A Fast and Furious Bayesian Network and Its Application of Identifying Colon Cancer to Liver Metastasis Gene Regulatory Networks

CNNGRN: A Convolutional Neural Network-Based Method for Gene Regulatory Network Inference From Bulk Time-Series Expression Data

Gene regulatory networks (GRNs) participate in many biological processes, and reconstructing them plays an important role in systems biology. Although many advanced methods have been proposed for GRN reconstruction, their predictive performance is far from the ideal standard, so it is urgent to design a more effective method to reconstruct GRN. Moreover, most methods only consider the gene expression data, ignoring the network structure information contained in GRN. In this study, we propose a supervised model named CNNGRN, which infers GRN from bulk time-series expression data via convolutional neural network (CNN) model, with a more informative feature. Bulk time series gene expression data imply the intricate regulatory associations between genes, and the network structure feature of ground-truth GRN contains rich neighbor information. Hence, CNNGRN integrates the above two features as model inputs. In addition, CNN is adopted to extract intricate features of genes and infer the potential associations between regulators and target genes. Moreover, feature importance visualization experiments are implemented to seek the key features. Experimental results show that CNNGRN achieved competitive performance on benchmark datasets compared to the state-of-the-art computational methods. Finally, hub genes identified based on CNNGRN have been confirmed to be involved in biological processes through literature.

Computational Tactics for Precision Cancer Network Biology

Network biology has garnered tremendous attention in understanding complex systems of cancer, because the mechanisms underlying cancer involve the perturbations in the specific function of molecular networks, rather than a disorder of a single gene. In this article, we review the various computational tactics for gene regulatory network analysis, focused especially on personalized anti-cancer therapy. This paper covers three major topics: (1) cell line's (or patient's) cancer characteristics specific gene regulatory network estimation, which enables us to reveal molecular interplays under varying conditions of cancer characteristics of cell lines (or patient); (2) computational approaches to interpret the multitudinous and massive networks; (3) network-based application to uncover molecular mechanisms of cancer and related marker identification. We expect that this review will help readers understand personalized computational network biology that plays a significant role in precision cancer medicine.

Fuzzy and Rough Set Theory Based Computational Framework for Mining Genetic Interaction Triplets From Gene Expression Profiles for Lung Adenocarcinoma

Genetic interactions are very helpful in understanding different disease and discovering drugs for it. Compared to the gene pairs that represent the genetic interactions between two genes, the gene triplets are more informative and useful. However, existing works on genetic interactions among gene triplets have primarily focused on detecting gene triplets from time series gene expression profiles. Generating the time series gene expression profiles for humans is quite impracticable but the labeled gene expression profiles are available for different diseases in case of humans. In this paper, a computational framework has been proposed to detect gene triplets from labeled gene expression profiles. First, it employs Rough Set Theory for extracting the key genes and then designs a fuzzy inference system for generating possible gene triplets. Further, Root Mean Squared Error measure has been used to prune out the irrelevant gene triplets. In the present work, the proposed computational framework has been applied to labeled lung adenocarcinoma dataset and can be applied to any other labeled gene expression dataset. The extracted gene triplets and their functionalities have been verified with existing biological literature and benchmark databases and the results of verification signify that the proposed framework is promising in terms of finding useful genetic triplets. Further, the proposed framework has been found more efficient as compared to an existing mutual information-based technique in terms of detecting known genetic interactions.

Estimation of Gene Regulatory Networks from Cancer Transcriptomics Data

Cancer is a genetic disease in which multiple genes are perturbed. Thus, information about the regulatory relationships between genes is necessary for the identification of biomarkers and therapeutic targets. In this review, methods for inference of gene regulatory networks (GRNs) from transcriptomics data that are used in cancer research are introduced. The methods are classified into three categories according to the analysis model. The first category includes methods that use pair-wise measures between genes, including correlation coefficient and mutual information. The second category includes methods that determine the genetic regulatory relationship using multivariate measures, which consider the expression profiles of all genes concurrently. The third category includes methods using supervised and integrative approaches. The supervised approach estimates the regulatory relationship using a supervised learning method that constructs a regression or classification model for predicting whether there is a regulatory relationship between genes with input data of gene expression profiles and class labels of prior biological knowledge. The integrative method is an expansion of the supervised method and uses more data and biological knowledge for predicting the regulatory relationship. Furthermore, simulation and experimental validation of the estimated GRNs are also discussed in this review. This review identified that most GRN inference methods are not specific for cancer transcriptome data, and such methods are required for better understanding of cancer pathophysiology. In addition, more systematic methods for validation of the estimated GRNs need to be developed in the context of cancer biology.

Impact of GFRA1 gene reactivation by DNA demethylation on prognosis of patients with metastatic colon cancer

BACKGROUND

The expression of the membrane receptor protein GFRA1 is frequently upregulated in many cancers, which can promote cancer development by activating the classic RET-RAS-ERK and RET-RAS-PI3K-AKT pathways. Several therapeutic anti-GFRA1 antibody-drug conjugates are under development. Demethylation (or hypomethylation) of GFRA1 CpG islands (dmGFRA1) is associated with increased gene expression and metastasis risk of gastric cancer. However, it is unknown whether dmGFRA1 affects the metastasis of other cancers, including colon cancer (CC).

AIM

To study whether dmGFRA1 is a driver for CC metastasis and GFRA1 is a potential therapeutic target.

METHODS

CC and paired surgical margin tissue samples from 144 inpatients and normal colon mucosal biopsies from 21 noncancer patients were included in this study. The methylation status of GFRA1 islands was determined by MethyLight and denaturing high-performance liquid chromatography and bisulfite-sequencing. Kaplan-Meier analysis was used to explore the effect of dmGFRA1 on the survival of CC patients. Impacts of GFRA1 on CC cell proliferation and migration were evaluated by a battery of biological assays in vitro and in vivo. The phosphorylation of AKT and ERK proteins was examined by Western blot analysis.

RESULTS

The proportion of dmGFRA1 in CC, surgical margin, and normal colon tissues by MethyLight was 68.4%, 73.4%, and 35.9% (median; nonparametric test, P = 0.001 and < 0.001), respectively. Using the median value of dmGFRA1 peak area proportion as the cutoff, the proportion of dmGFRA1-high samples was much higher in poorly differentiated CC samples than in moderately or well-differentiated samples (92.3%% vs 55.8%, Chi-square test, P = 0.002) and significantly higher in CC samples with distant metastasis than in samples without (77.8% vs 46.0%, P = 0.021). The overall survival of patients with dmGFRA1-low CC was significantly longer than that of patients with dmGFRA1-high CC (adjusted hazard ratio = 0.49, 95% confidence interval: 0.24-0.98), especially for 89 CC patients with metastatic CC (adjusted hazard ratio = 0.41, 95% confidence interval: 0.18-0.91). These data were confirmed by the mining results from TCGA datasets. Furthermore, GFRA1 overexpression significantly promoted the proliferation/invasion of RKO and HCT116 cells and the growth of RKO cells in nude mice but did not affect their migration. GFRA1 overexpression markedly increased the phosphorylation levels of AKT and ERK proteins, two key molecules in two classic GFRA1 downstream pathways.

CONCLUSION

GFRA1 expression is frequently reactivated by DNA demethylation in CC tissues and is significantly associated with a poor prognosis in patients with CC, especially those with metastatic CC. GFRA1 can promote the proliferation/growth of CC cells, probably by the activation of AKT and ERK pathways. GFRA1 might be a therapeutic target for CC patients, especially those with metastatic potential.