Competing Endogenous RNA (ceRNA) Network Analysis of Autophagy-Related Genes in Hepatocellular Carcinoma

The development of a novel signature based on the m6A RNA methylation regulator-related ceRNA network to predict prognosis and therapy response in sarcomas

Background: N⁶ methyladenosine (m⁶A)-related noncoding RNAs (including lncRNAs and miRNAs) are closely related to the development of cancer. However, the gene signature and prognostic value of m⁶A regulators and m⁶A-associated RNAs in regulating sarcoma (SARC) development and progression remain largely unexplored. Therefore, further research is required.

Methods: We obtained expression data for RNA sequencing (RNA-seq) and miRNAs of SARC from The Cancer Genome Atlas (TCGA) datasets. Correlation analysis and two target gene prediction databases (miRTarBase and LncBase v.2) were used to deduce m⁶A-related miRNAs and lncRNAs, and Cytoscape software was used to construct ceRNA-regulating networks. Based on univariate Cox regression and least absolute shrinkage and selection operator (LASSO) Cox regression analyses, an m⁶A-associated RNA risk signature (m6Ascore) model was established. Prognostic differences between subgroups were explored using Kaplan–Meier (KM) analysis. Risk score-related biological phenotypes were analyzed in terms of functional enrichment, tumor immune signature, and tumor mutation signature. Finally, potential immunotherapy features and drug sensitivity predictions for this model were also discussed.

Results: A total of 16 miRNAs, 104 lncRNAs, and 11 mRNAs were incorporated into the ceRNA network. The risk score was obtained based on RP11-283I3.6, hsa-miR-455-3p, and CBLL1. Patients were divided into two risk groups using the risk score, with patients in the low-risk group having longer overall survival (OS) than those in the high-risk group. The receiver operating characteristic (ROC) curves indicated that risk characteristic performed well in predicting the prognosis of patients with SARC. In addition, lower m6Ascore was also positively correlated with the abundance of immune cells such as monocytes and mast cells activated, and several immune checkpoint genes were highly expressed in the low-m6Ascore group. According to our analysis, lower m6Ascore may lead to better immunotherapy response and OS outcomes. The risk signature was significantly associated with the chemosensitivity of SARC. Finally, a nomogram was constructed to predict the OS in patients with SARC. The concordance index (C-index) for the nomogram was 0.744 (95% CI: 0.707–0.784). The decision curve analysis (DCA), calibration plot, and ROC curve all showed that this nomogram had good predictive performance.

Conclusion: This m6Ascore risk model based on m⁶A RNA methylation regulator-related RNAs may be promising for clinical prediction of prognosis and might contain potential biomarkers for treatment response prediction for SARC patients.

Codon Usage Bias in Autophagy-Related Gene 13 in Eukaryotes: Uncovering the Genetic Divergence by the Interplay Between Nucleotides and Codon Usages

Synonymous codon usage bias is a universal characteristic of genomes across various organisms. Autophagy-related gene 13 (atg13) is one essential gene for autophagy initiation, yet the evolutionary trends of the atg13 gene at the usages of nucleotide and synonymous codon remains unexplored. According to phylogenetic analyses for the atg13 gene of 226 eukaryotic organisms at the nucleotide and amino acid levels, it is clear that their nucleotide usages exhibit more genetic information than their amino acid usages. Specifically, the overall nucleotide usage bias quantified by information entropy reflected that the usage biases at the first and second codon positions were stronger than those at the third position of the atg13 genes. Furthermore, the bias level of nucleotide ‘G’ usage is highest, while that of nucleotide ‘C’ usage is lowest in the atg13 genes. On top of that, genetic features represented by synonymous codon usage exhibits a species-specific pattern on the evolution of the atg13 genes to some extent. Interestingly, the codon usages of atg13 genes in the ancestor animals (Latimeria chalumnae, Petromyzon marinus, and Rhinatrema bivittatum) are strongly influenced by mutation pressure from nucleotide composition constraint. However, the distributions of nucleotide composition at different codon positions in the atg13 gene display that natural selection still dominates atg13 codon usages during organisms’ evolution.

Long non‑coding RNA ZSCAN16‑AS1 promotes the malignant properties of hepatocellular carcinoma by decoying microRNA‑451a and consequently increasing ATF2 expression

The importance of long noncoding RNAs (lncRNAs) in the oncogenicity of hepatocellular carcinoma (HCC) has been widely studied. However, the detailed functions of ZSCAN16 antisense RNA 1 (ZSCAN16‑AS1) have seldom been explored in HCC until the present study. In the present study, experiments were performed to clarify whether ZSCAN16‑AS1 is implicated in the oncogenesis and progression of HCC and to explore the possible underlying mechanisms. ZSCAN16‑AS1 expression was analyzed using reverse transcription‑quantitative PCR. The effects of ZSCAN16‑AS1 on the biological behavior of HCC cells were demonstrated by functional experiments. The direct binding capacity of ZSCAN16‑AS1 with microRNA‑451a (miR‑451a) was indicated by the luciferase reporter assay and RNA immunoprecipitation. The high expression of ZSCAN16‑AS1 was confirmed in HCC by The Cancer Genome Atlas database and the cohort of the present study. Survival data revealed that patients with a high ZSCAN16‑AS1 level had worse prognosis compared with those with a low ZSCAN16‑AS1 level. Following ZSCAN16‑AS1 knockdown, HCC cell proliferation, migration and invasion were curbed, whereas cell apoptosis was promoted in vitro. The absence of ZSCAN16‑AS1 restricted tumor growth of HCC cells in vivo. Mechanistically, ZSCAN16‑AS1 acted as a competing endogenous RNA by decoying miR‑451a in HCC cells. Furthermore, activating transcription factor 2 (ATF2), a direct target of miR‑451a, was under the regulation of ZSCAN16‑AS1, which was exerted by sequestering miR‑451a. In addition, miR‑451a knockdown or ATF2 resumption reversed the proliferation suppression, apoptosis promotion and migration and invasion inhibition triggered by ZSCAN16‑AS1 silencing. In conclusion, ZSCAN16‑AS1, a pro‑oncogenic lncRNA, aggravated the malignancy of HCC by controlling the miR‑451a/ATF2 axis. An understanding of the competing endogenous RNA network of ZSCAN16‑AS1/miR‑451a/ATF2 in HCC might be instrumental in the development of attractive targets for molecular therapy.

A Novel Autophagy-Related lncRNA Gene Signature to Improve the Prognosis of Patients with Melanoma

Objective

Autophagy and long noncoding RNAs (lncRNAs) have been the focus of research on the pathogenesis of melanoma. However, the autophagy network of lncRNAs in melanoma has not been reported. The purpose of this study was to investigate the lncRNA prognostic markers related to melanoma autophagy and predict the prognosis of patients with melanoma.

Methods

We downloaded RNA sequencing data and clinical information of melanoma from the Cancer Genome Atlas. The coexpression of autophagy-related genes (ARGs) and lncRNAs was analyzed. The risk model of autophagy-related lncRNAs was established by univariate and multivariate Cox regression analyses, and the best prognostic index was evaluated combined with clinical data. Finally, gene set enrichment analysis was performed on patients in the high- and low-risk groups.

Results

According to the results of the univariate Cox analysis, only the overexpression of LINC00520 was associated with poor overall survival, unlike HLA-DQB1-AS1, USP30-AS1, AL645929, AL365361, LINC00324, and AC055822. The results of the multivariate Cox analysis showed that the overall survival of patients in the high-risk group was shorter than that recorded in the low-risk group (p < 0.001). Moreover, in the receiver operating characteristic curve of the risk model we constructed, the area under the curve (AUC) was 0.734, while the AUC of T and N was 0.707 and 0.658, respectively. The Gene Ontology was mainly enriched with the positive regulation of autophagy and the activation of the immune system. The results of the Kyoto Encyclopedia of Genes and Genomes enrichment were mostly related to autophagy, immunity, and melanin metabolism.

Conclusion

The positive regulation of autophagy may slow the transition from low-risk patients to high-risk patients in melanoma. Furthermore, compared with clinical information, the autophagy-related lncRNA risk model may better predict the prognosis of patients with melanoma and provide new treatment ideas.