scholarly journals cSurvival: a web resource for biomarker interactions in cancer outcomes

2021 ◽  
Author(s):  
Xuanjin Cheng ◽  
Yongxing Liu ◽  
Jiahe Wang ◽  
Yujie Chen ◽  
Andrew Gordon Robertson ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Cell Line ◽  
Clinical Outcomes ◽  
Large Scale ◽  
Interaction Analysis ◽  
Cancer Prognosis ◽  
High Expression ◽  
Joint Analysis ◽  
Experimental Cell ◽  
Web Resource

Survival analysis is a technique to identify prognostic biomarkers and genetic vulnerabilities in cancer studies. Large-scale consortium-based projects have profiled >11,000 adult and >4,000 paediatric tumor cases with clinical outcomes and multi-omics approaches. This provides a resource for investigating molecular-level cancer etiologies using clinical correlations. Although cancers often arise from multiple genetic vulnerabilities and have deregulated gene sets (GSs), existing survival analysis protocols can report only on individual genes. Additionally, there is no systematic method to connect clinical outcomes with experimental (cell line) data. To address these gaps, we developed cSurvival (https://tau.cmmt.ubc.ca/cSurvival). cSurvival provides a user-adjustable analytical pipeline with a curated, integrated database, and offers three main advances: (a) joint analysis with two genomic predictors to identify interacting biomarkers, including new algorithms to identify optimal cutoffs for two continuous predictors; (b) survival analysis not only at the gene, but also the GS level; and (c) integration of clinical and experimental cell line studies to generate synergistic biological insights. To demonstrate these advances, we report three case studies. We confirmed findings of autophagy-dependent survival in colorectal cancers and of synergistic negative effects between high expression of SLC7A11 and SLC2A1 on outcomes in several cancers. We further used cSurvival to identify high expression of the Nrf2-antioxidant response element pathway as a main indicator for lung cancer prognosis and for cellular resistance to oxidative stress-inducing drugs. Together, these analyses demonstrate cSurvival's ability to support biomarker prognosis and interaction analysis via gene- and GS-level approaches and to integrate clinical and experimental biomedical studies.

scholarly journals A Gradient Boosting Algorithm for Survival Analysis via Direct Optimization of Concordance Index

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Yifei Chen ◽  
Zhenyu Jia ◽  
Dan Mercola ◽  
Xiaohui Xie
Keyword(s):  
Survival Analysis ◽  
Large Scale ◽  
Proportional Hazards Model ◽  
Cancer Prognosis ◽  
Gradient Boosting ◽  
Nonparametric Model ◽  
Concordance Index ◽  
Direct Optimization ◽  
Hazard Functions ◽  
Boosting Method

Survival analysis focuses on modeling and predicting the time to an event of interest. Many statistical models have been proposed for survival analysis. They often impose strong assumptions on hazard functions, which describe how the risk of an event changes over time depending on covariates associated with each individual. In particular, the prevalent proportional hazards model assumes that covariates are multiplicatively related to the hazard. Here we propose a nonparametric model for survival analysis that does not explicitly assume particular forms of hazard functions. Our nonparametric model utilizes an ensemble of regression trees to determine how the hazard function varies according to the associated covariates. The ensemble model is trained using a gradient boosting method to optimize a smoothed approximation of the concordance index, which is one of the most widely used metrics in survival model performance evaluation. We implemented our model in a software package called GBMCI (gradient boosting machine for concordance index) and benchmarked the performance of our model against other popular survival models with a large-scale breast cancer prognosis dataset. Our experiment shows that GBMCI consistently outperforms other methods based on a number of covariate settings. GBMCI is implemented in R and is freely available online.

Soil/Tire Interaction Analysis Using FEM and FVM

2005 ◽  
Vol 33 (1) ◽  
pp. 38-62 ◽  
Author(s):  
S. Oida ◽  
E. Seta ◽  
H. Heguri ◽  
K. Kato
Keyword(s):  
Large Scale ◽  
Interaction Analysis ◽  
Yield Criterion ◽  
Surrounding Medium ◽  
Flow Analysis ◽  
Measurement Data ◽  
Traction Force ◽  
Elastoplastic Material ◽  
The Road ◽  
Soil Flow

Abstract Vehicles, such as an agricultural tractor, construction vehicle, mobile machinery, and 4-wheel drive vehicle, are often operated on unpaved ground. In many cases, the ground is deformable; therefore, the deformation should be taken into consideration in order to assess the off-the-road performance of a tire. Recent progress in computational mechanics enabled us to simulate the large scale coupling problem, in which the deformation of tire structure and of surrounding medium can be interactively considered. Using this technology, hydroplaning phenomena and tire traction on snow have been predicted. In this paper, the simulation methodology of tire/soil coupling problems is developed for pneumatic tires of arbitrary tread patterns. The Finite Element Method (FEM) and the Finite Volume Method (FVM) are used for structural and for soil-flow analysis, respectively. The soil is modeled as an elastoplastic material with a specified yield criterion and a nonlinear elasticity. The material constants are referred to measurement data, so that the cone penetration resistance and the shear resistance are represented. Finally, the traction force of the tire in a cultivated field is predicted, and a good correlation with experiments is obtained.

scholarly journals Identification of a juvenile-hormone signaling inhibitor via high-throughput screening of a chemical library

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Takumi Kayukawa ◽  
Kenjiro Furuta ◽  
Keisuke Nagamine ◽  
Tetsuro Shinoda ◽  
Kiyoaki Yonesu ◽  
...  
Keyword(s):  
Juvenile Hormone ◽  
Cell Line ◽  
Signaling Pathway ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Ex Vivo ◽  
Agricultural Field ◽  
Chemical Library ◽  
Field Development

Abstract Insecticide resistance has recently become a serious problem in the agricultural field. Development of insecticides with new mechanisms of action is essential to overcome this limitation. Juvenile hormone (JH) is an insect-specific hormone that plays key roles in maintaining the larval stage of insects. Hence, JH signaling pathway is considered a suitable target in the development of novel insecticides; however, only a few JH signaling inhibitors (JHSIs) have been reported, and no practical JHSIs have been developed. Here, we established a high-throughput screening (HTS) system for exploration of novel JHSIs using a Bombyx mori cell line (BmN_JF&AR cells) and carried out a large-scale screening in this cell line using a chemical library. The four-step HTS yielded 69 compounds as candidate JHSIs. Topical application of JHSI48 to B. mori larvae caused precocious metamorphosis. In ex vivo culture of the epidermis, JHSI48 suppressed the expression of the Krüppel homolog 1 gene, which is directly activated by JH-liganded receptor. Moreover, JHSI48 caused a parallel rightward shift in the JH response curve, suggesting that JHSI48 possesses a competitive antagonist-like activity. Thus, large-scale HTS using chemical libraries may have applications in development of future insecticides targeting the JH signaling pathway.

scholarly journals 3DIV update for 2021: a comprehensive resource of 3D genome and 3D cancer genome

2020 ◽  
Vol 49 (D1) ◽  
pp. D38-D46
Author(s):  
Kyukwang Kim ◽  
Insu Jang ◽  
Mooyoung Kim ◽  
Jinhyuk Choi ◽  
Min-Seo Kim ◽  
...  
Keyword(s):  
Cell Line ◽  
Large Scale ◽  
Three Dimensional ◽  
Cancer Cell Line ◽  
Cancer Genome ◽  
Structural Variations ◽  
3D Genome ◽  
Tightly Coupled ◽  
Regulatory Effects ◽  
The Impact

Abstract Three-dimensional (3D) genome organization is tightly coupled with gene regulation in various biological processes and diseases. In cancer, various types of large-scale genomic rearrangements can disrupt the 3D genome, leading to oncogenic gene expression. However, unraveling the pathogenicity of the 3D cancer genome remains a challenge since closer examinations have been greatly limited due to the lack of appropriate tools specialized for disorganized higher-order chromatin structure. Here, we updated a 3D-genome Interaction Viewer and database named 3DIV by uniformly processing ∼230 billion raw Hi-C reads to expand our contents to the 3D cancer genome. The updates of 3DIV are listed as follows: (i) the collection of 401 samples including 220 cancer cell line/tumor Hi-C data, 153 normal cell line/tissue Hi-C data, and 28 promoter capture Hi-C data, (ii) the live interactive manipulation of the 3D cancer genome to simulate the impact of structural variations and (iii) the reconstruction of Hi-C contact maps by user-defined chromosome order to investigate the 3D genome of the complex genomic rearrangement. In summary, the updated 3DIV will be the most comprehensive resource to explore the gene regulatory effects of both the normal and cancer 3D genome. ‘3DIV’ is freely available at http://3div.kr.

scholarly journals A circular RNAs dataset landscape reveals potential signatures for the detection and prognosis of early-stage lung adenocarcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Zhiying Chen ◽  
Jiahui Wei ◽  
Min Li ◽  
Yongjuan Zhao
Keyword(s):  
Functional Analysis ◽  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Target Genes ◽  
Interaction Analysis ◽  
Early Stage ◽  
Gene Interaction ◽  
Gene Expression Omnibus ◽  
Circular Rnas ◽  
Cerna Network

Abstract Background This study aimed to identify potential circular ribonucleic acid (circRNA) signatures involved in the pathogenesis of early-stage lung adenocarcinoma (LAC). Methods The circRNA sequencing dataset of early-stage LAC was downloaded from the Gene Expression Omnibus database. First, the differentially expressed circRNAs (DEcircRNAs) between tumour and non-tumour tissues were screened. Then, the corresponding miRNAs and their target genes were predicted. In addition, prognosis-related genes were identified using survival analysis and further used to build a network of competitive endogenous RNAs (ceRNAs; DEcircRNA–miRNA–mRNA). Finally, the functional analysis and drug–gene interaction analysis of mRNAs in the ceRNA network was performed. Results A total of 35 DEcircRNAs (30 up-regulated and 5 down-regulated circRNAs) were identified. Moreover, 135 DEcircRNA–miRNA and 674 miRNA–mRNA pairs were predicted. The survival analysis of these target mRNAs revealed that 60 genes were significantly associated with survival outcomes in early-stage LAC. Of these, high levels of PSMA 5 and low levels of NAMPT, CPT 2 and TNFSF11 exhibited favourable prognoses. In addition, the DEcircRNA–miRNA–mRNA network was constructed, containing 5 miRNA–circRNA (hsa_circ_0092283/hsa-miR-762/hsa-miR-4685-5p; hsa_circ_0070610/hsa-let-7a-2-3p/hsa-miR-3622a-3p; hsa_circ_0062682/hsa-miR-4268) and 60 miRNA–mRNA pairs. Functional analysis of the genes in the ceRNA network showed that they were primarily enriched in the Wnt signalling pathway. Moreover, PSMA 5, NAMPT, CPT 2 and TNFSF11 had strong correlations with different drugs. Conclusion Three circRNAs (hsa_circ_0062682, hsa_circ_0092283 and hsa_circ_0070610) might be potential novel targets for the diagnosis of early-stage LAC.

scholarly journals High expression of TMEM33 predicts poor prognosis and promotes cell proliferation in cervical cancer

2022 ◽  
Author(s):  
Hanxiang Chen ◽  
Yongqing Li ◽  
Shaoming Zhang ◽  
Yunshan Wang ◽  
Lili Wang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cell Proliferation ◽  
Cancer Cells ◽  
Gene Networks ◽  
Cox Regression ◽  
Interaction Analysis ◽  
Transmembrane Protein ◽  
Functional Enrichment ◽  
High Expression ◽  
Cervical Cancer Cells

Abstract Background As one of the most common cancer among women worldwide, the prognosis of patients with advanced cervical cancer remains unsatisfactory. A study indicated that transmembrane protein 33 (TMEM33) was implicated in tumor recurrence, while its role in cervical cancer has not been elucidated. Methods TMEM33 expression in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) was primarily screened in The Cancer Genome Altas (TCGA), and further validated in Gene Expression Omnibus (GEO) database. The Kaplan–Meier plotter analysis and Cox regression were constructed to evaluate the prognostic value of TMEM33 in CESC. Functional enrichment analysis was performed with GO, KEGG and GSEA tools. Protein-protein interaction analysis and correlated gene networks were conducted using STING and GEPIA2 websites, respectively. The expression of TMEM33 in cervical cancer cells were examined by immunoblotting and RT-qPCR. Finally, CCK-8 assay and colony formation assay were performed to investigate the role of TMEM33 in cervical cancer cell proliferation. Results TMEM33 expression was significantly elevated in CESC compared with normal tissues. High expression of TMEM33 was associated with poor prognostic clinical characteristics in CESC patients. KM-plotter analysis revealed that patients with increased TMEM33 had shorter overall survival (OS), progress free interval (PFI), and disease specific survival (DSS). Moreover, Multivariate Cox analysis further confirmed that high TMEM33 expression was an independent risk factor for OS in patients with CESC. TMEM33 was associated with immune cell infiltration, and its expression was correlated with tumorigenesis-related genes RNF4, OCIAD1, TMED5, DHX15, MED28 and LETM1. More importantly, knockdown of TMEM33 in cervical cancer cells decreased the expression of those genes and inhibited cell proliferation. Conclusions Increased TMEM33 in cervical cancer can serve as an independent prognostic marker and might play a role in tumorigenesis by promoting cell proliferation.

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