TREM1 as a Potential Prognostic Biomarker Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma

doi:10.21203/rs.3.rs-2230975/v1

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TREM1 as a Potential Prognostic Biomarker Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma

https://doi.org/10.21203/rs.3.rs-2230975/v1

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Journal Publication

published 23 May, 2023

Read the published version in World Journal of Surgical Oncology →

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Background: This study aims to find immune-related hub gene as potential novel biomarker for KIRC.

Methods: We constructed a KIRC immune prognostic signature. The receiver operating characteristic (ROC) curves, survival curves, univariate and multivariate Cox regression analysis were utilized to validate the signature. Immunohistochemical staining was used to detect the expression of TRME1 in renal clear cell carcinoma tissues. The clinical characteristics, the status of the tumor microenvironment, and immune infiltration were analyzed through the ESTIMATE and CIBERSORT algorithms for the hub gene, while the Gene Set Enrichment Analysis and PPI analysis were performed to predict the function of the hub gene.

Results: In Immunohistochemical staining, we found that the expression of TREM1 was significantly upregulated with increasing tumor grade in renal clear cell carcinoma, and elevated TREM1 expression was associated with poor prognosis.The CIBERSORT and ESTIMATE algorithms revealed that TREM1 was correlated with the infiltration of 12 types of immune cells. Therefore, it was determined that TREM1 was involved in numerous classical pathways in the immune response via GSEA analysis.

Conclusions: The results suggest that TREM1 may act as an implicit novel prognostic biomarker in KIRC that could be utilized to facilitate immunotherapeutic strategy.

Immune-related signature

Kidney renal clear cell carcinoma

The Cancer Genome Atlas

Tumor immune microenvironment

TREM1

Globally, kidney renal clear cell carcinoma (KIRC) is one of the most common pathological types of renal cell carcinoma (RCC), wherein it accounts for approximately 75% of RCC. The incidence of this disease continues to increase (1). Although KIRC can be detected in its early stages and surgical resection is the mainstream strategy for its mitigation, up to one-third of patients with KIRC will still develop metastases (2). Meanwhile, KIRC is insensitive to conventional radiotherapy and chemotherapy and has higher rate of recurrence and metastasis than other subtypes of RCC. Therefore, the treatment of this disease relies on targeted therapy and immunotherapy (3). The utilization of immune checkpoint inhibitors (ICIs) to alleviate tumor-associated suppression of anticancer immune responses, such as programmed cell death-1 (PD-1) and the Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), has revolutionized the treatment of advanced KIRC and significantly improved the survival status of patients with this disease (4,5). However, patients with advanced KIRC still have a poor prognosis despite advances in treatment, primarily due to drug resistance (6). Such responses usually rely on the dynamic interactions between the tumor and the tumor microenvironment (TME) (7).

The TME, which includes tumor cells, local stromal cells, and other immune cells (8), is crucial in tumor progression and metastasis. Moreover, most non-tumor cell populations, such as stromal and immune cells, have been identified as prognostic markers of tumorigenesis. Previous studies have demonstrated that inflammation in TME, in which infiltrated diverse inflammatory cells contribute to tumorigenesis, is generally considered as a hallmark of cancer (9,10). For example, lower densities of CD8+ T are associated with the recurrence of KIRC (11). Cancer immunotherapy mainly exerts effects with some proteins to enhance the function of or restore immune cells in the TME. Considering the importance of immunity in the development and the treatment of cancer, discovering novel KIRC immune-related biomarkers may be useful for immunotherapy.

The triggering receptor expressed on myeloid cells 1 (TREM1), one of the members of the immunoglobulin superfamily, is mainly expressed on neutrophils and monocytes/macrophages (12). TREM1 is also involved in the innate immune response associated with various infectious and non-infectious inflammation responses and autoimmune disorders (13,14). Recent studies have shown a close link between TREM1 and cancer, as TREM1 is selectively expressed on tumor-associated macrophages (TAMs) in non-small cell lung cancer (15), activates in the liver Kupffer cells and promotes the progression of hepatocellular carcinoma (16). Moreover, the increased abundance of TREM1 in neutrophils may promote the development of colorectal cancer (17). Nevertheless, the association of TREM1 with immune infiltration in KIRC has not been investigated.

In this paper, we identified the prognostic immune-related genes (IRGs) and immune-related lncRNAs (irlncRNAs) based on the TCGA database. Subsequently, differential expression analysis combined with Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein and protein interaction (PPI) analysis were performed to find the immune-related hub gene in high- and low-risk groups by utilizing the LASSO regression model. Furthermore, we performed immune microenvironment analysis, immune cell infiltration analysis, clinical correlation analysis, and GSEA analysis for TREM1 to determine their potential involvement in immune infiltration in KIRC.

1.Chemicals and Antibody.

All chenmical reagents were purchased from Beijing Sinopharm Chemical Reagents Co.Ltd. (Beijing, China). TREM-1 antibody was from Santa Cruz Biotechnology, Inc. (Cat#: sc-9013. Texas,USA).

2.Kidney cancer tissue

The formalin-fixed, paraffin-embedded (FFPE) kidney cancer TMA used in this work was obtained from department of Pathology, Taizhou Hospital. To improve the reproducibility of pathologists and reduce the intra/inter-observer variability, the traditional 4-tier Fuhrman grading system was re-categorized into a simplified Fuhrman grading system with low-grade (grades 1 and 2) and high-grade (grades 3 and 4). Fuhrman grading was accomplished by a subspecialized genitourinary pathologist with 8 years of diagnostic experience. It includes 2 cases of benign tissue and invasive kidney cancer with Grade Fuhrman grade I and Fuhrman grade III. The use of human kidney cancer tissue samples was approved by the Institutional Ethic Committee of Taizhou Hospital of Zhejiang Province.

3.Immuno histochemical

Briefly, paraffin-embedded 3 μm thick sections were deparaffinized, rehydrated, submerged into citric acid buffer for heat-induced antigen retrieval, immersed in 0.3% hydrogen peroxide to block endogenous peroxidase activity, blocked with 3% bovine serum albumin, incubated with primary antibodies at 4°C overnight and developed using the DAKO ChemMate Envision Kit/HRP (Dako-Cytomation, USA). The sections were then counterstained with hematoxylin, dehydrated, cleared and mounted. The tissues exhibiting brown staining in the cytoplasm, nucleus or membrane were considered positive. The slides were scanned using Leica SCN400 slide scanner (Leica Biosystems, Wetzlar, Germany). The optical density (OD) of protein expression was quantitatively determined using Image J software.

4. Data collection and pre-processing

The data of 539 KIRC and 72 normal samples were obtained from the TCGA database (https://portal.gdc.cancer.gov/). From the ImmPort database (https://www.immport.org/home), a list of recognized 2484 IRGs was obtained.

5. Establishment of weighted co-expression network and immune prognostic signature

The weighted gene co-expression network analysis (WGCNA) was used to identify the modules associated with survival time and status. We obtained a list of irlncRNAs that were co-expressed with the IRGs through Pearson correlation analysis (18). Then, we conducted a univariate Cox analysis of the irlncRNAs to identify the prognostic irlncRNAs.

6. Construction of the prognostic IRGs and irlncRNA signature model

To construct the prognostic signature model, the Lasso regression analysis was applied using the R package glmnet. The stability of the model was verified via univariate and multivariate Cox regression analysis in combination with the clinicopathological characteristics correlation analysis. Kaplan-Meier (KM) survival curves and receiver operating characteristic curves (ROC) were drawn using the survminer and timeROC

R packages, respectively.

7. Differential gene analysis and functional enrichment analysis

Differentially expressed IRGs were analyzed through the limma R package using the following screening criteria: false discovery rate (FDR) < 0.05 and log2 |fold change| >

1. Functional enrichment analyses using GO and KEGG were conducted for differentially expressed IRGs using the clusterProfiler R package.

8. Exploring the hub gene and validation of its mRNA expression level and prognostic value

The hub gene was screened from the IRGs in the GO enrichment results using the String website (https://string-db.org/). Then, the mRNA expression levels of the hub gene in KIRC were assessed in TIMER and TCGA database. The Kaplan-Meier (KM) survival curve, univariate and multivariate Cox regression analyses were plotted to investigate the prognostic value using the corresponding R packages.

9. Exploring the relationship between hub gene expression and immunocytes and their marker genes

The ESTIMATE algorithm was applied to evaluate the tumor microenvironment scores. Then, the corresponding survival analysis was performed using the survminer R package. To evaluate the relationship between the proportion of 22 immune cell types and the hub gene, CIBERSORT, a newly developed deconvolution algorithm, was applied. Then, the correlation between TREM1 expression and the gene markers of the main immune cells was verified in TCGA.

10. Gene Set Enrichment Analysis and PPI network analysis of the hub gene Gene set enrichment analysis (GSEA) was used to analyze the immune-related KEGG pathways of the hub gene through the R package (19). To further investigate the role of the hub gene, we constructed the PPI networks using the String website (https://www.string-db.org/) and GeneMANIA (http://genemania.org/).

1. Identification of prognostic IRGs and irlncRNAs

We identified 1358 differentially expressed IRGs associated with KIRC using the limma package in the R language. Subsequently, the clinical data of KIRC and 1358 IRGs were analyzed and classified through WGCNA analysis using the R language

(Figure 1A). A total of 200 prognostic IRGs were obtained by univariate Cox analysis (p < 0.05). Then, we utilized co-expression analysis to obtain irlncRNAs that were coexpressed with IRGs (| r | > 0.6, p < 0.001). A total of 364 irlncRNAs were identified, of which 275 were distinguished as prognostic via univariate Cox analysis (p < 0.01).

2. Construction of the prognostic IRGs and irlncRNAs signature model

The previously acquired 200 IRGs and 275 irlncRNAs were used to construct the LASSO Cox regression model regarding the prognosis of KIRC (Figure 1B-1C). We integrated the expression profiles of 3 hub genes (AR, TREM1, PTX3) and 3 hub lncRNAs (HOTAIRM1, AC003092.1, DLGAP1-AS2) involved in model construction (Figure 2A). The risk score was calculated by multiplying the expression level of each gene or lncRNA and its corresponding coefficient. The 530 KIRC samples were divided into low- and high-risk groups based on the median risk score (Figure 2B-2D). KaplanMeier analysis showed that the long-term survival time of patients with KIRC in the high-risk group was shorter than that in the low (Figure 2E). ROC curve analysis showed that the AUC for 3-year survival was 0.718. (Figure 2F).

3. Model validation and clinical relationship assessment

Both univariate and multivariate Cox regression results showed that the tumor stage, tumor grade, and the risk score of this model were all associated with the prognosis of KIRC (Figure 2G). These results demonstrated that our model could be used as the independent prognostic factor for KIRC. The relationship between risk groups and clinicopathological characteristics noted that the risk score was correlated with survival status, tumor grade, and tumor stage but not with the age or gender of the patients (Table 1).

4. GO, KEGG, and PPI Analyses

We conducted differential gene expression analysis and obtained 503 DEGs, including 66 IRGs. Then, we performed GO and KEGG analysis of these DEGs (Figure 3A). To search for immune-related DEGs, we selected genes enriched via GO involved in the immune response, including B-cell activation, B-cell mediated immunity, T-cell activation, etc. Next, we constructed the PPI network for these selected immune-related DEGs using string and cytoscape software (Figure 3B). The number of nodes in the top 20 immune-related DRGs is shown in Figure 3C. Eventually, we selected a significant module from the MCODE plugin analysis that included the following genes: IL-6, CXCL1, CXCL13, CXCL8, CXCL2, CXCL6, PI3, NFKBIZ and TREM1 (Figure 3D).

5. Correlation between TREM1 expression and KIRC

We analyzed the clinical relationship between the nine immune-related DEGs and KIRC according to the ranking order obtained from the ENCODE results and selected the hub gene TREM1. The oncomine database analysis revealed that TREM1 was highly expressed in different types of cancer, including KIRC (Figure 4A). We further validated the relationship of TREM1 to KIRC using the TCGA database, and the results were similar to before (Figure 4B-4C). The TREM1 levels were gradually upregulated along with the tumor grades and stage 3 to stage 4 in KIRC (Figure 4D). Furthermore, the analysis of the clinical characteristics revealed that TREM1 was correlated with survival status, grade, and TNM stage in KIRC (Table 2). The survival analysis showed that KIRC patients with high TREM1 expression have a shorter survival time than those with low expression (Figure 4E). Univariate and multifactorial Cox regression results indicated that TREM1 is an independent prognostic factor for KIRC (Table 3).

6. Correlation of TREM1 with immune infiltration and their gene markers in

KIRC

The ESTIMATE results revealed that the presence of stromal cells and immune cells in the high-TREM1 expression group was more abundant than the group with the low expression (Figure 5A). The survival analysis showed a negative correlation between the immune score and survival time (Figure 5B). The CIBERSORT analysis results displayed higher contents of activated memory CD4+ T cells, monocytes, M0 and M2 macrophages, resting dendritic cells, activated resting mast cells, and neutrophils in the high-TREM1 expression group than in the low-TREM1 expression group. In contrast, a low expression of TREM1 yielded higher concentrations of CD8+ T cells, resting NK cells, activated NK cells, M1 macrophages, and resting mast cells (Figure 5C). In addition, the different abundances of the infiltrated immune cells between the high- and low-TREM1 expression groups were further confirmed through the correlation analysis conducted between the different marker subsets of immune cells and TREM1 (20). These findings validate that TREM1 is strongly linked with innate immunity since the major innate immune cell types such as dendritic cells, neutrophils, natural killer cells, and macrophages were strongly associated with TREM1 expression (Table 4) (21).

7. TREM-1 showed correlation IHC.

In the presence of TRME1, uniform cytoplasmic and membrane staining showed positive expression of TRME1. In benign tissues, TREM1 expression was very low, but high in tumor tissue areas. And TREM1 expression increased with increasing Fuhrman grade. OD values: Grade 3, vs Grade 1: 0.308vs0.199. (Figure 7).

8. Prediction of the function of TREM1

GSEA analysis showed that TREM1 was involved in various immune-related pathways, including B-cell receptor, JAK-STAT, NOD-like receptor, Toll-like receptor, and Tcell receptor signaling pathway (Figure 6A). Moreover, the string and GeneMANIA results showed that the function of TREM1 and its associated molecules (TYROBP, CD274, SFTPD, and VSIG4) were primarily related to the negative regulation of mononuclear cell, lymphocyte and leukocyte proliferation and the negative regulation of lymphocyte activation (Figure 6B-6C). These results indicate that TREM1 is closely related to immune infiltration.

TREM1, functions as an effective amplifier of the pro-inflammatory innate immune response, has a pivotal role in cancer progression (14,22). In this study, we constructed an immune-related prognostic model where it was evaluated that the overall survival time of the high-risk group was significantly shorter than the low-risk group (p < 0.001). Subsequently, we identified that one of the immune-related hub gene responsibles for this difference is TREM1. The expression of TREM1 is significantly increased in KIRC tissues than in adjacent normal tissues (p < 0.001) and is gradually upregulated with increasing pathologic stages and grades. Consistently, patients with higher expression of TREM1 tended to have a worse prognosis than those with lower expression (p = 0.005). The univariate and multivariate Cox regression analyses indicated that TREM1 was a high-risk independent prognostic factor in KIRC. Evidence from various studies demonstrated an association between TREM1 overexpression with the progression of several types of cancer, including thyroid carcinoma, colorectal carcinoma, and hepatocellular carcinoma (17,22,23). Therefore, TREM1 might be identified as a novel and potential biomarker that could be of interest for further research in KIRC.

The TME could affect the prognosis of patients in the primary location, as there is complicated dynamic crosstalk among neoplastic and non-neoplastic cells, including adjacent stromal cells and infiltrating immune cells. In addition, immune and stromal scores were significantly associated with the expression of TREM1. Our findings showed that only the group with a higher immune score had a poor survival prognosis. Previous studies have shown that macrophages, which play an essential role in modulating the tumor immune microenvironment, are generally divided into two categories: M1 macrophages of the classical activation pathway and M2 macrophages of the alternative activation pathway. In this study, we found that compared to M1 macrophages, the content of M2 macrophages was more abundant in the high TREM1 expression group. However, the functions of the two are different, with M1 macrophages inhibiting tumor progression and M2 macrophages having a tumorpromoting phenotype (24,25). Furthermore, Yoshihiro et al. revealed that TAMs, especially M2-polarized TAMs, contribute to the activation of STAT3, which can promote cancer cell survival, angiogenesis, and immunosuppression in the tumor microenvironment (26). The content of Tregs is also increased in high TREM1 group, which could produce immunosuppressive cytokines and immune-inhibitory receptors to disturb the activation of anti-tumor T cell responses, leading to a worse prognosis.

We further found that TREM1 levels in KIRC correlate with T cell exhaustion markers (PD-1 and CTLA-4). Hence, these data revealed that TREM1 might play a critical role in regulating the tumor microenvironment in KIRC that may influence the prognosis of patients.

To explore the underlying molecular mechanism of TREM1, we further conducted GSEA and constructed the PPI network. GSEA results revealed that the pathways involving TREM1 were mainly enriched in the B-cell receptor, JAK/STAT, NOD-like receptor, T-cell receptor, and Toll-like receptor signaling pathways. These pathways have previously been reported to be involved in the immune response in KIRC (27-29). As reported, the effect of inhibiting the JAK/STAT signaling pathway on the suppression of immune activity appears promising and could become a strategy to prevent tumor progression (30). The top 10 related genes that may have a similar function in KIRC were predicted by constructing a string network. A recent study uncovered TREM2/APOE/C1Q-positive macrophage infiltration as a potential prognostic biomarker and a candidate therapeutic target (31). Further research revealed that TYROBP, HMGB1, and TLR4 were also significantly associated with the progression of KIRC (32).

However, this research had a few limitations. First, although we confirmed that TREM1 was highly expressed in tumor tissue compared with benign tissue, and the expression was positively correlated with Fuhrman grade, by immunohistochemistry experiment. However, due to the limitation of the small number of tumor tissue sections, the expression and distribution of TREM1 and the invasion of immune cells were not simultaneously investigated.. Second, the bioinformatics analysis needs to be supported by future biological experiments that involve in vivo and in vitro methods. In summary, these results indicate that TREM1 may have the potential to drive the progression of KIRC. Therefore, this gene may serve as an independent prognostic predictor and be a candidate novel biomarker for KIRC diagnosis.

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. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).

Consent for publication

Not applicable.

Data Availability

All data generated or analysed during this study are included in this published article.

Conflicts of Interest

All authors have completed the ICMJE uniform disclosure form (Available at http://dx.doi.org/10.1136/bmjopen-2020-038887). The authors have no conflicts of interest to declare.

Acknowledgements

Funding: No funding was used in this study.Thanks to the open TCGA public database.

Contributions:

Conception and design: Y Pu, D Cai; (II) Administrative support: G Wu, Q Guo, S Liu; (III) Research implementation: Y Pu, D Cai, L Jin, F Xu, E Ye, L Wu; (IV) Collection and assembly: Y Pu, D Cai; (V) Data analysis and interpretation: Y Pu, D Cai, L Jin; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.

Author details

¹Yaling Pu,School of Medicine,Shao Xing University,Department of Neurology, Taizhou Hospital of Zhejiang Province, No.150 Ximen Street, Taizhou,Linhai 317000, Zhejiang, China. ^5*Gang Wu, Department of Pharmacy, Taizhou Hospital of Zhejiang Province affiliated to Wenzhou Medical University, No.150 Ximen Street, Taizhou,Linhai 317000, Zhejiang, China.

Footnote

Reporting Checklist: The authors have completed the TRIPOD reporting checklist (Available at http://dx.doi.org/10.7326/M14-0697)

Peer Review File: Available at http://dx.doi.org/10.21037/ tau-20-1348

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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TREM1 as a Potential Prognostic Biomarker Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma

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