INHBA expression in patients with GC
The comparison of INHBA mRNA expression among various cancer types and normal tissues in TIMER database revealed significantly higher INHBA expression in bladder urothelial carcinoma (BLCA), breast invasive carcinoma (BRCA), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), rectum adenocarcinoma (READ) and stomach adenocarcinoma (STAD). By contrast, INHBA expression was significantly lower than adjacent normal tissues in kidney renal papillary cell carcinoma (KIRP), lung adenocarcinoma (LUAD)and lung squamous cell carcinoma (LUSC) (Figure 1A). And then we compared the expression level of INHBA in GC tumor tissues with this in normal tissues by using Oncomine and GEPIA databases. The results uncovered that expression of INHBA was significantly higher in GC tissues when compare with normal tissues (P<0.05) (Figure 1B, C).
When analyzed the expression level of INHBA in, respectively, Cho GC, DErrico GC, Deng GC, Cui GC, Chen GC, TCGA GC and Wang GC, GSE81948, GSE54129 and GSE13911 datasets, scatter plot showing the expression level of INHBA was fundamentally upregulated in GC patients tumor tissues compared with adjacent normal tissues (Figure 2). And previous studies confirmed that INHBA protein was highly expressed in GC tumor tissues by using immunohistochemistry and western blotting[23, 46, 47]. Further analysis of diverse clinical pathological characteristics of 415 GC samples in the UALCAN database indicated higher transcriptional level of INHBA. In stage subgroup (normal-vs-Stage I, normal-vs-Stage II, normal-vs-Stage III and normal-vs-Stage IV) analysis, tumor grade subtype (normal-vs-grade 1, normal-vs-grade 2 and normal-vs-grade 3), nodal metastasis status subgroup (normal-vs-N0, normal-vs-N1, normal-vs-N2 and normal-vs-N3), gender subgroup (normal-vs-male and normal-vs-female), TP53 mutation status subgroup (normal-vs-TP53 mutant and normal-vs-nonmutant), race subgroup (normal-vs-Caucasian, normal-vs-African American and normal-vs-Asian), HPV status subgroup (normal-vs-with HPV infection and normal-vs-without HPV infection status) analysis the INHBA expression was fundamentally higher in GC patients, as well as in age subgroup analysis (Figure 3). These findings suggest that INHBA expression levels can serve as a potential diagnostic biomarker in GC. And we validated the protein and mRNA expression of INHBA in GC tissues, the results showed that INHBA was significantly upregulated in GC tissues compared to normal tissues (Figure 4).
Diagnostic value of INHBA in patients with GC
According to the difference of INHBA in GC, we get further to explore the diagnostic value of INHBA for distinguishing GC patients from healthy individuals, we performed ROC curve based on the data from Cho GC, DErrico GC, Deng GC, Cui GC, Chen GC, TCGA GC and Wang GC, GSE81948, GSE54129 and GSE13911 datasets, respectively. The results showed that INHBA had high diagnostic value for distinguishing GC patients from healthy individuals (Cho GC, AUC=0.670, 95 CI% [0.524-0.788], Figure 5A; DErrico GC, AUC=0.652, 95 CI% [0.517-0.759], Figure 5B; Deng GC, AUC=0.817, 95 CI% [0.768-0.864], Figure 5C; Cui GC, AUC=0.838, 95 CI% [0.776-0.901], Figure 5D; Chen GC, AUC=0.942, 95 CI% [0.874-0.975], Figure 5E; Wang GC, AUC=0.836, 95 CI% [0.636-0.917], Figure 5F; TCGA GC, AUC=0.756, 95 CI% [0.711-0.801], Figure 5G; GSE81948 GC, AUC=1, 95 CI% [1-1], Figure 5H; GSE54129 GC, AUC=0.991, 95 CI% [0.924-0.996], Figure 4I; GSE13911 GC, AUC=0.970, 95 CI% [0.877-0.973], Figure 5J).
The correlation between the expression level of INHBA and prognosis in patients with GC
According to the difference of INHBA in GC, we get further analyzed the correlation between INHBA expression and prognosis in patients with GC, so it is necessary to clarify whether INHBA act as the promoter or suppressor of GC. Kaplan Meier plotter was utilized to investigate the relationship between the expression of INHBA and the prognosis, including OS, FPS and PPS, in patients with GC. The results showed that the high expression of INHBA in GC was significantly related to the worse OS (HR=1.31; 95% CI [1.08-1.58]; P=0.015), FPS (HR=1.32; 95% CI [1.08-1.61]; P=0.007) and PPS (1.43; 95% CI [1.12-1.82]; P=0.004), respectively (Figure 6A, 6B, 6I, 6J, 6Q, 6R). We further analyzed the correlation between the expression levels of INHBA and clinicopathological subtypes, such as gender, HER2 status and Lauren classification. In gender subtypes, high expression of INHBA in male patients with GC had poor OS (HR=1.34; 95% CI [1.06-1.69]; P=0.016), FPS (HR=1.37; 95% CI [1.07-1.73]; P=0.010) and PPS (1.62; 95% CI [1.22-2.16]; P=0.00085), respectively (Figure 6A, 6D, 6I, 6L, 6Q, 6T). In HER2 status subtypes, high expression of INHBA in HER2-positive patients with GC had worse OS (HR=1.78; 95% CI [1.36-2.34]; P<0.011), FPS (HR=2.15; 95% CI [1.53-3.01]; P<0.011) and PPS (HR=1.94; 95% CI [1.35-2.79]; P<0.011), respectively (Figure 6A, 6E, 6I, 6M, 6Q, 6U). In Lauren classification subtypes, high expression of INHBA in intestinal patients with GC had worse OS (HR=1.65; 95% CI [1.18-2.32]; P=0.004) and FPS (HR=1.60; 95% CI [1.09-2.35]; P=0.016) (Figure 6A, 6G, 6I, 6O), however, high expression of INHBA in diffuse patients with GC had worse PPS (HR=1.69; 95% CI [1.11-2.57]; P=0.014) (Figure, 6Q, 6X). These results suggested that INHBA may serve as a potential biomarker for specific subtypes gastric cancer.
Immune infiltrates in correlation with INHBA in GC
Previous studies had shown that tumor infiltration was significantly related to the progression and prognosis of GC[48-50]. So, we utilized TIMER database to investigate whether the expression levels of INHBA in GC tumor was correlated with immune infiltration. The results showed that INHBA was negatively correlated with B cell while positively correlated with Macrophage, Neutrophil and Dendritic cell infiltration (P<0.05, Figure 7A). Cumulative survival analysis uncovered that Macrophage of immune infiltrates statistically significant (P<0.05) of INHBA in GC indicating that Macrophage negatively affecting the prognosis, it is worthy to get further investigate (Figure 7B). Finally, somatic copy number alterations are characterized by GISTIC 2.0, including deep deletion (-2), arm-level deletion (-1), diploid/normal (0), arm-level gain (1), and high amplification (2). Box plots are presented to show the distributions of each immune subset at each copy number status with INHBA in GC (Figure 7C).
Relationship between INHBA expression and immune cell type markers in GC
We feather analyzed the correlation between the expression of INHBA and different immune cells type markers in GC based on TIMER database. The results showed that INHBA in GC was positively correlated with CD38 in B cells (Table 1). INHBA in GC was also positively correlated with CD8A in CD8+ T cells. Similarly, INHBA in GC was positively correlated with MPO, FCGR3B, FPR1, CSF3R in Neutrophils and CD209 in dendritic cells (Table 1). INHBA in GC was positively correlated with CD68, CD84, CD163, MS4A4A in macrophages (Table 1). These results further confirmed that INHBA in GC were correlated to immune infiltration.
Table 1. Correlation analysis between INHBA and immune cell type markers in TCGA-STAD cohort via TIMER database.
Cell type
|
Gene marker
|
None
|
Purity
|
Cor
|
P
|
Cor
|
P
|
B cells
|
CD19
|
-0.023
|
6.04E-01
|
-0.041
|
4.28E-01
|
|
FCRL2
|
0.021
|
6.66E-01
|
-0.005
|
9.20E-01
|
|
CD38
|
0.136
|
5.00E-03
|
0.091
|
7.56E-01
|
|
MS4A1
|
-0.021
|
6.74E-01
|
-0.061
|
2.38E-01
|
CD8+ T cells
|
CD8A
|
0.120
|
1.50E-02
|
0.083
|
1.09E-01
|
|
CD8B
|
0.037
|
4.51E-01
|
0.017
|
7.47E-01
|
Neutrophils
|
MPO
|
0.223
|
4.50E-06
|
0.215
|
2.38E-05
|
|
FCGR3B
|
0.153
|
1.73E-03
|
0.130
|
1.16E-02
|
|
FPR1
|
0.334
|
5.68E-13
|
0.328
|
5.92E-11
|
|
CSF3R
|
0.287
|
2.52E-09
|
0.263
|
1.94E-07
|
|
S100A12
|
0.064
|
1.96E-01
|
0.030
|
2.66-01
|
Macrophages
|
CD68
|
0.209
|
1.76E-05
|
0.196
|
1.27E-04
|
|
CD84
|
0.208
|
1.98E-05
|
0.193
|
1.54E-04
|
|
CD163
|
0.312
|
8.29E-11
|
0.281
|
2.67E-08
|
|
MS4A4A
|
0.310
|
1.10E-10
|
0.292
|
6.76E-09
|
Dendritic cells
|
CD209
|
0.137
|
9.14E-03
|
0.101
|
5.00E-02
|
|
CD1C
|
0.005
|
9.24E-01
|
-0.018
|
8.01E-01
|
STAD, stomach adenocarcinoma; Cor, r value of Spearman’s correlation; Purity, correlation adjusted by purity; Bold represents P<0.05.
Prognostic analysis of INHBA expression in GC based on immune cells
We have confirmed that the INHBA expression was correlated with the immune infiltration in GC, and the expression of INHBA was also related to the poor prognosis of the patients with GC. Thus, we speculated that expression n of INHBA in GC affected the prognosis partly due to immune infiltration. Then we performed prognostic analysis based on the expression levels of INHBA of GC in related immune cells subgroup via Kaplan Meier plotter database. The results uncovered that the high expression of INHBA of GC in enriched CD4+ T cells (HR=2.12; 95% CI [1.17-3.84]; P=0.011), CD8+ T cells (HR=1.97; 95% CI [1.12-3.18]; P=0.0046) and Macrophage (HR=1.79; 95% CI [1.05-3.03]; P=0.029) cohort had worse overall survival respectively (Figure 8B, 8C, 8D), while B cells cohort had no statistical significance (Figure 7A). The high expression of INHBA of GC in enriched B cells (HR=2.62; 95% CI [1-6.85]; P=0.042), CD4+ T cells (HR=4.08; 95% CI [1.35-12.32]; P=0.0073), CD8+ T cells (HR=6.91; 95% CI [1.57-30.38]; P=0.0034) and Macrophage (HR=3.24; 95% CI [0.94-11.12]; P=0.048) cohort had worse relapse free survival respectively (Figure 8E, 8F, 8G, 8H). The above results suggested that high INHBA expression in GC may affect prognosis partly because of immune infiltration.
Genetic alteration differences of INHBA in GC
In order to explore the sequence alterations of INHBA in GC, we then used cBioPortal database to investigate the sorts and frequency of INHBA modification in GC from STAD patients sequencing data in the TCGA. As shown in Figure 9A 69 of 1590 (4%) GC patients were altered. Further study suggested that mRNA upregulation and mutation are the most common types of INHBA in patients with GC (Figure 9B). Besides, the results of Kaplan-Meier plotter and log-rank test demonstrated no significantly statistical difference in overall survival (OS) and disease-free survival (DFS) in cases with and without INHBA alterations (P-value was 0.972 and 0.524, respectively. Figure 9C, 9D)
The correlation between INHBA expression and methylation around the promoter region
Previous studies had shown that DNA promoter methylation is a meaningful pattern which affect tumorigenesis of tumors[51-53]. To explore the correlation between INHBA expression and DNA methylation, then the methylation levels of INHBA in GC were performed by using of MEXPRESS database. Figure 10 shows that default MEXPRESS plot for INHBA in gastric cancer in the samples sorted based on the INHBA expression value. The results showed week significant methylation level change between tumor and normal tissues, which indicates that INHBA expression might not controlled by DNA methylation.
Functional enrichment analysis of genes co-expressed with GC
In order to uncovered the potential function of the INHBA, we performed protein-protein interaction (PPI) network, GO function and KEGG pathway enrichment analysis via Metascape database. The PPI network as shown in Figure 11A and 11B. As Figure 11C-E shows that the most significant enriched GO terms were regulation transmembrane receptor protein serine/threonine kinase signaling pathway, BMP signaling pathway, SMAD protein signal transduction, nodal signaling pathway, cell proliferation and metabolic process. The most enriched KEGG pathways were TGF-beta signaling pathway and PID ALK1/2 pathway.
GO function and KEGG pathway enrichment analysis of co-expression genes correlated with INHBA in GC
LinkedOmics was utilized to uncovered mRNA sequencing information from GC patients in the TCGA-STAD cohort. Spearman’s test was conducted to analyze correlations between INHBA and genes differentially expressed in GC (red represents positively related genes and green represents negatively related genes) (Figure 12A). The top 50 genes that were positively and negatively correlated with INHBA were shown in heat maps (Figure 12B-C). Significantly GO and KEGG functional enrichment analysis were conducted by gene set enrichment analysis (GSEA) suggested that these genes differentially expressed in correlation with INHBA in GC were mainly enriched collagen metabolic process, extracellular structure organization, cellular response to vascular endothelial growth factor stimulus, connective tissue development and DNA replication, and so on biological process (Figure 12D). Essentially molecular functions and cellular component were collagen binding, extracellular matrix structural constituent, growth factor, Wnt-protein binding and SMAD binding, collagen trimer, endoplasmic reticulum lumen and so on (Figure 12E-F). KEGG pathway analysis showed that cancer-related signaling pathways were enriched, including TGF-beta signaling pathway, ECM-receptor signaling pathway, AGE-RAGE signaling pathway and so on (Figure 12G).