CD96 Correlates With Immune Infiltration and Impacts Patient Prognosis: A Pan-Cancer Analysis

Abstract

Background: Immunotherapy has significantly improved patient outcomes, but encountered obstacles recently. CD96, a novel immune checkpoint expressed on T cells and natural killer (NK) cells, is essential for regulating immune functions. However, how CD96 correlating with immune infiltration and patient prognosis in pan-cancer remains unclear.

Methods: HPA, TCGA, GEO, GTEx, Oncomine, TIMER2.0, PrognoScan, Linkedomics, Metascape, and GEPIA2 databases were used to analyze CD96 in cancers. Visualization of data was mostly achieved by R language, version 4.0.2.

Results: In general, CD96 was differentially expressed between most cancer and adjacent normal tissues. CD96 significantly impacted the prognosis of diverse cancers. Especially, high CD96 expression was associated with poorer overall survival (OS) and disease-specific survival (DSS) in the TCGA lower grade glioma (LGG) cohort (OS, HR = 2.18, 95% CI = 1.79-2.66, P < 0.001). The opposite association was significantly observed in skin cutaneous melanoma (SKCM) cohort (OS, HR = 0.96, 95% CI = 0.94-0.98, P < 0.001). Notably, SKCM samples demonstrated the highest CD96 mutation frequency among all cancer types. Furthermore, in most cancers, CD96 expression level was significantly correlated with expression levels of recognized immune checkpoints and abundance of multiple immune infiltrates including CD8+ T cells, dendric cells (DCs), macrophages, monocytes, NK cells, neutrophils, regulatory T cells (Tregs), and follicular helper T cells (Tfh). CD96 was identified as a risk factor, protective factor, and irrelevant variable in LGG, SKCM and adrenocortical carcinoma (ACC), respectively. CD96 related genes were involved in negative regulation of leukocyte in LGG, however, involved in multiple positive immune processes in SKCM. Furthermore, CD96 was significantly associated with particular immune marker subsets. Importantly, it strongly correlated with markers of type 1 helper T cell (Th1) in SKCM, but not in LGG or ACC either.

Conclusions: CD96 participates in diverse immune responses, governs immune cell infiltration, and impacts malignant properties of various cancer types, thus standing as a potential biomarker for determining patient prognosis and immune infiltration in multiple cancers, especially in glioma and melanoma.

Keywords: CD96; bioinformatics; biomarker; cancer; immune infiltration; prognosis.

Figure 1

CD96 expression profiles in human normal and cancer tissues. (A) CD96 expression profiles in normal human tissues. (B) The protein expression profiles of CD96 in human normal tissues. (C–F) Representative IHC images of CD96 expression in normal lymph node tissues, normal spleen tissues, breast duct carcinoma tissues, and malignant melanoma tissues.

Figure 2

CD96 expression levels in different types of human cancers. (A) Increased or decreased CD96 in datasets of different cancers compared with normal tissues in the Oncomine database. (B) CD96 expression levels in different tumor types from TCGA database were analyzed by TIMER2.0 (*P < 0.05, **P < 0.01, ***P < 0.001). (C) Comparisons of CD96 expression levels between tumor tissues from TCGA database and normal tissues from GTEx database (*P < 0.05, **P < 0.01, ***P < 0.001).

Figure 3

Survival analysis comparing the high and low expression of CD96 in different types of cancer in the GEO dataset and TCGA dataset. (A–H) Survival curves in eight cohorts (GSE5287, GSE13507, GSE19615, GSE2034, GSE17537, GSE8894, GSE17260, and GSE19234) with significance. (I, J) Relation between CD96 expression and patient prognosis (OS and DSS) of different cancers in TCGA database (*P < 0.05, **P < 0.01, ***P < 0.001). (K–R) Survival curves of OS with significance in eight cancer types (BLCA, CESC, GBM, HNSC, LGG, SKCM, THYM, and UVM) in TCGA.

Figure 4

CD96 mutation landscape. (A) CD96 mutation frequency in multiple TCGA pan-cancer studies according to the cBioPortal database. (B) The general mutation count of CD96 in various TCGA cancer types by the cBioPortal database. (C) Mutation diagram of CD96 in different cancer types across protein domains.

Figure 5

The genome-wide correlation between CD96 and other signatures from the TCGA database (Cancer Regulome program).

Figure 6

Correlations between CD96 and immune checkpoints, as well as other variables of interest. (A) The correlations between CD96 and confirmed immune checkpoints in multiple cancers (*P < 0.05, **P < 0.01, ***P < 0.001). (B) The correlations between CD96 and essential genes involved in MMR in multiple cancers (*P < 0.05, **P < 0.01, ***P < 0.001). (C, D) The correlations of CD96 expression and TMB, MSI in cancers.

Figure 7

Associations of CD96 expression to tumor purity and immune infiltration. (A) Top three scatter plots of correlation between CD96 and stromal score, immune score, ESTIMATE score in various cancers. (B) The correlations of CD96 expression and immune infiltration in cancers.

Figure 8

CD96 expression and function profiles in three representative cancers. (A) CD96 expression levels in different grades or stages in LGG, SKCM, and ACC, respectively (*P < 0.05, **P < 0.01, ***P < 0.001). (B) 232, 421, and 77 related genes were identified in LGG, SKCM, and ACC cohorts. Circos plot showed overlaps in genes (purple curves) and enriched ontology terms (blue curves) between three lists based on their functions or shared pathways. (C, D) GO and KEGG analysis of CD96-related signatures in LGG, SKCM, and ACC.