Angiogenesis related gene signatures predict prognosis and guide therapeutic strategies in renal clear cell carcinoma

Abstract

Kidney tumors are hypervascular tumors with crucial antiangiogenic effects in tumor therapy. This study aimed to develop a predictive model for kidney renal clear cell carcinoma (KIRC) by utilizing angiogenesis-related genes to formulate targeted therapy and immunotherapy strategies. Angiogenesis-related genes were screened via the GeneCard and Molecular Signatures Database (MSigDB). The KIRC data downloaded from The Cancer Genome Atlas (TCGA) were randomly divided into an experimental cohort and a validation cohort. In the experimental cohort, a risk score prediction model was constructed through successive analyses via univariate Cox regression, LASSO regression, and multivariate Cox regression. Receiver operating characteristic (ROC) curves were employed to assess the sensitivity of the model's predictions. The model's stability and generalizability were subsequently validated in both the validation cohort and the E-MTAB-1980 cohort. Subsequently, the TCGA-KIRC dataset was stratified into two distinct groups: a localized tumor cohort and a progression/metastasis cohort, based on tumor staging criteria. The efficacy of the prognostic prediction model was evaluated within each subgroup. A nomogram model was developed in conjunction with each independent prognostic factor to accurately predict patient outcomes. Additionally, single-cell and intercellular communication analyses were conducted via KIRC single-cell data obtained from the Gene Expression Omnibus (GEO) database. The effects of immunotherapy and targeted therapy on patients were predicted via prognostic modeling. A total of 260 angiogenesis-related genes were identified through screening in the GeneCards and Molecular Signatures Database(MSigDB). We subsequently developed a risk model comprising five genes: MEOX2, PLG, PROX1, TEK, and TIMP1. Survival analysis indicated that the prognosis for high-risk patients was significantly poorer than that for low-risk patients (P < 0.001), and the model demonstrated satisfactory accuracy in predicting 1-, 3-, and 5-year survival rates. This finding was further validated in both internal and external validation cohorts. The model demonstrated applicability for prognostic predictions in both the localized tumor cohort and the progression/metastasis cohort, with proficiency in forecasting the prognosis of patients diagnosed with metastatic renal cancer. The AUC values for 1, 3, and 5 years were recorded at 0.691, 0.709, and 0.773, respectively. We successfully constructed a nomogram model to facilitate accurate prognostic predictions for patients. Analysis of single-cell data revealed that PLG was expressed predominantly in tumor cell clusters, whereas TEK was highly expressed primarily in pericytes. TIMP1 was found to be highly expressed in vascular smooth muscle cells. In contrast, MEOX2 and PROX1 were highly expressed in specific cell clusters but presented low expression levels across the overall cell population. Cell communication analysis indicated that the modeling gene TEK was involved in the angiogenic pathway, with the interaction between the ligand ANGPT2 and the receptor ITGA5-ITGB1 being particularly prominent in this study. Furthermore, the immune dysfunction and rejection scores for high-risk patients within the non-localized renal cancer cohort were markedly elevated compared to those observed in the low-risk group. In terms of targeted pharmacological intervention, individuals classified in the low-risk group exhibited a heightened sensitivity to sorafenib. The KIRC prognostic prediction model, which is based on five angiogenesis-related genes, demonstrated reliable performance, indicating that high-risk patients have a significantly poorer prognosis than low-risk patients do. The developed nomogram model effectively visualizes and accurately predicts patient prognosis. It is essential to highlight that individuals diagnosed with low-risk metastatic KIRC may experience greater advantages from the administration of immunotherapy and sorafenib.

Keywords: Angiogenesis; Immunotherapy; Kidney renal clear cell carcinoma; Prognosis; Targeted therapy.

Fig. 1

Heatmap of differentially expressed genes.

Fig. 2

Modeling gene screening process. (A) Forty-six prognosis-related genes with hazard ratios (HRs) > 1 were associated with poor prognosis in patients, and HRs < 1 indicated protective genes. (B) The horizontal axis represents the value of the independent variable lambda, whereas the vertical axis represents the coefficients of the independent variable. (C) The relationship between partial likelihood deviance and log(λ) was plotted via the LASSO regression model. (D) Visualization of gene expression differences used for modeling. ***P < 0.001.

Fig. 3

Risk modeling and validation training cohort: A,B,C; validation group: D,E,F; E-MTAB-1980 cohort, G, H,I. Survival analysis (A, D, G) indicated that the prognosis for high-risk patients was significantly poorer. In the accompanying scatterplot, delineated by a dotted line, higher risk scores correspond to high-risk patients. The mortality rate among high-risk patients is elevated across all cohorts (B, E, H). Additionally, C, F, and I present the time-dependent receiver operating characteristic (ROC) curves for each cohort, where a higher area under the curve (AUC) value signifies an improved predictive capability of the model.

Fig. 4

KIRC tumor staging subgroup analysis: Prognostic analysis within localized kidney tumors (A, stage I, II) and advanced/metastatic kidney tumors (B, stage III, IV); (C) risk combined tumor staging prognostic analysis; (D,E)subgroup survival time prediction ROC curve.

Fig. 5

Independent prognostic factor analysis: (A) Univariate and (B) multivariate Cox regression was performed to screen for independent prognostic factors. In the gender analysis, males are compared to females.

Fig. 6

Nomogram modeling and performance testing: (A) Thenomogram serves as a predictive tool for estimating the overall survival rates of patients at 1, 3, and 5 years, based on the cumulative scores. The time-dependent ROC curve indicates that a higher AUC correlates with increased nomogram (B) prediction accuracy. (C) ROC curves for multivariate, with nomogram modelshowing higher predictive accuracy than other factors from AUC values.Additionally, the calibration curves for the nomogram model (D, E, and F) illustrate the model’s performance; the diagonal dashed line represents the ideal prediction outcome, whereas the red line reflects the actual prediction results of the nomogram.

Fig. 7

Single-cell analysis of KIRC: The analysis of single cells was conducted on the five included samples (A) Single-cell data were categorized into 16 distinct cell clusters (B). Bubble plots illustrate the modeled gene expression within each cell cluster (C), with larger circles representing higher expression levels within a cell cluster and darker colors indicating elevated expression levels.The number of cells within each cluster was also quantified (D).

Fig. 8

Analysis of KIRC cell communication: (A) Graph of the number of cellular communications; the thickness of the connecting lines is related to the number of intercellular communications; the thicker the connecting lines are, the greater the number of intercellular communications is, and the arrows of the connecting lines point in the direction of signaling. (B) Intensity of the cellular communication graph; the thicknesses of the lines and the arrows have the same meaning as before.

Fig. 9

Analysis of the angiogenic “ANGPT” pathway: (A) Heatmap of cell-to-cell communication within the “ANGPT” signaling pathway, with vertical coordinates indicating the sending cells and horizontal coordinates indicating the receiving cells; (B) Levels of the contribution of major receptor‒ligand pairs within the signaling pathway, with the interaction between the ligand ANGPT2 and the receptor (ITGA5 + ITGB1) being the most dominant; (C) Receptor‒ligand gene expression in each endocytosis; the cellular communication network involves the different ligand‒receptor pairs (D, E, F).

Fig. 10

Somatic mutation analysis: Analysis of the top 20 genes with the highest mutation rates in the KIRC high- and low-risk groups (A, B); Violin plots of TMB differences between the high- and low-risk groups (C); prognostic impact of gene mutations (D); K‒M curves for risk-combined mutations, with the worst prognosis in the high-mutation population of high-risk patients (E).

Fig. 11

Immunotherapy and targeted therapy treatment predictions: (A) Differences in tumor immune dysfunction and exclusion scores in advanced/metastatic KIRC patients; (B, C) targeted therapy effect prediction chart and correlation scatter plot.