Identification of Potential Biomarkers for Anti-PD-1 Therapy in Melanoma by Weighted Correlation Network Analysis

Abstract

Melanoma is the most malignant form of skin cancer, which seriously threatens human life and health. Anti-PD-1 immunotherapy has shown clinical benefits in improving patients’ overall survival, but some melanoma patients failed to respond. Effective therapeutic biomarkers are vital to evaluate and optimize benefits from anti-PD-1 treatment. Although the establishment of immunotherapy biomarkers is well underway, studies that identify predictors by gene network-based approaches are lacking. Here, we retrieved the existing datasets (GSE91061, GSE78220 and GSE93157, 79 samples in total) on anti-PD-1 therapy to explore potential therapeutic biomarkers in melanoma using weighted correlation network analysis (WGCNA), function validation and clinical corroboration. As a result, 13 hub genes as critical nodes were traced from the key module associated with clinical features. After receiver operating characteristic (ROC) curve validation by an independent dataset (GSE78220), six hub genes with diagnostic significance were further recovered. Moreover, these six genes were revealed to be closely associated not only with the immune system regulation, immune infiltration, and validated immunotherapy biomarkers, but also with excellent prognostic value and significant expression level in melanoma. The random forest prediction model constructed using these six genes presented a great diagnostic ability for anti-PD-1 immunotherapy response. Taken together, IRF1, JAK2, CD8A, IRF8, STAT5B, and SELL may serve as predictive therapeutic biomarkers for melanoma and could facilitate future anti-PD-1 therapy.

Integrin Alpha-2 as a Potential Prognostic and Predictive Biomarker for Patients With Lower-Grade Glioma

“…In this study, three independent LGG cohorts—TCGA cohort and CGGA seq1 and CGGA seq2 cohorts—were downloaded and used for analysis. Data of immunotherapeutic cohorts were downloaded from IMvigor210 cohort (a BLCA immunotherapy cohort, n = 398) ( 13 ) and two melanoma immunotherapy cohorts, including GSE78220 cohort ( n = 26) and GSE91061 cohort (pre-treatment, n = 51) ( 14 ). Data of single-cell RNA sequencing analysis was downloaded from GSE84465 cohort.…”

Dissecting Response to Cancer Immunotherapy by Applying Bayesian Network Analysis to Flow Cytometry Data

“…In this section, we will illustrate BN validation and comparison with alternative multivariate analysis methods using one particular BN example—responders vs. nonresponders at day 1, Adaptive panel, naïve CD4+ T cells ( Figure 7 , same as Figure S43 ). Our interest in this specific subset (naïve CD4+ T cells) was heightened by the observations that, according to the BN analysis, CXCR3 appeared to be a very strong response predictor in naïve CD4+ T cells ( Figure 7 ), and also by the recent literature suggesting an important role of CXCR3 in response to immunotherapy [ 28 , 29 ]. (All standard machine learning and statistical analyses in this section were carried out using the Scikit-learn machine learning toolkit [ 30 ], with default parameters unless otherwise noted).…”

Integrative Multi−Omics Analysis Reveals Candidate Biomarkers for Oral Squamous Cell Carcinoma

“…The Gene Expression Profiling Interactive Analysis (GEPIA) online platform provides fast evaluation between the survival effect and the expression profile analysis of DEGs in a given cancer type. To validate aforementioned hub genes, the relative expression levels of these genes in head and neck squamous cell carcinoma (HNSC) were identified with statistical cut−off of |log 2 FC| > 1 and p−value < 0.05 as previously described (22). In addition, the overall survival (OS) effect of hub genes in HNSC was estimated by calculating the log−rank p−value and the hazard ratio (HR).…”