Construction of a risk model for predicting colorectal cancer prognosis and immunotherapy based on angiogenesis-related gene

doi:10.21203/rs.3.rs-3342290/v1

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Construction of a risk model for predicting colorectal cancer prognosis and immunotherapy based on angiogenesis-related gene

https://doi.org/10.21203/rs.3.rs-3342290/v1

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Background

Angiogenesis is closely associated with the prognosis and immune microenvironment of patients with colorectal cancer (CRC), which is regulated by numerous angiogenesis related genes (ARGs). This study aimed to construct a prognostic model based on ARGs to provide effective guidance for the prognosis and immunotherapy response of CRC patients.

Methods

CRC data for CRC patients was extracted from the public data to analyze the difference of immune cell infiltration abundance between Cluster 1 (high ARGs, C1) and Cluster 2 (low ARGs, C2).

Results

The results showed that C2 had a lower abundance of immune cell infiltration, especially CD4 + T cells. Furthermore, a model based on differential expression genes (DEGs) of C1 and C2 was established. Validation indicated that the ARGs-DEGs model could be well applied to predict the survival of CRC patients, thus determining prognostic risk stratification. The univariate and multivariate COX regression analysis of risk score combined with multiple clinical features showed that risk score of ARGs-DEGs model was an independent risk factor for CRC patients. Further construction of Norman chart combined with multiple clinical features could more accurately predict the prognosis of CRC patients. In addition, our further study demonstrated that intercellular connectivity and extracellular matrix remodeling signaling pathways may be potential mechanisms of the ARGs-DEGs model.

Conclusions

From our findings that the distribution of immune checkpoints was significantly obvious differences between the high-risk and low-risk cohorts, we conclude that the low-risk cohorts had a potentially better response to immunotherapy.

Colorectal cancer

Angiogenesis

Risk model

Norman chart

Immunotherap

Colorectal cancer (CRC), which originates from the colorectal epithelium, is one of the most common gastrointestinal malignancies in the world, resulting in a large number of patient deaths each year[1]. Traditional CRC treatment strategies, including surgery excision, chemotherapy, radiotherapy and targeted drugs, have made significant advances over the past decades. These surprising advances have significantly improved outcomes for CRC patients. However, for patients with advanced CRC, the above therapeutic options are unable to further improve the prognosis[2]. TNM staging system is currently a widely accepted indicator for the prognosis of CRC, but its poor specificity and accuracy greatly limit its application[3]. Therefore, it is crucial to explore more sensitive and specific markers to provide guidance for the prognosis of CRC.

Tumor angiogenesis is an extremely complex process, which is regulated by a variety of pro-angiogenic factors and angiogenic inhibitors, including vascular endothelial growth factor (VEGF), platelet-derived growth factor (PDGF), fibroblast growth factor (FGF), angiostatin and endostatin, and their dynamic interactions regulate tumor angiogenesis[4–9]. Angiogenic factors derive from a wide range of sources and are mainly secreted by tumors or interstitial tissues. Tumor vessels are essential for the survival of malignant tumors, including CRC, which not only provides nutrition for them, but also provides a pathway for metastasis[10]. Therefore, anti-angiogenesis therapy is considered to be one of the promising methods for inhibiting tumor progression. With the rapid development of the new generation of transcriptional sequencing, the research on the pathogenesis of CRC has been further expanded to the molecular level. Tumor molecular typing based on various sequencing results will facilitate the development of individualized CRC treatment options and provide new research ideas for overcoming tumorss[11]. Accumulating of studies have strongly confirmed that the role of angiogenesis-related genes (ARGs) in the development of solid tumor, including CRCr[12–14]. Zou et al. constructed a model based on ARGs classification in skin cutaneous melanoma (SKCM) to help stratify patients and predict prognosis and immunotherapy response[15]. To our knowledge, no studies have been reported on the prognostic features of CRC from the perspective of ARGs.

The immune checkpoint inhibitors (ICIs) based on many immune checkpoints have been widely used in CRC treatment and achieved good results in recent decade, which makes immunotherapy gain great attention[16]. However, in clinical applications, the number of patients who benefit from immunotherapy is small, which greatly limits the application of ICIs[17]. Despite molecular research on CRC has been advanced, precise prognostic biomarkers and predictors of immunotherapy response are still lacking. Currently, PD-L1, tumor mutation load (TMB), microsatellite instability, or DNA mismatch repair defects (MSI/dMMR) are recognized indicator for predicting the response of ICIs in a variety of tumors, including CRC[18–20]. However, its reliability has been questioned more and more due to its insufficient accuracy and validity. Therefore, there is an extreme need to explore new biomarkers that can effectively assess the response to immunotherapy. In view of previous studies, angiogenesis is involved in the migration of immune cells to regulate tumor immune microenvironment (TIM)[21]. We firmly believe that the study of ARGs of CRC can provide assist for the treatment and prognostic management of CRC.

In this study, we identified and validated the ARGs in CRC and their relevance to patient prognosis and TIM. Then, we established an ARG-based prognostic model in CRC to provide guidance for the supervision of patient prognosis and the implementation of individualized treatment programs.

Data Acquisition

Transcriptome expression data and corresponding clinical information of CRC patients were downloaded from the TCGA[22]. A total of 615 samples were collected and cases with incomplete clinical data were excluded. Finally, a total of 544 patients were enrolled in the follow-up study.

Procedure of Clustering Analysis

First, we conducted univariate Cox analysis to screen ARGs and obtain a series of gene sets. Then, the unsupervised non-negative matrix decomposition algorithm was used to cluster the combined gene sets of 544 CRC patients, so as to obtain the optimal number of clusters.

GSVA and Evaluation of Tumor-Immune Microenvironment

To compare the differences in activated signaling pathways between C1 and C2 clusters, we performed gene set variation analysis (GSVA) [23]. We applied the CIBERSORT (https://cibersortx.stanford.edu/) algorithm to estimate infiltration levels of 22 different immune cells between C1 and C2 patients[24, 25].

Construction of Prognostic Model

We developed a prognostic model via LASSO regression analysis based on the differential expression genes (DEGs) of C1 and C2 [26]. The formula for the risk score model was established as follows: risk score = ∑(βi∗Expi).

Assessment of the Immune Landscape of CRC

In this study, we analyzed the specific gene expression profile of immune and stromal cells in CRC tissue via the R package "estimation". Stromal and Immune score were performed by calculating purity scores and tumor microenvironmental cell infiltration by the ESTIMATE algorithm[27]. Then, the proportion of 16 types of infiltrating immune cells was calculated by the CIBERSORT algorithm based on normalized gene expression data.

Evaluating the predictive efficacy of risk scores for immunotherapy

The Immunophenotype Score (IPS) is a superior predictor of response to ICIs that quantifies the determinants of tumor immunogenicity and characterizes the characteristic immune landscape and cancer antigen within the tumor[28].

Statistical Analysis

R software (version 3.6.3) was conducted for calculation and statistical analysis in the present study. For quantitative data, the Student's t-test was used for statistical significance of normally distributed variables. For comparison of survival differences, we applied K-M curve and Log-rank test. p value less than 0.05 was considered statistically significant.

Identification of Angiogenesis Gene Subtypes in Colorectal Cancer

First, we inputted angiogenesis, neovascularization and vasculogenesis respectively into the GeneCards website to obtain different three gene sets. Then, these gene sets were intersected and plotted in a Venn diagram to obtain a total of 260 genes associated with angiogenesis (Fig. 1A). It is well known that angiogene-related genes (ARGs) are key factors in tumor angiogenesis[29]. Based on the above hypothesis, we stratified samples with qualitatively different CRC based on the expression of 260 angiogenesis genes using consensus clustering analysis of the NMF algorithm (Supplementary Fig. 1). As shown in Fig. 1B, we identified two different clusters of modified patterns, including 314 cases in C1 and 229 cases in C2. The further survival analysis implied that C2 had a better prognosis than C1 (Fig. 1C). Finally, we used heat maps to show the relationship between ARGs and clinicopathological features in two typing patterns (Fig. 1D).

Analysis of GSVA and immune function in angiogenesis-related genotyping

To preliminarily unveil the biomolecular mechanism of two different patterns of angiogenesis, we performed GSVA enrichment analyses in patients with C1 and C2. As shown in Fig. 2A, C1 patients showed significant enrichment in the intercellular junction pathway, such as VASCULAR SMOOTH MUSCLE CONTRACTION and FOCAL ADHESION. However, C2 patients presented significant enrichment in metabolism-related pathways, such as AMINOACYL TRNA BIOSYNTHESIS and LYSINE DEGRADATION. Further analysis of the difference in immunoinfiltration between the patients of different pattern cluster demonstrated that C2 patients showed high infiltration abundance of effector memory CD4 + T cells and activated CD4 + T cells, while C1 patients showed higher infiltration of M0 macrophages. (Fig. 2B, C).

Construction and Evaluation of ARGs-DEGs risk core model

The above data showed that CRC classification by ARGs was significantly different in survival. Therefore, we further established a risk model for predicting CRC prognosis based on the two subtypes. As shown in supplementary Table 1, through differential expression genes (DEGs) analysis, we obtained a gene set containing 1735 genes. Then, univariate Cox regression analysis of 1735 ARGs-DEGs was performed, and 38 genes significant associated with the prognosis of CRC were obtained (Supplementary Fig. 2). Furthermore, we applied LASSO regression analysis based on the 38 ARGs-DEGs and constructed a prognostic model contained 13 genes, which including PRKAR2A, CPT2, CALB2, IRF7, ARL8A, ENO2, DPP7, MPC1, CTNNA1, INHBB, SEZ6L2, DHRS7 and GSR (Supplementary Fig. 3A-B). To verify the accuracy and effectiveness of ARGs-DEGs model, all CRC patients were randomly divided into a training cohort (n = 383) and a validation cohort (n = 160) at 7:3. In the training CRC cohort, the median risk score divides CRC patients into low risk and high risk cohorts (Fig. 3A-B). The expression of 13 genes in the gene signature in the high-risk and low-risk groups was visualized as a heat map (Fig. 3C). Kaplan-Meier curve showed that CRC patients in the low-risk group had a better prognosis (Fig. 3D). In training cohort, the values of the 1-year, 3-year and 5-year ROC curves were 0.685, 0.696 and 0.773, respectively (Fig. 3E). In the validation cohort, the accuracy and validity of the ARGs-DEGs model was almost consistent to the training cohort (Supplementary Fig. 4A-E). These data provided strong evidence for the accuracy and effectiveness of ARGs-DEGs model.

Construction and validation of the nomogram

The ARGs-DEGs model demonstrated accurate prognostic performance for CRC, and we intended to further expand the clinical value. Univariate and multiariate Cox regression analyses revealed that the riskscore of ARGs-DEGs model is an independent risk factor for CRC (Fig. 4A-B). In order to better predict the survival of patients with colorectal cancer, we further constructed a norman chart with various clinical features and risk scores (Fig. 4C). Further analysis revealed that the Norman chart was more accurate in evaluating the prognosis of CRC patients, especially at 1, 3 and 5 years. (Fig. 4D).

Pathway enrichment analysis of ARGs-DEGs model

The ARGs-DEGs model presented excellent predictive performance for the prognosis of CRC, and we intended to preliminarily explore its underlying mechanism. According to the stratification of risk scores, KEGG and HALLMARKER analyses were carried out for TCGA-CRC patients by GESA. In the KEGG dataset, the signal pathways enriched in the high-risk group were mainly intercellular junctions, extracellular matrix receptors and adhesion patch junction signaling pathways, and the intracellular metabolism-related pathways were enriched in the low-risk group (Fig. 5A, B). In the HALLMARK dataset, the signal pathways enriched in the high-risk group were mainly epithelial mesenchymal transition and inflammatory response pathways, and the cell cycle and oxidative phosphorylation-related pathways were mainly enriched in the low-risk group (Fig. 5C, D).

Estimation of the association of tumor immune profile with the ARGs-DEGs model

Previous reports have confirmed the close relationship between tumor vessels and TIM, we further evaluated the relationship between ARGs-DEGs model and immune profile in CRC[30]. As shown in Fig. 6A-C, Immunescore, Stromalscore and Estimatescores were significantly lower in the high-risk group compared to the low-risk group. Interestingly, Tumorpurity was significantly higher in the high-risk group (Fig. 6A-D). In addition, CIBERSORT was applied to evaluate the difference in immune cell infiltration in different risk groups, and we found that the proportions of T cells CD4 naive, T cells CD4 memory resting, T cells CD4 memory activated, T cells follicular helper, Dendritic cells resting, Dendritic cells activated and Eosinophils were higher in the low-risk group. However, the proportions of Tregs and Macrophages were higher in the high-risk group (Fig. 6E, F). To clarify the correlation between risk scores and immune checkpoints, we conducted correlation analysis of risk scores with 12 immune checkpoints (Fig. 7A). The results demonstrated that the riskscore is positively correlated with PDCD1, HAVCR2 and VSIR, while CD226 and CD96 were up-regulated in the low-risk group (Fig. 7B-M).

Immune escape capacity and immunotherapy efficacy of CRC predicted by risk score

ICIs, represented by CTLA-4/PD-1 inhibitors, undoubtedly bring a great breakthrough for anti-tumor therapy[31]. Traditionally, PD-L1 and MSI have been used as biomarkers to evaluate response to ICIs, but their accuracy is insufficient. With advances in transcriptome sequencing, TIDE and IPS are increasingly being used to evaluate immunotherapy responsess[32]. We divided CRC patients into four groups by using the IPS evaluation system combined with the expression levels of PD1 and CTLA4, namely ips_PD1_negative_CTLA4_negative (PD- and CTLA-), ips_PD1_positive_CTLA4_negative (PD + and CTLA-), ips_PD1_negative_CTLA4_positive (PD- and CTLA+) and ips_ PD1_positive_CTLA4_positive (PD + and CTLA+). Our analysis showed that CRC patients with low risk scores had higher IPS scores, which suggesting that CRC patients with low risk may be more sensitive to immunotherapy (Fig. 8A-D). Finally, we further analyzed the correlation between the riskscores of ARGs-DEGs model and immune dysfunction, exclusion, MSI and TIDE. As shown in Fig. 8E-G, the high-risk patients had more obvious immune dysfunction and immune exclusion, and MSI levels were higher than those of high-risk patients. However, compared with the level of TIDE of high risk patients, low risk patients are lower. (Fig. 8H).

A variety of studies showed that angiogenesis is implicated in the carcinogenesis, development and metastasis of CRC[33, 34]. The angiogenesis translation studies in CRC reveal that several angiogenesis-related variables may be linked to prognosis[35]. Previous studies confirm that tumor angiogenesis is regulated by numerous genes with extremely complex interactions between these geness[15, 36, 37]. In the era of cancer precision medicine, multi-gene approaches can better describe tumor biology characteristics and guide clinical decision-making.

In this study, we performed cluster analysis of CRC samples by ARGs, and defined patients as C1 and C2 according to NMF typing. KM survival analysis shows that there is a significant difference in survival between the two subtypes, which indirectly proves the important role of ARGS in CRC. GSVA analysis of both subtypes revealed that the C1 was mainly enriched in pathways such as vascular smooth muscle junctions, intercellular gap junctions, and extracellular mechanism remodeling, and the C2 was mainly enriched in intracellular metabolism-related pathways. Patients with C1 and C2 have different immune characteristics. We further constructed a prognostic model based on ARGs-DEGs. To evaluate the clinical application value of ARGs-based risk score, we constructed a nomogram that combines several important clinical features, including age, gender, TNM stage and riskscore of ARGs-DEGs model. It has been verified that Norman chart derived from ARGs-DEGs model has a superior performance in evaluating the prognosis of patients with CRC. At present, the TNM staging system is the most commonly used criteria for classification and prognostic evaluation of CRC patients, but the tumor heterogeneity greatly limits its application[38, 39]. The riskscore of ARGs-DEGs model makes the nomogram more reliable, as it correlates with the results of both the training and validation sets. Therefore, the Norman chart based on the ARGs-DEGs model is worth to be promoted in clinical practice to predict the prognosis of CCR.

KEGG and HALLMARK were used to explore the underlying mechanisms of the ARGs-DEGs model. The results showed that the high-risk group of CRC was mainly enriched in intercellular junctions, extracellular matrix remodeling, and immune-related pathways, which was consistent with previous reports that the above functional pathways were related to vascular function[40–42]. More importantly, low cytotoxic cell infiltration was shown in the high-risk group. A variety of previous reports reveal that there is a significant correlation between CD4 + cell infiltration and immune cell activity in tumors[43, 44].This is consistent with our analysis of immunotherapy, and high-risk group had higher TIDE scores due to a greater tendency to express PD-L1 immunosuppressive molecules, suggesting that immunotherapy is more effective in the low-risk group.

This study has great application value in guiding the prognosis of CRC, but there are still significant deficiencies. The transcriptome and clinical data used in this study were derived from public database, and were not combined with clinical data to enhance their reliability. At the same time, the mechanism of the ARGs model in the role of TIM has not been further explored, which is worth expanding in our follow-up research.

In this study, we revealed the important role of ARGs in the prognosis and immune profile of CRC. The risk score model based on ARGs-DEGs may provide a novel research direction for evaluating the prognosis and immunotherapy response for CRC.

Acknowledgments

The authors would like to express our sincere thanks for sharing the data from The Cancer Genome Atlas (TCGA) database.

Author Contributions

The authors confirm their contribution to the paper as follows: study conception and design: WL and LMN; data collection: JLZ, XTQ; draft manuscript preparation: SXH. All authors compared different studies and approved the final version of the manuscript.

Funding Statement

This study was supported by Young and Middle-aged teacher Education Research Project of Fujian Province (JAT220286) .

Data Availability

The RNA sequencing data and corresponding clinical information of CRC patients were obtained from TCGA data portal (https://tcga-data.nci.nih.gov/tcga/).

Ethics statement

This study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Affiliated Hospital of Putian University.

Conflict of interest statement

All authors in this study declare no conflict of interest.

Consent for publication

Not applicable.

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Construction of a risk model for predicting colorectal cancer prognosis and immunotherapy based on angiogenesis-related gene

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials and Method

Data Acquisition

Procedure of Clustering Analysis

GSVA and Evaluation of Tumor-Immune Microenvironment

Construction of Prognostic Model

Assessment of the Immune Landscape of CRC

Evaluating the predictive efficacy of risk scores for immunotherapy

Statistical Analysis

Results

Identification of Angiogenesis Gene Subtypes in Colorectal Cancer

Analysis of GSVA and immune function in angiogenesis-related genotyping

Construction and Evaluation of ARGs-DEGs risk core model

Construction and validation of the nomogram

Pathway enrichment analysis of ARGs-DEGs model

Estimation of the association of tumor immune profile with the ARGs-DEGs model

Immune escape capacity and immunotherapy efficacy of CRC predicted by risk score

Discussion

Conclusions

Declarations

References

Supplemental Material

Additional Declarations

Status:

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