Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net

Zhang, Lin; Liu, Hui; Huang, Yufei; Wang, Xuesong; Chen, Yidong; Meng, Jia

doi:10.1109/tcbb.2015.2511758

Cited by 23 publications

(5 citation statements)

References 45 publications

(45 reference statements)

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“…Yamasaki et al reported that prothymosin-α (PTMA) and purine nucleoside phosphorylase (PNP) were directly regulated by miR-1-1, miR-1-2, miR-133a-1, and miR-133a-2. Silencing of these genes significantly inhibited cell proliferation, invasion and apoptosis in BC cells (33, 34). Jin et al (35) reported that miR-15a inhibited the components of TGF-β signaling pathways in LNCaP cell line, which might be related to the progression and metastasis of prostate cancer.…”

Section: Discussionmentioning

confidence: 99%

Development of a 21-miRNA Signature Associated With the Prognosis of Patients With Bladder Cancer

et al. 2019

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Objective: To develop a prognostic signature for patients with bladder cancer (BC). Methods: We identified differentially expressed miRNAs between normal bladder tissue and bladder cancer in the TCGA-BCLA dataset and evaluated prognostic values of these miRNAs. Then, a 21-miRNA signature was constructed based on the results of Cox proportional hazards regression model. Furthermore, functional enrichment analyses were conducted to explore the potential effects of the target genes of these 21 miRNAs. Results: Seventy six differentially expressed miRNAs were identified, among which 21 miRNAs including hsa-let-7c, mir-143, mir-944, mir-192, mir-590, mir-490, mir-141, mir-93, mir-1-2, mir-200c, mir-133a-1, mir-1-1, mir-133b, mir-20a, mir-185, mir-19a, mir-19b-2, mir-19b-1, mir-17, mir-15a, and mir-133a-2 were demonstrated to be significantly correlated with the overall survival (OS) of bladder cancer patients using Kaplan-Meier survival analysis and Log-rank test. The results of Chi-square test and multivariable logistic regression analysis showed that the 21-miRNA signature was significantly associated with the diagnosis type and T stage of bladder cancer. Univariate and multivariable survival analyses indicated that the 21-miRNA signature was an independent factor in predicting the overall survival of patients with bladder cancer. The results of functional enrichment analysis suggested that the target genes of these 21 miRNAs were mostly enriched in critical cancer-related biological processes and pathways, and the PPI network suggested that 60 targeted genes interacted with a minimum of 30 genes were at the hub of the whole network. In addition, we performed a multivariate nomogram and decision curve analysis (DCA) to evaluate the clinical application of 21-microRNA signature. Conclusion: We introduced a 21-miRNA signature which was associated the prognosis of patients of bladder cancer, and inspirational ideas for the future basic and clinical exploration.

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Section: Discussionmentioning

confidence: 99%

Development of a 21-miRNA Signature Associated With the Prognosis of Patients With Bladder Cancer

et al. 2019

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“… 8 The development of cancer may be related to interactions between several key genes. 9 Lung cancer prognosis-associated biomarkers have been proposed on the basis of omics data, including DNA methylation, 10 gene expression, 11 microRNA, 12 and long noncoding RNA. 13 However, most studies are limited to a single type of omics data, which results in less accurate prognostic models.…”

mentioning

confidence: 99%

Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects

Zhang

Chen

Dong

et al. 2020

Chest

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BACKGROUND: DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (GÂG) interactions.RESEARCH QUESTION: Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis? STUDY DESIGN AND METHODS: Biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated.RESULTS: Twenty-six pairs of biomarkers with GÂG interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P ¼ 5.10 Â 10 -17 ) and 34.85% (95% CI, 26.33%-41.87%; P ¼ 2.52 Â 10 -18 ) for 3-and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC 3 year , 0.88 [95% CI, 0.83-0.93]; and AUC 5 year , 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. GÂG interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3-and 5-year survival, respectively. INTERPRETATION:The integration of epigenetic and transcriptional biomarkers with main effects and GÂG interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.

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“…For example, under the assumption that miRNA-mRNA target pairs will have correlated expression, Gade et al built a bipartite network of mRNA and miRNA target information in order to fuse miRNA and mRNA expression profiles and guide selection of non-redundant mRNA and miRNA features [121]. A method called GIREN uses an interaction network to group features, assuming that proximity in the network indicates functional redundancy [129].…”

Section: Network Analysis Across Tumor Cohortsmentioning

confidence: 99%

The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine

Öztürk

Dow

Carlin

et al. 2018

Journal of Molecular Biology

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Precision cancer medicine promises to tailor clinical decisions to patients using genomic information. Indeed, successes of drugs targeting genetic alterations in tumors, such as imatinib that targets BCR-ABL in chronic myelogenous leukemia, have demonstrated the power of this approach. However, biological systems are complex, and patients may differ not only by the specific genetic alterations in their tumor, but also by more subtle interactions among such alterations. Systems biology and more specifically, network analysis, provides a framework for advancing precision medicine beyond clinical actionability of individual mutations. Here we discuss applications of network analysis to study tumor biology, early methods for N-of-1 tumor genome analysis, and the path for such tools to the clinic.

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Cancer Progression Prediction Using Gene Interaction Regularized Elastic Net

Cited by 23 publications

References 45 publications

Development of a 21-miRNA Signature Associated With the Prognosis of Patients With Bladder Cancer

Development of a 21-miRNA Signature Associated With the Prognosis of Patients With Bladder Cancer

Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects

The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine

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