Pseudogenes in Cancer: State of the Art

Coding, or non-coding, that is the question

The advent of high-throughput sequencing uncovered that our genome is pervasively transcribed into RNAs that are seemingly not translated into proteins. It was also found that non-coding RNA transcripts outnumber canonical protein-coding genes. This mindboggling discovery prompted a surge in non-coding RNA research that started unraveling the functional relevance of these new genetic units, shaking the classic definition of "gene". While the non-coding RNA revolution was still taking place, polysome/ribosome profiling and mass spectrometry analyses revealed that peptides can be translated from non-canonical open reading frames. Therefore, it is becoming evident that the coding vs non-coding dichotomy is way blurrier than anticipated. In this review, we focus on several examples in which the binary classification of coding vs non-coding genes is outdated, since the same bifunctional gene expresses both coding and non-coding products. We discuss the implications of this intricate usage of transcripts in terms of molecular mechanisms of gene expression and biological outputs, which are often concordant, but can also surprisingly be discordant. Finally, we discuss the methodological caveats that are associated with the study of bifunctional genes, and we highlight the opportunities and challenges of therapeutic exploitation of this intricacy towards the development of anticancer therapies.

Isolation of a Virulent Clostridium perfringens Strain from Elaphurus davidianus and Characterization by Whole-Genome Sequence Analysis

Clostridium perfringens (C. perfringens) is an important veterinary pathogen and a noteworthy threat to human and animal health. Recently, there has been a significant rise in the number of moose fatalities caused by this rare, endemic species in China. Currently, there is an increasing trend in conducting whole-genome analysis of C. perfringens strains originating from pigs and chickens, whereas fewer studies have been undertaken on Elaphurus davidianus-originating strains at the whole-genome level. Our laboratory has identified and isolated five C. perfringens type A from affected Elaphurus davidianus. The current study identified the most potent strain of C. perfringens, which originated from Elaphurus davidianus, and sequenced its genome to reveal virulence genes and pathogenicity. Our findings show that strain CX1-4 exhibits the highest levels of phospholipase activity, hemolytic activity, and mouse toxicity compared to the other four isolated C. perfringens type A strains. The chromosome sequence length of the CX1-4 strain was found to be 3,355,389 bp by complete genome sequencing. The current study unveils the genomic characteristics of C. perfringens type A originating from Elaphurus davidianus. It provides a core foundation for further investigation regarding the prevention and treatment of such infectious diseases in Elaphurus davidianus.

Improving platelet-RNA-based diagnostics: a comparative analysis of machine learning models for cancer detection and multiclass classification

Liquid biopsy demonstrates excellent potential in patient management by providing a minimally invasive and cost-effective approach to detecting and monitoring cancer, even at its early stages. Due to the complexity of liquid biopsy data, machine-learning techniques are increasingly gaining attention in sample analysis, especially for multidimensional data such as RNA expression profiles. Yet, there is no agreement in the community on which methods are the most effective or how to process the data. To circumvent this, we performed a large-scale study using various machine-learning techniques. First, we took a closer look at existing datasets and filtered out some patients to assert data collection quality. The final data collection included platelet RNA samples acquired from 1397 cancer patients (17 types of cancer) and 354 asymptomatic, presumed healthy, donors. Then, we assessed an array of different machine-learning models and techniques (e.g., feature selection of RNA transcripts) in pan-cancer detection and multiclass classification. Our results show that simple logistic regression performs the best, reaching a 68% cancer detection rate at a 99% specificity level, and multiclass classification accuracy of 79.38% when distinguishing between five cancer types. In summary, by revisiting classical machine-learning models, we have exceeded the previously used method by 5% and 9.65% in cancer detection and multiclass classification, respectively. To ease further research, we open-source our code and data processing pipelines (https://gitlab.com/jopekmaksym/improving-platelet-rna-based-diagnostics), which we hope will serve the community as a strong baseline.

Pathological roles of miRNAs and pseudogene-derived lncRNAs in human cancers, and their comparison as prognosis/diagnosis biomarkers

This review examines and compares the diagnostic and prognostic capabilities of miRNAs and lncRNAs derived from pseudogenes in cancer patients. Additionally, it delves into their roles in cancer pathogenesis. Both miRNAs and pseudogene-derived lncRNAs have undergone thorough investigation as remarkably sensitive and specific cancer biomarkers, offering significant potential for cancer detection and monitoring. . Extensive research is essential to gain a complete understanding of the precise roles these non-coding RNAs play in cancer, allowing the development of novel targeted therapies and biomarkers for improved cancer detection and treatment approaches.