Stratification From Heterogeneity of the Cell-Death Signal Enables Prognosis Prediction and Immune Microenvironment Characterization in Esophageal Squamous Cell Carcinoma

Zhang, Yiyuan; Chen, Yanxing

doi:10.3389/fcell.2022.855404

Cited by 8 publications

(7 citation statements)

References 50 publications

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“…The new mechanism, however, has illustrated that CD8+ T cells suppress tumor growth by inducing ferroptosis, pyroptosis, and necroptosis, which prompted a reconsideration of the relationship between tumor cell death and immune activation ( 17 ). In this study, we observed that apoptosis, necroptosis, pyroptosis, and ferroptosis positively correlated with a highly infiltrated immune microenvironment and highly expressed immune-checkpoint genes, which was consistent with many previous tumor cell death research ( 46 – 48 ). Furthermore, compared with patients in the low-cell-death group, patients in the high-cell-death groups (CS3 and CS4) had a lower expression of tumor-associated molecular pathways and higher enrichment of immune response biological pathways.…”

Section: Discussionsupporting

confidence: 92%

Crosstalk of four kinds of cell deaths defines subtypes of cutaneous melanoma for precise immunotherapy and chemotherapy

Wan

Wei

et al. 2022

Front. Immunol.

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BackgroundCell death patterns can give therapeutic and biological clues that facilitate the development of individualized treatments for this lethal form of skin cancer.MethodsWe employed unsupervised clustering to establish robust classifications based on the four kinds of cell death-associated gene expression of 462 melanoma patients in the Cancer Genome Atlas (TCGA) and tested their reproducibility in two independent melanoma cohorts of 558 patients. We then used dimensionality reduction of graph learning to display the different characteristics of cell death patterns and immune microenvironments.ResultsWe examined 570 cell death-associated gene expression data of melanoma patients for exploration, independent verification, and comprehensive classification of five reproducible melanoma subtypes (CS1 to CS5) with different genomic and clinical features. Patients in death-inactive subtypes (CS1, CS2, and CS5) had the least immune and stromal cell infiltration, and their prognosis was the poorest. A death-active subtype (CS4), on the other hand, had the highest infiltrated immune and stromal cells and elevated immune-checkpoints. As a result, these patients had the highest response to immunotherapy and the best prognosis. An additional subtype (CS3) had more diversified cell death and immune characteristics with moderate prognoses. Based on graph learning, we successfully divided the CS3 subtype into two subgroups (group A and group B) with distinct survival outcomes and immune features. Finally, we identified eight potential chemical drugs that were specifically targeted for the therapy of melanoma subtypes.ConclusionsThis research defines the intrinsic subtypes of melanoma based on the crosstalk of four kinds of cell deaths, which affords a blueprint for clinical strategies and guiding precise immunotherapy and chemotherapy for melanoma patients.

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Section: Discussionsupporting

confidence: 92%

Crosstalk of four kinds of cell deaths defines subtypes of cutaneous melanoma for precise immunotherapy and chemotherapy

Wan

Wei

et al. 2022

Front. Immunol.

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“…It can be seen that there are almost no significant differences between the two cohorts. There were 138 ICD-related genes acquired from previous studies ( Zhang and Chen, 2022 ; Supplementary Table S1 ). Eleven prognostic ICD-related genes were identified by univariate Cox regression analysis, and we obtained 1,320 ICD-related lncRNAs using Pearson correlation analysis.…”

Section: Resultsmentioning

confidence: 99%

“…Based on previous research, 138 ICD-related genes were identified ( Zhang and Chen, 2022 ). Pearson correlation analysis was performed to determine the association between lncRNAs and ICD-related genes.…”

Section: Methodsmentioning

confidence: 99%

Immune cell death-related lncRNA signature as a predictive factor of clinical outcomes and immune checkpoints in gastric cancer

Zhang

Thakur

et al. 2023

Front. Pharmacol.

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Background: Immune cell death (ICD) is a type of tumor cell death that has recently been shown to activate and regulate tumor immunity. However, the role of ICD-related long non-coding RNAs (lncRNAs) in gastric cancer remains to be clarified.Methods: We obtained 375 tumor samples from the Cancer Genome Atlas (TCGA) database and randomly assigned them to training and verification groups. LASSO and Cox regression analysis were utilized to identify ICD-related lncRNAs and establish a risk model. The changes in the immune microenvironment of the two groups were compared by examining the tumor-infiltrating immune cells.Results: We established a tumor signature based on nine ICD-related lncRNAs. In light of the receiver operating characteristic and Kaplan–Meier curves, the prognostic values of this risk model were verified. Multivariate regression analysis showed that the risk score was an independent risk factor for the prognosis of patients in both the training cohort (HR 2.52; 95% CI: 1.65–3.87) and validation cohort (HR 2.70; 95% CI: 1.54–4.8). A nomogram was developed to predict the 1-, 3-, and 5-year survival of patients with gastric cancer, and the signature was linked to high levels of immunological checkpoint expression (B7-H3, VSIR).Conclusions: An ICD-related lncRNA signature could predict the immune response and prognosis of patients with gastric cancer. This prognostic signature could be employed to independently monitor the efficacy of immunotherapy for gastric cancer patients.

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“…Research into the relationship of pyroptosis with ESCC is rare, but studies on this subject are gradually emerging. By comprehensive analyzing 857 patients’ transcriptomes and 124 patients’ proteomic profiles, Zhang and Chen discovered that the pathway of necroptosis, ferroptosis, and pyroptosis is closely linked with the activation of immunity in ESCC ( 14 ). Zheng et al found that a high level of STAT3β expression resulted in promoting the sensitivity of cisplatin and strengthening the reliant pyroptosis of gasdermin E (GSDME) in ESCC cells after exposuring to cisplatin ( 15 ).…”

Section: Discussionmentioning

confidence: 99%

A novel pyroptosis-related model for prognostic prediction in esophageal squamous cell carcinoma: a bioinformatics analysis

Shi¹,

Liu²,

Wang³

et al. 2023

J Thorac Dis

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Background: Esophageal squamous cell carcinoma (ESCC) has a poor prognosis, and the 5-year survival rate is less than 30%. Better differentiation of patients at high risk of recurrence or metastasis could guide clinical treatment. The close relationship between pyroptosis and ESCC has been recently reported. Herein, we aimed to identify genes associated with pyroptosis in ESCC and construct a prognostic risk model.Methods: RNA-seq data of ESCC was obtained from the The Cancer Genome Atlas (TCGA) database.Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were used to calculate the pyroptosis-related pathway score (Pys). Weighted gene co-expression network analysis (WGCNA) and univariate Cox regression were used to screen for pyroptotic genes associated with prognosis, and Lasso regression was used to establish a risk score. Finally, the T test was used to compare the relationship between the model and tumor-node-metastasis (TNM) stage. Furthermore, we compared the difference of immune infiltrating cells and immune checkpoints between the low-and high-risk groups.Results: Using WGCNA, 283 genes were significantly associated with N staging and Pys. Among them, univariate Cox analysis suggested that 83 genes were associated with prognosis of ESCC patients. After that, AADAC, GSTA1, and KCNS3 were identified as prognostic signatures separating high-and low-risk groups. Patients in the high-and low-risk groups had significantly different distributions of T (P=0.018) and N staging (P<0.05). Moreover, the 2 groups had remarkably different immune infiltrating cell scores and immune checkpoint expressions.Conclusions: Our study identified 3 prognosis pyroptosis-related genes in the ESCC and successfully build a prognostic model. AADAC, GSTA1, and KCNS3 may serve as promising therapeutic targets in ESCC.

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Stratification From Heterogeneity of the Cell-Death Signal Enables Prognosis Prediction and Immune Microenvironment Characterization in Esophageal Squamous Cell Carcinoma

Cited by 8 publications

References 50 publications

Crosstalk of four kinds of cell deaths defines subtypes of cutaneous melanoma for precise immunotherapy and chemotherapy

Crosstalk of four kinds of cell deaths defines subtypes of cutaneous melanoma for precise immunotherapy and chemotherapy

Immune cell death-related lncRNA signature as a predictive factor of clinical outcomes and immune checkpoints in gastric cancer

A novel pyroptosis-related model for prognostic prediction in esophageal squamous cell carcinoma: a bioinformatics analysis

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