Angiogenesis-Related Molecular Subtypes and a Novel Prognostic Signature in Clear Cell Renal Cell Carcinoma Patients

Abstract

Background

This study aimed to develop and validate a novel angiogenesis-related gene (ARG) signature and molecular subtypes by bioinformatics analysis.

Materials and Methods

The transcriptome data and clinical data were obtained from TCGA and ICGC database. We performed consensus clustering analysis to identify angiogenesis molecular subtypes for ccRCC. Univariate and multivariate Cox regression analyses were used to develop a novel ARG-related signature as a prognostic biomarker for ccRCC. Internal and external validation were then performed in TCGA and ICGC cohort, respectively.

Results

We identified a total of two angiogenesis molecular subtypes of ccRCC. The overall survival (OS) of subtype 1 ccRCC was significantly decreased compared with that of subtype 2 ccRCC (P=0.001). These two molecular subtypes have significantly different tumor microenvironment and immune checkpoint inhibitor sensitivities (P<0.05). Besides, we developed a novel signature based on three ARGs (including MSX1, TIMP1 and JAG2) for subtype 1 ccRCC. The difference in OS between high- and low-risk group was statistically significant in training cohort (P=0.009), test cohort (P=0.024), the whole type 1 cohort (P<0.001), and validation cohort (P=0.041). The AUC for one-year OS prediction was 0.732, 0.710, 0.725, and 0.645 in training cohort, test cohort, the whole type 1 cohort, and validation cohort, respectively. Independent prognostic analysis showed that this signature was an independent predictor for OS of subtype 1 ccRCC (P=0.028914). The power of this prognostic signature was superior to other signatures reported in previous studies.

Conclusion

We developed and successfully validated a novel ARG signature for predicting prognosis of subtype 1 ccRCC, which was superior to several previous signatures.

Keywords:

• clear cell renal cell carcinoma
• angiogenesis
• molecular subtypes
• signature
• prognosis

Data Sharing Statement

All data generated or analyzed during the present study were downloaded from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov), International Cancer Genome Consortium (ICGC) database (https://dcc.icgc.org/), and Molecular Signatures Database (MSigDB) (https://www.gsea-msigdb.org/gsea/msigdb).

Ethics Approval and Consent to Participate

This study was conducted in accordance with the Ethical Standards of the Institutional Ethics Committee of First Affiliated Hospital of Fujian Medical University and with the 1964 Helsinki declaration and its later amendments or comparable Ethical Standards.

Author Contributions

All authors contributed to data analysis, drafting or revising the article, gave final approval of the version to be published, agreed to the submitted journal, and agreed to be accountable for all aspects of the work.

Disclosure

All authors declare no conflicts of interest.

Additional information

Funding

There is no funding to report.