Dataset Information

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.

SUBMITTER: Wang X

PROVIDER: S-EPMC7230292 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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

Wang Xuanyi X Chai Zixuan Z Li Yinghong Y Long Fei F Hao Youjin Y Pan Guizhi G Liu Mingwei M Li Bo B

Genes 20200417 4

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, ...[more]

PMID: 32316408

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Project description:BackgroundMelanoma is a highly aggressive skin cancer with a poor prognosis and mortality. Immune checkpoint blockade (ICB) therapy (e.g., anti-PD-1 therapy) has opened a new horizon in melanoma treatment, but some patients present a non-responsive state. Cancer-associated fibroblasts (CAFs) make up the majority of stromal cells in the tumor microenvironment (TME) and have an important impact on the response to immunotherapy. There is still a lack of identification of CAFs-related predictors for anti-PD-1 therapy, although the establishment of immunotherapy biomarkers is well underway. This study aims to explore the potential CAFs-related gene panel for predicting the response to anti-PD-1 therapy in melanoma patients and elucidating their potential effect on TME.MethodsThree gene expression datasets from melanoma patients without anti-PD-1 treatment, in a total of 87 samples, were downloaded from Gene Expression Omnibus (GEO) as the discovery sets (GSE91061) and validation sets (GSE78220 and GSE122220). The CAFs-related module genes were identified from the discovery sets by weighted gene co-expression network analysis (WGCNA). Concurrently, we utilized differential gene analysis on the discovery set to obtain differentially expressed genes (DEGs). Then, CAFs-related key genes were screened with the intersection of CAFs-related module genes and DEGs, succeeded by supervised machine learning-based identification. As a consequence of expression analysis, gene set enrichment analysis, survival analysis, staging analysis, TME analysis, and correlation analysis, the multidimensional systematic characterizations of the key genes were uncovered. The diagnostic performance of the CAFs-related gene panel was assessed by receiver operating characteristic (ROC) curves in the validation sets. Eventually, the CAFs-related gene panel was verified by the expression from the single-cell analysis.ResultsThe six-gene panel associated with CAFs were finally identified for predicting the response to anti-PD-1 therapy, including CDK14, SYNPO2, TCF4, GJA1, CPXM1, and TFPI. The multigene panel demonstrated excellent combined diagnostic performance with the area under the curve of ROC reaching 90.5 and 75.4% ~100% in the discovery and validation sets, respectively.ConclusionConfirmed by clinical treatment outcomes, the identified CAFs-related genes can be used as a promising biomarker panel for prediction to anti-PD-1 therapy response, which may serve as new immunotherapeutic targets to improve survival outcomes of melanoma patients.

Project description:BackgroundThere is still an unmet need for therapeutic drugs for patients with an abdominal aortic aneurysm (AAA), especially for candidates unsuitable for surgical or interventional repair. Therefore, the purpose of this in silico study is to identify significant genes and regulatory mechanisms in AAA patients to predicate the potential therapeutic compounds for significant genes.MethodsThe GSE57691 dataset was obtained from Gene Expression Omnibus (GEO) and used to identify the differentially expressed genes (DEGs) and weighted correlation network analysis (WGCNA). The biological function of DEGs was determined using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). AAA-related genes were obtained from the Comparative Toxicogenomics Database (CTD) using the keywords: aortic aneurysm and abdominal. The hub genes in AAA were obtained by overlapping DEGs, WGCNA-based hub genes, and CTD-based genes. The diagnostic values of hub genes were determined using ROC curve analysis. Hereby, a TF-miRNA-hub gene network was constructed based on the miRnet database. Using these data, potential therapeutic compounds for the therapy of AAA were predicted based on the Drug Gene Interaction Database (DGIdb).ResultsA total of 218 DEGs (17 upregulated and 201 downregulated) and their biological function were explored; 4093 AAA-related genes were derived by text mining. Three hub modules and 144 hub genes were identified by WGCNA. asparagine synthetase (ASNS), axin-related protein 2 (AXIN2), melanoma cell adhesion molecule (MCAM), and the testis-specific Y-encoded-like protein 1 (TSPYL1) were obtained as intersecting hub genes and the diagnostic values were confirmed with ROC curves. As potential compounds targeting the hub genes, asparaginase was identified as the target compound for ASNS. Prednisolone and abiraterone were identified as compounds targeting TSPYL1. For MCAM and TSPYL1, no potential therapeutic compound could be predicted.ConclusionUsing WGCNA analysis and text mining, pre-existing gene expression data were used to provide novel insight into potential AAA-related protein targets. For two of these targets, compounds could be predicted.

Project description:BACKGROUND:Uveal melanoma accounts for 85% of the ocular melanomas and has an increased risk of hematogenous spread, most commonly to the liver. After curative intent therapy like surgery and radiation, fifty percent of patients present with distant metastasis. Metastatic uveal melanoma (MUM) does not harbor typically targetable mutations, e.g., BRAF as in cutaneous melanoma. As a result, there is no proven therapy for MUM. Various chemotherapy and immunotherapy regimens have been tried and only partial response (PR) is the best that has been achieved in most of the cases. Here, we present a case of MUM treated with combination immune checkpoint therapy (ipilimumab and nivolumab) following the progression with single-agent nivolumab and demonstrating a durable response without recurrence more than 22 months from the last treatment. CASE PRESENTATION:A 72-year-old Caucasian man presented with ciliary body melanoma of the left eye and underwent curative-intent enucleation but six months later developed diffuse hepatic metastases. He initially was treated with nivolumab 3 mg/kg every two weeks for four cycles but restaging scan showed a significant progression of the disease with increasing LDH. With the FDA approval for the combination of nivolumab 1mg/kg with Ipilimumab 3 mg/kg every three weeks for metastatic melanoma, this combination was given for four cycles with continuous rise in LDH to 993 unit/L (110-220 unit/L) until finishing cycle four of the treatment. Three weeks later, maintainence nivolumab 3mg/kg was initiated but two weeks later, he developed grade 4 liver toxicity with ALT 1565 unit/L (0-55 unit/L). A presumptive diagnosis of autoimmune hepatitis was made, nivolumab was stopped and oral prednisone 1mg/kg was started with quick resolution of elevated transaminases. Restaging abdominal MRI one month after the first and last dose of maintenance nivolumab showed PR and continuous shrinkage of the metastatic lesions with no hypermetabolic activity even on PET/CT. He is 22 months' post-treatment and continues to do well without any evidence of active disease. CONCLUSION:Although, limited response has been shown to single agent immune checkpoint inhibitors and chemotherapy, our patient showed durable response with anti-CTLA-4 and anti-PD-1 combination therapy in MUM.

Project description:Background: Burn injury is a life-threatening disease that does not have ideal biomarkers. Therefore, this study first applied weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) screening methods to identify pivotal genes and diagnostic biomarkers associated with the skin burn process. Methods: After obtaining transcriptomic datasets of burn patient skin and normal skin from Gene Expression Omnibus (GEO) and performing differential analysis and functional enrichment, WGCNA was used to identify hub gene modules associated with burn skin processes in the burn patient peripheral blood sample dataset and determine the correlation between modules and clinical features. Enrichment analysis was performed to identify the functions and pathways of key module genes. Differential analysis, WGCNA, protein-protein interaction analysis, and enrichment analysis were utilized to screen for hub genes. Hub genes were validated in two other GEO datasets, tested by immunohistochemistry for hub gene expression in burn patients, and receiver operating characteristic curve analysis was performed. Finally, we constructed the specific drug activity, transcription factors, and microRNA regulatory network of the five hub genes. Results: A total of 1,373 DEGs in GSE8056 were obtained, and the top 5 upregulated genes were S100A12, CXCL8, CXCL5, MMP3, and MMP1, whereas the top 5 downregulated genes were SCGB1D2, SCGB2A2, DCD, TSPAN8, and KRT25. DEGs were significantly enriched in the immunity, epidermal development, and skin development processes. In WGCNA, the yellow module was identified as the most closely associated module with tissue damage during the burn process, and the five hub genes (ANXA3, MCEMP1, MMP9, S100A12, and TCN1) were identified as the key genes for burn injury status, which consistently showed high expression in burn patient blood samples in the GSE37069 and GSE13902 datasets. Furthermore, we verified using immunohistochemistry that these five novel hub genes were also significantly elevated in burn patient skin. In addition, MCEMP1, MMP9, and S100A12 showed perfect diagnostic performance in the receiver operating characteristic analysis. Conclusion: In conclusion, we analyzed the changes in genetic processes in the skin during burns and used them to identify five potential novel diagnostic markers in blood samples from burn patients, which are important for burn patient diagnosis. In particular, MCEMP1, MMP9, and S100A12 are three key blood biomarkers that can be used to identify skin damage in burn patients.

Dataset Information

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

Publications

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

Similar Datasets

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