Briefings in Functional Genomics最新文献_第9页

Predicting drug synergy using a network propagation inspired machine learning framework. 利用网络传播启发的机器学习框架预测药物协同作用。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-07-19 DOI: 10.1093/bfgp/elad056

Qing Jin, Xianze Zhang, Diwei Huo, Hongbo Xie, Denan Zhang, Lei Liu, Yashuang Zhao, Xiujie Chen

{"title":"Predicting drug synergy using a network propagation inspired machine learning framework.","authors":"Qing Jin, Xianze Zhang, Diwei Huo, Hongbo Xie, Denan Zhang, Lei Liu, Yashuang Zhao, Xiujie Chen","doi":"10.1093/bfgp/elad056","DOIUrl":"10.1093/bfgp/elad056","url":null,"abstract":"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.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"429-440"},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139106947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Competing endogenous RNAs in head and neck squamous cell carcinoma: a review. 头颈部鳞状细胞癌中竞争性内源性RNA的研究进展。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-07-19 DOI: 10.1093/bfgp/elad049

Avantika Agrawal, Vaibhav Vindal

{"title":"Competing endogenous RNAs in head and neck squamous cell carcinoma: a review.","authors":"Avantika Agrawal, Vaibhav Vindal","doi":"10.1093/bfgp/elad049","DOIUrl":"10.1093/bfgp/elad049","url":null,"abstract":"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.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"335-348"},"PeriodicalIF":2.5,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71523465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning. 利用多模态深度学习从组织病理学图像预测胃癌肿瘤突变负荷

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad032

Jing Li, Haiyan Liu, Wei Liu, Peijun Zong, Kaimei Huang, Zibo Li, Haigang Li, Ting Xiong, Geng Tian, Chun Li, Jialiang Yang

{"title":"Predicting gastric cancer tumor mutational burden from histopathological images using multimodal deep learning.","authors":"Jing Li, Haiyan Liu, Wei Liu, Peijun Zong, Kaimei Huang, Zibo Li, Haigang Li, Ting Xiong, Geng Tian, Chun Li, Jialiang Yang","doi":"10.1093/bfgp/elad032","DOIUrl":"10.1093/bfgp/elad032","url":null,"abstract":"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.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"228-238"},"PeriodicalIF":2.5,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9965530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development. 基于 Omics 的深度学习方法用于肺癌决策和疗法开发。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad031

Thi-Oanh Tran, Thanh Hoa Vo, Nguyen Quoc Khanh Le

{"title":"Omics-based deep learning approaches for lung cancer decision-making and therapeutics development.","authors":"Thi-Oanh Tran, Thanh Hoa Vo, Nguyen Quoc Khanh Le","doi":"10.1093/bfgp/elad031","DOIUrl":"10.1093/bfgp/elad031","url":null,"abstract":"Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated. As omics data grow, artificial intelligence models, particularly deep learning, are prominent in developing more rapid and effective methods to potentially improve lung cancer patient diagnosis, prognosis and treatment strategy. This decade has seen genome-based deep learning models thriving in various lung cancer tasks, including cancer prediction, subtype classification, prognosis estimation, cancer molecular signatures identification, treatment response prediction and biomarker development. In this study, we summarized available data sources for deep-learning-based lung cancer mining and provided an update on recent deep learning models in lung cancer genomics. Subsequently, we reviewed the current issues and discussed future research directions of deep-learning-based lung cancer genomics research.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"181-192"},"PeriodicalIF":2.5,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10281428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping of long stretches of highly conserved sequences in over 6 million SARS-CoV-2 genomes. 在 600 多万个 SARS-CoV-2 基因组中绘制高度保守的长序列图。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad027

Akhil Kumar, Rishika Kaushal, Himanshi Sharma, Khushboo Sharma, Manoj B Menon, Vivekanandan P

{"title":"Mapping of long stretches of highly conserved sequences in over 6 million SARS-CoV-2 genomes.","authors":"Akhil Kumar, Rishika Kaushal, Himanshi Sharma, Khushboo Sharma, Manoj B Menon, Vivekanandan P","doi":"10.1093/bfgp/elad027","DOIUrl":"10.1093/bfgp/elad027","url":null,"abstract":"We identified 11 conserved stretches in over 6.3 million SARS-CoV-2 genomes including all the major variants of concerns. Each conserved stretch is ≥100 nucleotides in length with ≥99.9% conservation at each nucleotide position. Interestingly, six of the eight conserved stretches in ORF1ab overlapped significantly with well-folded experimentally verified RNA secondary structures. Furthermore, two of the conserved stretches were mapped to regions within the S2-subunit that undergo dynamic structural rearrangements during viral fusion. In addition, the conserved stretches were significantly depleted for zinc-finger antiviral protein (ZAP) binding sites, which facilitated the recognition and degradation of viral RNA. These highly conserved stretches in the SARS-CoV-2 genome were poorly conserved at the nucleotide level among closely related β-coronaviruses, thus representing ideal targets for highly specific and discriminatory diagnostic assays. Our findings highlight the role of structural constraints at both RNA and protein levels that contribute to the sequence conservation of specific genomic regions in SARS-CoV-2.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"256-264"},"PeriodicalIF":2.5,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9824704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Widespread transcriptomic alterations of transient receptor potential channel genes in cancer. 癌症中瞬时受体电位通道基因的广泛转录组变化。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad023

Tao Pan, Yueying Gao, Gang Xu, Lei Yu, Qi Xu, Jinyang Yu, Meng Liu, Can Zhang, Yanlin Ma, Yongsheng Li

引用次数: 0

Integration of hybrid and self-correction method improves the quality of long-read sequencing data. 混合和自校正方法的整合提高了长读数测序数据的质量。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad026

Tao Tang, Yiping Liu, Binshuang Zheng, Rong Li, Xiaocai Zhang, Yuansheng Liu

引用次数: 0

DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information. DeepCMI：基于图的模型，可准确预测具有多种信息的 circRNA-miRNA 相互作用。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad030

Yue-Chao Li, Zhu-Hong You, Chang-Qing Yu, Lei Wang, Lun Hu, Peng-Wei Hu, Yan Qiao, Xin-Fei Wang, Yu-An Huang

{"title":"DeepCMI: a graph-based model for accurate prediction of circRNA-miRNA interactions with multiple information.","authors":"Yue-Chao Li, Zhu-Hong You, Chang-Qing Yu, Lei Wang, Lun Hu, Peng-Wei Hu, Yan Qiao, Xin-Fei Wang, Yu-An Huang","doi":"10.1093/bfgp/elad030","DOIUrl":"10.1093/bfgp/elad030","url":null,"abstract":"Recently, the role of competing endogenous RNAs in regulating gene expression through the interaction of microRNAs has been closely associated with the expression of circular RNAs (circRNAs) in various biological processes such as reproduction and apoptosis. While the number of confirmed circRNA-miRNA interactions (CMIs) continues to increase, the conventional in vitro approaches for discovery are expensive, labor intensive, and time consuming. Therefore, there is an urgent need for effective prediction of potential CMIs through appropriate data modeling and prediction based on known information. In this study, we proposed a novel model, called DeepCMI, that utilizes multi-source information on circRNA/miRNA to predict potential CMIs. Comprehensive evaluations on the CMI-9905 and CMI-9589 datasets demonstrated that DeepCMI successfully infers potential CMIs. Specifically, DeepCMI achieved AUC values of 90.54% and 94.8% on the CMI-9905 and CMI-9589 datasets, respectively. These results suggest that DeepCMI is an effective model for predicting potential CMIs and has the potential to significantly reduce the need for downstream in vitro studies. To facilitate the use of our trained model and data, we have constructed a computational platform, which is available at http://120.77.11.78/DeepCMI/. The source code and datasets used in this work are available at https://github.com/LiYuechao1998/DeepCMI.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"276-285"},"PeriodicalIF":2.5,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10291543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prediction of strand-specific and cell-type-specific G-quadruplexes based on high-resolution CUT&Tag data. 基于高分辨率 CUT&Tag 数据预测链特异性和细胞类型特异性 G-四重链。

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad024

Yizhi Cui, Hongzhi Liu, Yutong Ming, Zheng Zhang, Li Liu, Ruijun Liu

{"title":"Prediction of strand-specific and cell-type-specific G-quadruplexes based on high-resolution CUT&Tag data.","authors":"Yizhi Cui, Hongzhi Liu, Yutong Ming, Zheng Zhang, Li Liu, Ruijun Liu","doi":"10.1093/bfgp/elad024","DOIUrl":"10.1093/bfgp/elad024","url":null,"abstract":"G-quadruplex (G4), a non-classical deoxyribonucleic acid structure, is widely distributed in the genome and involved in various biological processes. In vivo, high-throughput sequencing has indicated that G4s are significantly enriched at functional regions in a cell-type-specific manner. Therefore, the prediction of G4s based on computational methods is necessary instead of the time-consuming and laborious experimental methods. Recently, G4 CUT&Tag has been developed to generate higher-resolution sequencing data than ChIP-seq, which provides more accurate training samples for model construction. In this paper, we present a new dataset construction method based on G4 CUT&Tag sequencing data and an XGBoost prediction model based on the machine learning boost method. The results show that our model performs well within and across cell types. Furthermore, sequence analysis indicates that the formation of G4 structure is greatly affected by the flanking sequences, and the GC content of the G4 flanking sequences is higher than non-G4. Moreover, we also identified G4 motifs in the high-resolution dataset, among which we found several motifs for known transcription factors (TFs), such as SP2 and BPC. These TFs may directly or indirectly affect the formation of the G4 structure.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"265-275"},"PeriodicalIF":2.5,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9683854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Prognostic signature analysis and survival prediction of esophageal cancer based on N6-methyladenosine associated lncRNAs. 基于N6-甲基腺苷相关lncRNA的食管癌预后特征分析和生存预测

IF 2.5 3区生物学

Briefings in Functional Genomics Pub Date : 2024-05-15 DOI: 10.1093/bfgp/elad028

Ting He, Zhipeng Gao, Ling Lin, Xu Zhang, Quan Zou

{"title":"Prognostic signature analysis and survival prediction of esophageal cancer based on N6-methyladenosine associated lncRNAs.","authors":"Ting He, Zhipeng Gao, Ling Lin, Xu Zhang, Quan Zou","doi":"10.1093/bfgp/elad028","DOIUrl":"10.1093/bfgp/elad028","url":null,"abstract":"Esophageal cancer (ESCA) has a bad prognosis. Long non-coding RNA (lncRNA) impacts on cell proliferation. However, the prognosis function of N6-methyladenosine (m6A)-associated lncRNAs (m6A-lncRNAs) in ESCA remains unknown. Univariate Cox analysis was applied to investigate prognosis related m6A-lncRNAs, based on which the samples were clustered. Wilcoxon rank and Chi-square tests were adopted to compare the clinical traits, survival, pathway activity and immune infiltration in different clusters where overall survival, clinical traits (N stage), tumor-invasive immune cells and pathway activity were found significantly different. Through least absolute shrinkage and selection operator and proportional hazard (Lasso-Cox) model, five m6A-lncRNAs were selected to construct the prognostic signature (m6A-lncSig) and risk score. To investigate the link between risk score and clinical traits or immunological microenvironments, Chi-square test and Spearman correlation analysis were utilized. Risk score was found connected with N stage, tumor stage, different clusters, macrophages M2, B cells naive and T cells CD4 memory resting. Risk score and tumor stage were found as independent prognostic variables. And the constructed nomogram model had high accuracy in predicting prognosis. The obtained m6A-lncSig could be taken as potential prognostic biomarker for ESCA patients. This study offers a theoretical foundation for clinical diagnosis and prognosis of ESCA.","PeriodicalId":55323,"journal":{"name":"Briefings in Functional Genomics","volume":" ","pages":"239-248"},"PeriodicalIF":2.5,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9886829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0