Identification and Validation of Cellular Senescence-Related Signature to Predict Survival and Immunotherapeutic Responses in Skin Cutaneous Melanoma

Identification and Validation of Cellular Senescence-Related Signature to Predict Survival and Immunotherapeutic Responses in Skin Cutaneous Melanoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification and Validation of Cellular Senescence-Related Signature to Predict Survival and Immunotherapeutic Responses in Skin Cutaneous Melanoma Mengna Li, Xintao Cen, Yan Yan, Li Li, Wei Lai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4943989/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Skin cutaneous melanoma (SKCM) is the most lethal skin cancer with an increasing incidence worldwide. Cellular senescence plays essential roles in tumorigenesis, development, and immune modulation in cancers. However, the correlations of the cellular senescence with tumor progression and tumor immune microenvironment (TIME) in SKCM were poorly understood. Methods: In this study, we explored the expression profiles of 279 cellular senescence-related genes (SRGs) in 469 SKCM patients included from the TCGA database. The univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were conducted to construct a cellular senescence-related signature (CSRS). Kaplan–Meier survival curves as well as receiver operating characteristic (ROC) curve were used to validate the predictive ability of prognostic signature. Consensus clustering analysis was performed to stratify SKCM patients into different clusters and compared them in overall survival. The GSE65904 dataset was further used to validate the stability and applicability of the CSRS. Then, we explored the correlations of the CSRS with tumor-infiltrating immune cells and response to immunotherapy. Finally, the expression levels of prognosis related SRGs were validated based on immunohistochemistry, and the function of RUVBL2 was explored in SKCM cells. Results: We developed a prognostic prediction CSRS for patients with SKCM and verified patients in low-risk group were associated with better prognosis. Moreover, the correlation analysis showed that the CSRS could predict the infiltration of immune cells and immune status of the immune microenvironment in SKCM, and patients with low-risk score might benefit from immunotherapy. Our results implied that a high level of cellular senescence may stimulate immunosurveillance mechanisms and potentiate the tumor suppressive function for SKCM in a senescence-associated secretory phenotype (SASP)-depended manner. In addition, all the SKCM patients in this study were classified into three clusters based on the mRNA expression profiles of 113 SRGs, which revealed that cluster 1 suffered a poor prognosis relative to clusters 2 and 3. Finally, we found that RUVBL2 was significantly upregulated in SKCM cells, and knockdown of RUVBL2 inhibited the SKCM cells proliferation. Conclusions: The CSRS developed in this study can be applied not only as a prognostic tool but also as guidance for individualized immunotherapy for SKCM patients. Skin cutaneous melanoma Cellular senescence Prognostic signature Tumor microenvironment Immunotherapy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 | INTRODUCTION Skin cutaneous melanoma (SKCM) is the most lethal skin cancer with an increasing incidence worldwide. In2020, an estimated 325,000 persons worldwide were diagnosed as having melanoma, and approximately 57,000 persons died of the disease. If 2020 rates continue, the burden from melanoma is estimated to increase to 510,000 new cases (a roughly 50% increase) and to 96,000 deaths (a 68% increase) by 2040 [ 1 ]. Since 1998, the American Joint Committee on Cancer (AJCC) melanoma staging system has served as a foundation for clinical classification. However, clinical outcome of patients with similar or even identical clinical and histological features varies considerably. Notwithstanding current progress in the research of melanoma pathogenesis has facilitated the emergence of some novel therapies against SKCM such as targeted therapy and immunotherapy, curbing disease relapse in SKCM remains an unmet objective [ 2 – 4 ]. Therefore, it is urgently required to explore novel prognostic signature for SKCM patients that can predict survival. The tumor microenvironment (TME) is a complex ecosystem comprised of various cell types including malignant cells, cancer-associated fibroblasts (CAFs), endothelium, keratinocytes, adipocytes, and various types of immune cells. Tumor-infiltrating immune cells gradually exhibit malfunction in the dynamic interplay with melanoma cells and finally become “accomplices” of melanoma immune escape. The immune checkpoint inhibitors (ICIs) such as programmed cell death protein 1 (PD-1, nivolumab and pembrolizumab), programmed cell death ligand 1 (PD-L1, atezolizumab), and cytotoxic T lymphocyte antigen 4 (CTLA-4, ipilimumab) are applied in SKCM immunotherapy, which underscores the importance of the interactions between melanoma cells and immune cells in the TME. Previous accumulating evidence has revealed that combination therapy with nivolumab (anti-PD1) and ipilimumab (anti-CTLA4) has vastly improved long-term outcomes in metastatic malignant melanoma, with half of treated patients alive at 5 years [ 5 ]. However, the low response rate and inevitable occurrence of resistance to currently available targeted therapies have posed the obstacle in the path of SKCM management to obtain further amelioration [ 6 ]. Therefore, accurate predictive tools that assess the immune status of the TME and response to immunotherapy for SKCM patients are urgently needed. Cellular senescence is a hallmark of aging defined by stable exit from the cell cycle in response to cellular damage and stress [ 7 – 9 ]. Despite cellular senescence is an oncosuppressive mechanism and a potent anticancer therapeutic strategy, their persistence in tissues can have deleterious effects [ 10 , 11 ]. The senescence-associated secretory phenotype (SASP), where senescent cells produce a variety of secreted proteins including inflammatory cytokines, chemokines, matrix remodelling factors, growth factors and so on, plays pivotal but varying roles in the TME. The impact of SASP on the tissue microenvironment in vivo varies depending on the types of cells undergoing senescence, the cause of senescence, or the trigger of the innate immune pathway for SASP [ 12 ]. On the one hand, SASP factors can stimulate immunosurveillance mechanisms and potentiate the tumor suppressive function of senescent cells by guiding the immune system to mount anticancer responses. On the other hand, SASP contributes to tumor progression and relapse through protecting tumors from immune clearance, providing growth factors, and enhancing angiogenesis [ 13 , 14 ]. There is growing evidence that senescent cells may contribute to tumorigenesis, development, and immune modulation [ 15 ]. However, the correlations of the cellular senescence with tumor progression and tumor immune microenvironment (TIME) in SKCM were poorly understood. In this systematic study, we evaluated the expression levels of 279 cellular senescence-related genes (SRGs) in SKCM tissues and normal skin tissues, and developed a prognostic prediction CSRS for SKCM patients. Subsequently, we evaluated the relationship between CSRS and SKCM patients’ clinical characteristics, immune cell infiltration and immunotherapy efficacy based on CSRS. In addition, we also identified three cellular senescence clusters with distinct prognosis based on the 279 SRGs expression. Further exploration of the mechanisms implied that RUVBL2 expression was upregulated in SKCM and promoted the proliferation of SKCM cells. 2 | MATERIALS AND METHODS 2.1 | Data Acquisition and Processing Expression data and corresponding clinical follow-up information of 469 SKCM patients were obtained from The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov ) database. The normal skin tissues data was extracted from the TCGA and Genotype-Tissue Expression Project (GTEx, https://gtexportal.org/home/ ). A total of 279 SRGs (Supplementary Table 1) were obtained from the CellAge [ 16 ] ( https://genomics.senescence.info/cells/ ). The GSE65904 (n = 214) dataset[ 17 ]was extracted from the Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih.gov/geo/ ). We also downloaded the IMvigor210 immunotherapy data cohort, a set of expression profiling data and corresponding clinical information for 348 metastatic urothelial cancer patients treated with anti-PD-L1 agent ( http://research-pub.gene.com/IMvigor210CoreBiologies/ )[ 18 ]. 2.2 | Specimens collection We collected 5 SKCM specimens and 5 normal skin tissues from the Department of Dermatology, Third Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). Patients enrolled were informed consent and this study was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). 2.3 | Enrichment analysis Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were applied using the R package ‘clusterProfiler’. A protein-protein interaction (PPI) network was constructed using the Cytoscape webtool ( https://cytoscape.org/ ) to explore the interactions between these differentially expressed SRGs. Gene set enrichment analysis (GSEA) was conducted to identify the differences in biological processes among different clusters using Java GSEA software (version 4.1.0). 2.4 | Consensus Clustering To elucidate the biological characteristics and prognostic values of SRGs, we employed the “ConsensusClusterPlus” package in R to cluster the SKCM patients into different subgroups [ 19 ]. The empirical cumulative density function (CDF) plot is aimed to determine the optimum cluster number (k) for the sample distribution to reach an approximate maximum, which means the maximum stability. Principal Component Analysis (PCA) was performed using R package to assess the distribution of gene expression among different subtypes. Furthermore, Kaplan-Meier survival analysis (log-rank test) was applied to verify the performance of various clusters to predict survival differences. 2.5 | Construction and validation of cellular senescence-related signature (CSRS) Univariate Cox analysis was conducted to identify the differentially expressed SRGs related to overall survival (OS) in TCGA-SKCM dataset ( p < 0.001). Then, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select the prognostic SRGs. The risk score of each SKCM patient was obtained according to the following formula: risk score = Coef(Gene1) × Expr(Gene1) + Coef(Gene2) ×Expr(Gene2) +… + Coef(Genen) × Expr(Genen), in which Expr(Genen) represents the expression of a particular gene and Coef(Genen) is the coefficient obtained from univariate Cox analysis of genes. GSE65904 (n = 214) were used as an external validation dataset. The patients in TCGA-SKCM dataset and validation dataset were classified either into the high-risk (≥ median number) or the low-risk (< median number) groups according to the median risk score. Afterwards, the survival curve, risk score distribution, heatmap, and receiver operating characteristic (ROC) curve were analyzed. 2.6 | Signature Genes Analyses The Gene Expression Profiling Interactive Analysis (GEPIA2[ 20 ], http://gepia2.cancer-pku.cn/ ) database and UALCAN[ 21 ]( http://ualcan.path.uab.edu ) web tool were used to analyze the signature genes. 2.7 | Correlation Between Immune Cell Infiltration and CSRS To analyze the correlation between CSRS and different immune cell infiltration, RNA seq-derived infiltrating immune cell populations were estimated by TIMER, EPIC, xCELL, CIBERSORT and quanTIseq algorithms in TIMER2.0 [ 22 ] ( http://timer.comp-genomics.org/ ). The Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to quantitate the differences in the infiltration levels of immunocytes between the high- and low-risk subgroups using the “GSVA” package in R. Tumor purity, immune score, stromal score, and ESTIMATE score were calculated for each SKCM sample using the “ESTIMATE” package in the R program. 2.8 | Assessment of CSRS and Immunotherapeutic Responses Immunophenotype score (IPS) could well predict the response of ICIs, the higher the score the better the therapeutic response to immune checkpoint (PD-1 and CTLA4) inhibitors. The immunogenicity is determined by four major categories of genes, including effector cells, major histocompatibility complex (MHC) molecules, immunomodulators and immunosuppressive cells. The scores of IPS were calculated using a scale ranging from 0–10 based on representative cell type gene expression z-scores. We downloaded the IPS scoring in SKCM from The Cancer Immunome Atlas (TCIA [ 23 ], https://tcia.at ). Then, we also extracted clinical information from the IMvigor210 dataset to assess the difference (responding or non-responding) in high/low-risk group between the anti-PD-L1 immunotherapy groups. 2.9 | Tumor mutational burden (TMB) analysis The mutation data for SKCM patients were downloaded from the TCGA data portal ( https://portal.gdc.cancer.gov/ ). Package “maftools” was used to analyze TMB differences between the high- and low-risk subgroups. 2.10 | Cell culture and small interfering RNA (siRNA) transfection sequences Normal human epidermal melanocytes were maintained in basal medium supplemented with 0.5% fetal bovine serum (FBS) and 1% human melanocyte growth supplement (ScienCell, USA). SKCM cell lines A375 and SK-Mel-5 were kindly provided by Procell Life Science&Technology Co. Ltd. All cell lines were authenticated using the short tandem repeat method, performed using this cell bank. Cells were cultured in DMEM supplemented with 10% FBS, and cultured at 37°C with 5% carbon dioxide. For generation of siRNA-mediated knock down, cells were transfected with 50 nM RUVBL2 siRNA duplexes or 50 nM non-targeting control siRNA using lipofectamine 3000 reagent (Invitrogen, USA) in Opti-MEM medium (Gibco, USA) following the manufacturer's instructions. The RUVBL2 siRNA sequences were as follows: si- RUVBL2-1 TCCTGATCATGGCCACCAA; si-RUVBL2-2 GCGAGAAAGACAC GAAGCA. 2.11 | RNA isolation and quantitative real-time polymerase chain reaction (qRT-PCR) Total RNA was extracted using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) and then reverse transcribed to cDNA using a Revert Aid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific). The real-time polymerase chain reaction (RT-qPCR) was performed using a SYBR Fast qRT-PCR Master Mix Kit (Kapa Biosystems, Wilmington, MA, USA) and a Light Cycler 480 system (Roche, Basel, Switzerland) according to the manufacturer’s instructions. For each sample and index, the samples were studied in triplicate, with GAPDH mRNA expression measured as an internal reference. The primer sequences used for qRT-PCR were as follows: RUVBL2 Forward: 5’-AAGAAGATGTGGAGATGAG-3’, Reverse: 5’-CAGGAAGA GTGAGT AGAC-3’; GAPDH Forward:5’-CTGGGCTACACTGAGCACC-3’; Reverse: 5’-AAG TGGTCGTTGAGGGCAATG-3’. Fold changes were calculated using the relative quantification (2 −ΔΔCt ) method. 2.12 | Detection of cell viability Cells were seeded into 96-well plates (2000 cells/well) 1 day before the proliferation assay. The medium of each well was replaced by Cell Counting Kit-8 (Dojindo, Kamimashiki-gun, Kumamoto, Japan) following the manufacturer’s instruction 2 h before testing. The absorbance at a wavelength of 450 nm was measured with an enzyme mark instrument (Thermo Fisher Scientific). 2.13 | 5-ethynyl-20-deoxyuridine (EdU) staining Melanoma cells were seeded into 6-well plates at 1x10 5 cells per well and were cultured for 48 h. An EdU (5-ethynyl-20-deoxyuridine) Apollo-567 Kit (RiboBio, China) was applied to quantify cell proliferation. The EdU and 4,6-diamino-2-phenyl indole (DAPI) fluorescent dyes were added to melanoma cells, and the cells were visualized under a fluorescent microscope. Five random views per each condition were used to calculate the average EdU ratio (% vs. DAPI). 2.14 | Immunohistochemistry (IHC) staining and evaluation Paraffin slides were incubated at 4°C overnight with primary antibodies against FOXM1 (ab207298, 1: 200), NOTCH3 (ab300527, 1: 200) and NADPH oxidase 4 (ab133303, 1: 100). Next, the slides were incubated with secondary antibody and followed by DAB staining and hematoxylin counterstaining.The immunohistochemical staining was evaluated according to the percentage of positive cells and the staining intensity. Staining intensity was scored as 0 (negative), 1 (weak), 2 (moderate) or 3 (strong). The expression proportion was scored as 1 (0–25%), 2 (26–50%), 3 (51–75%) or 4 (76–100%). The final score of each sample was obtained by multiplying the signal intensity and the expression proportion. 2.15 | Statistical Analysis R software (version 4.1.3, https://www.r-project.org ), GraphPad Prism 8.0 and SPSS Statistics V25.0 were used to analysis data and plot graphs. Data were presented as mean ± SD of at least three independent experiments, and differences between two groups were compared using student’s t-test. Rank correlations were assessed by the performance of spearman’s correlation coefficient test among different variables. The Kaplan-Meier survival curve for OS analysis was plotted with the R package “survminer”. We considered p < 0.05 was statistically significant. 3 | RESULTS 3.1 | Characterize the expression pattern of SRGs and identification of cellular senescence-related subtypes in SKCM The overall study flow diagram was depicted in Fig. 1 . First, we screened 7896 cellular senescence-related differentially expressed genes (DEGs) between normal skin samples (n = 558) and SKCM samples (n = 469), based on the thresholds of an adjusted FDR 1 filters. Among them, 3857 DEGs were significantly upregulated in SKCM samples as compared to the normal skin samples, while the remaining 4039 were markedly downregulated. Then, we identified 113 differentially expressed SRGs by taking the intersection of DEGs and SRGs sets. Among them, 68 genes were upregulated, whereas 45 were downregulated (Fig. 2 A; Supplementary Fig. 1A, Supplementary Table 2).Subsequently, we performed GO and KEGG analyses to clarify the biological process of the 113 differentially expressed SRGs in SKCM. As expected, the DEGs were remarkably enriched in cellular senescence- and cell cycle-related pathways in SKCM samples (Supplementary Fig. 1B,C; Supplementary Table 3,4). A PPI network was performed to further explore the interactions among these 113 DEGs (Supplementary Fig. 1D). We performed a consensus clustering analysis to classify all SKCM patients based on the expression patterns of 113 SRGs. The optimal division (K = 3) was the optimal number of clusters according to the consensus matrix (Fig. 2 B), consensus CDF curves (Fig. 2 C), and relative change in the area under the CDF curves (Fig. 2 D). PCA analysis showed that SKCM patients could be well divided into three clusters (Fig. 2 E).The survival curve results showed that cluster 1 suffered a worse prognosis relative to clusters 2 and 3 (Fig. 2 F). In addition, The chisquare analysis demonstrated statistically significant differences in the age, T stage and breslow between the three clusters (Fig. 2 G). 3.2 | Development and validation of CSRS for SKCM We performed univariate Cox proportional hazard regression analysis and found a total of 15 out of the 113 differentially expressed SRGs were significantly associated with OS ( p < 0.001, Supplementary Table 5). Among them, ITSN2, CBX7, MAP4K1, ABI3, BCL6, HK3, NDRG1, and NOX4 were the protective factors with the hazard ratio (HR) 1 (Fig. 3 A). Besides, the correlation among the 15 potential prognostic DEGs were also analyzed, and the relationships between each two of them were almost positively correlated (Fig. 3 B). Afterwards, LASSO Cox regression was performed to narrow down the range of candidate genes and eliminate the risk of overfitting, and the penalty parameter was selected based on the minimum criterion. Then, a 12-gene prognostic signature was constructed according to the optimum λ value (Fig. 3 C). We observed that high expression of MVK, NOTCH3, FOXM1, SFN and RUVBL2 genes in TCGA dataset was consistently indicative of poor prognosis for SKCM patients, while high expression of ITSN2, CBX7, ABI3, BCL6, HK3, NDRG1 and NOX4 genes had a significantly longer OS time (Supplementary Fig. 2). Moreover, multivariate Cox regression analysis were also conducted, which indicated that NOTCH3 was risk factors and NDRG1 was the protective factor for SKCM patients (Fig. 3 D). We then established a risk score formula based on the expression of the 12 SRGs for SKCM patients: Risk score =(0.0271 ×expression value of MVK) + (-0.0459 × expression value of ITSN2) + (-0.0749 × expression value of CBX7) + (0.2481 ×expression value of NOTCH3) + (0.1034 × expression value of FOXM1)+ (-0.0861 × expression value of ABI3)+ (0.0823 × expression value of SFN)+ (0.0498 × expression value of RUVBL2)+ (-0.1105 × expression value of BCL6)+ (-0.2307 × expression value of HK3)+ (-0.1952 × expression value of NDRG1)+ (-0.1708 × expression value of NOX4).The risk score of every patient was then calculated using this formula, and SKCM cases were divided into low- and high-risk groups according to the median value of the risk score. We also analyzed the mRNA expression levels of the 15 candidate signature genes in SKCM by TCGA data, and found eight genes (PSMB5, MAP4K1, AKT1, FOXM1, ABI3, RUVBL2, HK3 and NOX4) were upregulated while seven genes (MVK, ITSN2, CBX7, NOTCH3, SFN, BCL6 and NDRG1) were downregulated (p < 0.001) (Fig. 3 E). The distribution of the CSRS risk scores, the survival status, and a heatmap exhibiting the expression profiles of 12 prognosis-related SRGs in the high- and low-risk groups are presented in Fig. 4 A. Kaplan-Meier survival analysis demonstrated that the high-risk group had a shorter OS than that of the low-risk group in TCGA-SKCM dataset ((Fig. 4 B, HR = 2.261, 95% CI:1.712–2.985, log-rank p < 0.0001). The 5-years survival rate of the high-risk group was 24.23%, which was significantly lower than that of the low-risk group (41.85%). Time-dependent ROC analysis was performed to evaluate the sensitivity and specificity of the CSRS, and the areas under the curve (AUC) values of 2, 3 and 5 year were 0.68, 0.64, and 0.65 (Fig. 4 C). In addition, significant difference in OS time was observed in both early-stage (HR = 2.691, 95% CI: 1.859–3.894, log-rank p < 0.0001) and advanced-stage SKCM (Fig. 4 D, HR = 2.5, 95% CI: 1.773–3.526, log-rank p < 0.0001). Afterwards, we performed the univariate and multivariate Cox regression analyses to examine whether the risk score could act as an independent prognosis variable of SKCM. The age, T stage, N stage, M stage, TNM stage, breslow depth, clark stage, ulceration status, location and risk score were correlated with OS in univariable analysis. Multivariate Cox regression analyses further identified that the risk score was an independent prognostic factor (HR = 2.136, 95% CI: 1.534–2.973, p < 0.001) for SKCM patients (Fig. 4 E). To validate the predictive reliability of the CSRS, we calculated the risk scores of samples in GSE65904 dataset using the same formula and similarly classified the samples into high-risk and low-risk groups. The risk score distribution, survival status and heatmap for the expression profile of CSRS in GSE65904 dataset are visualized in Fig. 4 F. Patients with high-risk scores had a higher mortality rate than those in the low-risk group (HR = 1.895, 95% CI: 1.29–2.783, p = 0.00111; Fig. 4 G). The AUC values of 2, 3 and 5 year in GSE65904 dataset were 0.63, 0.63, and 0.58 (Fig. 4 H). 3.3 | Correlations between the CSRS and clinicopathological factors We compared the differences in clinicopathological factors between high- and low-risk groups, including age, gender, T stage, N stage, M stage, TNM stage, breslow depth, clark stage, ulceration status and location. As Fig. 5 showed, the risk score of patients with breslow tumor thickness more than 3mm was higher than that of patients with no more than 3mm ( P = 0.0048), and patients with lymphatic metastasis showed a remarkably higher risk score than those with primary tumor ( P 60), gender (female and male), T stage (T0–2 and T3–4),N stage (N0 and N1–3), M stage (M0 and M1), TNM stage (stage I-II and stage III-IV), breslow depth (> 3mm and ≤ 3mm), clark stage (clark I-Ⅲ and stage Ⅳ-Ⅴ), ulceration status (yes and no) and location (primary and metastasis). The Kaplan-Meier survival curve showed that samples from the high-risk group had worse prognosis compared with those belonging to the low-risk group in all the subgroups (Supplementary Fig. 3). These results suggest that CSRS can accurately and reliably predict the survival outcome of patients with SKCM. 3.4 | Signature Genes Analysis We found markedly low levels of MVK, ITSN2, CBX7, NOTCH3, SFN, BCL6 and NDRG1 in SKCM from the GEPIA2 database, while those of FOXM1, RUVBL2 and HK3 were substantially high ( p < 0.05; Fig. 6 A). FOXM1, SFN and RUVBL2 were risk factors in many other types of cancer, including adrenocortical carcinoma, brain lower grade glioma, liver hepatocellular carcinoma and lung adenocarcinoma (Fig. 6 B). Then, we analyzed the relationship between methylation and the expression of the 12 signature genes, which may account for the abnormal expression of these signature genes. Significantly lower methylation levels of FOXM1 promoters were found in SKCM samples compared with normal skin samples, while the methylation levels of BCL6, NDRG1 and HK3 promoters were higher in SKCM (Fig. 6 C). No significant differences were present in the expression of methylation levels between tumor and normal tissues in the other eight signature genes. In addition, we also obtained protein structures of the 12 signature genes from the PDB database (Fig. 7 ). 3.5 | Biological Processes Analysis of Signature genes To explore the potential biological processes for the prognostic risk signature, we screened 4788 DEGs between high- and low-risk groups with the criteria FDR < 0.01 and |log2FC| ≥ 1. Among them, 1961 genes were downregulated in high-risk group, while 2827 genes were upregulated (Supplementary Fig. 4; Supplementary Table 6). GO and KEGG pathways analyses were performed based on the DEGs. Intriguingly, the results indicated that the DEGs were mainly involved in immunological regulation-related biological processes, such as immune response, T cell activation and B cell activation (Supplementary Fig. 5A,B). Besides, functional annotation was also performed between high- and low-risk groups using GSEA. The result showed that enriched gene sets of the HALLMARK collection in the high-risk group were mainly involved in tumor-related pathways, including oxidative phosphorylation, IFN-γ response, MYC targets, IFN-α response and E2F targets, which are closely related to the malignant proliferation and immune microenvironment of tumor (Supplementary Fig. 5C). These results suggest that CSRS may have a strong correlation with tumor-infiltrating immune cells. 3.6 | CSRS Is Associated with Alterations in SASP As mentioned above, SASP, where senescent cells produce a variety of secreted proteins including inflammatory cytokines, chemokines, proteases, growth factors and so on, plays pivotal but varying roles in the TME. We explored the correlations between CSRS and SASPs, and found that SASPs including interleukins (IL-1A, IL-1B, IL-6, IL-7, IL13 and IL-15), soluble or shed receptors or ligands (FAS,ICAM1,ICAM3,IL6ST, PLAUR,TNFRSF1A, TNFRSF1B, TNFRSF10C and TNFRSF11B), chemokines (CCL1, CCL3, CCL8, CCL13, CCL25, CCL26, CXCL5, and CXCL11), growth factors and regulators (ANG, FGF2, FGF7, HGF, IGFBP2, IGFBP3, IGFBP7, and VEGFA), and proteases and regulators (CTSB, MMP12 and SERPINE1) were significantly downregulated in high-risk group (Fig. 8 A). It has been reported that SASP promotes immune clearance of damaged cells. Therefore, our results suggest that patients with high CSRS scores may exhibit an attenuated immune response depend on SASP. 3.7 | Association between CSRS and TIME The differences of immune cell infiltration between high- and low-risk groups were analyzed to explore the correlations between the CSRS and TIME. We found that the infiltration levels of B cells, CD4 + T cells, and CD8 + T cells were lower in high-risk groups, while the CAFs were significantly higher in high-risk subgroup (Fig. 8 B). To further explore the relationship between the CSRS and immune status, we performed the expression profiles of 29 immune signature gens sets (16 types of immune cells and 13 immune-related pathways) in high- and low risk groups. The ssGSEA analysis showed that compared to the low-risk group, patients in the high-risk group had lower levels of immune cell infiltration and immune-related functions and pathways (Fig. 8 C-E). These results indicated that patients with high CSRS scores exhibit lower levels of immune infiltration, consistent with our above analysis. The ESITIMATE algorithm revealed that patients in the high-risk group had higher tumor purity and lower ESITIMATE scores, immune scores, and stromal scores compared with patients in the low-risk group (Fig. 8 F). Survival analysis showed that the patients with lower immune scores, lower stromal scores, lower ESTIMATE score, or higher tumor purity had a worse prognosis (Supplementary Fig. 6). These results indicated that there was a significant correlation of the CSRS-based risk score with the TIME. 3.8 | Association of CSRS with immunotherapy efficacy Given the association between CSRS and immune infiltration, we further compared the expression pattern of immune checkpoint genes between patients in high and low-risk groups, which resulted in most of the immune checkpoints being significantly overexpressed in the low-risk group (Fig. 9 A). Then, we confirmed that immune checkpoint genes (PD-L1, PD1, CTLA4, LAG3,TIM3) were overexpression in the low-risk group in GSE65904 dataset (Fig. 9 B). The levels of expression of PD-1 and CTLA-4 were negatively correlated with the risk score (Fig. 9 C). Afterwards, the survival analysis of the four groups stratified by CSRS and immune checkpoint gene expression was conducted. Our results showed that patients with low risk had prolonged OS compared to those with high risk among all the groups stratified by PD-L1,PD1 and CTLA4 expression in TCGA dataset (Fig. 9 D). We then assessed immunogenicity by IPS scoring to predict patient response to immune checkpoint blockade (anti-PD1 and/or anti-CTLA4), with higher IPS scores indicating better predicted immunotherapy efficacy. We found that anti-PD1, anti-CTLA4 and anti-PD1-CTLA4 combination therapy was more effective in the low-risk group (Fig. 9 E). Subsequent validation of the CSRS for predicting immunotherapy efficacy by external immunotherapy datasets showed that patients with lower CSRS scores had a higher prognosis in the IMvigor210 cohort (Fig. 9 F), and that patients in the low-risk group also exhibited a significantly higher prognosis among the PD-L1 high or low groups (Fig. 9 G). Finally, we compared survival distribution of patient groups classified by CSRS and TMB level. Our results showed that patients with high CSRS scores suffered unfavourable OS irrespective of patients’ TMB level in TCGA and IMvigor210 datasets (Fig. 9 H, I).These results indicated that patients with low CSRS scores may respond better to the immunotherapy. 3.9 | Validation of Signature Gene Expressions in SKCM Tissues To verify the reliability of the CSRS, five SKCM samples and five normal skin samples were collected to test the protein expression levels of FOXM1,NOX4,NOTCH3 by IHC. As expected, IHC staining revealed the protein expression level of FOXM1 and NADPH oxidase 4 were significantly elevated in SKCM tissues compared with normal skin tissues, while NOTCH3 was markedly downregulated (Fig. 10 A). 3.10 | RUVBL2 knockdown induces significant anticancer activity in SKCM Experiments were performed to test the potential function of RUVBL2 in SKCM cells. Firstly, we verified that RUVBL2 mRNA levels in A375 and SK-Mel-5 cells were significantly higher than those in normal human melanocytes (Fig. 10 B). Then, the silencing effect of RUVBL2 with two individual small interfering RNAs (siRNAs) was detected by qPCR, which showed that si-RUVBL2 could effectively knock down the expression of RUVBL2 mRNA in A375 and SK-Mel-5 cells, respectively (Fig. 10 C). The results of CCK8 experiments showed that the proliferation ability of A375 and SK-Mel-5 cells in the si-RUVBL2 group was significantly lower than that in the negative control group at 48, 72, and 96 h (Fig. 10 D). As expected, EdU staining experiments showed that knockdown of RUVBL2 significantly inhibited the proliferation of A375 and SK-Mel-5 cells (Fig. 10 E). Therefore, these findings support that RUVBL2 promotes tumorigenesis in SKCM. 4 | DISCUSSION Cutaneous melanoma is the most lethal type of skin cancer that originates from the malignant transformation of melanocytes. Nevertheless, a variety of SKCM patients are treated with limited and similar therapies on account of lacking reliable and effective predictive tools to estimate patients’ prognosis. Therefore, it is meaningful and necessary to identify accurate biomarkers to construct prognostic signatures for better predicting patients with SKCM and help to make decisions with regard to therapy. In the current study, we analyzed the mRNA expression patterns of 279 SRGs in SKCM and constructed the SRGs prognostic signature, which was well validated in GSE65904 dataset. Moreover, the CSRS was also significantly correlated with TIME and the response to immunotherapy, providing new insights into the correlations between cellular senescence and TIME in SKCM. The 12 SRGs in our prognostic prediction CSRS consist of MVK, NOTCH3, FOXM1, SFN, RUVBL2 as risk factors and ITSN2, CBX7, ABI3, BCL6, HK3, NDRG1, NOX4 as protective factors. Among them, FOXM1, ABI3, RUVBL2, HK3 and NOX4 were significantly upregulated in SKCM tissues as compared to the normal skin tissues, while MVK, ITSN2, CBX7, NOTCH3, SFN, BCL6 and NDRG1 were markedly downregulated. Several studies displayed that these 12 SRGs played an essential role in tumorigenesis and development. RUVBL2 is highly conserved ATPases that belong to the AAA + superfamily, which was found to be involved in the remodeling of chromatin, DNA damage repair, and regulation of the cell cycle, all of which help to play essential roles in cancer [ 24 – 26 ]. However, RUVBL2 was rarely reported associated with SKCM progression. In this study, we found that mRNA expression level of RUVBL2 was significantly elevated in SKCM, and knockdown of RUVBL2 significantly suppressed cell proliferation of A375 and SK-Mel-5 melanoma cell lines, indicating that RUVBL2 promotes tumorigenesis in SKCM. It has been demonstrated that increased Forkhead Box M1 (FOXM1) expression in melanoma has previously been associated with accelerated tumor progression and poor prognosis as well as suppression of the senescence phenotype [ 27 , 28 ], NADPH oxidase 4 (NOX4) was up-regulated in more than half of melanoma cell lines [ 29 – 31 ], Notch3 expression was significant decreased in in human tumor cell lines as well as melanoma samples compared to normal tissues[ 32 , 33 ]. In this study, we also detected the protein expression level of FOXM1, NOX4 and NOTCH3 by IHC staining, which of the results verified that FOXM1 and NADPH oxidase 4 were significantly elevated, while NOTCH3 was reduced in SKCM tissues. Hexokinase 3 (HK3) may become potential oncogenes across a variety of cancer types [ 34 ]. Sulforaphane (SFN) induces cell differentiation, melanogenesis and also inhibit the proliferation of melanoma cells [ 35 ].The transcription factor B cell lymphoma 6 (Bcl6) is essential in maintaining the lineage stability of Treg cells in TME[ 36 , 37 ]. N-myc downstream regulated gene 1 (NDRG1) has been identified as a protein involved in the differentiation of epithelial cells, which is an oncogenic signaling disruptor that plays a key role in multiple cancers [ 38 , 39 ]. Taken together, studies as mentioned above revealed the reasonability and accuracy of CSRS in SKCM tumorigenesis and development, and more experiments are needed to further elucidate the effect of these SRGs on tumorigenesis in SKCM. The TME is composed of many different cellular and acellular components that together drive tumor growth, invasion, metastasis, and response to therapies. Increasing realization of the significance of the TME in cancer biology has shifted cancer research from a cancer-centric model to one that considers the TME as a whole[ 40 ]. SKCM with relatively high proliferative capacity and aggressiveness largely results from various immunosuppressive mechanisms that often work in concert to help tumor cells evade innate and adaptive immune detection and destruction. The characteristic of immune evasion depends on the interaction between tumor cells and the surrounding TME. In this study, we also explored the effect of cellular senescence on the tumor immune infiltrate and whether this would impact the response to ICIs. Our results showed that B cells, CD4 + T cells and CD8 + T cells were significantly enriched in low-risk groups while CAFs, which are observed in almost all solid tumor types, were positively correlated with the CSRS score in SKCM. Correspondingly, high-risk subgroup manifested lower levels of infiltration of immune cells, implicating less process in immune activation. This result suggested that patients with higher CSRS score might have an immunosuppressive TME, which prevented immune clearance of tumor cells. Previous studies have emphasized the importance of immune checkpoint genes in modulating immune infiltration [ 41 , 42 ], and our results revealed significant relevance between cellular senescence and tumor immunity. Thus, we compared the expression pattern of immune checkpoint genes between high- and low-risk patients with SKCM. We found that the low-risk group had higher immune checkpoint genes expression and could benefit better from ICIs relative to the high-risk group. These results indicated that the CSRS score was coupled with specific immune checkpoint factors as predictive biomarkers of immunotherapy response for SKCM patients. SASP comprises pro-inflammatory cytokines or chemokines, extracellular matrix proteases and growth factors that influence the cellular microenvironment. Some of these secreted molecules may have autocrine effects that enforce cellular senescence, whereas others may exert non-cell-autonomous effects that favor tumorigenesis in nearby non-senescent cells. Thus, SASP can exert both beneficial and detrimental effects, depending on the senescence signal, tissue context and secreted molecules. In this study, lower levels of SASP including some cytokines and chemokines and lower levels of infiltration of immune cells were observed in high-risk group, suggesting that SASP may promote the immune activation and prevent tumorigenesis for SKCM patients. Thus, SKCM can be viewed as a paradigm of cellular senescence evasion. Matrix metalloproteinases (MMPs) are not only extracellular matrix remodeling enzymes but regulators of several cellular functions including growth, migration, invasion and gene expression. Notably, some upregulated SASP factors in high-risk group, including MMP3, MMP14 and PLAU, may not only influence melanoma metastasis by extracellular matrix degradation, but also via regulation of genes involved in several pro-tumorigenic functions including tumor cell growth and motility. Consequently, we distinguished different SASP affecting tumorigenesis and immune modulation as potential mechanisms underlying immune escape and tumor progression in SKCM. Taken together, our results implied that a high level of cellular senescence may stimulate immunosurveillance mechanisms and potentiate the tumor suppressive function for SKCM in a SASP-depended manner. The strength of our study was that it was the first time for us to carry out the systematic analysis of SRGs in SKCM, and our SRG-based signature was successfully created, which benefits prognosis and immunotherapy for SKCM patients. However, several limitations should be mentioned. Firstly, the data for our analysis were obtained from public databases, which may have led to some case selection bias in case selection. Secondly, it is necessary to collect a large amount of clinical case data to further validate the accuracy of the results. Thirdly, further in vivo and in vitro experiments are needed to clarify the roles of the SRGs on tumorigenesis, development, and immune modulation in SKCM. 5 | CONCLUSIONS In this study, we constructed and validated a CSRS based on 279 SRGs. We found that CSRS can effectively predict the prognosis and immunotherapy outcome of patients with SKCM, which was validated by an external dataset. Importantly, the CSRS was significantly associated with the immune cell infiltration levels of SKCM patients and involved in the regulation of the immune microenvironment in SKCM by SASP. In addition, we elucidated the expression of SRGs for SKCM patients, which could stratify patients into three subgroups with different prognosis. In an era when immunotherapy holds great promise for cancer treatment, CSRS involved in the regulation of the TIME through SASP was a robust biomarker for the prognosis and immunotherapeutic response in SKCM. ABBREVIATIONS SKCM Skin Cutaneous Melanoma CSRS Cellular Senescence-related Signature SRGs Cellular Senescence-Related Genes TCGA The Cancer Genome Atlas GEO Gene Expression Omnibus GTEx Genotype-Tissue Expression Program AJCC American Joint Committee on Cancer ROC Receiver Operating Characteristic PD-1 Programmed cell death 1 PD-L1 Programmed cell death Ligand 1 CTLA4 Cytotoxic T-lymphocyte-associated protein 4 LAG3 Lymphocyte Activating 3 ICIs Immune checkpoint inhibitors OS Overall survival TMB Tumor mutation burden IC50 Half maximal inhibitory concentration PPI Protein–protein interaction KM Kaplan–Meier ssGSEA Single-sample gene set enrichment analysis GSEA Gene set enrichment analysis GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes LASSO Least Absolute Shrinkage and Selection Operator TME Tumor Microenvironment TIME Tumor Immune Microenvironment CAFs Cancer-associated Fibroblasts SASP Senescence-associated Secretory Phenotype CDF Cumulative Density Function PCA Principal Component Analysis IPS Immunophenotype Score DEGs Differentially Expressed Genes HR Hazard Ratio Declarations FUNDING DECLARATION This study was supported by the National Natural Science Foundation of China (Nos. 82203901, 82373497). ACKNOWLEDGMENTS We acknowledge the contributions from TCGA and GEO databases. AUTHOR CONTRIBUTIONS LM designed the study, performed the data analysis and drafted the manuscript. LM and CX performed the in vitro experiments. LL and YY provided statistical advice. LW revised the manuscript and supervised the acquisition of the data. All authors read and approved the final manuscript. DATA AVAILABILITY STATEMENT The datasets supporting the conclusions of this article are available in the TCGA and GEO database. The datasets supporting the conclusions of this article are also included within the article and its additional files. CONFLICT OF INTEREST STATEMENT The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ETHICS STATEMENT The studies involving human participants were reviewed and approved by The Ethical Committee and Institutional Review Board of the Third Affiliated Hospital of Sun Yat-sen University. The patients/participants provided their written informed consent to participate in this study. References Arnold M, Singh D, Laversanne M, Vignat J, Vaccarella S, Meheus F, Cust AE, de Vries E, Whiteman DC, Bray F. Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040. JAMA Dermatol. 2022;158(5):495-503. doi:10.1001/jamadermatol.2022.0160. Long GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma.Lancet.2023;402(10400):485-502. doi: 10.1016/S0140-6736(23)00821-8. Perez M, Chakraborty A, Lau LS, Mohammed NBB, Dimitroff CJ. Melanoma-associated glycosyltransferase GCNT2 as an emerging biomarker and therapeutic target. Br J Dermatol. 2021;185(2):294-301. doi: 10.1111/bjd.19891. Curti BD, Faries MB. Recent Advances in the Treatment of Melanoma. 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Advantages of targeting the tumor immune microenvironment over blocking immune checkpoint in cancer immunotherapy. Signal Transduct Target Ther. 2021;6(1):72. doi: 10.1038/s41392-020-00449-4. Additional Declarations No competing interests reported. Supplementary Files FigureS1.tif Supplementary Figure 1| Identification of differentially expressed SRGs in SKCM. (A) Heatmap of significantly differentially expressed SRGs in TCGA dataset. GO enrichment analysis (B) and KEGG pathway enrichment analysis (C) of differentially expressed SRGs. (D) PPI analysis of differentially expressed SRGs. FigureS2.tif Supplementary Figure 2| Kaplan-Meier curves for patients with high and low expression of 12 signature genesin TCGA dataset. FigureS3.tif Supplementary Figure 3| The stratified survival analyses in different subgroups. (A) age, (B) gender, (C) T stage, (D) M stage, (E) N stage, (F) TNM stage, (G) breslow depth, (H) clark stage, (I) ulceration status and (J) tumor location. FigureS4.pdf Supplementary Figure 4 | Heatmap of significantly differentially expressed genes between the high- and low-risk groups in TCGA dataset. FigureS5.tif Supplementary Figure 5 | GO enrichment analysis (A) and KEGG pathway enrichment analysis (B) of DEGs. (C) GSEA of hallmark gene sets compared between the high- and low- risk groups in TCGA dataset. FigureS6.tif Supplementary Figure 6 |The Kaplan-Meier curves of OS in patients with diffrent immune score, stromal score, ESTIMATE score, and tumor purity. SupplementaryTables.xlsx Supplementary Table 1|Cellular senescence-related genes Supplementary Table 2|Cellular senescence-related DEGs in TCGA SKCM cohort Supplementary Table 3| GO enrichment analysis of cellular senescence-related DEGs Supplementary Table 4|KEGG enrichment analysis of cellular senescence-related DEGs Supplementary Table 5| Univariate Cox analysis of cellular senescence-related genes in TCGA Cohort Supplementary Table 6|DEGs between high- and low- risk group in TCGA cohort Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4943989","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":345633485,"identity":"36352fc4-6a1a-4a52-bcc8-172598ff3f08","order_by":0,"name":"Mengna Li","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Mengna","middleName":"","lastName":"Li","suffix":""},{"id":345633486,"identity":"5b4c2de1-d288-421d-9941-1889187910d9","order_by":1,"name":"Xintao Cen","email":"","orcid":"","institution":"Shenzhen Institute of Dermatology","correspondingAuthor":false,"prefix":"","firstName":"Xintao","middleName":"","lastName":"Cen","suffix":""},{"id":345633487,"identity":"76a062aa-ffe7-4de6-95db-de875923e868","order_by":2,"name":"Yan Yan","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Yan","suffix":""},{"id":345633488,"identity":"338e16b6-0d01-42b9-8d91-490c88beb744","order_by":3,"name":"Li Li","email":"","orcid":"","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Li","suffix":""},{"id":345633489,"identity":"b3228c82-ef03-43ea-9b14-db4d3e67355d","order_by":4,"name":"Wei Lai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBAC/hnpDxh4gAw2BgbGBwkVNYS1SNzJMYBpYTZ4cOYYYS0GcTkMYC0gXZIPW5iJ0CKXw/jgbZudPB9777GKxAY2Bv727gT8WqTTHxvObUs2bOM5l3YjcYcMg8SZsxsIaEkwk+ZtO8DYJpFjdiPxDBuDgUQuAS2SD8x/A7XYg7QUJLYxE6El4oEZM1BLIkgLA1FaJG68MZaccy45uY3njLFEwpljPAT9AozKhx/elNnZzm/vMfz4o6JGjr+9F78WDMBDmvJRMApGwSgYBVgBAKeSSBcQqFYbAAAAAElFTkSuQmCC","orcid":"","institution":"Third Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":true,"prefix":"","firstName":"Wei","middleName":"","lastName":"Lai","suffix":""}],"badges":[],"createdAt":"2024-08-20 09:50:48","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4943989/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4943989/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66637402,"identity":"d6824fcb-b8a0-4402-a736-850018192819","added_by":"auto","created_at":"2024-10-15 05:41:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":437314,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the study design.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/1424f2e2f8c47031696a2b92.png"},{"id":66636314,"identity":"0fb3d2fd-f931-4273-9c48-18838228ea90","added_by":"auto","created_at":"2024-10-15 05:33:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":433583,"visible":true,"origin":"","legend":"\u003cp\u003eConsensus clustering analysis of 279 SRGs. (A) Venn diagram showing 113 overlapping genes in DEGs and SRGs. (B) Consensus clustering matrix at K = 3. (C) The CDF curves for clusters at k = 2 to 8. (D) The relative change in area under CDF curves for different clusters from k = 2 to 8. (E) PCA plot for the three clusters. (F) Survival analysis for SKCM samples is stratified to the three clusters. (G) Heatmap and the clinical parameters of the three clusters. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e\u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/b427575cf587afaf6ac76e6f.png"},{"id":66637403,"identity":"2ee901b5-ad87-4072-9e24-35893730be21","added_by":"auto","created_at":"2024-10-15 05:41:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":591618,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of prognostic cellular senescence-related DEGs.(A) Forrest plot of the univariate Cox analysis of 15 candidate genes related to the prognosis of SKCM patients. (B) Correlation network of 15 candidate prognostic genes. (C) LASSO coefficient profiles and cross validation for tuning parameter selection in the LASSO regression. (D) Forrest plot of the multivariate Cox analysis of 12 signature genes. (E) The expression level of the 15 candidate prognostic genes in SKCM tissues and normal skin tissues. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/8c323356aa04297faf204770.png"},{"id":66639533,"identity":"1a9d051b-1801-4846-b4a7-0f076e673803","added_by":"auto","created_at":"2024-10-15 06:00:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":585229,"visible":true,"origin":"","legend":"\u003cp\u003eDevelopment of CSRS in TCGA dataset. (A) The distribution of the risk score, survival status and heatmap visualizing the expression of 12 signature genes in TCGA dataset. (B) Kaplan-Meier curves of overall survival (OS) in total SKCM patients of the TCGA dataset based on risk score. (C) The predictive value of the prognostic model measured by receiver operating characteristic (ROC) curves at 2, 3, and 5 years in TCGA dataset. (D) Kaplan-Meier curves of OS in patients with early-stage and advanced-stage SKCM based on risk score. (E) Univariable and multivariable Cox regression analysis of CSRS and OS in TCGA dataset. (F) The distribution of the risk score, survival status and heatmap visualizing the expression of 12 signature genes in GSE65904dataset.(G) Kaplan-Meier curves of OS in total SKCM patients of the GSE65904 dataset based on risk score.(H) The predictive value of the prognostic model measured by ROC curves at 2, 3, and 5 years in GSE65904 dataset.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/75717a9502c6b4a068147d86.png"},{"id":66636313,"identity":"41323fcc-4d15-453f-a140-dd0cb30ff793","added_by":"auto","created_at":"2024-10-15 05:33:30","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":178994,"visible":true,"origin":"","legend":"\u003cp\u003eThe correlation between CSRS and patients’ clinicopathological parameters, including age, gender, T stage, N stage, M stage, TNM stage, breslow depth, clark stage, location and ulceration.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/2c96b41a4d5c5984bbbaa18c.png"},{"id":66636329,"identity":"9c45a232-13e0-4916-acef-50b1bcd3d95c","added_by":"auto","created_at":"2024-10-15 05:33:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":517310,"visible":true,"origin":"","legend":"\u003cp\u003eSignature genes analysis. (A) Analysis of 12 signature genes expression in GEPIA 2 database. (B) Compare the survival contribution of 12 signature genes in multiple cancer types using the GEPIA 2 database. (C) Analysis of promoter methylation levels of the FOXM1, BCL-6, NDRG1 and HK3 between SKCM tissues and normal skin tissues using UALCAN database. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/03bea0c56ad6139ed9510ec3.png"},{"id":66637405,"identity":"8f332bb4-2b06-43e2-9dd5-5d9a1754a6e2","added_by":"auto","created_at":"2024-10-15 05:41:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":645273,"visible":true,"origin":"","legend":"\u003cp\u003eProtein structures of 12 signature genes of CSRS.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/8515243c8d614959f29ea022.png"},{"id":66639529,"identity":"b1fd896f-afb2-47ca-8827-d8f024e84eb1","added_by":"auto","created_at":"2024-10-15 06:00:09","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":430481,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation between CSRS and TME.\u003cstrong\u003e \u003c/strong\u003e(A) Expression of interleukins, soluble or shed receptors or ligands, chemokines, growth factors and regulators, and proteases and regulators between high- and low-risk groups. (B) Correlation analysis between risk scores and different immune cells estimated by TIMER, CIBERSORT-ABS, QUANTISEQ, xCELL and EPIC. (C) Heatmap indicates the scores for tumor purity and the TME between the high- and low-risk groups. (D) The differences in the proportions of 16 immune cells between the high- and low-risk groups. (E) The differences in the proportions of 13 immune-related pathways between the high- and low-risk groups. (F) The distributions of the immune score, ESTIMATE score, stromal score, and tumor purity between the high- and low-risk groups. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/ee2b723cd13bc6220266dd53.png"},{"id":66636319,"identity":"2c71b07d-044a-4887-9fdf-201992a77a45","added_by":"auto","created_at":"2024-10-15 05:33:31","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":383277,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of immunotherapeutic responses between the high- and low-risk groups. (A) Comparison of the expression levels of immune checkpoint genes between the high- and low-risk groups in TCGA dataset. (B) Comparison of the expression levels of PD-L1, PD-1, CTLA-4, LAG3 and TIM3 between the high- and low-risk in GSE65904 dataset. (C) The correlation between risk score and PD-1 or CTLA4 expression. (D) Kaplan-Meier survival curves of OS among four patient groups divided by the CSRS and PD-L1/PD-1/CTLA-4 in TCGA dataset.(E) The distribution of IPS in the high- and low-risk groups in TCGA dataset. (F) Kaplan-Meier curves for high- and low-risk groups in the IMvigor210 dataset. (G) Kaplan-Meier curves for four patient groups stratified by CSRS and PD-L1 expression in the IMvigor210 dataset. (H) The distributions of the TMB between the high- and low-risk groups. (I) Kaplan-Meier survival curves of OS among four patient groups divided by the CSRS and TMB. *\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/0bbc5d2ca2848e8f9ee26004.png"},{"id":66636323,"identity":"cd871e54-f566-47d9-947c-56ee3232f001","added_by":"auto","created_at":"2024-10-15 05:33:31","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1396703,"visible":true,"origin":"","legend":"\u003cp\u003eRUVBL2\u003cstrong\u003e \u003c/strong\u003eknockdown induces significant anticancer activity in SKCM (A) The representative images of IHC staining of three signature genes (FOXM1,NOX4,NOTCH3) from SKCM tissues and normal skin tissues. (B) Quantitative real-time polymerase chain reaction (qRT-PCR) results showed relative mRNA expression of RUVBL2 in SKCM cell lines (A375 and SK-Mel-5) and normal human epidermal melanocytes (MC). (C) QRT-PCR verified the mRNA expression level of RUVBL2 in A375 and SK-Mel-5 cells after transfection with si-RUVBL2. Cell Counting Kit-8 assay (D) and 5-ethynyl-20-deoxyuridine (EdU) staining assay (E) in A375 and SK-Mel-5 cells after transfection with si-RUVBL2. *\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.05, **\u003cem\u003eP\u003c/em\u003e \u0026lt;0.01, ***\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001, ns, no significance.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/8181da1ac65887a5d4a7b1b7.png"},{"id":71654674,"identity":"65358b00-7d9a-425d-aa8b-d1d2775fc89d","added_by":"auto","created_at":"2024-12-17 12:47:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6412372,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/82c283a9-e5c5-408a-a4b8-6e28a6830ca7.pdf"},{"id":66636328,"identity":"0b9ae041-e2ec-421b-a18e-425d17f58807","added_by":"auto","created_at":"2024-10-15 05:33:31","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":56102064,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1|\u003c/strong\u003e Identification of differentially expressed SRGs in SKCM. \u0026nbsp;(A) Heatmap of significantly differentially expressed SRGs in TCGA dataset. GO enrichment analysis (B) and KEGG pathway enrichment analysis (C) of differentially expressed SRGs. (D) PPI analysis of differentially expressed SRGs.\u003c/p\u003e","description":"","filename":"FigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/ca7828a07e0e7db849e4b387.tif"},{"id":66639460,"identity":"c5697900-0a45-45c2-a018-b2351844879d","added_by":"auto","created_at":"2024-10-15 06:00:01","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10716284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2| \u003c/strong\u003eKaplan-Meier curves for patients with high and low expression of 12 signature genesin TCGA dataset.\u003c/p\u003e","description":"","filename":"FigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/34cd24836c302888cf9b7cdb.tif"},{"id":66639461,"identity":"9220ad42-cbae-498c-811c-d8bd4dcdb8d2","added_by":"auto","created_at":"2024-10-15 06:00:01","extension":"tif","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18055632,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3| \u003c/strong\u003eThe stratified survival analyses in different subgroups. (A) age, (B) gender, (C) T stage, (D) M stage, (E) N stage, (F) TNM stage, (G) breslow depth, (H) clark stage, (I) ulceration status and (J) tumor location.\u003c/p\u003e","description":"","filename":"FigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/50eba3e6cf4c9957a66d8689.tif"},{"id":66637406,"identity":"b3823714-6170-4e5a-b6e3-353c5c3b35c4","added_by":"auto","created_at":"2024-10-15 05:41:31","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":9835615,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4 |\u003c/strong\u003e Heatmap of significantly differentially expressed genes between the high- and low-risk groups in TCGA dataset.\u003c/p\u003e","description":"","filename":"FigureS4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/0ef7ac883cca4643162cc7fc.pdf"},{"id":66639534,"identity":"4965edf7-7c43-4f2b-abd1-c65ca6c96343","added_by":"auto","created_at":"2024-10-15 06:00:14","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":7106252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5 |\u003c/strong\u003e GO enrichment analysis (A) and KEGG pathway enrichment analysis (B) of DEGs. (C) GSEA of hallmark gene sets compared between the high- and low- risk groups in TCGA dataset.\u003c/p\u003e","description":"","filename":"FigureS5.tif","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/fe7a2e8a7f5fd2f84007865f.tif"},{"id":66636326,"identity":"00c1c749-a906-4595-b047-af4a732b006f","added_by":"auto","created_at":"2024-10-15 05:33:31","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":8625420,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 6 |\u003c/strong\u003eThe Kaplan-Meier curves of OS in patients with diffrent immune score, stromal score, ESTIMATE score, and tumor purity.\u003c/p\u003e","description":"","filename":"FigureS6.tif","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/b8206a0cd082721052e9cc42.tif"},{"id":66636327,"identity":"8aac3a24-51a7-42ae-b734-0acf1193d192","added_by":"auto","created_at":"2024-10-15 05:33:31","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1263369,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1|\u003c/strong\u003eCellular senescence-related genes\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 2|\u003c/strong\u003eCellular senescence-related DEGs in TCGA SKCM cohort\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 3| \u003c/strong\u003eGO enrichment analysis of cellular senescence-related DEGs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 4|\u003c/strong\u003eKEGG enrichment analysis of cellular senescence-related DEGs\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 5| \u003c/strong\u003eUnivariate Cox analysis of cellular senescence-related genes in TCGA Cohort\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table 6|\u003c/strong\u003eDEGs between high- and low- risk group in TCGA cohort\u003c/p\u003e","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4943989/v1/154b5d121126d653ba7c7d06.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification and Validation of Cellular Senescence-Related Signature to Predict Survival and Immunotherapeutic Responses in Skin Cutaneous Melanoma","fulltext":[{"header":"1 | INTRODUCTION","content":"\u003cp\u003eSkin cutaneous melanoma (SKCM) is the most lethal skin cancer with an increasing incidence worldwide. In2020, an estimated 325,000 persons worldwide were diagnosed as having melanoma, and approximately 57,000 persons died of the disease. If 2020 rates continue, the burden from melanoma is estimated to increase to 510,000 new cases (a roughly 50% increase) and to 96,000 deaths (a 68% increase) by 2040 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Since 1998, the American Joint Committee on Cancer (AJCC) melanoma staging system has served as a foundation for clinical classification. However, clinical outcome of patients with similar or even identical clinical and histological features varies considerably. Notwithstanding current progress in the research of melanoma pathogenesis has facilitated the emergence of some novel therapies against SKCM such as targeted therapy and immunotherapy, curbing disease relapse in SKCM remains an unmet objective [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, it is urgently required to explore novel prognostic signature for SKCM patients that can predict survival.\u003c/p\u003e \u003cp\u003eThe tumor microenvironment (TME) is a complex ecosystem comprised of various cell types including malignant cells, cancer-associated fibroblasts (CAFs), endothelium, keratinocytes, adipocytes, and various types of immune cells. Tumor-infiltrating immune cells gradually exhibit malfunction in the dynamic interplay with melanoma cells and finally become \u0026ldquo;accomplices\u0026rdquo; of melanoma immune escape. The immune checkpoint inhibitors (ICIs) such as programmed cell death protein 1 (PD-1, nivolumab and pembrolizumab), programmed cell death ligand 1 (PD-L1, atezolizumab), and cytotoxic T lymphocyte antigen 4 (CTLA-4, ipilimumab) are applied in SKCM immunotherapy, which underscores the importance of the interactions between melanoma cells and immune cells in the TME. Previous accumulating evidence has revealed that combination therapy with nivolumab (anti-PD1) and ipilimumab (anti-CTLA4) has vastly improved long-term outcomes in metastatic malignant melanoma, with half of treated patients alive at 5 years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, the low response rate and inevitable occurrence of resistance to currently available targeted therapies have posed the obstacle in the path of SKCM management to obtain further amelioration [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Therefore, accurate predictive tools that assess the immune status of the TME and response to immunotherapy for SKCM patients are urgently needed.\u003c/p\u003e \u003cp\u003eCellular senescence is a hallmark of aging defined by stable exit from the cell cycle in response to cellular damage and stress [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite cellular senescence is an oncosuppressive mechanism and a potent anticancer therapeutic strategy, their persistence in tissues can have deleterious effects [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The senescence-associated secretory phenotype (SASP), where senescent cells produce a variety of secreted proteins including inflammatory cytokines, chemokines, matrix remodelling factors, growth factors and so on, plays pivotal but varying roles in the TME. The impact of SASP on the tissue microenvironment in vivo varies depending on the types of cells undergoing senescence, the cause of senescence, or the trigger of the innate immune pathway for SASP [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. On the one hand, SASP factors can stimulate immunosurveillance mechanisms and potentiate the tumor suppressive function of senescent cells by guiding the immune system to mount anticancer responses. On the other hand, SASP contributes to tumor progression and relapse through protecting tumors from immune clearance, providing growth factors, and enhancing angiogenesis [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. There is growing evidence that senescent cells may contribute to tumorigenesis, development, and immune modulation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the correlations of the cellular senescence with tumor progression and tumor immune microenvironment (TIME) in SKCM were poorly understood.\u003c/p\u003e \u003cp\u003eIn this systematic study, we evaluated the expression levels of 279 cellular senescence-related genes (SRGs) in SKCM tissues and normal skin tissues, and developed a prognostic prediction CSRS for SKCM patients. Subsequently, we evaluated the relationship between CSRS and SKCM patients\u0026rsquo; clinical characteristics, immune cell infiltration and immunotherapy efficacy based on CSRS. In addition, we also identified three cellular senescence clusters with distinct prognosis based on the 279 SRGs expression. Further exploration of the mechanisms implied that RUVBL2 expression was upregulated in SKCM and promoted the proliferation of SKCM cells.\u003c/p\u003e"},{"header":"2 | MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 | Data Acquisition and Processing\u003c/h2\u003e \u003cp\u003eExpression data and corresponding clinical follow-up information of 469 SKCM patients were obtained from The Cancer Genome Atlas (TCGA, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. The normal skin tissues data was extracted from the TCGA and Genotype-Tissue Expression Project (GTEx, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gtexportal.org/home/\u003c/span\u003e\u003cspan address=\"https://gtexportal.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A total of 279 SRGs (Supplementary Table\u0026nbsp;1) were obtained from the CellAge [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://genomics.senescence.info/cells/\u003c/span\u003e\u003cspan address=\"https://genomics.senescence.info/cells/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The GSE65904 (n\u0026thinsp;=\u0026thinsp;214) dataset[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]was extracted from the Gene Expression Omnibus database (GEO, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"http://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We also downloaded the IMvigor210 immunotherapy data cohort, a set of expression profiling data and corresponding clinical information for 348 metastatic urothelial cancer patients treated with anti-PD-L1 agent (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://research-pub.gene.com/IMvigor210CoreBiologies/\u003c/span\u003e\u003cspan address=\"http://research-pub.gene.com/IMvigor210CoreBiologies/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 | Specimens collection\u003c/h2\u003e \u003cp\u003eWe collected 5 SKCM specimens and 5 normal skin tissues from the Department of Dermatology, Third Affiliated Hospital of Sun Yat-sen University (Guangzhou, China). Patients enrolled were informed consent and this study was approved by the Ethics Committee of the Third Affiliated Hospital of Sun Yat-sen University (Guangzhou, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 | Enrichment analysis\u003c/h2\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were applied using the R package \u0026lsquo;clusterProfiler\u0026rsquo;. A protein-protein interaction (PPI) network was constructed using the Cytoscape webtool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to explore the interactions between these differentially expressed SRGs. Gene set enrichment analysis (GSEA) was conducted to identify the differences in biological processes among different clusters using Java GSEA software (version 4.1.0).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 | Consensus Clustering\u003c/h2\u003e \u003cp\u003eTo elucidate the biological characteristics and prognostic values of SRGs, we employed the \u0026ldquo;ConsensusClusterPlus\u0026rdquo; package in R to cluster the SKCM patients into different subgroups [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The empirical cumulative density function (CDF) plot is aimed to determine the optimum cluster number (k) for the sample distribution to reach an approximate maximum, which means the maximum stability. Principal Component Analysis (PCA) was performed using R package to assess the distribution of gene expression among different subtypes. Furthermore, Kaplan-Meier survival analysis (log-rank test) was applied to verify the performance of various clusters to predict survival differences.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 | Construction and validation of cellular senescence-related signature (CSRS)\u003c/h2\u003e \u003cp\u003eUnivariate Cox analysis was conducted to identify the differentially expressed SRGs related to overall survival (OS) in TCGA-SKCM dataset (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Then, least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to select the prognostic SRGs. The risk score of each SKCM patient was obtained according to the following formula: risk score\u0026thinsp;=\u0026thinsp;Coef(Gene1) \u0026times; Expr(Gene1)\u0026thinsp;+\u0026thinsp;Coef(Gene2) \u0026times;Expr(Gene2) +\u0026hellip; + Coef(Genen) \u0026times; Expr(Genen), in which Expr(Genen) represents the expression of a particular gene and Coef(Genen) is the coefficient obtained from univariate Cox analysis of genes. GSE65904 (n\u0026thinsp;=\u0026thinsp;214) were used as an external validation dataset. The patients in TCGA-SKCM dataset and validation dataset were classified either into the high-risk (\u0026ge;\u0026thinsp;median number) or the low-risk (\u0026lt;\u0026thinsp;median number) groups according to the median risk score. Afterwards, the survival curve, risk score distribution, heatmap, and receiver operating characteristic (ROC) curve were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 | Signature Genes Analyses\u003c/h2\u003e \u003cp\u003eThe Gene Expression Profiling Interactive Analysis (GEPIA2[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gepia2.cancer-pku.cn/\u003c/span\u003e\u003cspan address=\"http://gepia2.cancer-pku.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database and UALCAN[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e](\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ualcan.path.uab.edu\u003c/span\u003e\u003cspan address=\"http://ualcan.path.uab.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) web tool were used to analyze the signature genes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 | Correlation Between Immune Cell Infiltration and CSRS\u003c/h2\u003e \u003cp\u003eTo analyze the correlation between CSRS and different immune cell infiltration, RNA seq-derived infiltrating immune cell populations were estimated by TIMER, EPIC, xCELL, CIBERSORT and quanTIseq algorithms in TIMER2.0 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://timer.comp-genomics.org/\u003c/span\u003e\u003cspan address=\"http://timer.comp-genomics.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Single Sample Gene Set Enrichment Analysis (ssGSEA) algorithm was used to quantitate the differences in the infiltration levels of immunocytes between the high- and low-risk subgroups using the \u0026ldquo;GSVA\u0026rdquo; package in R. Tumor purity, immune score, stromal score, and ESTIMATE score were calculated for each SKCM sample using the \u0026ldquo;ESTIMATE\u0026rdquo; package in the R program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 | Assessment of CSRS and Immunotherapeutic Responses\u003c/h2\u003e \u003cp\u003eImmunophenotype score (IPS) could well predict the response of ICIs, the higher the score the better the therapeutic response to immune checkpoint (PD-1 and CTLA4) inhibitors. The immunogenicity is determined by four major categories of genes, including effector cells, major histocompatibility complex (MHC) molecules, immunomodulators and immunosuppressive cells. The scores of IPS were calculated using a scale ranging from 0\u0026ndash;10 based on representative cell type gene expression z-scores. We downloaded the IPS scoring in SKCM from The Cancer Immunome Atlas (TCIA [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tcia.at\u003c/span\u003e\u003cspan address=\"https://tcia.at\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Then, we also extracted clinical information from the IMvigor210 dataset to assess the difference (responding or non-responding) in high/low-risk group between the anti-PD-L1 immunotherapy groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 | Tumor mutational burden (TMB) analysis\u003c/h2\u003e \u003cp\u003eThe mutation data for SKCM patients were downloaded from the TCGA data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://portal.gdc.cancer.gov/\u003c/span\u003e\u003cspan address=\"https://portal.gdc.cancer.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Package \u0026ldquo;maftools\u0026rdquo; was used to analyze TMB differences between the high- and low-risk subgroups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 | Cell culture and small interfering RNA (siRNA) transfection sequences\u003c/h2\u003e \u003cp\u003eNormal human epidermal melanocytes were maintained in basal medium supplemented with 0.5% fetal bovine serum (FBS) and 1% human melanocyte growth supplement (ScienCell, USA). SKCM cell lines A375 and SK-Mel-5 were kindly provided by Procell Life Science\u0026amp;Technology Co. Ltd. All cell lines were authenticated using the short tandem repeat method, performed using this cell bank. Cells were cultured in DMEM supplemented with 10% FBS, and cultured at 37\u0026deg;C with 5% carbon dioxide.\u003c/p\u003e \u003cp\u003eFor generation of siRNA-mediated knock down, cells were transfected with 50 nM RUVBL2 siRNA duplexes or 50 nM non-targeting control siRNA using lipofectamine 3000 reagent (Invitrogen, USA) in Opti-MEM medium (Gibco, USA) following the manufacturer's instructions. The RUVBL2 siRNA sequences were as follows: si- RUVBL2-1 TCCTGATCATGGCCACCAA; si-RUVBL2-2 GCGAGAAAGACAC GAAGCA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 | RNA isolation and quantitative real-time polymerase chain reaction (qRT-PCR)\u003c/h2\u003e \u003cp\u003eTotal RNA was extracted using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) and then reverse transcribed to cDNA using a Revert Aid First Strand cDNA Synthesis Kit (Thermo Fisher Scientific). The real-time polymerase chain reaction (RT-qPCR) was performed using a SYBR Fast qRT-PCR Master Mix Kit (Kapa Biosystems, Wilmington, MA, USA) and a Light Cycler 480 system (Roche, Basel, Switzerland) according to the manufacturer\u0026rsquo;s instructions. For each sample and index, the samples were studied in triplicate, with GAPDH mRNA expression measured as an internal reference. The primer sequences used for qRT-PCR were as follows: RUVBL2 Forward: 5\u0026rsquo;-AAGAAGATGTGGAGATGAG-3\u0026rsquo;, Reverse: 5\u0026rsquo;-CAGGAAGA GTGAGT AGAC-3\u0026rsquo;; GAPDH Forward:5\u0026rsquo;-CTGGGCTACACTGAGCACC-3\u0026rsquo;; Reverse: 5\u0026rsquo;-AAG TGGTCGTTGAGGGCAATG-3\u0026rsquo;. Fold changes were calculated using the relative quantification (2\u003csup\u003e\u0026minus;ΔΔCt\u003c/sup\u003e) method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12 | Detection of cell viability\u003c/h2\u003e \u003cp\u003eCells were seeded into 96-well plates (2000 cells/well) 1 day before the proliferation assay. The medium of each well was replaced by Cell Counting Kit-8 (Dojindo, Kamimashiki-gun, Kumamoto, Japan) following the manufacturer\u0026rsquo;s instruction 2 h before testing. The absorbance at a wavelength of 450 nm was measured with an enzyme mark instrument (Thermo Fisher Scientific).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 | 5-ethynyl-20-deoxyuridine (EdU) staining\u003c/h2\u003e \u003cp\u003eMelanoma cells were seeded into 6-well plates at 1x10\u003csup\u003e5\u003c/sup\u003e cells per well and were cultured for 48 h. An EdU (5-ethynyl-20-deoxyuridine) Apollo-567 Kit (RiboBio, China) was applied to quantify cell proliferation. The EdU and 4,6-diamino-2-phenyl indole (DAPI) fluorescent dyes were added to melanoma cells, and the cells were visualized under a fluorescent microscope. Five random views per each condition were used to calculate the average EdU ratio (% vs. DAPI).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 | Immunohistochemistry (IHC) staining and evaluation\u003c/h2\u003e \u003cp\u003eParaffin slides were incubated at 4\u0026deg;C overnight with primary antibodies against FOXM1 (ab207298, 1: 200), NOTCH3 (ab300527, 1: 200) and NADPH oxidase 4 (ab133303, 1: 100). Next, the slides were incubated with secondary antibody and followed by DAB staining and hematoxylin counterstaining.The immunohistochemical staining was evaluated according to the percentage of positive cells and the staining intensity. Staining intensity was scored as 0 (negative), 1 (weak), 2 (moderate) or 3 (strong). The expression proportion was scored as 1 (0\u0026ndash;25%), 2 (26\u0026ndash;50%), 3 (51\u0026ndash;75%) or 4 (76\u0026ndash;100%). The final score of each sample was obtained by multiplying the signal intensity and the expression proportion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 | Statistical Analysis\u003c/h2\u003e \u003cp\u003eR software (version 4.1.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.r-project.org\u003c/span\u003e\u003cspan address=\"https://www.r-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), GraphPad Prism 8.0 and SPSS Statistics V25.0 were used to analysis data and plot graphs. Data were presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD of at least three independent experiments, and differences between two groups were compared using student\u0026rsquo;s t-test. Rank correlations were assessed by the performance of spearman\u0026rsquo;s correlation coefficient test among different variables. The Kaplan-Meier survival curve for OS analysis was plotted with the R package \u0026ldquo;survminer\u0026rdquo;. We considered \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 | RESULTS","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1 | Characterize the expression pattern of SRGs and identification of cellular senescence-related subtypes in SKCM\u003c/h2\u003e \u003cp\u003eThe overall study flow diagram was depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. First, we screened 7896 cellular senescence-related differentially expressed genes (DEGs) between normal skin samples (n\u0026thinsp;=\u0026thinsp;558) and SKCM samples (n\u0026thinsp;=\u0026thinsp;469), based on the thresholds of an adjusted FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and log2 | fold-change (FC) | \u0026gt; 1 filters. Among them, 3857 DEGs were significantly upregulated in SKCM samples as compared to the normal skin samples, while the remaining 4039 were markedly downregulated. Then, we identified 113 differentially expressed SRGs by taking the intersection of DEGs and SRGs sets. Among them, 68 genes were upregulated, whereas 45 were downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA; Supplementary Fig.\u0026nbsp;1A, Supplementary Table\u0026nbsp;2).Subsequently, we performed GO and KEGG analyses to clarify the biological process of the 113 differentially expressed SRGs in SKCM. As expected, the DEGs were remarkably enriched in cellular senescence- and cell cycle-related pathways in SKCM samples (Supplementary Fig.\u0026nbsp;1B,C; Supplementary Table\u0026nbsp;3,4). A PPI network was performed to further explore the interactions among these 113 DEGs (Supplementary Fig.\u0026nbsp;1D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe performed a consensus clustering analysis to classify all SKCM patients based on the expression patterns of 113 SRGs. The optimal division (K\u0026thinsp;=\u0026thinsp;3) was the optimal number of clusters according to the consensus matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB), consensus CDF curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and relative change in the area under the CDF curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). PCA analysis showed that SKCM patients could be well divided into three clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE).The survival curve results showed that cluster 1 suffered a worse prognosis relative to clusters 2 and 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF). In addition, The chisquare analysis demonstrated statistically significant differences in the age, T stage and breslow between the three clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2 | Development and validation of CSRS for SKCM\u003c/h2\u003e \u003cp\u003eWe performed univariate Cox proportional hazard regression analysis and found a total of 15 out of the 113 differentially expressed SRGs were significantly associated with OS (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Supplementary Table\u0026nbsp;5). Among them, ITSN2, CBX7, MAP4K1, ABI3, BCL6, HK3, NDRG1, and NOX4 were the protective factors with the hazard ratio (HR)\u0026thinsp;\u0026lt;\u0026thinsp;1, while PSMB5, MVK, AKT1, NOTCH3, FOXM1, SFN, and RUVBL2 were risk factors having a hazard ratio (HR)\u0026thinsp;\u0026gt;\u0026thinsp;1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Besides, the correlation among the 15 potential prognostic DEGs were also analyzed, and the relationships between each two of them were almost positively correlated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Afterwards, LASSO Cox regression was performed to narrow down the range of candidate genes and eliminate the risk of overfitting, and the penalty parameter was selected based on the minimum criterion. Then, a 12-gene prognostic signature was constructed according to the optimum λ value (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). We observed that high expression of MVK, NOTCH3, FOXM1, SFN and RUVBL2 genes in TCGA dataset was consistently indicative of poor prognosis for SKCM patients, while high expression of ITSN2, CBX7, ABI3, BCL6, HK3, NDRG1 and NOX4 genes had a significantly longer OS time (Supplementary Fig.\u0026nbsp;2). Moreover, multivariate Cox regression analysis were also conducted, which indicated that NOTCH3 was risk factors and NDRG1 was the protective factor for SKCM patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). We then established a risk score formula based on the expression of the 12 SRGs for SKCM patients: Risk score =(0.0271 \u0026times;expression value of MVK) + (-0.0459 \u0026times; expression value of ITSN2) + (-0.0749 \u0026times; expression value of CBX7) + (0.2481 \u0026times;expression value of NOTCH3) + (0.1034 \u0026times; expression value of FOXM1)+ (-0.0861 \u0026times; expression value of ABI3)+ (0.0823 \u0026times; expression value of SFN)+ (0.0498 \u0026times; expression value of RUVBL2)+ (-0.1105 \u0026times; expression value of BCL6)+ (-0.2307 \u0026times; expression value of HK3)+ (-0.1952 \u0026times; expression value of NDRG1)+ (-0.1708 \u0026times; expression value of NOX4).The risk score of every patient was then calculated using this formula, and SKCM cases were divided into low- and high-risk groups according to the median value of the risk score. We also analyzed the mRNA expression levels of the 15 candidate signature genes in SKCM by TCGA data, and found eight genes (PSMB5, MAP4K1, AKT1, FOXM1, ABI3, RUVBL2, HK3 and NOX4) were upregulated while seven genes (MVK, ITSN2, CBX7, NOTCH3, SFN, BCL6 and NDRG1) were downregulated (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe distribution of the CSRS risk scores, the survival status, and a heatmap exhibiting the expression profiles of 12 prognosis-related SRGs in the high- and low-risk groups are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA. Kaplan-Meier survival analysis demonstrated that the high-risk group had a shorter OS than that of the low-risk group in TCGA-SKCM dataset ((Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, HR\u0026thinsp;=\u0026thinsp;2.261, 95% CI:1.712\u0026ndash;2.985, log-rank \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). The 5-years survival rate of the high-risk group was 24.23%, which was significantly lower than that of the low-risk group (41.85%). Time-dependent ROC analysis was performed to evaluate the sensitivity and specificity of the CSRS, and the areas under the curve (AUC) values of 2, 3 and 5 year were 0.68, 0.64, and 0.65 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). In addition, significant difference in OS time was observed in both early-stage (HR\u0026thinsp;=\u0026thinsp;2.691, 95% CI: 1.859\u0026ndash;3.894, log-rank \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) and advanced-stage SKCM (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, HR\u0026thinsp;=\u0026thinsp;2.5, 95% CI: 1.773\u0026ndash;3.526, log-rank \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Afterwards, we performed the univariate and multivariate Cox regression analyses to examine whether the risk score could act as an independent prognosis variable of SKCM. The age, T stage, N stage, M stage, TNM stage, breslow depth, clark stage, ulceration status, location and risk score were correlated with OS in univariable analysis. Multivariate Cox regression analyses further identified that the risk score was an independent prognostic factor (HR\u0026thinsp;=\u0026thinsp;2.136, 95% CI: 1.534\u0026ndash;2.973, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for SKCM patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo validate the predictive reliability of the CSRS, we calculated the risk scores of samples in GSE65904 dataset using the same formula and similarly classified the samples into high-risk and low-risk groups. The risk score distribution, survival status and heatmap for the expression profile of CSRS in GSE65904 dataset are visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF. Patients with high-risk scores had a higher mortality rate than those in the low-risk group (HR\u0026thinsp;=\u0026thinsp;1.895, 95% CI: 1.29\u0026ndash;2.783, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00111; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). The AUC values of 2, 3 and 5 year in GSE65904 dataset were 0.63, 0.63, and 0.58 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.3 | Correlations between the CSRS and clinicopathological factors\u003c/h2\u003e \u003cp\u003eWe compared the differences in clinicopathological factors between high- and low-risk groups, including age, gender, T stage, N stage, M stage, TNM stage, breslow depth, clark stage, ulceration status and location. As Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e showed, the risk score of patients with breslow tumor thickness more than 3mm was higher than that of patients with no more than 3mm (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0048), and patients with lymphatic metastasis showed a remarkably higher risk score than those with primary tumor (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003ePatients were divided into different subgroups according to the following clinical variables, including age (\u0026le;\u0026thinsp;60 and \u0026gt;\u0026thinsp;60), gender (female and male), T stage (T0\u0026ndash;2 and T3\u0026ndash;4),N stage (N0 and N1\u0026ndash;3), M stage (M0 and M1), TNM stage (stage I-II and stage III-IV), breslow depth (\u0026gt;\u0026thinsp;3mm and \u0026le;\u0026thinsp;3mm), clark stage (clark I-Ⅲ and stage Ⅳ-Ⅴ), ulceration status (yes and no) and location (primary and metastasis). The Kaplan-Meier survival curve showed that samples from the high-risk group had worse prognosis compared with those belonging to the low-risk group in all the subgroups (Supplementary Fig.\u0026nbsp;3). These results suggest that CSRS can accurately and reliably predict the survival outcome of patients with SKCM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.4 | Signature Genes Analysis\u003c/h2\u003e \u003cp\u003eWe found markedly low levels of MVK, ITSN2, CBX7, NOTCH3, SFN, BCL6 and NDRG1 in SKCM from the GEPIA2 database, while those of FOXM1, RUVBL2 and HK3 were substantially high (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). FOXM1, SFN and RUVBL2 were risk factors in many other types of cancer, including adrenocortical carcinoma, brain lower grade glioma, liver hepatocellular carcinoma and lung adenocarcinoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Then, we analyzed the relationship between methylation and the expression of the 12 signature genes, which may account for the abnormal expression of these signature genes. Significantly lower methylation levels of FOXM1 promoters were found in SKCM samples compared with normal skin samples, while the methylation levels of BCL6, NDRG1 and HK3 promoters were higher in SKCM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). No significant differences were present in the expression of methylation levels between tumor and normal tissues in the other eight signature genes. In addition, we also obtained protein structures of the 12 signature genes from the PDB database (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 | Biological Processes Analysis of Signature genes\u003c/h2\u003e \u003cp\u003eTo explore the potential biological processes for the prognostic risk signature, we screened 4788 DEGs between high- and low-risk groups with the criteria FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.01 and |log2FC| \u0026ge; 1. Among them, 1961 genes were downregulated in high-risk group, while 2827 genes were upregulated (Supplementary Fig.\u0026nbsp;4; Supplementary Table\u0026nbsp;6). GO and KEGG pathways analyses were performed based on the DEGs. Intriguingly, the results indicated that the DEGs were mainly involved in immunological regulation-related biological processes, such as immune response, T cell activation and B cell activation (Supplementary Fig.\u0026nbsp;5A,B). Besides, functional annotation was also performed between high- and low-risk groups using GSEA. The result showed that enriched gene sets of the HALLMARK collection in the high-risk group were mainly involved in tumor-related pathways, including oxidative phosphorylation, IFN-γ response, MYC targets, IFN-α response and E2F targets, which are closely related to the malignant proliferation and immune microenvironment of tumor (Supplementary Fig.\u0026nbsp;5C). These results suggest that CSRS may have a strong correlation with tumor-infiltrating immune cells.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.6 | CSRS Is Associated with Alterations in SASP\u003c/h2\u003e \u003cp\u003eAs mentioned above, SASP, where senescent cells produce a variety of secreted proteins including inflammatory cytokines, chemokines, proteases, growth factors and so on, plays pivotal but varying roles in the TME. We explored the correlations between CSRS and SASPs, and found that SASPs including interleukins (IL-1A, IL-1B, IL-6, IL-7, IL13 and IL-15), soluble or shed receptors or ligands (FAS,ICAM1,ICAM3,IL6ST, PLAUR,TNFRSF1A, TNFRSF1B, TNFRSF10C and TNFRSF11B), chemokines (CCL1, CCL3, CCL8, CCL13, CCL25, CCL26, CXCL5, and CXCL11), growth factors and regulators (ANG, FGF2, FGF7, HGF, IGFBP2, IGFBP3, IGFBP7, and VEGFA), and proteases and regulators (CTSB, MMP12 and SERPINE1) were significantly downregulated in high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eA). It has been reported that SASP promotes immune clearance of damaged cells. Therefore, our results suggest that patients with high CSRS scores may exhibit an attenuated immune response depend on SASP.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.7 | Association between CSRS and TIME\u003c/h2\u003e \u003cp\u003eThe differences of immune cell infiltration between high- and low-risk groups were analyzed to explore the correlations between the CSRS and TIME. We found that the infiltration levels of B cells, CD4\u0026thinsp;+\u0026thinsp;T cells, and CD8\u0026thinsp;+\u0026thinsp;T cells were lower in high-risk groups, while the CAFs were significantly higher in high-risk subgroup (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eB). To further explore the relationship between the CSRS and immune status, we performed the expression profiles of 29 immune signature gens sets (16 types of immune cells and 13 immune-related pathways) in high- and low risk groups. The ssGSEA analysis showed that compared to the low-risk group, patients in the high-risk group had lower levels of immune cell infiltration and immune-related functions and pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eC-E). These results indicated that patients with high CSRS scores exhibit lower levels of immune infiltration, consistent with our above analysis.\u003c/p\u003e \u003cp\u003eThe ESITIMATE algorithm revealed that patients in the high-risk group had higher tumor purity and lower ESITIMATE scores, immune scores, and stromal scores compared with patients in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eF). Survival analysis showed that the patients with lower immune scores, lower stromal scores, lower ESTIMATE score, or higher tumor purity had a worse prognosis (Supplementary Fig.\u0026nbsp;6). These results indicated that there was a significant correlation of the CSRS-based risk score with the TIME.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.8 | Association of CSRS with immunotherapy efficacy\u003c/h2\u003e \u003cp\u003eGiven the association between CSRS and immune infiltration, we further compared the expression pattern of immune checkpoint genes between patients in high and low-risk groups, which resulted in most of the immune checkpoints being significantly overexpressed in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA). Then, we confirmed that immune checkpoint genes (PD-L1, PD1, CTLA4, LAG3,TIM3) were overexpression in the low-risk group in GSE65904 dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB). The levels of expression of PD-1 and CTLA-4 were negatively correlated with the risk score (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC). Afterwards, the survival analysis of the four groups stratified by CSRS and immune checkpoint gene expression was conducted. Our results showed that patients with low risk had prolonged OS compared to those with high risk among all the groups stratified by PD-L1,PD1 and CTLA4 expression in TCGA dataset (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eD). We then assessed immunogenicity by IPS scoring to predict patient response to immune checkpoint blockade (anti-PD1 and/or anti-CTLA4), with higher IPS scores indicating better predicted immunotherapy efficacy. We found that anti-PD1, anti-CTLA4 and anti-PD1-CTLA4 combination therapy was more effective in the low-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eE). Subsequent validation of the CSRS for predicting immunotherapy efficacy by external immunotherapy datasets showed that patients with lower CSRS scores had a higher prognosis in the IMvigor210 cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eF), and that patients in the low-risk group also exhibited a significantly higher prognosis among the PD-L1 high or low groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eG). Finally, we compared survival distribution of patient groups classified by CSRS and TMB level. Our results showed that patients with high CSRS scores suffered unfavourable OS irrespective of patients\u0026rsquo; TMB level in TCGA and IMvigor210 datasets (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eH, I).These results indicated that patients with low CSRS scores may respond better to the immunotherapy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.9 | Validation of Signature Gene Expressions in SKCM Tissues\u003c/h2\u003e \u003cp\u003eTo verify the reliability of the CSRS, five SKCM samples and five normal skin samples were collected to test the protein expression levels of FOXM1,NOX4,NOTCH3 by IHC. As expected, IHC staining revealed the protein expression level of FOXM1 and NADPH oxidase 4 were significantly elevated in SKCM tissues compared with normal skin tissues, while NOTCH3 was markedly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.10 | RUVBL2 knockdown induces significant anticancer activity in SKCM\u003c/h2\u003e \u003cp\u003eExperiments were performed to test the potential function of RUVBL2 in SKCM cells. Firstly, we verified that RUVBL2 mRNA levels in A375 and SK-Mel-5 cells were significantly higher than those in normal human melanocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eB). Then, the silencing effect of RUVBL2 with two individual small interfering RNAs (siRNAs) was detected by qPCR, which showed that si-RUVBL2 could effectively knock down the expression of RUVBL2 mRNA in A375 and SK-Mel-5 cells, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eC). The results of CCK8 experiments showed that the proliferation ability of A375 and SK-Mel-5 cells in the si-RUVBL2 group was significantly lower than that in the negative control group at 48, 72, and 96 h (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eD). As expected, EdU staining experiments showed that knockdown of RUVBL2 significantly inhibited the proliferation of A375 and SK-Mel-5 cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eE). Therefore, these findings support that RUVBL2 promotes tumorigenesis in SKCM.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 | DISCUSSION","content":"\u003cp\u003eCutaneous melanoma is the most lethal type of skin cancer that originates from the malignant transformation of melanocytes. Nevertheless, a variety of SKCM patients are treated with limited and similar therapies on account of lacking reliable and effective predictive tools to estimate patients\u0026rsquo; prognosis. Therefore, it is meaningful and necessary to identify accurate biomarkers to construct prognostic signatures for better predicting patients with SKCM and help to make decisions with regard to therapy. In the current study, we analyzed the mRNA expression patterns of 279 SRGs in SKCM and constructed the SRGs prognostic signature, which was well validated in GSE65904 dataset. Moreover, the CSRS was also significantly correlated with TIME and the response to immunotherapy, providing new insights into the correlations between cellular senescence and TIME in SKCM.\u003c/p\u003e \u003cp\u003eThe 12 SRGs in our prognostic prediction CSRS consist of MVK, NOTCH3, FOXM1, SFN, RUVBL2 as risk factors and ITSN2, CBX7, ABI3, BCL6, HK3, NDRG1, NOX4 as protective factors. Among them, FOXM1, ABI3, RUVBL2, HK3 and NOX4 were significantly upregulated in SKCM tissues as compared to the normal skin tissues, while MVK, ITSN2, CBX7, NOTCH3, SFN, BCL6 and NDRG1 were markedly downregulated. Several studies displayed that these 12 SRGs played an essential role in tumorigenesis and development. RUVBL2 is highly conserved ATPases that belong to the AAA\u0026thinsp;+\u0026thinsp;superfamily, which was found to be involved in the remodeling of chromatin, DNA damage repair, and regulation of the cell cycle, all of which help to play essential roles in cancer [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, RUVBL2 was rarely reported associated with SKCM progression. In this study, we found that mRNA expression level of RUVBL2 was significantly elevated in SKCM, and knockdown of RUVBL2 significantly suppressed cell proliferation of A375 and SK-Mel-5 melanoma cell lines, indicating that RUVBL2 promotes tumorigenesis in SKCM. It has been demonstrated that increased Forkhead Box M1 (FOXM1) expression in melanoma has previously been associated with accelerated tumor progression and poor prognosis as well as suppression of the senescence phenotype [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], NADPH oxidase 4 (NOX4) was up-regulated in more than half of melanoma cell lines [\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], Notch3 expression was significant decreased in in human tumor cell lines as well as melanoma samples compared to normal tissues[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, we also detected the protein expression level of FOXM1, NOX4 and NOTCH3 by IHC staining, which of the results verified that FOXM1 and NADPH oxidase 4 were significantly elevated, while NOTCH3 was reduced in SKCM tissues. Hexokinase 3 (HK3) may become potential oncogenes across a variety of cancer types [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Sulforaphane (SFN) induces cell differentiation, melanogenesis and also inhibit the proliferation of melanoma cells [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].The transcription factor B cell lymphoma 6 (Bcl6) is essential in maintaining the lineage stability of Treg cells in TME[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. N-myc downstream regulated gene 1 (NDRG1) has been identified as a protein involved in the differentiation of epithelial cells, which is an oncogenic signaling disruptor that plays a key role in multiple cancers [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Taken together, studies as mentioned above revealed the reasonability and accuracy of CSRS in SKCM tumorigenesis and development, and more experiments are needed to further elucidate the effect of these SRGs on tumorigenesis in SKCM.\u003c/p\u003e \u003cp\u003eThe TME is composed of many different cellular and acellular components that together drive tumor growth, invasion, metastasis, and response to therapies. Increasing realization of the significance of the TME in cancer biology has shifted cancer research from a cancer-centric model to one that considers the TME as a whole[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. SKCM with relatively high proliferative capacity and aggressiveness largely results from various immunosuppressive mechanisms that often work in concert to help tumor cells evade innate and adaptive immune detection and destruction. The characteristic of immune evasion depends on the interaction between tumor cells and the surrounding TME. In this study, we also explored the effect of cellular senescence on the tumor immune infiltrate and whether this would impact the response to ICIs. Our results showed that B cells, CD4\u0026thinsp;+\u0026thinsp;T cells and CD8\u0026thinsp;+\u0026thinsp;T cells were significantly enriched in low-risk groups while CAFs, which are observed in almost all solid tumor types, were positively correlated with the CSRS score in SKCM. Correspondingly, high-risk subgroup manifested lower levels of infiltration of immune cells, implicating less process in immune activation. This result suggested that patients with higher CSRS score might have an immunosuppressive TME, which prevented immune clearance of tumor cells. Previous studies have emphasized the importance of immune checkpoint genes in modulating immune infiltration [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and our results revealed significant relevance between cellular senescence and tumor immunity. Thus, we compared the expression pattern of immune checkpoint genes between high- and low-risk patients with SKCM. We found that the low-risk group had higher immune checkpoint genes expression and could benefit better from ICIs relative to the high-risk group. These results indicated that the CSRS score was coupled with specific immune checkpoint factors as predictive biomarkers of immunotherapy response for SKCM patients.\u003c/p\u003e \u003cp\u003eSASP comprises pro-inflammatory cytokines or chemokines, extracellular matrix proteases and growth factors that influence the cellular microenvironment. Some of these secreted molecules may have autocrine effects that enforce cellular senescence, whereas others may exert non-cell-autonomous effects that favor tumorigenesis in nearby non-senescent cells. Thus, SASP can exert both beneficial and detrimental effects, depending on the senescence signal, tissue context and secreted molecules. In this study, lower levels of SASP including some cytokines and chemokines and lower levels of infiltration of immune cells were observed in high-risk group, suggesting that SASP may promote the immune activation and prevent tumorigenesis for SKCM patients. Thus, SKCM can be viewed as a paradigm of cellular senescence evasion. Matrix metalloproteinases (MMPs) are not only extracellular matrix remodeling enzymes but regulators of several cellular functions including growth, migration, invasion and gene expression. Notably, some upregulated SASP factors in high-risk group, including MMP3, MMP14 and PLAU, may not only influence melanoma metastasis by extracellular matrix degradation, but also via regulation of genes involved in several pro-tumorigenic functions including tumor cell growth and motility. Consequently, we distinguished different SASP affecting tumorigenesis and immune modulation as potential mechanisms underlying immune escape and tumor progression in SKCM. Taken together, our results implied that a high level of cellular senescence may stimulate immunosurveillance mechanisms and potentiate the tumor suppressive function for SKCM in a SASP-depended manner.\u003c/p\u003e \u003cp\u003eThe strength of our study was that it was the first time for us to carry out the systematic analysis of SRGs in SKCM, and our SRG-based signature was successfully created, which benefits prognosis and immunotherapy for SKCM patients. However, several limitations should be mentioned. Firstly, the data for our analysis were obtained from public databases, which may have led to some case selection bias in case selection. Secondly, it is necessary to collect a large amount of clinical case data to further validate the accuracy of the results. Thirdly, further in vivo and in vitro experiments are needed to clarify the roles of the SRGs on tumorigenesis, development, and immune modulation in SKCM.\u003c/p\u003e"},{"header":"5 | CONCLUSIONS","content":"\u003cp\u003eIn this study, we constructed and validated a CSRS based on 279 SRGs. We found that CSRS can effectively predict the prognosis and immunotherapy outcome of patients with SKCM, which was validated by an external dataset. Importantly, the CSRS was significantly associated with the immune cell infiltration levels of SKCM patients and involved in the regulation of the immune microenvironment in SKCM by SASP. In addition, we elucidated the expression of SRGs for SKCM patients, which could stratify patients into three subgroups with different prognosis. In an era when immunotherapy holds great promise for cancer treatment, CSRS involved in the regulation of the TIME through SASP was a robust biomarker for the prognosis and immunotherapeutic response in SKCM.\u003c/p\u003e"},{"header":"ABBREVIATIONS","content":"\u003cp\u003eSKCM \u0026nbsp; Skin Cutaneous Melanoma \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCSRS \u0026nbsp; Cellular Senescence-related Signature \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSRGs \u0026nbsp; Cellular Senescence-Related Genes\u003c/p\u003e\n\u003cp\u003eTCGA \u0026nbsp; The Cancer Genome Atlas\u003c/p\u003e\n\u003cp\u003eGEO \u0026nbsp; Gene Expression Omnibus\u003c/p\u003e\n\u003cp\u003eGTEx \u0026nbsp; Genotype-Tissue Expression Program\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAJCC \u0026nbsp; American Joint Committee on Cancer\u003c/p\u003e\n\u003cp\u003eROC \u0026nbsp; Receiver Operating Characteristic \u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePD-1 \u0026nbsp; Programmed cell death 1\u003c/p\u003e\n\u003cp\u003ePD-L1 \u0026nbsp;Programmed cell death Ligand 1\u003c/p\u003e\n\u003cp\u003eCTLA4 \u0026nbsp; Cytotoxic T-lymphocyte-associated protein 4\u003c/p\u003e\n\u003cp\u003eLAG3 \u0026nbsp; Lymphocyte Activating 3\u003c/p\u003e\n\u003cp\u003eICIs \u0026nbsp; Immune checkpoint inhibitors\u003c/p\u003e\n\u003cp\u003eOS \u0026nbsp; Overall survival\u003c/p\u003e\n\u003cp\u003eTMB \u0026nbsp; Tumor mutation burden\u003c/p\u003e\n\u003cp\u003eIC50 \u0026nbsp; Half maximal inhibitory concentration\u003c/p\u003e\n\u003cp\u003ePPI \u0026nbsp; Protein–protein interaction\u003c/p\u003e\n\u003cp\u003eKM \u0026nbsp; Kaplan–Meier\u003c/p\u003e\n\u003cp\u003essGSEA \u0026nbsp; Single-sample gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eGSEA \u0026nbsp; Gene set enrichment analysis\u003c/p\u003e\n\u003cp\u003eGO \u0026nbsp; Gene Ontology\u003c/p\u003e\n\u003cp\u003eKEGG \u0026nbsp; Kyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n\u003cp\u003eLASSO \u0026nbsp; Least Absolute Shrinkage and Selection Operator\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTME \u0026nbsp;Tumor Microenvironment\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTIME \u0026nbsp; Tumor Immune Microenvironment \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCAFs \u0026nbsp; Cancer-associated Fibroblasts\u003c/p\u003e\n\u003cp\u003eSASP \u0026nbsp; Senescence-associated Secretory Phenotype\u003c/p\u003e\n\u003cp\u003eCDF \u0026nbsp;Cumulative Density Function\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCA \u0026nbsp;Principal Component Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIPS \u0026nbsp;Immunophenotype Score\u003c/p\u003e\n\u003cp\u003eDEGs \u0026nbsp;Differentially Expressed Genes \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHR \u0026nbsp;Hazard Ratio\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFUNDING DECLARATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Nos. 82203901, 82373497).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the contributions from TCGA and GEO databases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLM designed the study, performed the data analysis and drafted the manuscript. LM and CX performed the in vitro experiments. LL and YY\u0026nbsp;provided statistical advice. LW revised the manuscript and supervised the acquisition of the data. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets supporting the conclusions of this article are available in the TCGA and GEO database. The datasets supporting the conclusions of this article are also included within the article and its additional files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONFLICT OF INTEREST STATEMENT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICS STATEMENT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by The Ethical Committee and Institutional Review Board of the Third Affiliated Hospital of Sun Yat-sen University. The patients/participants provided their written informed consent to participate in this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eArnold M, Singh D, Laversanne M, Vignat J, Vaccarella S, Meheus F, Cust AE, de Vries E, Whiteman DC, Bray F. Global Burden of Cutaneous Melanoma in 2020 and Projections to 2040. JAMA Dermatol. 2022;158(5):495-503. doi:10.1001/jamadermatol.2022.0160.\u003c/li\u003e\n \u003cli\u003eLong GV, Swetter SM, Menzies AM, Gershenwald JE, Scolyer RA. Cutaneous melanoma.Lancet.2023;402(10400):485-502. doi: 10.1016/S0140-6736(23)00821-8.\u003c/li\u003e\n \u003cli\u003ePerez M, Chakraborty A, Lau LS, Mohammed NBB, Dimitroff CJ. Melanoma-associated glycosyltransferase GCNT2 as an emerging biomarker and therapeutic target. Br J Dermatol. 2021;185(2):294-301. doi: 10.1111/bjd.19891.\u003c/li\u003e\n \u003cli\u003eCurti BD, Faries MB. Recent Advances in the Treatment of Melanoma. 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Advantages of targeting the tumor immune microenvironment over blocking immune checkpoint in cancer immunotherapy. Signal Transduct Target Ther. 2021;6(1):72. doi: 10.1038/s41392-020-00449-4.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Skin cutaneous melanoma, Cellular senescence, Prognostic signature, Tumor microenvironment, Immunotherapy","lastPublishedDoi":"10.21203/rs.3.rs-4943989/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4943989/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Skin cutaneous melanoma (SKCM) is the most lethal skin cancer with an increasing incidence worldwide.\u003cstrong\u003e \u003c/strong\u003eCellular senescence plays essential roles in tumorigenesis, development, and immune modulation in cancers. However, the correlations of the cellular senescence with tumor progression and tumor immune microenvironment (TIME) in SKCM were poorly understood.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eIn this study, we explored the expression profiles of 279 cellular senescence-related genes (SRGs) in 469 SKCM patients included from the TCGA database. The univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis were conducted to construct a cellular senescence-related signature (CSRS). Kaplan–Meier survival curves as well as receiver operating characteristic (ROC) curve were used to validate the predictive ability of prognostic signature. Consensus clustering analysis was performed to stratify SKCM patients into different clusters and compared them in overall survival. The GSE65904 dataset was further used to validate the stability and applicability of the CSRS. Then, we explored the correlations of the CSRS with tumor-infiltrating immune cells and response to immunotherapy. Finally, the expression levels of prognosis related SRGs were validated based on immunohistochemistry, and the function of RUVBL2 was explored in SKCM cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We developed a prognostic prediction CSRS for patients with SKCM and verified patients in low-risk group were associated with better prognosis. Moreover, the correlation analysis showed that the CSRS could predict the infiltration of immune cells and immune status of the immune microenvironment in SKCM, and patients with low-risk score might benefit from immunotherapy. Our results implied that a high level of cellular senescence may stimulate immunosurveillance mechanisms and potentiate the tumor suppressive function for SKCM in a senescence-associated secretory phenotype (SASP)-depended manner. In addition, all the SKCM patients in this study were classified into three clusters based on the mRNA expression profiles of 113 SRGs, which revealed that cluster 1 suffered a poor prognosis relative to clusters 2 and 3. Finally, we found that RUVBL2 was significantly upregulated in SKCM cells, and knockdown of RUVBL2 inhibited the SKCM cells proliferation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The CSRS developed in this study can be applied not only as a prognostic tool but also as guidance for individualized immunotherapy for SKCM patients.\u003c/p\u003e","manuscriptTitle":"Identification and Validation of Cellular Senescence-Related Signature to Predict Survival and Immunotherapeutic Responses in Skin Cutaneous Melanoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 05:33:25","doi":"10.21203/rs.3.rs-4943989/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c79713e2-a489-4b28-8ef2-df89262e7822","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-17T12:39:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 05:33:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4943989","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4943989","identity":"rs-4943989","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}