Molecular Characteristics, Clinical Significance, and Cancer Immune Interactions of Angiogenesis-Associated Genes in clear cell renal cell carcinoma

doi:10.21203/rs.3.rs-2490747/v1

Download PDF

Research Article

Molecular Characteristics, Clinical Significance, and Cancer Immune Interactions of Angiogenesis-Associated Genes in clear cell renal cell carcinoma

https://doi.org/10.21203/rs.3.rs-2490747/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Immunotherapy have developed into important options for the treatment of various cancers. Angiogenesis is an important behavior in cancer, however, we do not yet know the relationship between angiogenesis and clinical outcome and immune infiltration in clear cell renal cell carcinoma (ccRCC).

Methods: Based on the data from The Cancer Genome Atlas-Kidney renal clear cell carcinoma (TCGA-KIRC), we systematically evaluated 36 angiogenesis-associated genes (AAGs) and comprehensively identiﬁed the correlation between angiogenesis and transcriptional patterns, potential mechanisms, prognosis, and relationship with immunity in ccRCC. We applied the AAG_score to quantify individual patient subtypes of angiogenesis.

Results: We investigated AAG mutations in ccRCC samples at genetic level. We determined two different molecular subtypes and observed that they correlate with prognosis. Next, an AAG_score for predicting overall survival (OS)was established. Furthermore, we built a highly reliable nomogram model by using AAG_score and other clinical variables and evaluated the performance of the model. Last, we found the difference in tumor microenvironment (TME) between two AAG_score groups.

Conclusion: We constructed the AAG_score for predicting the prognosis of ccRCC patients, which contributes to the understanding of the impact of angiogenesis on immunotherapy in ccRCC patients and helps to improved immunotherapeutic strategies.

Renal cell carcinoma (RCC) accounts for approximately 3% of all cancers in adults, with an annual incidence of more than 400,000 cases and an estimated 431,288 new cases in 2020 [1, 2]. About 25% of patients develop metastatic disease at the time of initial diagnosis, and 20–40% relapse after local nephrectomy [3]. Clear cell RCC (ccRCC) is the most common subtype of RCC and the pathologic type of the vast majority of metastatic RCC. Surgical intervention is the primary treatment for advanced RCC due to its insensitivity to radiotherapy. However, in patients with advanced RCC, where surgical intervention is ineffective or the opportunity for surgery has been lost, targeted therapy becomes the standard first-line treatment for advanced RCC, yet almost all patients eventually develop resistance[4, 5]. Recent studies have demonstrated the efficacy of immune checkpoint inhibitors (ICIs) in the treatment of RCC, but only some patients benefit from them, possibly due in part to the immunosuppressive tumor microenvironment (TME) in RCC. Studies have shown that RCC TME is characterized by extensive immune infiltration, hypervascularization, and fibrosis compared to other solid tumors, which is the basis for immunotherapy applications in RCC[6–8].

TME is a dynamic network of spatial tissues consisting of tumor cells, immune cells, endothelial cells, structural molecules, extracellular matrix, and neuroendocrine, adipocyte, or stromal cells[9]. It is characterized by the formation of new blood vessels, which are continuous and disorganized. Tumor cells generally promote angiogenesis and inflammation, thereby evading surveillance and clearance from the immune system[10]. Thus, overall analysis of the relationship between angiogenesis and TME may uncover different neoplastic immunophenotypes and provide a boost to the predictive power of immunotherapy. Angiogenesis inhibition remains one of the most active approaches in the treatment of advanced ccRCC. Most current studies, however, focus on identifying the role of individual genes associated with angiogenesis.

In this study, we systematically analyzed the expression of AAGs and their impact on the development, prognosis and TME of patients with ccRCC. Using the TCGA database, we identified two distinct subgroups of angiogenesis in ccRCC. We then evaluated the molecular features, prognostic significance, and intensity of infiltrating immune cells of the angiogenesis clusters identified. In addition, we achieved an AAG_score that accurately predicted clinical outcome in ccRCC patients and immunotherapy effect. We anticipate that this study contributes to the development of viable immunotherapy for ccRCC.

Data Collection and Website

The RNA expression data, somatic mutation and corresponding clinicopathological information of ccRCC were extracted from the TCGA-KIRC program. Samples lacking significant clinicopathological or survival information were excluded from further analysis. A total of 36 AAGs were obtained from the MSigDB Team (Hallmark Gene set) (Table S1). A protein-protein interaction network (PPI) was performed by STING website (https://string-db.org/)[11]. The cBioPortal website (https://www.cbioportal.org/) was used to analyse the 36AAGs gene alterations[12].

Consensus Clustering Analysis of AAGs

Consensus Clustering was employed to define distinct angiogenesis-related patterns by the k-means algorithms[13]. The quantity, along with cluster coherence, was constructed by the Consensus Clustering Algorithm, which is available in the “ConsensuClusterPlus” package[14]. We ran 1000 iterations to ensure stability across these categories. Gene set variation analysis (GSVA) was performed with the KEGG gene set (c2.cp.kegg. v7.4)[15] in order to identify biological functional differences in the AAGs. In addition, differences in overall survival (OS) between different patterns were assessed with the Kaplan–Meier analysis obtained by the "survival" and "survminer" software packages[16].

Relationship between ccRCC Molecular Patterns and TME

We evaluate the immune and stromal scores of ccRCC patients with the ESTIMATE algorithm[17]. The levels of 28 immune cell subtypes from each patient were then calculated with the single sample gene set enrichment analysis (ssGSEA) algorithm[18]. Next, we assessed the association between the two subsets on the expression of PD-1, PD-L1 and CTLA-4.

DEG identification and Functional Enrichment Analysis

We used the package "DESeq2" with criteria of |log2-fold change (FC)| ≥ 1and p-value < 0.05 to identify differentially expressed genes(DEGs) in the separate subgroups of angiogenesis[19]. Based on these DEGs, GO and KEGG analysis was performed with the package “clusterProfiler”[20].

Establishment of an Angiogenesis-Associated AAG_Score

We constructed an AAG_score to quantitatively evaluate angiogenesis in individual patients with ccRCC. We normalized DEG expression data from separate angiogenesis clusters across ccRCC specimens and selected genes with intersection. Differential evaluation demonstrated that 329 DEGs exist between the two angiogenesis groups. We then performed univariate Cox regression (uniCox) analysis for the DEGs. Genes associated with survival were retained for further analyses. We performed LASSO and multivariate Cox regression (multiCox) analysis to generate gene scores associated with angiogenesis with the following algorithm:

AAG_score =\({\sum }_{i = 1}^{n}Coef i*Expr i\)

Coef i, corresponding coefficient; Expr I, expression of a gene

Relationship between Prognostic AAG_Score and clinical variables

The correlation between AAG_score and clinical variables was investigated. We performed both uniCox and multiCox regression analyses to determine if AAG_score was an independent prognostic predictor. In addition, the levels of immune cell infiltrate and immune checkpoint (ICP) were compared in the different subgroups of risk scores. Furthermore, we investigated the correlations between AAG_score and tumor mutation burden (TMB) score, microsatellite instability (MSI) score, and tumor neoantigen (NEO).

Establishment of a Predictive Nomogram

The nomogram was represented to provide valuable clinical predictions for ccRCC patients with their riskscores and other clinicopathological features, especially regarding OS at 1, 3, and 5 years. We then performed a calibration curve analysis and a decision curve analysis (DCA) to verify the clinical reliability of the established nomogram.

Statistical Analysis

We apply the software package R (version 4.1.2) and its relevant packages to process, analyse and present the data. A two-sided P < 0.05 was considered valid.

Genetic Mutation Landscape of AAGs in KIRC

We first identified the expression levels of the 36 AAGs in tumor specimens and normal specimens with the TCGA-KIRC dataset. A total of 31 DEGs were found, and most of the DEGs were abundant in the tumor samples (Fig. 1A). A protein-protein interaction (PPI) analysis through the STRING website was established to reveal the interactivity of AAGs, which indicated that VEGFA, SPP1, POSTN, COL3A1, VCAN, ITGAV, UTN and TIMP1 were hub genes (Fig. 1B). Next, we determined the incidence of CNVs and somatic mutations of 36 AAGs in KIRC by cBioPortal website (https://www.cbioportal.org/). As depicted in Fig. 1C, 92 of 448 (21%) ccRCC samples presented genetic mutations, and the findings suggested ITGAV as the gene with the highest mutation incidence, followed by FGFR1 and COL5A2, among the 36 AAGs. Furthermore, we explored OS in the Altered and Unaltered groups (Fig. 1D). The findings indicated a substantial difference in the genomic background and expression levels of AAGs between ccRCC and normal specimens, suggesting the potential role of AAGs in ccRCC tumorigenesis.

Construction of Angiogenesis Subgroups in ccRCC

Figure S1 shows the detailed flowchart for this work. A total of 530 ccRCC patients from TCGA-KIRC were enrolled in this study to reveal the relationship between angiogenesis and tumorigenesis. Full details of these patients were listed in Table S2. uniCox and Kaplan–Meier analysis were performed in Table S3 to identify the prognostic values of 36 AAGs in patients with ccRCC. The correlation network of the AAG interactions in the KIRC patients was then presented in Fig. 2A and Table S4. We performed a consensus cluster analysis to determine the relationship between the AAG and ccRCC subtypes. Patients with ccRCC were classified based on these AAG expression levels.

We found that the optimal clustering variable was 2 (Fig. 2B), and the ccRCC patients in the full cohort were well dispersed across both cluster 1 (n = 23) and cluster 2 (n = 507). The results of the PCA analysis also supported the excellent inter-group distribution (Fig. 2C). In addition, OS time for both clusters was discussed, and a significant difference in survival was observed (Fig. 2D).

Characteristics of the TME in Different Subgroups

According to the findings of GSVA analysis, cluster 2 was abundant in cancer-associated pathways (RCC, pancreatic cancer, bladder cancer), and metabolism-associated pathways (NOTCH signaling pathway, mTOR signaling pathway) (Fig. 3A and Table S5). To identify the relationship between AAGs and the TME of ccRCC, we explore the infiltrating levels of 28 subpopulations of human immune cell subpopulations in the two clusters with the ssGSEA algorithm (Table S6). Figure 3B shows that the levels of activated B cell, activated CD8 T cell, CD56dim NK cell, Central memory CD4 T cell, NK T cell, T follicular helper cell, and type 1 T helper cell enrichment were clearly higher in cluster2 than in cluster1. However, there was no difference in the expression of 3 critical ICPs (PD-1, PD-L1, and CTLA-4) (Figs. 3C–E). The abundance of immune and stromal elements in the TME could be assessed by TME scores, in addition, we ran the ESTIMATE algorithm to obtain the TME scores in the different clusters, including the stromal score, the immune score, and the estimation score. The findings indicated patients in cluster2 had higher stromal score (Fig. 3F).

Identification of Subgroups Based on DEGs

To investigate the underlying biological activity of angiogenesis subgroups, we obtained 329 angiogenesis clusters-associated DEGs with the “DSEeq2” package and conducted functional enrichment analysis (Table S7). DEGs associated with these angiogenesis subgroups were primarily enriched in immune-associated biological processes (Fig. 4A). KEGG analysis demonstrated the abundance of cancer-associated pathways (Fig. 4B). Then, we performed uniCox analysis to determine the survival significance of 234 coding genes, and 161 coding genes were extracted with a criterion of p < 0.05 (Table S8).

Construction and Evaluation of the Prognostic AAG_Score

The AAG_score was created based on cluster-associated DEGs. The LASSO regression analysis and multiCox analysis for 161 angiogenesis cluster-associated prognostic DEGs were conducted to establish an optimal predictive model. The LASSO regression analysis and partial likelihood deviance on the prognostic genes were shown in Fig. 4C and D. Ultimately, we acquired nine genes (HS3ST1, CFH, SRPX2, TFPI2, ITGB8, GALNT15, CLEC18A, GRIK3, and SLC23A3), and the AAG_score was listed in TableS9. TableS10 show the specific coefficients. Patients' distributions in the two angiogenesis clusters and two AAG_score groups were displayed in Fig. 5A. We found a substantial difference in AAG_score between angiogenesis clusters (Figs. 5B). AAG_scores in cluster 1 were higher than AAG_scores in cluster 2. Using the aforementioned survival analysis, we identified that patients with higher risk scores were correlated with worse survival outcomes. Furthermore, Kaplan-Meier analysis indicated that low-risk patients had a better OS over high-risk patients (Fig. 5C), and the AUCs of 1-, 3-, and 5-years OS were 0.679, 0.665, and 0.678, respectively (Fig. 5D). The risk plot indicated that as AAG_score increased, OS time decreased while mortality increased (Figs. 5E).

Clinical Correlation Analysis of the Prognostic AAG_Score

We discussed the interplay between AAG_score and various clinical parameters in order to determine the relationship between AAG_score and clinicopathological characteristics (age, gender, stage, grade). We found increased risk scores in the higher stage, grade, and male patients (Fig. 6A-D). In addition, the independent prognostic value of the AAG_score for ccRCC patients was assessed. To explore the prognostic independence of multiple clinical factors, we conducted both uniCox and multiCox analyses. As presented in Fig. 6E-F. age, stage, grade, gender, and risk score in the cohort demonstrated significant differences.

Constructing Nomogram Based on AAG_score and Clinical Features

Due to the high correlation between risk scores and patient outcomes, we combined clinical parameters to create a nomogram. This nomogram was used to estimate OS at 1, 3, and 5 years for ccRCC patients (Fig. 7A). Furthermore, we estimated the AUC values of the nomogram for predicting 1-, 3-, and 5-year OS, respectively (Fig. 7B). As shown in Figs. 7C–E, the calibration curves of this established nomogram presented moderate accuracy between actual observations and predicted values. In addition, we found that this prognostic model with diverse clinical factors had a greater net benefit in predicting prognosis (Figs. 7F- H).

Assessment of TME and Checkpoints in Each Groups

We used the ssGSEA algorithm to assess the correlation between AAG_score and immune cell infiltration. As depicted in Fig. 8A, Activated B cell, Activated CD4 T cell, Activated CD8 T cell, Effector memory CD4 T cell, Effector memory CD8 T cell, Gamma delta T cell, Macrophage, Mast cell, MDSC, Type 1 T helper cell was higher in high- riskscore group, while the opposite performance was observed in relationship with AAG_score and CD56 bright natural killer cell, CD56 dim natural killer cell, Central memory CD4 T cell, Immature dendritic cell, Memory B cell, Natural killer cell, Neutrophil, Plasmacytoid dendritic cell, Regulatory T cell. Moreover, the AAG_score was positively linked to stromal score, and immune score (Fig. 8B). Additionally, we assessed the relationship between ICPs and this prognostic signature. Figure 8C demonstrates that 4 ICPs were discrepantly represented in the two risk subgroups, such as SIGLEC15, CD274, HAVCR2 and PDCD1LG2.

Association of AAG_Score With TMB, MSI, and NEO

Several studies found TMB and MSI to be valuable predictive indicators of tumor immune response, and patients with elevated TMB or MSI may benefit from ICP inhibitors[21–23]. However, we found that TMB, MSI, and NEO did not differ between the low-and high-risk groups (Fig. 8D-F).

RCC is one of the most vascularized tumor types due to the tendency to inactivate the tumor suppressor genes von Hippel-Lindau (VHL) in the early stages. VHL is a negative regulator of hypoxia-inducible factor (HIF), and VHL deficiency leads to upregulation of HIF, which in turn causes massive release of vascular endothelial growth factor (VEGF) and other factors, resulting in tumor vascularization. Abnormal angiogenesis a physical barrier that limits the effectiveness of immunotherapy and can also indirectly inhibit the effectiveness of immunotherapy through hypoxia and the formation of an acidic microenvironment, thereby increasing immunosuppression [24], Anti-tumor immune cells, such as T-lymphocytes and NK cells, can become unresponsive under acidic conditions, followed by apoptosis. Immune-suppressive components such as myeloid cells and Treg cells sustain tumor growth through the acidic environment of the tumor.[25, 26] Accumulating evidence has highlighted the inevitable link between angiogenesis and intrinsic immunity, and targeting angiogenesis may serve an essential role in augmenting cancer immunotherapy [27–29] Many reports, however, have focused only on a single AAG or a specific immune cell subtype. Thus, further clarification of the holistic impact and infiltrative characteristics of the TME regulated by the combinatorial action of diverse AAGs is needed.

The aim of this research was to identify transcriptional alterations and AAG expression based on the cohort of TCGA–KIRC, most AAGs, are upregulated in ccRCC patients and are associated with prognosis. With the unsupervised clustering approach, we then divided the ccRCC patients into 2 angiogenesis subgroups (Cluster1 and 2). There were significant discrepancies between the two groups in clinical outcome, immune infiltration, and function. We performed GO and KEGG enrichment analyses based on DEGs linked to the subgroup signature. LASSO and multiCox regression, AAG_score was set up to quantify subgroups of angiogenesis. The cluster1 with the poorer clinical outcomes had the higher AAG_score. OS was unfavorable in patients with a high AAG_score, suggesting that a high AAG_score may predict an unfavorable prognosis. Angiogenesis is implicated in the malignant behavior of various tumors including ccRCC[30, 31]. Our function enrichment analysis consistently showed significant enhancement of pathways related to cancer and metabolism, confirming the existing findings.

AAG_score was remarkedly pertinent to the clinicopathological characteristics of ccRCC. We performed a multiCox regression analysis on the clinical parameters and the riskscore, the results indicated that AAG_score was an independent predictor for the survival outcomes of ccRCC patients' disease. ROC validated its predictive robustness across 1-, 3-, and 5-year OSs. Therefore, AAG_score is a reliable predictor of patient prognosis. The accumulation of genetic mutations leads to cancer. TMB and MSI are potent predictors of ICIs efficacy in many malignancies. Tumor neoantigens generated by tumor-specific mutations and causing immune infiltration are generally considered prerequisites for ICIs to function. Although RCC is highly immune-invasive, its markers such as TMB are significantly lower than other highly immune-invasive cancers. (e.g., lung adenocarcinoma and melanoma)[32]Our results also showed that there was no discrepancy in TMB between low and high AAG_scores.

It has long been recognized that ccRCC is a highly immune-infiltrating tumor, which was the basis for the earliest cytokine-based immunotherapy applied to ccRCC. As research into TME has intensified, more and more immunotherapeutic strategies have emerged. The primary components of the TME are stromal cells and immune cells, and immune and stromal scores are linked to clinicopathological features and prognosis in ccRCC[33] When we calculated these scores with the ESTIMATE algorithm, we found that a high AAG_score group obviously had higher immune and stromal scores than a group with a low AAG_score. Which suggested that angiogenesis may be associated with TME involvement, thereby regulating the occurrence and development of neoplasia. We identified that higher enrichment of Tregs, natural killer cells and DCs were correlated with low AAG_score. The enrichment of Tregs, inhibiting the anti-tumor immunoreactivity, was related to poor survival[31]. This is concordant with our findings of abundant Tregs in the TME of patients with low AAG_scores. In addition, previous reports demonstrated that angiogenesis factors could serve as immune modulators, and the immune system may participate in carcinogenesis through the induction of pathological vasculature[34]. Targeting angiogenesis may therefore be a valuable regulatory strategy for CCRCC immunotherapies.

Although the signature we constructed has sufficient accuracy, our study still has some limitations. For example, more independent cohorts with a higher number of samples should be used to verify the prognostic power of this signature. And the interactions and mechanisms between risk scores and TME should be evaluated by in vitro and in vivo experiments.

Conflict of interest statements

All authors contributed to the article and approved the submitted version. All authors declare no competing interests.

Acknowledgments

We acknowledge TCGA and GEO database for providing their platforms and contributors for uploading their meaningful datasets.

Funding

This study did not receive funding support.

Ethics statement

TCGA and GEO belong to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open source data, so there are no ethical issues and other conflicts of interest.

Availability of data and materials:

Data available on TCGA and GEO database.

Authors' contributions

ZW, JL, WJ and XJ: study concept and design.

TZ, XZ, and YZ: acquisition of data.

ZW, and JL: analysis and interpretation of data.

WJ, and XJ: critical revision of the manuscript.

ZW and SX: statistical analysis.

All authors contributed to the article and approved the submitted version.

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F: Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: a cancer journal for clinicians 2021, 71(3):209-249.
Bukavina L, Bensalah K, Bray F, Carlo M, Challacombe B, Karam JA, Kassouf W, Mitchell T, Montironi R, O'Brien T et al: Epidemiology of Renal Cell Carcinoma: 2022 Update. European urology 2022, 82(5):529-542.
Dabestani S, Thorstenson A, Lindblad P, Harmenberg U, Ljungberg B, Lundstam S: Renal cell carcinoma recurrences and metastases in primary non-metastatic patients: a population-based study. World journal of urology 2016, 34(8):1081-1086.
Ballesteros PA, Chamorro J, Roman-Gil MS, Pozas J, Gomez Dos Santos V, Granados AR, Grande E, Alonso-Gordoa T, Molina-Cerrillo J: Molecular Mechanisms of Resistance to Immunotherapy and Antiangiogenic Treatments in Clear Cell Renal Cell Carcinoma. Cancers 2021, 13(23).
Capitanio U, Montorsi F: Renal cancer. Lancet (London, England) 2016, 387(10021):894-906.
Simonaggio A, Epaillard N, Pobel C, Moreira M, Oudard S, Vano YA: Tumor Microenvironment Features as Predictive Biomarkers of Response to Immune Checkpoint Inhibitors (ICI) in Metastatic Clear Cell Renal Cell Carcinoma (mccRCC). Cancers 2021, 13(2).
Mier JW: The tumor microenvironment in renal cell cancer. Current opinion in oncology 2019, 31(3):194-199.
Vuong L, Kotecha RR, Voss MH, Hakimi AA: Tumor Microenvironment Dynamics in Clear-Cell Renal Cell Carcinoma. Cancer discovery 2019, 9(10):1349-1357.
Heidegger I, Pircher A, Pichler R: Targeting the Tumor Microenvironment in Renal Cell Cancer Biology and Therapy. Frontiers in oncology 2019, 9:490.
Jin MZ, Jin WL: The updated landscape of tumor microenvironment and drug repurposing. Signal transduction and targeted therapy 2020, 5(1):166.
Szklarczyk D, Gable AL, Nastou KC, Lyon D, Kirsch R, Pyysalo S, Doncheva NT, Legeay M, Fang T, Bork P et al: The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic acids research 2021, 49(D1):D605-d612.
Gao J, Aksoy BA, Dogrusoz U, Dresdner G, Gross B, Sumer SO, Sun Y, Jacobsen A, Sinha R, Larsson E et al: Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science signaling 2013, 6(269):pl1.
Sabah A, Tiun S, Sani NS, Ayob M, Taha AY: Enhancing web search result clustering model based on multiview multirepresentation consensus cluster ensemble (mmcc) approach. PloS one 2021, 16(1):e0245264.
Seiler M, Huang CC, Szalma S, Bhanot G: ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data. Omics : a journal of integrative biology 2010, 14(1):109-113.
Hänzelmann S, Castelo R, Guinney J: GSVA: gene set variation analysis for microarray and RNA-seq data. BMC bioinformatics 2013, 14:7.
Rich JT, Neely JG, Paniello RC, Voelker CC, Nussenbaum B, Wang EW: A practical guide to understanding Kaplan-Meier curves. Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery 2010, 143(3):331-336.
Meng Z, Ren D, Zhang K, Zhao J, Jin X, Wu H: Using ESTIMATE algorithm to establish an 8-mRNA signature prognosis prediction system and identify immunocyte infiltration-related genes in Pancreatic adenocarcinoma. Aging 2020, 12(6):5048-5070.
Huang L, Wu C, Xu D, Cui Y, Tang J: Screening of Important Factors in the Early Sepsis Stage Based on the Evaluation of ssGSEA Algorithm and ceRNA Regulatory Network. Evolutionary bioinformatics online 2021, 17:11769343211058463.
Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology 2014, 15(12):550.
Yu G, Wang LG, Han Y, He QY: clusterProfiler: an R package for comparing biological themes among gene clusters. Omics : a journal of integrative biology 2012, 16(5):284-287.
Cristescu R, Mogg R, Ayers M, Albright A, Murphy E, Yearley J, Sher X, Liu XQ, Lu H, Nebozhyn M et al: Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy. Science (New York, NY) 2018, 362(6411).
Liu L, Bai X, Wang J, Tang XR, Wu DH, Du SS, Du XJ, Zhang YW, Zhu HB, Fang Y et al: Combination of TMB and CNA Stratifies Prognostic and Predictive Responses to Immunotherapy Across Metastatic Cancer. Clinical cancer research : an official journal of the American Association for Cancer Research 2019, 25(24):7413-7423.
Samstein RM, Lee CH, Shoushtari AN, Hellmann MD, Shen R, Janjigian YY, Barron DA, Zehir A, Jordan EJ, Omuro A et al: Tumor mutational load predicts survival after immunotherapy across multiple cancer types. Nature genetics 2019, 51(2):202-206.
Jain RK: Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science (New York, NY) 2005, 307(5706):58-62.
Shen C, Kaelin WG, Jr.: The VHL/HIF axis in clear cell renal carcinoma. Seminars in cancer biology 2013, 23(1):18-25.
Huber V, Camisaschi C, Berzi A, Ferro S, Lugini L, Triulzi T, Tuccitto A, Tagliabue E, Castelli C, Rivoltini L: Cancer acidity: An ultimate frontier of tumor immune escape and a novel target of immunomodulation. Seminars in cancer biology 2017, 43:74-89.
Rivera LB, Bergers G: Intertwined regulation of angiogenesis and immunity by myeloid cells. Trends in immunology 2015, 36(4):240-249.
Trenti A, Tedesco S, Boscaro C, Trevisi L, Bolego C, Cignarella A: Estrogen, Angiogenesis, Immunity and Cell Metabolism: Solving the Puzzle. International journal of molecular sciences 2018, 19(3).
Qing X, Xu W, Liu S, Chen Z, Ye C, Zhang Y: Molecular Characteristics, Clinical Significance, and Cancer Immune Interactions of Angiogenesis-Associated Genes in Gastric Cancer. Frontiers in immunology 2022, 13:843077.
Annese T, Tamma R, De Giorgis M, Ribatti D: microRNAs Biogenesis, Functions and Role in Tumor Angiogenesis. Frontiers in oncology 2020, 10:581007.
Sajib S, Zahra FT, Lionakis MS, German NA, Mikelis CM: Mechanisms of angiogenesis in microbe-regulated inflammatory and neoplastic conditions. Angiogenesis 2018, 21(1):1-14.
Lee M, Samstein RM, Valero C, Chan TA, Morris LGT: Tumor mutational burden as a predictive biomarker for checkpoint inhibitor immunotherapy. Human vaccines & immunotherapeutics 2020, 16(1):112-115.
Mennitto A, Huber V, Ratta R, Sepe P, de Braud F, Procopio G, Guadalupi V, Claps M, Stellato M, Daveri E et al: Angiogenesis and Immunity in Renal Carcinoma: Can We Turn an Unhappy Relationship into a Happy Marriage? Journal of clinical medicine 2020, 9(4).
Ribatti D, Crivellato E: Immune cells and angiogenesis. Journal of cellular and molecular medicine 2009, 13(9a):2822-2833.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Molecular Characteristics, Clinical Significance, and Cancer Immune Interactions of Angiogenesis-Associated Genes in clear cell renal cell carcinoma

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Data Collection and Website

Consensus Clustering Analysis of AAGs

Relationship between ccRCC Molecular Patterns and TME

DEG identification and Functional Enrichment Analysis

Establishment of an Angiogenesis-Associated AAG_Score

Relationship between Prognostic AAG_Score and clinical variables

Establishment of a Predictive Nomogram

Statistical Analysis

Results

Genetic Mutation Landscape of AAGs in KIRC

Construction of Angiogenesis Subgroups in ccRCC

Characteristics of the TME in Different Subgroups

Identification of Subgroups Based on DEGs

Construction and Evaluation of the Prognostic AAG_Score

Clinical Correlation Analysis of the Prognostic AAG_Score

Constructing Nomogram Based on AAG_score and Clinical Features

Assessment of TME and Checkpoints in Each Groups

Association of AAG_Score With TMB, MSI, and NEO

Discussion

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1