Gene expression analysis data
To clarify the effects of ABAT in various human cancers, TIMER2.0 was applied to explore the expression levels of ABAT in multiple cancers in the TCGA database, as shown in Figure 1(a). The difference of ABAT expression between normal tissues and some cancers was shown with p< 0.001, including BRCA, COAD, KICH, KIRC, KIRP, LIHC, LUSC, THCA, and UCEC; but HNSC, PCPG, and STAD showed p<0.05.
To supplement the missing normal tissue control for some tumors, authors selected normal tissues corresponding to those tumors from the GETx database for comparison. Then they obtained the results in Figure 1(b), which shows the expression of ABAT of BRCA, LAML, TGCT have significant differences with normal tissues (p<0.05). However, we didn't find significant differences in ACC, DLBC, LGG, OV, SARC,THYM, and UCS.
CPTAC, which was used to express the protein level of ABAT, integrated genomic and proteomic data to identify and describe the proteins of tumors and normal tissues and explored the candidate proteins as tumor biomarkers. Figure 1(c) indicated that Clear cell renal cell carcinoma, LUAD, Breast cancer showed significant expression differences of protein level of ABAT with normal tissues, respectively (p<0.05 Figure 1(c)). However, there was no significant difference by comparing Ovarian cancer, Colon cancer, Uterine corpus endometrial carcinoma, and Pediatric Brain Cancer with normal tissues (p>0.05 Figure 1(c)).
GEPIA2.0 was used to clarify the expression level of ABAT in different pathological stages of tumors as Figure 1(d) shows, BRCA, Esophageal carcinoma (ESCA), KIRC, KIRP, LIHC, LUAD, and PAADindicated significantly different expressions of ABAT in pathological stages (p<0.05).
Survival analysis data
GEPIA2.0 was also applied to determine the prognostic of ABAT by the tumor data of TCGA and GEO databases. The low expression of ABAT in ACC (p=0.019), KIRC (p=0.00013), KIRP (p=0.033), LGG (p=0.019), and LUAD (p=0.000075) that showed in Figure 2(a) was highly associated with the poor OS. Moreover, the relationship between OS and ABAT expression levels in LIHC could be reserved for about 80 months (p=0.0021). As showed in Figure 2(b), the increased ABAT expression in CESC (p=0.39), DLBC (p=0.7), THCA (p=0.54), UCS (p=0.48) and UVM (p=0.22) seem to associate with increased OS rate, but not significantly. The DFS analysis of ABAT was shown in Figure 2(c), the low expression of ABAT in ACC (p=0.0091), KIRC (p=0.0034), KIRP (p=0.037), and PRAD (p=0.0083) was associated with the lower DFS. The relationship between OS and ABAT expression level in BRCA (p=0.0017), LGG (p=0.0063) was reserved for about 150 months. In Figure 2(d), the high expression level of ABAT in CESC (p=0.17), DLBC (p=0.24), THCA (p=0.5), UCEC (p=0.42) and UVM (p=0.24) show a not significant association with high DFS rate.
Genetic alteration analysis data
Previous researches have reported the single-nucleotide polymorphisms (SNPs) of ABAT were associated with some diseases, i.e., affective disorder[35]. Here, the cBioportal was selected to process the tumor data of TCGA for exploring ABAT genetic mutation levels in various cancers. As Figure 3(a) shows, the top 1 alteration of frequency of ABAT was BLCA (>5%) with "Amplification" as the primary type. The main component of SKCM and UCEC is "Mutation" at about 4% alteration frequency. Interestingly, UCS, ASCC, DLBC, and ESCA have all gene "Amplification". The types of "Mutation" and "Amplification" were the majority part of ABAT genetic alteration.
The sites, types, and case number of ABAT genetic alterations are shown in Figure 3(b), which offers 106 genetic alteration data, including 88 "Missense", 8 "Truncating", 5 "Splice", and 5" SV/Fusion". The alteration of site R436*/Q has been found in 2 UCEC cases, 1 SKCM case, and 1 HNSC case, which the missense mutation may cause. The missense mutation of ABAT was the primary type of genetic alteration. Figure 3(c) presented the R436 site in the 3D structure of the ABAT protein.
The survival analysis of ABAT genetic alteration also used the cBioportal. We explored the OS, DFS, Disease-specific Survival (DSS), and Progression-Free Survival (PFS) of ABAT genetic alteration in UCEC and BRCA. Figure3(d) indicates that patients with UCEC with altered ABAT showed better prognosis in OS (p=0.0272) and PFS (p=0.0273), but not DFS (p=0.275) or DSS (p=0.0685). However, there was no significant association between the alteration of the ABAT gene and the difference in BRCA prognosis.
Immune infiltration analysis data
The tumor-infiltrating immune cell is the main component of the tumor microenvironment and is associated with cancer initiation, progression, or metastasis [36, 37]. Kwa and Chen reported that the tumor stromal microenvironment could regulate the function of tumor-infiltrating immune cells[38, 39].
The EPIC, MCPCOUNTER, XCELL, and TIDE algorithms were used through TIMER2.0 to clarify the correlation between ABAT and tumor-infiltrating immune cells in various cancers from TCGA. Figure 4(a) shows a significant positive correlation between ABAT and cancer-related fibroblasts of BRCA, CESC, HNSC, HNSC-HPV-, LUSC, SKCM, SKCM-Metastasis, and TGCT. The negative correlation between ABAT and cancer-associated fibroblasts of ESCA, KIRC, KIRP, PCPG, and PRAD can also be observed in Figure 4(a). The cancer-associated fibroblasts (CAFs) of KIRC, KIRP, and PRAD are negatively associated with ABAT, and these tumors show a low OS and DFS when ABAT expression decreases. The CAFs of CESC, THCA, UVM and a few tumors positively correlate with ABAT expression. The survival-related analyses show that high expression of ABAT in these tumors seems linked to a not significant favorable prognosis in these tumors in this study.
One of the algorithms was used to get the scatter plot of the relationship between cancer-related fibroblasts and ABAT in individual tumors. For instance, the ABAT expression level in BRCA is positively associated with the infiltration level of the cancer-related fibroblasts (Figure 4(b) Rho=0.237, p=4.09e-14) based on the EPIC algorithm.
Enrichment analysis of SND1-related partners
The different pathway enrichment analysis was conducted to identify targeted ABAT combining proteins and their corresponding expression-related genes for exploring the molecular mechanism of ABAT during tumor development. We selected the 50 ABAT-binding proteins with experimental evidence from STRING and used Cytoscape software for decoration to get Figure 5 (a). The tool GEPIA2 was used to select the top 100 genes most close to ABAT from the TCGA database and draw scatter plots. Figure 5(b) indicated some genes that have a positive association with the ABAT expression level, such as ASTN1 (Astrotactin 1) (R=0.81), APC2 (Adenomatous polyposis coli 2) (R=0.82), ATCAY (caytaxin) (R=0.77), etc. And then, we used the gene and TIMER2.0 to draw the heatmap of the correlation between ABAT and those genes in cancers (Figure 5(b)). As Figure 5(c) shows, most specific cancers positively associate ABAT and the above genes. The intersection analysis of the 50 ABAT-binding proteins and the top 100 genes showed a joint member, ALDH5A1, in Figure 5(d).
The above two groups have combined in Metascape for exploring the results of Gene Ontology annotation. Figure 5(e, f, g) indicated the cell junction organization might produce the essential benefits of ABAT during the tumor pathogenesis. The majority of genes may also be associated with cell behaviors, such as the biosynthesis and metabolism of amino acids, carbohydrate metabolic process, regulation of trans-synaptic signaling, etc.