A new metabolic signature contributes to disease progression and predicts worse survival in melanoma

Wan, Mengdi; Zhuang, Binyu; Dai, Xiaohui; Zhang, Liang; Zhao, Fangqing; You, Yan

doi:10.1080/21655979.2020.1822714

Cited by 9 publications

(9 citation statements)

References 39 publications

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“…Most BC cells exhibit distinct metabolic characteristics that promote their proliferation and progression, which is called metabolic reprogramming [ 10 ]. An altered metabolism has profound effects on the tumor microenvironment [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of Monocarboxylate Transporter 4 (MCT4) expression in breast cancer prognosis and immune infiltration via integrated bioinformatics analysis

et al. 2021

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Lactate blunts the anticancer immune response in breast cancer (BC). However, little is known about the exact effect of lactate transporters such as monocarboxylate transporter 4 (MCT4) on immunotherapy. In this study, we investigated the expression status and prognostic value of MCT4 in BC through large-scale transcriptome data. Our results showed that MCT4 was overexpressed in BC, particularly in the basal-like molecular subtype. Overexpression of MCT4 was significantly correlated with high BC lesion grade and poor prognosis. Enrichment analysis indicated that the MCT4-related genes were involved in immune-and metabolism-related bioprocesses, such as myeloid leukocyte activation, the adaptive immune system, and catabolic process. We also found that the expression of MCT4 in BC lesions was associated with immune cell infiltration and glycolytic rate-limiting enzymes like pyruvate kinase M2 (PKM2) and hexokinases-3 (HK3). Our observations indicate that MCT4 may play a pivotal role in the maintenance of the tumor immune microenvironment (TIME) through metabolic reprogramming. The enzymes of the glycolysis pathway (MCT4, PKM2, and HK3) may thus serve as new targets to modulate the TIME and enhance immunotherapy efficiency.

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Section: Introductionmentioning

confidence: 99%

Comprehensive Analysis of Monocarboxylate Transporter 4 (MCT4) expression in breast cancer prognosis and immune infiltration via integrated bioinformatics analysis

et al. 2021

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“…Furthermore, rank-based enrichment analysis showed significantly more modulated genes in GSEA analysis. Previous studies have shown that these potential signaling pathways are not only related to melanin production and metabolism, but also involved in the development of vitiligo [ 31 , 32 ].…”

Section: Discussionmentioning

confidence: 99%

Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies

Zhang

Guo

et al. 2021

Bioengineered

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This study aims to explore biomarkers associated with vitiligo and analyze the pathological role of immune cell infiltration in the disease. We used the robust rank aggregation (RRA) method to integrate three vitiligo data sets downloaded from gene expression omnibus database, identify the differentially expressed genes (DEGs) and analyze the functional correlation. Then, the comprehensive strategy of combined weighted gene coexpression network analysis (WGCNA) and logical regression of the selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) machine learning algorithm are employed to screen and biomarkers associated with vitiligo. Finally, the immune cell infiltration of vitiligo was evaluated by CIBERSORT, and the correlation between biomarkers and infiltrating immune cells was analyzed. Herein, we identified 131 robust DEGs, and enrichment analysis results showed that robust DEGs and melanogenesis were closely associated with vitiligo development and progression. TYR, TYRP1, DCT and LARP7 were identified as vitiligo-related biomarkers. Immune infiltration analysis demonstrated that CD4 T Cell, CD8 T Cell, Tregs, NK cells, dendritic cells, and macrophages were involved in vitiligo’s pathogenesis. In summary, we adopted a comprehensive strategy to screen biomarkers related to vitiligo and explore the critical role of immune cell infiltration in vitiligo. Abbreviations : TYR, Tyrosinase; TYRP1, Tyrosinase-related protein-1; DCT, dopachrome tautomerase; LARP7, La ribonucleoprotein domain family, member-7; RRA, robust rank aggregation; DEGs, differentially expressed genes; WGCNA, weighted gene coexpression network analysis; LASSO, logical regression of the selection operator; SVM-RFE, support vector machine recursive feature elimination; RF, random forest; GWAS, Genome-wide association study; FasL, Fas-Fas ligand; Tregs, T-regulatory cells; NK, natural killer; GEPCs, gene expression profiling chips; GO, gene ontology; GSEA, gene set enrichment analysis; FDR, false discovery rate; AUC, area under the curve; ROC, receiver-operating characteristic; BP, biological process; CC, cellular component; MF, molecular function.

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“…Recent studies have built several gene signatures related to autophagy [7][8][9] , immunity [10][11][12][13][14][15][16][17][18][19][20][21][22] , metabolic reprogramming 23 , and metastasis 24 using public databases such as The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Genetype Titanium Expression (GTEx) databases. Gene signatures related to ferroptosis, a type of programmed cell death characterized by the accumulation of reactive oxygen species caused by iron accumulation and lipid peroxidation 25 , have also been constructed.…”

Section: Introductionmentioning

confidence: 99%

Ferroptosis-Related lncRNA Signature Predicts Prognosis and Immunotherapy Efficacy in Cutaneous Melanoma

Niu

Yang

et al. 2022

Preprint

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Ferroptosis-related lncRNAs are promising biomarkers for predicting the prognosis of many cancers. However, a ferroptosis-related signature to predict the prognosis of cutaneous melanoma (CM) has not been identified. The purpose of our study was to construct a ferroptosis-related lncRNA signature to predict prognosis and immunotherapy efficacy in CM. Ferroptosis-related differentially expressed genes (FDEGs) and lncRNAs (FDELs) were identified using TCGA, GTEx, and FerrDb datasets. We performed Cox and LASSO regressions to identify key FDELs, and constructed a risk score to stratify patients into high- and low-risk groups. A nomogram was developed for clinical use. We performed gene set enrichment analyses (GSEA) to identify significantly enriched pathways. Differences in the tumor microenvironment (TME) between the 2 groups were assessed using 7 algorithms. To predict the efficacy of immune checkpoint inhibitors (ICI), we analyzed the association between PD1 and CTLA4 expression and the risk score. Finally, differences in Tumor Mutational Burden (TMB) and molecular drugs Sensitivity between the 2 groups were performed. Here, we identified 5 lncRNAs (AATBC, AC145423.2, LINC01871, AC125807.2, and AC245041.1) to construct the risk score. The AUC of the lncRNA signature was 0.743 in the training cohort and was validated in the testing and entire cohorts. Kaplan-Meier analyses revealed that the high-risk group had poorer prognosis. Multivariate Cox regression showed that the lncRNA signature was an independent predictor of OS with higher accuracy than traditional clinicopathological features. The 1-, 3-, and 5-year survival probabilities for CM patients were 92.7%, 57.2%, and 40.2% with an AUC of 0.804, indicating a good accuracy and reliability of the nomogram. GSEA showed that the high-risk group had lower ferroptosis and immune response. TME analyses confirmed that the high-risk group had lower immune cell infiltration (e.g., CD8+ T cells, CD4+ memory-activated T cells, and M1 macrophages) and lower immune functions (e.g., immune checkpoint activation). Low-risk patients whose disease expressed PD1 or CTLA4 were likely to respond better to ICIs. The analysis demonstrated that the TMB had significantly difference between low- and high- risk groups. Chemotherapy drugs, such as sorafenib, Imatinib, ABT.888 (Veliparib), Docetaxel, and Paclitaxel showed Significant differences in the estimated IC50 between the two risk groups. Overall, our novel ferroptosis-related lncRNA signature was able to accurately predict the prognosis and ICI outcomes of CM patients. These ferroptosis-related lncRNAs might be potential biomarkers and therapeutic targets for CM.

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A new metabolic signature contributes to disease progression and predicts worse survival in melanoma

Cited by 9 publications

References 39 publications

Comprehensive Analysis of Monocarboxylate Transporter 4 (MCT4) expression in breast cancer prognosis and immune infiltration via integrated bioinformatics analysis

Comprehensive Analysis of Monocarboxylate Transporter 4 (MCT4) expression in breast cancer prognosis and immune infiltration via integrated bioinformatics analysis

Identification of TYR, TYRP1, DCT and LARP7 as related biomarkers and immune infiltration characteristics of vitiligo via comprehensive strategies

Ferroptosis-Related lncRNA Signature Predicts Prognosis and Immunotherapy Efficacy in Cutaneous Melanoma

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