Analysis on methylation and expression of
            <i>PSMB8</i>
            and its correlation with immunity and immunotherapy in lung adenocarcinoma

Xie, Tongji; Fan, Guangyu; Huang, Liling; Lou, Ning; Han, Xiaohong; Xing, Puyuan; Shi, Yuankai

doi:10.2217/epi-2022-0282

Epigenomics

2022

DOI: 10.2217/epi-2022-0282

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Analysis on methylation and expression of PSMB8 and its correlation with immunity and immunotherapy in lung adenocarcinoma

Tongji Xie

Guangyu Fan

Liling Huang

et al.

Abstract: Aim: To find biomarkers for immunity and immunotherapy in lung adenocarcinoma (LUAD) through multiomics analysis. Materials & methods: The multiomics data of patients with LUAD were downloaded from the TCGA and GEO databases. CIBERSORT, quanTIseq, ESTIMATEScore, k-means clustering, gene set enrichment analysis, gene set variation analysis, immunophenoscore and logistic regression were used in this study. Results: PSMB8 HypoMet-HighExp group patients have more active immune-related pathways, more antitumor … Show more

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2024

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Cited by 2 publications

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Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study

Xie,

Fan,

Tang

et al. 2024

Cancer Immunol Immunother

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Background Existing biomarkers and models for predicting response to programmed cell death protein 1 monoclonal antibody in advanced squamous-cell non-small cell lung cancer (sqNSCLC) did not have enough accuracy. We used data from the ORIENT-3 study to construct artificial neural network (ANN) systems to predict the response to sintilimab for sqNSCLC. Methods Four ANN systems based on bulk RNA data to predict disease control (DC), immune DC (iDC), objective response (OR) and immune OR (iOR) were constructed and tested for patients with sqNSCLC treated with sintilimab. The mechanism exploration on the bulk and the spatial level were performed in patients from the ORIENT-3 study and the real world, respectively. Findings sqNSCLC patients with different responses to sintilimab showed each unique transcriptomic spectrum. Four ANN systems showed high accuracy in the test cohort (AUC of DC, iDC, OR and iOR were 0.83, 0.89, 0.93 and 0.94, respectively). The performance of ANN systems was better than that of linear model systems and showed high stability. The mechanism exploration on the bulk level suggested that patients with lower ANN system scores (worse response) had a higher ratio of immune-related pathways enrichment. The mechanism exploration on the spatial level indicated that patients with better response to immunotherapy had fewer clusters of both tumor and cytotoxicity T cell spots. Interpretation The four ANN systems showed high accuracy, robustness and stability in predicting the response to sintilimab for patients with sqNSCLC. Supplementary Information The online version contains supplementary material available at 10.1007/s00262-024-03886-0.

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Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study

Xie,

Fan,

Tang

et al. 2024

Cancer Immunol Immunother

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Myeloid cell differentiation-related gene signature for predicting clinical outcome, immune microenvironment, and treatment response in lung adenocarcinoma

Wu,

Liu,

Liu

et al. 2024

Sci Rep

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Considering the key role of myeloid cell differentiation—related genes in the tumor microenvironment (TME), we aimed to build a prognostic risk model using these genes for Lung adenocarcinoma (LUAD). The mRNA gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were downloaded as the training and validation sets. Then, “edgeR” R package was applied to screen out the differentially expressed genes (DEGs) and univariate cox regression, backward stepwise selection analyses were performed to construct a prognostic model for LUAD. ESTIMATE, TIMER, XCELL, CIBERSORT abs, QUANTISEQ, MCPCOUNTER, EPIC, and CIBERSORT algorithms were conducted to access the association of risk levels with the stromal and immune cell infiltration levels in LUAD. Six genes (F2RL1, PRKDC, TNFSF11, INHA, PLA2G3 and TUBB1) were utilized to construct the prognostic model. The risk model showed excellent prognostic performance for LUAD in both TCGA and GEO datasets. Also, compared to the low-risk patients, the high-risk patients had higher expression of immune checkpoint molecules and showed a lower IC50 value to the chemotherapy agents. Our findings provided a myeloid cell differentiation—related gene signature that could effectively predict prognosis and guide treatment strategies for LUAD patients.

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Analysis on methylation and expression of PSMB8 and its correlation with immunity and immunotherapy in lung adenocarcinoma

Cited by 2 publications

References 82 publications

Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study

Artificial neural network systems to predict the response to sintilimab in squamous-cell non-small-cell lung cancer based on data of ORIENT-3 study

Myeloid cell differentiation-related gene signature for predicting clinical outcome, immune microenvironment, and treatment response in lung adenocarcinoma

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