An R package for divergence analysis of omics data

Dinalankara, Wikum; Ke, Qian; Geman, Donald; Marchionni, Luigi

doi:10.1371/journal.pone.0249002

Cited by 2 publications

(2 citation statements)

References 21 publications

(15 reference statements)

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“…Thus, the initial network was comprised of 1,880,969 valid gene-metabolite pairs, involving 18,483 distinct genes and 111 distinct metabolites. For each gene-metabolite pair, a binary variable indicating whether the specific patient exhibits a "divergent" status compared to the normal range was derived using the divergence framework (Dinalankara et al, 2018(Dinalankara et al, , 2021. Next, we filtered out rare and independent gene-metabolite pairs based on binary divergence indicators, resulting in a network of 3,679 candidate pairs accounting for 12,245 unique genes and 71 unique metabolites.…”

Section: Multi-omics Covering Network Captures Inter-patient Heteroge...mentioning

confidence: 99%

“…Before constructing the covering network, we derived divergence statistics (Dinalankara et al, 2018), a binary random variable, for every tumor sample using the Divergence (Dinalankara et al, 2021) R/Bioconductor package to indicate whether each gene expression and metabolite abundance is aberrant compared to the baseline range calculated based on 48 normal samples. We define a gene-metabolite pair as divergent if the divergence statistics of the gene is correlated with that of the metabolite using Chi-squared test.…”

Section: Construction Of a Covering Networkmentioning

confidence: 99%

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Multi-omics biomarkers aid prostate cancer prognostication

Omar

Benedetti

et al. 2022

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Effective biomarkers and diagnostic tools are urgently needed in clinical settings for improved management of prostate cancer patients, especially to reduce over-treatment of indolent tumors and for early identification of aggressive disease. Gene expression signatures are currently the "gold standard" to provide guide clinical decision, however their clinical utility and interpretability is questionable. Multi-modal molecular profiling provides an holistic approach to systematically unravel the biological complexity underlying cancer pathogenesis, hence biomarkers developed using such an integrated approach hold the potential to more accurately capture cancer-driving alterations than signatures based on a single omics modality. Currently, however, robust and reproducible multi-omics biomarkers are still lacking for prostate cancer. In this study, we analyzed transcriptomics and metabolomics profiles jointly in a prostate cancer cohort and identified two prognostic signatures with high statistical powers (signature1: EGLN3, succinate, trans-4-hydroxyprolin; and signature2: IL6, SLC22A2, histamine). Our approach leveraged a priori biological knowledge of the cellular metabolism and gene circuitry, enabling the identification of dysregulated network modules. Functional bioinformatics analyses suggest that these signatures can capture relevant molecular alterations in prostate cancer tissues, including dysregulations of cellular signaling, cell cycle progression, and immune system modulation, stratifying patients in distinct risk groups. Next, we trained two gene expression signatures as a proxy for the multi-omics ones, extending our investigation to publicly available data, further confirming their prognostic values in independent patient cohorts. In summary, the analysis of multi-modal molecular grounded in cellular network biology represents a promising approach for the development of robust prognostic biomarkers of detecting and discriminating high grade disease.

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Section: Multi-omics Covering Network Captures Inter-patient Heteroge...mentioning

confidence: 99%

Section: Construction Of a Covering Networkmentioning

confidence: 99%

Multi-omics biomarkers aid prostate cancer prognostication

Omar

Benedetti

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

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Exploring the Diagnosis of Immune-Related Genes in Metabolic Syndrome Based on Three Algorithms

Liu

Zhang

et al. 2023

Preprint

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Background The pathogenesis of Metabolic Syndrome (MetS) remains largely unexplored. This study aims to explore the immune-related genes in MetS. Methods The microarray expression dataset GSE98895 was downloaded from the Gene Expression Database (GEO) and the immune-related genes were downloaded from the immune database. The samples of patients with MetS and non-MetS samples were analyzed by CIBORCORT method. The differential expression genes (DEGs) and Immune-related DEGs were extracted. Immune-related DEGs MetS were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment analyses. Protein-Protein Interaction (PPI) network was constructed by string online database and Cytoscape software. We used three algorithms of lasso, SVM-REF, and random forest to screen the attributes of MetS-related differential expression genes and obtained hub genes. These obtained hub genes were utilized to construct the nomogram model. The predictability of each hub gene was also identified by receiver operating characteristic (ROC) curves. The hub genes were then analyzed by GSEA (Gene set Enrichment analysis) and ssGSEA (single-sample Gene Set Enrichment analysis). Results 20 tissue samples from healthy subjects and 20 tissue samples from patients with MetS were obtained. We obtained 946 MetS-related differential expression genes from dataset GSE98895 and 1793 immune-related genes from the immune database. Immune-related genes and MetS-related genes were taken from intersection, and we got 63 immune-related differential expression genes. The expression of dendritic cells and resting mast cells in the samples of MetS had lower expression than those of normal samples. DEGs were mainly enriched in receptor ligand activity, as well as, signaling receptor activator activity by GO analysis. KEGG enrichment analysis indicated immune-related differential expression genes that were enriched in cytokine-cytokine receptor interaction. 13 genes were selected by the LASSO regression analysis (DEFB114, IL19, TNFRSF21, NFYB, CX3CR1, BMP8B, JAG1, DUOX1, IL2, OPRD1, NR1I2, JUN, and MMP9), 10 genes were selected by Random Forest algorithm (IFNG, CX3CR1, TNFRSF21, JUN, MCHR2, MMP9, PGLYRP1, IL1R2, SEMA3F, and CD40 ), and 17 genes were obtained by SVM-REF algorithm(TNFRSF21, JUN, BMP8B, NFYB, DUOX1, DEFB114, NR1I2, IFNG, MMP9, SST, IL2, OPRD1, DEFB103A, GAL, SLIT1, JAG1, SERPIND1). From the intersection of these three algorithms, we obtained three hub genes—JUN, MMP9, and TNFRSF21. The nomogram model of the three hub genes demonstrated good reliability and validity. The predictability of each hub gene was also identified by receiver operating characteristic (ROC) curves, AUC values, all greater than 0.7. GSEA enrichment analysis showed that the up-regulated functions of JUN were mainly concentrated in the amphetamine addition, MMP9 was mainly concentrated in arrhythmogenic right ventricular cardiomyopathy, and TNFSRF21 was mainly concentrated in cocaine addiction. ssGSEA indicated via enrichment analysis that MMP9 was mainly associated with TNFA Signaling via NFKB. In addition, KRAS Signaling, Dn and TNFRSF21 were mainly associated with TGF-β Signaling Pathway and Angiogenesis. Conclusion MMP9, JUN, and TNFRSF21 may be targets for diagnosis and treatment of MetS.

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An R package for divergence analysis of omics data

Cited by 2 publications

References 21 publications

Multi-omics biomarkers aid prostate cancer prognostication

Multi-omics biomarkers aid prostate cancer prognostication

Exploring the Diagnosis of Immune-Related Genes in Metabolic Syndrome Based on Three Algorithms

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