Subcellular localization and relevant mechanisms of human cancer-related micropeptides

Noncoding RNA-encoded peptides in cancer: biological functions, posttranslational modifications and therapeutic potential

In the present era, noncoding RNAs (ncRNAs) have become a subject of considerable scientific interest, with peptides encoded by ncRNAs representing a particularly promising avenue of investigation. The identification of ncRNA-encoded peptides in human cancers is increasing. These peptides regulate cancer progression through multiple molecular mechanisms. Here, we delineate the patterns of diverse ncRNA-encoded peptides and provide a synopsis of the methodologies employed for the identification of ncRNAs that possess the capacity to encode these peptides. Furthermore, we discuss the impacts of ncRNA-encoded peptides on the biological behavior of cancer cells and the underlying molecular mechanisms. In conclusion, we describe the prospects of ncRNA-encoded peptides in cancer and the challenges that need to be overcome.

Long non‑coding RNA SNHG1 promotes autophagy in vascular smooth muscle cells induced by facilitating CLEC7A

Long non‑coding RNAs serve a crucial role in autophagy of vascular smooth muscle cells (VSMCs). The present study aimed to investigate the effect of small nucleolar RNA host gene 1 (SNHG1) on autophagy in VSMCs and the associated underlying mechanisms. Rapamycin was used to induce autophagy in VSMCs and the effects of SNHG1 on the proliferation and migration of VSMCs and the change in phenotype were tested following overexpression and silencing of SNHG1. The target gene of SNHG1 was predicted and validated. SNHG1‑regulated autophagy of VSMCs via C‑type lectin domain family 7 member A (CLEC7A) was determined by combined silencing of SNHG1 and overexpression of CLEC7A. Rapamycin‑induced autophagy in VSMCs changed the cell phenotype from contractile to synthetic, with decreased expression of α‑smooth muscle actin and smooth muscle protein 22a and increased expression of osteopontin. Overexpression of SNHG1 caused the same change in phenotype while the opposite change was observed following SNHG1 silencing. Overexpression of SNHG1 promoted the proliferation and migration of VSMCs. CLEC7A was identified as a target gene of SNHG1 and a direct binding relationship between them was confirmed by RNA immunoprecipitation and RNA pull‑down assays. Overexpression of SNHG1 increased the expression of CLEC7A. The expression of both SNHG1 and CLEC7A was increased during autophagy of VSMCs. Overexpression of SNHG1 promoted autophagy of VSMCs and silencing of CLEC7A reduced this effect of SNHG1. In conclusion, SNHG1 and CLEC7A were increased in VSMCs following autophagy. SNHG1 promotes the conversion of VSMCs from a contractile phenotype to a synthetic phenotype by facilitating CLEC7A expression.

Multi-Omic Approaches in Cancer-Related Micropeptide Identification

Despite the advances in modern cancer therapy, malignant diseases are still a leading cause of morbidity and mortality worldwide. Conventional treatment methods frequently lead to side effects and drug resistance in patients, highlighting the need for novel therapeutic approaches. Recent findings have identified the existence of non-canonical micropeptides, an additional layer of the proteome complexity, also called the microproteome. These small peptides are a promising class of therapeutic agents with the potential to address the limitations of current cancer treatments. The microproteome is encoded by regions of the genome historically annotated as non-coding, and its existence has been revealed thanks to recent advances in proteomic and bioinformatic technology, which dramatically improved the understanding of proteome complexity. Micropeptides have been shown to be biologically active in several cancer types, indicating their therapeutic role. Furthermore, they are characterized by low toxicity and high target specificity, demonstrating their potential for the development of better tolerated drugs. In this review, we survey the current landscape of known micropeptides with a role in cancer progression or treatment, discuss their potential as anticancer agents, and describe the methodological challenges facing the proteome field of research.

DRpred: A Novel Deep Learning-Based Predictor for Multi-Label mRNA Subcellular Localization Prediction by Incorporating Bayesian Inferred Prior Label Relationships

The subcellular localization of messenger RNA (mRNA) not only helps us to understand the localization regulation of gene expression but also helps to understand the relationship between RNA localization pattern and human disease mechanism, which has profound biological and medical significance. Several predictors have been proposed for predicting the subcellular localization of mRNA. However, there is still considerable room for improvement in their predictive performance, especially regarding multi-label prediction. This study proposes a novel multi-label predictor, DRpred, for mRNA subcellular localization prediction. This predictor first utilizes Bayesian networks to capture the dependencies among labels. Subsequently, it combines these dependencies with features extracted from mRNA sequences using Word2vec, forming the input for the predictor. Finally, it employs a neural network combining BiLSTM and an attention mechanism to capture the internal relationships of the input features for mRNA subcellular localization. The experimental validation on an independent test set demonstrated that DRpred obtained a competitive predictive performance in multi-label prediction and outperformed state-of-the-art predictors in predicting single subcellular localizations, obtaining accuracies of 82.14%, 93.02%, 80.37%, 94.00%, 90.58%, 84.53%, 82.01%, 79.71%, and 85.67% for the chromatin, cytoplasm, cytosol, exosome, membrane, nucleolus, nucleoplasm, nucleus, and ribosome, respectively. It is anticipated to offer profound insights for biological and medical research.

MicroProteinDB: A database to provide knowledge on sequences, structures and function of ncRNA-derived microproteins

Omics-based technologies have revolutionized our comprehension of microproteins encoded by ncRNAs, revealing their abundant presence and pivotal roles within complex functional landscapes. Here, we developed MicroProteinDB (http://bio-bigdata.hrbmu.edu.cn/MicroProteinDB), which offers and visualizes the extensive knowledge to aid retrieval and analysis of computationally predicted and experimentally validated microproteins originating from various ncRNA types. Employing prediction algorithms grounded in diverse deep learning approaches, MicroProteinDB comprehensively documents the fundamental physicochemical properties, secondary and tertiary structures, interactions with functional proteins, family domains, and inter-species conservation of microproteins. With five major analytical modules, it will serve as a valuable knowledge for investigating ncRNA-derived microproteins.

Micropeptides: potential treatment strategies for cancer

Some noncoding RNAs (ncRNAs) carry open reading frames (ORFs) that can be translated into micropeptides, although noncoding RNAs (ncRNAs) have been previously assumed to constitute a class of RNA transcripts without coding capacity. Furthermore, recent studies have revealed that ncRNA-derived micropeptides exhibit regulatory functions in the development of many tumours. Although some of these micropeptides inhibit tumour growth, others promote it. Understanding the role of ncRNA-encoded micropeptides in cancer poses new challenges for cancer research, but also offers promising prospects for cancer therapy. In this review, we summarize the types of ncRNAs that can encode micropeptides, highlighting recent technical developments that have made it easier to research micropeptides, such as ribosome analysis, mass spectrometry, bioinformatics methods, and CRISPR/Cas9. Furthermore, based on the distribution of micropeptides in different subcellular locations, we explain the biological functions of micropeptides in different human cancers and discuss their underestimated potential as diagnostic biomarkers and anticancer therapeutic targets in clinical applications, information that may contribute to the discovery and development of new micropeptide-based tools for early diagnosis and anticancer drug development.