CD99 is a potential diagnostic and immunological biomarker in pan-cancer

Abstract

Background: As a cell adhesion factor, cluster of differentiation 99 (CD99) mainly plays a role in regulating cell differentiation. In cancer research, CD99 primarily focuses on sarcoma. Recent studies have revealed that CD99 plays a critical role in the initiation and progression of several cancers. However, a comprehensive pan-cancer analysis of CD99 has not been performed.

Methods: In this study, we utilized advanced bioinformatics techniques to conduct an in-depth investigation of CD99 expression across diverse tumors, its prognostic and diagnostic implications, the predominant modes of genetic alteration, correlations with immune cell infiltration and immune checkpoints, enrichment analyses of related gene expression, and mechanisms of drug resistance.

Results: Our findings revealed that CD99 was significantly upregulated in numerous common cancers and was associated with both diagnostic and prognostic outcomes. Notably, genetic alterations of CD99 predominantly manifest as deletions in sarcoma. Furthermore, CD99 exhibited strong correlations with nearly all infiltrating immune cells and their corresponding checkpoints. Enrichment analyses further emphasized the potential role of CD99 in epithelial-mesenchymal transition (EMT) pathways and leukocyte migration. Additionally, our investigations into drug resistance indicated that CD99 expression was primarily linked to resistance against antitumor agents such as 5-fluorouracil and belinostat.

Conclusions: Through this comprehensive pan-cancer analysis, we elucidated novel roles for CD99 in various cancer types, providing important insights for clinical treatment strategies and drug development.

Keywords: CD99; Drug sensitivity; Immunotherapy; Pan-cancer; Prognosis.

Fig. 1

The analysis of CD99 expression. A The differences in CD99 mRNA expression between normal tissue data in the GTEx database and tumor tissue data in the TCGA database. B The differences in CD99 protein expression were based on the UALCAN database using CPTAC data. *, P < 0.05; **, P < 0.01; ***, P < 0.001, ns, no significance

Fig. 2

Genetic mutations of CD99 analyzed using the cBioPortal tool. A Frequency of CD99 mutations in different tumor types. B Types, sites and number of cases with CD99 genetic alterations in pan-cancer from cBioPortal. CD99, cluster of differentiation 99; TCGA, The Cancer Genome Atlas

Fig. 3

The ROC curve about the diagnostic value of CD99. The association between CD99 expression and prognostic value was evaluated using R software

Fig. 4

The relationship between prognosis and CD99 expression. The patients’ overall survival (OS) (A) and recurrence-free survival (RFS) (B) in a high or low CD99 level across diverse tumor types were demonstrated by Kaplan–Meier plotter

Fig. 5

The relationship between CD99 expression and tumor-infiltrating immune cells, and immune checkpoints in pan-cancer. Analyzed the correlation between CD99 expression and the infiltration degrees of immune cells (A), and immune checkpoint molecules (B) in tumors by the R software. *, P < 0.05; **, P < 0.01

Fig. 6

The relationship between CD99 expression and TMB, MSI, and RNA methylation modifications. The correlation between CD99 expression and TMB (A) or MSI (B) according to the TCGA database. The correlation between mRNA expression of CD99 and RNA modification (m1A, m5C, m6A) using Sangerbox (A). *, P < 0.05

Fig. 7

Enrichment analysis of CD99. A The associated pathway analysis of CD99 in the GSCA database. B The CD99-associated protein-protein interaction networks were obtained from the STRING online database. C The GO and KEGG enrichment analysis of CD99-related genes by the “clusterProfiler” of R software

Fig. 8

Drug sensitivity analysis of CD99 in pan-cancer. A The drug sensitivity analysis of CD99 in GDSC. B The drug sensitivity analysis of CD99 in CTRP. GSCA, Gene Set Cancer Analysis

Fig. 9

The role of CD99 in A375 and U251 cells. A RT-qPCR analysis of CD99 expression in A375 cells after they were transfected with siRNA-CD99. B Proliferation assay to evaluate the cell count of A375 cells following siRNA-CD99 transfection. C Transwell assay to assess the migration capacity of A375 cells after they were transfected with siRNA-CD99. D RT-qPCR analysis of CD99 expression in U251 cells after they were transfected with siRNA-CD99. E Proliferation assay to evaluate the cell count of U251 cells following siRNA-CD99 transfection. F Transwell assay to assess the migration capacity of U251 cells after they were transfected with siRNA-CD99. **, P < 0.01; ***, P < 0.001