Transmembrane serine protease 2, a SARS-CoV-2 internalization protease, correlates with clinical outcome, molecular features, and immunotherapy response in colorectal cancer

Introduction

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of coronavirus disease 2019 (COVID-19) which has led to a worldwide healthcare emergency (1,2). Since December 2019, 475,391,321 cases and 6,287,850 deaths related to COVID-19 have been reported globally (https://www.worldometers.info/coronavirus/). Transmembrane serine protease 2 (TMPRSS2) has been identified as a key host cell factor for viral entry and pathogenesis of SARS-CoV-2, through a proteolytic process of the SARS-CoV-2 Spike (S) protein (3-5).

Colorectal cancer (CRC) is one of the most common types of malignant disease and the leading cause of cancer-related death globally (6). Although the past decades have witnessed advances in surgical procedures and adjuvant treatment schemes, the prognosis of patients with CRC with advanced stage remains poor (7,8). Surgical treatment followed by adjuvant chemotherapy is currently is the standard treatment scheme (9). Previous reports have suggested that patients with cancer are more susceptible to infections due to the immunosuppression caused by tumor burden and postoperative chemotherapy (10,11). In several bioinformatics analysis studies, lung and stomach cancer tissues expressed higher TMPRSS2 expression levels than did matched healthy tissues (12-14). However, recent studies have revealed that TMPRSS2 expression is downregulated in kidney and head and neck cancer tissues, indicating that these patients may be more resistant to SARS-CoV-2 infection (15,16). Research indicated that TMPRSS2 is involved in the signal transduction between cancer cells and the extracellular environment (17). Ko et al. revealed that TMPRSS2 expression was regulated by androgen and can promote prostate cancer cell invasion and metastasis (18). Of note, studies concerning the relationship between TMPRSS2 expression and CRC are conflicting (14,19,20), and the potential functions of TMPRSS2 gene and its correlation with molecular features and immunotherapy response have not been investigated in relation to CRC.

In this present study, The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were used to investigate the association between TMPRSS2 expression and oncogenic outcomes, molecular features, and immunotherapy response in patients with CRC. We present this article in accordance with the REMARK reporting checklist (available at https://jgo.amegroups.com/article/view/10.21037/jgo-23-641/rc).

Methods

Data availability and materials

Messenger RNA (mRNA) expression data of patients with CRC from TCGA were downloaded from the Genomic Data Commons (GDC) Data Portal (https://portal.gdc.cancer.gov/). Patients with RNA-sequencing (RNA-seq) data and complete follow-up information were included in this study. Three GEO datasets, GSE39582 (N=557), GSE33113 (N=90) and GSE17538 (N=200), produced by the Affymetrix HG-U133 plus 2.0 platform were included. CEL files were downloaded and normalized for GEO microarray data using robust multichip average method. The identified TCGA and GEO datasets were combined into a single dataset and the ComBat (combating batch effects when combining batches of gene expression microarray data) method was used to remove the batch effects among the TCGA and GEO cohorts. This method was implemented in the SVA R package. Other three datasets [GSE32323 (17 paired normal and cancer tissues), GSE110225 (17 paired normal and cancer tissues), GSE44076 (98 paired normal and cancer tissues)] from the GEO database were identified to compare the TMPRSS2 expression between normal and tumor tissues. The basic clinicopathological features of the identified patients from the GEO and TCGA databases are presented in the supplementary table (available at https://cdn.amegroups.cn/static/public/jgo-23-641-1.docx). This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Functional annotation of TMPRSS2-related genes

To identify TMPRSS2-associated genes, the linear models for microarray data (LIMMA) method was used to generate the differentially expressed genes (DEGs) between low and high TMPRSS2 expression groups. The threshold for significance was set as an adjusted P value <0.05 and fold change >1.5. Functional annotation of Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) biological process enrichment was further performed in the R package “clusterProfiler” (R version 3.4.2, The R Foundation for Statistical Computing; https://www.r-project.org/).

Identification of microRNA (miRNA) and transcriptional factors targeting TMRPSS2

miRWalk (http://mirwalk.umm.uni-heidelberg.de/) was used to predict the potential miRNAs targeting TMPRSS2. Moreover, the University of California, Santa Cruz (USCS) Genome browser (https://genome.ucsc.edu/) integrating the JASPAR database was used to determine the potential transcriptional factors regulating TMPRSS2, identify the modulating networks for TMPRSS2, and provide intervention alternatives.

Characterization of immune cell infiltration and evaluation of therapeutic response to immune checkpoint blockade

To quantify the relative abundance of immune cell infiltration and characterize the features of the CRC tumor microenvironment (TME) based on different TMPRSS2 expression levels, the CIBERSORT algorithm was implemented, and the calculated enrichment score of each cell was used to represent their abundance level in the TME. CIBERSORT is a deconvolution method that characterizes the cell composition of complex tissue from their gene expression profiles. It employs linear support vector regression, a machine learning approach, to deconvolute a mixture of gene expression. To evaluate the therapeutic response to immune checkpoint blockade based on TMPRSS2 expression, two cohorts of anti-programmed cell death protein 1 (PD-1) (21) and anti-cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) (22) were selected to analyze and compare the response rate between patients with low and high TMPRSS2 expression.

Statistical analyses

All statistical analyses were performed using R software. Data are presented as the mean ± standard deviation. To compare differences between groups, the Wilcoxon rank-sum test was used for data with a skewed distribution, while the Student’s t-test was used for data with a normal distribution. The X-tile program determined the best cutoff value to separate patients into low and high TMPRSS2 expression groups. The Kaplan-Meier method was used to plot survival curves, and the log-rank test was used to analyze significance. A P value <0.05 was considered significant.

Results

TMPRSS2 was downregulated in CRC tissues and was correlated with superior clinical outcomes

We examined TCGA and three datasets (GSE32323, GSE110225, GSE44076) from GEO database to compare the expression difference between cancer tissue and adjacent normal tissue. The results showed that TMPRSS2 was significantly downregulated in cancer tissues among the four cohorts (Figure 1A-1D). The comparison of TMPRSS2 expression between normal and cancer tissues among other solid tumors is shown in Figure S1. To further test the prognostic value of TMPRSS2 in patients with CRC, a total of 1,387 patients from TCGA and GEO databases were included. Based on the best cutoff value, patients with a low expression of TMPRSS2 showed notably worse survival than did patients with a high expression in TCGA (P=0.015; Figure 1E), GSE39582 (P=0.016; Figure 1F), GSE33113 (P=0.023; Figure 1G), GSE17538 (P=0.011; Figure 1H), and the combined (Figure 1I, P=0.01) cohorts. Further correlation analysis showed that low TMPRSS2 expression was significantly associated with advanced stage (Figure 1J). These results revealed that TMPRSS2 is a promising prognostic factor in CRC.

Figure 1 Expression difference of TMPRSS2 between normal and cancerous tissues in the GSE32323 (A), GSE110225 (B), GSE44076 (C), and TCGA (D) datasets. The Kaplan-Meier survival curves comparing low and high expression of TMPRSS2 in TCGA (E), GSE39582 (F), GSE33113 (G), GSE17538 (H), and the combined cohort (I). Correlation plot between TMPRSS2 expression and clinical factors (J). TMPRSS2, transmembrane serine protease 2; TCGA, The Cancer Genome Atlas; NA, not applicable; CMS, consensus molecular subtypes.

Identification of TMPRSS2-associated biological signaling pathways

To identify TMPRSS2 expression-associated genes, we combined TCGA and GEO cohorts and performed differential expression analysis between TMPRSS2 low and high expression groups. A total of 336 genes with fold change >1.5 and false-discovery rate (FDR) <0.05 were identified (the supplementary table is available at https://cdn.amegroups.cn/static/public/jgo-23-641-2.docx), with the top 10 differentially expressed genes being listed in Figure 2A. Next, we conducted function enrichment analysis based on the upregulated and downregulated genes in the TMPRSS2 low-expression group. The GO and KEGG enrichment analysis indicated that a low expression of TMPRSS2 was significantly associated with tumor metastasis-related biological processes or pathways, including extracellular matrix (ECM) organization, extracellular structure organization, focal adhesion, and ECM-receptor interaction (Figure 2B-2E); meanwhile, a high expression of TMPRSS2 was associated with energy metabolism-related biological processes or pathways, including generation of precursor metabolites and energy, small-molecule catabolic process, fatty acid catabolic process, and citrate cycle (Figure 2F-2I). In summary, low TMPRSS2 expression was notably associated with tumor progression-related pathways.

Figure 2 Heatmap of differentially expressed genes between the low and high TMPRSS2 expression groups (A). Functional annotation of GO BP (B), GO MF (C), GO CC (D), and KEGG (E) based on the upregulated genes in the low TMPRSS2 expression group. Functional annotation of GO biological process (F), GO molecular function (G), GO cellular component (H), and KEGG (I) based on the upregulated genes in the high TMPRSS2 expression group. TMPRSS2, transmembrane serine protease 2; RFS, relapse free survival; KEGG, Kyoto Encyclopedia of Genes and Genomes; GO, Gene Ontology; BP, biological process; MF, molecular function; CC, cellular component.

Prediction of miRNAs and transcriptional factors targeting TMPRSS2

To identify the potential regulators of TMPRSS2 expression, we attempted to explore the potential miRNAs and transcriptional factors targeting TMPRSS2. First, we used the miRWalk tool to search for miRNAs that could putatively target TMPRSS2 (the supplementary table is available at https://cdn.amegroups.cn/static/public/jgo-23-641-3.docx). Based on TCGA database, we examined six of the identified miRNAs (miR_379_5p, let_7i_5p, miR_513b_3p, miR_140_5p, miR_371a_5p, miR_708_5p) whose expression levels were inversely correlated with TMPRSS2 expression (Figure 3A) and were significantly upregulated in cancer tissues (Figure 3B). Next, we used the UCSC genome database integrating JASPAR to predict the possible transcriptional factors targeting TMPRSS2. A list of transcriptional factors targeting TMPRSS2 with the setting the track score set to >600 is provided in Figure 3C.

Figure 3 Correlation plot between indicated miRNAs and TMPRSS2 in TCGA CRC dataset (A). Box-plot analysis representing the expression levels of miRNAs in normal and cancer tissues from patients with CRC in TCGA (B). Screenshot of the identified transcriptional factors with a track core >600 in the UCSC Genome Browser integrating the JASPAR database (C). *, P<0.05; **, P<0.01. TMPRSS2, transmembrane serine protease 2; miRNAs, microRNAs; TCGA, The Cancer Genome Atlas; CRC, colorectal cancer; UCSC, University of California Santa Cruz.

Characterization of the infiltrating immune cells in the TME among the different TMPRSS2 expression groups

To more precisely characterize the degree of immune infiltration between the TMPRSS2 low and high expression groups, we performed CIBERSORT analysis; the resultant proportions of 22 kinds of immune cell components in CRC tissues are visualized in Figure 4A. A heatmap was then used to plot the differences in the immune cell infiltrations between the TMPRSS2 low and high-expression groups (Figure 4B). Compared with those in the high TMPRSS2 expression group, the infiltration levels of activated natural killer (NK) cells, resting NK cells, CD8⁺ T cells, and M0, M1, and M2 macrophages were significantly higher in the low TMPRSS2 expression group (Figure 4C). In summary, low TMPRSS2 expression was significantly associated with immune-activating cell infiltration.

Figure 4 The proportions of different immune cell components in CRC samples with low and high TMPRSS2 expression (A). Heatmap for the infiltration levels of immune cells in the low and high TMPRSS2 expression groups (B). Box plots for the differences in infiltration levels of immune cells between the low and high TMPRSS2 expression groups (C). ns, no significant; *, P<0.05; **, P<0.01; ***, P<0.001. NK, natural killer; TMPRSS2, transmembrane serine protease 2; CRC, colorectal cancer.

Potential of TMPRSS2 expression as an indicator of immunotherapy response

Based on the differences in immune cell infiltration between patients with low and high TMPRSS2 expression, we hypothesized that TMPRSS2 might be indicative of immunotherapy response. Tumor purity, heterogeneity, and immune checkpoint inhibitor (ICI) gene expression are established markers for immunotherapy response. We then explored the differences in tumor purity, heterogeneity, and ICI gene expression between the low and high TMPRSS2 expression groups. We found that immune (Figure 5A), stromal (Figure 5B), and estimate scores (Figure 5C) were significantly higher in the low TMPRSS2 expression group than in the high TMPRSS2 expression group. Subsequently, the mutant-allele tumor heterogeneity (MATH) score was used to evaluate tumor heterogeneity between the low and high expression groups. The results showed that patients with low TMPRSS2 expression had significantly higher MATH scores than those with high TMPRSS2 expression (Figure 5D). We next compared the expression level of ICI genes [programmed death-ligand 1 (PD-L1) and CTLA-4] between patients with low and high TMPRSS2 expression. It was found that patients with low TMPRSS2 expression tended to show higher PD-L1 and CTLA-4 expression (Figure 5E,5F). Consensus molecular subtypes (CMS) are important molecular classification in CRC and is also an indicator for prognosis and immunotherapy response evaluation (23). We thus compared TMPRSS2 expression differences among CMS types and found that the CMS4 type was associated with the worst prognosis, while the immune-activated CMS1 type was associated with relatively low TMPRSS2 expression (Figure 5G).

Figure 5 Box plots for the differences of immune score (A), stromal score (B), ESTIMATE score (C), MATH score (D), PD-L1 expression (E), and CTLA-4 expression (F) between the low and high TMPRSS2 expression groups. Comparison of TMPRSS2 expression among patients with different CMS types (G). Distribution of response rate to anti-PD-1 (H) and anti-CTLA-4 (I) in patients with low and high TMPRSS2 expression. TMPRSS2, transmembrane serine protease 2; MATH, mutant-allele tumor heterogeneity; PD-L1, programmed death-ligand 1; CTLA-4, cytotoxic T-lymphocyte-associated protein 4; CMS, consensus molecular subtypes; PD-1, programmed death 1; CR, complete response; PR, partial response; SD, stable disease; PD, progressive disease.

PD-1 and CTLA-4 inhibitors are two representative immunotherapies. We next investigated whether TMPRSS2 expression could be used to predict effective response to these immune checkpoint blockade therapies. In both the anti-PD-1 cohort (21) and anti-CTLA-4 cohort (22), patients with low TMPRSS2 expression displayed more effective clinical response to anti-PD-1or CTLA-4 immunotherapies as compared to those patients with high TMPRSS2 expression (response rate of anti-PD-1 cohort: 38% vs. 22%, Figure 5H; response rate of anti-CTLA-4 cohort: 57% vs. 32%, Figure 5I). Overall, these findings suggest that TMPRSS2 could be a reliable biomarker for predicting immunotherapy response.

Discussion

SARS-CoV-2 is a newly identified coronavirus capable of infecting humans and rapidly inducing acute lung failure and multi-organ damage. TMPRSS2 is one of the most important factors mediating the susceptibility to SARS-CoV-2 and infection enhancement. Thus, exploring the susceptibility to coronavirus and the physiological functions of TMPRSS2 in humans is essential to the ongoing global drive to manage this pandemic. Previous reports have suggested that patients with cancer are more susceptible to infections, and several bioinformatics analyses have shown that cancer tissues have higher TMPRSS2 expression levels than matched healthy tissues in lung and stomach cancer (24,25). As previously reported, patients with COVID-19 usually exhibit multi-organ damage, especially aerodigestive disease (26). We can speculate that a high expression of TMPRSS2 in these tissues facilitates the susceptibility to SARS-CoV-2. However, studies concerning the implications of TMPRSS2 expression in patients with CRC remain conflicting. Wang et al. showed that TMPRSS2 was highly expressed in CRC which suggests severe symptoms of SARS-CoV-2 Infection (27). We thus applied multiomics tools to explore the role of TMPRSS2 expression in CRC and evaluated the correlation between TMPRSS2 expression and immunotherapy response to glean new insights into the impact of COVID-19 infection on CRC.

To our knowledge, this is the first study to analyze the difference in TMPRSS2 expression between normal and cancer tissues and its prognostic value based on multiple datasets. We found that TMPRSS2 was highly expressed in normal colorectal tissues. In addition, a list of miRNAs and transcriptional factors that could putatively target TMPRSS2 were identified. Notably, TMPRSS2 expression was associated with immune cell infiltration, and a low expression of TMPRSS2 was significantly correlated with higher immunotherapy response.

TMPRSS2 mediates the entry of SARS-CoV-2 into host cells. The low level of TMPRSS2 in CRC cancerous tissue was confirmed in this study, which indicates that patients with CRC may be more resistant to SARS-CoV-2 infection. CRC is a type of immunogenic cancer. It was found that the spontaneous anticancer immune response may help to increase survival duration, while the immune escape may decrease survival (28). First lymphocytes will eliminate tumor cells while some cells are resilient and they survive and reach in equilibrium phase and thus escaping immune surveillance (29). Increased PD-1 expression was found to be able to cause tumor infiltrating lymphocytes to be terminally exhausted (29). Recently, the application of several ICIs including anti-PD-1 and anti-CTLA-4 has been widely adopted in CRC (28). The aim of immunotherapy is to activate the immunosuppressive networks in the TME. Several immune-related markers have been used for predicting the response to immunotherapy (30). Our study revealed that a low expression of TMPRSS2 was related to the infiltration of resting NK cells, activated NK cells, plasma cells, CD8⁺ T cells, and M0, M1, and M2 macrophages, indicating the possible relation between TMPRSS2 expression and immunotherapy response. Tumor purity, heterogeneity, and ICI gene expression level are established indicators for immunotherapy response. We found that patients with a low expression of TMPRSS2 tended to have higher tumor heterogeneity and ICI gene expression level, which correlated with a higher immunotherapy response rate. As expected, a low expression of TMPRSS2 was found to be associated with a higher response rate in both the anti-PD-1 and anti-CTLA-4 cohorts.

Although this is the first study to demonstrate a correlation between TMPRSS2 expression and the prognosis, molecular features, and immunotherapy response in patients with CRC, several limitations should be noted. First, all the data were obtained from public databases, and further prospective validation is needed. Second, samples of patients with CRC with SARS-CoV-2 infection should be tested to confirm the conclusions of this study. Finally, the mechanisms underlying the correlations reported in this study remain unclear and should be further investigated.

Conclusions

In conclusion, CRC tumor cells may be more resistant to SARS-CoV-2 infection due to decreased TMPRSS2 expression. In addition, the expression levels of this gene may represent a novel biomarker for the prognosis and immunotherapy response prediction of patients with CRC.

Acknowledgments

We would like to acknowledge the editors and reviewers for their helpful comments on this paper.

Funding: This work was supported by the National Natural Science Foundation of China (No. 82102850; No. 82002416), the China Postdoctoral Science Foundation (No. 2020M671399), the Natural Science Foundation of Jiangsu Province (No. BK20201089), and the Jiangsu Province Postdoctoral Fund (No. 247755).

Footnote

Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://jgo.amegroups.com/article/view/10.21037/jgo-23-641/rc

Peer Review File: Available at https://jgo.amegroups.com/article/view/10.21037/jgo-23-641/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jgo.amegroups.com/article/view/10.21037/jgo-23-641/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. This study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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