SNAI1 is a prognostic biomarker and correlated with immune infiltrates in gastrointestinal cancers

The six EMT-related genes expression in human cancers

To analysis the expression of EMT-related genes in human cancers, the Oncomine database was used to analysis the EMT-related genes mRNA levels in human cancers. Specifically, as shown in Figure 1A, the SNAI1 expression was higher in colorectal cancer, esophageal cancer, gastric cancer, and kidney cancer. To further analysis EMT markers in human cancers, we compare the expression level of EMT-related genes in TCGA dataset (Supplementary Figure 1A–1E). Specifically, as shown in Figure 1B, compared with normal tissues, the SNAI1 expression was significantly higher in bladder urothelial carcinoma (BLCA), 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), stomach adenocarcinoma (STAD). However, SNAI1 expression was significantly lower in kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), Prostate adenocarcinoma (PRAD), thyroid carcinoma (THCA), corpus Endometrial Carcinoma (UCEC).

Survival analysis of six EMT-related genes in cancers

Next, to inspect whether six EMT-related genes were related with prognosis in cancer patients, GEPIA site was used to analysis the prognosis of genes in cancers by using the TCGA dataset (Supplementary Figures 2–7). Notably, high SNAI1 expression levels was closely related with poorer prognosis of overall survival (OS) in STAD (p=0.002), COAD (p=0.013), ESCA (p=0.031), KIRP (p=0.045), LGG (p<0.001), LUAD (p=0.026), LUSC (p<0.001), MESO (p=0.007), OV (p=0.048), and THYM (p=0.001) (Figure 2A, 2B and Supplementary Figure 4). Simultaneously, high SNAI1 expression levels was moderate closely related with poorer prognosis of OS in READ (p=0.093) (Figure 2C). Therefore, we focus on SNAI1 expression and prognosis in gastrointestinal cancers.

Next, to inspect whether SNAI1 expression was related with subtype of gastrointestinal cancers, we divide gastrointestinal cancers into five subtypes (CIN, EBV, HM-SNV, HM-indel) [15]. We found high SNAI1 expression was significantly related with subtype of STAD (p=0.008) and COAD (p=0.008), and moderate closely related with READ (p=0.096) (Figure 1C–1E). To validate the prognostic potential of SNAI1 in STAD, we used Kaplan-Meier plotter database to validate the prognostic potential of SNAI1 in STAD. Interestingly, a cohorts including GSE62254, GSE14210, GSE15459, GSE22377, GSE29272, and GSE51105 indicated that high SNAI1 expression was closely related with poorer prognosis in STAD (OS HR = 2.09, 95% CI =1.73 to 2.52, P<0.001; FP HR = 2.06, 95% CI =1.68 to 2.52, P<0.001; PFS HR = 2.46, 95% CI = 1.97 to 3.08, P<0.001) (Figure 2D–2F). Moreover, two cohorts including GSE17536, GSE39582 indicated that high SNAI1 expression was closely related with poorer prognosis in CRC (OS HR = 1.50, 95% CI =1.03 to 2.18, P=0.035) (Figure 2G–2I).

High expression of SNAI1 correlates with clinical relevance of STAD

Next, we examined the associationship between the SNAI1 expression and clinical relevance of STAD. As shown in Table 1, high SNAI1 expression was closely related with poorer prognosis in female (OS HR = 2.24, P<0.001; PFS HR =1.91, P = 0.002) and male (OS HR = 2.27, P <0.001; PFS HR =2.43, P<0.001). Moreover, high SNAI1 expression was closely related with poorer OS and PFS in stage 1 (OS HR = 3.63, P=0.011; PFS HR =2.88, P = 0.023), stage 2 (OS HR = 2.03, P=0.021; PFS HR =1.93, P = 0.030), stage 3 (OS HR = 1.98, P<0.001; PFS HR =2.06, P <0.001), and stage 4 (OS HR = 2.06, P<0.001; PFS HR =2.30, P <0.001) of STAD, and poorer OS and PFS in TNM stage. Furthermore, high SNAI1 expression was closely related with poorer prognosis in the lauren classification (OS HR = 1.89, P<0.001; PFS HR =2.33, P <0.001), moderate differentiation (OS HR = 1.94, P=0.046; PFS HR =2.27, P =0.011), negative (OS HR = 1.98, P<0.001; PFS HR =2.02, P <0.001) and positive (OS HR = 2.14, P<0.001; PFS HR =2.36, P <0.001) HER-2 status (Table 1). These results suggest that high SNAI1 expression can impact the prognosis in STAD with lymph node metastasis, and SNAI1 is an independent prognostic marker but can also predict the clinicopathological features of STAD.

SNAI1 expression was correlated with tumor-infiltrating lymphocytes (TILs)

TILs are an independent predictor in cancers [16, 17]. Therefore, TIMER database was used to infer the relations between abundance of TILs and expression of SNAI1. The relationship between SNAI1 expression and TILs in different types of cancer was shown in Supplementary Figure 8. Specifically, SNAI1 expression was negative closely related with infiltrating levels of tumor purity and B cell in STAD, and positively closely related with macrophages in STAD. Moreover, SNAI1 expression was negative closely related with infiltrating levels of tumor purity and B cell in COAD, and positively closely related with CD4+T cells, macrophages, dendritic cells, and neutrophils in COAD. Finally, SNAI1 expression was negative closely related with infiltrating levels of B cell and CD8+ T cells in READ, and positively closely related with CD4+T cells and dendritic cells in READ (Figure 3A–3C).

Next, to examine which TILs was related with gastrointestinal cancers, TISIDB database was used to infer the relations between abundance of 28 TILs and expression of SNAI1. The landscape of relationship between SNAI1 expression and TILs in different types of cancer was shown in Figure 4A. The relations between abundance of 28 TIL types and expression of SNAI1 in gastrointestinal cancers was significant correlated (Figure 4B–4R and Supplementary Figure 9). Next, we detected the associations between SNAI1 expression and immune subtypes in gastrointestinal cancers, and we divided the cells into six immunophenotypes (C1: wound healing, C2: IFN-gamma dominant, C3: inflammatory, C4: lymphocyte depleted, C5: immunologically quiet, C6: TGF-β dominant) [20]. Specifically, SNAI1 expression was correlated with immune subtypes (wound healing, IFN-gamma dominant, inflammatory, lymphocyte depleted, TGF-β dominant) in STAD and COAD, but not in READ (Figure 4S–4U). These results suggest that SNAI1 expression was associated with tumor-infiltrating immune cells in gastrointestinal cancers.

Validation SNAI1 in gastrointestinal cancers

To further validate SNAI1 in gastrointestinal cancers, RT-qPCR was used to detect the SNAI1 mRNA expression in STAD, CRC, and paired adjacent normal tissue (PANT). Compared with the PANT group, the SNAI1 mRNA level was significantly higher in the CRC and GC group (Figure 6A, 6C). Next, we measured the SNAI1 protein level by immunohistochemistry and western blot, and the result showed compared with the PANT group, the SNAI1 level was significantly higher in the CRC and GC group (Figures 5A, 5B and 6A, 6C). Additionally, Kaplan–Meier and Cox’s proportional hazards regression model survival analysis revealed that patients with high expression levels of SNAI1 had shorter overall survival in CRC (HR = 1.71, 95% CI = 1.28 to 2.94, P = 0.023) and GC (HR = 1.68, 95% CI = 1.23 to 2.57, P = 0.022) (Figure 6B, 6D). Simultaneously, high expression of SNAI1 correlates with clinical relevance of CRC and GC (Table 2). To verify the relationship between SNAI1 and the diverse immune infiltrating cells, we focused on the correlations between SNAI1 and immune marker sets of various immune cells in gastrointestinal cancers. We analyzed the correlations between SNAI1 expression and immune marker genes of different immune cells, included CD8+ T cell, T cell (general), B cell, monocyte, tumor-associated macrophage (TAM), M1 macrophage, M2 macrophage, neutrophils, natural killer cell, dendritic cell, T helper cell (Th) 1, Th2, follicular helper T cell (Tfh), Th17, regulatory T cell (Treg), and T cell exhaustion. It is only when SNAI1 is related to all the markers of an immune cell that we think that SNAI1 is related to immune cells. The results revealed the SNAI1 expression level was significantly correlated with M2 macrophage in STAD, and was significantly correlated with monocyte, TAM, M2 macrophage, dendritic cell, Th1, and treg in CRC (Table 3). Correlation results between SNAI1 and TILsare similar to those in TCGA database (Figure 4B–4R and Supplementary Figure 9). These findings suggest that SNAI1 may regulate M2 macrophage in gastrointestinal cancers.

Data source and processing

Using TIMER (Tumor Immune Estimation Resource, https://cistrome.shinyapps.io/timer/) site to analysis the expression of EMT markers in cancers, the mRNA profiling information is from TCGA (The Cancer Genome Atlas, https://cancergenome.nih.gov/) database [37]. We also Oncomine database (https://www.oncomine.org/resource/login.html) to analysis the expression of SNAI1 in cancers [38]. Bayes test was used to select EMT markers with a change >=2 fold and a P value cutoff of 0.05 was defined as statistically significant.

Survival analysis

GEPIA (Gene Expression Profiling Interactive Analysis, http://gepia.cancer-pku.cn/) site was used to analysis the prognosis of EMT markers in cancers by using the TCGA dataset [39]. A Cox P-value < 0.05 was defined as statistically significant. Moreover, we also used Kaplan-Meier plotter database (http://kmplot.com/analysis/index.php?p=service&cancer=gastric) to validate the prognosis of SNAI1 in gastrointestinal cancers [40]. Kaplan-Meier plotter can assess the effect of 54,675 genes on survival using 10,461 cancer samples. These samples include 5,143 breast, 1,816 ovarian, 2,437 lung, and 1,065 gastric cancer samples on the HGU133 Plus 2.0 array with a mean follow-up of 69, 40, 49, and 33 months, respectively. The hazard ratio (HR) with 95% confidence intervals (CI) and log-rank p-value were estimated.

Immune infiltration

To revealed the immune infiltration of SNAI1 in gastrointestinal cancers, we used TISIDB (tumor-immune system interactions and drugbank, http://cis.hku.hk/TISIDB/index.php) database to infer the relations between abundance of tumor-infiltrating lymphocytes (TILs) and expression of SNAI1. The immune-related signatures of 28 TIL types from Charoentong’s study, which can be viewed in the download page. The relative abundance of TILs was inferred by using genomic variation analysis based on gene expression profiles [41].

Patients

A total of 440 gastrointestinal cancers patients (200 CRC patients, 240 GC patients) from January 2009 to December 2018, were consecutively recruited in analyses, with clinical information. The clinicopathological characteristics were showed in Table 4.

Immunohistochemistry

200 colorectal cancer tissues, 240 gastric cancer tissues and corresponding adjacent tissues were collected to explore the expression of SNAI1 in the tissue samples by using immunohistochemical staining (IHC). IHC staining was performed according to the manufacturer’s instructions. Tissue samples were fixed in 10% formalin embedded in paraffin and cut into slices (4μm). The frozen tissue sections were dewaxed in xylene and rehydrated in descending grades of ethanol. Endogenous peroxidase activity was blocked by 0.3% H₂O₂. After heat-induced antigen retrieval, the sections were blocked with 10% rabbit serum for 10min. Subsequently, the sections were incubated with primary antibodies against SNAI1 (1:200) overnight at 4°C, and incubated with an anti-rabbit secondary antibody (1:2,000) for 30 min at 37°C. After that, the sections were visualized with DAB for 10 min at 37°C and counterstained with hematoxylin for 30 sec at 37°C, followed by dehydration with gradient ethanol and sealing with neutral gum.

Quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays

Total RNA from cells or tissues was isolated using TRIzol (Invitrogen, Canada) reagent, the specific operation is carried out with reference to the instructions for the operation of the kit. RNA (1 μg) was converted into cDNA using the RevertAid First Strand cDNA Synthesis Kit (Takara, China). qRT-PCR was performed using SYBR Green Mixture (Takara, China) in the ABI StepOne-Plus System (ABI7500, USA). Target gene expression was normalized against GAPDH. The primer sequences are 5′-CACCTCCAGACCCACTCAGAT-3′ (sense) and 5′-CCTGAGTGGGGTGGGAGCTTCC-3′ (antisense)

Western blot

Using the kit to measure protein concentration. An equal amount of protein is added to the gel. The protein was resolved on SDS-PAGE under denatured reducing conditions and transferred to nitrocellulose membranes. The membranes were blocked with 5% non-fat dried milk at room temperature for 30 min and incubated with primary antibodies. The membranes were washed and incubated with horseradish peroxidase-conjugated secondary antibody. Scanning with the Odyssey two-color infrared fluorescence imaging system.

Statistical analyses

The expression of EMT-related genes in cancer was using TIMER and Oncomine database. The survival curve was generated by GEPIA, PrognoScan, and Kaplan Meier diagrams. One-Way ANOVA was used to compare the expression level of SNAI1 in tumor and normal tissues. To control FDR, the p-value was set to less than 0.05. If any independent value is lost, the whole sample is excluded from statistical analysis. Above all, statistical analysis was performed in R version 3.5. p-values < 0.05 were considered statistically significant.