An analysis on the expression of PAI-1 in pan-cancer
To evaluate the potential function and expression of PAI-1 in a variety of cancers, the study analyzed the expression of PAI-1 first, as shown in Fig.1. The expression level of PAI-1 in BRCA, COAD, ESCA, GBM, HNSC, KIRC, READ, STAD or THCA was significantly higher than in normal tissue. However, the level was significantly down-regulated in CHOL, KICH, KIRP, or LIHC. There was no significant difference for the level in BLCA, LUAD, LUSC, PRAD and UCEC. Similarly, the mRNA expression data of gastric cancer were analyzed, and through a combination of Kaplan-Meier analysis and COX regression model, 82 survival-related genes highly correlated with prognosis of gastric cancer were screened out (Supplementary Table S1), including high-risk PAI-1. Subsequently, the expression of PAI-1 in various cancer tissues was verified by the GEPIA database. As shown in Fig.2A-2H, the expression was up-regulated obviously in BRCA, COAD, ESCA, GBM, HNSC, KIRC, READ, and STAD, while down-regulated in KICH, KIRP, LIHC, LUSC, THCA, and UCEA (Fig.2I-2N). Overall, the expression levels of PAI-1 in BRCA, COAD, ESCA, GBM, HNSC, KIRC, READ, and STAD obviously increased, while in KICH, KIRP, and LIHC decreased, indicating that PAI-1 might be closely related to the occurrence, development, invasion and metastasis of these 11 cancers.
An analysis on the prognosis of PAI-1 in various cancers
The survival analysis for PAI-1 in these 11 cancers was conducted based on the GEPIA online database. As shown in Fig.3 and Fig.4, in terms of overall survival (OS), the high expression levels of PAI-1 in HNSC, STAD, KIRP and LIHC indicated a poor prognosis, while in terms of recurrence-free survival (RFS), the up-regulation of PAI-1 expression in GBM, KIRC, STAD, KICH and KIRP represented a poor prognosis. In combination with the expression levels of PAI-1 in cancer tissues and normal tissues, the study found that only the up-regulation of PAI-1 expression level worsened the prognosis of gastric cancer, and there was no statistical significance for the survival prediction related to other malignant tumors. To sum up, as a potential prognostic biomarker, PAI-1 may indicate poor prognosis of gastric cancer.
Prediction and analysis on upstream miRNAs of PAI-1
The upstream miRNAs of PAI-1 were predicted based on the Starbase online database, and the miRNAs that could be predicted by three databases simultaneously were captured for further analysis. According to the mechanism of action of miRNA on target gene mRNA, miRNA should be negatively correlated with target gene. Subsequently, the correlation between PAI-1 and these miRNAs was calculated, and at last, 10 closely related miRNAs were obtained. As shown in Fig.5A, a miRNA- PAI-1 regulatory network that met the above conditions for screening was visualized through Cytoscape software, and the data concerning correlation between the 10 miRNAs and PAI-1 were described in Table 1. According to the theory, candidate miRNAs should be poorly expressed in gastric cancer tissues compared to normal tissues, while only miR-30e-5p and miR-30c-5p conformed. Meanwhile, the expression levels of miR-30e-5p and miR-30c-5p in gastric cancer tissues and normal tissues were detected, as shown in Fig. 5B-5C. Among the candidate miRNAs, only miR-30c-5p had expression level lowered in gastric cancer with statistical significance. As shown in Fig.5D, Also, a survival analysis on miR-30c-5p in gastric cancer based on the Kaplan-Meier plotter database found that up-regulation of miR-30c-5p expression level in gastric cancer was associated with poor prognosis of gastric cancer. All these findings suggested that miR-30c-5p might be the most promising upstream regulatory gene in gastric cancer.
Table 1. The expression correlation between predicted miRNAs and SERPINE1 in STAD
Gene
|
miRNA
|
R value
|
pvalue
|
logFC
|
SERPINE1
|
miR-19b-3p
|
-0.223967703
|
1.36E-05
|
1.285571282
|
SERPINE1
|
miR-19a-3p
|
-0.206224287
|
6.37E-05
|
1.901414418
|
SERPINE1
|
miR-30d-5p
|
-0.200104292
|
0.000105202
|
0.273812907
|
SERPINE1
|
miR-30b-5p
|
-0.172364077
|
0.000856495
|
0.261855105
|
SERPINE1
|
miR-148a-3p
|
-0.171064971
|
0.000938155
|
0.448863061
|
SERPINE1
|
miR-30c-5p
|
-0.1603171
|
0.001945506
|
-0.162681261
|
SERPINE1
|
miR-196b-5p
|
-0.156509062
|
0.002493569
|
4.694990524
|
SERPINE1
|
miR-30e-5p
|
-0.14994663
|
0.003777251
|
-0.030222247
|
SERPINE1
|
miR-942-5p
|
-0.115381271
|
0.026106396
|
1.051408458
|
SERPINE1
|
miR-196a-5p
|
-0.105013598
|
0.042986363
|
4.890843882
|
Prediction and analysis on the upstream lncRNAs of miR-30c-5p
According to the competitive endogenous RNA hypothesis, lncRNA may be involved in the expression and regulation of target genes through competitive binding with miRNA. Through miRNA, the lncRNA that can bind to it may be derived reversely based on this mechanism. Thus, a total of 101 lncRNAs that may bind to miR-30c-5p were obtained through a prediction based on the Starbase database, and the lncRNA-miR-30c-5p regulatory network was constructed using Cytoscape software (Supplementary Fig.1). A screening based on the mechanism of competitive binding with miRNA identified 6 lncRNAs(GATA3-AS1, NORAD, KCNH7-AS1, LINC02863, CASC15, and LINC01094) expression up-regulated significantly in gastric cancer as compared with those in the normal group and significantly correlated with miR-30c-5p (P < 0.05, |R| > 0.1). For their expression levels, shown Fig.6A-6F. Subsequently, the 6 lncRNAs were performed correlation analysis with PAI-1, and the results were as shown in Table 2. Obviously, the p value for correlation of GATA3-AS1, NORAD, and KCNH7-AS1 with PAI-1 was greater than 0.05, NORAD was negatively correlated with PAI-1, and therefore, there was no statistical significance. Next, LINC02863, CASC15 and LINC01094 were analyzed for survival, and the results showed that in gastric cancer with high expression levels of CASC15 and LINC01094, the OS was poor. Finally, again with the GEPIA database, the prognosis of CASC15 and LINC01094 in gastric cancer was verified. As shown in Figure 7, only patients with high expression level of CASC15 in gastric cancer had poor OS and RFS, indicating that the up-regulation of CASC15 expression level was related to the poor prognosis of gastric cancer. Therefore, CASC15 may be the most promising upstream lncRNA of miR-30c-5p/ PAI-1.
Table 2. Correlation analysis between lncRNA and miR-30c-5p or lncRNA
and SERPINE1 in STAD
lncRNA
|
miRNA
|
R value
|
p value
|
GATA3-AS1
|
miR-30c-5p
|
-0.115965869a
|
0.02530644*
|
NORAD
|
miR-30c-5p
|
-0.166618219a
|
0.001275214*
|
LINC02863
|
miR-30c-5p
|
-0.129356938a
|
0.012523149*
|
CASC15
|
miR-30c-5p
|
-0.127152482a
|
0.014169844*
|
LINC01094
|
miR-30c-5p
|
-0.146472635a
|
0.004676307*
|
KCNH7-AS1
|
miR-30c-5p
|
-0.116009984a
|
0.025250466*
|
lncRNA
|
Gene
|
R value
|
p value
|
GATA3-AS1
|
SERPINE1
|
0.096288048
|
0.063567773
|
NORAD
|
SERPINE1
|
-0.014045404
|
0.787063572
|
LINC02863
|
SERPINE1
|
0.109654194a
|
0.034498615*
|
CASC15
|
SERPINE1
|
0.294656742a
|
8.16E-09*
|
LINC01094
|
SERPINE1
|
0.321744257a
|
2.65E-10*
|
KCNH7-AS1
|
SERPINE1
|
0.001144345
|
0.982450299
|
aThese results are statistically significant
|
*p value < 0.05
|
PAI-1 positively correlates with immune cell infiltration in gastric cancer
The copy number variation (CNV) of gene, a variant form of DNA mutation, has been reported to be closely related to human tumors.The study analyzed the relations of various copy numbers of PAI-1 with B cell, CD8+T cell, CD4+T cell, macrophage, neutrophil, or dendritic cell infiltration degree using TIMER database. The CNV degree was indicated by Deep Deletion, Arm−level Deletion, Diploid/Normal, Arm−level Gain and high Amplification. As shown in Fig.8, in gastric cancer with CNV of PAI-1, except for deep deletion, the infiltration degree of most immune cells was significantly reduced, indicating that the PAI-1 might mediated immune cells infiltration. Also, the correlation between PAI-1 and the infiltration degree of the 6 types of immune cells was verified through tumor purity correction. As shown in Fig.9B and Fig.9D-9F, the expression level of PAI-1 had a significant positive correlation with the infiltration degree of CD8+T cell, Macrophage, Neutrophil, and Dendritic cell. As shown in Fig.9A and Fig.9C, the correlation between B cell, CD4+T cell and expression level of PAI-1 was no statistical significance. Therefore, CiberSort was further used to analyze the proportion of each of the 22 types of immune cells in all samples (Fig.10). Then, PAI-1 was divided into high and low expression groups according to the corresponding expression levels, and the violin diagram was used to observe the difference in infiltration degree of various immune cells for different PAI-1 expression levels. The results showed that the infiltration degrees of NK cells resting, Monocytes, Dendritic cells activated, Mast cells activated, Eosinophils, and Neutrophils in the high PAI-1 expression group were significantly increased as compared with those in the low PAI-1 expression group, and the infiltration degree of NK cells activated in the high PAI-1 expression group was significantly decreased as compared with that in the low PAI-1 expression group (Fig.11A). Furthermore, as shown in Fig.11B, in the analysis concerning correlation between PAI-1 expression level and immune cell infiltration degree, the PAI-1 expression level had a significant positive correlation with the infiltration degrees of NK cells resting, Monocytes, Dendritic cells activated, Mast cells activated, Eosinophils, and Neutrophils, and a negative correlation with the infiltration degree of NK cells activated. Two methods indicated that the infiltration degrees of these seven immune cells were correlated with PAI-1.
Correlation between PAI-1 expression level and biomarkers of immune cells in gastric cancer
To further explore the immunization of PAI-1 in gastric cancer, the Spearman's rank correlation coefficient method was used for analyzing the correlation between PAI-1 and biomarkers of immune cells. As shown in Table 3, PAI-1 had a significant positive correlation with CD8+T cell’s biomarker (CD8A), CD4+T cell’s biomarker (CD4), M1 macrophage’s biomarkers (IRF5 and PTGS2), M2 macrophage’s biomarkers (CD163, VSIG4 and MS4A4A), neutrophil’s biomarkers (ITGAM, CCR7) and dendritic cell’s biomarkers (HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, NRP1, and ITGAX). Based on the table, the correlation between the PAI-1 expression in gastric cancer tissues and the biomarkers of immune cells was also verified through GEPIA database, and the results showed that PAI-1 had a significant positive correlation with B cell’s biomarker (CD79A), CD8+T cell’s biomarkers (CD8A, CD8B), CD4+T cell’s biomarker (CD4), M1 macrophage’s biomarkers (IRF5 and PTGS2), M2 macrophage’s biomarkers (CD163, VSIG4 and MS4A4) A), neutrophil’s biomarkers (ITGAM, CCR7) and dendritic cell’s biomarkers (HLA-DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1C, NRP1, and ITGAX). Generally, these conclusions have supported the positive correlation between PAI-1 expression and immune cells infiltration.
Table 3. Correlation analysis between SERPINE1 and biomarkers of immune cells in STAD determined by Spearman's correlation analysis and GEPIA database.
ImmuneCell
|
Biomarker
|
Spearman's correlation
|
GEPIA
|
P value
|
R value
|
P value
|
R value
|
B cell
|
CD19
|
0.63845434
|
0.024330413
|
0.059
|
0.094
|
CD79A
|
0.060862322
|
0.096902492
|
0.029
|
0.11
|
CD8+ T cell
|
CD8A
|
0.00217794
|
0.157962453
|
0.0003
|
0.18
|
CD8B
|
0.241480665
|
0.060616452
|
0.034
|
0.11
|
CD4+ T cell
|
CD4
|
2.15E-06
|
0.242599158
|
3.70E-09
|
0.29
|
M1 macrophage
|
NOS2
|
0.779449455
|
-0.01450199
|
0.72
|
-0.018
|
IRF5
|
0.036128571
|
0.108277847
|
0.00013
|
0.19
|
PTGS2
|
3.83E-13
|
0.365259757
|
3.80E-19
|
0.42
|
M2 macrophage
|
CD163
|
2.01E-13
|
0.369159176
|
6.80E-16
|
0.39
|
VSIG4
|
5.28E-12
|
0.348344294
|
4.40E-17
|
0.4
|
MS4A4A
|
4.27E-09
|
0.298813289
|
1.20E-12
|
0.34
|
Neutrophil
|
CEACAM8
|
0.781027213
|
0.014402196
|
0.21
|
0.063
|
ITGAM
|
7.07E-09
|
0.29469496
|
1.10E-11
|
0.33
|
CCR7
|
8.83E-06
|
0.227836842
|
5.50E-08
|
0.26
|
Dendritic cell
|
HLA-DPB1
|
0.027667102
|
0.113754238
|
0.002
|
0.15
|
HLA-DQB1
|
0.006184357
|
0.141246331
|
0.015
|
0.12
|
HLA-DRA
|
0.03018002
|
0.111993173
|
0.01
|
0.13
|
HLA-DPA1
|
0.026490874
|
0.114626237
|
0.0075
|
0.13
|
CD1C
|
0.073171952
|
0.092639663
|
0.013
|
0.12
|
NRP1
|
0
|
0.486063716
|
6.40E-31
|
0.53
|
ITGAX
|
6.62E-12
|
0.346809648
|
7.30E-16
|
0.38
|
Association of PAI-1 with common immune checkpoints in gastric cancer tissues
The commonly used immune checkpoints, including PD1, CD274 and CTLA4, are important checkpoints in the tumor immune escape mechanism. Based on the above analyses, it can be preliminarily assumed that PAI-1 has a potential for guiding the occurrence and development of gastric cancer. Therefore, the correlation of PAI-1 with PD1, CD274 and CTLA4 was analyzed using TIMER after tumor purity correction. The results showed that PAI-1 had a significant positive correlation with PD1, CD274 and CTLA4 (Fig.12A-12C). The validation of GEPIA database has also confirmed the above results (Figure 12D-12F). The results have supported the involvement of tumor immune escape mechanism in gastric cancer development regulated by PAI-1.