Briefings in Functional Genomics最新文献

Circular RNAs (circRNAs) are a class of noncoding RNA molecules that are widely found in cells. Recent studies have revealed the significant role played by circRNAs in human health and disease treatment. Several restrictions are encountered because forecasting prospective circRNAs and medication sensitivity connections through biological research is not only time-consuming and expensive but also incredibly ineffective. Consequently, the development of a novel computational method that enhances both the efficiency and accuracy of predicting the associations between circRNAs and drug sensitivities is urgently needed. Here, we present DGATCCDA, a computational method based on deep learning, for circRNA-drug sensitivity association identification. In DGATCCDA, we first construct multimodal networks from the original feature information of circRNAs and drugs. After that, we adopt DeepWalk-aware graph attention networks to sufficiently extract feature information from the multimodal networks to obtain the embedding representation of nodes. Specifically, we combine DeepWalk and graph attention network to form DeepWalk-aware graph attention networks, which can effectively capture the global and local information of graph structures. The features extracted from the multimodal networks are fused by layer attention, and eventually, the inner product approach is used to construct the association matrix of circRNAs and drugs for prediction. The ultimate experimental results obtained under 5-fold cross-validation settings show that the average area under the receiver operating characteristic curve value of DGATCCDA reaches 91.18%, which is better than those of the five current state-of-the-art calculation methods. We further guide a case study, and the excellent obtained results also show that DGATCCDA is an effective computational method for exploring latent circRNA-drug sensitivity associations.

Identification of individual-level differentially expressed genes (DEGs) is a pre-step for the analysis of disease-specific biological mechanisms and precision medicine. Previous algorithms cannot balance accuracy and sufficient statistical power. Herein, RankCompV2, designed for identifying population-level DEGs based on relative expression orderings, was adjusted to identify individual-level DEGs. Furthermore, an optimized version of individual-level RankCompV2, named as RankCompV2.1, was designed based on the assumption that the rank positions of genes and relative rank differences of gene pairs would influence the identification of individual-level DEGs. In comparison to other individualized analysis algorithms, RankCompV2.1 performed better on statistical power, computational efficiency, and acquired coequal accuracy in both simulation and real paired cancer-normal data from ten cancer types. Besides, single sample GSEA and Gene Set Variation Analysis analysis showed that pathways enriched with up-regulated and down-regulated genes presented higher and lower enrichment scores, respectively. Furthermore, we identified 16 genes that were universally deregulated in 966 triple-negative breast cancer (TNBC) samples and interacted with Food and Drug Administration (FDA)-approved drugs or antineoplastic agents, indicating notable therapeutic targets for TNBC. In addition, we also identified genes with highly variable deregulation status and used these genes to cluster TNBC samples into three subgroups with different prognoses. The subgroup with the poorest outcome was characterized by down-regulated immune-regulated pathways, signal transduction pathways, and apoptosis-related pathways. Protein-protein interaction network analysis revealed that OAS family genes may be promising drug targets to activate tumor immunity in this subgroup. In conclusion, RankCompV2.1 is capable of identifying individual-level DEGs with high accuracy and statistical power, analyzing mechanisms of carcinogenesis and exploring therapeutic strategy.

Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of disease have revealed that circRNA can be considered as a potential biomarker. Therefore, understanding the association of circRNA and diseases can help to forecast some disorders of life activities. However, traditional biological experimental methods are time-consuming. The most common method for circRNA-disease association prediction on the basis of machine learning can avoid this, which relies on diverse data. Nevertheless, topological information of circRNA and disease usually is not involved in these methods. Moreover, circRNAs can be associated with diseases through miRNAs. With these considerations, we proposed a novel method, named THGNCDA, to predict the association between circRNAs and diseases. Specifically, for a certain pair of circRNA and disease, we employ a graph neural network with attention to learn the importance of its each neighbor. In addition, we use a multilayer convolutional neural network to explore the relationship of a circRNA-disease pair based on their attributes. When calculating embeddings, we introduce the information of miRNAs. The results of experiments show that THGNCDA outperformed the SOTA methods. In addition, it can be observed that our method gives a better recall rate. To confirm the significance of attention, we conducted extensive ablation studies. Case studies on Urinary Bladder and Prostatic Neoplasms further show THGNCDA's ability in discovering known relationships between circRNA candidates and diseases.

The cases of inflammatory bowel disease (IBD) are increasing rapidly around the world. Due to the multifactorial causes of IBD, there is an urgent need to understand the pathogenesis of IBD. As such, the usage of high-throughput techniques to profile genetic mutations, microbiome environments, transcriptome and proteome (e.g. lipidome) is increasing to understand the molecular changes associated with IBD, including two major etiologies of IBD: Crohn disease (CD) and ulcerative colitis (UC). In the case of transcriptome data, RNA sequencing (RNA-seq) technique is used frequently. However, only protein-coding genes are analyzed, leaving behind all other RNAs, including non-coding RNAs (ncRNAs) to be unexplored. Among these ncRNAs, long non-coding RNAs (lncRNAs) may hold keys to understand the pathogenesis of IBD as lncRNAs are expressed in a cell/tissue-specific manner and dysregulated in a disease, such as IBD. However, it is rare that RNA-seq data are analyzed for lncRNAs. To fill this gap in knowledge, we re-analyzed RNA-seq data of CD and UC patients compared with the healthy donors to dissect the expression profiles of lncRNA genes. As inflammation plays key roles in the pathogenesis of IBD, we conducted loss-of-function experiments to provide functional data of IBD-specific lncRNA, lung cancer associated transcript 1 (LUCAT1), in an in vitro model of macrophage polarization. To further facilitate the lncRNA research in IBD, we built a web database, IBDB (https://ibd-db.shinyapps.io/IBDB/), to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in IBD patients compared with healthy donors.

Genome sequencing data have become increasingly important in the field of personalized medicine and diagnosis. However, accurately detecting genomic variations remains a challenging task. Traditional variation detection methods rely on manual inspection or predefined rules, which can be time-consuming and prone to errors. Consequently, deep learning-based approaches for variation detection have gained attention due to their ability to automatically learn genomic features that distinguish between variants. In our review, we discuss the recent advancements in deep learning-based algorithms for detecting small variations and structural variations in genomic data, as well as their advantages and limitations.

Combination therapy is a promising strategy for cancers, increasing therapeutic options and reducing drug resistance. Yet, systematic identification of efficacious drug combinations is limited by the combinatorial explosion caused by a large number of possible drug pairs and diseases. At present, machine learning techniques have been widely applied to predict drug combinations, but most studies rely on the response of drug combinations to specific cell lines and are not entirely satisfactory in terms of mechanism interpretability and model scalability. Here, we proposed a novel network propagation-based machine learning framework to predict synergistic drug combinations. Based on the topological information of a comprehensive drug-drug association network, we innovatively introduced an affinity score between drug pairs as one of the features to train machine learning models. We applied network-based strategy to evaluate their therapeutic potential to different cancer types. Finally, we identified 17 specific-, 21 general- and 40 broad-spectrum antitumor drug combinations, in which 69% drug combinations were validated by vitro cellular experiments, 83% drug combinations were validated by literature reports and 100% drug combinations were validated by biological function analyses. By quantifying the network relationships between drug targets and cancer-related driver genes in the human protein-protein interactome, we show the existence of four distinct patterns of drug-drug-disease relationships. We also revealed that 32 biological pathways were correlated with the synergistic mechanism of broad-spectrum antitumor drug combinations. Overall, our model offers a powerful scalable screening framework for cancer treatments.

Our understanding of RNA biology has evolved with recent advances in research from it being a non-functional product to molecules of the genome with specific regulatory functions. Competitive endogenous RNA (ceRNA), which has gained prominence over time as an essential part of post-transcriptional regulatory mechanism, is one such example. The ceRNA biology hypothesis states that coding RNA and non-coding RNA co-regulate each other using microRNA (miRNA) response elements. The ceRNA components include long non-coding RNAs, pseudogene and circular RNAs that exert their effect by interacting with miRNA and regulate the expression level of its target genes. Emerging evidence has revealed that the dysregulation of the ceRNA network is attributed to the pathogenesis of various cancers, including the head and neck squamous cell carcinoma (HNSCC). This is the most prevalent cancer developed from the mucosal epithelium in the lip, oral cavity, larynx and pharynx. Although many efforts have been made to comprehend the cause and subsequent treatment of HNSCC, the morbidity and mortality rate remains high. Hence, there is an urgent need to understand the holistic progression of HNSCC, mediated by ceRNA, that can have immense relevance in identifying novel biomarkers with a defined therapeutic intervention. In this review, we have made an effort to highlight the ceRNA biology hypothesis with a focus on its involvement in the progression of HNSCC. For the identification of such ceRNAs, we have additionally highlighted a number of databases and tools.

Acute myeloid leukemia (AML) is a type of blood cancer with diverse genetic variations and DNA methylation alterations. By studying the interaction of gene mutations, expression, and DNA methylation, we aimed to gain valuable insights into the processes that lead to block differentiation in AML. We analyzed TCGA-LAML data (173 samples) with RNA sequencing and DNA methylation arrays, comparing FLT3 mutant (48) and wild-type (125) cases. We conducted differential gene expression analysis using cBioPortal, identified DNA methylation differences with ChAMP tool, and correlated them with gene expression changes. Gene set enrichment analysis (g:Profiler) revealed significant biological processes and pathways. ShinyGo and GeneCards were used to find potential transcription factors and their binding sites among significant genes. We found significant differentially expressed genes (DEGs) negatively correlated with their most significant methylation probes (Pearson correlation coefficient of -0.49, P-value <0.001) between FLT3 mutant and wild-type groups. Moreover, our exploration of 450 k CpG sites uncovered a global hypo-methylated status in 168 DEGs. Notably, these methylation changes were enriched in the promoter regions of Homebox superfamily gene, which are crucial in transcriptional-regulating pathways in blood cancer. Furthermore, in FLT3 mutant AML patient samples, we observed overexpress of WT1, a transcription factor known to bind homeobox gene family. This finding suggests a potential mechanism by which WT1 recruits TET2 to demethylate specific genomic regions. Integrating gene expression and DNA methylation analyses shed light on the impact of FLT3 mutations on cancer cell development and differentiation, supporting a two-hit model in AML. This research advances understanding of AML and fosters targeted therapeutic strategy development.

RNA modifications include not only methylation modifications, such as m6A, but also acetylation modifications, which constitute a complex interaction involving "writers," "readers," and "erasers" that play crucial roles in growth, genetics, and disease. N4-acetylcytidine (ac4C) is an ancient and highly conserved RNA modification that plays a profound role in the pathogenesis of a wide range of diseases. This review provides insights into the functional impact of ac4C modifications in disease and introduces new perspectives for disease treatment. These studies provide important insights into the biological functions of post-transcriptional RNA modifications and their potential roles in disease mechanisms, offering new perspectives and strategies for disease treatment.

Tumor mutational burden (TMB) is a significant predictive biomarker for selecting patients that may benefit from immune checkpoint inhibitor therapy. Whole exome sequencing is a common method for measuring TMB; however, its clinical application is limited by the high cost and time-consuming wet-laboratory experiments and bioinformatics analysis. To address this challenge, we downloaded multimodal data of 326 gastric cancer patients from The Cancer Genome Atlas, including histopathological images, clinical data and various molecular data. Using these data, we conducted a comprehensive analysis to investigate the relationship between TMB, clinical factors, gene expression and image features extracted from hematoxylin and eosin images. We further explored the feasibility of predicting TMB levels, i.e. high and low TMB, by utilizing a residual network (Resnet)-based deep learning algorithm for histopathological image analysis. Moreover, we developed a multimodal fusion deep learning model that combines histopathological images with omics data to predict TMB levels. We evaluated the performance of our models against various state-of-the-art methods using different TMB thresholds and obtained promising results. Specifically, our histopathological image analysis model achieved an area under curve (AUC) of 0.749. Notably, the multimodal fusion model significantly outperformed the model that relied only on histopathological images, with the highest AUC of 0.971. Our findings suggest that histopathological images could be used with reasonable accuracy to predict TMB levels in gastric cancer patients, while multimodal deep learning could achieve even higher levels of accuracy. This study sheds new light on predicting TMB in gastric cancer patients.