Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer

Cheng Shen; Bing Zheng; Zhan Chen; Wei Zhang; Xinfeng Chen; Siyang Xu; Jianfeng Ji; Xingxing Fang; Chunmei Shi

doi:10.1016/j.heliyon.2024.e27710

Identification of prognostic models for glycosylation-related subtypes and tumor microenvironment infiltration characteristics in clear cell renal cell cancer

Heliyon. 2024 Mar 13;10(6):e27710. doi: 10.1016/j.heliyon.2024.e27710. eCollection 2024 Mar 30.

Authors

Cheng Shen^{1

2}, Bing Zheng¹, Zhan Chen^{1

2}, Wei Zhang¹, Xinfeng Chen¹, Siyang Xu³, Jianfeng Ji⁴, Xingxing Fang⁵, Chunmei Shi¹

Affiliations

¹ Department of Urology, Affiliated Hospital 2 of Nantong University, China.
² Medical Research Center, Affiliated Hospital 2 of Nantong University, China.
³ Clinical Medicine Specialty, Xinglin College of Nantong University, China.
⁴ Department of Burn and plastic surgery, Affiliated Hospital 2 of Nantong University, China.
⁵ Nephrology Department, Affiliated Hospital 2 of Nantong University, China.

Abstract

Background: One of the most fatal forms of cancer of the urinary system, renal cell carcinoma (RCC), significantly negatively impacts human health. Recent research reveals that abnormal glycosylation contributes to the growth and spread of tumors. However, there is no information on the function of genes related to glycosylation in RCC.

Methods: In this study, we created a technique that can be used to guide the choice of immunotherapy and chemotherapy regimens for RCC patients while predicting their survival prognosis. The Cancer Genome Atlas (TCGA) provided us with patient information, while the GeneCards database allowed us to collect genes involved in glycosylation. GSE29609 was used as external validation to assess the accuracy of prognostic models. The "ConsensusClusterPlus" program created molecular subtypes based on genes relevant to glycosylation discovered using differential expression analysis and univariate Cox analysis. We examined immune cell infiltration as measured by estimate, CIBERSORT, TIMER, and ssGSEA algorithms, Tumor Immune Dysfunction and Exclusion (TIDE) and exclusion of tumour stemness indices (TSIs) based on glycosylation-related molecular subtypes and risk profiles. Stratification, somatic mutation, nomogram creation, and chemotherapy response prediction were carried out based on risk factors.

Results: We built and verified 16 gene signatures associated with the prognosis of ccRCC patients, which are independent prognostic variables, and identified glycosylation-related genes by bioinformatics research. Cluster 2 is associated with lower human leukocyte antigen expression, worse overall survival, higher immunological checkpoints, and higher immune escape scores. In addition, cluster 2 had significantly better angiogenic activity, mesenchymal EMT, and stem ability scores. Higher immune checkpoint genes and human leukocyte antigens are associated with lower overall survival and a higher risk score. Higher estimated and immune scores, lesser tumor purity, lower mesenchymal EMT, and higher stem scores were all characteristics of the high-risk group. High amounts of tumor-infiltrating lymphocytes, a high mutation load, and a high copy number alteration frequency were present in the high-risk group.Discussion.According to our research, the 16-gene prognostic signature may be helpful in predicting prognosis and developing individualized treatments for patients with renal clear cell carcinoma, which may result in new personalized management options for these patients.

Keywords: Glycosylation-related genes (GRGs); Immune infiltration; Molecule subtypes; Renal clear cell carcinoma; Signature.