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. 2019 Oct 29;8(1):28.
doi: 10.1186/s40169-019-0245-6.

Network vulnerability-based and knowledge-guided identification of microRNA biomarkers indicating platinum resistance in high-grade serous ovarian cancer

Xin Qi  1 , Chunjiang Yu  1   2 , Yi Wang  1 , Yuxin Lin  1 , Bairong Shen  3
Affiliations

Network vulnerability-based and knowledge-guided identification of microRNA biomarkers indicating platinum resistance in high-grade serous ovarian cancer

Xin Qi et al. Clin Transl Med. .

Abstract

Background: High-grade serous ovarian cancer (HGSC), the most common ovarian carcinoma type, is associated with the highest mortality rate among all gynecological malignancies. As chemoresistance has been demonstrated as the major challenge in improving the prognosis of HGSC patients, we here aimed to identify microRNA (miRNA) biomarkers for predicting platinum resistance and further explore their functions in HGSC.

Results: We developed and applied our network vulnerability-based and knowledge-guided bioinformatics model first time for the study of drug-resistance in cancer. Four miRNA biomarkers (miR-454-3p, miR-98-5p, miR-183-5p and miR-22-3p) were identified with potential in stratifying platinum-sensitive and platinum-resistant HGSC patients and predicting prognostic outcome. Among them, miR-454-3p and miR-183-5p were newly discovered to be closely implicated in platinum resistance in HGSC. Functional analyses highlighted crucial roles of the four miRNA biomarkers in platinum resistance through mediating transcriptional regulation, cell proliferation and apoptosis. Moreover, expression patterns of the miRNA biomarkers were validated in both platinum-sensitive and platinum-resistant ovarian cancer cells.

Conclusions: With bioinformatics modeling and analysis, we identified and confirmed four novel putative miRNA biomarkers, miR-454-3p, miR-98-5p, miR-183-5p and miR-22-3p that could serve as indicators of resistance to platinum-based chemotherapy, thereby contributing to the improvement of chemotherapeutic efficiency and optimization of personalized treatments in HGSC.

Keywords: Bioinformatics model; Ovarian cancer; Platinum resistance; miRNA biomarker; miRNA-mRNA regulatory network.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Structural and functional characteristics of PRMNH. a Layout of PRMNH consisting of 190 regulations between 26 miRNAs and 140 mRNAs with differential expression patterns. b Intersection between known platinum resistance-associated miRNA biomarkers and up- or down-regulated miRNAs in PRMNH. The light red and green ellipses represent up-regulated and down-regulated miRNAs in PRMNH, respectively; light yellow ellipses represent known miRNA biomarkers involved in platinum resistance. miRNAs with properties of both hub and bottleneck nodes were highlighted with blue color. c Top 10 significantly enriched GO terms at the biological process level of miRNA target genes in PRMNH. The fan area represents the number of target genes implicated in the corresponding GO term. d Degree distribution of all nodes in PRMNH
Fig. 2
Fig. 2
Schematic description of criteria for identifying platinum resistance-associated miRNA biomarkers based on miRNA-mRNA regulatory relationships. a Schematic description of miRNA-mRNA regulatory types. b NSR distribution of the identified miRNA biomarkers, all miRNAs in PRMNH and all miRNAs in the human miRNA-mRNA regulatory network. Statistical significance was calculated using the Wilcoxon rank-sum test
Fig. 3
Fig. 3
Prognostic performance of the identified miRNA biomarkers evaluated based on ROC curve and Kaplan–Meier survival analyses. a Sensitivity and specificity of miRNA biomarkers in predicting platinum resistance in HGSC were assessed by ROC curve analysis and AUC values. The dashed diagonal line in the ROC plot refers to AUC = 0.5, which means discrimination with random chance. b, c Kaplan–Meier survival curve analyses for progression-free survival (PFS) (b) and overall survival (OS) (c) of HGSC patients were conducted to evaluate the prognostic performance of miRNA biomarkers in predicting survival times. P-values were calculated using the log-rank test
Fig. 4
Fig. 4
Gene ontology (GO) and pathway enrichment analyses of target genes of the identified miRNA biomarkers. a Clusters of miRNA biomarker target-enriched GO terms at the biological process level. In the functional enrichment map, each node refers to a GO term and is grouped based on GO similarity; each edge represents there were shared genes between two connecting GO terms. Node size is determined by the gene number in each GO term. Node color intensity represents enrichment significance. Edge thickness is determined by the number of shared genes between two connecting GO terms. b Top 15 significantly enriched IPA pathways of target genes of the identified miRNA biomarkers
Fig. 5
Fig. 5
The ovarian cancer pathway enriched by target genes of the identified miRNA biomarkers in IPA. Objects with purple circles or triangles are acting locus by mapped genes
Fig. 6
Fig. 6
Validation of expression patterns of the identified miRNA biomarkers in A2780 and/or OVCAR3 ovarian cancer cells. The orange color represents the fold change of miRNAs between platinum-resistant and platinum-sensitive HGSC patients; the blue color represents the fold change of miRNAs between platinum-resistant and platinum-sensitive A2780 cell lines; the green color represents the fold change of miRNAs between platinum-resistant and platinum-sensitive OVCAR3 cell lines
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