An Effective Graph Clustering Method to Identify Cancer Driver Modules

Zhang, Wei; Zeng, Yifu; Wang, Lei; Liu, Yue; Cheng, Yinan

doi:10.3389/fbioe.2020.00271

Cited by 3 publications

(2 citation statements)

References 58 publications

(68 reference statements)

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“…The STRING database 2 was used to analyze protein–protein interactions (PPI) among ferroptosis modulators. In this study, we propose an MCL cluster to identify cancer driver modules that combine mutex, functional similarity, and connectivity in the PPI network ( Zhang et al, 2020 ). Pearson correlation analysis was used to reveal the associations among different regulators in different modules.…”

Section: Methodsmentioning

confidence: 99%

Analysis of Ferroptosis-Mediated Modification Patterns and Tumor Immune Microenvironment Characterization in Uveal Melanoma

Jin

Wang

et al. 2021

Front. Cell Dev. Biol.

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Uveal melanoma (UVM) is an intraocular malignancy in adults in which approximately 50% of patients develop metastatic disease and have a poor prognosis. The need for immunotherapies has rapidly emerged, and recent research has yielded impressive results. Emerging evidence has implicated ferroptosis as a novel type of cell death that may mediate tumor-infiltrating immune cells to influence anticancer immunity. In this study, we first selected 11 ferroptosis regulators in UVM samples from the training set (TCGA and GSE84976 databases) by Cox analysis. We then divided these molecules into modules A and B based on the STRING database and used consensus clustering analysis to classify genes in both modules. According to the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), the results revealed that the clusters in module A were remarkably related to immune-related pathways. Next, we applied the ESTIMATE and CIBERSORT algorithms and found that these ferroptosis-related patterns may affect a proportion of TME infiltrating cells, thereby mediating the tumor immune environment. Additionally, to further develop the prognostic signatures based on the immune landscape, we established a six-gene-regulator prognostic model in the training set and successfully verified it in the validation set (GSE44295 and GSE27831). Subsequently, we identified the key molecules, including ABCC1, CHAC1, and GSS, which were associated with poor overall survival, progression-free survival, disease-specific survival, and progression-free interval. We constructed a competing endogenous RNA network to further elucidate the mechanisms, which consisted of 29 lncRNAs, 12 miRNAs, and 25 ferroptosis-related mRNAs. Our findings indicate that the ferroptosis-related genes may be suitable potential biomarkers to provide novel insights into UVM prognosis and decipher the underlying mechanisms in tumor microenvironment characterization.

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Section: Methodsmentioning

confidence: 99%

Analysis of Ferroptosis-Mediated Modification Patterns and Tumor Immune Microenvironment Characterization in Uveal Melanoma

Jin

Wang

et al. 2021

Front. Cell Dev. Biol.

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“…With the development of high-throughput sequencing technology, massive amounts of DNA sequencing data have been released, making it feasible to comprehensively analyze cancer-related somatic mutation data from the data level. In this process, the key challenge is to identify driver gene sets with biologically similar functions, that is, driver pathways or modules, which are often disturbed in cancer cells and lead to products of tumorigenic properties [1,6,7].…”

Section: Introductionmentioning

confidence: 99%

Definition of a New Metric With Mutual Exclusivity and Coverage for Identifying Cancer Driver Modules

2020

Self Cite

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Identification of cancer driver modules or pathways is a key step in understanding cancer pathogenesis and exploring patient-specific treatments. Numerous studies have shown that some genes with low mutation frequency are also important for the cancer progression, while previous research have focused on identifying high-frequency mutation genes. In this study, we propose a new framework with a new metric to identify driver modules with low-frequency mutation genes, called iCDModule. Inspired by the gravity model, we integrate the coverage and mutual exclusivity in mutation information, define a new metric between gene pairs, called mutation impact distance, to help identifying potential driver genes sets, including those have extremely low mutation rates but play an important role in functional networks. A genetic network is constructed by combining the defined mutation impact distance and then the driver module identification problem is formalized as the maximum clique solution problem, and an improved ant colony optimization algorithm is used to solve it. iCDModule is applied to TCGA breast cancer, glioblastoma, ovarian cancer to test performance. Experiments show that it can accurately identify known cancer driver modules and pathways, and also detect driver modules containing low-frequency mutation genes. iCDModule is significantly better than other existing methods in identifying driver modules.

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The Causal Pivot: A Structural Approach to Genetic Heterogeneity and Variant Discovery in Complex Diseases

Shaw,

Williams,

Tan

et al. 2024

Preprint

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We develop a structural causal methodology to analyze genetic heterogeneity in complex diseases. Our method, the Causal Pivot (CP), exploits one known cause to detect the contribution of a second suspected cause. We employ polygenic scores (PRS) as a known cause; we consider selected rare variation (RV) and RV ensembles as candidate causes. We condition on disease to determine a method that incorporates outcome induced association. We derive a conditional maximum likelihood procedure for both binary and quantitative traits and develop a likelihood ratio test (CP-LRT). We demonstrate the CP-LRT power and robustness to misspecification of the RV rate compared to alternatives. We then use data from the UK Biobank (UKBB) to examine performance. As demonstrations, we consider hypercholesterolemia (HC, LDL-c direct >= 4.9 mmol/L; nc=24656), breast cancer (BC, female, ICD10 C50; nc=12479), and Parkinsons Disease (PD, ICD10 G20, nc=2940). For PRS we use UKBB data; for RV we select ClinVar pathogenic and likely pathogenic variants in known disease genes. For HC we focus on LDLR, for BC we focus on BRCA1, and in PD we consider GBA. Using CP-LRT we detect RV signals for all three demonstration diseases. Cross-disease analyses serve as comparators. We extend our CP-LRT approach using matching and inverse probability weighting to address potential confounding driven by ancestry. Finally, we present a CP oligogenic load analysis in PD using lysosomal storage. Taken together, our work demonstrates the versatility of the PRS-RV CP as a method of inference and discovery in complex disease genetics.

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An Effective Graph Clustering Method to Identify Cancer Driver Modules

Cited by 3 publications

References 58 publications

Analysis of Ferroptosis-Mediated Modification Patterns and Tumor Immune Microenvironment Characterization in Uveal Melanoma

Analysis of Ferroptosis-Mediated Modification Patterns and Tumor Immune Microenvironment Characterization in Uveal Melanoma

Definition of a New Metric With Mutual Exclusivity and Coverage for Identifying Cancer Driver Modules

The Causal Pivot: A Structural Approach to Genetic Heterogeneity and Variant Discovery in Complex Diseases

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