Downregulation of ATP binding cassette subfamily a member 10 acts as a prognostic factor associated with immune infiltration in breast cancer

Abstract

The human ATP binding cassette (ABC) family of transporter proteins plays an important role in the maintenance of homeostasis in vivo. The aim of this study is to evaluate the potential diagnostic, prognostic, and therapeutic value of the ABCA10 gene in BRCA. We found that ABCA10 expression was downregulated in different subgroups of breast cancer and strongly correlated with pathological stage in BRCA patients. Low expression of ABCA10 was associated with BRCA patients showing shorter overall survival (OS). ABCA10 expression may be regulated by promoter methylation, copy number variation (CNV) and kinase, and is associated with immune infiltration. Our study also demonstrated the potential role of ABCA10 modifications in tumor microenvironment (TME) cellular infiltration. Nevertheless, the regulatory mechanism remains unknown and immunotherapy is marginal in BRCA. We demonstrate the expression of different ABCA10 modulators in breast cancer associated with genetic variants, deletions, tumor mutation burden (TMB) and TME. Mutations in ABCA10 are positively associated with different immune cells in six different immune databases and play an important role in immune cell infiltration in breast cancer. Overall, this study provides evidence that ABCA10 could become the potential targets for precision treatment and new biomarkers in the prognosis of breast cancer.

Keywords: ABCA10; biomarker; breast cancer; multi-omics; prognosis.

Figure 1

ABCA10 was significantly mutated in breast cancer compared with normal breast tissue. (A) Waterfall Plot of the top 30 mutated genes from TCGA. The bar plot indicates the number of genetic mutations per patient, while the right bar plot displays the number of genetic mutations per gene. (B) The mRNA expression levels of ABCA10 in multiple cancers on ONCOMINE database. Red background with numbers indicates the studies including ABCA10 expression levels meeting our selection standards (with P-values <0.05 and expression fold changes >1.5-fold change and expressed gene rank in the top 10% as our selection threshold) in cancer tissue; Blue (the same selection threshold) in normal tissues. (C, D) Volcano and scatter and volcano plots exhibiting genes associated with alterations in ABCA10 CNA frequency. (E) Box blot representing the 10 most frequently altered genes. (F) Mutation counts in patients with different kinds of cervical cancer in the TCGA dataset. (G) The distribution and correlation of CNVs are labeled as gains and losses, presented as visual ratios.

Figure 2

Frequency and type of ABCA10 alterations in breast cancer. (A) Analysis of various mutations in the ABCA10 gene in human cancer data. (B) The graphical view showing the ABCA10 protein domain and the location of specific mutations. (C) The illustration of the definition of somatic cell copy alteration in ABCA10 deletion, arm and chromosome levels. (D) Expression of ABCA10 in different types of mutated tumor tissues.

Figure 3

Transcriptional level of ABCA10 in BRCA. (A) Overall survival estimates for ABCA10 mRNA levels from Kaplan-Meier plotter database. (B) Expression of ABCA10 in BRCA and normal tissues. (C, D) ABCA10 expression in normal, BRCA primary tumor and metastatic tumor from different datasets. Violin (E) and box plot (F) to evaluate ABCA10 mRNA expression in BRCA patients based on pathological stage. (G) The mRNA expression level of ABCA10 among different subtypes of BC from TCGA database. (H, I) Significance of dependency of ABCA10 in 84 BRCA cell lines and different subtypes based on the CRISPR screen. (J) mRNA expression of ABCA10 in normal breast cells and multiple breast cancer cells. (K) qPCR analysis of ABCA10 in 30 paired BRCA and non-tumor tissues. N and T represent non-tumor and tumor tissues, respectively. (L) Representative images of ABCA10 staining in BRCA tissues. (M) IHC scores of ABCA10 expression in BRCA tissues. ^**P < 0.01, ^***P < 0.001.

Figure 4

Expression of ABCA10 gene in breast cancer in Oncomine database. ABCA10 mRNA levels from (A and B) Curtis Breast statistics cohort, (C and D) TCGA Breast Statistics cohort, (E) Gluck Breast Statistics cohort (F) Radvanyi Breast Statistics cohort in BRCA and normal tissue. Note: p < 0.05 indicates statistical significance; ABCA10 was among the top 10% overexpressed genes in all four different datasets of BRCA.

Figure 5

Association between ABCA10 gene expression and clinical pathological parameters in patients with breast cancer. (A, B) ABCA10 mRNA expression levels were shown in breast cancer patients by bee swarm in DNA microarray datasets and RNA-sequencing datasets. (Abbreviations: ER: estrogen receptor; PR: progesterone receptor; HER2: human epidermal growth factor receptor 2).

Figure 6

Functional prediction and enrichment analysis of ABCA10 expression in breast cancer. The predictability and descriptiveness between mRNA expression and shRNA (A) and sgRNA (B) functions are plotted with breast cancer cell lines. (C) Genes with shRNA/sgRNA overlap are identified in the positive correlation and negative correlation Venn diagram analysis. (D) Pearson test was used to analyze the differential gene expression related to ABCA10 in BRCA. (E) The top 20 functions of ABCA10 in BRCA are used for enrichment analysis.

Figure 7

Expression of common mutated genes and ABCA10 in BRCA. (A) Relationship between ABCA10 and the six highly mutated genes in breast cancer. (B) Gene_Mutation module comparing PIK3CA mutation status among ABCA10 gene expression in pan-cancer. (C) Statistics of PIK3CA mutation status among ABCA10 gene expression in breast cancer (n = 1017). (D) Gene_Mutation module comparing TP53 mutation status among ABCA10 gene expression in pan-cancer. (E) Statistics of TP53 mutation status among ABCA10 gene expression in breast cancer (n = 1017).

Figure 8

Correlation of ABCA10 expression with immune infiltration level in BRCA. (A) Immune cell bars show the expression of the ABCA10 gene. (B) The infiltration level of various immune cells under different copy numbers of ABCA10 in BRCA. (C) The correlation between ABCA10 expression level and immune infiltration. ^*P < 0.05, ^**P < 0.01, ^***P < 0.001.

Figure 9

Correlation of ABCA10 expression with immune infiltration level in BRCA.

Figure 10

Inhibition of ABCA10 expression in breast cancer cells by pharmacogenomic mapping. (A) Lypressin treatment simulated the effects of ABCA10 inhibition on breast cancer cell lines. (B) Analyses were performed to explore the similarity between ABCA10 and drug-induced genetic characteristics in multiple cancer cell lines to assess the effects.