mRNA expression module analysis of WGCNA
After data preprocessing, for the mRNA sequencing data, the samples (n=29) were well clustered and 5880 of 23520 mRNAs were remained for further analysis (Fig. 1a). Using the discovery set (GSE132542 dataset), 17 mRNA network modules were identified using WGCNA. The modules were assigned a color label (black, brown, yellow, midnightblue, pink, salmon, red, turquoise, purple, tan, cyan, lightcyan, magenta, greenyellow, blue, green, and grey) (Fig. 1b). Correlations between these gene network modules and clinical data were analyzed. Module brown was found to be markedly positive correlated with imatinib-resistant (r = 0.54, p = 0.002); Module yellow was found to be markedly positive correlated with imatinib-resistant (r = 0.43, p = 0.02) (Fig. 1c). To reveal the functions of the mRNAs of the modules correlated with imatinib-resistant of GIST, GO enrichment analysis was performed. According to the results, the mRNAs in the brown module (n=786) and yellow module (n=582), they were involved with cell division, mitotic nuclear division, chromosome segregation, G1/S transition of mitotic cell cycle, sister chromatid cohesion, DNA replication, DNA replication initiation, spindle organization, G2/M transition of mitotic cell cycle, cell proliferation, mitotic sister chromatid segregation, mitotic cytokinesis, and mitotic spindle organization. (Fig. 1d and Table1).
Weighted Gene co-expression network analysis (WGCNA) of miRNA
After data preprocessing, for the miRNA sequencing data, the samples (n=53) were well clustered and 722 miRNAs were remained for further analysis (Fig. 2a). According to the module analysis of miRNA coexpression networks, 7 miRNA network modules were identified, and assigned the color labels red, blue, green yellow, brown, turquoise, and grey. The correlation analysis of modules and clinical prognostic data revealed that module red was significantly negative associated with imatinib-resistant of GIST (r = -0.29, p = 0.04) (Fig. 2b). Cytoscape 3.6.1 was used to visualize the miRNA network in the red module (n=23) (Fig. 2c). To reveal the functions of the miRNAs of the modules correlated with imatinib-resistant of GIST, GO enrichment analysis was performed using mirPath v.3. According to the results, 210 GO terms were totally enriched in biological process (BP). G1/S transition of mitotic cell cycle, G2/M transition of mitotic cell cycle, and cell proliferation were common pathways of the gene modules and miRNA module. (Fig. 2d).
Associations of miRNA target genes and gene network modules
The thresholds for the differentially expressed genes (DEGs) were set as the values of p<0.05 and false discovery rate (FDR)<0.1 between Imatinib-Naive and Imatinib-Resistant GIST Samples. 436 significantly differentially expressed genes were detected between the two groups. 102 genes were found in module brown and 16 genes were found in module yellow of WGCNA such as CDK1, AURKB, AURKA, CD44, HMMR, BIRC5 and CCNB1 (Fig. 3a and Table 2). The Connectivity Map (Cmap) was used to predict small-molecule drugs against GIST. We found 28 small-molecule drugs could target CDK1 such as JNJ-7706621, Terameprocol, 1-Azakenpaullone, and Kenpaullone. We found 24 small-molecule drugs could target AURKB such as JNJ-7706621, tozasertib and orantinib. We found 25 small-molecule drugs could target AURKA such as JNJ-7706621, tozasertib and orantinib. We found 1 small-molecule drugs could target CD44 such as hyaluronic-acid. We found 2 small-molecule drugs could target BIRC5 such as Terameprocol and YM-15. We found 2 small-molecule drugs could target CCNB1 such as Kenpaullone and 1-Azakenpaullone. These small-molecule drugs might have a therapeutic effect against imatinib-resistant GIST. (Table 3). A two-sample t-test (p<0.05) Benjamini-Hochberg false discovery rate (FDR) (FDR<20%) was used to determine differentially expressed microRNAs between Imatinib-Naive and Imatinib-Resistant GIST Samples. 35 significantly differentially expressed miRNAs (DEMs) were detected between the two groups (Table 4). Three miRNAs (miR-539, miR-376b and miR-18b) were found in both in module red of WGCNA and DEMs. The results are shown in the Venn diagram (Fig. 3b).
Three miRNAs (miR-539, miR-376b and miR-18b) contained in the modulel red and DEMs were chosen for target genes. A total of 265 target genes were screened out based on the information in TargetScan, miRanda, and miRwalk. Three critical miRNAs target gene regulatory network (miR-539, miR-376b and miR-18b) and the regulatory network of 120 mRNA gene in module brown and yellow were then constructed. Interestingly, Two target gene APOBEC3B and SPAG5 of miR-539 are also in network of mRNA gene in module brown (Fig. 3c).
Relationship between the expression of APOBEC3B/SPAG5 and immune infiltration
To investigate the association between differentially expressed genes (DEGs) and the tumor microenvironment, the 8 immune cell faction was calculated by EPIC. We found that imatinib-naive group has a lower density of CD4+ T cells and endothelial cells compared to the imatinib-resistant group (Fig. 4a). The correlation between APOBEC3B/SPAG5 expression and immune invasion in sarcoma using the Tumor-Immune System Interactions (TISIDB) (Fig. 4b). We found that both the expression of APOBEC3B (rho=0.509, p<2.2e-16) and SPAG5 (rho=0.468, p<2.2e-16) was positive correlated with the infiltration levels of activated CD4+ T cells (Fig.4c).
Clinical correlation between miR-539, TGF-β1 and infiltrating CD4+ T cells in imatinib-naive and imatinib-resistant GIST.
We compared the expressions of miR-539, APOBEC3B, SPAG5 and TGF-β in imatinib-naive (n=14) and imatinib-resistant tumor samples (n=15). Consistent with bioinformatics results, imatinib-resistant tumor samples exhibited significantly downregulation of miR-539 (p=0.0005) (Fig.5a). We found that the mRNA relative expression of APOBEC3B and SPAG5 in imatinib-resistant tumor samples significantly increased as compared with tumor samples in imatinib-naive group (p=0.0051 and 0.0035, respectively) (Fig.5b). As expected, the protein relative expression of APOBEC3B and SPAG5 in imatinib-resistant tumor samples were significantly increased (p=0.0051 and 0.0085, respectively) (Fig.5c). We detected Transforming growth factor (TGF)-β1 and interleukin (IL)-10, known as important immunosuppressive cytokines, by ELISA. We found that TGF-β1 secretion of imatinib-resistant tumor samples were significantly decreased compared with tumor samples in imatinib-naive group (p=0.0051) (Fig.5d). The proportions of CD4+ T cells, CD8+ T cells , NK cells and B cells in tumor-infiltrating lymphocytes from imatinib-naive and imatinib-resistant patients were analyzed via flow cytometry (FCM). We found that the proportions of CD4+ T cells of imatinib-resistant tumor samples were significantly increased compared with tumor samples in imatinib-naive group (p=0.003) (Fig.5e). We analyzed the correlation of miR-539 expressions and CD4+ T cells proportions in 29 GIST patients. We observed a significantly negative correlation between miR-539 expressions and CD4+ T cells proportionsin GIST patients (P < 0.001, R2 = 0.5353) (Fig.5f).
Overexpression of miR-539 sensitized imatinib resistant GIST48 cells and increased the secretion of TGF-b1 by inhibiting APOBEC3B and SPAG5.
We investigated whether miR-539 can change the effects of imatinib on GIST48, a imatinib-resistant GIST cell line. We found that miR-539 can significantly sensitized the inhibitory effects of GIST48 cells proliferation induced by imatinib (Fig.6a). ELISA was employed to measure the concentration of TGF-β1 and IL-10 secreted by in the supernatants of GIST48 cells. We found that both miR-539 and imatinib can significantly rise the TGF-β1 secretion of GIST48 cells, compared with GIST48 cells treated with imatinib only (p=0.0231) (Fig.6b). miR-539 targeted a site at 3’-UTR of APOBEC3B and SPAG5 mRNA analyzed by bioinformatics. (Fig.6c). We identified that APOBEC3B and SPAG5 protein expression was significantly downregulated in miR-539 and imatinib as compared with cells GIST48 cells treated with imatinib only (p=0.001) (Fig.6d).