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Recently, many studies have shown that lncRNA can mediate the regulation of TF-gene in drug sensitivity. However, there is still a lack of systematic identification of lncRNA-TF-gene regulatory triplets for drug sensitivity. In this study, we propose a novel analytic approach to systematically identify the lncRNA-TF-gene regulatory triplets related to the drug sensitivity by integrating transcriptome data and drug sensitivity data. Totally, 1570 drug sensitivity-related lncRNA-TF-gene triplets were identified, and 16 307 relationships were formed between drugs and triplets. Then, a comprehensive characterization was performed. Drug sensitivity-related triplets affect a variety of biological functions including drug response-related pathways. Phenotypic similarity analysis showed that the drugs with many shared triplets had high similarity in their two-dimensional structures and indications. In addition, Network analysis revealed the diverse regulation mechanism of lncRNAs in different drugs. Also, survival analysis indicated that lncRNA-TF-gene triplets related to the drug sensitivity could be candidate prognostic biomarkers for clinical applications. Next, using the random walk algorithm, the results of which we screen therapeutic drugs for patients across three cancer types showed high accuracy in the drug-cell line heterogeneity network based on the identified triplets. Besides, we developed a user-friendly web interface-DrugSETs (http://bio-bigdata.hrbmu.edu.cn/DrugSETs/) available to explore 1570 lncRNA-TF-gene triplets relevant with 282 drugs. It can also submit a patient's expression profile to predict therapeutic drugs conveniently. In summary, our research may promote the study of lncRNAs in the drug resistance mechanism and improve the effectiveness of treatment. [ABSTRACT FROM AUTHOR]

Background: Immunotherapy is a revolutionary strategy in cancer therapy, but the resistance of which is one of the important challenges. Detecting the regulation of immune cells and biomarkers concerning immune checkpoint blockade (ICB) therapy is of great significance.Methods: Here, we firstly constructed regulation networks for 11 immune cell clusters by integrating biological pathway data and single cell sequencing data in metastatic melanoma with or without ICB therapy. We then dissected these regulation networks and identified differently expressed genes between responders and non-responders. Finally, we trained and validated a logistic regression model based on ligands and receptors in the regulation network to predict ICB therapy response.Results: We discovered the regulation of genes across eleven immune cell stats. Functional analysis indicated that these stat-specific networks consensually enriched in immune response corrected pathways and highlighted antigen processing and presentation as a core pathway in immune cell regulation. Furthermore, some famous ligands like SIRPA, ITGAM, CD247and receptors like CD14, IL2 and HLA-G were differently expressed between cells of responders and non-responders. A predictive model of gene sets containing ligands and receptors performed accuracy prediction with AUCs above 0.7 in a validation dataset suggesting that they may be server as biomarkers for predicting immunotherapy response.Conclusions: In summary, our study presented the gene-gene regulation landscape across 11 immune cell clusters and analysis of these networks revealed several important aspects and immunotherapy response biomarkers, which may provide novel insights into immune related mechanisms and immunotherapy response prediction. [ABSTRACT FROM AUTHOR]

Numerous studies have shown that copy number variation (CNV) in lncRNA regions play critical roles in the initiation and progression of cancer. However, our knowledge about their functionalities is still limited. Here, we firstly provided a computational method to identify lncRNAs with copy number variation (lncRNAs-CNV) and their driving transcriptional perturbed subpathways by integrating multidimensional omics data of cancer. The high reliability and accuracy of our method have been demonstrated. Then, the method was applied to 14 cancer types, and a comprehensive characterization and analysis was performed. LncRNAs-CNV had high specificity in cancers, and those with high CNV level may perturb broad biological functions. Some core subpathways and cancer hallmarks widely perturbed by lncRNAs-CNV were revealed. Moreover, subpathways highlighted the functional diversity of lncRNAs-CNV in various cancers. Survival analysis indicated that functional lncRNAs-CNV could be candidate prognostic biomarkers for clinical applications, such as ST7-AS1, CDKN2B-AS1 and EGFR-AS1. In addition, cascade responses and a functional crosstalk model among lncRNAs-CNV, impacted genes, driving subpathways and cancer hallmarks were proposed for understanding the driving mechanism of lncRNAs-CNV. Finally, we developed a user-friendly web interface-LncCASE (http://bio-bigdata.hrbmu.edu.cn/LncCASE/) for exploring lncRNAs-CNV and their driving subpathways in various cancer types. Our study identified and systematically characterized lncRNAs-CNV and their driving subpathways and presented valuable resources for investigating the functionalities of non-coding variations and the mechanisms of tumorigenesis. [ABSTRACT FROM AUTHOR]

Breast cancer has become the most common cancer that leads to women's death. Breast cancer is a complex, highly heterogeneous disease classified into various subtypes based on histological features, which determines the therapeutic options. System identification of effective drugs for each subtype remains challenging. In this work, we present a computational network biology approach to screen precision drugs for different breast cancer subtypes by considering the impact intensity of candidate drugs on the pathway crosstalk mediated by miRNAs. Firstly, we constructed and analyzed the subtype-specific risk pathway crosstalk networks mediated by miRNAs. Then, we evaluated 36 Food and Drug Administration (FDA)-approved anticancer drugs by quantifying their effects on these subtype-specific pathway crosstalk networks and combining with survival analysis. Finally, some first-line treatments of breast cancer, such as Paclitaxel and Vincristine, were optimized for each subtype. In particular, we performed precision screening of subtype-specific therapeutic drugs and also confirmed some novel drugs suitable for breast cancer treatment. For example, Sorafenib was applicable for the basal subtype treatment, Irinotecan was optimum for Her2 subtype treatment, Vemurafenib was suitable for the LumA subtype treatment, and Vorinostat could apply to LumB subtype treatment. In addition, the mechanism of these optimal therapeutic drugs in each subtype of breast cancer was further dissected. In summary, our study offers an effective way to screen precision drugs for various breast cancer subtype treatments. We also dissected the mechanism of optimal therapeutic drugs, which may provide novel insight into the precise treatment of cancer and promote researches on the mechanisms of action of drugs. [ABSTRACT FROM AUTHOR]

Background: Individualized drug response prediction is vital for achieving personalized treatment of cancer and moving precision medicine forward. Large-scale multi-omics profiles provide unprecedented opportunities for precision cancer therapy.Methods: In this study, we propose a pipeline to identify subpathway signatures for anticancer drug response of individuals by integrating the comprehensive contributions of multiple genetic and epigenetic (gene expression, copy number variation and DNA methylation) alterations.Results: Totally, 46 subpathway signatures associated with individual responses to different anticancer drugs were identified based on five cancer-drug response datasets. We have validated the reliability of subpathway signatures in two independent datasets. Furthermore, we also demonstrated these multi-omics subpathway signatures could significantly improve the performance of anticancer drug response prediction. In-depth analysis of these 46 subpathway signatures uncovered the essential roles of three omics types and the functional associations underlying different anticancer drug responses. Patient stratification based on subpathway signatures involved in anticancer drug response identified subtypes with different clinical outcomes, implying their potential roles as prognostic biomarkers. In addition, a landscape of subpathways associated with cellular responses to 191 anticancer drugs from CellMiner was provided and the mechanism similarity of drug action was accurately unclosed based on these subpathways. Finally, we constructed a user-friendly web interface-CancerDAP ( http://bio-bigdata.hrbmu.edu.cn/CancerDAP/ ) available to explore 2751 subpathways relevant with 191 anticancer drugs response.Conclusions: Taken together, our study identified and systematically characterized subpathway signatures for individualized anticancer drug response prediction, which may promote the precise treatment of cancer and the study for molecular mechanisms of drug actions. [ABSTRACT FROM AUTHOR]