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98 records · Page 6Linked to original sources

PubMed2026

Deletion of the MALAT1 RNA 3' end promotes transcript decay and inhibits proliferation in gastric and breast cancer cells.

The long non-coding RNA MALAT1 is a conserved oncogenic driver whose function relies on a 3' triple-helix motif. While its biochemistry is well-characterized in vitro, the endogenous requirement for this motif in regulating the stability of the transcript and other genes residing in its locus remains unclear. In this study, we employed a dual-sgRNA CRISPR-Cas9 approach to systematically excise triple-helix-forming sequences from the native MALAT1 locus in gastric (AGS) and breast (MCF7) cancer cells. Our findings demonstrate that the 3' end strongly contributes to MALAT1 stability. Perturbations ranging from genomic deletions to a single-base changes trigger transcript collapse and rapid exonucleolytic decay, while the biogenesis of the small RNA mascRNA (a byproduct of MALAT1, also involved in cancer) remains decoupled and unaffected. In cellulo, DMS probing reveals that edited transcripts retain structural complexity in the 3' region. Phenotypically, structural disruption of the 3' end significantly impairs proliferation of both cancer cellular models. These results identify the 3' triple-helix as a determinant of MALAT1 stability and provide endogenous validation for its role in the analyzed AGS and MCF7 cells.

Cancer↗

PubMed2026

Hidden proteins encoded by non-canonical open reading frames: A review.

There is increasing evidence that translation is not limited to annotated protein-coding genes. Ribosome profiling sequencing, mass spectrometry-based proteomics, and immunopeptidomics have identified the productive translation of non-canonical open reading frames (ORFs). This suggests that the functional proteome includes not only conserved proteins but also proteins hidden in non-coding RNAs and de novo proteins. Some of these translated products are functional peptides, while others may be non-functional, potentially arising from evolutionary events. Several non-canonical ORF-encoded peptides have been found to regulate multiple physiological and pathological functions, particularly in cancer, immunity, and inflammation, indicating that they have potential as biomarkers and novel therapeutic targets. To better understand the diversity of functional peptides and translated non-canonical ORFs based on existing data, we summarize their classification according to transcriptional features and supporting evidence, including non-canonical ORFs located in ncRNAs and canonical mRNAs. This review provides a concise summary of the origin, discovery methods, and classification of non-canonical ORFs. It offers insights into the origins and functions of non-canonical ORF-encoded peptides from an evolutionary perspective, while also exploring the biological functions and regulatory mechanisms of these non-canonical ORF-encoded hidden proteins in tumorigenesis and progression.

Open Reading Frames↗

PubMed2025

Long non-coding RNA NEAT1 promotes colorectal cancer progression via interacting with SIRT1.

Nuclear-enriched abundant transcript 1 (NEAT1), a long noncoding RNA, is found to be significantly dysregulated in different types of cancer, including colorectal cancer (CRC). Nevertheless, there is still much to learn about the precise functions and processes of NEAT1 in the progression of CRC. Using The Cancer Genome Atlas (TCGA) database and 50 CRC specimens from the First Affiliated Hospital of Dali University, we assessed the expression of NEAT1 to determine its clinical impact. Through gene set enrichment analysis (GSEA), Cancer Single-cell State Atlas (CancerSEA), and immune infiltration studies, we elucidated key functions of NEAT1. We utilized Cell Counting Kit-8 (CCK8), wound healing, and Transwell assays to investigate the role of NEAT1 in the progression of CRC. Through the use of GSEA and immunohistochemistry, additional investigations were conducted to unveil the downstream targets of NEAT1 and gain insights into their regulatory dynamics. Our in vitro studies confirmed the regulatory role of NEAT1 in CRC. Findings indicate that increased NEAT1 expression correlates with adverse outcomes in colorectal tissues. In the CRC model, reduced levels of NEAT1 lead to reduced cell proliferation, invasion, and migration. Additionally, NEAT1 influenced immune cell infiltration in CRC and functioned as an oncogene by upregulating Sirtuin 1 (SIRT1) expression. This study demonstrates that NEAT1 promotes CRC progression and metastasis through a SIRT1-mediated mechanism, suggesting its potential as a prognostic biomarker and therapeutic target for CRC.

RNA, Long Noncoding↗

PubMed2026

Machine learning approaches for cancer prognosis and diagnosis via non-coding RNA: a comprehensive review.

Non-coding RNAs (ncRNAs), once considered genomic dark matter, are now established as key regulators of gene expression with widespread roles in cellular homeostasis and disease. In cancer, ncRNA expression is frequently and systematically dysregulated, and many of these molecules circulate in stable, protected form within biofluids, offering a compelling basis for non-invasive or minimally invasive diagnostic strategies. However, their clinical translation remains substantially hindered to date due to biological complexity, technical noise, and high dimensionality inherent to ncRNA expression datasets. In this context, machine learning (ML) has emerged as a powerful analytical tool to address these challenges, enabling the identification of subtle, reproducible ncRNA signatures predictive of diverse malignancies. This review critically evaluates ML-driven frameworks for cancer diagnosis and prognosis across four ncRNA subclasses, namely miRNAs, lncRNAs, circRNAs, and piRNAs, while also acknowledging the biophysical and thermodynamic models that reinforce ncRNA bioinformatics. Despite substantial methodological progress in ML-based cancer diagnosis and prognosis, key challenges persist, including tumor biological heterogeneity, limited multicenter validation, and the lack of widely adopted standardized protocols for preprocessing, normalization, and reporting workflows. Furthermore, many current ML models lack interpretability in biological or clinical context, constraining their translational utility. By synthesizing recent advances and identifying unresolved barriers, this review charts a roadmap for developing a robust, clinically actionable ncRNA biomarker platform for cancer detection. With global cancer incidence projected to exceed 35 million annual cases by 2050, validated ncRNA-ML-driven frameworks hold potential to revolutionize early-stage detection and personalized therapeutic strategies, thereby reducing the escalating socio-economic burden of cancer worldwide.

Humans↗

PubMed2025

Genome-Wide Mining of lncRNAs Reveals Their Potential Regulatory Role in the Evolution of Viviparity.

Reproduction in vertebrates usually involves egg-laying (oviparity) or live-bearing (viviparity). Oviparity is the ancestral trait from which viviparity has independently evolved more than 100 times in squamate reptiles. This transition involves a series of physiological and structural changes, including the degeneration of eggshell and the evolution of a placenta and differences in the temporal and spatial expression patterns of some functional genes that drive the structural transformation. Long non-coding RNAs (lncRNAs) play important roles in the regulation of gene expression, yet it remains unclear whether they participate in gene expression shifts during the transition from oviparity to viviparity, and if so how. Therefore, we employ deep mining to identify novel lncRNAs of a closely related oviparous-viviparous pair of lizards (Phrynocephalus przewalskii and P. vlangalii). We construct cis- and trans-regulatory networks between lncRNAs and target genes using the transcriptomic data of oviduct or uteri tissues across reproductive periods. Results show that lncRNAs that regulate eggshell gland developmental genes in the oviparous lizard are lost or less expressed in the viviparous lizard. A number of lncRNAs involved in the regulation of placental development and embryo attachment in viviparous species have no orthologs in oviparous species, and others show little or no expression. Accordingly, lncRNAs may play important regulatory roles in the physiological and structural changes in the transition from oviparity to viviparity. These results open doors to the further elucidation of genetic regulatory networks.

Animals↗

PubMed2025

Liver cancer-specific prognostic model developed using endoplasmic reticulum stress-related LncRNAs and LINC01011 as a potential therapeutic target.

Liver cancer is a serious malignancy worldwide, and long noncoding RNAs (lncRNAs) have been implicated in its prognosis.It remains unclear how lncRNAs related to endoplasmic reticulum stress (ERS) influence liver cancer prognosis. Here, we analyzed RNA and clinical data from the Cancer Genome Atlas and sourced ERS-related genes from the Molecular Signatures Database. Co-expression analysis identified ERS-related lncRNAs, and Cox regression analysis as well as least absolute shrinkage and selection operator regression highlighted three lncRNAs for a prognostic model. Based on median risk scores, we classified patients into two risk groups. The high-risk group displayed poor prognosis, and this finding was validated in the test set. According to consistency clustering, the patients were assigned to two clusters, and tumor microenvironment scores were computed. Patients with a high mutation burden had worse outcomes. Furthermore, immune infiltration analysis indicated more immune cells and mutations in checkpoint molecules among high-risk individuals. Drug sensitivity varied between the risk groups. LINC01011 was selected for functional assays. Colony formation assay and CCK-8 assay revealed that silencing LINC01011 suppressed liver cancer cell proliferation. Transwell and scratch assays indicated that silencing LINC01011 inhibited liver cancer cell migration. Western blotting assay revealed that inhibiting LINC01011 induced apoptosis and simultaneously inhibited epithelial-mesenchymal transition. These findings confirm the validity of the prognostic model and indicate that LINC01011 could serve as a potential research target.

Humans↗

PubMed2026

A Comparative Analysis of the Methylation Status of Non-Coding RNA Promoters in Fibroid and Matched Myometrium.

Uterine fibroids exhibit dysregulated expression of non-coding RNAs (ncRNAs), although the underlying mechanisms remain incompletely understood. We investigated promoter DNA methylation and its relationship with ncRNA expression in fibroids. Genomic DNA from eight paired fibroid and matched myometrial tissues was analyzed using MeDIP-chip to identify differentially methylated ncRNA promoters. Selected candidates were validated by methylation-specific PCR (MSP) in 16 paired samples, and transcript expression was assessed by qRT-PCR in 68-94 paired specimens. MeDIP-chip identified 538 lncRNAs and 61 miRNAs with differential promoter methylation, including 300 hypermethylated and 238 hypomethylated lncRNAs and 47 hypermethylated and 14 hypomethylated miRNAs. Promoter methylation was not significantly correlated with transcript expression (r = -0.1224). MSP confirmed hypermethylation of LINC-PINT and MIR9-3 and hypomethylation of WT1-AS and TTLL10-AS1. Correspondingly, LINC-PINT and MIR9-3 expression was decreased, whereas WT1-AS and TTLL10-AS1 expression was increased in fibroids. However, LINC-PINT and TTLL10-AS1 methylation did not fully correspond with MeDIP-chip findings. These results reveal widespread ncRNA promoter methylation alterations in uterine fibroids but demonstrate that genome-wide methylation does not consistently predict transcript expression, highlighting the complexity of ncRNA epigenetic regulation and the importance of locus-specific validation.

Humans↗

PubMed2022

Flnc: Machine Learning Improves the Identification of Novel Long Noncoding RNAs from Stand-Alone RNA-Seq Data.

Long noncoding RNAs (lncRNAs) play critical regulatory roles in human development and disease. Although there are over 100,000 samples with available RNA sequencing (RNA-seq) data, many lncRNAs have yet to be annotated. The conventional approach to identifying novel lncRNAs from RNA-seq data is to find transcripts without coding potential but this approach has a false discovery rate of 30-75%. Other existing methods either identify only multi-exon lncRNAs, missing single-exon lncRNAs, or require transcriptional initiation profiling data (such as H3K4me3 ChIP-seq data), which is unavailable for many samples with RNA-seq data. Because of these limitations, current methods cannot accurately identify novel lncRNAs from existing RNA-seq data. To address this problem, we have developed software, Flnc, to accurately identify both novel and annotated full-length lncRNAs, including single-exon lncRNAs, directly from RNA-seq data without requiring transcriptional initiation profiles. Flnc integrates machine learning models built by incorporating four types of features: transcript length, promoter signature, multiple exons, and genomic location. Flnc achieves state-of-the-art prediction power with an AUROC score over 0.92. Flnc significantly improves the prediction accuracy from less than 50% using the conventional approach to over 85%. Flnc is available via GitHub platform.

RNA-seq↗