In this study, a robust 13-IRGP signature was employed to estimate prognosis in SKCM, which can identify and provide new insights into immunological biomarkers.
Recently, with the development of genomics and transcriptomics, studies about IRGs or prognostic signatures in cancers, including melanoma, have been increasingly reported. In one study of melanoma, Huang et al. [13] first found that 63 IRGs were associated with the OS of patients. Based on Cox regression and LASSO analysis, they identified an IRG signature including 8 IRGs, namely PSME1, CDC42, CMTM6, HLADQB1, HLA-C, CXCR6, CD8B, and TNFSF13, which could better predict the prognosis of patient compared with existing recorded data. For uveal melanoma, an immune-related signature with two genes of MANEAL and SLC44A3, was established [11]. In addition, the researchers revealed a moderate association between the immune checkpoints that contained PD-1, CTLA-4, IDO, and TIGIT and such signature. Li et al. [26] used RNA-seq data from 527 head and neck cancer patients from the TCGA database to establish a prognostic prediction model using multivariable Cox regression analysis. Finally, they identified a 10-IRG signature including SEMA3G, GNRH1, ZAP70, PLAU, SFTPA2, CCL26, DKK1, GAST, PDGFA and STC1. Other studies on IRG signatures in human cancers include breast cancer [12], colorectal cancer [27], hepatocellular cancer [28], lung cancer [29], and gliomas [30]. Therefore, based on TCGA and microarray methods, IRG signatures for predicting the prognosis of patients have increasingly become the focus of research.
We constructed a 13-IRGP prognostic signature for SKCM in this study. There have been no reports about IRGP signatures for SKCM until now. However, in colorectal cancer [17], Wu et al. used six public cohorts, including a training cohort (n = 565) together with five independent validation cohorts (n = 572, 290, 90 177 and 68). They established a 19-IRGP signature that contained 36 unique genes with an obvious relation to patients’ survival. When combined sex, stage, and other clinical factors, the IRGP signature and clinical factor combination showed a higher prognostic accuracy than the individual IRGP signature. In another study of hepatocellular carcinoma [16], researchers also identified a signature of 33 IRGPs that can predict clinical outcomes based on TCGA and GEO data. Compared with traditional studies about gene signatures, since pairwise comparison helped to generate our IRGP, the calculation of risk score was decided by the gene expression of the same patient. The obtained prognostic signature is capable of overcoming the batch effect of various platforms on the one hand and shows no need for scaling and normalization of data on the other hand.
In our study, LASSO penalized Cox regression was adopted for constructing a 13-IRGP prognostic signature of SKCM. We also validated this signature in testing and validation datasets. The training set, the TCGA dataset, and the independent dataset held an average AUC of 0.79, 0.756, and 0.818, respectively. The significant survival differences between the Risk-H and Risk-L groups were determined by K-M survival plots. Similar results were also validated in the TCGA, independent, and GSE65904 datasets. The above results proved the accuracy and robustness of our prognostic signature in SKCM patients.
Our 13-IRGP signature consists of a total of 25 IRGs in SKCM. Here, IFIT5_vs_CASP6 and NFKBIE_vs_CXCL10 were two IRGPs with the largest coefficients. IFIT5, as one of the human IFIT gene families, can affect different biological activities of cancers, such as antiviral immune response, host innate immunity, replication, PAMP recognition, as well as double-stranded RNA signaling [31]. In bladder cancer, it was shown to be positively correlated with pathological characteristics and predicts poor prognosis in patients [32]. Additionally, this gene is capable of inducing the epithelial-mesenchymal transition (EMT) as well as promoting the migration and invasion of cells [32]. CXCL10, as a "key driver chemokine" as well as an effective target for treating autoimmune diseases [33], has been a target for novel cancer therapy in immune activation functions [34]. In melanoma, CXCL10 was reported as a key candidate gene by integrated bioinformatics analysis [35]. The above research results indicated that the IRGs in our signature may be involved in the occurrence and development of SKCM.
However, the study exhibits some limitations. First, adopting retrospective analysis, the study requires a prospective cohort for validating study results and this signature. Second, the molecular functions of the genes in the IRGP signature are still unknown. Finally, RNA-seq and microarray data were used to construct the signature in the study, thus it is necessary to verify the model with IHC or western blotting in a large sample size of clinical tissues.
To sum up, a 13-IRGP prognostic signature is developed here, which will serve as a suitable predictive method for identifying SKCM patients who may be better treated with immunotherapy.