Prognostic Implications of Metabolism Related Gene Signature in Cutaneous Melanoma

Zeng, Furong; Su, Juan; Peng, Cong; Liao, Mengting; Guo, Ying; Chen, Xiang; Deng, Guangtong

doi:10.3389/fonc.2020.01710

Cited by 20 publications

(18 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To validate the antioxidant gene signatures and evaluate their prognostic value, patients with sarcoma in the TCGA-SARC and GEO datasets were classified into low- and high-risk groups according to the median value of the risk score calculated from the identified antioxidant gene signatures [ 36 ]. Survival analysis was then performed using the log-rank test to compare the difference in OS or DFS between the two risk groups.…”

Section: Methodsmentioning

confidence: 99%

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Quan

Cao

et al. 2022

Aging

View full text Add to dashboard Cite

Background: Oxidative stress plays a critical role in tumorigenesis, tumor development, and resistance to therapy. A systematic analysis of the interactions between antioxidant gene expression and the prognosis of patients with sarcoma is lacking but urgently needed. Methods: Gene expression and clinical data of patients with sarcoma were derived from The Cancer Genome Atlas Sarcoma (training cohort) and Gene Expression Omnibus (validation cohorts) databases. Least absolute shrinkage, selection operator regression, and Cox regression were used to develop prognostic signatures for overall survival (OS) and disease-free survival (DFS). Based on the signatures and clinical features, two nomograms for predicting 2-, 4-, and 6-year OS and DFS were established. Results: On the basis of the training cohort, we identified five-gene (CHAC2, GPX5, GSTK1, PXDN, and S100A9) and six-gene (GGTLC2, GLO1, GPX7, GSTK1, GSTM5, and IPCEF1) signatures for predicting OS and DFS, respectively, in patients with sarcoma. Kaplan-Meier survival analysis of the training and validation cohorts revealed that patients in the high-risk group had a significantly poorer prognosis than those in the low-risk group. On the basis of the signatures and other independent risk factors, we established two models for predicting OS and DFS that showed excellent calibration and discrimination. For the convenience of clinical application, we built web-based calculators (OS: https://quankun.shinyapps.io/sarcOS/; DFS: https://quankun.shinyapps.io/sarcDFS/). Conclusions: The antioxidant gene signature models established in this study can be novel prognostic predictors for sarcoma.

show abstract

Section: Methodsmentioning

confidence: 99%

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

Quan

Cao

et al. 2022

Aging

View full text Add to dashboard Cite

show abstract

“…To assess the performance of the TRS, we compared it with the infiltration levels of CD8+ T cells and 8 published prognostic signatures of melanoma (39)(40)(41)(42)(43)(44)(45)(46). The infiltration levels of CD8+ T cells in melanoma patients were calculated with the CIBERSORT algorithm (32).…”

Section: Comparison Of Prognosis Prediction Performance With Published Prognostic Signatures In Melanomamentioning

confidence: 99%

“…To further estimate the performance of the refined TRS on prognosis prediction, we collected 8 published gene signatures (39)(40)(41)(42)(43)(44)(45)(46) and compared their performance in the five cohorts. To keep consistency of the scores in prognosis prediction, we calculated the negative value of TRS scores and CD8+ T cell infiltration levels as the corresponding risk scores.…”

Section: Trs Contributes To Longer Overall Survival Times In Melanoma Patientsmentioning

confidence: 99%

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

Yan

Ping

et al. 2021

Front. Immunol.

View full text Add to dashboard Cite

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.

show abstract

“…The two datasets used in this project were an RNA array GSE15605 dataset (8)(9)(10)(11)(12)(13) from the GEO database and SKCM RNA-seq data from the TCGA database (https://tcga-data.nci.nih.gov/tcga/). The GSE42352 dataset consisted of 16 normal skin tissues (GSM390208-GSM390223) and 46 primary skin melanoma tissues (GSM390224-GSM390269) in the SKCM group.…”

Section: Data Sourcesmentioning

confidence: 99%

New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation

Zhou

Han

et al. 2021

Front. Oncol.

View full text Add to dashboard Cite

Skin cutaneous melanoma (SKCM) is the most aggressive and fatal type of skin cancer. Its highly heterogeneous features make personalized treatments difficult, so there is an urgent need to identify markers for early diagnosis and therapy. Detailed profiles are useful for assessing malignancy potential and treatment in various cancers. In this study, we constructed a co-expression module using expression data for cutaneous melanoma. A weighted gene co-expression network analysis was used to discover a co-expression gene module for the pathogenesis of this disease, followed by a comprehensive bioinformatics analysis of selected hub genes. A connectivity map (CMap) was used to predict drugs for the treatment of SKCM based on hub genes, and immunohistochemical (IHC) staining was performed to validate the protein levels. After discovering a co-expression gene module for the pathogenesis of this disease, we combined GWAS validation and DEG analysis to identify 10 hub genes in the most relevant module. Survival curves indicated that eight hub genes were significantly and negatively associated with overall survival. A total of eight hub genes were positively correlated with SKCM tumor purity, and 10 hub genes were negatively correlated with the infiltration level of CD4+ T cells and B cells. Methylation levels of seven hub genes in stage 2 SKCM were significantly lower than those in stage 3. We also analyzed the isomer expression levels of 10 hub genes to explore the therapeutic target value of 10 hub genes in terms of alternative splicing (AS). All 10 hub genes had mutations in skin tissue. Furthermore, CMap analysis identified cefamandole, ursolic acid, podophyllotoxin, and Gly-His-Lys as four targeted therapy drugs that may be effective treatments for SKCM. Finally, IHC staining results showed that all 10 molecules were highly expressed in melanoma specimens compared to normal samples. These findings provide new insights into SKCM pathogenesis based on multi-omics profiles of key prognostic biomarkers and drug targets. GPR143 and SLC45A2 may serve as drug targets for immunotherapy and prognostic biomarkers for SKCM. This study identified four drugs with significant potential in treating SKCM patients.

show abstract

Prognostic Implications of Metabolism Related Gene Signature in Cutaneous Melanoma

Cited by 20 publications

References 48 publications

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

A web-based calculator for predicting the prognosis of patients with sarcoma on the basis of antioxidant gene signatures

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

New Prognostic Biomarkers and Drug Targets for Skin Cutaneous Melanoma via Comprehensive Bioinformatic Analysis and Validation

Contact Info

Product

Resources

About