Association of RYR2 Mutation With Tumor Mutation Burden, Prognosis, and Antitumor Immunity in Patients With Esophageal Adenocarcinoma

Han, Xinwei; Liu, Long; Jiao, Dechao; Guo, Chunguang; Wang, Libo; Li, Zhaonan; Sun, Zhenqiang

doi:10.3389/fgene.2021.669694

Cited by 36 publications

(32 citation statements)

References 30 publications

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“…Clustering of ferroptosis was performed using the consensus clustering method to identify structure over many clustering realizations robustly ( 33 , 34 ). The survival curves for the prognostic analysis were generated via the Kaplan-Meier method, and log-rank tests were used to identify the significance of differences.…”

Section: Methodsmentioning

confidence: 99%

Characterization of Two Ferroptosis Subtypes With Distinct Immune Infiltration and Gender Difference in Gastric Cancer

Yao

et al. 2021

Front. Nutr.

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Background: Iron is an essential nutrient involved in the redox cycle and the formation of free radicals. The reprogramming of iron metabolism is the main link to tumor cell survival. Ferroptosis is an iron-dependent form of regulated cell death associated with cancer; the characteristics of ferroptosis in cancers are still uncertain. This study aimed to explore the application value and gender difference of ferroptosis in prognosis and immune prediction to provide clues for targeted therapy of gastric cancer.Methods: We comprehensively evaluated the ferroptosis levels of 1,404 gastric cancer samples from six independent GC cohorts based on ferroptosis-related specific genes and systematically correlated ferroptosis with immune cell infiltrating and gender characteristics. The ferroptosis score was constructed to quantify the ferroptosis levels of individual tumors using principal component analysis (PCA) algorithms.Results: We identified two distinct ferroptosis subtypes in gastric cancer, namely Subtype-A and Subtype-B. We found that male patients in Subtype-B had the worst prognosis in contrast with the other groups. Three sex hormone receptors (AR, ER, and PR) in Subtype-B tumor patients were higher than in Subtype-A tumor patients in GC, while the HER2 displayed an opposite trend. We developed a risk model termed ferroptosis score to evaluate ferroptosis levels within individual tumors. The low-ferroptosis score group was characterized by activation of immune cells and increased mutation burden, which is also linked to increased neoantigen load and enhanced response to anti-PD-1/L1 immunotherapy. The patients with a low-ferroptosis score showed a high microsatellite instability status (MSI-H) and had a higher response to immunotherapy. Furthermore, the patients with low-ferroptosis scores have a lower estimated IC50 in the several chemotherapy drugs, including paclitaxel, gemcitabine, and methotrexate.Conclusions: We revealed that sex hormone receptors and immune cell infiltration were markedly different between ferroptosis subtypes in GC patients. The results suggested that gender difference may be critical when the ferroptosis-related strategy is applied in GC treatment. Further, ferroptosis levels were identified with an extreme variety of prognosis and tumor immune characteristics, which might benefit GC individualized treatment.

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Section: Methodsmentioning

confidence: 99%

Characterization of Two Ferroptosis Subtypes With Distinct Immune Infiltration and Gender Difference in Gastric Cancer

Yao

et al. 2021

Front. Nutr.

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“…Firstly, we used the ESTIMATE algorithm to calculate the stromal score of all samples ( Yoshihara et al, 2013 ). ESTIMATE is a prevalent R package, which is widely utilized in the cancer-related studies ( Liu et al, 2021c ; 2021d ; 2021e ). Then, the single sample gene set enrichment analysis (ssGSEA) was performed to derive the immune enrichment score based on the 29 immune gene sets ( Bindea et al, 2013 ).…”

Section: Methodsmentioning

confidence: 99%

A Novel Lactate Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironment in Hepatocellular Carcinoma

et al. 2022

Front. Cell Dev. Biol.

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Hepatocellular carcinoma (HCC) is the main subtype of primary liver cancer with high malignancy and poor prognosis. Metabolic reprogramming is a hallmark of cancer and has great importance on the tumor microenvironment (TME). As an abundant metabolite, lactate plays a crucial role in cancer progression and the immunosuppressive TME. Nonetheless, the potential roles of lactate in HCC remain unclear. In this study, we downloaded transcriptomic data of HCC patients with corresponding clinical information from the TCGA and ICGC portals. The TCGA-HCC dataset used as the training cohort, while the ICGC-LIRI-JP dataset was served as an external validation cohort. Cox regression analysis and the LASSO regression model were combined to construct the lactate metabolism-related gene signature (LMRGS). Then, we assessed the clinical significance of LMRGS in HCC. Besides, enriched molecular functions, tumor mutation burden (TMB), infiltrating immune cells, and immune checkpoint were comprehensively analyzed in different LMRGS subgroups. In total, 66 differentially expressed lactate metabolism-related genes (LMRGs) were screened. The functions of LMRGs were mainly enriched in mitochondrial activity and metabolic processes. The LMRGS comprised of six key LMRGs (FKTN, PDSS1, PET117, PUS1, RARS1, and RNASEH1) had significant clinical value for independently predicting the prognosis of HCC patients. The overall survival and median survival of patients in the LMRGS-high group were significantly shorter than in the LMRGS-low group. In addition, there were differences in TMB between the two LMRGS subgroups. The probability of genetic mutations was higher in the LMRGS-high group. Most importantly, the LMRGS reflected the TME characteristics. In the LMRGS-high group, the immune microenvironment presented a suppressed state, accompanied by more inhibitory immune cell infiltration, including follicular helper T cells and regulatory T cells. Additionally, the expression of inhibitory checkpoint molecules was much higher in the LMRGS-high group. Our study suggested that the LMRGS was a robust biomarker to predict the clinical outcomes and evaluate the TME of patients with HCC.

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“…The single-sample gene set enrichment analysis (ssGSEA) ( Yi, et al, 2020 ), which can estimate the relative score of a specific type of immune cell at the level of a single sample, was utilized to evaluate the relative abundance of 28 immune cells according to the specific gene signatures curated from the previously published literature ( Charoentong, et al, 2017 ) via the R package “GSVA”. The ssGSEA is a popular bioinformatics algorithm, which was extensively utilized in cancer-related studies ( Liu, et al, 2021a ; Liu, et al, 2021b ; Liu, et al, 2021c ; Liu, et al, 2021d ; Liu, et al, 2021e ; Liu, et al, 2021f ; Liu, et al, 2021g ).…”

Section: Methodsmentioning

confidence: 99%

Identifying a Hypoxia-Related Long Non-Coding RNAs Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Hepatocellular Carcinoma

Tang

Wang

et al. 2021

Front. Genet.

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Background: Both hypoxia and long non-coding RNAs (lncRNAs) contribute to the tumor progression in hepatocellular carcinoma (HCC). We sought to establish a hypoxia-related lncRNA signature and explore its correlation with immunotherapy response in HCC.Materials and Methods: Hypoxia-related differentially expressed lncRNAs (HRDELs) were identified by conducting the differential gene expression analyses in GSE155505 and The Cancer Genome Atlas (TCGA)- liver hepatocellular carcinoma (LIHC) datasets. The HRDELs landscape in patients with HCC in TCGA-LIHC was dissected by an unsupervised clustering method. Patients in the TCGA-LIHC cohort were stochastically split into the training and testing dataset. The prognostic signature was developed using LASSO (least absolute shrinkage and selection operator) penalty Cox and multivariable Cox analyses. The tumor immune microenvironment was delineated by the single-sample gene set enrichment analysis (ssGSEA) algorithm. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was applied to evaluate the predictive value of the constructed signature in immunotherapeutic responsiveness.Results: A total of 55 HRDELs were identified through integrated bioinformatical analyses in GSE155505 and TCGA-LIHC. Patients in the TCGA-LIHC cohort were categorized into three HRDELs-specific clusters associated with different clinical outcomes. The prognostic signature involving five hypoxia-related lncRNAs (LINC00869, CAHM, RHPN1-AS1, MKLN1-AS, and DUXAP8) was constructed in the training dataset and then validated in the testing dataset and entire TCGA-LIHC cohort. The 5-years AUC of the constructed signature for prognostic prediction reaches 0.705 and is superior to that of age, AJCC stage, and histopathological grade. Patients with high-risk scores consistently had poorer overall survival outcomes than those with low-risk scores irrespective of other clinical parameters status. The low-risk group had more abundance in activated CD8+ T cell and activated B cell and were predicted to be more responsive to immunotherapy and targeted therapy than the high-risk group.Conclusion: We established a reliable hypoxia-related lncRNAs signature that could accurately predict the clinical outcomes of HCC patients and correlate with immunotherapy response and targeted drug sensitivity, providing new insights for immunotherapy and targeted therapy in HCC.

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Association of RYR2 Mutation With Tumor Mutation Burden, Prognosis, and Antitumor Immunity in Patients With Esophageal Adenocarcinoma

Cited by 36 publications

References 30 publications

Characterization of Two Ferroptosis Subtypes With Distinct Immune Infiltration and Gender Difference in Gastric Cancer

Characterization of Two Ferroptosis Subtypes With Distinct Immune Infiltration and Gender Difference in Gastric Cancer

A Novel Lactate Metabolism-Related Gene Signature for Predicting Clinical Outcome and Tumor Microenvironment in Hepatocellular Carcinoma

Identifying a Hypoxia-Related Long Non-Coding RNAs Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Hepatocellular Carcinoma

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