Exploration of the immune cell infiltration-related gene signature in the prognosis of melanoma

Single intravenous administration of oncolytic adenovirus TILT-123 results in systemic tumor transduction and immune response in patients with advanced solid tumors

BACKGROUND

A limitation of approved oncolytic viruses is their requirement for intratumoral (i.t.) injection. TILT-123 (igrelimogene litadenorepvec, Ad5/3-E2F-D24-hTNFα-IRES-hIL-2) is a chimeric oncolytic adenovirus suitable for intravenous (i.v.) delivery due to its capsid modification and dual selectivity devices. It is armed with tumor necrosis alpha and interleukin-2 for promoting T-cell activation and lymphocyte trafficking to tumors, thereby enhancing the antitumor immune response. Here, we present the findings after a single i.v. administration of TILT-123 in three phase I dose escalation clinical trials.

METHODS

Patients with advanced solid tumors initially received a single i.v. dose of TILT-123 ranging from 3 × 109 to 4 × 1012 viral particles (VP). Blood was collected at baseline, 1, 16, and 192 h (7 days) post-treatment for bioavailability and serum analysis. Tumor biopsies were collected prior to treatment and 7 days post-treatment for analysis of viral presence and immunological effects. Patients did not receive any other cancer therapies during this period.

RESULTS

Across all three trials (TUNIMO, TUNINTIL, and PROTA), 52 total patients were treated with i.v. TILT-123. Overall, TILT-123 was found to be well-tolerated, with no dose-limiting toxicities observed. Post-treatment tumor biopsies showed expression of viral genes, presence of TILT-123 adenovirus proteins or DNA, and changes in immune cell infiltration from baseline. Increased virus dose did not lead to increased virus detection in tumors. Median overall survival was longer in patients with confirmed presence of TILT-123 in post-treatment biopsies (280 versus 190 days, p = 0.0405).

CONCLUSION

TILT-123 demonstrated safety and significant intratumoral immunomodulation following a single i.v. administration, warranting further investigation.

TRIAL REGISTRATIONS

TUNIMO-NCT04695327. Registered 4 January 2021, https://clinicaltrials.gov/study/NCT04695327 . TUNINTIL-NCT04217473. Registered 19 December 2019, https://clinicaltrials.gov/study/NCT04217473 . PROTA-NCT05271318. Registered 4 February 2022, https://clinicaltrials.gov/study/NCT05271318 .

Machine learning in the prediction of immunotherapy response and prognosis of melanoma: a systematic review and meta-analysis

Background

The emergence of immunotherapy has changed the treatment modality for melanoma and prolonged the survival of many patients. However, a handful of patients remain unresponsive to immunotherapy and effective tools for early identification of this patient population are still lacking. Researchers have developed machine learning algorithms for predicting immunotherapy response in melanoma, but their predictive accuracy has been inconsistent. Therefore, the present systematic review and meta-analysis was performed to comprehensively evaluate the predictive accuracy of machine learning in melanoma response to immunotherapy.

Methods

Relevant studies were searched in PubMed, Web of Sciences, Cochrane Library, and Embase from their inception to July 30, 2022. The risk of bias and applicability of the included studies were assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed on R4.2.0.

Results

A total of 36 studies consisting of 30 cohort studies and 6 case-control studies were included. These studies were mainly published between 2019 and 2022 and encompassed 75 models. The outcome measures of this study were progression-free survival (PFS), overall survival (OS), and treatment response. The pooled c-index was 0.728 (95%CI: 0.629-0.828) for PFS in the training set, 0.760 (95%CI: 0.728-0.792) and 0.819 (95%CI: 0.757-0.880) for treatment response in the training and validation sets, respectively, and 0.746 (95%CI: 0.721-0.771) and 0.700 (95%CI: 0.677-0.724) for OS in the training and validation sets, respectively.

Conclusion

Machine learning has considerable predictive accuracy in melanoma immunotherapy response and prognosis, especially in the former. However, due to the lack of external validation and the scarcity of certain types of models, further studies are warranted.

Identification of the role of MCM6 in bladder cancer prognosis, immunotherapy response, and in vitro experimental investigation using multi-omics analysis

BACKGROUND

The tumor-promoting effects of MCM6 in numerous tumors have been widely revealed, yet its specific role in bladder cancer (BLCA) is still elusive. The objective of this research was to explore the underlying impact of MCM6 on BLCA.

METHODS

Integrating transcriptomic and proteomic data, MCM6 was identified to be strongly correlated with BLCA through weighted gene co-expression network analysis(WGCNA) and venn analyses. Then, the clinical value of MCM6 was validated with public database data. The different molecular/immune characteristics and the benefit of immunotherapy were also found in MCM6-defined subgroups. Additionally, single-cell RNA sequencing (scRNA-seq) data was choose for quantify MCM6 expression in the distinct BLCA cell types. The biological role of MCM6 were evaluated via in vitro functional experiments.

RESULTS

It was testified that the MCM6 could distinguish patients outcome in TCGA and GEO cohorts. Moreover, compared with the MCM6 low-expression group, the MCM6 high-expression group was related to more tumor-promoting related pathways, aggressive phenotypes, and benefit from immunotherapy. Analysis of scRNA-seq data resulted in MCM6 was mainly expressed in BLCA epithelial cells and the proportion of MCM6-expressing tumor epithelial cells is higher than the normal epithelial cells. Moreover, vitro experiments demonstrated that MCM6 knockdown repressed proliferation, cell cycle, migration, and invasion of BLCA cells.

CONCLUSION

This research indicated MCM6 is a promising marker for both prognosis and immunotherapy benefit and could promote the cells proliferation, invasion and migration in BLCA.

Adjuvant Therapy for High-Risk Melanoma: An In-Depth Examination of the State of the Field

The consideration of systemic adjuvant therapy is recommended for patients with stage IIB-IV melanoma who have undergone surgical resection due to a heightened risk of experiencing melanoma relapse and mortality from melanoma. Adjuvant therapy options tested over the past three decades include high-dose interferon-α, immune checkpoint inhibitors (pembrolizumab, nivolumab), targeted therapy (dabrafenib-trametinib for BRAF mutant melanoma), radiotherapy and chemotherapy. Most of these therapies have been demonstrated to enhance relapse-free survival (RFS) but with limited to no impact on overall survival (OS), as reported in randomized trials. In contemporary clinical practice, the adjuvant treatment approach for surgically resected stage III-IV melanoma has undergone a notable shift towards the utilization of nivolumab, pembrolizumab, and BRAF-MEK inhibitors, such as dabrafenib plus trametinib (specifically for BRAF mutant melanoma) due to the significant enhancements in RFS observed with these treatments. Pembrolizumab has obtained regulatory approval in the United States to treat resected stage IIB-IIC melanoma, while nivolumab is currently under review for the same indication. This review comprehensively analyzes completed phase III adjuvant therapy trials in adjuvant therapy. Additionally, it provides a summary of ongoing trials and an overview of the main challenges and future directions with adjuvant therapy.

The role of systemic inflammatory response index (SIRI) and tumor-infiltrating lymphocytes (TILs) in the prognosis of patients with laryngeal squamous cell carcinoma

OBJECTIVE

Systemic inflammatory response index (SIRI) values and tumor-infiltrating lymphocytes (TILs) are associated with the prognosis of various tumors. There is minimal evidence of those two as prognostic markers in laryngeal squamous cell carcinoma (LSCC). In this study, we aimed to examine the predictive value of SIRI and tumor-infiltrating CD3+/CD4+/CD8+ T cells in the prognosis of patients who underwent partial or total laryngectomy.

STUDY DESIGN

A total of 78 patients with LSCC who underwent total or partial laryngectomy at the Eye, Ear, Nose, and Throat Hospital of Fudan University between 2013 and 2015 were retrospectively analyzed.

METHODS

The tumor tissues of 78 LSCC patients were retrospectively evaluated using immunohistochemical staining for CD3+ /CD4+ /CD8+ -cells. The overall survival (OS) and disease-free survival (DFS) rates were recorded using the Kaplan-Meier method.

RESULTS

Patients with high immunoscore (IS) (3-4) had prolonged survival (P < 0.001 for OS). High SIRI values were independently associated with poorer OS and DFS (P = 0.018 for OS; P = 0.016 for DFS). CD8+ TILs and SIRI values showed a- negative association (P < 0.01). Patients with low SIRI values and high IS had better 5-year OS and DFS than those with high SIRI values and low IS (P < 0.001 for OS; P = 0.0014 for DFS). Patients with 'hot' tumor had a higher 5-year OS than those with 'excluded' or 'cold' phenotype.

CONCLUSIONS

The SIRI values and the density of TILs may help predict LSCC patients' outcomes after surgery. The combination of SIRI and IS may be a new component of the tumor, nodes, and metastases (TNM) classification of cancer and prognostic factor for T-cell-target immunotherapy.

Chromosome instability-associated prognostic signature and cluster investigation for cutaneous melanoma cases

Chromosomal instability (CIN) is closely associated to the early detection of several clinical tumours. In this study, the authors first established a novel prognostic model of melanoma using the hub genes of CIN, based on the datasets of The cancer genome atlas-skin cutaneous melanoma (TCGA-SKCM) and GSE65904 cohorts. Based on the risk scores of our model, the disease-specific survival (DSS) prognosis was worse in the high-risk group. Combining risk score, stage, age, ulceration, and clark factors, a Nomogram was generated to predict 1, 3, 5-year survival rates, which indicated a good clinical validity. Our finding also showed a correlation between high/low risk and tumour infiltration levels of 'activated CD8 T cells' and 'effector memory CD8 T cells'. Moreover, the authors first performed a CIN-based tumour clustering analysis using TCGA-SKCM cases, and identified two melanoma clusters, which exhibit the distinct DSS prognosis and the tumour-infiltrating levels of CD8 T cells. Taken together, a promising CIN-related prognostic signature and clustering for melanoma cases were first established in our study.

Development and validation of ferroptosis-related lncRNA signature and immune-related gene signature for predicting the prognosis of cutaneous melanoma patients

Ferroptosis, a form of cell death caused by iron-dependent peroxidation of lipids, plays an important role in cancer. Recent studies have shown that long noncoding RNAs (lncRNAs) are involved in the regulation of ferroptosis in tumor cells and are also closely related to tumor immunity. Immune cell infiltration in the tumor microenvironment affects the prognosis and clinical outcome of immunotherapy in melanoma patients, and immune cell classification may be able to accurately predict the prognosis of melanoma patients. However, the prognostic value of ferroptosis-related lncRNAs (FRLs) in melanoma has not been thoroughly explored, and it is difficult to define the immune characteristics of melanoma. We used The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) database, and the FerrDb database to identify FRLs. FRLs with prognostic value were evaluated in an experimental cohort utilizing univariate, LASSO (least absolute shrinkage and selection operator) and multivariate Cox regression, followed by in vitro assays evaluating the expression levels and the biological functions of three candidate FRLs. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were used to assess the validity of the risk model, and the drug sensitivity of FRLs was examined by drug sensitivity analysis. The differentially expressed genes between the high- and low-risk groups in the risk model were enriched in the immune pathway, and we further found immune gene signatures (IRGs) that could predict the prognosis of melanoma patients through a series of methods including single-sample Gene Set Enrichment Analysis (ssGSEA). Finally, two GEO cohorts were used to validate the predictive accuracy and reliability of these two signature models. Our findings suggest that FRLs and IRGs have the potential to predict the prognosis of patients with cutaneous melanoma.

Screening of prognosis-related Immune cells and prognostic predictors in Colorectal Cancer Patients

OBJECTIVE

To accurately screen potential immune cells that can predict the survival of colorectal cancer (CRC) patients and identify related prognostic predictors.

METHODS

The sample data of CRC patients were downloaded from the GEO database as a training set to establish a prognosis-scoring model and screen prognosis-related immune cells. The sample data of CRC patients from the TCGA database were used as the validation set. Simultaneously, cancer tissue samples from 116 patients with CRC diagnosed pathologically in Shanghai Dongfang Hospital were collected to analyze the relationship of prognosis-related immune cells with patients' survival, and clinical and pathological parameters, and to screen prognostic predictors.

RESULTS

Prognosis-related immune cells screened from GEO and TCGA databases mainly included Follicular Helper T cells (Tfh), Monocytes and M2 Macrophages. In the training set, the 2,000- and 4,000-day survival rates were 48.3% and 10.7% in the low-risk group (N = 234), and 42.1% and 7.5% in the high-risk group (N = 214), respectively. In the validation set, the 2,000- and 4,000-day survival rates were 34.8% and 8.6% in the low-risk group (N = 187), and 28.9% and 6.1% in the high-risk group (N = 246), respectively. The prognosis of patients in the high-risk group was worse than that in the low-risk group (P < 0.05). Furthermore, the screened primary prognostic predictors were CD163 and CD4 + CXCR5. CD163 protein expression was distributed in Monocytes and M2 Macrophages. The 1,000- and 2,000-day survival rates were 56.1% and 7.0% in the CD163 low-expression group, and 40.7% and 1.7% in the high-expression group (N = 214), respectively, showing a worse prognosis in the high-expression group than that in the low-expression group. Meanwhile, the immune marker CD4 + CXCR5 could identify Tfh. The 1,000- and 2,000-day survival rates were 63.9% and 5.6% in the CD4 + CXCR5 high-expression group, and 33.3% and 2.8% in the low-expression group (N = 214), respectively, with a better prognosis in the high-expression group than that in the low-expression group.

CONCLUSION

Prognostic-related immune cells of CRC mainly include Tfh cells, Monocytes and M2 Macrophages. Monocytes and M2 Macrophages correlate negatively, while Tfh cells correlate positively with the prognosis of CRC patients. Immune markers CD163 and CD4 + CXCR5 can be considered as the prognostic predictors of CRC with clinical value of the application.

Discovery and Validation of a SIT1-Related Prognostic Signature Associated with Immune Infiltration in Cutaneous Melanoma

Signaling threshold regulating transmembrane adaptor 1 (SIT1) encodes a disulfide-linked homodimeric lymphocyte-specific glycoprotein involved in immune cell activation. However, the relationship between SIT1 and the prognosis of skin cutaneous melanoma (SKCM) and tumor-infiltrating lymphocytes remains elusive. Here, we first compared the differences in SIT1 expression levels between SKCM tissues and adjacent normal tissues. Next, we found that the immune cell infiltration levels and signature pattern of immune infiltration were positively associated with the SIT1 gene mRNA levels. TCGA_SKCM RNA-seq data unveiled that the SIT1 upregulated several immune-associated signaling pathways in GSEA analysis. The high expression of SIT1 was closely related to improved survival in patients with SKCM. A pathway enrichment analysis of SIT1-associated immunomodulators indicated the involvement of the NF-κB signaling pathways. Based on SIT1-associated immunomodulators, we built a 13-gene signature by LASSO Cox regression which served as an independent prognostic factor for the survival of melanoma patients. By using the signature risk score, we achieved a good prediction result for the immunotherapy response and survival of SKCM patients. Our findings provided evidence for SIT1's implication in tumor immunity and survival of SKCM patients. The nominated immune signature is a promising predictive model for prognosis and immunotherapy sensitivity in SKCM patients.

Integrated molecular analyses of an interferon-γ based subtype with regard to outcome, immune characteristics, and immunotherapy in bladder cancer and experimental verification

This study attempted to explore the role of interferon-γ related genes (IRGs) in the prognosis and immunotherapy of bladder cancer (BC). Based on data downloaded from public databases, molecular subtypes with different IRG expression patterns were determined via nonnegative matrix factorization clustering. On the basis of IRGs, interferon-γ related gene signature (IRGS) was developed through Cox regression analyses. We identified that two molecular subgroups with different outcome and immune profiles. It was proved that IRGS possessed prediction efficiency for BC prognosis. Compared with low IRGS group, high IRGS group was related to less anti-cancer immune cells infiltration, less tumor mutation burden score, more cancer stem cell index, and less benefit from immunotherapy. Differential expression of six model genes (IRF5, LATS2, MTHFD2, VAMP8, HLA-G and PTPN6) was validated between paired tissues by RT-qPCR. This study presents a prognostic model, which could serve as an indicator for the benefit of BC immunotherapy.

Impaired expression of serine/arginine protein kinase 2 (SRPK2) affects melanoma progression

Melanoma is one of the most aggressive tumors, and its lethality is associated with the ability of malignant cells to migrate and invade surrounding tissues to colonize distant organs and to generate widespread metastasis. The serine/arginine protein kinases 1 and 2 (SRPK1 and SRPK2) are classically related to the control of pre-mRNA splicing through SR protein phosphorylation and have been found overexpressed in many types of cancer, including melanoma. Previously, we have demonstrated that the pharmacological inhibition of SRPKs impairs pulmonary colonization of metastatic melanoma in mice. As the used compounds could target at least both SRPK1 and SRPK2, here we sought to obtain additional clues regarding the involvement of these paralogs in melanoma progression. We analyzed single-cell RNA sequencing data of melanoma patient cohorts and found that SRPK2 expression in melanoma cells is associated with poor prognosis. Consistently, CRISPR-Cas9 genome targeting of SRPK2, but not SRPK1, impaired actin polymerization dynamics as well as the proliferative and invasive capacity of B16F10 cells in vitro. In further in vivo experiments, genetic targeting of SRPK2, but not SRPK1, reduced tumor progression in both subcutaneous and caudal vein melanoma induction models. Taken together, these findings suggest different functional roles for SRPK1/2 in metastatic melanoma and highlight the relevance of pursuing selective pharmacological inhibitors of SRPK2.

Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View

The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.

Single-Cell Transcriptomic Analysis Reveals a Tumor-Reactive T Cell Signature Associated With Clinical Outcome and Immunotherapy Response In Melanoma

The infiltration of tumor-reactive T cells in the tumor site is associated with better survival and immunotherapy response. However, tumor-reactive T cells were often represented by the infiltration of total CD8+ T cells, which was confounded by the presence of bystander T cells. To identify tumor-reactive T cells at the cancer lesion, we performed integration analyses of three scRNA-seq data sets of T cells in melanoma. Extensive heterogeneous functional states of T cells were revealed in the tumor microenvironment. Among these states, we identified a subset of tumor-reactive T cells which specifically expressed tumor-reactive markers and T cell activation signature, and were strongly enriched for larger T cell receptor (TCR) clones. We further identified and validated a tumor-reactive T cell signature (TRS) to evaluate the tumor reactivity of T cells in tumor patients. Patients with high TRS scores have strong immune activity and high mutation burden in the TCGA-SKCM cohort. We also demonstrated a significant association of the TRS with the clinical outcomes of melanoma patients, with higher TRS scores representing better survival, which was validated in four external independent cohorts. Furthermore, the TRS scores exhibited greater performance on prognosis prediction than infiltration levels of CD8+ T cells and previously published prognosis-related signatures. Finally, we observed the capability of TRS to predict immunotherapy response in melanoma. Together, based on integrated analysis of single-cell RNA-sequencing, we developed and validated a tumor-reactive-related signature that demonstrated significant association with clinical outcomes and response to immunotherapy.

Effects of RNA methylation N6-methyladenosine regulators on malignant progression and prognosis of melanoma

Background

Melanoma is an extremely aggressive type of skin cancer and experiencing a expeditiously rising mortality in a current year. Exploring new potential prognostic biomarkers and therapeutic targets of melanoma are urgently needed. The ambition of this research was to identify genetic markers and assess prognostic performance of N6-methyladenosine (m6A) regulators in melanoma.

Methods

Gene expression data and corresponding clinical informations of melanoma patients as well as sequence data of normal controls are collected from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. Quantitative real-time PCR (qRT-PCR) analysis was carried out to detect the RNA expression of IGF2BP3 in A375 cell line, melanoma tissues, and normal tissues. Western blot, cell proliferation, and migration assays were performed to assess the ability of IGF2BP3 in A375 cell line.

Results

Differently expressed m6A regulators between tumor samples and normal samples were analyzed. A three-gene prognostic signature including IGF2BP3, RBM15B, and METTL16 was constructed, and the risk score of this signature was identified to be an independent prognostic indicator for melanoma. In addition, IGF2BP3 was verified to promote melanoma cell proliferation and migration in vitro and associate with lymph node metastasis in clinical samples. Moreover, risk score and the expression of IGF2BP3 were positively associated with the infiltrating immune cells and these hub genes made excellent potential drug targets in melanoma.

Conclusion

We identified the genetic changes in m6A regulatory genes and constructed a three-gene risk signature with distinct prognostic value in melanoma. This research provided new insights into the epigenetic understanding of m6A regulators and novel therapeutic strategies in melanoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02163-9.

Development and Validation of a CD8+ T Cell Infiltration-Related Signature for Melanoma Patients

Aim

Immunotherapy shows efficacy in only a subset of melanoma patients. Here, we intended to construct a risk score model to predict melanoma patients’ sensitivity to immunotherapy.

Methods

Integration analyses were performed on melanoma patients from high-dimensional public datasets. The CD8+ T cell infiltration related genes (TIRGs) were selected via TIMER and CIBERSORT algorithm. LASSO Cox regression was performed to screen for the crucial TIRGs. Single sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithm were used to evaluate the immune activity. The prognostic value of the risk score was determined by univariate and multivariate Cox regression analysis.

Results

184 candidate TIRGs were identified in melanoma patients. Based on the candidate TIRGs, melanoma patients were classified into three clusters which were characterized by different immune activity. Six signature genes were further screened out of 184 TIRGs and a representative risk score for patient survival was constructed based on these six signature genes. The risk score served as an indicator for the level of CD8+ T cell infiltration and acted as an independent prognostic factor for the survival of melanoma patients. By using the risk score, we achieved a good predicting result for the response of cancer patients to immunotherapy. Moreover, pan-cancer analysis revealed the risk score could be used in a wide range of non-hematologic tumors.

Conclusions

Our results showed the potential of using signature gene-based risk score as an indicator to predict melanoma patients’ sensitivity to immunotherapy.

Tumor Immune Microenvironment Characterization Identifies Prognosis and Immunotherapy-Related Gene Signatures in Melanoma

Background

The tumor microenvironment (TME) involves infiltration of multiple immune cell subsets, which could influence the prognosis and clinical characteristics. The increasing evidence on the role of tumor-infiltrating lymphocytes (TILs) in primary and metastatic melanomas supports that the immune system is involved in the progression and outcomes of melanoma. However, the immune infiltration landscape in melanoma has not been systematically elucidated.

Methods

In this study, we used CIBERSORT and ESTIMATE algorithms to analyze immune infiltration pattern of 993 melanoma samples. Then we screened differential expression genes (DEGs) related to immune subtypes and survival. The immune cell infiltration (ICI) score was constructed by using principal-component analysis (PCA) based on immune signature genes from DGEs. Gene set enrichment analysis (GSEA) was applied to explore high and low ICI score related pathways. Finally, the predictive ability of ICI score was evaluated in survival prognosis and immunotherapy benefit.

Result

We identified three ICI clusters and three gene clusters associated with different immune subtypes and survival outcomes. Then the ICI score was constructed, and we found that high ICI score exhibited activated immune characteristics and better prognosis. High ICI score was significantly enriched in immune pathways and highly expressed immune signature genes. More importantly, we confirmed that melanoma patients with high ICI score had longer overall survival and rate of response to immunotherapy.

Conclusion

We presented a comprehensive immune infiltration landscape in melanoma. Our results will facilitate understanding of the melanoma tumor microenvironment and provide a new immune therapy strategy.