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A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma.

ABSTRACT: Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (real and artificial) signify that the suggested method outstrips two known earlier seed-centric local methods. Additionally, we compared it with other global multilayer and single-layer methods. Eventually, we applied our method on a biological two-layer network of Colon Adenocarcinoma (COAD), reconstructed from transcriptomic and post-transcriptomic datasets, and assessed the output modules. The functional enrichment consequences infer that the modules of interest hold biomolecules involved in the pathways associated with the carcinogenesis.

SUBMITTER: Pournoor E

PROVIDER: S-EPMC8351981 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma.

Pournoor Ehsan E Mousavian Zaynab Z Nowzari-Dalini Abbas A Masoudi-Nejad Ali A

PloS one 20210809 8

Regardless of all efforts on community discovery algorithms, it is still an open and challenging subject in network science. Recognizing communities in a multilayer network, where there are several layers (types) of connections, is even more complicated. Here, we concentrated on a specific type of communities called seed-centric local communities in the multilayer environment and developed a novel method based on the information cascade concept, called PLCDM. Our simulations on three datasets (r ...[more]

PMID: 34370784

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Project description:BackgroundColon cancer (CC) is treatable if detected in its early stages. Improved CC detection assays that are highly sensitive, specific, and available at point of care are needed. In this study, we systematically selected and tested methylated markers that demonstrate high sensitivity and specificity for detection of CC in tissue and circulating cell-free DNA.MethodsHierarchical analysis of 22 candidate CpG loci was conducted using The Cancer Genome Atlas (TCGA) COAD 450K HumanMethylation database. Methylation of 13 loci was analyzed using quantitative multiplex methylation-specific PCR (QM-MSP) in a training set of fresh frozen colon tissues (N = 53). Hypermethylated markers were identified that were highest in cancer and lowest in normal colon tissue using the 75th percentile in Mann-Whitney analyses and the receiver operating characteristic (ROC) statistic. The cumulative methylation status of the marker panel was assayed in an independent test set of fresh frozen colon tissues (N = 52) using conditions defined and locked in the training set. A minimal marker panel of 6 genes was defined based on ROC area under the curve (AUC). Plasma samples (N = 20 colorectal cancers, stage IV and N = 20 normal) were tested by cMethDNA assay to evaluate marker performance in liquid biopsy.ResultsIn the test set of samples, compared to normal tissue, a 6-gene panel showed 100% sensitivity and 90% specificity for detection of CC, and an AUC of 1.00 (95% CI 1.00, 1.00). In stage IV colorectal cancer plasma versus normal, an 8-gene panel showed 95% sensitivity, 100% specificity, and an AUC of 0.996 (95% CI 0.986, 1.00) while a 5-gene subset showed 100% sensitivity, 100% specificity, and an AUC of 1.00 (95% CI 1.00, 1.00), highly concordant with our observations in tissue.ConclusionsWe identified high performance methylated DNA marker panels for detection of CC. This knowledge has set the stage for development and implementation of novel, automated, self-contained CC detection assays in tissue and blood which can expeditiously and accurately detect colon cancer in both developed and underdeveloped regions of the world, enabling optimal use of limited resources in low- and middle-income countries.

Dataset Information

A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma.

Publications

A propagation-based seed-centric local community detection for multilayer environment: The case study of colon adenocarcinoma.

Similar Datasets

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