Immune-related basement membrane genes predict the immunotherapy and prognosis of ccRCC patients

doi:10.21203/rs.3.rs-2602234/v1

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Immune-related basement membrane genes predict the immunotherapy and prognosis of ccRCC patients

https://doi.org/10.21203/rs.3.rs-2602234/v1

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Background

Basement membrane (BM) genes are an important factor in the process of clear cell renal cell carcinoma (ccRCC). Thus, identifying BM genes with prognostic values in ccRCC is critical.

Methods

The samples from TCGA were separated randomly into 2 cohorts, the training cohort, and the validation cohort. Univariate and multivariate Cox regression analyses were applied to identify prognostic BM genes. Then, nomogram was applied to predict prognosis at different clinicopathological stages and risk scores. GO and KEGG analyses were applied to identify the differentially expressed genes. Moreover, CIBERSORT and ESTIMATE scores were calculated between the high- and low-risk cohort.

Results

A prognostic risk model of four BM genes, including ADAMTS14, COL7A1, HSPG2, and TIMP3, was constructed. There were also significant differences in survival time between the high- and low-risk cohort for the validation cohort and the entire cohort. The risk model was validated as a new independent prognostic factor for ccRCC by univariate and multivariate Cox regression together with clinicopathological characteristics. The model can also analyze the possibility of immune escape and response to immunotherapy in ccRCC patients. In addition, the results of a pan-cancer analysis showed that these four model genes were associated with immune-related genes in a variety of cancers.

Conclusion

The signature of four BM genes had a significant prognostic value for ccRCC. They may be promising targets for therapy, especially immune therapy.

Basement membrane

ccRCC

Predict

Prognosis

Immune therapy

Renal cell carcinoma (RCC) is the deadliest tumor of the urinary system. In recent decades, the incidence of RCC increase rapidly, among which clear cell RCC (ccRCC) is the most common one, accounting for 75–80% of cases [1–3]. ccRCC is mainly derived from renal tubular epithelial adenocarcinoma. Surgery is the main treatment for ccRCC, but the recurrence rate after radical surgery is still more than 40%. Patients with distant metastasis have a high mortality rate [1, 2, 4]. Immunotherapy and/or molecular targeted therapy can be used in patients with advanced stage [5]. Therefore, it is essential to find new immunological features, and therapeutic targets to improve the diagnosis and prognosis of ccRCC patients.

Basement membrane (BM) is an ancient and special extracellular matrix, which locates on the basal surface of epithelial and endothelial tissues [6]. Functionally, BM is very important for providing physical and biochemical clues to the overlying cells, which can shape the tissue into the correct size and shape. In addition, there is much evidence that BM is involved in the regulation of various signaling pathways and life activities, which are closely related to the development of tumors and other diseases, such as embryogenesis, homeostasis, and immunity, etc. [7–15]. Recently, a comprehensive network of 222 human gene proteins and their animal homologs localized to BM was defined, which provided us with new opportunities and directions for studying BM-related diseases.

As far as we know, the achievement of renal physiological functions depends on the normal structure and function of BM. In addition, many renal diseases are associated with malfunction or destruction of BM [16]. Therefore, we hypothesize that the occurrence, immunophenotype, and prognosis of ccRCC may also be related to BM. Based on this hypothesis, we constructed a BM-related signature using RNA-seq data of TCGA and BM genes. We then validated our model in terms of immunotherapy and prognosis, respectively. We finally determined that BM-related signature had a very good prognostic efficacy.

2.1 The expression levels of BM genes

We extracted the gene expression data of ccRCC from The Cancer Genome Atlas (TCGA) database, including 72 normal tissue samples and 539 ccRCC tissue samples (Supplementary Table S1). BM genes were acquired from the literature (Supplementary Table S2) [9]. Then, we extracted the expression data of BM-related genes from the samples and analyzed the expression characteristics of these genes using the Limma package (Supplementary Table S3).

2.2 The relationship between BM genes

We investigated the network of 222 BM genes in the STRING database to establish a protein-protein interaction (PPI) network of BM genes.

2.3 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses

GO and KEGG approaches were used to estimate the biological functions and signal transduction mechanisms of multiple genes [17].

2.4 Construction of a risk model and nomogram associated with BM genes

The expression information of 221 BM genes in ccRCC samples (due to one missing gene expression data) was matched with their clinical survival data. Patients were randomly divided into the training cohort (n = 265) and validation cohort (n = 266). The training cohort was used to construct a prognosis signature of BM genes. Firstly, a univariate Cox analysis was performed to determine the prognostic value of the BM genes. Secondly, based on the candidate BM genes with p < 0.01 in univariate Cox screening analysis, the “glmnet” package was used to construct a least absolute shrinkage and selection operator (LASSO) regression model [18]. Finally, the collected BM genes were introduced into a multivariate Cox model to obtain the hazard ratio (HR) and regression coefficient for each BM gene used in the construction of the BM genes’ risk signature using the “survival” package. The risk score of BM genes was calculated using the following formula:

$$Riskscore = \sum {Exp}_{BMi}\times {Coef}_{BMi}$$

Correlation analysis was conducted to explore the relationship between risk score and the expression levels of model factors, including ADAMTS14, COL7A1, HSPG2, and TIMP3. According to the risk score, patients were divided into high- and low-risk groups according to the median risk score. Subsequently, Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curve analysis were performed using the "survival" package to analyze the prognostic factors in overall survival (OS). The validation cohort and the entire cohort were used to verify the prognostic signature based on the training cohort.

2.5 Clinical values of a risk model associated with BM genes

Log-rank test was used to analyze the relationship between clinicopathological characteristics of ccRCC patients and high- and low-risk groups. To verify the clinical value of the risk model, univariate and multivariate Cox regression analyses were applied to identify the relationship between age, gender, grade, clinical stage, risk score values, and OS in ccRCC patients.

2.6 The relationship between the constructed model and immune infiltrating cells based on BM genes

CIBERSORT and ESTIMATE methods were used to detect the expression level of immune cell infiltration in 539 ccRCC tissues [19, 20]. The expression level of immune cells in 539 ccRCC patients were divided into two groups: group 1 and group 2. A T-test was used to determine whether there were differences in the expression level of immune cells between the two groups based on the BM genes. In addition, correlation analysis was performed to determine the relationship between ccRCC risk score and immune cell infiltration.

2.7 The relationship between the constructed BM genes risk model and immune cell markers

The expression level of immune cell markers in 539 ccRCC tissues from the TCGA database was obtained. The risk score was combined with genetic data of immune cell markers of ccRCC patients, and t-test was used to explore the expression level of immune cell markers between the two groups. Similarly, correlation analysis was performed to explore the relationship between risk score and immune cell markers in ccRCC tissues.

2.8 Pan-cancer analysis of the model genes

Pan-cancer analysis of model genes was conducted using online tools (http://sangerbox.com/home.html) to reveal the expression features and relationships between model genes, immune regulatory genes and immune checkpoint genes.

2.9 Statistical analysis

Limma package was used to detect the expression level of BM genes and BMs regulated gene subgroup-related genes in ccRCC tissues. Cox analysis, LASSO analysis, and K-M survival analysis were used to identify BMs regulated genes associated with the prognosis of ccRCC patients. Correlation analysis was performed to determine the relationship between the BM gene-based model and immune cell infiltration (p < 0.05).

3.1 Identification of expression level of BM genes in ccRCC tissues

BM genes with differential expression levels between normal and ccRCC tissues were screened. The volcano plot showed a significant difference between the two groups. Among them, 151 (68%) BM genes showed statistically differences with a threshold of p < 0.05 and |logFC|>1.5 (Fig. 1A).

3.2 The mechanism and function of BM genes

GO and KEGG analyses were performed to explore the function and signaling pathway of BM genes. GO enrichment analysis revealed that BM genes were involved in extracellular matrix organization, extracellular structure organization, external encapsulating structure organization, cell-substrate adhesion, cell-matrix adhesion, integrin-mediated signaling pathway, gastrulation, formation of primary germ layer, endodermal cell differentiation, and endoderm formation (Fig. 1B). KEGG analysis showed that BM genes participated in ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, human papillomavirus infection, small cell lung cancer, protein digestion and absorption, hypertrophic cardiomyopathy, dilated cardiomyopathy, arrhythmogenic right ventricular cardiomyopathy, and amoebiasis, et al (Fig. 1C). Moreover, we investigated the network of BM genes in STRING database and revealed the relationship between BM genes from the protein-protein interaction (PPI) network (Fig. 1D).

3.3 Construction of BM genes-related prognostic signature

Univariate Cox regression analysis of the training cohort showed that 103 BM genes were associated with the prognosis of ccRCC. Subsequently, LASSO and multivariate Cox regression analysis were performed to identify four BM genes (ADAMTS14, COL7A1, HSPG2, and TIMP3) (Fig. 2A-C). A BM genes-based signature was established according to the following risk score formula:

$$Riskscore =0.38\times ADAMTS14+0.33\times COL7A1-0.28\times HSPG2-0.30\times TIMP3$$

3.4 Assessment of BM genes-related prognostic signature

ADAMTS14 and COL7A1 were risk factors for ccRCC, while HSPG2 and TIMP3 were protective factors. The formula was applied to calculate their risk scores, which showed a median risk score of 0.854. Accordingly, the training cohort was divided into high-risk group (n = 133) and low-risk group (n = 133) according to the median risk score (Fig. 3A). With the increase of risk score, the survival time was gradually shortened, and the mortality rate was gradually increased (Fig. 3B). Finally, the expression levels of OS-related BM genes were presented in the form of heat map (Fig. 3C). Log-rank test showed that there was a significant difference between the two groups (Fig. 3D). The time-dependent ROC curve showed that OS-related BM genes had a predictive effect on prognosis, and the area under the curve (AUC) was 0.79 (Fig. 3E). Subsequently, we validated the prognostic BM genes signature in the validation cohort and the entire cohort. The results showed significant differences between high- and low-risk groups in both the validation cohort and the entire cohort. Moreover, the AUC of the validation cohort and the entire cohort were 0.68 and 0.74, respectively, indicating that BM genes’ prognostic signature were robust (Fig. 3F-O).

3.5 Clinical values of BM genes related prognostic signature

Univariate Cox regression showed that the risk score was a risk factor affecting the prognosis of ccRCC patients (HR:1.128 ~ 1.216, p < 0.001), and a high-risk score predicted a shorter OS time. Furthermore, age (HR:1.018 ~ 1.045, p < 0.001), stage (HR:1.635 ~ 2.129, p < 0.001), and grade (HR:1.877 ~ 2.829, p < 0.001) were also closely related to the prognosis of ccRCC patients (Fig. 4A). Although clinical characteristics were controlled, risk score remained an independent risk factor (HR:1.061 ~ 1.155, p < 0.001) (Fig. 4B). Moreover, ROC analysis showed that the prognostic value of risk score (0.739) was higher than that of age (0.654), gender (0.505), and grade (0.720), but slightly lower than that of stage (0.801) (Fig. 4C). In addition, the time-dependent ROC curve showed that AUC of the entire cohort at 1-, 3-, and 5-year were 0.739, 0.713, and 0.728, respectively, showing high robustness (Fig. 4D). Similarly, when the entire cohort was subdivided according to age, gender, stage, and grade, all the high-risk groups had lower OS than the low-risk groups (Fig. 4E-L). In all, these data illustrated that BM genes-related prognostic signature was strongly associated with the prognosis of ccRCC patients.

3.6 Construction and assessment of nomogram, as well as enrichment analysis of the model-related different expression genes (DEGs)

Nomogram is a common prognostic visualization tool in oncology that allows quantification of patient survival based on an inclusive index score. According to multivariate Cox regression, age, stage, and risk score were used as indicators for the construction of nomogram. The total scores were obtained by age, stage, risk score, and individual indicator score that predicted 1-, 3-, and 5-year survival probabilities (Fig. 5A). In addition, the calibration curves showed consistent predicted survival times at 1-, 3-, and 5-year compared to the reference line (Fig. 5B).

We further screened DEGs between the high- and low-risk groups. The screening threshold was |log FC|>1 and p < 0.05. As a result, a gene set consisting of 615 genes was obtained. Based on this gene set, GO and KEGG analyses on DEGs were applied to reveal the functions and signaling pathways. The results of GO analysis showed that the model-related DEGs were involved in humoral immune response, complement activation (classical pathway), regulation of complement activation, complement activation, humoral immune response mediated by circulating immunoglobulin, regulation of humoral immune response, enzyme inhibitor activity, immunoglobulin complex, blood microparticle, collagen-containing extracellular matrix, external side of plasma membrane, apical plasma membrane, apical part of cell, antigen binding, glycosaminoglycan binding, endopeptidase inhibitor activity, and protein-lipid complex binding (Fig. 5C). KEGG analysis demonstrated that DEGs were involved in signaling pathways including complement and coagulation cascades, cholesterol metabolism, fat digestion and absorption, mineral absorption, rheumatoid arthritis, and PPAR signaling pathway (Fig. 5D).

3.7 Immune cell infiltration in ccRCC correlates with risk model of BM genes

From the above enrichment results, we noted that BM gene model-related DEGs were involved in multiple immune processes. So we further analyzed the immune characteristics of high- and low-risk groups. There were significant differences between high- and low-risk groups in the expression level of B cells naïve, B cells memory, plasma cells, T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, T cells regulatory (Tregs), T cells gamma delta, NK cells resting, monocytes, macrophages M0, macrophages M1, dendritic cells resting, and mast cells resting, which were completely consistent with our expected (Fig. 6A). Correlation analysis showed that risk score was significantly correlated with the level of the stromal score, macrophages M1, NK cells resting, T cells CD4 memory resting, B cells naïve, neutrophils, macrophages M0, T cells regulatory, T cells CD8, mast cells resting, monocytes, T cells follicular helper, plasma cells, macrophages M2, NK cells activated, T cells CD4 memory activated, and B cells memory (Fig. 6B).

3.8 Immune function associated with the risk model of BM genes

According to the expression characteristics of the low- and high-risk group, there were significant differences in type II IFN response, T cell co-inhibition, T cell co-stimulation, and inflammation-promoting (Fig. 6C).

3.9 Immune escape and immunotherapy prediction

Jiang et al. [21] designed a computational architecture to calculate the possibility of tumor immune escape, called tumor immune dysfunction and exclusion (TIDE). Researchers can calculate TIDE scores on the website: http://tide.dfci.harvard.edu/ with cancer expression feature data. The higher the scores are, the greater the likelihood of cancer immune escapes. The TIDE scores of the low- and high-risk group were calculated, and the result revealed that the high-risk group had a higher TIDE score, suggesting that the high-risk group had a higher possibility of immune escape and a poorer response to immunotherapy (Fig. 6D).

3.10 Analysis of the four genes associated with a pan-cancer risk model

We examined the expression differences of ADAMTS14, COL7A1, HSPG2, and TIMP3 in 43 cancers compared with the corresponding normal tissues and found that they were differentially expressed in most cancers (Fig. 7A-D). We then analyzed the association between the expression of these four genes in a variety of cancers and the expression of immune regulatory genes and immune checkpoint genes. The results showed that these four genes were associated with the expression of immune regulatory genes and immune checkpoint genes in most of 43 cancers, suggesting that these four genes affected cancer progression by interfering with immune regulation and immune checkpoint (Fig. 7E-L).

Many studies have shown that the development of ccRCC is accompanied by changes in gene expression [22–26]. For example, Wang et al. [22] discovered that the expression of NEK2 was significantly upregulated in ccRCC tissues, which was associated with poor prognosis of patients. Feng et al. [23] reported that overexpression of PFKFB4 mediated sunitinib resistance in ccRCC patients by promoting the pentose phosphate pathway. Blajan et al. [24] demonstrated that ABCB8 was overexpressed in ccRCC. Zhang et al. [25] uncovered that overexpression of ceruloplasmin was associated with carcinogenic pathways and poorer survival rates in ccRCC. Gao et al. [26] revealed that ADAM12 overexpression was associated with prognosis and immune cell infiltration in ccRCC.

In addition, numerous research demonstrated that BMs are also closely related to tumor development [27–30]. For example, BMs proteins were found to influence cancer progression by mediating EMT [27]. In addition, Chang et al. [28] indicated that BMs could act as a "blocker" in tumorigenesis by limiting tumor invasion and metastasis. Rodriguez-Teja et al. [29] discovered that BMs could be used as a target to modify tumor aggressiveness. Liu et al. [30] reported that BMs can promote tumorigenesis by decreasing the activation of T cells.

At present, targeted therapy and immunotherapy have become effective treatments for ccRCC [31–35]. Therefore, the impact of immune checkpoints and immune microenvironment on tumor prognosis is becoming more and more obvious. Immune checkpoint inhibitors have been shown to improve survival in ccRCC [32]. The relationship between immunotherapy and immune microenvironment has been well revealed [36, 37]. In this study, it was found that the expression levels of B cells naïve, B memory cells, plasma cells, T CD4 memory resting cells, T CD4 memory activated cells, T follicular helper cells, T regulatory cells, NK cells resting, monocytes, macrophages M1, dendritic cells resting, mast cells resting were found significantly different in the risk model constructed by ADAMTS14, COL7A1, HSPG2, and TIMP3. The risk score was significantly correlated with levels of the neutrophils, resting mast cells, macrophage M0, macrophage M1, macrophage M2, resting NK cells, activated NK cells, resting memory CD4 T cells, activated memory CD4 T cells, CD8 T cells, regulatory T cells, monocytes, follicular helper T cells, plasma cells, memory B cells, and naïve B cells. This suggests that this risk model constructed by BMs is related to ccRCC immune cell infiltration.

This study revealed the involvement of BMs and constructed a risk model for ccRCC using data from TCGA database. In the future, this model should be validated by a large number of clinical applications and cell experiments. BMs are generally involved in the occurrence and development of ccRCC. ADAMTS14, COL7A1, HSPG2, and TIMP3 are important prognostic factors in ccRCC patients. High risk scores are associated with poor prognosis and is an independent risk factor for poor prognosis in ccRCC patients. The risk score is correlated with multiple immune-related markers, suggesting that BMs affect prognosis of ccRCC patients by affecting the immune infiltration process, indicating a new direction for the prognosis of ccRCC patients.

In addition, we found that model-associated genes were functionally related to tumor immunity. Tumor immunity plays an important role in many cancers, so we performed a pan-cancer analysis of these four genes. The results showed that these four genes were differentially expressed in various cancers not only from the normal group but also from the expression of immunoregulatory genes and immune checkpoint genes.

In all, ccRCC patients have a poor prognosis, affecting the health of tens of millions of people every year. Many researchers have focused on improving patient outcomes. Our study elucidated how BM genes were related to the prognosis of ccRCC. It can be better used as an independent factor to predict the prognosis of cRCC patients, indicating a new direction for immunotherapy targeting ccRCC in the future.

ccRCC: clear cell renal cell cancer; BM: basement membrane; TCGA: The Cancer Genome Atlas database; PPI: protein-protein interaction; DEGs: differentially expressed genes; KM analysis: Kaplan Meier analysis; LASSO: least absolute shrinkage and selection operator; HR: hazard ratio; ROC curve: receiver operating characteristic curve; OS: overall survival; AUC: area under curve; GO :gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; TIDE: tumor immune escape called tumor immune dysfunction and exclusion;

Ethical Approval and Consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of supporting data

The data used in database can be found on TCGA website.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by National Natural Science Foundation of China (Grant No. 82072835) to K Wang, Key Research and Development Joint Program of Liaoning Province (Grant No. 2020JH 2/10300139) to K Wang, Natural Science Foundation of Liaoning Province (Grant No. 2019-MS-360) to K Wang, Shenyang Science and Technology Bureau Plan Projects (Grant No. 20-205-4-076) to K Wang, 345 Talent Project of Shengjing Hospital of China Medical University to K Wang, and Outstanding Scientific Fund of Shengjing Hospital to K Wang.

Authors’ contributions

KW and XC conceived the manuscript; JH, CZ, and YW reviewed the information. YW arranged the format of the figures. JH wrote the manuscript. CZ, YW, XW, XC, and KW critically reviewed the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable.

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Table 1 is available in the Supplementary Files section.

No competing interests reported.

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Immune-related basement membrane genes predict the immunotherapy and prognosis of ccRCC patients

Status:

Version 1

Abstract

Background

Methods

Results

Conclusion

Figures

1. Introduction

2. Materials And Methods

2.1 The expression levels of BM genes

2.2 The relationship between BM genes

2.3 Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses

2.4 Construction of a risk model and nomogram associated with BM genes

2.5 Clinical values of a risk model associated with BM genes

2.6 The relationship between the constructed model and immune infiltrating cells based on BM genes

2.7 The relationship between the constructed BM genes risk model and immune cell markers

2.8 Pan-cancer analysis of the model genes

2.9 Statistical analysis

3. Results

3.1 Identification of expression level of BM genes in ccRCC tissues

3.2 The mechanism and function of BM genes

3.3 Construction of BM genes-related prognostic signature

3.4 Assessment of BM genes-related prognostic signature

3.5 Clinical values of BM genes related prognostic signature

3.7 Immune cell infiltration in ccRCC correlates with risk model of BM genes

3.8 Immune function associated with the risk model of BM genes

3.9 Immune escape and immunotherapy prediction

3.10 Analysis of the four genes associated with a pan-cancer risk model

4. Discussion

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

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