Comprehensive analysis identifies IFI16 as a novel signature associated with overall survival and immune infiltration of skin cutaneous melanoma

Abstract

Background: Skin cutaneous melanoma (SKCM) is the most common skin tumor with high mortality. The unfavorable outcome of SKCM urges the discovery of prognostic biomarkers for accurate therapy. The present study aimed to explore novel prognosis-related signatures of SKCM and determine the significance of immune cell infiltration in this pathology.

Methods: Four gene expression profiles (GSE130244, GSE3189, GSE7553 and GSE46517) of SKCM and normal skin samples were retrieved from the GEO database. Differentially expressed genes (DEGs) were then screened, and the feature genes were identified by the LASSO regression and Boruta algorithm. Survival analysis was performed to filter the potential prognostic signature, and GEPIA was used for preliminary validation. The area under the receiver operating characteristic curve (AUC) was obtained to evaluate discriminatory ability. The Gene Set Variation Analysis (GSVA) was performed, and the composition of the immune cell infiltration in SKCM was estimated using CIBERSORT. At last, paraffin-embedded specimens of primary SKCM and normal skin tissues were collected, and the signature was validated by fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC).

Results: Totally 823 DEGs and 16 feature genes were screened. IFI16 was identified as the signature associated with overall survival of SKCM with a great discriminatory ability (AUC > 0.9 for all datasets). GSVA noticed that IFI16 might be involved in apoptosis and ultraviolet response in SKCM, and immune cell infiltration of IFI16 was evaluated. At last, FISH and IHC both validated the differential expression of IFI16 in SKCM.

Conclusions: In conclusion, our comprehensive analysis identified IFI16 as a signature associated with overall survival and immune infiltration of SKCM, which may play a critical role in the occurrence and development of SKCM.

Keywords: Bioinformatics; Feature selection algorithm; IFI16; Prognostic signature; Skin cutaneous melanoma.

Fig. 1

Flowchart of the research methodology

Fig. 2

Two-dimensional PCA cluster plot before and after correction and volcano plot of DEGs. A Two-dimensional PCA cluster plot of GSE130244, GSE3189, GSE7553 and GSE46517 datasets before batch effect correction. B Two-dimensional PCA cluster plot of GSE130244, GSE3189, GSE7553 and GSE46517 datasets after batch effect correction. C Volcano plot of differential expressed genes (DEGs); the red represents the up-regulated genes, the black represents no significant difference genes, and the green represents the down-regulated genes

Fig. 3

Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene ontology (GO) functional enrichment analysis of DEGs. A KEGG pathway enrichment analysis of DEGs. B GO functional enrichment analysis of DEGs; BP represents biological process, CC represents cell component, MF represents molecular function

Fig. 4

Screening and preliminary validation of feature genes. A Feature selection via the least absolute shrinkage and selection operator (LASSO) logistic regression model. B Feature selection via Boruta algorithm. C Venn diagram demonstrating the intersection of feature genes obtained by the two algorithms. D Preliminary validation of mRNA expression of intersected feature genes using the data from TCGA and GTEx databases

Fig. 5

Kaplan–Meier survival analysis of feature genes, the receiver operating characteristic (ROC) curves of the classification effectiveness of the prognostic biomarker IFI16, and the gene set variation analysis of IFI16. A Kaplan–Meier survival analysis of AFF1, AHNAK, CAT, CHIN3, COTL1, CXCL9, IFI16, IRF6, LAMB4, LSAMP, PCOLCE2, PLAT, SOX4 and ZBTB16. B ROC curves of the classification effectiveness of IFI16 in GSE130244, GSE3189, GSE7553 and GSE46517 datasets. C The gene set variation analysis of IFI16; the blue represents the functional annotations of gene sets with up-regulated IFI16, the grey represents the functional annotations of gene sets with no significant difference IFI16, and the green represents the functional annotations of gene sets with down-regulated IFI16

Fig. 6

Evaluation of immune cell infiltration and correlation between IFI16 and immune cells. A Bar plot of ratio of different immune cells in SKCM samples from TCGA database. B Correlation heatmap of immune cells. Red represents positive correlation, blue represents negative correlation, and darker color represents stronger correlation. C Correlation between IFI16 and infiltrating immune cells

Fig. 7

Fluorescence in situ hybridization (FISH) and immunohistochemical (IHC) staining of prognostic biomarker IFI16 in SKCM and normal skin tissue. A SKCM (×200) by FISH; B normal skin (×200) by FISH; C SKCM (×200) by IHC; D normal skin (×200) by FISH; E SKCM of location outside the trunk (×200) by IHC; F SKCM of location inside the trunk (×200) by IHC; G comparison of optical density value of FISH; H comparison of optical density value of IHC; I comparison of optical density value of IHC of SKCM sample with different clinical features