Database mining analysis revealed the role of the putative H+/sugar transporter solute carrier family 45 in skin cutaneous melanoma

Abstract

Metabolic reprogramming is common in various cancers. Targeting metabolism to treat tumors is a hot research topic at present. Among them, changes in glucose metabolism in cancer have been widely studied. The Warburg effect maintains a high metabolic level in the tumor, accompanied by changes in glucose transporters. The transmembrane transport of sugar was previously thought to be mediated by SGLT and GLUT. Recently, the Solute Carrier Family(SLC) 45 family may be the third sugar transporter. But the role and value of the SLC45 family in melanoma, a highly malignant skin tumor, is unclear. Our study found that the four members of the SLC45 family, SLC45A1-SLC45A4, were differentially expressed in melanoma, but only SLC45A2 and SLC45A3 had prognostic guiding values. Further analysis revealed that the co-expression patterns of SLC45A2 and SLC45A3 were enriched in multiple metabolic pathways, suggesting their potential role in melanoma. In addition, SLC45A2 and SLC45A3 are also associated with immune cell infiltration. In conclusion, SLC45A2 and SLC45A3 are good prognostic indicators for melanoma and have guiding value for the treatment of melanoma in the future.

Keywords: Melanoma; bioinformatics; immunotherapy; solute carrier; transporter.

Figure 1.

(a).Alteration frequency of SLC45A1 across cancers. (b).Alteration frequency of SLC45A2 across cancers. (c).Alteration frequency of SLC45A3 across cancers. (d).Alteration frequency of SLC45A4 across cancers

Figure 2.

(a-d). The 186 melanomas were divided into 167 G1 (primary) and 19 G2 (metastatic) groups. A total of 1809 normal skin samples were collected. Differential expression analysis was performed between the G1, G2, and normal groups. The results showed that compared with normal tissues, SLC45A1 was down-regulated in G1 and G2 groups, while SLC45A2, SLC45A3 and SLC45A4 were up-regulated in G1 and G2 groups, with statistical significance (P < 0.05). (e-h).Overall survival analysis of SLC45A1-A4 expression in melanoma (GEPIA): The high expression of SLC45A2 and SLC45A3 could significantly reduce the overall survival rate. (i-l).Disease-free survival analysis of SLC45A1-A4 expression in melanoma (GEPIA): The high expression of SLC45A2 and SLC45A3 could significantly reduce the disease-free survival rate

Figure 3.

(a-d). Clinical characteristic association of SLC45 family in melanoma:Each column represents a characteristic variable, different colors represent different types or stages, and lines represent the distribution of the same sample in different characteristic variables. (e). The heat map of the top 100 co-expressed genes with SLC45A2. (d). The heat map of the top 100 co-expressed genes with SLC45A3

Figure 4.

(a).GO enrichment analysis of co-expressed genes of SLC45A2. (b).GO enrichment analysis of co-expressed genes of SLC45A3. (c). KEGG enrichment analysis of co-expressed genes of SLC45A2. (d).KEGG enrichment analysis of co-expressed genes of SLC45A3

Figure 5.

(a).Protein-protein interaction (PPI) network for co-expressing genes of SLC45A2. (b).Protein-protein interaction (PPI) network for co-expressing genes of SLC45A2. (c). The top 10 proteins in the PPI network of SLC45A2 according to the MCC score. (d). The top 10 proteins in the PPI network of SLC45A2 according to the MCC score. (e). The proteins which are most closely associated with SLC45A2

Figure 6.

Immue infiltration linked to SLC45A2 and SLC45A3 in melanoma (TIMER): (a). The expression of SLC45A2 was significantly correlated with the infiltration of CD8 + T cells(p = 4.86e-03), macrophages(p = 1.59e-05), and neutrophils(p = 2.64e-08) in melanoma. (b).The expression of SLC45A3 was significantly correlated with the infiltration of B cells(p = 9.79e-03), macrophages(p = 4.24e-04) in melanoma