Sperm Autoantigenic Protein 17 Predicts the Prognosis and the Immunotherapy Response of Cancers: A Pan-Cancer Analysis

Abstract

Background: Sperm autoantigen protein 17 (SPA17) is a highly conserved mammalian protein that participates in the acrosome reaction during fertilization and is a recently reported member of the cancer-testicular antigen (CTA) family. It has been reported that the SPA17 expression is limited in adult somatic tissues and re-expressed in tumor tissues. Recently, studies have found that SPA17 regulates the progression of various cancers, but its role in cancer immunotherapy is not clear.

Methods: The pan-cancer and normal tissue transcriptional data were acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets. We explored the SPA17 pan-cancer genomic alteration analysis in the cBioPortal webtool. The Human Protein Atlas (HPA) and ComPPI websites were used to mine the SPA17 protein information. We performed a western blotting assay to validate the upregulated SPA17 expression in clinical glioblastoma (GBM) samples. The univariate Cox regression and Kaplan-Meier method were used to assess the prognostic role of SPA17 in pan-cancer. Gene Set Enrichment Analysis (GSEA) was used to search the associated cancer hallmarks with SPA17 expression in each cancer type. TIMER2.0 was the main platform to investigate the immune cell infiltrations related to SPA17 in pan-cancer. The associations between SPA17 and immunotherapy biomarkers were performed by Spearman correlation analysis. The drug sensitivity information from the Connectivity Map (CMap) dataset was downloaded to perform SAP17-specific inhibitor sensitivity analysis.

Findings: SPA17 was aberrantly expressed in most cancer types and exhibited prognosis predictive ability in various cancers. In addition, our results also show that SPA17 was significantly correlated with immune-activated hallmarks (including pathways and biological processes), immune cell infiltrations, and immunoregulator expressions. The most exciting finding was that SPA17 could significantly predict anti-PDL1 and anti-PD1 therapy responses in cancer patients. Finally, specific inhibitors, like irinotecan and puromycin, which correlate with SPA17 expression in different cancer types, were also screened using Connectivity Map (CMap).

Conclusions: Our results reveal that SPA17 was abnormally expressed in cancer tissues, and this expression pattern could be associated with immune cell infiltrations in tumor microenvironments. Clinically, SPA17 not only acted as a potent prognostic factor to predict the clinical outcomes of cancer patients but was also a promising immunotherapy predictive biomarker for cancer patients treated with immune-checkpoint inhibitors (ICIs).

Keywords: CMap; immunotherapy response; pan-cancer; prognostic biomarker; sperm autoantigenic protein 17 (SPA17).

Figure 1

Basic information of SPA17. (A) The level of SPA17 expression between tumor and normal tissues in each type of cancer based on the integrated data from TCGA and GTEx datasets. (B) Analysis of SPA17 change frequency in pan-cancer research according to cBioPortal database. (C) The SPA17 expression levels between SPA17-deletion and diploid TGCT patients. (D) The immunofluorescence images of SPA17 protein, nucleus, endoplasmic reticulum (ER), microtubules and the incorporative images in A-431 and U251 cell lines. (E) The protein-protein interaction (PPI) network presents the proteins interacting with SPA17. (F, G) Western blot protein detection of the SPA17 expression levels in paired GBM and adjacent normal tissues. The labelled asterisk indicated the statistical p-value (ns, p > 0.05, *p < 0.05, **p < 0.01 and ***p < 0.001).

Figure 2

(A) Based on the univariate Cox regression and Kaplan-Meier models, summary of the correlation between SPA17 expression and overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI) and progression-free interval (PFI). Red indicates that SPA17 is a risk factor for the prognosis of cancer patients, while blue represents a protective factor. Only p-values < 0.05 are shown. (B) The forest plot showed the prognostic role of SPA17 in cancers by univariate Cox regression. The red type of cancer represents SPA17 as a statistically significant risk factor, and blue represents a protective factor. (C–F) Kaplan-Meier overall survival curves of SPA17 in BRCA (C), KIRC (D), LGG (E) and TGCT (F).

Figure 3

The hallmarks gene set enrichment analysis (GSEA) of SPA17 in pan-cancer. The size of the circle represents the false discovery rate (FDR) value of each cancer enrichment item, and the color represents the normalized enrichment score (NES) of each enrichment item.

Figure 4

The correlations of SPA17 expression and the infiltration levels of CD4+ T cells, CAF, progenitor, Endo, Eos, HSC, Tfh, γδT, NKT, regulatory T cells (Tregs), B cells, neutrophils, monocytes, macrophages, dendritic cells, NK cells, Mast cells and CD8+ T cells in cancers. Positive correlation in red and negative correlation in blue.

Figure 5

(A) The Spearman correlation heatmap shows the correlation between the expression of SPA17 and 47 kinds of immune regulators in pan-cancer. Red represents positive correlation and blue represents negative correlation. (B) Correlations between SPA17 expression and tumor mutation burden in pan-cancer. (C) Correlations between SPA17 expression and microsatellite instability in pan-cancer. (D) Kaplan-Meier curve of low and high-SPA17 subgroup in IMvigor210 cohort (anti-PD-L1), and the proportion of tumors (including kidney cancer) in the low-SPA17 and high-SPA17 subgroups in the IMvigor210 cohort who responded to PD-1 therapy. (E) Kaplan-Meier curve of GSE91061 (anti-PD-L1 melanoma) in the low and high-SPA17 patient groups, and the proportion of melanoma patients in the GSE91061 low and high-SPA17 subgroups that responded to anti-PD-1 therapy. The labelled asterisk indicated the statistical p-value (*p < 0.05, **p < 0.01, ***p < 0.001).

Figure 6

Heatmap representing the rich fraction (positive in blue, negative in red) of each drug in the CMap database of each cancer. Components or drugs are ranked in descending number of enriched cancers from right to left.