSE76903. To further screen downstream genes targeted by the miRNAs, survival analysis and qRT-PCR were conducted to evaluate their prognostic role in HCC and construct a ceRNA regulatory network.

RESULTS

Three significantly upregulated circRNAs, hsa_circ_0002003, hsa_circ_0002454, and hsa_circ_0001394, and one significantly downregulated circRNA, hsa_circ_0003239, were identified and validated by qRT-PCR. Our in vitro data indicated that upregulation of hsa_circ_0002003 accelerated cell growth and metastasis. Mechanistically, DTYMK, DAP3, and STMN1, which were targeted by hsa-miR-1343-3p, were significantly downregulated in HCC cells when hsa_circ_0002003 was silenced and were significantly correlated with poor prognosis in patients with HCC.

CONCLUSION

Hsa_circ_0002003 may play critical roles in HCC pathogenesis and serve as a potential prognostic biomarker for HCC. Targeting the hsa_circ_0002003/hsa-miR-1343-3p/STMN1 regulatory axis could be an effective therapeutic strategy in patients with HCC.

Long non-coding RNAs (lncRNAs) in hepatocellular carcinoma progression: Biological functions and new therapeutic targets

Liver is an important organ in body that performs vital functions such as detoxification. Liver is susceptible to development of cancers, and hepatocellular carcinoma (HCC) is among them. 75-85% of liver cancer cases are related to HCC. Therefore, much attention has been directed towards understanding factors mediating HCC progression. LncRNAs are epigenetic factors with more than 200 nucleotides in length located in both nucleus and cytoplasm and they are promising candidates in cancer therapy. Directing studies towards understanding function of lncRNAs in HCC is of importance. LncRNAs regulate cell cycle progression and growth of HCC cells, and they can also induce/inhibit apoptosis in tumor cells. LncRNAs affect invasion and metastasis in HCC mainly by epithelial-mesenchymal transition (EMT) mechanism. Revealing the association between lncRNAs and downstream signaling pathways in HCC is discussed in the current manuscript. Infectious diseases can affect lncRNA expression in mediating HCC development and then, altered expression level of lncRNA is associated with drug resistance and radio-resistance. Biomarker application of lncRNAs and their role in prognosis and diagnosis of HCC are also discussed to pave the way for treatment of HCC patients.

Comprehensive molecular analyses of an autoimmune-related gene predictive model and immune infiltrations using machine learning methods in moyamoya disease

Background: Growing evidence suggests the links between moyamoya disease (MMD) and autoimmune diseases. However, the molecular mechanism from genetic perspective remains unclear. This study aims to clarify the potential roles of autoimmune-related genes (ARGs) in the pathogenesis of MMD. Methods: Two transcription profiles (GSE157628 and GSE141025) of MMD were downloaded from GEO databases. ARGs were obtained from the Gene and Autoimmune Disease Association Database (GAAD) and DisGeNET databases. Differentially expressed ARGs (DEARGs) were identified using "limma" R packages. GO, KEGG, GSVA, and GSEA analyses were conducted to elucidate the underlying molecular function. There machine learning methods (LASSO logistic regression, random forest (RF), support vector machine-recursive feature elimination (SVM-RFE)) were used to screen out important genes. An artificial neural network was applied to construct an autoimmune-related signature predictive model of MMD. The immune characteristics, including immune cell infiltration, immune responses, and HLA gene expression in MMD, were explored using ssGSEA. The miRNA-gene regulatory network and the potential therapeutic drugs for hub genes were predicted. Results: A total of 260 DEARGs were identified in GSE157628 dataset. These genes were involved in immune-related pathways, infectious diseases, and autoimmune diseases. We identified six diagnostic genes by overlapping the three machine learning algorithms: CD38, PTPN11, NOTCH1, TLR7, KAT2B, and ISG15. A predictive neural network model was constructed based on the six genes and presented with great diagnostic ability with area under the curve (AUC) = 1 in the GSE157628 dataset and further validated by GSE141025 dataset. Immune infiltration analysis showed that the abundance of eosinophils, natural killer T (NKT) cells, Th2 cells were significant different between MMD and controls. The expression levels of HLA-A, HLA-B, HLA-C, HLA-DMA, HLA-DRB6, HLA-F, and HLA-G were significantly upregulated in MMD. Four miRNAs (mir-26a-5p, mir-1343-3p, mir-129-2-3p, and mir-124-3p) were identified because of their interaction at least with four hub DEARGs. Conclusion: Machine learning was used to develop a reliable predictive model for the diagnosis of MMD based on ARGs. The uncovered immune infiltration and gene-miRNA and gene-drugs regulatory network may provide new insight into the pathogenesis and treatment of MMD.

LncRNA ASMTL-AS1/microRNA-1270 differentiate prognostic groups in gastric cancer and influence cell proliferation, migration and invasion

The objective of this study was to determine the expression levels of ASMTL-AS1 and miR-1270 in gastric cancer, and to explore whether ASMTL-AS1 and miR-1270 is associated with cancer prognosis and progression or not. ASMTL-AS1 and miR-1270 expression were quantified in gastric cancer tissues and adjacent normal tissues (n = 167) and cell lines. The potential of ASMTL-AS1 and miR-1270 as prognostic biomarkers was evaluated by the receiver operating characteristic (ROC) curve, Kaplan-Meier, and multivariate Cox regression analyses. The binding between ASMTL-AS1 and miR-1270 was verified by the Luciferase reporter assay and RNA pull-down assay. Functional roles of ASMTL-AS1/miR-1270 on cells were investigated in HGC-27 and NCI-N87 cells by MTS viability, Transwell migration, and Matrigel invasion assay. ASMTL-AS1 was significantly downregulated while miR-1270 was upregulated in gastric cancer tissues as compared with normal tissue and cell lines. According to the studies, ASMTL-AS1 and miR-1270 were related to unfavorable clinical parameters, such as the advanced TNM stage. Downregulated ASMTL-AS1 and upregulated miR-1270 were associated with reduced 5-year overall survival. Functional studies suggested that ASMTL-AS1 inhibits proliferation, migration, and invasion of HGC-27 and NCI-N87 cells by regulation of miR-1270. In summary, ASMTL-AS1 and miR-1270 are associated with poor prognosis of patients with gastric cancer. ASMTL-AS1 inhibited gastric cancer progression by regulating miR-1270. Therefore, ASMTL-AS1/miR-1270 may be a potential prognostic biomarker and novel strategy for gastric cancer targeted therapy.