Genomic Identification of RNA Editing Through Integrating Omics Datasets and the Clinical Relevance in Hepatocellular Carcinoma

Tumor neoantigens derived from RNA editing events show significant clinical relevance in melanoma patients treated with immunotherapy

This study aimed to investigate the clinical significance of RNA editing (RE) and RNA editing derived (RED-) neoantigens in melanoma patients treated with immunotherapy. Vardict and VEP were used to identify the somatic mutations. RE events were identified by Reditools2 and filtered by the custom pipeline. miRTar2GO was implemented to predict the RE whether located in miRNA targets within the 3' UTR region. NetMHCpan and NetCTLpan were used to identify and characterize RED-neoantigens. In total, 7116 RE events were identified, most of which were A-to-I events. Using our custom pipeline, 631 RED-neoantigens were identified that show a significantly greater peptide-MHC affinity, and facilitate epitope processing and presentation than wild-type peptides. The OS of the patients with high RED-neoantigens burden was significantly longer ( P  = 0.035), and a significantly higher RED-neoantigens burden was observed in responders ( P  = 0.048). The area under the curve of the RED-neoantigen was 0.831 of OS. Then, we validated the reliability of RED-neoantigens in predicting the prognosis in an independent cohort and found that patients with high RED-neoantigens exhibited a longer OS ( P  = 0.008). To our knowledge, this is the first study to systematically assess the clinical relevance of RED-neoantigens in melanoma patients treated with immunotherapy.

Research on Predicting the Occurrence of Hepatocellular Carcinoma Based on Notch Signal-Related Genes Using Machine Learning Algorithms

BACKGROUND/AIMS

Hepatocellular carcinoma, a highly malignant tumor, is difficult to diagnose, treat, and predict the prognosis. Notch signaling pathway can affect hepatocellular carcinoma. We aimed to predict the occurrence of hepatocellular carcinoma based on Notch signal-related genes using machine learning algorithms.

MATERIALS AND METHODS

We downloaded hepatocellular carcinoma data from the Cancer Genome Atlas and Gene Expression Omnibus databases and used machine learning methods to screen the hub Notch signal-related genes. Machine learning classification was used to construct a prediction model for the classification and diagnosis of hepatocellular carcinoma cancer. Bioinformatics methods were applied to explore the expression of these hub genes in the hepatocellular carcinoma tumor immune microenvironment.

RESULTS

We identified 4 hub genes, namely, LAMA4, POLA2, RAD51, and TYMS, which were used as the final variables, and found that AdaBoostClassifie was the best algorithm for the classification and diagnosis model of hepatocellular carcinoma. The area under curve, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of this model in the training set were 0.976, 0.881, 0.877, 0.977, 0.996, 0.500, and 0.932; respectively. The area under curves were 0.934, 0.863, 0.881, 0.886, 0.981, 0.489, and 0.926. The area under curve in the external validation set was 0.934. Immune cell infiltration was related to the expression of 4 hub genes. Patients in the low-risk group of hepatocellular carcinoma were more likely to have an immune escape.

CONCLUSION

The Notch signaling pathway was closely related to the occurrence and development of hepatocellular carcinoma. The hepatocellular carcinoma classification and diagnosis model established based on this had a high degree of reliability and stability.

Prognostic RNA-editing signature predicts immune functions and therapy responses in gliomas

Background: RNA-editing refers to post-transcriptional transcript alterations that lead to the formation of protein isoforms and the progression of various tumors. However, little is known about its roles in gliomas. Aim: The aim of this study is to identify prognosis-related RNA-editing sites (PREs) in glioma, and to explore their specific effects on glioma and potential mechanisms of action. Methods: Glioma genomic and clinical data were obtained from TCGA database and SYNAPSE platform. The PREs was identified with regression analyses and the corresponding prognostic model was evaluated with survival analysis and receiver operating characteristic curve. Functional enrichment of differentially expressed genes between risk groups was performed to explore action mechanisms. The CIBERSORT, ssGSEA, gene set variation analysis, and ESTIMATE algorithms were employed to assess the association between PREs risk score and variations of tumor microenvironment, immune cell infiltration, immune checkpoints, and immune responses. The maftools and pRRophetic packages were used to evaluate tumor mutation burden and predict drug sensitivity. Results: A total of thirty-five RNA-editing sites were identified as prognosis-related in glioma. Functional enrichment implied variation of immune-related pathways between groups. Notably, glioma samples with higher PREs risk score exhibited higher immune score, lower tumor purity, increased infiltration of macrophage and regulatory T cells, suppressed NK cell activation, elevated immune function score, upregulated immune checkpoint gene expression, and higher tumor mutation burden, all of which implied worse response to immune therapy. Finally, high-risk glioma samples are more sensitive to Z-LLNle-CHO and temozolomide, while the low-risk ones respond better to Lisitinib. Conclusion: We identified a PREs signature of thirty-five RNA editing sites and calculated their corresponding risk coefficients. Higher total signature risk score indicates worse prognosis and worse immune response and lower sensitivity to immune therapy. The novel PREs signature could help risk stratification, immunotherapy response prediction, individualized treatment strategy-making for glioma patients, and development of novel therapeutic approaches.

Preliminary identification and analysis of differential RNA editing between higher and lower backfat thickness pigs using DNA-seq and RNA-seq data

RNA editing is an essential post-transcriptional regulatory mechanism. However, few studies have investigated the functional RNA edits in the economic traits of livestock on a genome-wide scale. Pigs are one of the most important livestock species and their fat is the principal organ involved in the regulation of adipose deposition. Here, we used three full-sibling pairs, with each pair comprising a pig with higher backfat (BF) thickness and lower backfat thickness, to identify RNA editing events based on whole-genome and transcriptome sequencing data. A total of 60,903 non-redundant RNA editing sites with 59,472 (97.7%) A-to-G edits were detected using a revised bioinformatics pipeline. A specific sequence context with G preference was found one base downstream of the edited site, and the editing level was associated with the distribution of nucleotides across nearly sites. Moreover, the A-to-G editing sites mostly occurred in the pig-special short interspersed nuclear elements, Pre0_SS. Comparing the difference between pigs with higher BF and lower BF, we found 211 differentially edited sites (DESites). Functional enrichment analyses revealed a significant enrichment of genes containing DESites in terms of adipose deposition. The DESites located in the six adipose-related genes (SKP1, GSK3B, COL5A3, MDM4, NT5C2, and DENND2A) were selected as candidate RNA editing sites associated with adipose deposition, and thus require further evaluation. This study mined the potentially functional RNA editing sites in pig adipose tissue and indicated that RNA editing may play an important role in adipose deposition, which provides a new insight into the post-transcriptionally mediated regulation mechanism of fat development.

Global RNA editing identification and characterization during human pluripotent-to-cardiomyocyte differentiation

RNA editing is widely involved in stem cell differentiation and development; however, RNA editing events during human cardiomyocyte differentiation have not yet been characterized and elucidated. Here, we identified genome-wide RNA editing sites and systemically characterized their genomic distribution during four stages of human cardiomyocyte differentiation. It was found that the expression level of ADAR1 affected the global number of adenosine to inosine (A-to-I) editing sites but not the editing degree. Next, we identified 43, 163, 544, and 141 RNA editing sites that contribute to changes in amino acid sequences, variation in alternative splicing, alterations in miRNA-target binding, and changes in gene expression, respectively. Generally, RNA editing showed a stage-specific pattern with 211 stage-shared editing sites. Interestingly, cardiac muscle contraction and heart-disease-related pathways were enriched by cardio-specific editing genes, emphasizing the connection between cardiomyocyte differentiation and heart diseases from the perspective of RNA editing. Finally, it was found that these RNA editing sites are also related to several congenital and noncongenital heart diseases. Together, our study provides a new perspective on cardiomyocyte differentiation and offers more opportunities to understand the mechanisms underlying cell fate determination, which can promote the development of cardiac regenerative medicine and therapies for human heart diseases.

An ADAR1-dependent RNA editing event in the cyclin-dependent kinase CDK13 promotes thyroid cancer hallmarks

Background

Adenosine deaminases acting on RNA (ADARs) modify many cellular RNAs by catalyzing the conversion of adenosine to inosine (A-to-I), and their deregulation is associated with several cancers. We recently showed that A-to-I editing is elevated in thyroid tumors and that ADAR1 is functionally important for thyroid cancer cell progression. The downstream effectors regulated or edited by ADAR1 and the significance of ADAR1 deregulation in thyroid cancer remain, however, poorly defined.

Methods

We performed whole transcriptome sequencing to determine the consequences of ADAR1 deregulation for global gene expression, RNA splicing and editing. The effects of gene silencing or RNA editing were investigated by analyzing cell viability, proliferation, invasion and subnuclear localization, and by protein and gene expression analysis.

Results

We report an oncogenic function for CDK13 in thyroid cancer and identify a new ADAR1-dependent RNA editing event that occurs in the coding region of its transcript. CDK13 was significantly over-edited (c.308A > G) in tumor samples and functional analysis revealed that this editing event promoted cancer cell hallmarks. Finally, we show that CDK13 editing increases the nucleolar abundance of the protein, and that this event might explain, at least partly, the global change in splicing produced by ADAR1 deregulation.

Conclusions

Overall, our data support A-to-I editing as an important pathway in cancer progression and highlight novel mechanisms that might be used therapeutically in thyroid and other cancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-021-01401-y.

Epitranscriptomics: A New Layer of microRNA Regulation in Cancer

MicroRNAs are pervasive regulators of gene expression at the post-transcriptional level in metazoan, playing key roles in several physiological and pathological processes. Accordingly, these small non-coding RNAs are also involved in cancer development and progression. Furthermore, miRNAs represent valuable diagnostic and prognostic biomarkers in malignancies. In the last twenty years, the role of RNA modifications in fine-tuning gene expressions at several levels has been unraveled. All RNA species may undergo post-transcriptional modifications, collectively referred to as epitranscriptomic modifications, which, in many instances, affect RNA molecule properties. miRNAs are not an exception, in this respect, and they have been shown to undergo several post-transcriptional modifications. In this review, we will summarize the recent findings concerning miRNA epitranscriptomic modifications, focusing on their potential role in cancer development and progression.

Identification of A-to-I RNA editing profiles and their clinical relevance in lung adenocarcinoma

Adenosine-to-inosine (A-to-I) RNA editing is a widespread posttranscriptional modification that has been shown to play an important role in tumorigenesis. Here, we evaluated a total of 19,316 RNA editing sites in the tissues of 80 lung adenocarcinoma (LUAD) patients from our Nanjing Lung Cancer Cohort (NJLCC) and 486 LUAD patients from the TCGA database. The global RNA editing level was significantly increased in tumor tissues and was highly heterogeneous across patients. The high RNA editing level in tumors was attributed to both RNA (ADAR1 expression) and DNA alterations (mutation load). Consensus clustering on RNA editing sites revealed a new molecular subtype (EC3) that was associated with the poorest prognosis of LUAD patients. Importantly, the new classification was independent of classic molecular subtypes based on gene expression or DNA methylation. We further proposed a simplified model including eight RNA editing sites to accurately distinguish the EC3 subtype in our patients. The model was further validated in the TCGA dataset and had an area under the curve (AUC) of the receiver operating characteristic curve of 0.93 (95%CI: 0.91-0.95). In addition, we found that LUAD cell lines with the EC3 subtype were sensitive to four chemotherapy drugs. These findings highlighted the importance of RNA editing events in the tumorigenesis of LUAD and provided insight into the application of RNA editing in the molecular subtyping and clinical treatment of cancer.