Identification of DE_lncRNAs and WGCNA co-expression network
A total of 12289 lncRNAs in GSE115414 data set were obtained after reannotation. Based on the cut-off criteria, 1691 lncRNAs (including 643 upregulated and 1048 downregulated) were significantly differentially expressed, as shown in Figure 1a and 1b, and were used to construct a WGCNA co-expression network. In order to evaluate the outliers of 24 samples (8 control and 26 NAFLD), sample clustering methods were used based on the distribution of the expression values of the samples (Figure 1c), with no significant difference detected in the samples included in the WGCNA. The scale-free topology index and mean connectivity were applied during the current study to determine the soft threshold in WGCNA. The higher the scale-free topology index value was indicative of a strong probability of a non-scale feature. Power = 10 was selected in the event of the correlation coefficient between log (k) and log P (k) reached 0.9 for the first time (Figure 1d-f). Within the gene co-expression network module recognition, the maximum number of genes processed by computer was maxBlockSize = 5,000, the minimum number of genes of each module was minModuleSize = 30, and the module merge threshold was set as mergeCutHeight = 0.25. The 31 identified modules are depicted in Figure 1g, whereby the different modules were marked using different colors. As can be seen from a multidimensional scaling (MDS) for gene expression data of the three modules (Figure 1h), lncRNAs in black, red, green, and turquoise modules showed dissimilar expression. The co-expression of the lncRNAs of the modules in lightgreen was relatively high. The correlation between the module lncRNAs is illustrated in Figure 1i. In addition, three module values were calculated in each module and a clustering tree is presented in Figure 1j. In order to obtain the lncRNA modules closely related to PCa, the relevant clinical information of the sample was extracted from the microarray, and the correlation between the above three different color modules and the 3 clinical characteristics were analyzed. The heatmap of the module and the clinical correlation is demonstrated in Figure 1k. We observed that the grey module shared the closest correlation to PCa, which was identified to be a positive association. The correlation coefficient reached 0.9, which was considered to be an indication that the lncRNAs in this module were highly likely to be positively affected by the development of PCa. The lncRNAs in 31 modules were used to construct a co-expression network with 609 edges and 7274 nodes (Figure 1l) using Cytoscape software and the top 10 hub genes of the network was identified using cytohubba plug-in based on ‘Degree’ method (Figure 1m). The hub lncRNAs above are named LINC00313, LINC00393, AC007422.1, LINC00351, AC023796, AL133166, JRKL-AS1, MIR381HG, LINC01568, and AP001116.
Identification of DEGs and functional enrichment analysis in GSE55945 data set
A total of 24442 genes in GSE55945 data set were obtained after reannotation. Based on the cut-off criteria, 663 genes (including 210 upregulated and 453 downregulated) were significantly differentially expressed, as shown in Figure 2a and 2b. We performed GSEA and GSVA analysis to further investigate the potential functions of these 663 DEGs in prostate malignant tumor. As shown in Figure 2c, these DEGs in prostate malignant tumor group were mainly enriched in “glyoxylate and dicarboxylate metabolism”, “ribosome”, “fructose and mannose metabolism”, “homologous recombination”, and “maturity onset diabetes of the young” pathways based on GSEA results. GSVA confirmed that these gene sets were close relationship with circadian rhythm mammal, basal transcription factors, abc transporters, adherens junction, AKT signaling pathway, and ether lipid metabolism (Figure 2d). The results of GO analysis were divided into biological processes (BP), cellular component (CC) and molecular function (MF). GO results indicated that the these DEGs were significantly enriched in BP, CC, and MF (Figure 2e and 2f). BP includes morphogenesis of an epithelium, muscle contraction and protein heterotetramerization. CC includes extracellular matrix, contractile fiber, and myofibril. And MF includes actin binding, extracellular matrix structural constituent, and chromatin DNA binding.
Identification of DEGs and functional enrichment analysis in GSE102124 data set
A total of 30905 genes in GSE102124 data set were obtained after reannotation. Based on the cut-off criteria, 572 genes (including 62 upregulated and 510 downregulated) were significantly differentially expressed, as shown in Figure 2g and 2h. We performed GSEA and GSVA analysis to further investigate the potential functions of these 572 DEGs in PCa patients receiving treatment. As shown in Figure 2i, these DEGs in treated group were mainly enriched in “pyruvate metabolism”, “primary bile acid biosynthesis”, “glycolysis gluconeogenesis”, “arginine and proline metabolism”, and “nicotinate and nicotinamide metabolism” pathways based on GSEA results. GSVA confirmed that these gene sets were close relationship with protein export, glyoxylate and dicarboxylate metabolism, steroid biosynthesis, proteasome, peroxisome, N glycan biosynthesis, riboflavin metabolism, butanoate metabolism, sulfur metabolism, glycosylphosphatidylinositol gpi anchor biosynthesis, and valine leucine and isoleucine degradation (Figure 2j). GO results indicated that the these DEGs were significantly enriched in BP (carboxylic acid biosynthetic process, organic acid biosynthetic process, and Golgi vesicle transport), CC (endosome membrane, transport vesicle, and focal adhesion), and MF (exopeptidase activity, GDP binding, and myosin binding) (Figure 2k and 2l).
WGCNA co-expression network of GSE55945 data set
A total of 663 DEGs in GSE55945 data set were used to construct a WGCNA co-expression network. In order to evaluate the outliers of 21 samples (8 normal and 13 prostate malignant tumor), sample clustering methods were used based on the distribution of the expression values of the samples (Figure 3a), with no significant difference detected in the samples included in the WGCNA. Power = 20 was selected in the event of the correlation coefficient between log (k) and log P (k) reached 0.91 for the first time (Figure 3b-d). The module merge threshold was set as mergeCutHeight = 0.25. The 10 identified modules are depicted in Figure 3e, whereby the different modules were marked using different colors. As can be seen from a multidimensional scaling (MDS) for gene expression data of the 10 modules (Figure 3f). The co-expression of the genes of the modules in grey was relatively high. The correlation between the module genes is illustrated in Figure 3g. In addition, three module values were calculated in each module and a clustering tree is presented in Figure 3h. In order to obtain the gene modules closely related to malignant tumor, the relevant clinical information of the sample was extracted from the microarray, and the correlation between the above three different color modules and the 2 clinical characteristics were analyzed. The heatmap of the module and the clinical correlation is demonstrated in Figure 3i. We observed that the grey module shared the closest correlation to malignant tumor, which was identified to be a positive association. The correlation coefficient reached 0.69, which was considered to be an indication that the genes in this module were highly likely to be positively affected by the development of PCa. The genes in 10 modules were used to construct a co-expression network with 467 edges and 1113 nodes (Figure 3j) using Cytoscape software and the top 10 hub genes of the network was identified using cytohubba plug-in based on ‘Degree’ method (Figure 3k). The hub lncRNAs above are named CDKN2A, PTEN, NCAM1, BDNF, KRT19, SPP1, CAV1, CALM1, KRT5, and BMP2.
WGCNA co-expression network of GSE102124 data set
A total of 572 DEGs in GSE102124 data set were used to construct a WGCNA co-expression network. In order to evaluate the outliers of 22 samples (3 control and 19 treated), sample clustering methods were used based on the distribution of the expression values of the samples (Figure 3l), with no significant difference detected in the samples included in the WGCNA. Power = 20 was selected in the event of the correlation coefficient between log (k) and log P (k) reached 0.75 for the first time (Figure 3m-o). The module merge threshold was set as mergeCutHeight = 0.25. The 10 identified modules are depicted in Figure 3p, whereby the different modules were marked using different colors. As can be seen from a multidimensional scaling (MDS) for gene expression data of the 10 modules. The co-expression of the genes of the modules in grey was relatively high (Figure 3q). The correlation between the module genes is illustrated in Figure 3r. In addition, three module values were calculated in each module and a clustering tree is presented in Figure 3s. In order to obtain the gene modules closely related to treated group, the relevant clinical information of the sample was extracted from the microarray, and the correlation between the above three different color modules and the 2 clinical characteristics were analyzed. The heatmap of the module and the clinical correlation is demonstrated in Figure 3t. We observed that the grey module shared the closest correlation to treated group, which was identified to be a positive association. The correlation coefficient reached 0.9, which was considered to be an indication that the genes in this module were highly likely to be positively affected by the development of PCa. The genes in 10 modules were used to construct a co-expression network with 327 edges and 1080 nodes (Figure 3u) using Cytoscape software and the top 10 hub genes of the network was identified using cytohubba plug-in based on ‘Degree’ method (Figure 3v). The hub lncRNAs above are named GAPDH, HSPA5, CANX, CDH1, HSP90B1, PTEN, SEC61A1, VEGFA, RPN1, and DNAJC3.
Construction of lncRNA-associated ceRNA network
LINC00313 was the top one hub gene of WGCNA co-expression network in GSE115414 data set. PTEN was the only one overlapping DEG between GSE55945 and GSE102124 data sets (Figure 3w).
miRNAs targeted by LINC00313 and PTEN were screened via using StarBase database. A ceRNA network was constructed using lncRNA–mRNA–miRNA pairs (Figure 3x). Furthermore, CytoNCA, a Cytoscape plugin, was used for centrality analysis of the PPI network to identify crucial genes. Therefore, LINC00313-miR-19a-3p-PTEN was identified as a core network (Figure 3y).
Low LINC00313 expression was detected in PCa tumor tissues and dox-resistant PCa cells, and had a poor survival
RT-PCR showed that LINC00313 expression is lower in PCa tumor tissues than that in matched adjacent tissues (Figure 4a). Low LINC00313 expression was correlated with a longer OS in patients with PC (Figure 4b). To investigate the relationship between LINC00313 expression and the clinicopathological features of the patients with PCa. Our results showed that LINC00313 expression was significantly associated with histological grade, preoperative PSA, Gleason score, angiolymphatic invasion, and biochemical recurrence, respectively (P < 0.05; Table 1 and Figure 4c) and was not associated with age, pathological stage, lymph node metastasis, and surgical margin status. Univariate and multivariate Cox regression analyses of overall survival (OS; Figure 4d and 4e) suggested that LINC00313 is an independent prognostic factor in PCa (P < 0.05). We further found that LINC00313 expression is significantly decreased in dox-resistant PC cells (PC3-DR, and DU145-DR) (Figure 4g).
LINC00313 overexpression resulted in inhibited viability, proliferation, invasion, and migration, and induced apoptosis and cell cycle arrested at the G1 phase in dox-resistant PCa cells, in addition to the regulation of protein expression
Vectors expressing LINC00313 were transfected into PC3-DR, and DU145-DR cells, followed by verification of high LINC00313 expression (Figure 5a). The proliferation of dox-resistant PC cells was significantly inhibited in response to LINC00313 overexpression, as determined by CCK-8 and colony formation assays (Figure 5b and 5c). Flow cytometry showed that overexpression of LINC00313 promotes apoptosis in dox-resistant PCa cells and induces cell cycle arrest at the G1 phase (Figure 5d and 5e). As expected, the invasion and migration of dox-resistant PCa cells were significantly inhibited in response to LINC00313 overexpression (Figure 5f and 5g). Next, we found that LINC00313 overexpression resulted in increased protein levels of p27, Bax, cleaved caspase-3/9, and E-cadherin, and decreased cyclinD1, Ki67, Bcl-2, N-cadherin, Vimentin, MMP-2, and PIP3 protein levels in PC3-DR, and DU145-DR cells (Figure 5h-i).
LINC00313 inhibition resulted in increased viability, proliferation, invasion, and migration, and suppressed apoptosis and cell cycle progression in dox-resistant PCa cells, in addition to the regulation protein expression
SiRNA targeting LINC00313#1 or LINC00313#2 were transfected into PC3-DR, and DU145-DR cells, followed by verification of low LINC00313 expression (Figure 6a-c). The proliferation of dox-resistant PCa cells was significantly increased in response to LINC00313 inhibition, as determined by CCK-8 and colony formation assays (Figure 6d-g). Flow cytometry analysis showed that inhibition of LINC00313 inhibits apoptosis and cell cycle progression in dox-resistant PCa cells (Figure 6h and 6i). As expected, the invasion and migration of dox-resistant PCa cells were significantly upregulated in response to LINC00313 inhibition (Figure 6j-l). Next, we found that LINC00313 inhibition resulted in decreased protein levels of p27, Bax, cleaved caspase-3/-9, and E-cadherin and increased cyclinD1, Ki67, Bcl-2, N-cadherin, Vimentin, MMP-2, and PIP3 protein levels in PC3-DR, and DU145-DR cells (Figure 6k and 6l).
LINC00313 affects the oncogenicity of dox-resistant PCa cells in vivo
PC3-DR cells transfected with vectors expressing LINC00313 were subcutaneously injected into nude mice. Tumor weights and volumes were measured using an electronic balance and Vernier calipers (Figure 7a). LINC00313 overexpression resulted in reduced the tumor volume and weight (Figure 7b and 7c). Next, DU145-DR cells transfected with siRNA against LINC00313 were subcutaneously injected into nude mice; this was followed by similar analyses of tumor weight and volume (Figure 7d). Inhibition of LINC00313 increased the tumor volume and weight (Figure 7e and 7f). IHC staining showed that Ki67 expression was reduced and cleaved caspase-3 and E-cadherin levels were increased in response to LINC00313 overexpression in the tumor tissues of nude mice, whereas Ki67 expression was increased and cleaved caspase-3 and E-cadherin levels were decreased in response to LINC00313 inhibition (Figure 7g).
miR-19a-3p, identified as a target miRNA of LINC00313, was upregulated in tumor tissues of patients with PCa and in dox-resistant PCa cells and PTEN was target gene of miR-19a-3p.
The binding sites and modified sequence in the LINC00313 3′ UTR are shown in Figure 8a. In a dual-luciferase reporter assay, the luciferase activity in cells with LINC00313‐WT transfection was significantly decreased in response to an miR‐19a-3p mimic and was significantly increased in response to an miR‐19a-3p inhibitor. There were no alterations in the LINC00313‐MUT-transfected cells (Figure 8b). Furthermore, RT-qPCR confirmed that the levels of miR-19a-3p in PC3-DR, and DU145-DR cells were significantly reduced in response to overexpression and were increased in response to the inhibition of LINC00313 (Figure 8c and 8d). In addition, in a biotin-labeled pull‐down assay, miR-19a-3p was efficiently pulled down by LINC00313 (Figure 8e) but not by bio‐LINC00313 MUT. A Spearman’s correlation analysis proved that LINC00313 expression was negatively correlated with miR-19a-3p expression in 82 patients with PCa (Figure 8f). A miR‐19a‐3p mimic or inhibitor was transfected into PC3-DR, and DU145-DR cells, and the transfection efficiency was verified by RT-qPCR. RT-qPCR showed that the levels of miR-19a-3p in PC3-DR, and DU145-DR cells were significantly increased by the miR‐19a-3p mimic and inhibited by the miR‐19a-3p inhibitor (Figure 8g). The levels of miR-19a-3p in tumor tissues of patients with PCa and in dox-resistant PCa cells were significantly higher than those in normal adjacent tissues PCa cells (Figure 8h-i). Additionally, miRbase and TargetScan databases were used to predict the stem-loop structure of miR-19a-3p and the binding site to the PTEN, respectively. The stem-loop structure of miR-19a-3p and the binding site to the PTEN was presented in Figure 8j and 8k using R software (RNAcofold and RNAfold) and the MFE value was -9 kcal/mol. Dual-luciferase reporter assay suggested that the luciferase activity of cells with PTEN-WT transfection was significantly decreased by miR-19a-3p mimic and the luciferase activity of cells with PTEN-WT transfection was significantly increased by miR-19a-3p inhibitor, while there was no alteration in PTEN-MUT transfected group (Figure 8l and 8m). To further confirm this result, miR-19a-3p mimic or inhibitor was transfected into PC3-DR, and DU145-DR cells, and transfection efficiency was evaluated by RT-qPCR and western blot assays. Further, RT‐qPCR and western blot assays proved that the mRNA level of PTEN in PC3-DR, and DU145-DR cells was significantly reduced by miR-19a-3p mimic, while increased by miR-19a-3p inhibitor (Figure 8n-q). A Spearman’s correlation analysis proved that miR-19a-3p expression was negatively correlated with PTEN expression in 82 patients with PCa (Figure 8r). PTEN expression in tumor tissues of patients with PCa and in dox-resistant PCa cells were significantly lower than those in normal adjacent tissues PCa cells (Figure 8s and 8t).
LINC00313 regulates proliferation, apoptosis, cell cycle, invasion, and migration by targeting miR-19a-3p to affect PTEN expression, thereby regulating the activation of the PI3K/Akt signaling pathway
Firstly, western blot and RT-qPCR assays revealed that in PC3-DR cells with vectors expressing PTEN transfection, the protein and mRNA levels of PTEN was significantly increased, and in PC3-DR cells with siRNA targeting PTEN transfection, the protein and mRNA levels of PTEN was significantly decreased (Figure 9a). Next, CCK-8, colony formation, and flow cytometry assays demonstrated that the overexpression of PTEN counteracts the inhibition of proliferation and promotion of apoptosis by miR-19a-3p inhibitor (Figure 9b-d). As expected, miR-19a-3p inhibitor-induced G1 phase cell cycle arrest and inhibition of PC3-DR cells invasion and migration were reversed by overexpression of PTEN (Figure 9e-g). We found that miR-19a-3p inhibitor resulted in increased protein levels of p27, Bax, cleaved caspase-3, and E-cadherin, and decreased cyclinD1, Ki67, Bcl-2, N-cadherin, and MMP-2 protein levels in PC3-DR cells (Figure 9h). These functions of miR-19a-3p inhibitor in the above protein expression were reversed by overexpression of PTEN. Further, in PC3-DR cells with miR-19a-3p inhibitor transfection, PTEN protein expression increased significantly and PIP3 protein expression decreased significantly, meanwhile, the phosphorylation levels of PI3K and AKT suppressed significantly. However, overexpression of PTEN abolished the effects of miR-19a-3p inhibitor on the expression of cyclinD1, p27, Ki67, Bax, Bcl-2, cleaved caspase-3, E-cadherin, N-cadherin, MMP2, PTEN, PIP3, phosphorylation (phosph)-PI3K, and phosph-AKT in PC3-DR cells with miR-19a-3p inhibitor transfection. In a dual-luciferase reporter assay, the luciferase activity in cells with PTEN‐WT transfection was significantly increased in response to LINC00313 overexpression. There were no alterations in the PTEN‐MUT-transfected cells (Figure 9i). Vectors expressing LINC00313 were transfected into PC3-DR cells, followed by verification of high PTEN expression and siRNA targeting LINC00313#1 or LINC00313#2 were transfected into PC3-DR cells, followed by verification of low PTEN expression (Figure 9j). Our results showed that PTEN silencing promoted the proliferation, invasion, and migration and inhibited the apoptosis of PC3-DR cells (Figure 9k-o). Analogously, rescue experiments showed that the functions of overexpression of LINC00313 in inhibiting cell proliferation, invasion, and migration, promoting cell apoptosis, and inducing cell cycle arrest at the G1 phase were reversed by PTEN silencing in PCa cell.