Toll-like receptor 3 is a potential prognosis marker and associated with immune infiltration in stomach adenocarcinoma

Abstract

BACKGROUND:

Toll-like receptors participate in various biological mechanisms, mainly including the immune response and inflammatory response. Nevertheless, the role of TLRs in STAD remains unclear.

OBJECTIVE:

We aimed to explore the expression, prognosis performance of TLRs in STAD and their relationship with immune infiltration.

METHODS:

Student’s t-test was used to evaluate the expression of TLRs between STAD tissues and normal tissues. Kaplan-Meier method was applied to explored the prognosis value of TLRs in STAD. And qRT-PCR validated their expression and prognosis value. Spearman’s correlation analysis and Wilcoxon rank-sum test were used to assess the association between TLRs and immune infiltration in STAD.

RESULTS:

The mRNA level of TLR3 was downregulated in STAD. We summarized genetic mutations and CNV alteration of TLRs in STAD cohort. Prognosis analysis revealed that STAD patients with high TLR3 expression showed better prognosis in OS, FP and PPS. The result of qRT-PCR suggested that TLR3 expression was decreased in STAD tissues and STAD patients with high TLR3 mRNA level had a better OS. Univariate and multivariate cox regression analysis suggested TLR3 expression and clinical stage as independent factors affecting STAD patients’ prognosis. A positive association existed between TLR3 expression and the abundance of immune cells and the expression of various immune biomarkers. Furthermore, key targets related to TLR3 were identified in STAD, mainly including MIR-129 (GCAAAAA), PLK1, and V$IRF1_01.

CONCLUSIONS:

Our result demonstrated TLR3 as a prognosis marker and associated with immune infiltration in STAD.

Keywords

Biomarker bioinformatics analysis immune cell infiltration stomach adenocarcinoma Toll-like receptor

1. Introduction

Gastric cancer (GC) is considered one of the severe diseases worldwide; gastric cancer ranks fifth among all cancers in terms of incidence and is the third leading cause of cancer-related death [1]. Stomach adenocarcinoma (STAD) is the most common subtype of gastric cancer (GC). Eighty percent of STAD patients are diagnosed at advanced stages because of the lack of symptoms at early stages, endoscopy, and cancer screening [2, 3]. The prognosis of advanced STAD patients is so poor that the 5-year survival rate is less than 30% [4]. Currently, surgery and chemotherapy are still the primary approaches for the treatment of STAD [5]. Although new therapies, such as immune and targeted therapies, have been utilized in recent years, there has still limited improvement in the prognosis of STAD patients [3, 6]. Thus, more survival biomarkers and therapeutic targets for STAD are needed.

Toll-like receptors (TLRs) are a class of single, membrane-spanning, noncatalytic proteins, and this class consists of 10 members (TLR1–TLR10) in human [7]. TLRs are also called pattern-recognition receptors and are associated with various biological mechanisms, such as activating the immune system, inducing inflammatory responses and regulating the proliferation, apoptosis and invasiveness of various types of cancer cells [7, 8]. The potential applications of TLRs in cancer were also focused by clinicians. Numerous studies have revealed that some TLRs (TLR1 to TLR10) could be potential prognostic biomarkers or therapeutic targets in certain types of cancer, mainly including oral squamous cell carcinoma, pancreatic cancer, hepatocellular carcinoma, neuroblastoma, thoracic esophageal squamous cell carcinoma and colorectal cancer [9, 10, 11, 12, 13, 14]. In cancer therapy, some of TLR-targeted agents were under clinical estimation. A multi-center study reported that a combined therapy, including poly-ICLC (a TLR3 agonist), radiotherapy and temozolomide, was utilized in glioblastoma treatment, and the improved effects were presented [15]. In phase clinical trial, OM-174, a TLR2 and TLR4 agonist, was found to benefit with refractory solid tumor [16]. Genetic variations of TLRs also played a crucial role in cancer diagnosis and prevention. Semlali et al. reported two kinds of TLR2 single-nucleotide polymorphisms (SNPs) might predict risk of colon cancer development, and TLR6 SNP rs5743810 might be used in the diagnosis of breast cancer [17, 18]. The above evidences showed potential clinical value of TLRs in various human cancer types. Nevertheless, few studies systematically elucidated the specific functions of TLRs in STAD.

Based on the emergence of a variety of reliable bioinformatics databases and the development of gene sequencing technology, comprehensive analysis of TLRs has become feasible. In the current study, we investigated the transcription level, prognostic performance and relative subgroup analysis of TLRs in STAD to select potential prognostic biomarkers from TLRs. Drug sensitivity and immune cell infiltration analyses conducted on STAD tissues were used to explore the resistance of TLRs to chemotherapy and potential immunotherapeutic targets among the TLRs. Cancer pathway activity analyses, functional enrichment analyses, and protein-protein interaction networks were conducted to validate the biological functions and molecular mechanisms of TLRs in STAD.

2. Materials and methods

2.1 Datasets and preprocessing

The RNA sequencing (RNA-seq) data of STAD ( $N=$ 415) patients and the corresponding clinical information were obtained using the Cancer Genome Atlas (TCGA) database on July 1, 2020. The difference of the expression of TLRs in STAD and normal tissues in TCGA-STAD dataset were identified using “limma” and “reshape2” packages in R software v4.0.3 (R Foundation for Statistical Computing, Vienna, Austria). In addition, all STAD dataset, including DErrico gastric [19], Chen gastric [20] and Cho gastric [21] were extracted from Oncomine (www.oncomine.org) database [22]. These datasets in Oncomine were used to further confirm the expression level of TLRs in STAD. Student’s t-test was used to analyze whether the differences in the transcription levels of TLRs between STAD tissues and normal tissues were significant with a $P$ -value of 0.05, a fold change of 1.5 and a gene rank of 10%.

2.2 Mutation analysis, drug sensitivity analysis and pathway activity analysis of TLRs in STAD

The mutation frequency and oncoplot of 10 TLRs in STAD patients was generated by “maftools” packages. The location of CNV alteration of 10 TLRs on 23 chromosomes was drawn by “RCircos” package. Moreover, a PPI network was constructed for 10 TLRs with Search Tool for the Retrieval of Interacting Genes (STRING) [23]. In drug sensitivity analysis, gene expression profiling data of 265 small molecules was obtained from Genomics of Drug Sensitivity in Cancer (GDSC). Pearson correlation analysis was performed to analyze the coefficients between TLRs and drug-targets and the detailed method could be found in Rees et al. [24]. In cancer-related pathway activity analysis, TLRs expression was divided into High expression group and Low expression group by median expression. The difference of pathway activity score (PAS) between groups is defined by Student’s $t$ -test. When PAS (group High) $>$ PAS (group Low), we consider TLRs may have a activate effect to a pathway, otherwise have an inhibit effect to a pathway. A similar method has been applied in Ye et al. [25].

2.3 Prognosis analysis

Kaplan-Meier plotter (https://kmplot.com) [26] is a useful meta-analysis based tool that is capable of evaluating the effect of 54 k genes on survival in 21 cancer types. The sources for the databases include GEO and TCGA. Setting the medium value of TLRs the cutoff to distinguish the high and low expression groups in TCGA-STAD cohort, the prognostic value of TLRs in STAD was shown by overall survival (OS) plots, first progression (FP) plots and postprogression survival (PPS) plots. Using similar methods, the prognosis value of TLRs in OS analysis was also verified using GSE29272 and GSE62254 dataset.

2.4 Validation of the expression and prognosis value of TLRs in STAD

Approved by the Ethics Committee of the Second Affiliated Hospital of Nanchang University, we selected 48 STAD tissues and pair-normal gastric tissues from patients don’t receive any local or systemic treatment preoperatively (IRB No.2020(012)). Informed consent was obtained from each patient. All the Total RNA of STAD and normal tissue were extracted with TRIzol reagent (Vazyme, Nanjing). The synthesis of cDNAs corresponding to the mRNAs and qTR-PCR were performed using PrimeScript RT-polymerase (Vazyme). SYBR-Green Premix (Vazyme) with specific PCR primers (Sangon, Shanghai). Glyceraldehyde-3-phosphate dehydrogenase was used as an internal control. The 2 ${}^{\rm-\Delta\Delta Ct}$ method was used to calculate fold-changes. Primer sequences were showed in Supplementary Table 1. The difference of the mRNA level and the prognosis of TLRs in STAD were evaluated with Student’s $t$ -test and Kaplan-Meier analysis.

Table 1
The transcription levels of TLR1 to TLR10 in different types of STAD and normal stomach tissues (Oncomine)

	Fold change	$P$ -value	$t$ -test	Ref
TLR1	NA	NA	NA	NA
TLR2	5.595	1.22E-5	8.988	DErrico Gastric [19]
TLR3	$-$ 1.532	5.66E-5	$-$ 4.380	Chen Gastric [20]
	$-$ 1.549	0.005	$-$ 3.265	Chen Gastric [20]
	$-$ 1.604	0.010	$-$ 2.622	DErrico Gastric [19]
	$-$ 1.915	6.50E-4	$-$ 3.406	DErrico Gastric [19]
TLR4	NA	NA	NA	NA
TLR5	$-$ 1.535	0.003	$-$ 2.886	Cho Gastric [21]
TLR6	NA	NA	NA	NA
TLR7	NA	NA	NA	NA
TLR8	NA	NA	NA	NA
TLR9	$-$ 1.517	2.17E-5	$-$ 5.138	Cho Gastric [21]
TLR10	$-$ 1.754	9.70E-4	$-$ 3.284	DErrico Gastric [19]
	$-$ 1.813	0.005	$-$ 2.830	Cho Gastric [21]
	$-$ 1.685	0.031	$-$ 2.197	Cho Gastric [21]

Abbreviation: TLR, toll-like receptor.

2.5 Immune infiltration, TMB and MSI analysis

TIMER (https://cistrome.shinyapps.io/timer/) is a bioinformatics platform that explores the correlation between immune cell infiltration and target gene expression in cancer cells [27]. The following analyses were performed on the TCGA-STAD dataset ( $N=$ 415). The “Gene” module was used to validate the correlation between the expression of TLR3 and the abundance of B cells, CD8 $+$ T cells, CD4 $+$ T cells, macrophages, neutrophils, and dendritic cells. Next, the “Correlation” module was used to assess the association between TLR3 expression and the expression of immune biomarkers based on previous studies [28, 29, 30]. Spearman’s correlation analysis was utilized in the above analyses. Moreover, we explored the correlation between somatic copy number alterations (SCNAs) of TLR3 and immune infiltration level by the “SCNA” module, which was based on two-sided Wilcoxon rank-sum test. Using “ggstatsplot” in R software, we also assess the association between TLR3 expression and Tumor Mutation Burden (TMB) and Microsatellite Instability (MSI). TMB and MSI analysis were performed using Spearman’s correlation analysis.

Figure 1.

The transcription levels of TLRs in STAD. (A) The transcription levels of TLRs in STAD in Oncomine dataset. The numbers of datasets are shown in the figure. The red area represents upregulated expression of TLRs, while the blue area represents downregulated expression of TLRs. (B) The transcription levels of TLRs in STAD in TCGA dataset. Abbreviations: TLR, toll-like receptor; TCGA, the Cancer Genome Atlas; STAD, stomach adenocarcinoma. Note: * $p<$ 0.05, ** $p<$ 0.01, *** $p<$ 0.001.

2.6 Functional enrichment analysis and PPI network construction

LinkedOmics (http://www.linkedomics.org/login. php) is an online database that collects genomic data from 32 cancer types using TCGA dataset [31]. After obtained the genes that were positively or negatively correlated with TLR3 expression in the “LinkFinder” module, we then performed Gene Set Enrichment Analysis (GSEA) to identify the potential biological functions and molecular mechanisms of TLR3 in STAD with a $P$ -value of 0.05, a minimum number of gene sizes of 3 and a simulation of 500. The analysis included cellular component (CC), biological process (BP) and molecule function (MF) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, TLRs-associated kinase target, TLRs-associated miRNA target and TLRs-associated transcription factor target. Moreover, we then constructed PPI networks to illustrate the association and the relative functions of gene sets that GSEA identified as being enriched in STAD using GeneMANIA (http://genemania.org/), a web portal is designed to help researchers predict the function of target genes and gene sets [32].

Figure 2.

Subgroup analyses of TLR2 expression in STAD. Subgroup analyses based on race (A), age (B), TP53 mutation status (C), gender (D), H. pylori infection status (E), nodal metastasis status (F), individual cancer stages (G), tumor grade (H), and histological subtypes (I) were conducted. Abbreviations: TLR, Toll-like receptor; STAD, stomach adenocarcinoma. Note: * $P<$ 0.05, ** $P<$ 0.01, *** $P<$ 0.001.

3. Results

3.1 The expression of TLRs in STAD

In the Oncomine database, we explored the transcription level of TLRs (TLR1 to TLR10) in STAD tissues and normal tissues. As shown in Table 1 and Fig. 1A, TLR2 was overexpressed in STAD tissues, while TLR3, TLR5, TLR9 and TLR10 were downregulated in STAD tissues. DErrico et al. [19] showed elevated expression of TLR2 in STAD tissues (fold change $=$ 5.595, $P$ -value $=$ 1.22E-05). Chen et al. [20] provided two datasets that illustrated the reduced transcription level of TLR3 in STAD tissues. The fold changes and $P$ -values of these data were $-$ 1.532 ( $P$ -value $=$ 5.66E-05) and $-$ 1.549 ( $P$ -value $=$ 0.005), respectively. Another two datasets of DErrico et al. [19] also suggested that the transcription level of TLR3 was reduced in STAD tissues. The fold changes and $P$ -values of these data were $-$ 1.604 ( $P$ -value $=$ 0.010) and $-$ 1.915 ( $P$ -value $=$ 6.50E-04), respectively. According to the two datasets from Cho et al. [21], the transcription levels of TLR5 and TLR9 were reduced in STAD tissues. The fold changes and $P$ -values of these data were $-$ 1.535 ( $P$ -value $=$ 0.003) and $-$ 1.517 ( $P$ -value $=$ 2.17E-05), respectively. Moreover, the transcription level of TLR10 in STAD tissues was decreased in the Cho [21] and DErrico [19] datasets. The fold changes and $P$ -values of these data were $-$ 1.813 ( $P$ -value $=$ 0.005), $-$ 1.685 ( $P$ -value $=$ 0.031) and $-$ 1.754 ( $P$ -value $=$ 9.70E-04), respectively. We also detected the mRNA level of TLRs in STAD using TCGA-STAD dataset. As a result, the data suggested an upregulation of TLR2 ( $P$ -value $<$ 0.001), TLR4 ( $P$ -value $<$ 0.01), TLR6 ( $P$ -value $<$ 0.01), TLR7 ( $P$ -value $<$ 0.01), and TLR8 ( $P$ -value $<$ 0.01) and a downregulation of TLR3 ( $P$ -value $<$ 0.01) in STAD tissues compared with gastric tissues. Thus, the data from Oncomine and TCGA suggested that TLR2 was upregulated while TLR3 was downregulated in STAD (Fig. 1B).

We then explored the mRNA levels of TLR2 and TLR3 in different subgroups of STAD tissues. In the subgroup analyses based on patient race (Fig. 2A), patient age (Fig. 2B), TP53 mutation status (Fig. 2C), patient gender (Fig. 2D), H. pylori infection status (Fig. 2E), nodal metastasis status (Fig. 2F), individual cancer stage (Fig. 2G), tumor grade (Fig. 2H) and histological subtype (Fig. 2I), the transcription level of TLR2 was significantly elevated in STAD tissues. Nevertheless, the transcription level of TLR3 in STAD tissues was significantly decreased in these subgroups (Fig. 3A–I).

Figure 3.

Subgroup analyses of TLR3 expression in STAD. Subgroup analyses based on race (A), age (B), TP53 mutation status (C), gender (D), H. pylori infection status (E), nodal metastasis status (F), individual cancer stages (G), tumor grade (H) and histological subtypes (I) were conducted. Abbreviations: TLR, Toll-like receptor; STAD, stomach adenocarcinoma. Note: * $P<$ 0.05, ** $P<$ 0.01, *** $P<$ 0.001.

Figure 4.

cancer hallmark analysis of TLRs in STAD. (A-B) The mutation frequency and classification of 10 TLRs in STAD. (C) The CNV variation frequency of 10 TLRs in STAD cohort. (D) The location of CNV alteration of 10 TLRs on 23 chromosomes in STAD cohort. (E) PPI network of 10 TLRs. (F) Drug sensitivity analysis of TLRs in STAD. A positive correlation means that a sample with gene expression is resistant to the drug, and vice versa. (G) Cancer pathway activity of TLRs in STAD. The activation and inhibition of TLRs occurs through several common cancer signaling pathways. Abbreviations: TLR, Toll-like receptor; STAD, stomach adenocarcinoma.

Figure 5.

The prognostic value of TLR2/TLR3 in STAD. (A) OS of STAD patients with high/low expression of TLR2. (B) FP of STAD patients with high/low expression of TLR2. (C) PPS of STAD patients with high/low expression of TLR2. (D) OS of STAD patients with high/low expression of TLR3. (E) FP of STAD patients with high/low expression of TLR3. (F) PPS of STAD patients with high/low expression of TLR3. (G-H) OS of STAD patients with high/low expression of TLR3 in GSE29272 and GSE62254 cohort. Abbreviations: TLR, Toll-like receptor; OS, overall survival; FP, first progression; PPS, postprogression survival; HR, hazard ratio; STAD, stomach adenocarcinoma.

Figure 6.

The expression and prognosis value of TLR3 in STAD. (A) The relative expression of TLR3 in STAD tissues and normal tissues. (B) Survival curve revealed the overall survival of STAD patients with high/low TLR3 expression. (C) The ROC curve of TLR3 in predicting the prognosis of STAD. (D-E) Univariate and multivariate analysis of TLR3 and clinical characters in STAD. Abbreviations: TLR, Toll-like receptor; STAD, stomach adenocarcinoma.

3.2 Genetic alteration and co-expression of TLRs in STAD

As shown in Fig. 4A and B, 86 of 106 (81.13%) STAD sample experienced genetic mutations. Among all the forms of variant classification, Missense mutation was the most common form (Fig. 4B). The result also suggested that SNP ranked the top variant type and C $>$ T was the most common SNV class (Fig. 4A). Among these 10 TLRs in STAD, TLR4 was the highest mutation frequency gene followed by TLR9 and TLR8 (Fig. 4B). The result of CNV alteration frequency revealed that most of TLRs had copy number amplification while the CNV deletion frequency of TLR3 and TLR9 were widespread (Fig. 4C). Figure 4D showed the location of CNV alteration of these 10 TLRs on chromosomes. We also constructed a PPI network to explore the interactions of these TLRs, which was shown in Fig. 4E. The correlation network containing all TLRs in STAD was shown in Supplementary Fig. 1.

3.3 Drug sensitivity and pathway activity analysis of TLRs in STAD tissues

To develop a therapy target, it is important to analyze correlation between gene expression and exited drugs. We then explored the correlation between TLR expression in STAD tissues and drug sensitivity based on data from GDSC. As a result, STAD tissues with high TLR3 expression and low expression of TLR1, TLR7, TLR8 and TLR10 were resistant to most small molecules or drugs from GDSC (Fig. 4F). The pathway activity analysis results revealed that TLRs mainly activated the apoptosis pathway, EMT pathway, hormone ER pathway, RAS/MAPK pathway and RTK pathway and inhibited the cell cycle pathway, DNA damage response pathway and hormone AR pathway in STAD tissues (Fig. 4G).

3.4 The prognostic value of TLR2 and TLR3 in STAD

Above results found that TLR2 was upregulated while TLR3 was downregulated in STAD. We then examined the prognostic performance of TLR2 and TLR3 in STAD. OS, FP and PPS curves of TLR2 are presented in Fig. 5A–C, respectively. The result indicated a better PPS ( $P$ -value $=$ 0.006) but not OS ( $P$ -value $=$ 0.066) and FP ( $P$ -value $=$ 0.300) in STAD patients with high TLR2 expression. As shown in Fig. 5D–F, STAD patients with increased TLR3 levels showed a better OS rate ( $P$ -value $=$ 5.1E-05), FP rate ( $P$ -value $=$ 0.00017) and PPS rate ( $P$ -value $=$ 0.003), which indicated that TLR3 acted a prognostic biomarker in STAD predicting a favorable prognosis.

Figure 7.

The correlation between TLR3 and immune cell infiltration in STAD. (A) The correlation between TLR3 and the abundance of six types of tumor-infiltrating cells. (B) The correlation between SCNAs of TLR3 and immune infiltration levels in six tumor-infiltrating cells. (C-D) The correlation between TLR3 and TMB or MSI in STAD. Abbreviations: TLR, Toll-like receptor; cor, correlation coefficient; STAD, stomach adenocarcinoma; TMB, Tumor Mutation Burden; MSI, Microsatellite Instability. Note: * $P<$ 0.05, ** $P<$ 0.01, *** $P<$ 0.001.

Next, we explored the correlation between TLR3 expression and OS, FP and PPS in STAD with different pathological parameters. We found that high TLR3 expression was associated with better OS in male and female STAD patients and in STAD patients with tumor stage 3, intestinal Lauren classification, lymph nodal metastasis stage 1 and lymph nodal metastasis stage $1+2+3$ (Supplementary Table 2). High TLR3 expression was also relevant to better FP in male STAD patients and in patients with tumor stage 3, intestinal Lauren classification, lymph nodal metastasis stage 1 and lymph nodal metastasis stage $1+2+3$ (Supplementary Table 3). Moreover, high TLR3 expression was correlated with better PPS in male and female STAD patients and in STAD patients with tumor stage 3, lymph node metastasis stage 2, and diffuse Lauren classification(Supplementary Table 4). These results further verified the prognostic value of TLR3 in STAD.

In order to further verify this result, we also performed prognosis analysis using GSE29272 and GSE62254 dataset, which also suggested that STAD patients with increased TLR3 levels showed a better OS rate in GSE29272 (Fig. 5G, $P$ -value $=$ 0.0086) and GSE62254 (Fig. 5H, $P$ -value $=$ 0.0097) dataset.

3.5 Validation of the expression and prognostic value of TLR3 in STAD

qRT-PCR was performed to further confirm the expression and prognostic value of TLR3 in STAD. As expected, the data suggested a decreased expression of TLR3 in STAD tissues (Fig. 6A, $P$ -value $<$ 0.01). Moreover, the data also suggested that STAD patients with high TLR3 mRNA level had a better OS (Fig. 6B, $P$ -value $=$ 0.036) with an AUC of 0.767 (Fig. 6C). Interestingly, TLR3 expression and clinical stage were suggested as independent factors affecting the prognosis of STAD patients in univariate and multivariate cox regression analysis (Fig. 6D and E). This data further confirmed previous results.

Table 2
Correlation of TLR3 and gene markers on tumor-infiltrating cells in STAD (TIMER)

Description	Gene markers	STAD
		None		Purity
		Cor	$P$ -value	Cor	$P$ -value
CD8 $+$ T cell	CD8A	0.417	***	0.412	***
	CD8B	0.189	***	0.171	***
T cell (general)	CD3D	0.337	***	0.337	***
	CD3E	0.352	***	0.357	***
	CD2	0.383	***	0.382	***
B cell	CD19	0.177	***	0.167	**
	CD79A	0.182	***	0.180	***
Monocyte	CD86	0.304	***	0.289	***
	CD115 (CSF1R)	0.347	***	0.335	***
TAM	CCL2	0.123	*	0.098	0.0576
	CD68	0.258	***	0.238	***
	IL10	0.266	***	0.244	***
M1 Macrophage	INOS (NOS2)	0.148	**	0.150	**
	IRF5	0.129	**	0.106	*
	COX2 (PTGS2)	0.054	0.2718	0.045	0.3869
M2 Macrophage	CD163	0.370	***	0.355	***
	VSIG4	0.326	***	0.309	***
	MS4A4A	0.362	***	0.342	***
Neutrophil	CD66b (CEACAM8)	0.034	0.4888	0.019	0.7121
	CD11b (ITGAM)	0.349	***	0.336	***
	CCR7	0.269	***	0.264	***
Natural killer cell	KIR2DL1	0.180	***	0.157	**
	KIR2DL3	0.183	***	0.153	**
	KIR2DL4	0.297	***	0.279	***
	KIR3DL1	0.222	***	0.215	***
	KIR3DL2	0.234	***	0.211	***
	KIR3DL3	0.138	**	0.133	**
	KIR2DS4	0.113	*	0.109	*
Dendritic cell	HLA-DPB1	0.372	***	0.366	***
	HLA-DQB1	0.264	***	0.252	***
	HLA-DRA	0.407	***	0.403	***
	HLA-DPA1	0.363	***	0.353	***
	BDCA-1 (CD1C)	0.248	***	0.243	***
	BDCA-4 (NRP1)	0.231	***	0.212	***
	CD11c (ITGAX)	0.298	***	0.266	***
Th1	T-bet (TBX21)	0.380	***	0.383	***
	STAT4	0.360	***	0.365	***
	STAT1	0.405	***	0.383	***
	IFN-g (IFNG)	0.277	***	0.265	***
	TNF-a (TNF)	0.094	0.0568	0.084	0.1033
Th2	GATA3	0.228	***	0.223	***
	STAT6	0.291	***	0.285	***
	STAT5A	0.463	***	0.445	***
	IL13	0.029	0.5622	0.026	0.6075
Tfh	BCL6	0.312	***	0.310	***
	IL21	0.221	***	0.188	***
Th17	STAT3	0.413	***	0.412	***
	IL17A	$-$ 0.070	0.1567	$-$ 0.061	0.2352
Treg	FOXP3	0.248	***	0.231	***
	CCR8	0.313	***	0.299	***
	STAT5B	0.295	***	0.288	***
	TGFb (TGFB1)	0.154	**	0.152	**
T cell exhaustion	PD-1 (PDCD1)	0.271	***	0.253	***
	CTLA4	0.214	***	0.187	***
	LAG3	0.335	***	0.319	***
	TIM-3 (HAVCR2)	0.351	***	0.325	***
	GZMB	0.275	***	0.262	***

Abbreviations: TLR, Toll-like receptor; STAD, stomach adenocarcinoma; TAM, tumor-associated macrophage; Cor, correlation coefficient. Note: * $P<$ 0.05, ** $P<$ 0.01, *** $P<$ 0.001.

3.6 Immune infiltration associated with TLR3 in STAD

Above result showed that TLR3 was downregulated and acted as a prognostic biomarker in STAD and we selected TLR3 for further analysis. Comprehensive analyses were conducted to evaluate the correlation between TLR3 expression and immune cell infiltration. We found that TLR3 expression was positively associated with CD8 $+$ T cells (cor $=$ 0.270, $P$ -value $=$ 1.36E-07), CD4 $+$ T cells (cor $=$ 0.186, $P$ -value $=$ 3.45E-04), macrophages (cor $=$ 0.207, $P$ -value $=$ 5.79E-05), neutrophils (cor $=$ 0.284, $P$ -value $=$ 2.62E-08) and dendritic cells (cor $=$ 0.285, $P$ -value $=$ 2.42E-08) (Fig. 7A). Next, we explored the correlation between SCNAs of TLR3 and the infiltration levels of six immune cells in STAD tissues. Compared with normal tissues, SCNAs of TLR3 in STAD generally decreased the infiltration levels of CD8 $+$ T cells, CD4 $+$ T cells, macrophages, neutrophils and dendritic cells (Fig. 7B). However, there is no significant correlation between TLR3 expression and TMB score ( $P$ -value $=$ 0.987, Fig. 7B) or MSI score ( $P$ -value $=$ 0.958, Fig. 7C). The association between TLR3 expression in STAD tissues and the biomarkers of CD8 $+$ T cells, general T cells, B cells, monocytes, TAMs, M1 macrophages, M2 macrophages, neutrophils, natural killer cells, dendritic cells, Th1 cells, Th2 cells, Tfh cells, Th17 cells, Tregs and exhausted T cells was also evaluated. After adjusting for tumor purity, the expression of TLR3 was strongly associated with most of the biomarkers on various tumor-infiltrating immune cells in STAD samples (Table 2).

Figure 8.

Functional enrichment analysis of TLR3 in STAD. (A) The volcano plot of the genes positively (dark red dots) or negatively (dark green dots) associated with TLR3. (B) The heat map of the top 50 genes that positively correlated with TLR3. (C) The heat map of the top 50 genes that negatively correlated with TLR3. GO analyses, including BP analysis (D), CC analysis (E) and MF (F) analysis, were performed by GSEA. (G) KEGG pathway analysis was performed in GSEA. Abbreviations: TLR, Toll-like receptor; GSEA, gene set enrichment analysis; GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes; HR, hazard ratio; STAD, stomach adenocarcinoma.

Table 3

The key miRNA-target, kinase-target and transcription factor-target networks of TLR3 in STAD (LinkedOmics)

Enriched category	Geneset	LeadingEdgeNum
miRNA	GCAAAAA, MIR-129	59
target	GTGCAAA, MIR-507	44
	ATAGGAA, MIR-202	31
	GTGTTGA, MIR-505	36
	ACACTAC, MIR-142-3P	46
Kinase	Kinase_ PLK1	33
target	Kinase_ LCK	19
	Kinase_ JAK2	7
	Kinase_ HCK13	13
	Kinase_ JAK3	8
Transcription	V$IRF_Q6	85
factor	V$IRF1_01	68
target	V$IRF2_01	41
	V$IRF3_01	49
	V$E2F_Q4	92

Abbreviation: TLR, toll-like receptor; STAD, stomach adenocarcinoma; FDR, false discovery rate.

Figure 9.

PPI networks and functional analysis of the genes in the MIR-129 and PLK1 target networks. (A) The PPI networks and functional analysis of MIR-129 target networks. (B) The PPI networks and functional analysis of PLK1 target networks. Different colors of the network edge illustrate the bioinformatics methods applied: coexpression, genetic interactions, predicted, shared protein domains, physical interactions and colocalization. The different colors for the network nodes illustrate the biological functions of the set of enriched genes. Abbreviations: PPI, protein-protein interaction; MIR, microRNA.

3.7 GO and KEGG pathway analyses of genes associated with TLR3 in STAD

The “LinkFinder” module was used to explore the genes correlated with TLR3 in STAD samples. In total, 10322 genes (dark green dots) were negatively correlated with TLR3, while 9903 genes (dark red dots) were positively correlated with TLR3 (Fig. 8A). The top 50 genes that were positively and negatively associated with TLR3 in STAD samples are shown in Fig. 8B and C. Gene ontology (GO) analysis in the “LinkInterpreter” module demonstrated that the role of TLR3 in STAD primarily participated in the immune response, positive regulation of cytokine production, NF-KappaB signaling, response to interferon-gamma, cytokine binding, and catalytic activity of DNA (Fig. 8D–F). Moreover, KEGG pathway analysis revealed that TLR3 exerted biological functions in STAD mainly via the chemokine signaling pathway, NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, and Th1, Th2 and Th17 cell differentiation (Fig. 8G).

3.8 MicroRNA, kinase and transcription factor target networks of TLR3 in STAD

To validate the targets of TLR3 in STAD, we explored the top 5 most significant microRNA (miRNA), kinase, and transcription factor target networks by GSEA in LinkedOmics (Table 3). The top 5 most significant miRNA target networks were MIR-129 (GCAAAAA), MIR-507 (GTGCAAA), MIR-202 (ATAGGAA), MIR-505 (GTGTTGA) and MIR-142-3P (ACACTAC). Similarly, the top 5 most significant kinase target networks were PLK1, LCK, JAK2, JAK3 and HCK13. Furthermore, the top 5 most significant transcription factor target networks were V$IRF_Q6, V$IRF1_01, V$IRF2_01, V$IRF3_01 and V$E2F_Q4. We next constructed protein-protein interaction networks to validate the potential biological functions of MIR-129 (GCAAAAA) target networks, PLK1 target networks and V$IRF1_01 target networks by GeneMIANA. The gene set in the MIR-129 target network mainly participated in sequence-specific DNA binding, regulatory region DNA binding and protein phosphatase binding (Fig. 9A). The gene set in the PLK1 target networks was mainly involved in the regulation of the mitotic cell cycle and cell division (Fig. 9B). The gene set in the V$IRF1_01 target network was mainly correlated with the response to type I interferon, type I interferon signaling pathway, response to interferon-gamma and interferon-gamma-mediated signaling pathway(Supplementary Fig. 2).

4. Discussion

TLRs, a subset of pattern-recognition receptors (PRRs), are capable of responding to conserved microbial products and endogenous damaged molecules and are involved in the biological mechanism of pathogen invasion, cancer and tissue injury [7, 33]. The TLR signaling pathway can transmit signals from inappropriate immune enhancement and antitumor immunity to modulate the progression, metastasis, recurrence and drug resistance of cancer cells [34]. Accumulating studies have been conducted to determine the clinical value, biological functions and molecular mechanisms of TLRs in various types of cancers. However, we know very little about the specific functions of TLRs in STAD.

The above analysis showed that the transcription level of TLR2 in STAD tissues was significantly elevated. However, the transcription level of TLR3 in STAD tissues was substantially decreased. Some studies have reported that TLR2 is overexpressed in gastric cancer and pancreatic cancer, and TLR2 expression is associated with pathological parameters, such as nodal metastasis stage and microvascular invasion [35, 36]. Furthermore, Bonnin et al. [37] and Yuan et al. [11] reported that TLR3 is downregulated in hepatocellular carcinoma. The above evidence is similar to the results in our study. However, more studies that focus on the expression level of TLR3 in STAD are needed.

Attention has been given to the prognostic value of TLR2/TLR3 in STAD. Unexpectedly, TLR2 seemed not to be a potential biomarker for STAD. We next found that STAD patients with high TLR3 expression had a better prognosis in terms of OS, FP and PPS than those with low TLR3 expression, which indicated that high TLR3 expression might be a protective factor for the survival of STAD patients. Evidence on hepatocellular carcinoma, neuroblastoma and thoracic esophageal squamous cell carcinoma also showed that patients with high TLR3 expression had a favorable prognosis [11, 12, 13]. Some studies have reported that activation of TLR3 induces apoptosis in prostate cancer [38], non-small cell lung cancer [39], ovarian cancer [40], and hepatocellular carcinoma [11]. Inducing apoptosis of cancer cells may be one of the mechanisms by which TLR3 improves the prognosis of STAD patients.

Tumor-infiltrating immune cells play a pivotal role in eliminating cancer cells and hindering cancer progression [41]. The abundance of tumor-infiltrating immune cells affects the proliferation and progression of tumor cells, thus influencing the clinical outcomes of cancer patients [42]. We found that the expression level of TLR3 was positively associated with the abundance of CD8 $+$ T cells, CD4 $+$ T cells, macrophages, neutrophils and dendritic cells. TLR3 expression is strongly associated with most of the biomarkers of tumor-infiltrating immune cells. These results revealed that TLR3 expression might be related to the immune escape of STAD cells and that TLR3 might be a promising immunotherapeutic target of STAD.

The main functions of TLR3 in STAD were associated with the immune response, positive regulation of cytokine production, NF-KappaB signaling, response to interferon-gamma, cytokine binding, and catalytic activity of DNA. TLR3 exerts functions in STAD mainly through the chemokine signaling pathway, NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, and Th1, Th2 and Th17 cell differentiation. TLRs are primarily correlated with innate immunity; however, they also participate in the adaptive immune system mainly through activation of dendritic cells and regulation of cytokines, such as IL-1 $\beta$ , IL-6 and TNF [43]. Previous studies have proven that the Toll-like receptor signaling pathway leads to the activation of NF-kappaB, a transcription factor that participates in immune and inflammatory responses [7, 44]. Previous studies also confirmed the critical role of chemokine signaling pathways in tumorigenesis and progression of cancer cells [45, 46]. Moreover, Toll-like receptors and NOD-like receptors are capable of regulating tumor-specific immune responses together [47]. The above evidence supports the results of enrichment analyses.

We constructed PPI networks to validate the potential functions of the MIR-129 (GCAAAAA) target network, PLK1 target network and V$IRF1_01 target network. The functions of the MIR-129 (GCAAAAA) target network mainly involved the process of gene expression. Thus, TLR3 may influence the occurrence and progression of STAD by modulating the process of oncogene expression via the MIR-129 (GCAAAAA) target network. Overexpressed PLK1 causes destruction of checkpoints, which leads to excessive cell division and eventually results in the tumorigenesis and progression of cancer [48]. The functions of the PLK1 target network primarily involved the process of cell division. Hence, TLR3 may regulate the division of STAD cells by the PLK1 target network. Interferon regulatory factors (IRFs) are closely related proteins that mainly participate in regulating interferon-inducible genes in the interferon system [49]. The loss of functional interferon regulatory factors 1 (IRF1) plays a pivotal role in the development of GC [50]. The functions of the V$IRF1_01 target network was primarily responsible for the regulation of the interferon system. Thus, TLR3 may affect the occurrence and progression of STAD by regulating the interferon system via the V$IRF1_01 target network.

Some limitations still exist in the current study. First, our analyses were mainly conducted at the transcriptional level, and some of the results may not apply to studies based on the protein level. Second, relative fundamental and clinical studies that focus on the clinical value and molecular mechanisms of TLR3 in GC or STAD are rare compared to those that focus on TLR3 in other types of cancer. Thus, more studies are required to verify our results.

5. Conclusions

In the present study, we primarily explored the expression, the prognostic performance of TLRs in STAD, and the relationship between TLRs expression and immune infiltration. As a result, compared with normal stomach tissues, a significantly decreased mRNA level of TLR3 was detected in STAD tissues. And STAD patients in high TLR3 expression group showed a better prognosis in OS, FP and PPS. In addition, TLR3 expression was positively associated with abundance of CD8 $+$ T cells, CD4 $+$ T cells, macrophages, neutrophils and dendritic cells, and expression of various immune biomarkers. Thus, TLR3 is a promising prognosis marker and associated with immune cells infiltration in STAD. Our study provided insights that TLR3 might predict STAD patients’ prognosis, and TLR3 might be a novel immunotherapeutic target in STAD.

Author contributions

Conception: Linquan Wu, Aoxiao He and Zhihao Huang.

Interpretation or analysis of data: Jiakun Wang and Hongcheng Lu.

Preparation of the manuscript: Zhihao Huang and Qian Feng.

Revision for important intellectual content: Wenjun Liao and Qian Feng.

Supervision: Rongguiyi Zhang and Xiaoyun Xu.

Supplementary data

The supplementary files are available to download from http://dx.doi.org/10.3233/CBM-210354.

sj-pdf-1-cbm-10.3233_CBM-210354.pdf - Supplemental material

Supplemental material, sj-pdf-1-cbm-10.3233_CBM-210354.pdf

Footnotes

Acknowledgments

This article was supported by National Natural Science Foundation of China (No. 81860431) and National Natural Science Foundation of China (No. 82060447).

Conflict of interest

All authors declare no conflicts of interest in this work.

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Toll-like receptor 3 is a potential prognosis marker and associated with immune infiltration in stomach adenocarcinoma

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Materials and methods

2.1 Datasets and preprocessing

2.2 Mutation analysis, drug sensitivity analysis and pathway activity analysis of TLRs in STAD

2.3 Prognosis analysis

2.4 Validation of the expression and prognosis value of TLRs in STAD

Table 1 The transcription levels of TLR1 to TLR10 in different types of STAD and normal stomach tissues (Oncomine)

3.1 The expression of TLRs in STAD

3.3 Drug sensitivity and pathway activity analysis of TLRs in STAD tissues

3.4 The prognostic value of TLR2 and TLR3 in STAD

Table 2 Correlation of TLR3 and gene markers on tumor-infiltrating cells in STAD (TIMER)

3.8 MicroRNA, kinase and transcription factor target networks of TLR3 in STAD

4. Discussion

5. Conclusions

Author contributions

Supplementary data

sj-pdf-1-cbm-10.3233_CBM-210354.pdf - Supplemental material

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
The transcription levels of TLR1 to TLR10 in different types of STAD and normal stomach tissues (Oncomine)

Table 2
Correlation of TLR3 and gene markers on tumor-infiltrating cells in STAD (TIMER)