
==== Front
Saudi J Gastroenterol
Saudi J Gastroenterol
SJG
Saudi J Gastroenterol
Saudi Journal of Gastroenterology : Official Journal of the Saudi Gastroenterology Association
1319-3767
1998-4049
Wolters Kluwer - Medknow India

38813725
SJG-30-243
10.4103/sjg.sjg_424_23
Original Article
Prediction of the survival status and tumor microenvironment in colorectal cancer through genotyping analysis based on toll-like receptors
Peng Huaidu
Zhang Junshuo
Yang Zehuang
Chen Lixin
Chen Jinhong
Cai Chudong
Department of General Surgery, Shantou Central Hospital of Guangdong Province, Shantou, China
Address for correspondence: Dr. Huaidu Peng, Department of General Surgery, Shantou Central Hospital of Guangdong Province, No. 114 of Waima Road in Jinping Region, Shantou, 515031, Guangdong, China. E-mail: huaidupeng@163.com
Jul-Aug 2024
30 5 2024
30 4 243251
20 12 2023
02 5 2024
Copyright: © 2024 Saudi Journal of Gastroenterology
2024
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
Background:

Colorectal cancer (CRC) ranks third in both the incidence and mortality rates among male and female cancers, and it is the leading digestive system cancer. Due to the inter- and intratumor heterogeneity of cancer, the TNM system is insufficient for predicting prognosis, necessitating the use of molecular biomarkers for prognostic prediction. Toll-like receptors (TLRs) have been associated with CRC survival rates. This study focused on the investigation of the role and potential value of TLRs in CRC genotyping to aid in immunotherapy for CRC patients.

Methods:

Differential gene expression analysis was performed on CRC transcriptomic data from The Cancer Genome Atlas database. TLRs were referred from the literature, and their intersection with differentially expressed genes (DEGs) in CRC yielded TLR-DEGs. The expression patterns of TLR-DEGs were predicted using the STRING website, and copy number variations of TLR-DEGs were analyzed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted on TLR-DEGs. ConsensusClusterPlus R package was used for clustering CRC patients, and ESTIMATE and GSEAbase were employed to analyze immune characteristics of different subtypes. Immune phenotyping scores and tumor immune dysfunction and exclusion scores were evaluated. DEGs of different subtypes were analyzed, followed by GO and KEGG enrichment analyses, the protein–protein interaction (PPI) network analysis, and further selection of hub genes. The sensitivity of drugs was assessed using the identified hub genes.

Results:

We identified 37 TLR-DEGs, and the PPI analysis revealed their coexpression, although they were distributed on different chromosomes. Enrichment analyses indicated that the 37 TLR-DEGs were linked to cancer cell immune response. Based on these TLR-DEGs, CRC patients were classified into three subtypes. Cluster2 exhibited lower survival rates and higher immune infiltration levels and predicted poorer response to immune checkpoint inhibitor therapy. The intersection of DEGs from cluster2 and cluster1 with DEGs from cluster2 and cluster3 yielded a set of 426 commonly shared DEGs. Enrichment analyses revealed that these shared DEGs might regulate immune cell viability. Eight common hub genes for different subtypes were further identified to predict drug-related correlations.

Conclusion:

The developed TLR genotyping was used to predict the survival status and tumor microenvironment of CRC, providing a foundation for understanding the molecular mechanisms of TLR signaling and deepening its clinical significance.

Colorectal cancer
genotyping
hub genes
immune landscape
toll-like receptors
==== Body
pmcINTRODUCTION

Ranking third in both incidence and mortality rates among male and female cancers, colorectal cancer (CRC) is the leading digestive system cancer.[1] According to cancer statistics from 2020, the United States estimated 153,020 new cases and 52,550 deaths from CRC.[2] Common treatment options for CRC include surgery, chemotherapy, and radiotherapy, with approximately 24.9% of patients experiencing tumor recurrence after treatment.[3] Patients with CRC at different stages have considerably different survival times. For instance, the 5-year survival rates for stage I and II patients are 91% and 82%, respectively, while stage IV CRC patients have a 5-year survival rate of approximately 12%.[4] Due to the inter- and intratumor heterogeneity of cancer, the classical staging system, despite its widespread usage, is insufficient in predicting prognosis.[5] Therefore, identifying molecular biomarkers to improve the prognosis of CRC is of great significance.

Toll-like receptors (TLRs) take a crucial part in the innate immune system. They participate in the activation of innate immunity and the development of antigen-specific acquired immunity.[6] Increasing evidence suggests that alterations in TLR expression are linked to cancer development and can exert an influence on susceptibility to infections. Differential expression of TLRs and their associated proteins has been observed in CRC compared to healthy individuals and is correlated with patient survival.[78] For example, upregulation of TLR1, TLR2, TLR4, TLR8, and TLR9 genes has been found in CRC tissues compared to normal colon tissues.[9] These findings unearth the pivotal role of TLRs in CRC progression. Therefore, we conducted a study to identify CRC-specific TLRs for genotyping, aiming to predict CRC survival status and tumor microenvironment.

Using transcriptomic data of CRC from The Cancer Genome Atlas (TCGA) database, we identified differentially expressed genes (DEGs) and intersected them with TLRs to obtain TLR-DEGs. Based on TLR-DEGs, we performed clustering of CRC samples, analyzed the immune characteristics of different subtypes, and examined the functional enrichment of DEGs among subtypes. Furthermore, we analyzed the expression relationships of DEGs among different subtypes, identified hub genes, and assessed the sensitivity of these hub genes to drugs, aiming to provide valuable insights into the prognosis and treatment of CRC.

MATERIALS AND METHODS

Data acquisition

CRC-RNA sequencing transcriptomic data and corresponding clinical profiles (as of December 16, 2022) were obtained from TCGA database (https://portal.gdc.cancer.gov/), comprising 701 samples (normal: 51, tumor: 650). TLRs were referred from the literature,[10] and the data were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (https://www.kegg.jp/kegg) [Table S1].

Table S1 TLR-related genes

AKT1	
AKT2	
AKT3	
CASP8	
CCL3	
CCL4	
CCL5	
CD14	
CD40	
CD80	
CD86	
CHUK	
CTSK	
CXCL10	
CXCL11	
CXCL8	
CXCL9	
FADD	
FOS	
IFNA1	
IFNA10	
IFNA13	
IFNA14	
IFNA16	
IFNA17	
IFNA2	
IFNA21	
IFNA4	
IFNA5	
IFNA6	
IFNA7	
IFNA8	
IFNAR1	
IFNAR2	
IFNB1	
IKBKB	
IKBKE	
IKBKG	
IL12A	
IL12B	
IL1B	
IL6	
IRAK1	
IRAK4	
IRF3	
IRF5	
IRF7	
JUN	
LBP	
LY96	
MAP2K1	
MAP2K2	
MAP2K3	
MAP2K4	
MAP2K6	
MAP2K7	
MAP3K7	
MAP3K8	
MAPK1	
MAPK10	
MAPK11	
MAPK12	
MAPK13	
MAPK14	
MAPK3	
MAPK8	
MAPK9	
MYD88	
NFKB1	
NFKBIA	
PIK3CA	
PIK3CB	
PIK3CD	
PIK3CG	
PIK3R1	
PIK3R2	
PIK3R3	
PIK3R5	
RAC1	
RELA	
RIPK1	
SPP1	
STAT1	
TAB1	
TAB2	
TBK1	
TICAM1	
TICAM2	
TIRAP	
TLR1	
TLR2	
TLR3	
TLR4	
TLR5	
TLR6	
TLR7	
TLR8	
TLR9	
TNF	
TOLLIP	
TRAF3	
TRAF6	

TLR gene analysis

Differential gene expression analysis of the RNA-seq data between the normal and CRC groups was conducted by applying the R package edgeR (|logFC| > 0.585, FDR < 0.05). The intersection of DEGs and TLRs yielded TLR-DEGs. We calculated, integrated, and constructed the protein–protein interaction (PPI) network of TLR-DEGs by using the STRING website (https://string-db.org/) and then visualized it using Cytoscape software (version 3.7.1). The specific chromosomal locations of TLR genes were plotted using the RCircos package. Gene Ontology (GO) and KEGG enrichment analyses were conducted for TLR-DEGs, and the results were visualized using the R package ClusterProfiler.[11]

Subtype identification and immune analysis

ConsensusClusterPlusR package was used to perform K-Means consensus clustering on CRC patients, and the R package survival was employed to study the survival rates among subtypes. We calculated the ESTIMATE score, immune score, and stromal score for each subtype by utilizing the ESTIMATE method,[12] predicting the levels of infiltrating immune cells and stromal cells. Additionally, single-sample gene set enrichment analysis (ssGSEA) based on the expression levels of 29 immune-related feature genes was conducted utilizing the R package GSEAbase to analyze the immune abundance and immune functional abundance of each subtype.

Immunophenoscores (IPSs) of CRC were obtained from The Cancer Immunome Atlas (TCIA) (https://tcia.at), and violin plots were created utilizing the R package ggpubr based on subtype information. Higher IPS scores indicated better response to immunotherapy.

Tumor Immune Dysfunction and Exclusion (TIDE) score files were retrieved from the TIDE website (http://tide.dfci.harvard.edu), and the R package ggpubr was utilized to assess the potential differences in immune checkpoint inhibitor (ICI) response among different subtypes.[13]

Differential gene expression analysis and functional analysis

We employed R package edgeR to conduct differential gene expression analysis on the subtype-grouped samples (|logFC| > 1.0, FDR < 0.01). GO and KEGG enrichment analyses of the DEGs were performed by utilizing clusterProfiler R package. The relationships among DEGs were explored using STRING, and the PPI network was presented by using Cytoscape software (version 3.7.1). We evaluated and selected hub genes based on five common algorithms (MCC, MNC, Degree, Closeness, EPC).[14] The GeneMania website (http://www.genemania.org/) was utilized to establish a coexpression network of these shared hub genes.

Drug sensitivity prediction

Exploration of the CellMiner database (https://discover.nci.nih.gov/cellminer/) revealed the mechanisms of uncharacterized and structurally similar compounds. Z-score was utilized as a normalized index, greatly aiding in integrating data such as expression levels of gene transcripts and activity rates of drugs.[15] In order to further identify new potential targets and more effective therapeutic drugs, we utilized the CellMiner database to screen for antitumor drugs with their sensitivity tightly linked to shared hub genes.

RESULTS

Analysis of DEGs

Differential expression analysis was conducted on TCGA dataset between the normal group and CRC group, resulting in the identification of 8866 DEGs, including 3710 downregulated genes and 5156 upregulated genes [Figure 1a]. Intersection of the DEGs with TLR genes revealed 37 TLR-DEGs [Figure1b and Table S2]. Through PPI network exploration of the 37 TLR-DEGs, we found 37 nodes and 593 interaction connections [Figure 1c], with all 37 TLR-DEGs included. Further analysis of the copy number variation (CNV) distribution of the 37 TLR-DEGs showed that they were located on different chromosomes [Figure 1d]. GO and KEGG analyses were carried out to clarify the functional properties and biological impacts of these genes. The GO enrichment analysis showed remarkable enrichment of DEGs in biological functions such as positive regulation of defense response, cellular response to biotic stimuli, neutrophil chemotaxis, and positive regulation of MAPK cascade [Figure 1e]. The KEGG enrichment analysis displayed enrichment of the relevant genes in pathways such as NOD-like receptor signaling pathway, IL-17 signaling pathway, and TNF signaling pathway [Figure 1f]. Taken together, this study demonstrated the crucial importance of the 37 TLR-DEGs in the molecular mechanisms of cancer cell immune response.

Figure 1 Analysis and functional characterization of TLR-DEGs in CRC (a) Volcano plot of differential gene expression analysis in CRC. (b) Upset plot of DEGs and TLR-related genes. (c) PPI network of TLR-DEGs. (d) Chromosomal representation of TLRs’ CNV. (e and f) GO (e) and KEGG (f) enrichment analyses of TLR-DEGs in CRC

Table S2 TLR-DEGs in CRC

TLR3	
PIK3CG	
RIPK1	
TAB2	
MAPK10	
TLR7	
IRAK1	
SPP1	
MAP2K6	
CXCL8	
MAPK3	
MAP2K4	
CXCL11	
IRF3	
CCL5	
PIK3CD	
AKT3	
LY96	
PIK3R2	
CD14	
PIK3R5	
IFNAR2	
LBP	
TLR1	
TLR5	
CCL3	
TLR6	
FOS	
IL1B	
FADD	
TLR8	
MAPK12	
TNF	
CD80	
IL6	
CCL4	
CXCL10	

Subtyping and inter-subtype immune landscape of TLR-DEGs

Clustering analysis was carried out on the expression matrix of TLR-DEGs, and the optimal number of clusters was set to 3 [Figure 2a]. Survival analysis revealed that cluster2 exhibited a considerably lower survival rate compared to cluster1 and cluster3 [Figure 2b]. Further exploration of the immune landscape among different subtypes was conducted using the ESTIMATE algorithm to estimate stromal and immune cells in malignant tumors. The results displayed that compared to cluster1 and cluster3, cluster2 had considerably higher immune scores, stromal scores, and ESTIMATE scores, while exhibiting a considerably lower tumor purity score [Figure 2c-f]. Additionally, the ssGSEA algorithm revealed the differences in immune cells and their functional profiles among different subtypes. Cluster2 was observed to have higher immune levels in cells such as DCs, macrophages, T_helper_cells, and NK_cells. It also demonstrated higher abundances in functions such as Check-point, Type_II_IFN Response, and CCR, compared to cluster1 and cluster3 [Figure 2g]. Furthermore, after detecting the expression levels of immune checkpoint genes across different subtypes, it was observed that the majority of immune checkpoint genes were remarkably low-expressed in cluster1 and cluster3 compared to cluster2 [Figure 2h]. Collectively, these findings suggested that different subtypes of CRC might exhibit distinct responses to ICI therapy.

Figure 2 Subtyping and immune landscape of TLR-DEGs (a) Clustering analysis of genotyping. (b) Survival analysis of different subtypes. (c-f) Immune score (c), stromal score (d), ESTIMATE score (e), and tumor purity score (f) calculated by the ESTIMATE algorithm. (g) Heatmap depicted immune cell and immune functional profiles based on the ssGSEA analysis. (h) Expression levels of immune checkpoint genes across different subtypes. * indicates P < 0.05, ** indicates P < 0.01, *** indicates P < 0.001

Prediction of immunotherapy response

To predict the immunotherapy outcomes under ICI treatment, we analyzed the efficacy of immunotherapy among different subtypes using the IPS and TIDE scores. We observed that both cluster1 and cluster3 exhibited significantly higher IPS scores compared to cluster2 [Figure 3a-d] and had lower TIDE scores [Figure 3e]. These findings suggested that CRC patients in cluster1 and cluster3 might have a higher likelihood of benefiting from immunotherapy, with a lower chance of tumor immune evasion and a higher response rate to ICI treatment.

Figure 3 Immunotherapy prediction among different subtypes (a-d) IPS among different subtypes. (e) TIDE scores among different subtypes. * indicates P < 0.05, ** indicates P < 0.01, *** indicates P < 0.001, ns indicates P > 0.05

DEGs and functional analysis among different subtypes

To further investigate the functional differences among different subtypes, we analyzed the DEGs between cluster2 and cluster1 and between cluster2 and cluster3. A total of 1095 DEGs were identified between cluster2 and cluster1, with 228 downregulated and 867 upregulated DEGs [Figure 4a]. Additionally, there were 660 DEGs between cluster2 and cluster3, with 114 downregulated and 546 upregulated DEGs [Figure 4b]. The intersection of DEGs in cluster 2 and cluster 1 with those in cluster 2 and cluster3 resulted in a total of 426 shared DEGs [Figure 4c and d]. We performed GO and KEGG enrichment analyses to figure out the functions of these DEGs among different subtypes. The GO analysis revealed enrichment in biological processes such as regulation of humoral immune response, neutrophil chemotaxis, and acute inflammatory response for these shared DEGs [Figure 4e]. The KEGG enrichment analysis indicated enrichment in signaling pathways including cytokine–cytokine receptor interaction, IL-17 signaling pathway, and neutrophil extracellular trap formation [Figure 4f]. These DEGs were likely involved in regulating immune cell activities, thereby influencing disease progression. Furthermore, a PPI network was established by utilizing Cytoscape for the shared DEGs with a score greater than 0.7. The network exhibited 151 nodes and 466 interaction connections [Figure 4g]. By applying MCC, MNC, Degree, Closeness, and EPC algorithms, we collected the top 20 hub genes in the network [Table 1]. Among them, 8 hub genes were commonly shared [Figure 4h], including CRP, CCL3, CSF3, IL1B, CXCL10, CXCL8, IL17A, and CXCL9. We applied the GeneMANIA database to further analyze the coexpression network and associated functions of these hub genes. The results displayed complex coexpression networks for these genes, with 58.48% coexpression, 5.29% physical interactions, 7.83% colocalization, 13.13% predicted, and 15.27% shared protein domains. These genes also exhibited immune-related functions [Figure 4i].

Figure 4 DEGs and functional analysis among different subtypes (a and b) Volcano plots of DEGs between cluster2 and cluster1 (a) and between cluster2 and cluster3 (b). (c and d) Venn diagrams displaying upregulated DEGs (c) and downregulated DEGs (d). (e and f) GO (e) and KEGG (f) enrichment analyses of DEGs. (g) PPI network of DEGs. (h) Upset plot of hub genes. (i) Coexpression network of hub genes

Table 1 Top 20 hub genes in CytoHubba

MCC	MNC	Degree	Closeness	EPC	
SPRR3	SPRR2E	PNLIP	FCGR3A	FCGR3A	
SPRR2F	CCL7	SPRR2E	CCL7	CCL7	
SPRR2E	CRP	CRP	CRP	CRP	
SPRR2D	SPRR2D	SPRR2D	IL17F	IL17F	
SPRR2A	IVL	IVL	CCL3	CCL8	
SPRR1B	LCE3D	LCE3D	ESR1	IL22	
LCE3D	SPRR2A	SPRR2A	CSF3	LBP	
IVL	SPRR2F	SPRR2F	SERPINE1	CCL3	
IL1B	CCL3	CCL3	TREM1	CSF3	
IL17A	CSF3	CSF3	IL1B	SERPINE1	
CXCL9	IL1B	IL1B	FCGR3B	IL1B	
CXCL8	CXCL5	CXCL10	CXCL5	FCGR3B	
CXCL5	CXCL10	CXCL8	CXCL10	CXCL5	
CXCL10	CXCL8	KRT5	CXCL8	CXCL10	
CSF3	IL17A	IL17A	KRT5	CXCL8	
CRP	CXCL9	CXCL9	IL17A	IL17A	
CCL7	CASP14	CASP14	CXCL9	TNFAIP6	
CCL3L3	SPRR1B	SPRR1B	CCL3L3	CXCL9	
CCL3	FN1	FN1	MMP8	CCL3L3	
CASP14	SPRR3	SPRR3	FN1	FN1	

Analysis of drug sensitivity for shared hub genes

Furthermore, we conducted a drug sensitivity analysis to predict the potential applications of the shared hub genes. The CellMiner database was employed to explore the relationship between the shared hub genes and drug sensitivity. We found that CSF3 had a positive correlation with Rebimastat (0.942). CXCL9 was shown to have favorable relationships with Alectinib, Estramustine, brigatinib, and Carmustine (0.764, 0.634, 0.604, 0.539). CCL3 was positively linked to Rebimastat (0.727). IL17A had a negative correlation with Irofulven (-0.579). IL1B was found to be positively linked to Rebimastat (0.573) [Figure 5].

Figure 5 Correlation plot of drug sensitivity prediction using CellMiner

DISCUSSION

Like many other solid tumors, CRC is a heterogeneous and molecularly complex disease with distinctive molecular and phenotypic characteristics.[16] The classical staging system divides CRC patients into different prognostic groups according to the extent of the primary tumor, regional lymph nodes, and distant metastasis.[17] However, it is insufficient for patients with similar clinical characteristics. The TLR signaling pathway takes a pivotal part in inflammation, immune cell regulation, survival, and proliferation.[1819] Activation of various TLRs has been shown to facilitate the development of colitis-associated cancer.[20] TLR2 and TLR4 have prognostic implications in CRC.[21] To elucidate the function of TLR signaling, we compared the expression patterns of TLRs between CRC and normal samples and determined aberrantly expressed TLR genes related to CRC development, resulting in 37 TLR-DEGs. We explored their intrinsic connections through PPI and CNV analyses. Functional analyses of TLR-DEGs using GO and KEGG revealed their critical involvement in the molecular mechanisms of immune response in cancer cells, suggesting a significant impact of these 37 TLR-DEGs on the immune landscape of CRC.

The correlation between TLR expression and the prognosis of CRC has been reported, with adverse prognosis observed in CRC patients with high TLR8 expression.[22] According to 37 TLR-DEGs, we classified patients into three clusters and found that cluster2 exhibited a worse prognosis. Interestingly, TLR8 was identified as a common DEG in both cluster2 and the combined cluster1 and cluster3. By analyzing the immune landscape of subtypes, we discovered that compared to cluster1 and cluster3, cluster2 had higher immune scores, stromal scores, and ESTIMATE scores. Additionally, cluster2 showed elevated levels of immune cell types such as DCs, macrophages, T_helper_cells, and NK_cells, possibly contributing to TLR expression on both immune and nonimmune cells and high expression primarily in immune cells such as DCs, macrophages, NK_cells, and adaptive immune cells (T and B lymphocytes).[23] Furthermore, cluster2 exhibited higher expression of immune checkpoint molecules. The IPS and TIDE scores are predictive of the response to immunotherapy.[1324] By using IPS and TIDE scores, we predicted the response to ICI therapy in different subtypes and found that patients in cluster1 and cluster3 might benefit more from immunotherapy.

Through enrichment analysis of the DEGs between cluster2 and cluster1 and between cluster2 and cluster3, we identified potential regulatory effects of DEGs on the biological activities of immune cells, thereby influencing disease progression. Furthermore, using the PPI network analysis, we identified eight common hub genes (CRP, CCL3, CSF3, IL1B, CXCL10, CXCL8, IL17A, CXCL9). Previous studies have associated CRP with distant metastasis in T3CRC.[25] rBFT-1 promotes CRC proliferation through the CCL3-related molecular pathway.[26] CSF3 enhances tumor cells to grow and migrate and expands the proportion of stem-like cells in CRC cultures.[27] IL1B is considerably linked to the overall survival of CRC patients.[28] TRIB3 suppresses CD8+ T cell infiltration and induces immune evasion in CRC by inhibiting the STAT1-CXCL10 axis. FOXS1 promotes tumor progression in CRC by upregulating CXCL8.[29] Blocking IL-17A facilitates tumor response to anti-PD-1 immunotherapy in microsatellite-stable CRC.[30] Downregulation of the CXCL9/10-CXCR3 axis in CRC cells promotes the adenoma-to-adenocarcinoma transition.[31] Collectively, these identified hub genes play important roles in CRC. Additionally, we investigated the clinical implications of the hub genes and identified potential drugs, including rebimastat, alectinib, estramustine, brigatinib, carmustine, and irofulven. Among them, rebimastat is associated with hub genes CSF3, CXCL9, and CCL3. Rebimastat is a thiol-based second-generation matrix metalloproteinase inhibitor (MMPI) with potential antitumor activity.[32] These drugs may provide useful therapeutic options for different molecular subtypes of CRC.

However, the study had several issues to be addressed. First, the clustering of CRC patients was based on data obtained from TCGA, and prospective clinical studies would be needed for further validation. Furthermore, the clustering was performed using gene expression profiles without further validation through cellular and animal experiments. Last, drug sensitivity was predicted on the basis of gene expression profiles and has not been verified through cellular experiments.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.
==== Refs
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