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        <title>Frontiers in Bioinformatics | RNA Bioinformatics section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/bioinformatics/sections/rna-bioinformatics</link>
        <description>RSS Feed for RNA Bioinformatics section in the Frontiers in Bioinformatics journal | New and Recent Articles</description>
        <language>en-us</language>
        <generator>Frontiers Feed Generator,version:1</generator>
        <pubDate>2026-05-14T18:26:37.465+00:00</pubDate>
        <ttl>60</ttl>
        <item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1794098</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1794098</link>
        <title><![CDATA[Machine learning-based determination of sex-related bladder cancer biomarkers]]></title>
        <pubdate>2026-04-29T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Joseph R. Pizzi</author><author>Image Adhikari</author><author>Prakyat Prakash</author><author>Hangchuan Shi</author><author>Hiroshi Miyamoto</author><author>Feng Cui</author>
        <description><![CDATA[IntroductionBladder cancer exhibits sex-specific behavior, occurring more frequently in males but progressing to advanced stages more commonly in females. The activation of sex hormone receptors may explain these differences, but the exact genetic drivers remain poorly understood. Furthermore, current bladder cancer biomarkers have inconsistent sensitivities and specificities in practice, making early diagnosis a challenge.MethodsThis study approaches bladder cancer biomarker discovery through machine learning techniques on gender and disease-stratified RNA-seq data. Training sets limited to differentially expressed genes were subjected to four different feature selection methods: differential gene expression analysis adjusted p-value, recursive feature elimination with support vector machine, logistic regression, and an optimized random forest procedure. Gene panels were compared and aggregated across selection strategies and cross-validation folds to identify robust biomarkers for sex-specific bladder cancer development and progression.ResultsWhen applied to unseen datasets and limited to 50 genes or less, male and female-specific panels achieved areas under the receiver operating characteristic curve of 0.932 and 0.914, respectively, in distinguishing bladder cancer samples from non-tumor controls. In terms of enriched pathways, the male panel was associated with cell interactions and altered PI3K-AKT signaling, while the female panel was more closely connected to extracellular matrix reorganization. The panel differentiating male and female tumors had a poorer performance on external datasets compared to the sex-specific analyses, but still contained relevant genes.DiscussionGenes such as PRAC1 and PCDH11Y were identified as high-impact predictors related to sex hormones or chromosomes for male tumor development. In the female-specific panel, genes related to aberrant androgen signaling across tumor types like androgen receptor, PLXNA1, USP54, and PMEPA1 were influential. These results offer potential targets for further in vivo/vitro experimentation and provide a framework for constructing high-performance gene panels related to sex-specific bladder cancer biology.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1719535</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1719535</link>
        <title><![CDATA[Characterizing miRNA editing patterns in 5 types of cells using single-cell small RNA sequencing data]]></title>
        <pubdate>2026-04-09T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Chunyi Mao</author><author>Hao Guo</author><author>Wenping Xie</author><author>Yue Xu</author><author>Hongjia Zhang</author><author>Kang Luo</author><author>Jun Yang</author><author>Yun Zheng</author>
        <description><![CDATA[Numerous studies have identified a large number of miRNA editing sites via deep sRNA sequencing profiling of tissue samples. However, the single-cell landscape of miRNA editing patterns has remained largely unknown to date. To investigate miRNA editing and mutation characteristics at single cell level, this study analyzed miRNA editing and mutation events in 448 single-cell small RNA sequencing profiles from 5 different cell types. Our results revealed that PCA and clustering analysis, performed based on the editing levels of identified miRNA editing sites, could distinguish distinct cell types, indicating that miRNA editing patterns are cell-type-specific across different cellular populations. We further demonstrated that a subset of miRNA editing sites exhibited strict cell-type-specific editing patterns. Meanwhile, within the same cell type, the identified sites presented different distributions of editing levels in different cells. A fraction of sites showed highly variable editing levels among different cells of the same cell type, while some sites displayed relatively uniform and consistent editing patterns. An A-to-I editing site in hsa-mir-376c, i.e., hsa-mir-376c 48 A g, showed a significantly higher editing level in glioblastoma cells than in naive embryonic stem cells, suggesting a potential role in the initiation and progression of glioblastoma. Furthermore, our results also suggest that in leukemia cells, TENT4A, TENT5A, TENT5B, TENT5C, TENT5D, and TUT1 may mediate the non-templated nucleotide additions to the 3′ends of miRNAs.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1760987</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1760987</link>
        <title><![CDATA[An explainable-AI framework reveals novel lncRNAs specific for breast cancer subtypes]]></title>
        <pubdate>2026-03-10T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Jai Chand Patel</author><author>Avinash Veerappa</author><author>Chittibabu Guda</author>
        <description><![CDATA[BackgroundLong non-coding RNAs (lncRNAs) have emerged as important regulators in cancer biology; yet their potential for cancer subtyping remains underexplored particularly in the context of large-scale, multi-class supervised classification frameworks, due to limited publicly available data or their use only as auxiliary features in classification tasks.MethodsIn this study, we utilized an expansive set of 7,177 lncRNAs obtained from 1,021 breast cancer (BRCA) transcriptomics datasets for subtyping using an explainable artificial intelligence (AI) framework. lncRNA, mRNA, and miRNA features were used to build machine learning (ML) models individually and in combination. Four ML classifiers: Naïve Bayes, Random Forest, Artificial Neural Network, and XGBoost were employed to evaluate subtype classification performance.ResultsUsing lncRNAs alone, XGBoost demonstrated strong performance with an accuracy of 89.2% and AUROC of 0.99. Addition of miRNA or mRNA features to lncRNA marginally improved the accuracy to 90.8% and 92.2%, respectively, while using all the three features together provided no further gain. A sequential key feature identification pipeline (ANOVA, Boruta, SHAP) has identified interpretable subtype-specific biomarker panels, yielding 119, 66, 54, and 24 unique features for Luminal A, Luminal B, HER2+, and Basal subtypes, respectively. Further lncRNA characterization followed by survival analysis revealed significant subtype-specific novel lncRNAs, including CUFF.25255 (LumA), CUFF.20237 and CUFF.3888 (LumB), CUFF.22414 (HER2+), and CUFF.26607 and CUFF.1961 (Basal).ConclusionOur findings highlight the diagnostic and biomarker discovery potential of lncRNAs, and the explainable-AI framework implemented here provides a systematic large-scale evaluation of lncRNA-only and integrative models for multi-class BRCA subtyping for BRCA subtyping and can be adopted to other cancers using the existing cancer transcriptomics data in the public databases.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1758257</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1758257</link>
        <title><![CDATA[A clustering method for single-cell RNA sequencing data based on denoising and masking learning]]></title>
        <pubdate>2026-03-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shuang Xu</author><author>Wen Yan</author><author>Bin Zhang</author><author>Hong Qi</author><author>Kai Wang</author>
        <description><![CDATA[IntroductionSingle-cell RNA sequencing (scRNA-seq) enables high-throughput analysis of gene expression at single-cell resolution and plays a crucial role in studying cellular heterogeneity, tissue development, and disease mechanisms. However, scRNA-seq data are characterized by high dimensionality, sparsity, technical noise, and prevalent dropout events, which pose substantial challenges to conventional clustering approaches.MethodsTo address these challenges, we propose scDMAC, a novel clustering framework for single-cell RNA sequencing data based on denoising and masking learning. The method integrates a zero-inflated negative binomial (ZINB)-based denoising autoencoder with a masking autoencoder. First, the ZINB-based autoencoder models count distribution and dropout events to denoise gene expression data. Subsequently, a tailored masking strategy is applied to the denoised data to learn gene-wise correlations through reconstruction.ResultsExtensive experiments conducted on multiple benchmark scRNA-seq datasets demonstrate that scDMAC achieves superior clustering accuracy and stability compared with state-of-the-art methods. The proposed framework consistently improves clustering performance across diverse datasets, highlighting its robustness to noise and sparsity.DiscussionBy effectively combining probabilistic denoising with masking-based representation learning, scDMAC provides a powerful solution for addressing dropout and sparsity issues in scRNA-seq data. The improved clustering performance suggests that integrating distribution-aware denoising with feature reconstruction enhances the extraction of biologically meaningful representations, making scDMAC a promising tool for single-cell transcriptomic analysis.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2026.1787360</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2026.1787360</link>
        <title><![CDATA[From clinical phenotypes to genomic signatures: machine learning integration for precision tuberculosis treatment prediction ]]></title>
        <pubdate>2026-03-03T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Liping Li</author><author>Huanqing Liu</author><author>Qian Lei</author><author>Tingting Li</author>
        <description><![CDATA[BackgroundTuberculosis (TB) remains a major global health threat, causing approximately 1.5 million deaths each year. Despite progress in treatment, 15%–20% of patients still experience treatment failure or relapse, highlighting the urgent need for precise predictive tools for early identification of high-risk patients. Current methods based on clinical parameters have limitations in prediction accuracy and revealing potential biological mechanisms.MethodsThis study developed and validated an innovative multi-omics integration prediction model. We retrospectively collected clinical data from 467 tuberculosis patients and integrated transcriptomic data from three independent public cohorts (GSE19491, GSE31312, GSE83456), involving 3,240 differentially expressed genes. Through advanced feature engineering and bioinformatics analysis, key features were selected. We systematically evaluated 12 machine learning algorithms and adopted an ensemble learning strategy to construct the final model. Model performance was evaluated through strict cross-validation and prospective validation cohorts.ResultsClinical data analysis identified age, body mass index (BMI), and C-reactive protein (CRP) levels as significant predictors of treatment response. Transcriptomic analysis revealed 1,247 differentially expressed genes between responders and non-responders, enriched in immune response and metabolic pathways. Among the tested algorithms, the ensemble model based on Extra Trees performed the best, with an area under the curve (AUC) of 0.986, significantly superior to models using only clinical data (AUC = 0.850) or only genomic data (AUC = 0.820). Feature importance analysis confirmed CRP, specific gene features (such as DNA repair and interferon response pathways), age, and BMI as the most important predictors. External validation confirmed the model’s robustness (AUC = 0.972).ConclusionThis study successfully developed a high-precision prediction model integrating clinical and genomics data, capable of early identification of high-risk patients with poor treatment response. The model demonstrates excellent prediction performance and generalization ability, providing a powerful tool for moving towards tuberculosis precision medicine, guiding individualized treatment strategies to improve patient prognosis and control the spread of drug resistance.Clinical Trial Registrationhttps://www.chictr.org.cn/, ChiCTR2300074328, 03/08/2023.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1676149</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1676149</link>
        <title><![CDATA[TRANSAID: a hybrid deep learning framework for translation site prediction with integrated biological feature scoring]]></title>
        <pubdate>2026-01-19T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Yan Li</author><author>Boran Wang</author><author>Zhen Liu</author><author>Wei Wei</author><author>Caiyi Fei</author><author>Shi Xu</author><author>Tiyun Han</author><author>Wei Geng</author><author>Zengding Wu</author>
        <description><![CDATA[IntroductionTranslation initiation and termination are critical regulatory checkpoints in protein synthesis, yet accurate computational prediction of their sites remains challenging due to training data biases and the complexity of full-length transcripts.MethodsTo address these limitations, we present TRANSAID (TRANSlation AI for Detection), a novel deep learning framework that accurately and simultaneously predicts translation initiation (TIS) and termination (TTS) sites from complete transcript sequences. TRANSAID’s hierarchical architecture efficiently processes long transcripts, capturing both local motifs and long-range dependencies. Crucially, the model was trained on a human transcriptome dataset that was rigorously partitioned at the gene level to prevent data leakage and included both protein-coding (NM) and non-coding (NR) transcripts.ResultsThis mixed-training strategy enables TRANSAID to achieve high fidelity, correctly identifying 73.61% of NR transcripts as non-coding. Performance is further enhanced by an integrated biological scoring system, improving “perfect ORF prediction” for coding sequences to 94.94% and “correct non-coding prediction” to 82.00%. The human-trained model demonstrates remarkable cross-species applicability, maintaining high accuracy on organisms from mammals to yeast. Beyond annotation, TRANSAID serves as a powerful discovery tool for novel coding events. When applied to long-read sequencing data, it accurately identified previously unannotated protein isoforms validated by mass spectrometry (76.28% validation rate). Furthermore, homology searches of high-scoring ORFs predicted within NR transcripts suggest a strong potential for identifying cryptic translation events.DiscussionAs a fully documented open-source tool with a user-friendly web server, TRANSAID provides a powerful and accessible resource for improving transcriptome annotation and proteomic discovery.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1690932</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1690932</link>
        <title><![CDATA[Altered circRNAs: a novel potential mechanism for the functions of extracellular vesicles derived from platelet-rich plasma]]></title>
        <pubdate>2026-01-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Lifeng Niu</author><author>Yanli Wang</author><author>Yao Gao</author><author>Jun Zhang</author>
        <description><![CDATA[Platelet-rich plasma (PRP) has been widely applied in clinical practice for tissue repair and regeneration. Recent studies have reported that large amounts of extracellular vesicles (EVs) derived from PRP (PRP-EVs) are also involved in the functions of tissue repair and regeneration, except for the secreted growth factors. However, the relevant mechanisms of PRP-EVs remain unknown. In this study, we attempted to reveal the potential circular RNA (circRNA) mechanisms of PRP-EVs using high-throughput RNA sequencing (RNA-seq) technique and bioinformatics analysis. Six healthy donors were enrolled in this study, including three donors for the isolation of PRP-EVs and three donors for the isolation of EVs derived from blood plasma (plasma-EVs). As a result, we confirmed that PRP activation by thrombin could significantly promote the formation and secretion of EVs, particularly those with diameters ranging from 50 to 200 nm. Moreover, 144 circRNAs were altered in PRP-EVs with a fold change ≥ 2.0 and p-value ≤ 0.05. Among these, 89 circRNAs were upregulated, whereas 55 circRNAs were downregulated. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and circRNA–miRNA–mRNA interaction network analyses were performed to predict the potential roles of circRNAs in PRP-EVs. GO analysis indicated that these altered circRNAs might be related to the physiological processes of cell genesis and development. The pathways that were most strongly correlated with the biological functions of PRP-EVs were the transforming growth factor β (TGF-β) signaling pathway and HIF-1 signaling pathway. In addition, the expression levels of five selected circRNAs were verified through RT-qPCR. In conclusion, this is the first study to explain a novel potential mechanism of the biological functions of PRP-EVs in terms of the altered circRNAs. Taken together, our findings in this study may lay the groundwork for the clinical application of PRP-EVs and provide possible novel targets for further research.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1696823</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1696823</link>
        <title><![CDATA[inDAGO: a user-friendly interface for seamless dual and bulk RNA-Seq analysis]]></title>
        <pubdate>2025-11-21T00:00:00Z</pubdate>
        <category>Technology and Code</category>
        <author>Gaetano Aufiero</author><author>Carmine Fruggiero</author><author>Nunzio D’Agostino</author>
        <description><![CDATA[Dual RNA-sequencing enables simultaneous profiling of protein-coding and non-coding transcripts from two interacting organisms, an essential capability when physical separation is difficult, such as in host-parasite or cross-kingdom interactions (e.g., plant-plant or host-pathogen systems). By allowing in silico separation of mixed reads, dual RNA-seq reveals the transcriptomic dynamics of both partners during interaction. However, existing analysis workflows often require programming expertise, limiting accessibility. We present inDAGO, a free, open-source, cross-platform graphical user interface designed for biologists without coding skills. inDAGO supports both bulk and dual RNA sequencing, with dual RNA sequencing further accommodating both sequential and combined approaches. The interface guides users through key analysis steps, including quality control, read alignment, read summarization, exploratory data analysis, and identification of differentially expressed genes, while generating intermediate outputs and publication-ready plots. Optimized for speed and efficiency, inDAGO performs complete analyses on a standard laptop (16 GB RAM) without requiring high-performance computing. We validated inDAGO using diverse real datasets to demonstrate its reliability and usability. inDAGO, available on CRAN (https://cran.r-project.org/web/packages/inDAGO/) and GitHub (https://github.com/inDAGOverse/inDAGO), lowers the technical barrier to dual RNA-seq by enabling robust, reproducible analyses, even for users without coding experience.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1629526</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1629526</link>
        <title><![CDATA[Unveiling the impact of interferon genes on the immune microenvironment of triple-negative breast cancer: identification of therapeutic targets]]></title>
        <pubdate>2025-10-08T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ying Liu</author><author>Jiayi Cai</author><author>Aamir Fahira</author><author>Kai Zhuang</author><author>Jiaojiao Wang</author><author>Zhi Zhang</author><author>Lin Yan</author><author>Yong Liu</author><author>Defang Ouyang</author><author>Zunnan Huang</author>
        <description><![CDATA[ObjectiveTriple-negative breast cancer (TNBC), a classic subtype of breast cancer, is challenging to treat due to the lack of drug-targeting receptors. This study aims to explore interferon-related prognostic molecular biomarkers in TNBC and their potential competing endogenous RNA (ceRNA) regulatory network in TNBC.MethodsRNA expression profiles and interferon genes were downloaded from the Cancer Genome Atlas (TCGA) database and the Gene Set Enrichment Analysis (GSEA) website, respectively. Univariate and multivariate Cox regression analyses were performed to identify prognostic genes and construct a risk model. Single-sample GSEA (ssGSEA) and the CellMiner database were used to explore the relationships between prognostic genes and both tumor immune microenvironment and drug sensitivity, respectively. The lncRNA-miRNA-mRNA network associated with prognosis was constructed using the ENCORI database. Finally, the potential interferon-associated lncRNA/miRNA/mRNA regulatory axis was identified through correlation analysis. The abnormal expressions of prognostic genes were validated in three TNBC tumor cell lines compared to normal mammary epithelial cells by using quantitative real-time polymerase chain reaction (qRT-PCR).ResultsThe TNBC prognostic signature comprising four interferon genes (STXBP1, LAMP3, CD276, and POLR2F) was identified, with their expression significantly correlated with the infiltration abundance of multiple immune cells and the drug sensitivity of 30 diverse drugs (ARQ-680, Fluphenazine, and Chelerythrine, etc.). Furthermore, an interferon-related genes prognostic ceRNA network was further constructed, consisting of 248 lncRNAs, 66 miRNAs, and 4 mRNAs. As a result, 5 interferon-related ceRNA regulatory axes (AC124067.4/hsa-miR-455-3p/STXBP1, RBPMS-AS1/hsa-miR-455-3p/STXBP1, DNMBP-AS1/hsa-miR-455-3p/STXBP1, FAM198B-AS1/hsa-miR-455-3p/STXBP1, LIFR-AS1/hsa-miR-455-3p/STXBP1) associated with TNBC progression were identified. QRT-PCR results showed that all four prognostic mRNAs were upregulated in TNBC cells.ConclusionThis study established a prognostic signature and a ceRNA network associated with interferon in TNBC, and identified five key regulatory axes. In the prognostic signature and the ceRNA axes, STXBP1, RBPMS-AS1, and FAM198B-AS1 were first reported as potential biomarkers of TNBC. These findings have the potential to provide new insights into the mechanisms driving TNBC tumorigenesis and development.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1633494</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1633494</link>
        <title><![CDATA[Association between dysregulated expression of Ca2+ and ROS-related genes and breast cancer patient survival]]></title>
        <pubdate>2025-09-22T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Sofia Ramos</author><author>João Gregório</author><author>Ana Sofia Fernandes</author><author>Nuno Saraiva</author>
        <description><![CDATA[The intricate interplay between Ca2+ and reactive oxygen species (ROS) signalling systems influences numerous cellular pathways. Dysregulated expression of genes associated with Ca2+ and ROS homeostasis can significantly impact cancer progression. Despite extensive research, various underlying mechanisms remain elusive, lacking a comprehensive unified perspective. Breast cancer (BC) remains the leading cause of cancer-related deaths among women, highlighting the pressing need to discover novel regulatory mechanisms, therapeutic targets, and potential biomarkers. In this study, we employed a bioinformatic approach based on data from The Cancer Genome Atlas to assess the association between combined dysregulation of specific pairs of genes involved in redox- or Ca2+-related cellular homeostases and patient outcome. These genes were selected by differences in their expression between normal and tumour tissues and in their individual association with patient survival rates. Cumulative proportion survival at the 5-year post-diagnosis was calculated for each quartile of expression within the population exhibiting either high or low expression of a second gene. Additional genes with expression positively or negatively correlated with the set of relevant gene pairs were identified, and a gene enrichment analysis was performed. Our results show that the simultaneous dysregulation of a selected number of gene pairs is substantially associated with BC patient survival. Notably, the expression dysregulation of these gene pairs is associated with altered expression of genes linked to cell cycle regulation, cell adhesion, and cell projection processes. This approach exhibits a significant potential to identify new prognostic biomarkers or drug targets for BC.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1605681</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1605681</link>
        <title><![CDATA[Bioinformatics analysis of lncRNA and mRNA differentially expressed in patients with cervical cancer]]></title>
        <pubdate>2025-08-01T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Xiaohua An</author><author>Xiaoxue Huang</author><author>Qiujie Yu</author><author>Yiyue Tang</author><author>Yan Wang</author><author>Huasu Chen</author><author>Yafei Zhang</author><author>Qianhao Huang</author><author>Yudi Rao</author><author>Guomei Hu</author><author>He Zha</author>
        <description><![CDATA[To verify the expression profile of long non-coding RNAs (lncRNAs) and mRNAs in cervical cancer, identify their clinical significance in HPV16-associated cervical cancer, and annotate the biological function of mRNAs. Three pairs of cancerous and paracancer tissues were selected in cervical squamous cell carcinoma (IB2 stage), high-throughput sequencing was utilized to determine the expression levels of lncRNAs and mRNAs. The detection results were validated by GEPIA database analysis and RT-qPCR. Functional annotations of differential mRNAs were conducted through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network states. Furthermore, the association between antisense lncRNA and mRNA in cervical cancer was analyzed to predict the biological functions of lncRNA. Finally, recombinant lentivirus CV224-HPV16 E6/E7 was transfected into HcerEpic to establish a stable cell line with overexpressed HPV16 E6/E7, then differential lncRNAs were detected by RT-qPCR. Compared to paracancerous tissues, there were 3,608 lncRNAs significantly upregulated and 4,383 lncRNAs significantly downregulated in cervical cancer tissues (Fold change >2 and P < 0.05). Additionally, 3,666 mRNAs were significantly upregulated, while 2,220 mRNAs were significantly downregulated (Fold change >2 and P < 0.05). GO/KEGG enrichment analysis showed that differentially expressed mRNA played a significant role in cell cycle and cell senescence, and was related to signal pathways such as cAMP and MAPK, forming a complex network among the proteins encoded by these mRNAs. Further analysis indicated that the 20 antisense lncRNAs with the most remarkable differences might exert biological functions by influencing their corresponding mRNAs. The results of RT-qPCR revealed that CDKN2B-AS1, HAGLROS and GATA6-AS1 were potentially regulated by HPV16 E6/E7, which were in accordance with those obtained from chip detection. In this study, differentially expressed lncRNAs associated with HPV16 infection were screened and explored their transcriptional molecular functions and biological pathways, providing a molecular basis for predicting diagnostic markers of cervical cancer.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1625145</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1625145</link>
        <title><![CDATA[Comparative transcriptome analysis of different tissues of Hylomecon japonica provides new insights into the biosynthesis pathway of triterpenoid saponins]]></title>
        <pubdate>2025-07-07T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Bing He</author><author>Teng Xu</author><author>Shaowei Xu</author><author>Huqiang Fang</author><author>Qingshan Yang</author>
        <description><![CDATA[Triterpenoid saponins are one of the main activities of roots and rhizomes of Hylomecon japonica, with various pharmacological activities such as antibacterial, anticancer, and anti-inflammatory. To elucidate the biosynthesis pathway of triterpenoid saponins in H. japonica, DNA nanoball sequencing technology was used to analyze the transcriptome of leaves, roots, and stems of H. japonica. Out of a total of 99,404 unigenes, 78,989 unigenes were annotated by seven major databases; 49 unigenes encoded 11 key enzymes in the biosynthesis pathway of triterpenoid saponins. Nine transcription factors were found to be involved in the metabolism of terpenoids and polyketides in H. japonica and a spatial structure model of squalene synthase in triterpenoid saponin biosynthesis was established. This study greatly enriched the transcriptome data of H. japonica, which is helpful for further analysis of the functions and regulatory mechanisms of key enzymes in the biosynthesis pathway of triterpenoid saponins.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1585794</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1585794</link>
        <title><![CDATA[CytoLNCpred-a computational method for predicting cytoplasm associated long non-coding RNAs in 15 cell-lines]]></title>
        <pubdate>2025-05-26T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Shubham Choudhury</author><author>Naman Kumar Mehta</author><author>Gajendra P. S. Raghava</author>
        <description><![CDATA[The function of long non-coding RNA (lncRNA) is largely determined by its specific location within a cell. Previous methods have used noisy datasets, including mRNA transcripts in tools intended for lncRNAs, and excluded lncRNAs lacking significant differential localization between the cytoplasm and nucleus. In order to overcome these shortcomings, a method has been developed for predicting cytoplasm-associated lncRNAs in 15 human cell-lines, identifying which lncRNAs are more abundant in the cytoplasm compared to the nucleus. All models in this study were trained using five-fold cross validation and tested on an validation dataset. Initially, we developed machine and deep learning based models using traditional features like composition and correlation. Using composition and correlation based features, machine learning algorithms achieved an average AUC of 0.7049 and 0.7089, respectively for 15 cell-lines. Secondly, we developed machine based models developed using embedding features obtained from the large language model DNABERT-2. The average AUC for all the cell-lines achieved by this approach was 0.665. Subsequently, we also fine-tuned DNABERT-2 on our training dataset and evaluated the fine-tuned DNABERT-2 model on the validation dataset. The fine-tuned DNABERT-2 model achieved an average AUC of 0.6336. Correlation-based features combined with ML algorithms outperform LLM-based models, in the case of predicting differential lncRNA localization. These cell-line specific models as well as web-based service are available to the public from our web server (https://webs.iiitd.edu.in/raghava/cytolncpred/).]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1575346</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1575346</link>
        <title><![CDATA[MORE-RNAseq: a pipeline for quantifying retrotransposition-capable LINE1 expression based on RNA-seq data]]></title>
        <pubdate>2025-05-22T00:00:00Z</pubdate>
        <category>Brief Research Report</category>
        <author>Yutaka Nakachi</author><author>Jianbin Du</author><author>Risa Watanabe</author><author>Yutaro Yanagida</author><author>Miki Bundo</author><author>Kazuya Iwamoto</author>
        <description><![CDATA[Retrotransposon long interspersed nuclear element-1 (LINE-1, L1) constitutes a large proportion of the mammalian genome. A fraction of L1s, which have no deleterious mutations in the structure, can amplify their copies via a process called retrotransposition (RT). RT affects genome stability and gene expression and is involved in the pathogenesis of many hereditary diseases. Measuring expression of RT-capable L1s (rc-L1s) among the hundreds of thousands of non rc-L1s is an essential step to understand the impact of RT. We developed mobile element-originated read enrichment from RNA-seq data (MORE-RNAseq), a pipeline for calculating expression of rc-L1s using manually curated L1 references in humans and mice. MORE-RNAseq allows for quantification of expression levels of overall (sum of the expression of all rc-L1s) and individual rc-L1s with consideration of the genomic context. We applied MORE-RNAseq to publicly available RNA-seq data of human and mouse cancer cell lines from the studies that reported increased L1 expression. We found the significant increase of rc-L1 expressions at the overall level in both inter- and intragenic contexts. We also identified differentially expressed rc-L1s at the locus level, which will be the important candidates for downstream analysis. We also applied our method to young and aged human muscle RNA-seq data with no prior information about L1 expression, and found a significant increase of rc-L1 expression in the aged samples. Our method will contribute to understand the role of rc-L1s in various physiological and pathophysiological conditions using standard RNA-seq data. All scripts are available at https://github.com/molbrain/MORE-RNAseq.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1571476</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1571476</link>
        <title><![CDATA[Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods]]></title>
        <pubdate>2025-05-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Ankita Lawarde</author><author>Masuma Khatun</author><author>Prakash Lingasamy</author><author>Andres Salumets</author><author>Vijayachitra Modhukur</author>
        <description><![CDATA[IntroductionMicroRNAs (miRNAs) regulate gene expression and play an important role in carcinogenesis through complex interactions with messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs). Despite their established influence on tumor progression and therapeutic resistance, the application of miRNA interaction networks for tumor tissue-of-origin (TOO) classification remains underexplored.MethodsWe developed a machine learning (ML) framework that integrates miRNA-mRNA-lncRNA interaction networks to classify tumors by their tissue of origin. Using transcriptomic profiles from 14 cancer types in The Cancer Genome Atlas (TCGA), we constructed co-expression networks and applied multiple feature selection techniques including recursive feature elimination (RFE), random forest (RF), Boruta, and linear discriminant analysis (LDA) to identify a minimal yet informative subset of miRNA features. Ensemble ML algorithms were trained and validated with stratified five-fold cross-validation for robust performance assessment across class distributions.ResultsOur models achieved an overall 99% classification accuracy, distinguishing 14 cancer types with high robustness and generalizability. A minimal set of 150 miRNAs selected via RFE resulted in optimal performance across all classifiers. Furthermore, in silico validation revealed that many of the top miRNAs, including miR-21-5p, miR-93-5p, and miR-10b-5p, were not only highly central in the network but also correlated with patient survival and drug response. In addition, functional enrichment analyses indicated significant involvement of miRNAs in pathways such as TGF-beta signaling, epithelial-mesenchymal transition, and immune modulation. Our comparative analysis demonstrated that models based on miRNA outperformed those using mRNA or lncRNA classifiers.DiscussionOur integrated framework provides a biologically grounded, interpretable, and highly accurate approach for tumor tissue-of-origin classification. The identified miRNA biomarkers demonstrate strong translational potential, supported by clinical trial overlap, drug sensitivity data, and survival analyses. This work highlights the power of combining miRNA network biology with ML to improve precision oncology diagnostics and supports future development of liquid biopsy-based cancer classification.]]></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2025.1545680</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2025.1545680</link>
        <title><![CDATA[An extended miRNA repertoire in Rattus norvegicus]]></title>
        <pubdate>2025-03-10T00:00:00Z</pubdate>
        <category>Data Report</category>
        <author>Julienne Lehmann</author><author>Ali Yazbeck</author><author>Jörg Hackermüller</author><author>Sebastian Canzler</author>
        <description></description>
      </item><item>
        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1487292</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1487292</link>
        <title><![CDATA[hsa-miR-548d-3p: a potential microRNA to target nucleocapsid and/or capsid genes in multiple members of the Flaviviridae family ]]></title>
        <pubdate>2025-01-14T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>H. W. Cayatineto</author><author>S. T. Hakim</author>
        <description><![CDATA[IntroductionFlaviviridae comprise a group of enveloped, positive-stranded RNA viruses that are mainly transmitted through either mosquitoes or tick bites and/or contaminated blood, blood products, or other body secretions. These viruses cause diseases ranging from mild to severe and are considered important human pathogens. MicroRNAs (miRNAs) are non-coding molecules involved in growth, development, cell proliferation, protein synthesis, apoptosis, and pathogenesis. These small molecules are even being used as gene suppressors in antiviral therapeutics, inhibiting viral replication. In the current study, we used bioinformatic tools to predict a possible miRNA sequence that could be complementary to the nucleocapsid (NP) and/or capsid (CP) gene of the Flaviviridae family and provide an inhibitory solution.MethodsBioinformatics is a field of science that includes tremendous computational analysis, logarithms, and sequence alignments. To predict the right alignments between miRNA and viral mRNA genomes, we used computational databases such as miRBase, NCBI, and Basic Alignment Search Tool–nucleotides (BLAST-n).ResultsOf the 2,600 mature miRNAs, hsa-miR-548d-3p revealed complementary sequences with the flavivirus capsid gene and bovine viral diarrhea virus (BVDV) capsid gene and was selected as a possible candidate to inhibit flaviviruses.ConclusionAlthough more detailed in vitro and in vivo studies are required to test the possible inhibitory effects of hsa-miR-548d-3p against flaviviruses, this computational study may be the first step to study further, developing a novel therapeutic for lethal viruses within the Flaviviridae family using suggested candidate miRNAs.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fbinf.2024.1493712</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fbinf.2024.1493712</link>
        <title><![CDATA[In silico identification of chilli genome encoded MicroRNAs targeting the 16S rRNA and secA genes of “Candidatus phytoplasma trifolii”]]></title>
        <pubdate>2025-01-06T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Vineeta Pandey</author><author>Aarshi Srivastava</author><author>Ramwant Gupta</author><author>Haitham E. M. Zaki</author><author>Muhammad Shafiq Shahid</author><author>Rajarshi K. Gaur</author>
        <description><![CDATA[Phytoplasma, a potentially hazardous pathogen associated with witches’ broom, is an economically harmful disease-producing bacteria that damages chilli cultivation. Phytoplasma-infected plants display various symptoms that indicate significant disruptions in normal plant physiology and behaviour. Diseases caused by phytoplasma are widespread and have a major economic impact on crop quality and yield. This work focuses on identifying and examining chilli microRNAs (miRNAs) as potential targets against the 16S rRNA and secA gene of “Candidatus Phytoplasma trifolii” (“Ca. P. trifolii”) through plant miRNA prediction algorithms. Mature chilli miRNAs (CA-miRNAs) were collected and used to hybridise the 16S rRNA and secA genes. A total of four common CA-miRNAs were picked according to genetic consensus. Three algorithms applied in the present study suggested that the physiologically relevant, top-ranked miR169b_2 has a possibly specific site at nucleotide position 1,006 for targeting the ‘Ca. P. trifolii’ 16S rRNA gene. The circos algorithm was then utilised to create the miRNA-mRNA regulatory network. The free energy between the miRNA:mRNA duplex was also computed, and the best value of −17.46 kcal/mol was obtained for CA-miR166c_2. Currently, there are no suitable commercial ‘Ca. P. trifolii’-resistant chilli crops. As a result, the expected biological data provide useful evidence for developing ‘Ca. P. trifolii’-resistant chilli plants.]]></description>
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