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Novel insights into small open reading frame-encoded micropeptides in hepatocellular carcinoma: A potential breakthrough

Traditionally, non-coding RNAs (ncRNAs) are regarded as a class of RNA transcripts that lack encoding capability; however, advancements in technology have revealed that some ncRNAs contain small open reading frames (sORFs) that are capable of encoding micropeptides of approximately 150 amino acids in length. sORF-encoded micropeptides (SEPs) have emerged as intriguing entities in hepatocellular carcinoma (HCC) research, shedding light on this previously unexplored realm. Recent studies have highlighted the regulatory functions of SEPs in the occurrence and progression of HCC. Some SEPs exhibit inhibitory effects on HCC, but others facilitate its development. This discovery has revolutionized the landscape of HCC research and clinical management. Here, we introduce the concept and characteristics of SEPs, summarize their associations with HCC, and elucidate their carcinogenic mechanisms in HCC metabolism, signaling pathways, cell proliferation, and metastasis. In addition, we propose a step-by-step workflow for the investigation of HCC-associated SEPs. Lastly, we discuss the challenges and prospects of applying SEPs in the diagnosis and treatment of HCC. This review aims to facilitate the discovery, optimization, and clinical application of HCC-related SEPs, inspiring the development of early diagnostic, individualized, and precision therapeutic strategies for HCC.

Annotation of uORFs in the OMIM genes allows to reveal pathogenic variants in 5'UTRs

An increasing number of studies emphasize the role of non-coding variants in the development of hereditary diseases. However, the interpretation of such variants in clinical genetic testing still remains a critical challenge due to poor knowledge of their pathogenicity mechanisms. It was previously shown that variants in 5'-untranslated regions (5'UTRs) can lead to hereditary diseases due to disruption of upstream open reading frames (uORFs). Here, we performed a manual annotation of upstream translation initiation sites (TISs) in human disease-associated genes from the OMIM database and revealed ∼4.7 thousand of TISs related to uORFs. We compared our TISs with the previous studies and provided a list of 'high confidence' uORFs. Using a luciferase assay, we experimentally validated the translation of uORFs in the ETFDH, PAX9, MAST1, HTT, TTN,GLI2 and COL2A1 genes, as well as existence of N-terminal CDS extension in the ZIC2 gene. Besides, we created a tool to annotate the effects of genetic variants located in uORFs. We revealed the variants from the HGMD and ClinVar databases that disrupt uORFs and thereby could lead to Mendelian disorders. We also showed that the distribution of uORFs-affecting variants differs between pathogenic and population variants. Finally, drawing on manually curated data, we developed a machine-learning algorithm that allows us to predict the TISs in other human genes.

Computational methods for ribosome profiling data analysis

Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is characterized under: Translation > Ribosome Structure/Function RNA Evolution and Genomics > Computational Analyses of RNA Translation > Translation Mechanisms Translation > Translation Regulation.