Increased NFATC4 Correlates With Poor Prognosis of AML Through Recruiting Regulatory T Cells

Despite that immune responses play important roles in acute myeloid leukemia (AML), immunotherapy is still not widely used in AML due to lack of an ideal target. Therefore, we identified key immune genes and cellular components in AML by an integrated bioinformatics analysis, trying to find potential targets for AML. Eighty-six differentially expressed immune genes (DEIGs) were identified from 751 differentially expressed genes (DEGs) between AML patients with fair prognosis and poor prognosis from the TCGA database. Among them, nine prognostic immune genes, including NCR2, NPDC1, KIR2DL4, KLC3, TWIST1, SNORD3B-1, NFATC4, XCR1, and LEFTY1, were identified by univariate Cox regression analysis. A multivariable prediction model was established based on prognostic immune genes. Kaplan–Meier survival curve analysis indicated that patients in the high-risk group had a shorter survival rate and higher mortality than those in the low-risk group (P < 0.001), indicating good effectiveness of the model. Furthermore, nuclear factors of activated T cells-4 (NFATC4) was recognized as the key immune gene identified by co-expression of differentially expressed transcription factors (DETFs) and prognostic immune genes. ATP-binding cassette transporters (ABC transporters) were the downstream KEGG pathway of NFATC4, identified by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). To explore the immune responses NFATC4 was involved in, an immune gene set of T cell co-stimulation was identified by single-cell GSEA (ssGSEA) and Pearson correlation analysis, positively associated with NFATC4 in AML (R = 0.323, P < 0.001, positive). In order to find out the immune cell types affected by NFATC4, the CIBERSORT algorithm and Pearson correlation analysis were applied, and it was revealed that regulatory T cells (Tregs) have the highest correlation with NFATC4 (R = 0.526, P < 0.001, positive) in AML from 22 subsets of tumor-infiltrating immune cells. The results of this study were supported by multi-omics database validation. In all, our study indicated that NFATC4 was the key immune gene in AML poor prognosis through recruiting Tregs, suggesting that NFATC4 might serve as a new therapy target for AML.

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A Four-Immune Gene Prognostic Risk Model for Colorectal Adenocarcinoma Based on the TCGA and Immport Data Sets

10.21203/rs.3.rs-136144/v1 ◽

2020 ◽

Author(s):

Boyang Xu ◽

Ziqi Peng ◽

Yue An ◽

Xue Yao ◽

Mingjun Sun

Keyword(s):

T Cells ◽

Colorectal Adenocarcinoma ◽

Immune Cell ◽

Risk Model ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Data Sets ◽

Immune Gene ◽

Immune Genes ◽

Follicular Helper

Abstract BackgroundAs one of the hot spots in oncology field, immune research provides new ideas for the diagnosis and treatment of tumors. Different histological types of colorectal cancer are different. Adenocarcinoma, as the type with the highest proportion, has a high research value. This study aims to build an immune gene prognostic risk model for colorectal adenocarcinoma to improve the diagnosis and prognosis prediction of colorectal adenocarcinoma.MethodsThe differentially expressed immune genes could be obtained from the gene expression data downloaded from The Cancer Genome Atlas (TCGA) and the immune gene data downloaded from the ImmPort Database. Univariate COX and multivariate COX analyses were used to construct the immune gene prognostic risk model of and the clinical application potential of this model. The correlation between the model and the immune cells infiltration and the influence of each immune cell on the survival were analyzed.Results5975 differentially expressed genes were obtained, and 497 differentially expressed immune genes were selected by combining the information of immune genes. Among them, 36 immune genes were associated with prognosis, and 4 immune genes (THRB, IL1RL2, LGR6, LTB4R2) were included in the prognostic risk model of immune genes. Patients with higher Risk Score had shorter survival. Compared with gender, age and pathological stage, the model has better prediction potential. In addition, the model was correlated with Macrophages M0, Macrophages M1, T cells follicular helper and NK cells activated. Among them, T cells follicular helper and Macrophages M0 were related to the survival of patients.ConclusionWe developed a prognostic risk model containing four immune genes, THRB, IL1RL2, LGR6 and LTB4R2, which accurately described the prognosis of the patient, and affected the survival of patients by influencing the infiltration of Macrophages M0 and T cells follicular helper.

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Construction of a Prognostic Model of Non-small Cell Lung Cancer Based on TCGA and ImmPort Databases and Analysis of Its Correlation With Tumor Microenvironment

10.21203/rs.3.rs-697201/v1 ◽

2021 ◽

Author(s):

Peng Song ◽

Xiaobin Ma ◽

Dongliang Yang

Keyword(s):

Risk Score ◽

Risk Group ◽

Assessment Model ◽

Differentially Expressed ◽

Immune Gene ◽

Overall Survival Rate ◽

Small Cell Lung ◽

Immune Genes ◽

Prognosis Model ◽

Nsclc Patients

Abstract PurposeBioinformatics methods are used to construct an immune gene prognosis assessment model for patients with non-small cell lung cancer (NSCLC), and to screen biomarkers that affect the occurrence and prognosis of NSCLC.MethodsThe transcriptomic data and clinicopathological data of NSCLC and cancer-adjacent normal tissues were downloaded from the Cancer Genome Atlas (TCGA) database and the immune-related genes were obtained from the IMMPORT database (http://www.immport.org/); then, the differentially expressed immune genes were screened out. Based on these genes, an immune gene prognosis model was constructed. The Cox proportional hazards regression model was used for univariate and multivariate analyses. Further, the correlations among the risk score, clinicopathological characteristics, tumor microenvironment, and the prognosis of NSCLC were analyzed.ResultsA total of 193 differentially expressed immune genes related to NSCLC were screened based on the "wilcox.test" in R language, and Cox single factor analysis showed that 19 differentially expressed immune genes were associated with the prognosis of NSCLC (P <0.05). After including 19 differentially expressed immune genes with P<0.05 into the Cox multivariate analysis, an immune gene prognosis model of NSCLC was constructed (it included 13 differentially expressed immune genes). Based on the risk score, the samples were divided into the high-risk and low-risk groups. The Kaplan-Meier survival curve results showed that the 5-year overall survival rate in the high-risk group was 32.4%, and the 5-year overall survival rate in the low-risk group was 53.7%. The receiver operating characteristic (ROC) model curve confirmed that the prediction model had a certain accuracy (AUC=0.673). After incorporating multiple variables into the Cox regression analysis, the results showed that the immune gene prognostic risk score was an independent predictor of the prognosis of NSCLC patients. There was a certain correlation between the risk score and degree of neutrophil infiltration in the tumor microenvironment.ConclusionThe NSCLC immune gene prognosis assessment model was constructed based on bioinformatics methods, and it can be used to calculate the prognostic risk score of NSCLC patients. Further, this model is expected to provide help for clinical judgment of the prognosis of NSCLC patients.

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A prognostic model of non small cell lung cancer based on TCGA and ImmPort databases

Scientific Reports ◽

10.1038/s41598-021-04268-7 ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Dongliang Yang ◽

Xiaobin Ma ◽

Peng Song

Keyword(s):

Risk Score ◽

Risk Group ◽

Assessment Model ◽

Differentially Expressed ◽

Immune Gene ◽

Overall Survival Rate ◽

Small Cell Lung ◽

Immune Genes ◽

Prognosis Model ◽

Nsclc Patients

AbstractBioinformatics methods are used to construct an immune gene prognosis assessment model for patients with non-small cell lung cancer (NSCLC), and to screen biomarkers that affect the occurrence and prognosis of NSCLC. The transcriptomic data and clinicopathological data of NSCLC and cancer-adjacent normal tissues were downloaded from the Cancer Genome Atlas (TCGA) database and the immune-related genes were obtained from the IMMPORT database (http://www.immport.org/); then, the differentially expressed immune genes were screened out. Based on these genes, an immune gene prognosis model was constructed. The Cox proportional hazards regression model was used for univariate and multivariate analyses. Further, the correlations among the risk score, clinicopathological characteristics, tumor microenvironment, and the prognosis of NSCLC were analyzed. A total of 193 differentially expressed immune genes related to NSCLC were screened based on the "wilcox.test" in R language, and Cox single factor analysis showed that 19 differentially expressed immune genes were associated with the prognosis of NSCLC (P < 0.05). After including 19 differentially expressed immune genes with P < 0.05 into the Cox multivariate analysis, an immune gene prognosis model of NSCLC was constructed (it included 13 differentially expressed immune genes). Based on the risk score, the samples were divided into the high-risk and low-risk groups. The Kaplan–Meier survival curve results showed that the 5-year overall survival rate in the high-risk group was 32.4%, and the 5-year overall survival rate in the low-risk group was 53.7%. The receiver operating characteristic model curve confirmed that the prediction model had a certain accuracy (AUC = 0.673). After incorporating multiple variables into the Cox regression analysis, the results showed that the immune gene prognostic risk score was an independent predictor of the prognosis of NSCLC patients. There was a certain correlation between the risk score and degree of neutrophil infiltration in the tumor microenvironment. The NSCLC immune gene prognosis assessment model was constructed based on bioinformatics methods, and it can be used to calculate the prognostic risk score of NSCLC patients. Further, this model is expected to provide help for clinical judgment of the prognosis of NSCLC patients.

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Distinctive roles of age, sex, and genetics in shaping transcriptional variation of human immune responses to microbial challenges

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1714765115 ◽

2017 ◽

Vol 115 (3) ◽

pp. E488-E497 ◽

Cited By ~ 75

Author(s):

Barbara Piasecka ◽

Darragh Duffy ◽

Alejandra Urrutia ◽

Hélène Quach ◽

Etienne Patin ◽

...

Keyword(s):

T Cells ◽

Immune Responses ◽

Disease Risk ◽

Transcriptional Response ◽

Cellular Heterogeneity ◽

Specific Cell ◽

Immune Gene ◽

Immune Genes ◽

Regulatory Variants ◽

Age And Sex

The contribution of host genetic and nongenetic factors to immunological differences in humans remains largely undefined. Here, we generated bacterial-, fungal-, and viral-induced immune transcriptional profiles in an age- and sex-balanced cohort of 1,000 healthy individuals and searched for the determinants of immune response variation. We found that age and sex affected the transcriptional response of most immune-related genes, with age effects being more stimulus-specific relative to sex effects, which were largely shared across conditions. Although specific cell populations mediated the effects of age and sex on gene expression, including CD8+T cells for age and CD4+T cells and monocytes for sex, we detected a direct effect of these intrinsic factors for the majority of immune genes. The mapping of expression quantitative trait loci (eQTLs) revealed that genetic factors had a stronger effect on immune gene regulation than age and sex, yet they affected a smaller number of genes. Importantly, we identified numerous genetic variants that manifested their regulatory effects exclusively on immune stimulation, including aCandida albicans-specific master regulator at theCR1locus. These response eQTLs were enriched in disease-associated variants, particularly for autoimmune and inflammatory disorders, indicating that differences in disease risk may result from regulatory variants exerting their effects only in the presence of immune stress. Together, this study quantifies the respective effects of age, sex, genetics, and cellular heterogeneity on the interindividual variability of immune responses and constitutes a valuable resource for further exploration in the context of different infection risks or disease outcomes.

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A Four-Immune Gene Prognostic Risk Model for Colorectal Adenocarcinoma Based on the TCGA and ImmPort Data Sets

10.21203/rs.3.rs-120261/v1 ◽

2020 ◽

Author(s):

Boyang Xu ◽

Ziqi Peng ◽

Yue An ◽

Xue Yao ◽

Mingjun Sun

Keyword(s):

T Cells ◽

Colorectal Adenocarcinoma ◽

Immune Cell ◽

Risk Model ◽

The Cancer Genome Atlas ◽

Differentially Expressed ◽

Data Sets ◽

Immune Gene ◽

Immune Genes ◽

Follicular Helper

Abstract Background: As one of the hot spots in oncology field, immune research provides new ideas for the diagnosis and treatment of tumors. Different histological types of colorectal cancer are different. Adenocarcinoma, as the type with the highest proportion, has a high research value. This study aims to build an immune gene prognostic risk model for colorectal adenocarcinoma to improve the diagnosis and prognosis prediction of colorectal adenocarcinoma.Methods: The differentially expressed immune genes could be obtained from the gene expression data downloaded from The Cancer Genome Atlas (TCGA) and the immune gene data downloaded from the ImmPort Database. Univariate COX and multivariate COX analyses were used to construct the immune gene prognostic risk model of and the clinical application potential of this model. The correlation between the model and the immune cells infiltration and the influence of each immune cell on the survival were analyzed.Results: 5975 differentially expressed genes were obtained, and 497 differentially expressed immune genes were selected by combining the information of immune genes. Among them, 36 immune genes were associated with prognosis, and 4 immune genes (THRB, IL1RL2, LGR6, LTB4R2) were included in the prognostic risk model of immune genes. Patients with higher Risk Score had shorter survival. Compared with gender, age and pathological stage, the model has better prediction potential. In addition, the model was correlated with Macrophages M0, Macrophages M1, T cells follicular helper and NK cells activated. Among them, T cells follicular helper and Macrophages M0 were related to the survival of patients.Conclusion: We developed a prognostic risk model containing four immune genes, THRB, IL1RL2, LGR6 and LTB4R2, which accurately described the prognosis of the patient, and affected the survival of patients by influencing the infiltration of Macrophages M0 and T cells follicular helper.

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Identification and Validation of a Seven m6A-related lncRNAs Signature Predicting Prognosis of Ovarian Cancer

10.21203/rs.3.rs-915700/v1 ◽

2021 ◽

Author(s):

Yan Li ◽

Xiaoying Wang ◽

Yue Han ◽

Xun Li

Keyword(s):

Ovarian Cancer ◽

Regression Analysis ◽

Correlation Analysis ◽

Prognostic Value ◽

Cox Regression ◽

Risk Group ◽

Pearson Correlation ◽

Regression Analyses ◽

Cox Regression Analysis ◽

Pearson Correlation Analysis

Abstract Background: Long non-coding RNAs (lncRNAs) play an important role in angiogenesis, immune response, inflammatory response and tumor development and metastasis. m6 A (N6 - methyladenosine) is one of the most common RNA modifications in eukaryotes. The aim of our research was to investigate the potential prognostic value of m6A-related lncRNAs in ovarian cancer (OC).Methods: The data we need for our research was downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Pearson correlation analysis between 21 m6A regulators and lncRNAs was performed to identify m6A-related lncRNAs. Univariate Cox regression analysis was implemented to screen for lncRNAs with prognostic value. A least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression analyses was used to further reduct the lncRNAs with prognostic value and construct a m6A-related lncRNAs signature for predicting the prognosis of OC patients. Results: 275 m6A-related lncRNAs were obtained using pearson correlation analysis. 29 m6A-related lncRNAs with prognostic value was selected through univariate Cox regression analysis. Then, a seven m6A-related lncRNAs signature was identified by LASSO Cox regression. Each patient obtained a riskscore through multivariate Cox regression analyses and the patients were classified into high-and low-risk group using the median riskscore as a cutoff. Kaplan-Meier curve revealed that the patients in high-risk group have poor outcome. The receiver operating characteristic curve revealed that the predictive potential of the m6A-related lncRNAs signature for OC was powerful. The predictive potential of the m6A-related lncRNAs signature was successfully validated in the GSE9891, GSE26193 datasets and our clinical specimens. Multivariate analyses suggested that the m6A-related lncRNAs signature was an independent prognostic factor for OC patients. Moreover, a nomogram based on the expression level of the seven m6A-related lncRNAs was established to predict survival rate of patients with OC. Finally, a competing endogenous RNA (ceRNA) network associated with the seven m6A-related lncRNAs was constructed to understand the possible mechanisms of the m6A-related lncRNAs involed in the progression of OC.Conclusions: In conclusion, our research revealed that the m6A-related lncRNAs may affect the prognosis of OC patients and identified a seven m6A-related lncRNAs signature to predict the prognosis of OC patients.

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Identification the prognostic value of immune gene signature and infiltrating immune cells of esophageal cancer patients

10.21203/rs.3.rs-20941/v1 ◽

2020 ◽

Author(s):

Lin Wang ◽

Qian Wei ◽

Ming Zhang ◽

Lianze Chen ◽

Zinan Li ◽

...

Keyword(s):

T Cells ◽

Esophageal Cancer ◽

Signaling Pathway ◽

Immune Cells ◽

Roc Curves ◽

Survival Outcome ◽

Immune Gene ◽

Prognostic Signature ◽

Immune Genes ◽

Validation Set

Abstract Background Esophageal cancer (ESCA) is one of the deadliest solid malignancies with worse survival in the world. The poor prognosis of ESCA is not only related to malignant cells, but also affected by the microenvironment. We aimed to establish prognostic signature consisting of immune genes to predict the survival outcome of patients and estimate the prognosis value of infiltrating immune cells in tumor microenvironment (TME). Methods Based on integrated analysis of gene expression profiling and immune gene database, differentially immune-related genes were filtered out. Then, stepwise Cox regression analysis was applied to identify survival related immune genes and construct prognosis signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier (K-M) curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognosis signature and infiltrating immune cells by Tumor Immune Estimation Resource (TIMER) and CIBERSORT. Results Finally, we identified 303 differentially immune genes as candidate and constructed immune prognosis signature composed of six immune genes. Furthermore, we observed that the prognosis signature was enriched in cytokine-mediated signaling pathway, lymphocyte activation, immune effector process, cancer pathway, NF-kappa B signaling pathway. K-M survival curves showed that the prognosis signature indeed have good predictive ability in entire ESCA set ( P =0.003), validation set 1 ( P =0.008) and validation set 2 ( P =0.036). The area under the curve (AUC) of ROC curves validated the predictive accuracy of immune signature in three cohorts (AUC=0.757, 0.800 and 0.701), respectively. In addition, we identified the prognosis value of infiltrating-immune cells including activated memory CD4 T cells, T cells follicular helper cells and monocytes and provided a landscape of TME. Conclusions The results indicated that immune prognosis signature can be a novel biomarker to predict survival outcome, which can provide new targets for immunotherapy and individualized therapies in ESCA and open up a new prospect for improving the prognosis of ESCA patients in the era of immunotherapy.

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Identification of Prognostic Immune Genes in Bladder Urothelial Carcinoma

BioMed Research International ◽

10.1155/2020/7510120 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Qisheng Su ◽

Yan Sun ◽

Zunni Zhang ◽

Zheng Yang ◽

Yuling Qiu ◽

...

Keyword(s):

Transcription Factor ◽

Urothelial Carcinoma ◽

Proportional Hazards ◽

Differentially Expressed ◽

Immune Gene ◽

Hub Genes ◽

Immune Genes ◽

Proportional Hazards Regression ◽

Bladder Urothelial Carcinoma ◽

Gene Regulatory

Background. The aim of this study is to identify possible prognostic-related immune genes in bladder urothelial carcinoma and to try to predict the prognosis of bladder urothelial carcinoma based on these genes. Methods. The Cancer Genome Atlas (TCGA) expression profile data and corresponding clinical traits were obtained. Differential gene analysis was performed using R software. Reactome was used to analyze the pathway of immune gene participation. The differentially expressed transcription factors and differentially expressed immune-related genes were extracted from the obtained list of differentially expressed genes, and the transcription factor-immune gene network was constructed. To analyze the relationship between immune genes and clinical traits of bladder urothelial carcinoma, a multifactor Cox proportional hazards regression model based on the expression of immune genes was established and validated. Results. Fifty-eight immune genes were identified to be associated with the prognosis of bladder urothelial carcinoma. These genes were enriched in Cytokine Signaling in Immune System, Signaling by Receptor Tyrosine Kinases, Interferon alpha/beta signaling, and other immune related pathways. Transcription factor-immune gene regulatory network was established, and EBF1, IRF4, SOX17, MEF2C, NFATC1, STAT1, ANXA6, SLIT2, and IGF1 were screened as hub genes in the network. The model calculated by the expression of 16 immune genes showed a good survival prediction ability (p<0.05 and AUC = 0.778). Conclusion. A transcription factor-immune gene regulatory network related to the prognosis of bladder urothelial carcinoma was established. EBF1, IRF4, SOX17, MEF2C, NFATC1, STAT1, ANXA6, SLIT2, and IGF1 were identified as hub genes in the network. The proportional hazards regression model constructed by 16 immune genes shows a good predictive ability for the prognosis of bladder urothelial carcinoma.

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Let-7a Inhibits T-Cell Proliferation and IFN-γ Secretion by Down-Regulating STAT3 Expression in Patients with Psoriasis

Cellular Physiology and Biochemistry ◽

10.1159/000477120 ◽

2017 ◽

Vol 42 (1) ◽

pp. 115-125 ◽

Cited By ~ 6

Author(s):

Xiao-Ping Hu ◽

Qian Xie ◽

Chao-Feng Chen ◽

Wei Zhang ◽

Bo Yu

Keyword(s):

Cell Proliferation ◽

T Cells ◽

T Cell ◽

Correlation Analysis ◽

Mrna Expression ◽

Pearson Correlation ◽

T Cell Proliferation ◽

Control Group ◽

Pearson Correlation Analysis ◽

Ifn Γ

Objective: This study aimed to explore the effects of STAT3 targeting by let-7a on T-cell proliferation and IFN-γ secretion in psoriasis. Methods: From January 2013 to January 2015, 40 patients with psoriasis (psoriasis group) and 38 volunteers undergoing plastic surgery (control group) were enrolled in this study. Pearson correlation analysis was performed to evaluate the correlation between let-7a and STAT3 expression. T-cells were isolated and subjected to different transfection methods. A dual luciferase reporter assay was carried out to confirm STAT3 as a target gene of let-7a. Let-7a, STAT3 and IFN-γ mRNA expression was detected by quantitative real-time fluorescent polymerase chain reaction (qRT-PCR), and pSTAT3 protein levels were determined by Western blot. T-cell proliferation was evaluated with a cell counting kit-8 (CCK-8) assay. Results: The level of STAT3 mRNA and pSTAT3 was higher, but let-7a expression was lower in the psoriasis group than the control group. Pearson correlation analysis indicated that STAT3 expression was negatively correlated with let-7a expression. T-cells transfected with inhibitors exhibited greater IFN-γ mRNA expression and T-cell proliferation than transfected T-cells and T-cells transfected with a non-sense sequence, while T-cells transfected with let-7a mimics exhibited lower IFN-γ mRNA expression and T-cell proliferation than transfected T-cells and T-cells transfected with a non-sense sequence. This suggested that siRNA-STAT3 could reverse the increase in IFN-y mRNA expression and T-cell proliferation induced by let-7a inhibitors. Conclusion: Our results demonstrated that let-7a inhibits T-cell proliferation and IFN-γ secretion by down-regulating STAT3 in psoriasis.

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Genetic Variants in Immune Related Genes as Predictors of Responsiveness to BCG Immunotherapy in Metastatic Melanoma Patients

Cancers ◽

10.3390/cancers13010091 ◽

2020 ◽

Vol 13 (1) ◽

pp. 91

Author(s):

Romela Irene Ramos ◽

Misa A. Shaw ◽

Leland Foshag ◽

Stacey L. Stern ◽

Negin Rahimzadeh ◽

...

Keyword(s):

Immune Responses ◽

Disease Free Survival ◽

Phase Iii ◽

Stage Iii ◽

Immune Gene ◽

Nucleotide Polymorphisms ◽

Predictive Tool ◽

Immune Genes ◽

Ajcc Stage ◽

Melanoma Patients

Adjuvant immunotherapy in melanoma patients improves clinical outcomes. However, success is unpredictable due to inherited heterogeneity of immune responses. Inherent immune genes associated with single nucleotide polymorphisms (SNPs) may influence anti-tumor immune responses. We assessed the predictive ability of 26 immune-gene SNPs genomic panels for a clinical response to adjuvant BCG (Bacillus Calmette-Guérin) immunotherapy, using melanoma patient cohorts derived from three phase III multicenter clinical trials: AJCC (American Joint Committee on Cancer) stage IV patients given adjuvant BCG (pilot cohort; n = 92), AJCC stage III patients given adjuvant BCG (verification cohort; n = 269), and AJCC stage III patients that are sentinel lymph node (SLN) positive receiving no immunotherapy (control cohort; n = 80). The SNP panel analysis demonstrated that the responder patient group had an improved disease-free survival (DFS) (hazard ratio [HR] 1.84, 95% CI 1.09–3.13, p = 0.021) in the pilot cohort. In the verification cohort, an improved overall survival (OS) (HR 1.67, 95% CI 1.07–2.67, p = 0.025) was observed. No significant differences of SNPs were observed in DFS or OS in the control patient cohort. This study demonstrates that SNP immune genes can be utilized as a predictive tool for identifying melanoma patients that are inherently responsive to BCG and potentially other immunotherapies in the future.

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