Differential Co-expression and Regulatory Network Analysis Uncover the Relapse Factor and Mechanism of T Cell Acute Leukemia

Abstract

The pediatric T cell acute lymphoblastic leukemia (T-ALL) still remains a cancer with worst prognosis for high recurrence. Massive studies were conducted for the leukemia relapse based on diagnosis and relapse paired samples. However, the initially diagnostic samples may contain the relapse information and mechanism, which were rarely studied. In this study, we collected mRNA and microRNA (miRNA) data from initially diagnosed pediatric T-ALL samples with their relapse or remission status after treatment. Integrated differential co-expression and miRNA-transcription factor (TF)-gene regulatory network analyses were used to reveal the possible relapse mechanisms for pediatric T-ALL. We detected miR-1246/1248 and NOTCH2 served as key nodes in the relapse network, and they combined with TF WT1/SOX4/REL to form regulatory modules that influence the progress of T-ALL. A regulatory loop miR-429-MYCN-MFHAS1 was found potentially associated with the remission of T-ALL. Furthermore, we proved miR-1246/1248 combined with NOTCH2 could promote cell proliferation in the T-ALL cell line by experiments. Meanwhile, analysis based on the miRNA-drug relationships demonstrated that drugs 5-fluorouracil, ascorbate, and trastuzumab targeting miR-1246 could serve as potential supplements for the standard therapy. In conclusion, our findings revealed the potential molecular mechanisms of T-ALL relapse by the combination of co-expression and regulatory network, and they provide preliminary clues for precise treatment of T-ALL patients.

Keywords: T-ALL; co-expression; drug; network; relapse.

Figure 1

The Function Enrichment Analysis (A) The clustered heatmap of all DEGs in relapse and remission. Relatively higher expression is shown in red and lower expression is in green. (B) KEGG enrichment of DEGs by DAVID database (p value < 0.01). Number beside bar, counts enriched in KEGG pathway. (C) KEGG enrichment of DEMs by DIANA-miRPath (p value < 0.05). Number beside bar, targets of DEMs enriched in KEGG pathway; m/n, DEMs/targets.

Figure 2

The Expression of DEGs and DEMs with Stable Pattern Expression levels of top 5 upregulated and top 5 downregulated DEGs and all DEMs with stable expression profile in relapse and remission samples. R, relapse; N, remission.

Figure 3

The Workflow for Co-expression and miRNA-TF-Gene Network Analyses

Figure 4

The Function Analysis of Modules (A) The GO enrichment of 22 WGCNA modules (p value < 0.05). Purple, GO terms; green and dark green, modules; gray, function type. (B) KEGG pathways enriched by the specific gene sets (relapse/remission gene sets) (p value < 0.01).

Figure 5

The Regulatory Network and Crosstalk Analysis for Gene Set (A) KEGG pathway network formed by relapse gene sets. Blue nodes, DEGs; pink labels, KEGG pathways. (B) miRNA-TF-gene regulatory network for DEGs in remission gene sets. Hexagon, miRNA; diamond, transcriptional factor; ellipse, gene.

Figure 6

The Crosstalk Analysis for Remission Gene Set and Experimental Verification (A) KEGG pathway network formed by remission gene sets. Red nodes, upregulation in relapse; green nodes, downregulation in relapse; gray nodes, non-DEGs; pink labels, KEGG pathways. (B and C) Effects of miR-1246 (B) and miR-1248 (C) and NOTCH2 on cell proliferation in the Jurkat cell line. The numbers in the bar represent the means. Values represent the means ± SD (**p < 0.001 and *p < 0.01; ns, not significant). (D) NOTCH2-signaling pathway and possible mechanism of miR-1246/1248 on T-ALL relapse.

Figure 7

Drug Target Analysis Drug-miRNA network was constructed by drugs approved by the US Food and Drug Administration in the SM2miR database and DEMs.