Expression analysis data
The human DAPL1 gene is located on chromosome 2q24.1 (Fig. 1). Fig. S1 shows the evolutionary tree of the DAPL1 gene in different species. Our study aims at exploring the functional links of the human DAPL1 gene with breast cancer from different perspectives. We first performed gene expression analysis. As shown in Fig. 1B, based on the dataset of the TCGA-BRCA cohort, we observed a lower expression level of DAPL1 in the tissues of breast invasive carcinoma (P<0.01), compared with the normal tissues. A similar result was detected in the DAPL1 expression analysis of Radvanyi breast dataset (Fig. 1C, P<0.01). The immunohistochemical result further showed that the expression level of DAPL1 protein was higher in the normal tissues than that in the tissues of breast duct carcinoma and lobular carcinoma (Fig. 1D). Besides, the DAPL1 gene showed the different expression levels among different intrinsic subtypes and pathologic stages (Fig. 1E, P<0.01). Nevertheless, there is no significant statistical correlation between DAPL1 expression and some other factors of ER status, HER2 status, PR status, race, ethnicity, and radiation therapy (Fig. 1E). Therefore, the above suggested the potential role of the DAPL1 gene in the etiology of breast cancer.
Survival curve analysis data
Subsequently, we performed a correlation analysis between DAPL1 gene expression and the clinical overall survival outcome of breast cancer patients in TCGA-BRCA cohort. As shown in Fig. 2A, we observed a correlation between high expression of the DAPL1 gene and better clinical prognosis of overall survival (P=0.0028). When adjusted by the factors of cancer type (P=0.0043) or menopause status (P=0.0014), positive statistical results were detected as well (Fig. 2A). Meanwhile, based on the survival data in the GEO database, high DAPL1 expression was associated with a better prognosis of distant metastasis free survival (DMFS, Fig. 2B, P=0.0023), relapse free survival (RFS, P=0.0065), but a worse prognosis of post progression survival (PPS, P =0.023). In addition, we further conducted a series of subgroup analyses by the different clinical factors and observed distinct experimental results. For instance, the lowly expressed DAPL1 is associated with the worse prognosis of RFS (Table 1, P=0.04, HR=0.72) and DMFS (Table S1, P=0.011, HR=0.48), but a better prognosis of OS (Table S2, P= 0.0042, HR=2.84) and PPS (Table S3, P= 0.0016, HR=2.87) for the breast cancer patients with pathological grade 3. These results provide evidence regarding the association between the DAPL1 expression and clinical outcomes of breast cancer.
DNA methylation analysis data
We attempted to exploit the potential molecular mechanism from the point of DAPL1 DNA methylation based on the methylation data of TCGA-BRCA. Fig. 3A presents the waterfall plot of DAPL1 DNA methylation levels for each breast cancer case. We did not observe the statistically significant difference of the promoter methylation levels of DAPL1 between the normal and tumor tissues, or among the different pathological analyses and tumor types (Fig. 3B, all P>0.05). We also observed a similar negative result using the MEXPRESS method (Fig. S2). Thus, DNA methylation is not involved in the molecular mechanism of DAPL1 in the pathogenesis of breast cancer.
Genetic alteration analysis data
Based on the TCGA-BRCA data, we explore the possible molecular mechanisms from the perspective of genetic alteration. As shown in Fig. 4A, we only observed a genetic alteration frequency of ~ 0.5% for the breast ductal carcinoma cases of TCGA. We also did not observe a correlation between DAPL1 genetic alteration and the prognosis of overall survival (Fig. 4B, P=0.992), disease/pression free survival (Fig. 4C, P=0.317) for the total cancer patients. We then analyzed the correlation between DAPL1 gene expression and different mutation status of specific genes. As shown in Fig. 4D, compared with the wildtype status, the DAPL1 gene was lowly expressed in the mutated genes of MAP3K1, NUP98, CEP152M, and ZNF300 (P<0.001), whereas it was highly expressed in the mutated KIF2IB gene (P=0.003). Furthermore, we observed that the expression of DAPL1 gene was negatively correlated with the copy number variation (CNV) of the CCDC59 gene (Fig. 4E, R=-0.18, P<0.0001).
Immune cell infiltration analysis data
Also, we aimed to investigate whether the DAPL1 gene is involved in the etiology of breast cancer through immune cell infiltration. As shown in Table 2, the expression of DAPL1 gene in breast cancer was positively correlated with the infiltration level of immune cells: M1 macrophage, NK cell, B cell, Tfh, naive T cell, effector T cell, effector memory T cell, central memory T cell, resident memory T cell (all R>0, P<0.05)., but negatively correlated with the infiltration level of resting or effect Treg T cell (all R<0, P<0.05)
Enrichment analysis data
Based on the data of TCGA-BRCA cohort, we utilized the LinkedOmics approach to screen out a group of DAPL1 expression-correlated positively genes (e.g., MRRP2, KRT5, and LGALS7, etc.) and negatively related genes (e.g., TRPS1, ST3GAL1, ARFGEF1, TPD52, etc.) in Fig. 5A (heat map) and Fig. 5B (association plot). Our correlation analyses showed a strong correlation between the DAPL1 and the selected TRPS1, MRAP2, KRT5 and LGALS7 genes in the TCGA-BRCA cohort (Fig. 5C).
The GO analysis data of Fig. 6 identified the DAPL1-associated cellular component (e.g. DNA packaging complex, methyltransferase complex, etc.), molecular function (e.g. ubiquitin-like protein binding, ribonocleoprotein complex binding, etc), and biological process (e.g. ribonucleoprotein complex localization, translational elongation, etc.). KEGG pathway analysis data further showed a series of pathways, such as ubiquitin mediated proteolysis, RNA transports, cell adhesion molecules (CAMs), chemical carcinogenesis, and so on (Fig. 7A). In addition, GSEA data also suggested the correlation between DAPL1 expression and the biological issuses of DNA repair complex, nucleotide excision repair, translational elongation, ubiquitin-like protein binding, ubiquitin proteasome pathway, and proteasome (Fig. 7B).
DAPL1-correlated kinase analysis
After the prediction of potential phosphorylation sites and kinases, five sites of human DAPL1 protein [NP_001017920.2], namely S10, T35, T77, T86, and Y99, were identified (Fig. 8A). And the CDC2, MAPK were predicted as the potential catalytic kinases of DPAL1 protein at S10 site, while MAPK and PKA were for the T86 site (Fig. 8A). In addition, we performed an enrichment analysis of kinase pathway, and also identified several DAPL1-correlated kinases, such as PRKACA, PRKCA, GSK3B, ROCK, and CHEK1 (Fig. 8B).