Cuproptosis related genes associated with Jab1 shapes tumor microenvironment and pharmacological profile in nasopharyngeal carcinoma

Wang, Liping; Wang, Dujuan; Liu, Yang; Zeng, Xiaojiao; Zhang, Qian; Liu, Guohong; Pan, Yunbao

doi:10.3389/fimmu.2022.989286

Cited by 21 publications

(11 citation statements)

References 27 publications

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“…To evaluate the immune cell abundance and immune process activity in CRC samples, we utilized the GSVA and GSEABase packages to perform single‐sample GSEA (ssGSEA) analysis 26 . Box plots were generated using the ggpubr package to visualize the differences in immune cell abundance and immune processes between the high‐ and low‐risk groups.…”

Section: Methodsmentioning

confidence: 99%

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes

Zhang,

Fan,

2024

The Journal of Gene Medicine

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The progression and the metastatic potential of colorectal cancer (CRC) are intricately linked to the epithelial–mesenchymal transition (EMT) process. The present study harnesses the power of machine learning combined with multi‐omics data to develop a risk stratification model anchored on EMT‐associated genes. The aim is to facilitate personalized prognostic assessments in CRC. We utilized publicly accessible gene expression datasets to pinpoint EMT‐associated genes, employing a CoxBoost algorithm to sift through these genes for prognostic significance. The resultant model, predicated on gene expression levels, underwent rigorous independent validation across various datasets. Our model demonstrated a robust capacity to segregate CRC patients into distinct high‐ and low‐risk categories, each correlating with markedly different survival probabilities. Notably, the risk score emerged as an independent prognostic indicator for CRC. High‐risk patients were characterized by an immunosuppressive tumor milieu and a heightened responsiveness to certain chemotherapeutic agents, underlining the model's potential in steering tailored oncological therapies. Moreover, our research unearthed a putative repressive interaction between the long non‐coding RNA PVT1 and the EMT‐associated genes TIMP1 and MMP1, offering new insights into the molecular intricacies of CRC. In essence, our research introduces a sophisticated risk model, leveraging machine learning and multi‐omics insights, which accurately prognosticates outcomes for CRC patients, paving the way for more individualized and effective oncological treatment paradigms.

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Section: Methodsmentioning

confidence: 99%

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes

Zhang,

Fan,

2024

The Journal of Gene Medicine

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“…GO and KEGG enrichment analysis of DEGs was conducted using packages including ‘ClusterProfiler’, ‘org.Hs.eg.db’, ‘enrich plot’, ‘circle’, ‘RColorBrewer’, ‘dplyr’, ‘ComplexHeatmap’ and ‘ggplot2’ in R. Statistical significance was set at P < 0.05 after adjustment (Wang et al., 2022).…”

Section: Methodsmentioning

confidence: 99%

Regulatory mechanism of FCGR2A in macrophage polarization and its effects on intervertebral disc degeneration

Luo,

Jin,

Cui

et al. 2024

The Journal of Physiology

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Intervertebral disc degeneration (IDD) poses a significant health burden, necessitating a deeper understanding of its molecular underpinnings. Transcriptomic analysis reveals 485 differentially expressed genes (DEGs) associated with IDD, underscoring the importance of immune regulation. Weighted gene co‐expression network analysis (WGCNA) identifies a yellow module strongly correlated with IDD, intersecting with 197 DEGs. Protein–protein interaction (PPI) analysis identifies ITGAX, MMP9 and FCGR2A as hub genes, predominantly expressed in macrophages. Functional validation through in vitro and in vivo experiments demonstrates the pivotal role of FCGR2A in macrophage polarization and IDD progression. Mechanistically, FCGR2A knockdown suppresses M1 macrophage polarization and NF‐κB phosphorylation while enhancing M2 polarization and STAT3 activation, leading to ameliorated IDD in animal models. This study sheds light on the regulatory function of FCGR2A in macrophage polarization, offering novel insights for IDD intervention strategies. imageKey points This study unveils the role of FCGR2A in intervertebral disc (IVD) degeneration (IDD). FCGR2A knockdown mitigates IDD in cellular and animal models. Single‐cell RNA‐sequencing uncovers diverse macrophage subpopulations in degenerated IVDs. This study reveals the molecular mechanism of FCGR2A in regulating macrophage polarization. This study confirms the role of the NF‐κB/STAT3 pathway in regulating macrophage polarization in IDD.

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“…Univariate and multivariate Cox analyses were used to comprehensively assess the association between risk score and patient clinical information on OS. The survminer package was utilized for the analysis ( Wang et al, 2022 ).…”

Section: Methodsmentioning

confidence: 99%

Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes

et al. 2023

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Background: Breast cancer (BRCA) is regarded as a lethal and aggressive cancer with increasing morbidity and mortality worldwide. cGAS-STING signaling regulates the crosstalk between tumor cells and immune cells in the tumor microenvironment (TME), emerging as an important DNA-damage mechanism. However, cGAS-STING-related genes (CSRGs) have rarely been investigated for their prognostic value in breast cancer patients.Methods: Our study aimed to construct a risk model to predict the survival and prognosis of breast cancer patients. We obtained 1087 breast cancer samples and 179 normal breast tissue samples from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEX) database, 35 immune-related differentially expression genes (DEGs) from cGAS-STING-related genes were systematically assessed. The Cox regression was applied for further selection, and 11 prognostic-related DEGs were used to develop a machine learning-based risk assessment and prognostic model.Results: We successfully developed a risk model to predict the prognostic value of breast cancer patients and its performance acquired effective validation. The results derived from Kaplan-Meier analysis revealed that the low-risk score patients had better overall survival (OS). The nomogram that integrated the risk score and clinical information was established and had good validity in predicting the overall survival of breast cancer patients. Significant correlations were observed between the risk score and tumor-infiltrating immune cells, immune checkpoints and the response to immunotherapy. The cGAS-STING-related genes risk score was also relevant to a series of clinic prognostic indicators such as tumor staging, molecular subtype, tumor recurrence, and drug therapeutic sensibility in breast cancer patients.Conclusion: cGAS-STING-related genes risk model provides a new credible risk stratification method to improve the clinical prognostic assessment for breast cancer.

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Cuproptosis related genes associated with Jab1 shapes tumor microenvironment and pharmacological profile in nasopharyngeal carcinoma

Cited by 21 publications

References 27 publications

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes

Constructing a personalized prognostic risk model for colorectal cancer using machine learning and multi‐omics approach based on epithelial–mesenchymal transition‐related genes

Regulatory mechanism of FCGR2A in macrophage polarization and its effects on intervertebral disc degeneration

Development of a risk model to predict prognosis in breast cancer based on cGAS-STING-related genes

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