Identification of a Novel Survival-Related circRNA–miRNA–mRNA Regulatory Network Related to Immune Infiltration in Liver Hepatocellular Carcinoma

Abstract

Increasing studies have reported that circular RNAs (circRNAs) play critical roles in tumorigenesis and cancer progression. However, the underlying regulatory mechanisms of circRNA-related competing endogenous RNA (ceRNA) in liver hepatocellular carcinoma (LIHC) are still unclear. In the present study, we discovered dysregulated circRNAs through Gene Expression Omnibus (GEO) analysis and validated the expression of the top seven circRNAs with upregulated expression by qRT–PCR and Sanger sequencing. Then, the Cancer-Specific CircRNA Database (CSCD) was used to predict the downstream miRNAs of seven circRNAs, and expression and survival analyses through The Cancer Genome Atlas (TCGA) were performed to identify the key miRNA in LIHC. Thereafter, the hsa_circ_0017264-hsa-miR-195–5p subnetwork was successfully constructed. Subsequently, we predicted downstream target genes of hsa-miR-195–5p with TargetScan, miRDB, and mirtarbase and overlapped them with differentially expressed mRNAs to obtain 21 target genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to predict the biological and functional roles of these target genes. Finally, with Pearson correlation and prognostic value analysis, a survival-related hsa_circ_0017264-hsa-miR-195-5p-CHEK1/CDC25A/FOXK1 axis was established. Gene set enrichment analysis (GSEA) was performed to determine the function of CHEK1/CDC25A/FOXK1 in the ceRNA network. Moreover, immune infiltration analysis revealed that the ceRNA network was markedly associated with the levels of multiple immune cell infiltrates, immune cell biomarkers and immune checkpoints. Overall, the hsa_circ_0017264-hsa-miR-195-5p-CHEK1/CDC25A/FOXK1 network might provide novel insights into the potential mechanisms underlying LIHC onset and progression.

Machine learning to establish three sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with gastric cancer

“…All of the RNA was extracted from cells and tissues using TRIzol reagents (Ambion, Carlsbad, CA, USA) based on instructions provided by the kit manual. Total RNA was used as a template and reverse transcribed to cDNA with a GoScript Reverse Transcription (RT) System (Promega, Madison, WI, USA) according to the manufacturer's instructions [19]. Then, qRT-PCR detection was performed via GoTaq qPCR Master Mix (Promega) whose conditions were as follows: 95°C for 5 min, followed by 40 cycles of 94°C for 15 s, 52°C for 30 s, and 72°C for 30 s. GAPDH mRNA was chosen to normalize and the primer sequences were as follows: GLA: forward…”