Biomarker for predicting melanoma prognosis and immunotherapy response and application thereof

Abstract

The invention discloses a biomarker for predicting melanoma prognosis and immunotherapy response and application thereof, and relates to the technical field of biology, the biomarker is a CD27 gene or mRNA expressed by the CD27 gene or protein expressed by the CD27 gene, and the nucleotide sequence of the CD27 gene is shown as Seq ID No.1. AUC analysis shows that immunotherapy response patients in PRJEB23709 and GSE91061 can be effectively distinguished through CD27 expression, and it is prompted that CD27 expression can be used as a prediction biomarker of immunotherapy response.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and more particularly to a biomarker for predicting melanoma prognosis and immunotherapy response and applications thereof. Background Art

[0002] Melanoma is a skin cancer that arises from the malignant transformation of melanocytes and is one of the most aggressive skin cancers. If left untreated, it can rapidly spread to other parts of the body (Schadendorf et al., 2015). Although surgical treatment is effective for early-stage melanoma, advanced melanoma responds poorly to conventional treatments, and clinical management remains challenging (Davis et al., 2019). Currently, immunotherapy and molecular targeted therapy have become important treatment strategies for melanoma (Pavri et al., 2016). In recent years, immune checkpoint inhibitors have shown great potential in the treatment of melanoma (Jenkins and Fisher, 2021).

[0003] Immune checkpoint inhibitors (such as ipilimumab, nivolumab, and pembrolizumab) block the signals that cancer cells use to evade immune system attack (Rotte et al., 2018). Specifically, these inhibitors act on key molecules on the surface of immune cells, such as cytotoxic T lymphocyte-associated antigen 4 (CTLA-4) and programmed death receptor 1 (PD-1), which act as "brakes" on the immune response. By blocking these immune checkpoints, immune cells are able to more effectively recognize and eliminate cancer cells (Rotte et al., 2018). CTLA-4 inhibitors, by inhibiting CTLA-4 activity, enhance the immune system's ability to attack cancer cells (Postow et al., 2015). Clinical trials have demonstrated that these checkpoint inhibitors can induce durable anti-tumor responses and improve survival in patients with advanced melanoma (Weiss et al., 2019).

[0004] Despite the encouraging results achieved with these immunotherapies, several challenges and limitations remain (Jenkins and Fisher, 2021). One of the major obstacles is that not all patients respond to these treatments, and even those who do respond often experience side effects such as inflammation and autoimmune reactions. It is currently unclear which patients are most likely to benefit from immunotherapy. Identifying biomarkers to better predict treatment response may help improve the effectiveness of immune checkpoint inhibition therapy and provide better treatment outcomes for melanoma patients. Another challenge is the development of resistance to these treatments. Although many patients initially respond well to immunotherapy, some may eventually develop resistance and experience disease progression. This highlights the need to develop new treatment strategies to overcome drug resistance.

[0005] CD27 is a transmembrane glycoprotein primarily expressed on various immune cells, including T cells, B cells, and natural killer (NK) cells (Agematsu et al., 1994). As a member of the tumor necrosis factor receptor (TNFR) superfamily, CD27 plays a key role in regulating immune responses. CD27 signaling plays a crucial role in regulating anti-tumor immune responses. Binding of CD27 to its ligand CD70 on tumor-infiltrating lymphocytes (TILs) promotes T cell activation, differentiation, and survival, thereby enhancing the immune response against tumors. Studies have reported that melanoma resistance to immune checkpoint inhibitors (such as PD-1) is associated with a lack of CD8+ T cells in the tumor microenvironment, a deficiency that is due to insufficient dendritic cell recruitment (Corrales et al., 2017; Spranger et al., 2016). Other studies have shown that agonists targeting CD27 can bypass dendritic cell signaling, thereby promoting T cell activation and producing anti-tumor effects (Buchan et al., 2018b; Roberts et al., 2010). In recent years, the role of CD27 in tumors has attracted widespread attention and has gradually been recognized as a promising target for cancer immunotherapy ( Buchan et al., 2018a ).

[0006] Given its key role in regulating anti-tumor immune responses, CD27 has become an attractive target for cancer immunotherapy. Currently, several approaches are being developed to target CD27 and enhance anti-tumor immune responses. One approach is to use agonistic antibodies to activate CD27 signaling, enhancing T cell activation and survival. Preclinical studies have demonstrated the efficacy of CD27 agonists in promoting anti-tumor immune responses in various cancer models (Burris et al., 2017). Another approach is to combine CD27 agonists with other immunotherapies, such as immune checkpoint inhibitors or adoptive T cell therapy, to enhance their efficacy (Buchan et al., 2018a). Currently, combination therapies targeting CD27 are being tested in clinical trials for a variety of cancers.

[0007] Therefore, it is necessary to propose a biomarker for predicting melanoma prognosis and immunotherapy response and its application to solve the above problems. Summary of the Invention

[0008] The purpose of the present invention is to solve the problems raised in the background technology.

[0009] In order to achieve the above-mentioned purpose, the present invention specifically adopts the following technical solutions:

[0010] A biomarker for predicting melanoma prognosis and immunotherapy response, wherein the biomarker is the CD27 gene or its expressed mRNA or its expressed protein;

[0011] The nucleotide sequence of the CD27 gene is shown in Seq ID No.1.

[0012] Furthermore, the primers for the CD27 gene are:

[0013] Forward primer: ACTACTGGGCTCAGGGAAAGCT;

[0014] Reverse primer: GGATCACACTGAGCAGCCTTTC.

[0015] The application of biomarkers for predicting melanoma prognosis and immunotherapy response includes the above-mentioned biomarkers for predicting melanoma prognosis and immunotherapy response, and the CD27 gene is used to prepare a preparation, drug or kit for predicting melanoma prognosis and immunotherapy effect.

[0016] Furthermore, the kit includes specific primers or probes for CD27 gene, or specific antibodies for CD27 protein.

[0017] Furthermore, the preparations, drugs or kits are all used for real-time quantitative PCR, gene chips, immunohistochemistry, tissue chips, and high-throughput sequencing to predict the prognosis of melanoma and the effectiveness of melanoma immunotherapy.

[0018] Furthermore, the preparation, drug or kit for predicting the effect of melanoma immunotherapy is used to detect the expression level of CD27 mRNA or protein in melanoma tissue samples.

[0019] Compared with the prior art, the present invention has the following beneficial effects:

[0020] 1. CD27 expression is a powerful and practical biomarker that can predict melanoma immunotherapy response and prognosis. Its integration into clinical workflows, through either IHC or mRNA detection, can improve patient stratification and identify suitable candidates for CD27-targeted therapies. By promoting an immune-active tumor microenvironment and synergizing with existing immune checkpoint inhibitors, CD27 may become a potential therapeutic target. Combination strategies involving CD27 agonists and immune checkpoint inhibitors may provide new opportunities to improve outcomes for melanoma patients.

[0021] 2. AUC analysis showed that CD27 expression could effectively distinguish PRJEB23709 (AUC = 0.763, Figure 4 C) and GSE91061 (AUC=0.659, Figure 4 E) in patients with immunotherapy response, suggesting that CD27 expression can be used as a predictive biomarker for immunotherapy response. Survival analysis showed that melanoma patients in the high expression group had significantly longer progression-free survival than those in the low expression group (p = 0.033, Figure 4 F). Comparative analysis showed that the CD27 expression level of good responders was significantly higher than that of poor responders (p = 0.0019, Figure 4 G). CD27 gene expression levels analyzed by qPCR had an AUC of 0.688 for predicting immunotherapy response in the FAHZZU cohort ( Figure 4 H). In the FAHZZU cohort, tumor samples from 95 melanoma patients were stained with IHC for CD27 and PD-L1. Patients with positive CD27 IHC staining had significantly better progression-free survival compared with those with negative CD27 IHC staining (p = 0.023, Figure 5 C). Comparative analysis showed that the CD27 H-score of good responders was significantly higher than that of poor responders (p=0.0067, Figure 5 D). The AUC of CD27 expression level analyzed by IHC in predicting progression-free survival was significantly higher than that of PD-L1 (0.656 vs 0.46, Figure 5E), suggesting that CD27 may be a better biomarker for predicting immunotherapy response in melanoma patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 Correlation between CD27 gene expression, immune checkpoint genes, and melanoma prognosis, including:

[0023] (AH) CD27 gene expression was significantly positively correlated with the expression of immune checkpoint genes, including PDCD1, TIGIT, LAG3, HAVCR2, ID01, BTLA, CD274, and CTLA4.

[0024] (IK) Kaplan-Meier survival curves based on CD27 gene expression levels showed that the high expression group had longer overall survival (I), disease-specific survival (J), and progression-free survival (K).

[0025] (L) Multivariate risk regression analysis integrating tumor stage, gender, age, and CD27 gene expression. *p<0.05, **p<0.01.

[0026] Figure 2 The correlation between CD27 expression and immune cell infiltration levels and immune-related pathways is shown in Figure 2.

[0027] (A) Correlation between CD27 gene expression and tumor purity and immune cell infiltration levels, including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells, in metastatic SKCM.

[0028] (B) Box plot showing the difference in the absolute proportion of immune cells in the tumor microenvironment between the CD27 high expression group and the low expression group.

[0029] (C) Box plot showing the difference in leukocyte proportions between the high-expression group and the low-expression group estimated based on methylation data.

[0030] (D) Box plot showing the difference in the proportion of tumor-infiltrating lymphocytes (TILs) between the high-expression group and the low-expression group (assessed by pathological images).

[0031] (E) GSEA diagram showing the enriched signature gene sets and pathways in the CD27 high expression group (FDR < 0.05).

[0032] (F) Heat map showing the expression of four immunotherapy-related signature gene sets, including IFNγ-6, IFNγ expanded immunity 18, effector T cells, and IFNγ / effector T cells.

[0033] (G, J) Box plots of gene set enrichment scores for IFNγ-6 (G), IFNγ EI18 (H), effector T cells (I), and IFNγ / effector T cells (J). *p < 0.05, **p < 0.01, ****p < 0.0001; ns, not significant.

[0034] Figure 3 Methods: Prognostic analysis of 368 patients with metastatic melanoma stratified by BTLA methylation and mRNA expression.

[0035] (AB) Kaplan-Meier curves of overall survival of melanoma patients stratified by the median Beta values ​​of cg03995631 and cg24157392.

[0036] (C) Kaplan-Meier curves of overall survival of patients stratified by promoter methylation levels defined by the average beta value of two CpG sites.

[0037] (D) Kaplan-Meier curve of overall survival of patients stratified by BTLA gene expression.

[0038] (E) Multivariate Cox regression analysis of four variables (group [high / low promoter methylation group], sex, tumor stage, and age).

[0039] Figure 4 Correlation between BTLA mRNA expression and promoter methylation and immune cell infiltration in metastatic melanoma:

[0040] (A) Correlation between BTLA mRNA expression and tumor purity and immune cell (B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells) infiltration levels.

[0041] (B) Comparison of the absolute proportion of TME cells between high and low promoter methylation groups.

[0042] (CE) Box plots show the differences in leukocyte ratio, TIL percentage, and cytotoxic activity between the two groups. All statistical differences were compared using the Wilcoxon rank sum test; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

[0043] Figure 5 Transcriptome changes of molecules and pathways related to BTLA promoter methylation, including:

[0044] (A) Volcano plot showing differentially expressed genes between low and high promoter methylation groups; genes with a log2 fold change > 1 and a P value < 0.05 were considered upregulated in low promoter methylation melanomas.

[0045] (B) Gene set enrichment analysis of the signature gene sets and pathways enriched in the low promoter methylation group (FDR < 0.05).

[0046] (C) Correlation between the methylation levels of two CpG sites and the mRNA expression of immune checkpoint genes.

[0047] (D) Heat map showing normalized gene expression of four gene sets with significant differences between high and low promoter methylation groups.

[0048] (E, H) Boxplots of gene set enrichment scores. Boxplots show the median, first, and third quartiles, with whiskers extending to 1.5 times the interquartile range. P values ​​were calculated using a two-sided Wilcoxon rank-sum test. (E) IFNγ-6. (F) IFNγ-expanded immunity 18. (G) Effector T cells. (H) IFNγ / effector T cells. All statistical differences were compared using the Wilcoxon rank-sum test; *, P < 0.05; **, P < 0.01; ***, P < 0.001; ****, P < 0.0001.

[0049] Figure 6 Changes in immune molecules related to CD27 gene expression, including:

[0050] (A) Volcano plot showing differentially expressed genes between low- and high-CD27 expression groups; genes with log2 fold change > 1 and p < 0.05 were considered upregulated in the high CD27 expression group;

[0051] (BE) Box plots of gene set enrichment scores for high and low CD27 expression groups in primary melanoma, including IFNγ-6 (B), IFNγ expanded immune 18 (C), effector T cells (D), and IFNγ / effector T cells (E). ****p < 0.0001; NS, not significant.

[0052] Figure 7 The correlation between CD27 protein expression and immune cell infiltration and immunotherapy response is as follows:

[0053] (AB) CD27 / CD45R0 double immunofluorescence staining confirmed the co-localization of CD27 (yellow signal) and CD45R0 (red signal) in T cells;

[0054] (C) Representative images showing H&E staining of tissue and cell sections and IHC staining of CD27 and PD-L1 for high and low CD27 expression groups, respectively. 20×, 20x original magnification; 80×, 80x original magnification. DETAILED DESCRIPTION

[0055] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] See also Figure 1

[0057] The biomarker for predicting melanoma prognosis and immunotherapy response is the CD27 gene or its expressed mRNA or its expressed protein;

[0058] The nucleotide sequence of the CD27 gene is shown in Seq ID No.1.

[0059] The primers for the CD27 gene are:

[0060] Forward primer: ACTACTGGGCTCAGGGAAAGCT;

[0061] Reverse primer: GGATCACACTGAGCAGCCTTTC.

[0062] The CD27 gene is used to prepare preparations, drugs or kits for predicting the prognosis of melanoma and the effect of immunotherapy.

[0063] The kit includes specific primers or probes targeting the CD27 gene, or specific antibodies targeting the CD27 protein.

[0064] The preparations, drugs or kits are all used for real-time quantitative PCR, gene chips, immunohistochemistry, tissue chips and high-throughput sequencing to predict the prognosis of melanoma and the effectiveness of melanoma immunotherapy.

[0065] The preparation, drug or kit for predicting the effect of melanoma immunotherapy is used to detect the expression level of CD27 mRNA or protein in melanoma tissue samples.

[0066] Sample collection and resources

[0067] Some of the results analyzed in this study are based on skin cancer (SKCM) data generated by The Cancer Genome Atlas (TCGA, http: / / cancergenome.nih.gov / ). RNA-seq and clinicopathological data from 470 skin cancer samples were collected, including 366 metastatic SKCM tissues used as a discovery dataset and 104 primary SKCM tissues used as a validation dataset. Expression profiles generated using the Illumina human-6 v2.0 expression bead array platform and clinical data from 54 stage IV melanomas were obtained from the Gene Expression Omnibus database, dataset number GSE22153 (Jonsson et al., 2010). In addition, RNA-seq data from tumor tissues from 91 melanoma patients receiving immunotherapy were obtained from the European Nucleotide Archive, dataset number PRJEB23709. Clinical summaries of these patients are provided in Table S2 (Gide et al., 2019). RNA-seq and clinical data of 51 melanoma patients before anti-PD-1 inhibitor treatment were also collected from GEO (dataset number GSE91061) (Riaz et al., 2017).

[0068] Patient collection from the First Affiliated Hospital of Zhengzhou University

[0069] For the in-hospital validation cohort, formalin-fixed paraffin-embedded (FFPE) tumor blocks and clinical information were collected from 102 melanoma patients treated with PD-1 / PD-L1 immune checkpoint inhibition (ICB) at the First Affiliated Hospital of Zhengzhou University (FAHZZU). Response assessment was performed prospectively using the Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) (Schwartz et al., 2016). Good responders were defined as RECIST complete response (CR), partial response (PR), or stable disease (SD) lasting for more than 6 months; poor responders were defined as RECIST progression as the best response or SD lasting for 6 months or less. Detailed information on patient demographics and clinical characteristics, including sex, age, cancer stage, and immunotherapy response, was provided. Patient enrollment and sample analysis at FAHZZU were approved by the Institutional Research Board (IRB) of the First Affiliated Hospital of Zhengzhou University. All study procedures conformed to the ethical standards of the institutional research committee and adhered to the 1964 Declaration of Helsinki and its subsequent revisions.

[0070] Whole-transcriptome comparative analysis

[0071] Raw gene counts for 470 SKCM samples were downloaded from TCGA. Then, differential expression analysis between high and low CD27 expression groups was performed using DESeq2 (Love et al., 2014). Genes with a Benjamini-Hochberg-corrected p-value less than 0.05 and a log2-fold change greater than 1.0 were considered differentially expressed.

[0072] Gene set enrichment analysis

[0073] Fifty signature gene sets and 186 KEGG pathway signatures (Liberzon et al., 2015) were collected from the Molecular Marker Database (MSigDB v2022.1.Hs, https: / / www.gsea-msigdb.org / gsea / msigdb). Gene set enrichment analysis was performed using clusterProfiler (Wu et al., 2021) according to the high and low expression groups to identify gene sets associated with CD27 expression. To predict markers for immunotherapy, a six-gene IFNγ marker (IFNγ-6), a related 18-gene extended immune marker (IFNγ extended immune 18) (Ayers et al., 2017), an effector T cell marker (effector T cell) (Bolen et al., 2017), and a combined IFNγ effector T cell marker (Fehrenbacher et al., 2016) were collected and analyzed using the GSVA R package ( et al., 2013) to calculate the single-sample gene set enrichment score for each sample.

[0074] Immune cell infiltration estimation

[0075] This study used the TIMER 2.0 web application (Li et al., 2020) to estimate the correlation between CD27 gene expression and tumor purity and the infiltration levels of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. Data on the absolute proportions of 22 infiltrating immune cells were inferred from gene expression profiles using the CIBERSORT algorithm (Newman et al., 2015) and downloaded from the TIMER database (Li et al., 2020) (http: / / timer.cistrome.org / infiltration_estimation_for_tcga.CSv.gz). In addition, data on the proportion of leukocytes based on DNA methylation (TCGA_all_leuk_estimate.masked.20170107.tsv) were obtained from Thorsson's study (Thorsson et al., 2018) and can be retrieved from https: / / gdc.cancer.gov / about-data / publications / panimmune. The percentage of tumor-infiltrating lymphocytes (TILs) in TCGA tumor pathology images assessed by deep learning methods is shown in the supplementary table of Saltz's study (Saltz et al., 2018). Then, the differences in immune cell infiltration, leukocyte ratio, and TILs between the high and low promoter groups were compared.

[0076] Quantitative reverse transcription-PCR (qRT-PCR)

[0077] qRT-PCR analysis was used to assess CD27 mRNA expression levels in 102 FFPE melanoma specimens from the FAHZZU cohort. Total RNA was extracted using an FFPE DNA / RNA extraction kit (AmoyDx, Xiamen, China) according to the manufacturer's protocol. Subsequently, 500 ng of purified RNA was reverse transcribed into cDNA using HiScript IIIRT SuperMix for qPCR (+gDNA wiper) (Vazyme, Nanjing, China) according to the manufacturer's protocol. Amplification reactions were performed in 20 μL of Taq Pro Universal SYBR qPCR Master Mix (Vazyme, Nanjing, China). Each reaction contained 20 ng of cDNA template and 0.2 μM of primers (CD27 forward primer: actactgggctcagggaaagct, CD27 reverse primer: ggatcacactgagcagcctttc). The house genes ACTB and GAPDH were used as normalization references (ACTB forward primer: atgtggccgaggactttgatt, ACTB reverse primer: agtggggtggcttttaggatg; GAPDH forward primer: tgcaccaccaactgcttagc, GAPDH reverse primer: ggcatggactgtggtcatgag). qRT-PCR was performed using a Bio-Rad CFX96 Real-Time PCR Detection System (Bio-Rad Laboratories, Hercules, CA) with a temperature program of 95°C for 30 seconds, followed by 40 cycles of 95°C for 10 seconds and 60°C for 30 seconds. Relative CD27 expression levels were calculated using the ΔCT method.

[0078] Multiplex immunofluorescence and image acquisition

[0079] Multiplex immunofluorescence staining procedures were performed using a 3-color fluorescent immunohistochemistry kit (PhenoVision Bio Co., Ltd.) according to the manufacturer's protocol. Briefly, tissue sections underwent a series of preparative steps, including deparaffinization, rehydration, and antigen retrieval by immersion in citrate buffer (pH 6.0). Subsequently, sections underwent washing steps and were sequentially incubated with H₂O₂ and blocking reagent for 10 minutes each. Primary antibodies (CD27 from Proteintech and CD45RO from ZSbio) were then added and incubated at room temperature for 30 minutes. Bound primary antibodies were detected using PVB anti-Rb / Mm-HRP detection reagent, and signal amplification was performed using PVB-coupled streptavidin fluorochromes (PVB 520 and PVB 570) for 10 minutes. Nuclear contrast staining was performed using 4′,6-diamino-2-phenylindole (PhenoVision Bio Co., Ltd.), and sections were coverslipped. Finally, the prepared sections were imaged using the PhenoImage HT system (Akoya Biosciences) at 200× magnification.

[0080] Immunohistochemistry (IHC) analysis of CD27

[0081] 4-μm thick FFPE tissue sections were stained for CD27 using an anti-human CD27 rabbit monoclonal antibody (anti-CD27, 66308-1-Ig, Proteintech). The staining process was performed on a Roche (Ventana BenchMark ULTRA) immunohistochemistry / ISH automated staining module. The Roche Ventana UV AP-Red kit was used as the detection system. The staining protocol included the following sequential steps: (1) EZ dewaxing, 12 minutes; (2) CC1, 64 minutes; (3) incubation with primary antibody (CD27), 48 minutes; (4) UV Red Univ Mult, 12 minutes; (5) UV Red Enhancer, 4 minutes; (6) UV Fast Red A / UV Red Naphthol, 8 minutes; (7) UV Fast Red B, 8 minutes; (8) counterstaining with Mayer's hematoxylin, 12 minutes; and (9) post-counterstaining with bluing reagent, 4 minutes. For the evaluation of CD27 protein expression, IHC staining images were analyzed using the semiquantitative H-score method. The H-score was calculated as follows: (percentage of weakly stained cells) + (percentage of moderately stained cells × 2) + (percentage of strongly stained cells × 3) (McCarty et al., 1985).

[0082] Statistical analysis

[0083] All statistical analyses were performed in the R environment. Pearson correlation analysis was performed using the cor.test function in R. Continuous values ​​between the two groups were compared using the Wilcoxon-Mann-Whitney U test. Kaplan-Meier estimates and the log-rank test were used for survival analysis of overall survival (OS), progression-free survival (PFS), and disease-specific survival (DSS). Multivariate Cox proportional hazards models were used to investigate the associations of CD27 gene expression, clinical tumor stage, age, and sex with survival.

[0084] result

[0085] High CD27 mRNA expression is positively correlated with immune checkpoint genes and good prognosis

[0086] Therapy based on immune checkpoint inhibition has become the main weapon in the fight against cancer. Antibody drugs, such as anti-PD-1 / PD-L1 and anti-CTLA4, have a wide range of adaptability in different types of cancer and can produce lasting clinical responses when the treatment is effective, especially in melanoma (He and Xu, 2020). CD27 is a member of the tumor necrosis factor receptor superfamily and is involved in activating T cells by providing co-stimulatory signals. In order to study the relationship between CD27 and immune checkpoint genes, Pearson correlation analysis was performed on the mRNA expression of CD27 and immune checkpoint genes. It was found that CD27 was significantly positively correlated with multiple immune checkpoint genes, including PDCD1 (R=0.92, p<0.001, Figure 1 A), TIGIT (R=0.93, p<0.001, Figure 1 B), LAG3 (R=0.87, p<0.001, Figure 1 C), HAVCR2 (R=0.8, p<0.001, Figure 1 D), IDO1 (R=0.75, p<0.001, Figure 1 E), BTLA (R=0.75, p<0.001, Figure 1 F), CD274 (R=0.66, p<0.001, Figure 1 G) and CTLA4 (R=0.5, p<0.001, Figure 1H). Notably, TIGIT (Niebel et al., 2022), PDCD1 (Danilova et al., 2016), HAVCR2 (Holderried et al., 2019), CTLA4 (Mo et al., 2018), and BTLA (Dong et al., 2020) have been reported to be associated with good prognosis and immunotherapy response in melanoma. In this study, patients with high CD27 mRNA expression showed significantly prolonged overall survival (p < 0.001, Figure 1 I) and disease-specific survival (p < 0.001, Figure 1 J), but had no significant correlation with progression-free survival (p = 0.082, Figure 1 K). Multivariate survival analysis using Cox regression model showed that CD27 mRNA expression remained an independent prognostic factor for overall survival (OS) in metastatic SKCM (P < 0.001) ( Figure 1 L). Overall, high CD27 mRNA expression is positively correlated with immune checkpoint genes and favorable prognosis in melanoma and can serve as a predictive biomarker for favorable prognosis.

[0087] Correlation between CD27 gene expression and immune cell infiltration in the tumor microenvironment

[0088] Tumor-infiltrating immune cells have been reported to be independent prognostic predictors (Ropponen et al., 1997). Therefore, we investigated whether CD27 gene expression affects immune cell infiltration in the tumor microenvironment (TME) of metastatic SKCM. TIMER 2.0 web application analysis results showed that increased CD27 gene expression was negatively correlated with tumor purity ( Figure 2 A), suggesting that CD27 may play an important role in controlling tumor growth. When analyzing immune cells, CD27 gene expression was positively correlated with the infiltration levels of B cells, CD8+T cells, CD4+T cells, macrophages, neutrophils, and dendritic cells ( Figure 2 A), indicating that CD27 may affect the infiltration of lymphocytes. In addition, the infiltration levels of 22 immune cells inferred by CIBERSORT (Newman et al., 2015) were compared, and 16 immune cell types were found to be significantly enriched in the high expression group ( Figure 2 B), among which CD8+ T cells (Li et al., 2021), memory B cells and naive B cells (Ladanyi et al., 2011) have been reported to be associated with a good prognosis of SKCM. The proportion of white blood cells ( Figure 2 C) and the percentage of tumor-infiltrating lymphocytes assessed by pathological images ( Figure 2D) was also higher in the high-expression group. These results suggest that high CD27 mRNA expression is associated with high levels of immune cell infiltration, which in turn affects prognosis.

[0089] Alterations in immune molecular signatures associated with CD27 expression

[0090] To reveal the transcriptomic molecular characteristics associated with CD27 expression, differential expression analysis was performed on high and low expression groups of 365 metastatic SKCM samples. In the high expression group, 2688 significantly upregulated genes were identified, including 8 immune checkpoint genes (TIGIT, PDCD1, LAG3, BTLA, HAVCR2, CD274, C10orf54 and SIGLEC7 [p < 0.01]) ( Figure 6 A). Unbiased gene set enrichment analysis showed that the high CD27 expression group was significantly enriched in 9 signature gene sets and 44 KEGG pathways (FDR < 0.05). Notably, signature gene sets and KEGG pathways associated with anti-tumor immune response, including inflammatory response, interferon γ, B cell receptor signaling pathway, chemokine signaling pathway, cytokine-cytokine receptor interaction, natural killer cell-mediated cytotoxicity, and T cell receptor signaling pathway, were significantly enriched in the high expression group (FDR < 0.001) ( Figure 2 E). In addition, the expression of four gene sets previously reported to be associated with immunotherapy response was explored and found to be significantly upregulated in the high expression group ( Figure 2 F). Single-sample gene set enrichment scores for these gene sets, including the six-gene IFNγ marker (Ayers et al., 2017) ( Figure 2 G), related 18-gene IFNγ marker (Ayers et al., 2017) ( Figure 2 H), effector T cell markers (Bolen et al., 2017) ( Figure 2 I), combined IFNγ / effector T cell markers (Fehrenbacher et al., 2016) ( Figure 2 J), which was significantly higher in the high expression group than in the low expression group.

[0091] Correlation between CD27, immune cell infiltration, and favorable prognosis in primary SKCM

[0092] To validate the correlation of CD27 with biological and immune features in SKCM, the same analysis was performed on 104 primary SKCM samples from TCGA. Consistent with metastatic SKCM, elevated CD27 mRNA expression was negatively correlated with tumor purity (R = -0.527, p < 0.001, Figure 3A). In addition, CD27 mRNA expression levels were positively correlated with the infiltration levels of B cells, CD8+T cells, CD4+T cells, neutrophils, and dendritic cells ( Figure 3 A). Based on CD27 mRNA expression levels, 104 primary SKCM samples were divided into high-expression and low-expression groups. Similar to metastatic SKCM, the high CD27 expression group was found to have significantly higher proportions of multiple immune cell types (such as CD8+ T cells, activated memory CD4+ T cells, T follicular helper cells, activated NK cells, and M1 macrophages). Figure 3 B). White blood cell ratio estimated by DNA methylation ( Figure 3 C) was also higher in the high expression group. In addition, the expression patterns of four gene sets previously reported to be associated with immunotherapy response were analyzed and found to be significantly upregulated in the high expression group ( Figure 3 D). Single-sample gene set enrichment scores for these gene sets, including the six-gene IFNγ marker ( Figure 6 B) related 18-gene IFNγ markers ( Figure 6 C), effector T cell markers ( Figure 6 D) and combined IFNγ / effector T cell markers ( Figure 6 E), which was significantly higher in the high expression group than in the low expression group. Furthermore, it was verified that patients with higher CD27 mRNA expression levels showed significantly prolonged overall survival in the primary SKCM cohort (p = 0.028, Figure 3 E), progression-free survival (p=0.0013, Figure 3 F) and disease-specific survival (p = 0.019, Figure 3 G).

[0093] Validation of the correlation between CD27 gene expression and prognosis and immunotherapy response in an independent melanoma cohort

[0094] To validate the prognostic value of CD27 mRNA expression, an overall survival analysis was performed on a public independent dataset of 54 stage IV melanoma patients in GSE22153. As expected, patients with high CD27 mRNA expression levels had significantly prolonged overall survival (p = 0.014, Figure 4 A). To further validate the correlation between CD27 mRNA expression and immunotherapy response, survival analysis and area under the receiver operating characteristic (ROC) curve (AUC) analysis were performed on patients receiving melanoma immunotherapy in two public datasets to compare the sensitivity and specificity of predicting treatment response. In 91 melanoma patients receiving PD-1 / PD-L1 ICB therapy in PRJEB23709 (p = 0.00019, Figure 4B) and 51 melanoma patients who received PD-1 ICB in GSE91061 (p = 0.038, Figure 4 D), patients with high CD27 mRNA expression had significantly better overall survival than those with low CD27 mRNA expression. In addition, AUC analysis also showed that CD27 expression could effectively distinguish PRJEB23709 (AUC = 0.763, Figure 4 C) and GSE91061 (AUC=0.659, Figure 4 E) in patients with immunotherapy responses, suggesting that CD27 expression could serve as a predictive biomarker for immunotherapy response.

[0095] Validation of the correlation between CD27 gene expression and immunotherapy response in a FAHZZU cohort

[0096] To further validate the clinical application potential of CD27 gene expression in predicting immunotherapy response, we retrospectively collected clinical prognostic information and FFPE tumor tissues from 102 melanoma patients receiving immunotherapy. CD27 gene expression levels were quantitatively analyzed by qPCR, and patients were divided into high-expression and low-expression groups based on expression levels. Survival analysis showed that melanoma patients in the high-expression group had significantly longer progression-free survival than those in the low-expression group (p = 0.033, Figure 4 F). Comparative analysis showed that the CD27 expression level of good responders was significantly higher than that of poor responders (p = 0.0019, Figure 4 G). CD27 gene expression levels analyzed by qPCR had an AUC of 0.688 for predicting immunotherapy response in the FAHZZU cohort ( Figure 4 H).

[0097] Correlation between CD27 protein expression, immune cell infiltration and immunotherapy response in the FAHZZU cohort

[0098] Based on established immunological characteristics, CD27 has been documented as a surface receptor widely expressed on T and B lymphocyte populations, while CD45RO is used as a marker to identify human memory T cell subsets (Michie et al., 1992). To verify the presence of this expression profile in the melanoma microenvironment, multiple immunofluorescence staining experiments for CD27 and CD45RO were performed on three independent FFPE melanoma specimens. Imaging analysis showed spatial colocalization of CD27 (pseudo-colored yellow) and CD45RO (pseudo-colored red) in specific cell subsets ( Figure 5 A and Figure 7A&B). This co-expression pattern provides histological evidence that CD27-positive lymphocytes in melanoma tissue correspond to T cell or B cell lineages, confirming previous hematological findings in solid tumor immunobiology. Hematoxylin and eosin (HE) staining and IHC staining showed that tumors in the high CD27 expression group had more lymphocyte infiltration than those in the low CD27 expression group ( Figure 5 B and Figure 7 C). In the FAHZZU cohort, tumor samples from 95 melanoma patients were stained with IHC for CD27 and PD-L1. Patients with positive CD27 IHC staining had significantly better progression-free survival compared with those with negative CD27 IHC staining (p = 0.023, Figure 5 C). Comparative analysis showed that the CD27 H-score of good responders was significantly higher than that of poor responders (p=0.0067, Figure 5 D). The AUC of CD27 expression level analyzed by IHC in predicting progression-free survival was significantly higher than that of PD-L1 (0.656 vs 0.46, Figure 5 E), suggesting that CD27 may be a better biomarker for predicting immunotherapy response in melanoma patients.

[0099] discuss

[0100] Melanoma, a malignancy known for its aggressive behavior and propensity to evade immune responses, has seen transformative progress with immunotherapy, particularly immune checkpoint inhibitors (ICIs) targeting PD-1, CTLA-4, and LAG3 (Schadendorf et al., 2015). Despite these advances, a significant proportion of patients still exhibit initial or acquired resistance to ICIs, highlighting the urgent need for biomarkers to predict treatment efficacy and guide personalized therapy (Jenkins and Fisher, 2021). CD27, a co-stimulatory molecule in the tumor necrosis factor receptor superfamily, has emerged as a key regulator of T cell activation and memory formation (Agematsu et al., 1994). This study elucidates the prognostic and predictive value of CD27 in melanoma, demonstrating its association with an immune-active tumor microenvironment (TME), prolonged survival, and enhanced response to ICIs.

[0101] This analysis revealed strong co-expression of CD27 with immune checkpoint genes, including PDCD1, TIGIT, LAG3, HAVCR2, ID01, BTLA, CD274, and CTLA4, suggesting that high CD27 mRNA expression may reflect an immune-active tumor microenvironment (TME) that favors a better response to ICIs. These genes are hallmark features of T cell exhaustion, but paradoxically, they also mark an immune-reactive TME that can respond to checkpoint inhibition (He and Xu, 2020). For example, upregulation of PDCD1 is associated with T cell dysfunction, but its presence correlates with clinical response to anti-PD-1 therapy because PD-1+ T cells retain proliferative potential after receptor blockade (Danilova et al., 2016). Similarly, co-inhibition of TIGIT and LAG3 synergizes with PD-1 blockade to revitalize exhausted T cells, a strategy currently being evaluated in clinical trials (NCT04370704, NCT03470922) (Chauvin and Zarour, 2020). The co-regulation of CD27 with these inhibitory receptors suggests a dual role: although chronic antigen exposure drives the expression of exhaustion markers, CD27 may maintain T cell survival and effector function through CD70-mediated co-stimulation (Roberts et al., 2010). Preclinical models support this hypothesis, demonstrating that CD27 agonism can enhance CD8+ T cell persistence and synergize with anti-PD-1 therapy to inhibit tumor growth (Buchan et al., 2018a). Specifically, high CD27 mRNA expression correlates with longer overall survival (OS) and progression-free survival (PFS) in multiple cohorts.

[0102] High CD27 mRNA expression is associated with reduced tumor purity and increased infiltration of B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. CD8+ T cells are central to antitumor immunity, and their abundance predicts response to ICIs in various cancer types (Sade-Feldman et al., 2019). B cells, particularly those residing in tertiary lymphoid structures (TLS), contribute to antigen presentation and antibody production, promoting sustained immune responses (Helmink et al., 2020). In melanoma, the presence of TLS is associated with improved overall survival and response to PD-1 inhibitors, as shown in the CheckMate069 trial (Cabrita et al., 2020). Dendritic cells (DCs), which have professional antigen-presenting functions and play a key role in the priming of naive T cells (Del Prete et al., 2023), are enriched in tumors with high CD27 expression, likely due to CD27-CD70 interactions that promote DC maturation and cross-presentation (Penaloza-MacMaster et al., 2011). Macrophages further amplify antitumor immunity by secreting proinflammatory cytokines such as IL-12 and TNF-α (Mantovani et al., 2022). Notably, the role of CD27 in maintaining memory T cell immunity may explain the infiltration of CD45RO+ memory T cells observed in multiplex immunofluorescence experiments (Hendriks et al., 2000). The immune-enriched environment driven by CD27 creates a favorable environment for ICIs, as demonstrated by the longer progression-free survival observed in patients with high CD27 expression. The association between high CD27 mRNA expression and increased immune cell infiltration suggests that CD27 may play a key role in regulating the immune response in the tumor microenvironment and may enhance the therapeutic effect of ICIs.

[0103] Current biomarkers for melanoma immunotherapy, including PD-L1 expression, tumor mutational burden (TMB), and the interferon-γ (IFN-γ) gene signature, have limitations in sensitivity and utility (Newell et al., 2022). Due to inconsistent correlation with treatment outcomes and a lack of standardized assays, PD-L1 IHC expression has not yet been approved as a predictive biomarker for melanoma immunotherapy (Dupuis et al., 2018). The lack of standardization in TMB assessment leads to differences in measurement and interpretation between different sequencing methods, such as whole-exome sequencing (WES) and targeted next-generation sequencing (NGS) panels. This inconsistency complicates the establishment of universal TMB thresholds for predicting immunotherapy response. Furthermore, the high cost of TMB testing poses challenges to its widespread use in clinical practice (Moeckel et al., 2023). The interferon γ signature is used to predict immunotherapy response and is based on the mRNA expression of multiple genes (Ayers et al., 2017). However, RNA degradation in formalin-fixed, paraffin-embedded (FFPE) tissues poses a significant challenge to accurate quantification in clinical practice. The fixation and embedding process can lead to RNA fragmentation and chemical modification, increasing the difficulty of reliable mRNA expression analysis (Bai et al., 2024). In contrast, CD27 IHC demonstrated strong prognostic performance in the cohort (AUC = 0.656 for PFS prediction), outperforming PD-L1 (AUC = 0.46). This advantage stems from the stability of CD27 in FFPE tissues and its role in reflecting immune activation and inhibitory signals (Bai et al., 2024). In addition, the association of CD27 with the IFN-γ signature and effector T cell signature provides mechanistic insights into its predictive value. These characteristics make CD27 a practical biomarker, particularly in resource-limited settings where advanced genomic analysis is not available. Furthermore, the stability of CD27 in FFPE tissues makes it more practical than RNA-based biomarkers that are easily degraded.

[0104] In addition to its use as a prognostic marker, CD27 is also an attractive therapeutic target. Agonistic anti-CD27 antibodies, such as varlilumab, have shown promise in early clinical trials, demonstrating promising results in preclinical models by enhancing T cell activation and synergizing with PD-1 inhibition (Burris et al., 2017). In a phase I clinical trial (NCT02335918), varlilumab combined with nivolumab induced durable responses in patients with PD-1-naïve melanoma, with a disease control rate of 33% (Sanborn et al., 2022). Data support this approach, as tumors with high CD27 expression exhibit elevated expression of co-stimulatory molecules (such as GZMA and PRF1) and chemokines (CXCL9 and CXCL10), which recruit effector T cells. These findings provide a strong rationale for developing combination therapies targeting CD27, which may enhance the efficacy of existing immunotherapies and overcome resistance mechanisms.

[0105] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. The scope of patent protection of the present invention shall be based on the claims. Any equivalent structural changes made using the contents of the description of the present invention shall also be included in the scope of protection of the present invention.

Claims

1. Biomarkers for predicting melanoma prognosis and immunotherapy response, characterized by: The biomarker is the CD27 gene or its expressed mRNA or its expressed protein; The nucleotide sequence of the CD27 gene is shown in Seq ID No.

1.

2. The biomarker for predicting melanoma prognosis and immunotherapy response according to claim 1, wherein the primers for the CD27 gene are: Forward primer: ACTACTGGGCTCAGGGAAAGCT; Reverse primer: GGATCACACTGAGCAGCCTTTC.

3. Use of a biomarker for predicting melanoma prognosis and immunotherapy response, comprising the biomarker for predicting melanoma prognosis and immunotherapy response according to claim 1 or 2, characterized in that: The CD27 gene is used to prepare a preparation, a medicine or a kit for predicting the prognosis of melanoma and the effect of immunotherapy.

4. The use of a biomarker for predicting melanoma prognosis and immunotherapy response according to claim 3, characterized in that: The kit includes specific primers or probes targeting CD27 gene, or specific antibodies targeting CD27 protein.

5. The use of a biomarker for predicting melanoma prognosis and immunotherapy response according to claim 3, characterized in that: The preparations, drugs or kits are all used for real-time quantitative PCR, gene chips, immunohistochemistry, tissue chips and high-throughput sequencing to predict the prognosis of melanoma and the effectiveness of melanoma immunotherapy.

6. The use of a biomarker for predicting melanoma prognosis and immunotherapy response according to claim 3, characterized in that: The preparation, drug or kit for predicting the effect of melanoma immunotherapy is used to detect the expression level of CD27 mRNA or protein in melanoma tissue samples.

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