Integrated bioinformatics and statistical approaches to explore molecular biomarkers for breast cancer diagnosis, prognosis and therapies

Abstract

Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.

Fig 1. The pipeline of this study.

Fig 2

(A) Volcano plot of–log₁₀(P-value) against log₂FC values to display significantly upregulated and downregulated DEGs. (B) Heatmap of significantly upregulated and downregulated DEGs to observe the clustering performance of tumor and control groups by hierarchical clustering approach.

Fig 3. Protein-protein interaction (PPI) network of DEGs to select the key genes (KGs).

Blue color indicates downregulated and pink color indicates upregulated DEGs, big size and octagon shape indicate the KGs.

Fig 4

KGs regulatory network analysis results (A) KGs-TFs interaction network to identify key transcriptional regulators of KGs, (B) KGs-miRNAs interaction network to identify key post-transcriptional regulators of KGs. Here pink color octagon indicates the KGs in both A and B, blue color bigger size ellipse indicates key TFs in A and key miRNAs in B.

Fig 5

The prognostic powers of KGs were displayed by (A) Heatmap of hierarchical clustering (B) Multivariate survival curves with KGs and (C) ROC curves of prediction models with KGs.

Fig 6. Molecular docking simulation results for exploring candidate drugs against BC.

(A) Image of binding affinity scores of proposed ordered receptor proteins with the top 97 ordered meta-drug agents, (B) Image of binding affinity scores of proposed ordered receptor proteins with the proposed ordered candidate drugs only (C) Image of binding affinity scores of ordered proposed and already published candidate drugs against the top-ranked independent receptors published by others.

Fig 7. The 3D views of the selected strong binding interactions between drug targets and agents were displayed.

The key interacting amino acids and their binding types with potential targets were also shown.

Fig 8

Validation of the proposed KGs (receptors) and candidate drugs in favor of BC by the literature review (A) Validation of the proposed KGs: circles with blue color indicate downregulated KGs and pink color indicates upregulated KGs, and each connected network with a circle indicates the reference in which the KG is associated with BC, (B) Validation of the proposed candidate drugs: circles with red color indicate FDA approved and investigational drugs, green color indicate investigational drugs and ash color indicate unapproved drugs, and each connected network with a circle indicates the references in which our suggested drugs might be effective against BC treatment.