Introduction

Characterized by rapid progression and metastasis, cutaneous melanoma (CM) is considered as the most aggressive skin cancer, accounting for about 55,500 death worldwide annually [1]. During the past decades, some breakthroughs have been made and significantly improve the survival of metastatic CM patients. For example, the clinical application of BRAF inhibitors and MEK inhibitors, which can extend the progression-free survival (PFS) of BRAF V600-mutate CM patients to 11–14.9 months [1]. However, CM patients carrying BRAF V600 mutations only makes up 45–50% of total melanoma patients [2]. For wild-type BRAF CM (WT Bf-CM) patients, that accounts for about 30% of total CM patients, the response rate to MEK inhibitor is merely 10% [3,4,5]. Lack of therapeutic options resulted in the poor prognosis of WT Bf-CM. With a chemotherapy of dacarbazine, the median PFS of WT Bf-CM patients can be low as 1.5 months [6].

Recently, with the help of next-generation sequencing (NGS) technology, the high heterogeneity of CM has been demonstrated, and it turns out that some features of this heterogeneity such as tumor mutational burden, are associate with the prognosis of CM [7]. Moreover, the high heterogeneity of CM indicates the existence of various biomarkers for prognosis. Although studies have revealed different biomarkers or signature for survival prediction, few of them have focused on the patients with wild-type BRAF CM [8,9,10]. Little is known about which factors can contribute to the poor prognosis of WT Bf-CM patients, apart from less therapy options, failing the need of precise management of WT Bf-CM patients.

To address this issue, this paper analyzed the whole transcription profiles of tumor tissues from 211 patients in the TCGA database, and proposed and validated a novel 7-RNA based signature to predict the prognosis of with wild-type BRAF CM. Moreover, functional analysis and therapy response analysis were also performed, providing a preliminary bioinformatics evidence for the molecular mechanism and potential drug targets that could improving WT Bf-CM prognosis.

Materials and methods

Data extraction

The transcriptional profiles, mutation data and corresponding clinical information of 467 CM patients were downloaded from TCGA-SKCM project with an R package “TCGAbiolinks” [11]. After excluding patients with BRAF somatic mutations and incomplete survival information, 211 WT Bf-CM patients’ tumor samples were equally allocated into training group (n = 106) and testing group (n = 105). NRAS and NF-1 somatic mutational statuses of WT Bf-CM patients were extracted from mutation data. We also downloaded DNA methylation status of specific sites of the WT Bf-CM patients using R package “cgdsr”. RNA-seq data were alignment to the human genome (Gencode.v22 annotation) and the expression of RNAs were measured by Reads Per Kilobase per Million mapped reads (RPKM). RNA expression profiles were identified based on the two following criteria: 1) transcripts were expressed in all WT Bf-CM samples; 2) average of RPKM > 0.1. Eventually, a total of 14,202 RNAs in the profiles were enrolled.

Statistical analysis

In the training set, correlations between the expression of each RNA and overall survival (OS) of patients were calculated by univariate Cox regression and 251 RNAs with their P-values < 0.01 were considered significant to patients’ OS. Random forests were grown based on survival-related RNAs and variable hunting was implemented repeatedly using R package “randomForestSRC”, which output top ranked variables by frequency of occurrence. 7 OS-relevant characteristics were selected as the best and fitted into stepped multivariate Cox regression analysis. Risk scores were weighted by the expression and coefficients of 7 RNAs. The median of risk scores in the training set served as a cut-off, respectively dividing WT Bf-CM patients into high risk groups and low risk groups both in the training set and the testing set. Kaplan–Meier (KM) analysis and visualization were performed to evaluate the survival differences between high risk groups and low risk groups by the “survival” R package. For WT Bf-CM patients with at least 5-year follow ups, receiver operating characteristic (ROC) curves were used to show the sensitivities and specialties of signatures’ abilities to predict the 5-years OS using categorical variables. The area under ROC (AUC) scores and 95% confidence interval (CI) to estimate the reliabilities of signatures were calculated by R package “pROC” [12]. To demonstrate the overview information of risk scores, patients’ survival and expression of signature genes, data were listed in descending order by scaled risk scores and showed through R packages “ggplot2” and “heatmap” as previously described [13]. The results of univariable and multivariable Cox analysis of 7-RNA signature and clinical variables were visualized by the R package “forestplot”.

Nomogram and Decision Curve Analysis (DCA)

Three factors including our signature scores, age and tumor stage which were tightly associated with patients’ survival in multivariable Cox analysis were selected, integrated and used to establish a new model predicting 3-year and 5-year survival of WT Bf-CM patients. Nomogram was drawn with the help of R package “rms”. C-index and calibration curve were used to evaluate the predicting ability of nomogram. DCA is an effective method to estimate molecular signatures and prediction models, especially considering decision preference and clinical demands [14]. Visualization of DCA were achieved in the R statistics environment with the guide of “stdca” (https://github.com/matt-black/dcapy/blob/master/test/resource/stdca.R).

Functional analysis

To explore the function implications of the 7-RNA signature, the differently expression analysis between high risk groups and low risk groups were carried out using R package “edgeR” [15]. Differently expressed genes (DEG) were defined as absolute value of log2FoldChange > 0.8 and adjusted p values < 0.001. The potential interactions of 527 protein-coding DEGs were estimated in String and laid out using Cytoscape [16, 17]. The application in Cytoscape Mcode was used to find the core gene module using criteria of Mcode scores [18]. The top biological process (BP) enrichment results of all DEGs and expression of involved genes were demonstrated and clustered by BP terms using R packages “GOplot” [19]. Gene set enrichment analysis (GSEA) on interested enrichment sets in MSigDB was based on R package “clusterProfiler” [20].

Immune infiltration and drug prediction

To investigate the immune microenvironment of WT Bf-CM tumor samples, 24 sorts of immune cells of each sample were quantified using single sample GSEA (ssGSEA) based on marker genes in previous study and immune infiltration was evaluated by the results of Hierarchical cluster [21, 22]. Combination of immune infiltration results and clinical features was demodnstrated using R package “ComplexHeatmap” [23]. As previously described, we used an R package “pRRophetic” to predict the inhibitory concentration 50 (IC50) of various anti-tumor drugs based on expression profiles [24].

Results

Clinical characteristics of the study populations

A total of 211 WT Bf-CM patients’ samples was downloaded from TCGA. As is shown in Table 1, 64.0% of the samples were collected from male and 36% were from female. The median age of this cohort is 62, and the range of age is 25 ~ 87. 193 samples were matched with pathological stages according to the American Joint Committee on Cancer (AJCC) Cancer staging manual, the percentages of stage 0, I, II, III, IV were 1.9%, 16.6%, 29.9%, 39.3% and 3.8%, respectively. 25.5% of these samples featured with a Breslow thickness < 2 mm, 28.5% within the range of 2 mm to 5 mm, 25.1% > 5 mm. More information of the selected clinical characteristics of are listed in Table 1.

Table 1 Clinico-pathological characteristics of WT Bf-CM patients
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Construction and validation of the expression-based prognostic signature

In order to generate the training set and test set, 211 WT Bf-CM patients were divided equally into two set at random, and their clinical characteristics are shown in Table 1. By analyzing the transcriptome data of training set with univariate Cox regression, 251 RNA were shown significantly to correspond with the 5-year OS of WT Bf-CM patients (p < 0.01). Next, multivariate Cox stepwise regression was performed based on the results of random forests and an expression-based prognostic signature was constructed. This prognostic signature is based on the hazard ratio of 7-RNA and can be formulated as: Risk scores = 0.318*expression of CDC73 + 0.282*expression of RP1-69E11.3 + 0.882*expression of RP11-188D8.1 + 0.968*expression of RP11-116P24.2 + 0.486*expression of TRIB2 + 0.203*expression of VPS13D + 0.199*expression of CELF3. More information about the 7 RNA is listed in Table 2.

Table 2 7 RNAs significantly associated with the overall survival
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The risk scores of each patient from training set were calculated and the median score was used as cut-off for the stratification of high risk group and low risk group. Kaplan–Meier survival analysis and Log-rank test of this two groups showed a significantly poorer prognostics of high risk group compared to low risk group (39.8 months vs. 170.2 months, p < 0.001) (Fig. 1a). For the prognostic value of the proposed signature, ROC curve of training set was drawn and AUC was calculated to be 0.866 (Fig. 1a). To further validate the performance of the proposed signature, patients from test set were stratified similarly based on the median score from training set. Same analysis and tests were also performed with the high- and low-risk groups of test set. Besides the significant correspondence between risk-score and prognosis (59.3 months vs. 90.4 months, p = 0.005), the ROC curve (AUC = 0.699) reveals the considerable potential of this signature for predicting the prognosis of WT Bf-CM patients in the test set (Fig. 1b). Similar results were also observed in the entire WT Bf-CM set that high risk group had shorter OS than low risk group (47.9 months vs. 170.2 months, p < 0.001) and the AUC was 0.780 (Fig. 1c). Risk-related profiles revealed that more deaths occurred and expression of signature genes increased with the raise of risk scores (Fig. 1d).

Fig. 1
figure 1

The 7-RNA signature-related risk score and OS prediction of WT Bf-CM patients. KM analysis reveal the OS differences between high and low risk groups and ROC curves assess the predictive performance of the signature-related score in training set (a); in testing set (b) and in the entire set (c). P values were calculated by two-sided log-rank tests, total AUC values were estimated and 95% CI were computed with 2000 stratified bootstrap replicates. d Risk-related plots illustrate the risk scores, survival status of patients and heatmap of 7 RNAs’ expression

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Independence evaluation of the signature in prognostic prediction from clinical and pathological factors

Traditional clinical and pathological factors have been well-clarified to be associated with the prognosis of melanoma patients, including age, gender, pathological stage, Breslow depths and radiation therapy [1, 25]. Using univariable and multivariable analysis, age and stage were shown to be significantly associated with the prognosis of 211 WT Bf-CM patients in addition to the proposed signature (Fig. 2a). Therefore, age and stage were chosen to be compared with the proposed signature for its independence. As is shown in Fig. 2b, when applied in each subgroup of entire set divided according to the risk stratification of each factor, the proposed signature revealed considerable performances in prognosis prediction. These results support the independence of this expression-based prognostic signature. Finally, we also examined the performances of our signature in CM considering less common mutations including NF-1 mutant, NRAS-mutant, and triple negative CM [26]. The results are shown in Fig. S1 and further support that our 7-RNA signature was independent of NRAS and NF-1 mutant status in BRAT-WT CM.

Fig. 2
figure 2

Signature’s independence of clinical factors and stratification analysis. Univariable Cox regression and multivariable Cox regression analysis were performed, P-value (significance), Hazard Ratio and 95% CI were respectively shown in (a) and (b). KM and ROC analysis of regrou** cohorts based on age and stage were correspondingly demonstrated in (c) and (d)

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Comparison of the signature with other known prognostic biomarkers

Considering the burden brought by CM, several studies have proposed other biomarkers for CM prognosis prediction [8, 27,28,29,30,31]. Although none of them specifically aimed at WT Bf-CM, we compared our signature with them to examine the predictive performances in WT Bf-CM prognosis. As is shown in Fig. 3a and Table 3, the ROC analysis of time-dependent analysis in WT Bf-CM cohort proved that our signature had significant higher AUC than other biomarkers, and might be superior in WT Bf-CM prognosis prediction exclusively.

Fig. 3
figure 3

a Comparison of predictive performances of our 7-RNA signature and other known biomarkers/signatures. b The signature-based nomogram to predict 3-year and 5-year survival probability. c The calibration plot of nomogram predicted and real surviving proportions. d Net reduction interventions and (e) net benefit of decision curve analysis

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Table 3 The ROC results of our signature and other latest biomarkers of SKCM
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Construction and evaluation of a prognostic nomogram for WT Bf-CM

Nomogram has been a welcomed quantitative predictive tool for clinical practice. Based on the results from univariable and multivariable analysis above (Fig. 2a), a nomogram to predict 3 − year and 5-year survival of WT Bf-CM patients was constructed based on the risk scores of the proposed signature, age and pathological stage (Fig. 3b). The C-index of this nomogram was calculated to be 0.7505 and calibration plot was also drawn, exhibiting an acceptable accuracy for prediction (Fig. 3c). Additionally, DCA was performed to assess the efficiency of the proposed nomogram (Fig. 3d, e). When the threshold probability was lower than 25%, our nomogram would be more beneficial and when threshold probability higher than 25%, our signature would be more beneficial. These results supported the promising complementary use of our signature and nomogram.

Functional analysis based on the signature grou**

Cellular and molecular mechanism accounting for the prognosis of melanoma have always been a hot topic of cancer research. Thus, we aimed to find out what biological process might explain the predictive function of this signature. 865 genes were defined as DEGs between high risk and low risk group (Fig. 4a) and interactions of 527 proteins provided a landscape of potential molecular mechanisms (Fig. 4b). Using Mcode, a six-gene module with highest score (score 6) was selected as core module, which was closely associated with mRNA processing, splicing and metabolic process (Fig. 4b). BP enrichment analysis showed various processes were involved, including response to oxygen levels, RNA splicing, regulation of developmental growth, etc. (Fig. 4c). Besides, GSEA analysis indicated that the high-risk group was associated with the up-regulation of GO_NUCLEAR_SPECK (NES = 1.361, p = 0.026), GO_PYRIMIDINE_NUCLEOTIDE_BIOSYNTHETIC_PROCESS (NES = 1.573, p = 0.039), REACTOME_MITOTIC_PROMETAPHASE (NES = 1.413, p = 0.041) and TATA_01 (NES = 1.589, p = 0.005) (Fig. 4d). Taken together, these results supported that the predictive function of this signature might be related to the up-regulation of RNA splicing, transcription, and cellular proliferation in the high-risk group, and upregulation of these processes have been demonstrated to be linked to malignancy of cancer [32, 33].

Fig. 4
figure 4

a Volcano plot of DEGs between high risk and low risk group. b Protein–protein interactions (PPI) of DEGs in String and core module of PPI with the highest Mcode score. c GOplot of BP enrichment results showing term-clustered gene set and fold changes of genes. d GSEA results of specific enrichment set in MSigDB

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Evaluation for the therapeutic responses based on signature grou**

The treatment of cutaneous melanoma has made a considerable progress recently, especially for the application of immune-therapy and targeted drugs. Hence, we wondered if the proposed signature could provide clues for the therapeutic responses of WT Bf-CM patients. By estimating the immune infiltration of the WT Bf-CM cohort, we found that little differences in 24 sorts of immune cells infiltration (Fig. 5a). Similarly, the comparison of the expression of 9 immune-markers showed little differences between high- and low- risk groups (Fig. 5b). Of note, although the checkpoint therapies targeting programmed cell death 1 (PD-1), programmed cell death-ligand 1 (PD-L1) and cytotoxic T lymphocyte antigen 4 (CTLA-4) have been recommended for the treatment of WT Bf-CM, their expression didn’t seem to be high both in high- or low-risk groups, suggesting the poor responses of checkpoint therapy in WT Bf-CM patients. Interestingly, the analysis of responses to chemo-therapies showed significant differences of 9 chemotherapeutic drugs in estimated IC50 between high and low-risk groups, along with an increased IC50 to these 9 chemotherapies in high risk group (Fig. 5c). Among these 9 chemotherapeutic drugs, the targets of AKT.inhibitor.VIII, AS601245, GDC0941, MK.2206, PF.02341066 (Crizotinib), Rapamycin and Temsirolimus are involved in the regulation of PI3K/Akt/mTOR pathway [34,

Conclusion

Collectively, this study proposed a novel 7-RNA signature for the prognostic prediction of WT Bf-CM. As far as we are concerned, this is the first prognostic signature for WT Bf-CM, which might provide more assistance in the management of WT Bf-CM patients.