Prognosis Of Cutaneous Melanoma Patients Research Articles

Molecular Subtypes Based on PANoptosis Genes and Characteristics of Immune Infiltration in Cutaneous Melanoma.

Cutaneous melanoma (CM) remains the most life-threatening form of skin cancer. Further risk stratification and search for new prognostic targets for CM are of positive clinical significance. PANoptosis is defined as an inflammatory programmed cell death mediated by the PANoptosome complex and cannot be characterized by pyroptosis, apoptosis or necroptosis alone. Although PANoptosis is closely associated with many diseases including cancer, it has not been reported in CM. Combined with GTEx and TCGA database, we extracted 14 PANoptosis-related genes (PAGs). The molecular subtypes of CM were then analyzed by PAGs and their associations with the immune microenvironment and immunotherapy reactivity were analyzed. LASSO and univariate Cox analysis was performed on PAGs-related differentially expressed genes to establish PAGs characteristics that could effectively predict the prognosis of CM patients. Immune infiltration, tumor mutation burden analysis, immunotherapy response and drug sensitivity analysis were used to further analyze the causes of prognostic differences. Our study provides a new perspective on the role of PANoptosis in CM.

Pyroptosis-related lncRNA prognostic signatures for cutaneous melanoma and tumor microenvironment status.

Aims: To explore whether the expression of pyroptosis-related lncRNAsmakes a difference in the prognosis and antitumor immunity of cutaneous melanoma (CM) patients. Methods: A series of analyses were conducted to establish a prognostic risk model and validate its accuracy. Immune-related analyses were performed to further assess the associations among immune status, tumor microenvironmentand theprognostic risk model. Results: Eight pyroptosis-related lncRNAs relevant toprognosis were ascertained and applied to establish the prognostic risk model. The low-risk group had a higher overall survival rate. Conclusion: The established prognostic risk model presents better prediction ability for the prognosis of CM patients and provides new possibletherapeutic targets for CM.

PANoptosis-related prognostic signature predicts overall survival of cutaneous melanoma and provides insights into immune infiltration landscape

Cutaneous melanoma (CM) is a highly malignant tumor originating from melanocytes, and its metastasis and recurrence are the major causes of death in CM patients. PANoptosis is a newly defined inflammatory programmed cell death that crosstalk pyroptosis, apoptosis, and necroptosis. PANoptosis participates in the regulation of tumor progression, especially the expression of PANoptosis related genes (PARGs). Although pyroptosis, apoptosis, and necroptosis have received attention in CM, respectively, the link between them remains elusive. Therefore, this study aimed to investigate the potential regulatory role of PANoptosis and PARGs in CM and the relationship among PANoptosis, PARGs and tumor immunity. We identified 3 PARGs associated with prognosis in CM patients by The Cancer Genome Atlas. Risk model and nomogram were established. Enrichment analysis of differentially expressed genes indicated that CM was immune-related. Subsequent analyses indicated that prognosis-related PARGs were associated with immune scores and infiltration of immune cells in CM patients. In addition, immunotherapy and drug sensitivity results indicated an association between prognosis-related PARGs and drug resistance in CM patients. In conclusion, PARGs play a key role in the progression of tumors in CM patients. PARGs can be used not only for risk assessment and OS prediction in CM patients, but also reflect the immune landscape of CM patients, which can provide a novel reference for individualized tumor treatment.

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.

CDCA3 is a prognostic biomarker for cutaneous melanoma and is connected with immune infiltration.

Dysregulation of cell cycle progression (CCP) is a trait that distinguishes cancer from other diseases. In several cancer types, CCP-related genes serve as the primary risk factor for prognosis, but their role in cutaneous melanoma remains unclear. Data from cutaneous melanoma patients were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Using a Wilcoxon test, the level of CCP-related gene expression in cutaneous melanoma patient tissues was compared to that in normal skin tissues. Logistic analysis was then utilized to calculate the connection between the CCP-related genes and clinicopathological variables. The important functions of the CCP-related genes were further investigated using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). Univariate and multivariate Cox analyses and Kaplan-Meier analysis were used to estimate the association between CCP-related genes and prognosis. In addition, using Cox multivariate analysis, a nomogram was constructed to forecast the influence of CCP-related genes on survival rates. High expression of CCP-related genes was associated with TNM stage, age, pathological grade, and Breslow depth (P < 0.05). Multivariate analysis demonstrated that CCP-related genes were an independent factor in overall survival and disease-specific survival. High levels of gene expression originating from CCP were shown by GSEA to trigger DNA replication, the G1-S specific transcription factor, the mitotic spindle checkpoint, and the cell cycle. There was a negative association between CCP-related genes and the abundance of innate immune cells. Finally, we revealed that knockdown of cell division cycle-associated gene 3 (CDCA3) significantly suppressed the proliferation and migration ability of cutaneous melanoma cells. According to this study, CCP-related genes could serve as potential biomarkers to assess the prognosis of cutaneous melanoma patients and are crucial immune response regulators.

Identification and Validation of Ferroptosis-Related DNA Methylation Signature for Predicting the Prognosis and Guiding the Treatment in Cutaneous Melanoma

Cutaneous melanoma (CM) is one of the most aggressive skin tumors with a poor prognosis. Ferroptosis is a newly discovered form of regulated cell death that is closely associated with cancer development and immunotherapy. The aim of this study was to establish and validate a ferroptosis-related gene (FRG) DNA methylation signature to predict the prognosis of CM patients using data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. A reliable four-FRG DNA methylation prognostic signature was constructed via Cox regression analysis based on TCGA database. Kaplan-Meier analysis showed that patients in the high-risk group tended to have a shorter overall survival (OS) than the low-risk group in both training TCGA and validation GEO cohorts. Time-dependent receiver operating characteristic (ROC) analysis showed the areas under the curve (AUC) at 1, 3, and 5 years were 0.738, 0.730, and 0.770 in TCGA cohort and 0.773, 0.775, and 0.905 in the validation cohort, respectively. Univariate and multivariate Cox regression analyses indicated that the signature was an independent prognostic indicator of OS in patients with CM. Immunogenomic profiling showed the low-risk group of patients had a higher immunophenoscore, and most immune checkpoints were negatively associated with the risk signature. Functional enrichment analysis revealed that immune response and immune-related pathways were enriched in the low-risk group. In conclusion, we established and validated a four-FRG DNA methylation signature that independently predicts prognosis in CM patients. This signature was strongly correlated with the immune landscape, and may serve as a biomarker to guide clinicians in making more precise and personalized treatment decisions for CM patients.

HLA-DRB1: A new potential prognostic factor and therapeutic target of cutaneous melanoma and an indicator of tumor microenvironment remodeling.

Cutaneous melanoma (CM) is the most common skin cancer and one of the most aggressive cancers and its incidence has risen dramatically over the past few decades. The tumor microenvironment (TME) plays a crucial role in the occurrence and development of cutaneous melanoma. Nevertheless, the dynamics modulation of the immune and stromal components in the TME is not fully understood. In this study, 471 CM samples were obtained from TCGA database, and the ratio of tumor-infiltrating immune cells (TICs) in the TME were estimated using the ESTIMATE algorithms and CIBERSORT computational method. The differently expressed genes (DEGs) were applied to GO and KEGG function enrichment analysis, establishment of protein-protein interaction (PPI) network and univariate Cox regression analysis. Subsequently, we identified a predictive factor: HLA-DRB1 (major histocompatibility complex, class II, DR beta 1) by the intersection analysis of the hub genes of PPI network and the genes associated with the prognosis of the CM patients obtained by univariate Cox regression analysis. Correlation analysis and survival analysis showed that the expression level of HLA-DRB1 was negatively correlated with the Stage of the patients while positively correlated with the survival, prognosis and TME of melanoma. The GEPIA web server and the representative immunohistochemical images of HLA-DRB1 in the normal skin tissue and melanoma tissue from the Human Protein Atlas (HPA) database were applied to validate the expression level of HLA-DRB1. CIBERSORT analysis for the ratio of TICs indicated that 9 types of TICs were positively correlated with the expression level of HLA-DRB1 and only 4 types of TICs were negatively correlated with the expression level of HLA-DRB1. These results suggested that the expression level of HLA-DRB1 may be related to the immune activity of the TME and may affect the prognosis of CM patients by changing the status of the TME.

Significance of immunogenic cell death-related genes in prognosis prediction and immune microenvironment landscape of patients with cutaneous melanoma.

Cutaneous melanoma (CM) is one of the most life-threatening tumors. Although targeted therapies and immune checkpoint inhibitors have significantly improved patient outcomes over the past decades, they still have their efficacy limitations. Immunogenic cell death (ICD) induces regulated cell death through immunogenic signal secretion and exposure. Accumulated evidence suggests that the ICD process is an effective target for the treatment of a variety of tumor types, including CM. However, the research on ICD in CM is far from complete, and its clinical value has not been widely concerned. By analyzing the Cancer Genome Atlas (TCGA) database, we constructed a new risk model based on 4 ICD-related genes and validated its ability to predict the prognosis of CM patients. In addition, we comprehensively analyzed the tumor microenvironment (TME) of CM patients and showed a significant immunosuppressive TME in the high-risk group compared with the low-risk group. By Immunophenoscore (IPS), we further explored the correlation between the model and immunotherapy response. The data of Genomics of Drug Sensitivity in Cancer (GDSC) database were further extracted to analyze drug sensitivity and evaluate its correlation with the established risk model. In the end, differential expressed genes (DEGs) were analyzed by Gene Set Variation Analysis (GSVA) to preliminarily explore the possible signaling pathways related to the prognosis of ICD and CM. The results of this study provide new perspectives and insights for individualized and accurate treatment strategies for CM patients.

Identification of a predictive gene signature related to pyroptosis for the prognosis of cutaneous melanoma.

Pyroptosis is a form of inflammatory programmed cell death. However, because of no specific molecular biomarker, pyroptosis has not been considered as a novel therapeutic method to treat cutaneous melanoma (CM). Here, we identified pyroptosis genes that associate with the prognosis of CM patients and constructed an effective model for the prognostic prediction of CM patients. To identify genes related to pyroptosis that are differentially expressed in CM, we obtained gene expression data of CM patients and normal skin tissues from the Cancer Genome Atlas and the Genotype-Tissue Expression databases, and used another cohort obtained from Gene Expression Omnibus database for validation. Three genes (BST2, GBP5, and AIM2) that were associated with prognosis were found and incorporated into our prognostic model. Furthermore, we divided the patients into 2 groups: a high-risk group and a low-risk group. Functional analyses indicated that our model was correlated with patient survival and cancer growth. Multivariate and univariate Cox regressions revealed that the constructed model could serve as an independent prognostic factor for CM patients. Meanwhile, compared with other clinical characteristics, our model significantly improved the diagnostic accuracy. Gene function analysis revealed that pyroptosis genes BST2, GBP5, and AIM2 were differentially expressed in CM patients and positively associated with patient prognosis. Finally, a risk score was used to generate nomograms that displayed favorable discriminatory abilities for CM. In summary, our model could significantly predict the prognosis of CM patients and be used for the development of CM therapy.

Identification of an endoplasmic reticulum stress-associated gene signature to predict the immune status and prognosis of cutaneous melanoma.

Besides protecting normal cells from various internal and external perturbations, endoplasmic reticulum (ER) stress is also directly related to the pathogenesis of cutaneous melanoma (CM). However, due to the lack of specific molecular biomarkers, ER stress has not been considered a novel treatment target for CM. Here, we identified ER stress-related genes involved in the prognosis of CM patients and constructed an effective model for the prognostic prediction of these patients. First, gene expression data of CM and normal skin tissues from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases were retrieved to identify differentially expressed ER stress-related genes in CM. Meanwhile, an independent cohort obtained from the Gene Expression Omnibus (GEO) database was used for validation. The ER stress genes (ZBP1, DIABLO, GNLY, FASLG, AURKA, TNFRSF21, and CD40LG) that were associated with CM prognosis were incorporated into our prognostic model. The functional analyses indicated that the prognostic model was correlated with patient survival, gender, and cancer growth. Multivariate and univariate Cox regressions revealed that the constructed model could serve as an independent prognostic factor for CM patients. The pathway enrichment analysis showed that the risk model was enriched in different immunity and cancer progression-associated pathways. Moreover, the signature model was significantly connected with the immune subtypes, infiltration of immune cells, immune microenvironment, as well as tumor stem cells. The gene function analysis revealed that 7 ER stress genes were differentially expressed in CM patients and were significantly associated with prognosis and several antitumor drugs. Overall, our current model presented predictive value for the prognosis of CM patients and can be further used in the development of novel therapeutic strategies for CM.

Clinical significance and immune landscape of KIR2DL4 and the senescence-based signature in cutaneous melanoma.

Senescence is an effective barrier to tumor progression. Mutations that inhibit senescence and promote cell division are mandatory for the development of cancer. Therefore, it is particularly important to explore the differences between cutaneous melanoma (CM) patients with severe and mild degrees of senescence. We clustered all the patients with CM in the Cancer Genome Atlas (TCGA) database based on all the genes of the senescence pathway in the CellAge and MSigDB database. The prognosis, immunotherapy effect, tumor microenvironment score, NRAS mutation rate, expression of CD274, CTLA4, and PDCD1, and abundance of CD8+ T and natural killer (NK) cell infiltration in the younger group of patients (YG) were higher than those in the older group (OG). Compared with the American Joint Committee on Cancer (AJCC) stage, the risk scoring system stratified the risk of CM patients and guided immunotherapy more accurately. The nomogram model, which combined the AJCC stage and risk score, greatly improved the ability and accuracy of prognosis prediction. As KIR2DL4 is the core molecule in the risk scoring system (RSS), knocking down the KIR2DL4 of human NK cells in vitro can inhibit the cytotoxicity of NK cells and can also inhibit the secretion of tumor necrosis factor-α and interferon-γ by NK cells. In contrast, upregulation of KIR2DL4 can activate the MEK/ERK signaling pathway, which is the activation pathway of NK cells. Our RSS and nomogram model can accurately stratify the risk of CM patients and effectively predict the effect of immunotherapy and prognosis in CM patients.

Application of Deep Learning on the Prognosis of Cutaneous Melanoma Based on Full Scan Pathology Images.

Introduction The purpose of this study is to use deep learning and machine learning to learn and classify patients with cutaneous melanoma with different prognoses and to explore the application value of deep learning in the prognosis of cutaneous melanoma patients. Methods In deep learning, VGG-19 is selected as the network architecture and learning model for learning and classification. In machine learning, deep features are extracted through the VGG-19 network architecture, and the support vector machine (SVM) model is selected for learning and classification. Compare and explore the application value of deep learning and machine learning in predicting the prognosis of patients with cutaneous melanoma. Result According to receiver operating characteristic (ROC) curves and area under the curve (AUC), the average accuracy of deep learning is higher than that of machine learning, and even the lowest accuracy is better than that of machine learning. Conclusion As the number of learning increases, the accuracy of machine learning and deep learning will increase, but in the same number of cutaneous melanoma patient pathology maps, the accuracy of deep learning will be higher. This study provides new ideas and theories for computational pathology in predicting the prognosis of patients with cutaneous melanoma.

Core immune cell infiltration signatures identify molecular subtypes and promote precise checkpoint immunotherapy in cutaneous melanoma.

Yutao Wang, China Medical University, ChinaThe tumor microenvironment (TME) has been shown to impact the prognosis of tumors in patients including cutaneous melanoma (CM); however, not all components of TME are important. Given the aforementioned situation, the functional immune cell contents correlated with CM patient prognosis are needed to optimize present predictive models and reflect the overall situation of TME. We developed a novel risk score named core tumor-infiltrating immune cell score (cTICscore), which showed certain advantages over existing biomarkers or TME-related signatures in predicting the prognosis of CM patients. Furthermore, we explored a new gene signature named cTILscore−related module gene score (cTMGs), based on four identified TME-associated genes (GCH1, GZMA, PSMB8, and PLAAT4) showing a close correlation with the cTICscore, which was generated by weighted gene co-expression network analysis and least absolute shrinkage and selection operator analysis to facilitate clinical application. Patients with low cTMGs had significantly better overall survival (OS, P = 0.002,< 0.001, = 0.002, and = 0.03, respectively) in the training and validating CM datasets. In addition, the area under the curve values used to predict the immune response in four CM cohorts were 0.723, 0.723, 0.754, and 0.792, respectively, and that in one gastric cohort was 0.764. Therefore, the four-gene signature, based on cTICscore, might improve prognostic information, serving as a predictive tool for CM patients receiving immunotherapy.cutaneous melanoma, tumor microenvironment, prognosis, immunotherapy, cTICscore

Identification of m7G-associated lncRNA prognostic signature for predicting the immune status in cutaneous melanoma.

RNA modifications, including RNA methylation, are widely existed in cutaneous melanoma (CM). Among epigenetic modifications, N7-methylguanosine (m7G) is a kind of modification at 5’ cap of RNA which participate in maintaining the stability of mRNA and various cell biological processes. However, there is still no study concerning the relationship between CM and m7G methylation complexes, METTL1 and WDR4. Here, long non-coding RNA (lncRNAs) and gene expression data of CM from the Cancer Genome Atlas (TCGA) database were retrieved to identify differentially expressed m7G-related lncRNAs connected with overall survival of CM. Then, Cox regression analyses was applied to construct a lncRNA risk signature, the prognostic value of identified signature was further evaluated. As a result, 6 m7G-associated lncRNAs that were significantly related to CM prognosis were incorporated into our prognostic signature. The functional analyses indicated that the prognostic model was correlated with patient survival, cancer metastasis, and growth. Meanwhile, its diagnostic accuracy was better than conventional clinicopathological characteristics. The pathway enrichment analysis showed that the risk model was enriched in several immunity-associated pathways. Moreover, the signature model was significantly connected with the immune subtypes, infiltration of immune cells, immune microenvironment, as well as several m6A-related genes and tumor stem cells. Finally, a nomogram based on the calculated risk score was established. Overall, a risk signature based on 6 m7G-associated lncRNAs was generated which presented predictive value for the prognosis of CM patients and can be further used in the development of novel therapeutic strategies for CM.

Identification of TNFAIP8 as an Immune-Related Biomarker Associated With Tumorigenesis and Prognosis in Cutaneous Melanoma Patients.

Tumor necrosis factor-α–induced protein 8 (TNFAIP8) is a member of the TIPE/TNFAIP8 family which is associated with inflammation and tumorigenesis. The potential role of TNFAIP8 in a tumor immune microenvironment in skin cutaneous melanoma (SKCM) has not yet been investigated. The TNFAIP8 expression was evaluated via gene expression profiling interactive analysis (GEPIA). We also evaluated the influence of TNFAIP8 on overall survival via GEPIA and PrognoScan. After GO and KEGG pathway analyses, the correlation between the TNFAIP8 expression level and immune cells or gene markers of the immune infiltration level was explored by R-language. The result showed the TNFAIP8 expression was significantly reduced in SKCM in comparison with normal control. In SKCM, the TNFAIP8 expression in higher levels was associated with the better overall survival. The high expression of TNFAIP8 was positively correlated with the immune score and promoted immune cell infiltration in SKCM patients. TNFAIP8 can be a positive prognosis marker or new immunotherapy target in SKCM.

Prognostic Value of Interleukin-32 Expression and Its Correlation with the Infiltration of Natural Killer Cells in Cutaneous Melanoma.

Interleukin-32 (IL-32) is well known as a proinflammatory cytokine that is expressed in various immune cells and cancers. However, the clinical relevance of IL-32 expression in cutaneous melanoma has not been comprehensively studied. Here, we identified the prognostic value of IL32 expression using various systematic multiomic analyses. The IL32 expressions were significantly higher in cutaneous melanoma than in normal tissue, and Kaplan–Meier survival analysis showed a correlation between IL32 expression and good prognosis in cutaneous melanoma patients. In addition, we analyzed the correlation between IL32 expression and the infiltration of natural killer (NK) cells to identify a relevant mechanism between IL32 expression and prognosis in cutaneous melanoma (p = 0.00031). In the relationship between IL32 expression and the infiltration of NK cells, a negative correlation was found in resting NK cells (rho = −0.38, p = 3.95 × 10−17) whereas a strong positive correlation was observed only in active NK cells (rho = 0.374, p = 1.23 × 10−16). Moreover, IL32 expression was markedly positively correlated with the cytolytic molecules, such as granzyme and perforin. These data suggest that IL32 expression may increase patient survival through the infiltration and activation of NK cells, representative anticancer effector cells, in cutaneous melanoma. Collectively, this study provides the prognostic value of IL32 expression and its potential role as an effective predictive biomarker for NK cell infiltration in cutaneous melanoma.

Identification of CXCL13 as an Immune-Related Biomarker Associated with Tumorigenesis and Prognosis in Cutaneous Melanoma Patients.

BackgroundMelanoma is one of the most lethal tumors and its treatment is still challenging. It is urgent to detect novel therapy targets in melanoma.Material/MethodsThe GEO dataset was used to obtain a list of DEGS (differentially-expressed genes). Integrative bioinformatics analyses, including HPRD database, TCGA data, and TIMER, were performed to determine the role of CXCL13 in SKCM (skin cutaneous melanoma) progression and the immune environment. Furthermore, Pearson correlation coefficient analysis was used to measure correlations between CXCL13 and its co-expressed genes. Survival analysis, GO, and KEGG enrichment analysis were performed to investigate the role of CXCL13 in SKCM.ResultsA total of 41 DEGs were identified in 3 GEO datasets, and 4 out of 41 DEGs are hub genes. Among the 4 hub genes, CXCL13 is involved in the most KEGG terms. CXCL13 is co-expressed with well-known immune checkpoint blockade targets, and it was associated with better overall survival. In addition, CXCL13 levels in infiltrating immune cells (neutrophil and myeloid dendritic cells) affect prognosis and survival in SKCM. Functional enrichment analysis clarified that CXCL13-co-expressed top 30 genes were associated with immune signaling pathways. Network analysis identified CXCL13 as a hub gene that interacts with CXCR5 to participate in immune-related biological process.ConclusionsThis study found that CXCL13 is associated with SKCM tumorigenesis and prognosis and immune infiltrations. Our result suggests that CXCL13 has great potential in development of novel immunotherapy targets in melanoma.

Combining texture features of whole slide images improves prognostic prediction of recurrence-free survival for cutaneous melanoma patients

BackgroundAccurate prediction of recurrence-free survival (RFS) is important for the prognosis of cutaneous melanoma patients. The image-based pathological examination remains as the gold standard for diagnosis. It is of clinical interest to account for computer-aided processing of pathology image when performing prognostic analysis.MethodsWe enrolled in this study a total of 152 patients from TCGA-SKCM (The Cancer Genome Atlas Skin Cutaneous Melanoma project) with complete information in recurrence-related survival time, baseline variables (clinicopathologic variables, mutation status of BRAF and NRAS genes), gene expression data, and whole slide image (WSI) features. We preprocessed WSI to segment global or nucleus areas, and extracted 3 types of texture features from each region. We performed cross validation and used multiple evaluation metrics including C-index and time-dependent AUC to determine the best model of predicting recurrence events. We further performed differential gene expression analysis between the higher and lower-risk groups within AJCC pathologic tumor stage III patients to explore the underlying molecular mechanisms driving risk stratification.ResultsThe model combining baseline variables and WSI features had the best performance among models with any other types of data integration. The prognostic risk score generated by this model could provide a higher-resolution risk stratification within pathologically defined subgroups. We found the selected image features captured important immune-related variations, such as the aberration of expression in T cell activation and proliferation gene sets, and therefore contributed to the improved prediction.ConclusionsOur study provided a prognostic model based on the combination of baseline variables and computer-processed WSI features. This model provided more accurate prediction than models based on other types of data combination in recurrence-free survival analysis.Trial registrationThis study was based on public open data from TCGA and hence the study objects were retrospectively registered.

Identification of immune-related biomarkers associated with tumorigenesis and prognosis in cutaneous melanoma patients

BackgroundSkin cutaneous melanoma (SKCM) is one of the most malignant and aggressive cancers, causing about 72% of deaths in skin carcinoma. Although extensive study has explored the mechanism of recurrence and metastasis, the tumorigenesis of cutaneous melanoma remains unclear. Exploring the tumorigenesis mechanism may help identify prognostic biomarkers that could serve to guide cancer therapy.MethodIntegrative bioinformatics analyses, including GEO database, TCGA database, DAVID, STRING, Metascape, GEPIA, cBioPortal, TRRUST, TIMER, TISIDB and DGIdb, were performed to unveil the hub genes participating in tumor progression and cancer-associated immunology of SKCM. Furthermore, immunohistochemistry (IHC) staining was performed to validate differential expression levels of hub genes between SKCM tissue and normal tissues from the First Affiliated Hospital of Soochow University cohort.ResultsA total of 308 differentially expressed genes (DEGs) and 12 hub genes were found significantly differentially expressed between SKCM and normal skin tissues. Functional annotation indicated that inflammatory response, immune response was closely associated with SKCM tumorigenesis. KEGG pathways in hub genes include IL-10 signaling and chemokine receptors bind chemokine signaling. Five chemokines members (CXCL9, CXCL10, CXCL13, CCL4, CCL5) were associated with better overall survival and pathological stages. IHC results suggested that significantly elevated CXCL9, CXCL10, CXCL13, CCL4 and CCL5 proteins expressed in the SKCM than in the normal tissues. Moreover, our findings suggested that IRF7, RELA, NFKB1, IRF3 and IRF1 are key transcription factors for CCL4, CCL5, CXCL10. In addition, the expressions of CXCL9, CXCL10, CXCL13, CCL4 and CCL5 were positively correlated with infiltration of six immune cells (B cell, CD8+T cells, CD4+T cells, macrophages, neutrophils, dendritic cells) and 28 types of TILs. Among them, high levels of B cells, CD8+T cells, neutrophils and dendritic cells were significantly related to longer SKCM survival time.ConclusionIn summary, this study mainly identified five chemokine members (CXCL9, CXCL10, CXCL13, CCL4, CCL5) associated with SKCM tumorigenesis, progression, prognosis and immune infiltrations, which might help us evaluate several immune-related targets for cutaneous melanoma therapy.

Kinesin family member 18B: A contributor and facilitator in the proliferation and metastasis of cutaneous melanoma.

Melanoma is the most aggressive type of cutaneous tumor and the occurrence of metastasis makes it resistant to almost all available treatment and becomes incorrigible. Hence, identifying metastasis-related biomarkers and effective therapeutic targets will assist in preventing metastasis and ameliorating cutaneous melanoma. In our present study, we reported kinesin family member 18B (KIF18B) as a novel contributor in cutaneous melanoma proliferation and metastasis, and it was found to be of great significance in predicting the prognosis of cutaneous melanoma patients. Bioinformatics analysis based on ONCOMINE, The Cancer Genome Atlas, and Genotype-Tissue Expression database revealed that KIF18B was highly expressed in cutaneous melanoma and remarkably correlated with unfavorable clinical outcomes. Consistently, the results of the quantitative real-time polymerase chain reaction exhibited that the expression of KIF18B was significantly higher in cutaneous melanoma cell lines than that in normal cells. In vitro, biological assays found that knockdown of KIF18B in cutaneous melanoma cells noticeably repressed cell proliferation, migration, and invasion, while inducing cell apoptosis. Moreover, the protein expression of E-cadherin was enhanced while the expression of N-cadherin, vimentin, and Snail was decreased in M14 cells after knocking down KIF18B. In addition, the phosphorylation of phosphoinositide 3-kinase (PI3K) and extracellular-signal-regulated kinase (ERK) was significantly suppressed in M14 cells with silenced KIF18B. Above all, our results indicated that the repression of cutaneous melanoma cell migration and proliferation caused by KIF18B depletion suggested an oncogenic role of KIF18B in cutaneous melanoma, which acts through modulating epithelial-mesenchymal transition and ERK/PI3K pathway.