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Review
. 2022 Dec 26;12(1):101.
doi: 10.3390/cells12010101.

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

Qing Ye  1   2 , Nancy Lan Guo  1   3
Affiliations
Review

Inferencing Bulk Tumor and Single-Cell Multi-Omics Regulatory Networks for Discovery of Biomarkers and Therapeutic Targets

Qing Ye et al. Cells. .

Abstract

There are insufficient accurate biomarkers and effective therapeutic targets in current cancer treatment. Multi-omics regulatory networks in patient bulk tumors and single cells can shed light on molecular disease mechanisms. Integration of multi-omics data with large-scale patient electronic medical records (EMRs) can lead to the discovery of biomarkers and therapeutic targets. In this review, multi-omics data harmonization methods were introduced, and common approaches to molecular network inference were summarized. Our Prediction Logic Boolean Implication Networks (PLBINs) have advantages over other methods in constructing genome-scale multi-omics networks in bulk tumors and single cells in terms of computational efficiency, scalability, and accuracy. Based on the constructed multi-modal regulatory networks, graph theory network centrality metrics can be used in the prioritization of candidates for discovering biomarkers and therapeutic targets. Our approach to integrating multi-omics profiles in a patient cohort with large-scale patient EMRs such as the SEER-Medicare cancer registry combined with extensive external validation can identify potential biomarkers applicable in large patient populations. These methodologies form a conceptually innovative framework to analyze various available information from research laboratories and healthcare systems, accelerating the discovery of biomarkers and therapeutic targets to ultimately improve cancer patient survival outcomes.

Keywords: Prediction Logic Boolean Implication Networks (PLBINs); SEER-Medicare; biomarkers; electronic medical records (EMRs); multi-omics regulatory networks; network centrality; single cells; therapeutic targets.

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Conflict of interest statement

N.L.G. is the inventor of the patent PCT/US22/75136 filed and owned by West Virginia University. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Constructing a relevance network using Pearson Correlation Coefficient (PCC).
Figure 2
Figure 2
Example of a discrete Bayesian network.
Figure 3
Figure 3
Example of a static Bayesian network (A) and a dynamic Bayesian network (B).
Figure 4
Figure 4
Example of a Boolean network. (A). A simple Boolean network GN, E. (B). The state transition corresponding to the linkage graph G′N′, E′ of A. (C). The truth table of this network.
Figure 5
Figure 5
Example of a Boolean implication rule. (A). Inducing an implication rule based on quadrants of the categorized expression levels of gene A and gene B. The intermediate areas in gray are removed from the implication rule induction. (B). Six specific implication rules connecting gene A and gene B.
Figure 6
Figure 6
Contingency table of the Boolean implication rule and their corresponding error cells in prediction logic.
Figure 7
Figure 7
The basic structure of neural networks.
Figure 8
Figure 8
Distribution of centrality metrics in single-cell gene co-expression networks with CD27, CTLA4, or PD1 ranked within the top 10th percentile. Each subplot represented a centrality metric: (A). Degree centrality; (B). Closeness centrality; (C). Betweenness centrality; (D). VoteRank centrality. Each violin plot showed the distribution of the centrality metric in one specific network: I. T-cell PBL gene co-expression network in normal samples. II. T-cell PBL gene co-expression network in NSCLC patients. III. T-cell gene co-expression network in NSCLC tumors.
Figure 9
Figure 9
The comparison of centrality metrics of our published single B-cell network vs. randomly selected networks with the same number of genes. The p values showed the percentage of randomly selected genes having a higher ranked average centrality metric than the clinically relevant single B-cell network. Each column in the plot showed a centrality metric: I. Degree centrality; II. Eigenvector centrality; III. Closeness centrality; IV. Betweenness centrality; V. VoteRank centrality. Each row represented a single-cell gene co-expression network constructed in normal PBL T cells, NSCLC PBL T cells, tumor infiltrating T cells, normal B cells, and tumor infiltrating B cells, respectively. NS: not statistically significant.
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