Genomics, proteomics & bioinformatics最新文献_第3页

Genomic Insights into Divergent Evolutionary Strategies and Vector Capacities of Ticks. 蜱的不同进化策略和媒介能力的基因组见解。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-24 DOI: 10.1093/gpbjnl/qzag089

Wenqiang Shi, Jia-Jing Zheng, Yi-Fei Wang, Hua Wei, Yi Sun, Li-Feng Du, Di Tian, Xiao-Ming Cui, Na Jia, Ming-Zhu Zhang, Xiao-Yu Shi, Jin Luo, Weifei Yang, Ning Wang, Bai-Hui Wang, Wan-Ying Gao, Zhijun Yu, Tian-Hong Wang, Di Zhao, Ming-Xiang Wang, Zhongsi Wang, Zhi-Hong Liu, Jia-Fu Jiang, Guangyuan Liu, Wu-Chun Cao

{"title":"Genomic Insights into Divergent Evolutionary Strategies and Vector Capacities of Ticks.","authors":"Wenqiang Shi, Jia-Jing Zheng, Yi-Fei Wang, Hua Wei, Yi Sun, Li-Feng Du, Di Tian, Xiao-Ming Cui, Na Jia, Ming-Zhu Zhang, Xiao-Yu Shi, Jin Luo, Weifei Yang, Ning Wang, Bai-Hui Wang, Wan-Ying Gao, Zhijun Yu, Tian-Hong Wang, Di Zhao, Ming-Xiang Wang, Zhongsi Wang, Zhi-Hong Liu, Jia-Fu Jiang, Guangyuan Liu, Wu-Chun Cao","doi":"10.1093/gpbjnl/qzag089","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag089","url":null,"abstract":"Ticks, which are globally distributed, transmit a wide range of human and animal pathogens. Soft and hard ticks are two major tick families exhibiting distinct vector capacities and blood-feeding abilities. However, understanding the genetic basis of these differences is hindered by the scarcity of high-quality tick genomes, particularly for soft ticks. Here, we present high-quality genomes of two soft and two hard tick species of both evolutionary and medical importance, and compare the genomic characteristics of the two tick families. Compared with soft ticks, hard ticks exhibited faster genome evolution, characterized by larger genome sizes, higher rates of gene gain and loss, and a greater number of positively selected genes. Both tick families exhibited frequent positive selection across metabolism and organismal systems, sharing a core set of adaptive genes but undergoing refinement at different sites. Gene families underpinning various vector capacities, including those involved in cuticle structure, heme biosynthesis, egg waxing, and immune pathways, were identified. Additionally, we assembled the genomes of two novel Rickettsia endosymbionts from soft ticks, revealing high genomic divergence and lineage-specific adaptations to their hosts. These genomic resources provide fundamental insights into the genetic basis of tick biology and vector capacity, highlighting the divergent evolutionary strategies of blood-feeding arthropods shaped by their ecological niches.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148815288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bio-OS: An Integrated Cloud-native Environment for Data-Intensive Biomedical Research. Bio-OS：用于数据密集型生物医学研究的集成云原生环境。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-20 DOI: 10.1093/gpbjnl/qzag085

Jilong Liu, Liguang Yang, Qingyu Xiao, Yanqing Wang, Zhen Wang, Rui Zhang, Zhaoqiang Li, Jianwen Zhou, Hengshen Liu, Lu Yao, Kaixin Yang, Ziru Chen, Xiang Wan, Tsunghui Chang, Ye Li, Tao Huang, Wenming Zhao, Xin Zhang, Yixue Li

{"title":"Bio-OS: An Integrated Cloud-native Environment for Data-Intensive Biomedical Research.","authors":"Jilong Liu, Liguang Yang, Qingyu Xiao, Yanqing Wang, Zhen Wang, Rui Zhang, Zhaoqiang Li, Jianwen Zhou, Hengshen Liu, Lu Yao, Kaixin Yang, Ziru Chen, Xiang Wan, Tsunghui Chang, Ye Li, Tao Huang, Wenming Zhao, Xin Zhang, Yixue Li","doi":"10.1093/gpbjnl/qzag085","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag085","url":null,"abstract":"The increasing volume and complexity of biomedical data, especially driven by high-throughput biotechnologies, have highlighted the need for scalable, interoperable, and reproducible data analysis frameworks. While cloud computing has revolutionized data processing and collaboration in life sciences, challenges remain in building cloud platforms tailored to biomedical research and ensuring platform interoperability. To address these limitations, we propose Bio-OS, an open-source, cloud-native framework designed to support the rapid development of interoperable biomedical cloud platforms. Bio-OS aligns with the Global Alliance for Genomics and Health (GA4GH) cloud workstream standards-including Workflow Execution Service (WES), Task Execution Service (TES), Data Repository Service (DRS), and Tool Registry Service (TRS) - while introducing innovations such as interactive execution models, workspace-centric data management, and AI-driven enhancements. These features facilitate adherence to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, promoting reproducibility and collaboration in biomedical research. Deployments across multiple research institutions demonstrated Bio-OS's ability to build biomedical cloud platforms and validated its efficacy in supporting data-intensive applications in life sciences.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating the Penetrance of PRNP Mutations in Chinese Prion Disease Patients based on the Genome Databases. 基于基因组数据库估计中国朊病毒病患者PRNP突变的外显率。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-19 DOI: 10.1093/gpbjnl/qzag054

Ruhan A, Xiao-Xi Jia, Dong-Hua Zhou, Yue-Zhang Wu, Yuan Wang, Wei-Wei Zhang, Kang Xiao, Dong-Lin Liang, Li-Ping Gao, Wei Zhou, Cao Chen, Qi Shi, Xiao-Ping Dong

{"title":"Estimating the Penetrance of PRNP Mutations in Chinese Prion Disease Patients based on the Genome Databases.","authors":"Ruhan A, Xiao-Xi Jia, Dong-Hua Zhou, Yue-Zhang Wu, Yuan Wang, Wei-Wei Zhang, Kang Xiao, Dong-Lin Liang, Li-Ping Gao, Wei Zhou, Cao Chen, Qi Shi, Xiao-Ping Dong","doi":"10.1093/gpbjnl/qzag054","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag054","url":null,"abstract":"Genetic prion diseases (gPrDs) are directly caused by mutations in the prion protein gene (PRNP). Although several mutations have been strongly linked to disease manifestation, the penetrance of many variants remains uncertain. This study aimed to clarify the penetrance of PRNP variants by analyzing data from the Chinese surveillance system and comparing them to large genome databases representing East Asian and global genetic backgrounds. Our analysis included 301 gPrD cases with PRNP point mutations alongside 96,462 individuals of East Asian genetic descent and 784,714 from a global cohort. Notably, the D178N, E200K, and P102L mutations exhibited high penetrance irrespective of genetic background, underscoring their pathogenic potential. T188K, the most frequent variant in Chinese gPrD cases, also showed high penetrance across all cohorts despite its regional specificity. Conversely, estimates of E196A penetrance were low, particularly in the Chinese and East Asian cohorts. Variants such as S97N and R148H showed extremely low penetrance across all cohorts, while V180I and V210I displayed distinct regional and ethnic variations in both prevalence and penetrance. However, the penetrance of rare mutations could not be reliably estimated due to insufficient case numbers. These insights highlight the complex interplay between genetic variation and disease risk, necessitating further investigation into the genetic epidemiology of gPrD.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Characterization of Genetic Risk Factors of Cerebral Small Vessel Disease Based on Large-scale Transcriptome Profiling. 基于大规模转录组分析的脑血管病遗传危险因素表征

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-18 DOI: 10.1093/gpbjnl/qzag086

Fengyu Wang, Jingyao Zeng, Qiheng Qian, Jinlong Zou, Zhenping Gong, Rong Li, Junkui Shang, Jingfa Xiao, Jiewen Zhang

{"title":"Characterization of Genetic Risk Factors of Cerebral Small Vessel Disease Based on Large-scale Transcriptome Profiling.","authors":"Fengyu Wang, Jingyao Zeng, Qiheng Qian, Jinlong Zou, Zhenping Gong, Rong Li, Junkui Shang, Jingfa Xiao, Jiewen Zhang","doi":"10.1093/gpbjnl/qzag086","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag086","url":null,"abstract":"Cerebral small vessel disease (CSVD) is a major contributor to stroke and dementia, and it endangers the health of older individuals (>50 years old). Nevertheless, its clinical diagnosis predominantly depends on radiography. Moreover, omics studies and effective biomarkers for CSVD are still limited, and their pathogenesis has not been comprehensively clarified. To facilitate an in-depth understanding of CSVD at the molecular level and to characterize potential risk factors for this disease, we conducted a series of systematic transcriptome studies using peripheral blood samples from 91 Chinese patients with CSVD. By profiling the transcriptome heterogeneity between patients with CSVD and healthy individuals and conducting quantitative real-time PCR to validate differentially expressed genes, we established a comprehensive transcriptome map of CSVD and identified several statistically significant potential biomarkers for this disease. Our comparative analysis between dementia and non-dementia subgroups in CSVD patients highlights several differentially distributed neuroimaging and clinical features, such as total white matter hyperintensity severity, CSVD burden, neutrophil counts, and triglyceride levels, among others. More importantly, a CSVD-prediction model and a dementia-forecasting model have been constructed through machine learning methods, which achieve the average F1 scores of 0.93 and 0.73, respectively. Consequently, both models are anticipated to provide effective support for clinical predictions and diagnoses. In summary, this study delineates the transcriptome characteristics of CSVD, laying a foundation for research and clinical insights pertaining to CSVD.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Benchmarking Bayesian Colocalization Methods in Validating Mendelian Randomization-identified Targets. 验证孟德尔随机识别目标的基准贝叶斯共定位方法。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-18 DOI: 10.1093/gpbjnl/qzag087

Wenmin Zhang, Satoshi Yoshiji, Robert Sladek, Josée Dupuis, Tianyuan Lu

{"title":"Benchmarking Bayesian Colocalization Methods in Validating Mendelian Randomization-identified Targets.","authors":"Wenmin Zhang, Satoshi Yoshiji, Robert Sladek, Josée Dupuis, Tianyuan Lu","doi":"10.1093/gpbjnl/qzag087","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag087","url":null,"abstract":"Mendelian randomization (MR) is an important tool for identifying potential biomarkers and drug targets. Colocalization analysis is crucial for validating MR findings and guarding against confounding due to linkage disequilibrium. We aim to benchmark the performance of four Bayesian colocalization methods in validating MR-based target discoveries from circulating proteins for cardiometabolic traits. We assessed the associations between circulating levels of 1535 proteins and five cardiometabolic traits, followed by colocalization analyses using coloc, coloc+SuSiE, PWCoCo and SharePro. All methods demonstrated well-controlled false discoveries. SharePro demonstrated the highest frequency in supporting 160 (79.6%) of the 201 Bonferroni-significant protein-trait associations identified by MR, compared to coloc (supporting 40.3% of these associations), coloc+SuSiE (46.8%), and PWCoCo (45.8%), and was robust to varying prior colocalization probabilities. Protein-trait associations supported by SharePro were more likely to agree with significant gene-level associations identified in exome-wide association studies and implicate known drug targets. Eight protein-trait associations were exclusively supported by SharePro, suggesting potential cardiometabolic biomarkers or drug targets, such as HSF1 and HAVCR2. In summary, SharePro most often supports statistically significant associations identified through MR for cardiometabolic traits. Combining multiple lines of evidence using different methods may substantially increase the yield of biomarker and drug target discovery programs.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence in Post-translational Modification Site Prediction: Progress and Future Perspectives. 人工智能在翻译后修饰位点预测中的应用：进展与展望。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-18 DOI: 10.1093/gpbjnl/qzag088

Jiayi Ran, Xiaohan Zhang, Yunze Wang, Silin Chen, Jiaqi Deng, Martin Kosar, Bo Yang, Huiran Wang, Yishu Deng, Tailin Li, Yufei Liu, Lian Wang, Yijiang Guo, Xiaochen Zhang, Huaqiong Huang, Jianya Zhou, Jing Zheng, Zhihao Xu, Yong Tang, Jian Liu

{"title":"Artificial Intelligence in Post-translational Modification Site Prediction: Progress and Future Perspectives.","authors":"Jiayi Ran, Xiaohan Zhang, Yunze Wang, Silin Chen, Jiaqi Deng, Martin Kosar, Bo Yang, Huiran Wang, Yishu Deng, Tailin Li, Yufei Liu, Lian Wang, Yijiang Guo, Xiaochen Zhang, Huaqiong Huang, Jianya Zhou, Jing Zheng, Zhihao Xu, Yong Tang, Jian Liu","doi":"10.1093/gpbjnl/qzag088","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag088","url":null,"abstract":"Post-translational modifications (PTMs) are pivotal in modulating protein function and cellular processes. However, experimental identification of PTM sites remains costly and labor-intensive. Recent advances in artificial intelligence (AI) have enabled accurate and scalable in silico PTM site prediction from large-scale proteomic data. In this review, we provide a comprehensive and up-to-date overview of AI-driven PTM site prediction across more than ten PTM classes, covering single-PTM site prediction, multiple-PTM site prediction, inter-site crosstalk prediction, and functional prediction of modification sites. We systematically analyze and compare key AI frameworks, from conventional machine learning to deep learning, and summarize representative tools. We also identify key challenges and propose future directions for improvement. To facilitate application and ongoing progress, we provide practical guidelines for method selection and have established a dedicated website, which serves as a community benchmarking resource for the development of PTM site prediction tools. This website will be regularly updated with emerging prediction tools. By integrating comprehensive literature analysis with a dynamic online resource, we aim to provide a reliable foundation for understanding current capabilities and guiding the future development of PTM site prediction tools, thereby promoting the integration of AI into practical biomedical research applications.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MOFUN-CCC: A Multi-omics Intermediate Fusion Network for Digital White Blood Cell Count Prediction. MOFUN-CCC：用于数字白细胞计数预测的多组学中间融合网络。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-17 DOI: 10.1093/gpbjnl/qzag080

Molin Yue, Manqi Cai, Chongyue Zhao, Jing Liu, Kristina Gaietto, Shiyue Tao, Haoran Hu, Yanshuo Chen, Ying Ding, Heng Huang, Juan C Celedón, Jiebiao Wang, Wei Chen

{"title":"MOFUN-CCC: A Multi-omics Intermediate Fusion Network for Digital White Blood Cell Count Prediction.","authors":"Molin Yue, Manqi Cai, Chongyue Zhao, Jing Liu, Kristina Gaietto, Shiyue Tao, Haoran Hu, Yanshuo Chen, Ying Ding, Heng Huang, Juan C Celedón, Jiebiao Wang, Wei Chen","doi":"10.1093/gpbjnl/qzag080","DOIUrl":"10.1093/gpbjnl/qzag080","url":null,"abstract":"As the volume of omics data continues to grow exponentially, there is an increasing demand for innovative methodologies that combine multi-omics data to extract meaningful clinical insights. Absolute cell counts are a fundamental component of clinical evaluations for disease diagnosis, treatment, and patient management. While cellular deconvolution can estimate relative cell type proportions from bulk data, obtaining absolute cell counts from omics data remains rarely studied. In response to the clinical needs and challenges, we introduce a novel multi-modal deep learning model with intermediate fusion: multi-omics fusion neural network- computational cell counting (MOFUN-CCC). This model is designed to predict absolute cell counts directly by integrating gene expression and DNA methylation data within a supervised framework, assuming that the underlying true cell components are shared across the two omics data. Comprehensive evaluations, including cross-validation, independent data testing, and real-world applications, demonstrate the model's robustness, precision, and capacity to effectively capture biological variations. MOFUN-CCC represents a pioneering effort in the integration of multi-omics data for the prediction of absolute cell counts. With our user-friendly software (https://github.com/yuemolin/MOFUN-CCC) and web application (https://shiny.crc.pitt.edu/mofun_shiny/), this innovation holds the potential to make significant contributions to disease diagnosis, progression analysis, and clinical decision-making.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803514","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SPCC: Inferring Spatial Cell-Cell Interaction by Integrating Single-cell and Spatial Transcriptomics. SPCC：通过整合单细胞和空间转录组学推断空间细胞-细胞相互作用。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-17 DOI: 10.1093/gpbjnl/qzag083

Tiangang Wang, Yu Zhou, Xi Liu, Sijia Wu, Liyu Huang, Kexin Huang, Xiaobo Zhou

{"title":"SPCC: Inferring Spatial Cell-Cell Interaction by Integrating Single-cell and Spatial Transcriptomics.","authors":"Tiangang Wang, Yu Zhou, Xi Liu, Sijia Wu, Liyu Huang, Kexin Huang, Xiaobo Zhou","doi":"10.1093/gpbjnl/qzag083","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag083","url":null,"abstract":"Study of single-cell spatial biology reveals the importance of integrating single-cell and spatial data for capturing spatial structure at individual cell resolution in various fields. With the lack of cellular-level information in most spatial data, it is necessary to integrate single-cell and spatial data. Here, we developed a deep learning computational framework for alignment and mapping of unpaired single-cell and the spatial data by using adversarial joint-variational autoencoder and Random Forest model (SPCC). SPCC will generate a mapping matrix for single-cell and spatial positions that can transfer spatial location to individual cells. SPCC can be used to perform downstream analysis, such as cell-cell communication and spatial variable gene identification with pseudo-space information. Then, we validated the performance with current popular integration methods on three datasets with different spatial sequencing techniques, including the mouse somatosensory cortex data, breast cancer data and melanoma brain metastasis data. Our findings suggest that SPCC exhibits greater robustness against sequencing noise compared to previous methods. SPCC has successfully captured the precise spatial structure in multiple datasets. For example, our results indicated that PECAM1 can interact with SOX4 between cancer cells and endothelial cells, which was rarely identified by using previous tools. The PECAM1-SOX4 interaction can regulate the vascular adhesion in melanoma and further contribute to tumor cell metastasis. These results show that SPCC is highly sensitive and accurate to identify unique spatial cell interactions. SPCC is freely accessible at https://github.com/ploughhh/SPCC.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803485","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The LncRNA-Tumor Immune Microenvironment Nexus: Integrative Insights for Cancer Immunotherapy. lncrna -肿瘤免疫微环境关系：癌症免疫治疗的综合见解。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-17 DOI: 10.1093/gpbjnl/qzag084

Juwon Lee, Revathy Nadhan, Yong Sang Song, Danny N Dhanasekaran

{"title":"The LncRNA-Tumor Immune Microenvironment Nexus: Integrative Insights for Cancer Immunotherapy.","authors":"Juwon Lee, Revathy Nadhan, Yong Sang Song, Danny N Dhanasekaran","doi":"10.1093/gpbjnl/qzag084","DOIUrl":"https://doi.org/10.1093/gpbjnl/qzag084","url":null,"abstract":"Long non-coding RNAs (lncRNAs) have emerged as pivotal regulators of tumor-immune interactions, bridging epigenetic, transcriptional, and post-transcriptional control of immune signaling networks. Recent multi-omics analyses reveal that lncRNAs shape the tumor immune microenvironment (TIME) by influencing immune-cell differentiation, activation, and exhaustion, as well as cytokine, chemokine, and immune-checkpoint pathways. Acting through molecular scaffolding, chromatin remodeling, and competing endogenous RNA (ceRNA) mechanisms, lncRNAs orchestrate the recruitment and functional polarization of lymphoid and myeloid populations, thereby dictating the balance between antitumor immunity and immune suppression. Integrative evidence further links specific lncRNA signatures to PD-1/PD-L1 checkpoint regulation, metabolic reprogramming, and therapeutic resistance. Here, we synthesize current discoveries defining the lncRNA-TIME nexus and highlight advances in high-throughput sequencing, single-cell transcriptomics, and computational modeling that enable the mapping of these complex networks. We discuss the translational promise of targeting lncRNAs as biomarkers and therapeutic nodes to enhance immunotherapy efficacy. Collectively, these integrative insights position lncRNAs as critical molecular interfaces connecting tumor genomics with immune regulation and open new avenues for precision immuno-oncology.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148803446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

DBP: Adaptive and Interpretable Factor Analysis for Single-cell RNA-seq Data with Deep Beta Processes. DBP：单细胞RNA-seq数据深度β过程的适应性和可解释因子分析。

IF 7.9

Genomics, proteomics & bioinformatics Pub Date : 2026-08-14 DOI: 10.1093/gpbjnl/qzaf117

Runyan Liu, Shuofeng Hu, Guohua Dong, Tongtong Kan, Jinhui Shi, Jing Wang, Jiahao Zhou, Zhen He, Xiaomin Ying

{"title":"DBP: Adaptive and Interpretable Factor Analysis for Single-cell RNA-seq Data with Deep Beta Processes.","authors":"Runyan Liu, Shuofeng Hu, Guohua Dong, Tongtong Kan, Jinhui Shi, Jing Wang, Jiahao Zhou, Zhen He, Xiaomin Ying","doi":"10.1093/gpbjnl/qzaf117","DOIUrl":"10.1093/gpbjnl/qzaf117","url":null,"abstract":"Factor analysis is a method that condenses multiple variables into a few latent factors. It can be used to extract the underlying sources of biological variation in high-dimensional data and distill them into interpretable gene programs. However, existing factorization methods lack adaptability in selecting the optimal number of factors and interpretability in capturing biological variation. To address these concerns, we propose Deep Beta Process (DBP), a deep probabilistic framework for adaptive and interpretable factor analysis of single-cell transcriptomic data. DBP achieves adaptive selection of factors through a stick-breaking Beta process and performs batch correction using an adversarial learning strategy. We validate the flexible factor extraction and robust batch correction capabilities of DBP on simulated datasets. We also demonstrate its superior performance in dimensionality reduction and biological interpretability while explaining biological variation from both cell and gene perspectives using factor and loading matrices. The application of DBP to a gastric adenocarcinoma dataset reveals malignant epithelial cell heterogeneity, providing valuable insights for investigating the molecular mechanisms of disease onset and progression. DBP is available at https://github.com/labomics/DBP and https://ngdc.cncb.ac.cn/biocode/tool/BT007954.","PeriodicalId":94020,"journal":{"name":"Genomics, proteomics & bioinformatics","volume":" ","pages":""},"PeriodicalIF":7.9,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13686767/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145643932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0