PD-L1 regulation by SDH5 via β-catenin/ZEB1 signaling

Tuo, Zhan; Zong, Yan; Li, Jie; Xiao, Guangqin; Zhang, Furong; Li, Guiling; Wang, Sihua; Lv, Yi; Xia, Jiahong; Li, Jun

doi:10.1080/2162402x.2019.1655361

Cited by 14 publications

(14 citation statements)

References 28 publications

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“…Western blotting was performed as described previously 11 utilizing the following major antibodies: anti‐HK3 (1:500, Abclonal), anti‐CD274 (1:200, Abclonal) and anti‐GAPDH (1:5000, Abclonal).…”

Section: Methodsmentioning

confidence: 99%

“…Several studies have paid great attention to exploring the relationship between immune escape and the disorder of energy metabolism during the last decade 9,10 . Tumor glycolysis, that is, anaerobic glycolysis, affects tumor microenvironment and becomes the main obstacle to successfully targeting cancer with antitumor immune cells and other therapies 11 . Dysregulating tumor glycolysis could render tumor cells sensitive to natural killer cell (NK cell)‐mediated immunotherapy by upregulating stress‐inducible NKG2DLs (MIC‐A/B) and affecting the acidity of tumor microenvironment (TME), which increases the penetrability or infiltration of the antitumor immune system 12 .…”

Section: Introductionmentioning

confidence: 99%

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HK3 is correlated with immune infiltrates and predicts response to immunotherapy in non‐small cell lung cancer

Tuo

Zheng

Zong

et al. 2020

Clinical & Translational Med

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Background With the knowledge of tumor immunobiology deepening among researchers, the breakthroughs in the field of tumor immunotherapy in recent years have provided new approaches for cancer therapy. While patients who receive treatment are all at risk of side effects, about one‐fifth of them have sustained responses. It is crucial to figure out the underlying mechanism of how the immune system regulates the nonsmall cell lung cancer (NSCLC) microenvironment to improve the benefit of immunotherapy. Regarding glucose metabolism, the initial step is to generate glucose‐6‐phosphate by phosphorylating glucose with hexokinases‐3 (HK3). According to a recent study, HK3 has a functional role in the treatment of acute promyelocytic leukemia and colorectal cancer. Results Here, we studied the co‐expression relationship between the glycolytic pathway gene and the immune checkpoint gene and found that the expression of HK3 in tumor tissues may be related to immune status. By analyzing The Cancer Genome Atlas (TCGA) data, we found that the expression of HK3 was closely related to the main clinical features as well as to molecular characteristics. We also predicted that cases with low expression of HK3 were usually malignant entities and were shown to be obvious genomic aberrations of driver oncogenes. At the same time, gene ontology analysis based on significantly related genes in HK3 expression showed that HK3 expression was linked to inflammatory activity and immune response. Additionally, HK3 showed a remarkable trend in predicting the efficacy of immunotherapy for patients receiving Keytruda (PD‐1 monoclonal antibody) treatment. Conclusions This is the first comprehensive study to characterize HK3 expression in NSCLC from molecular and clinical aspects.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HK3 is correlated with immune infiltrates and predicts response to immunotherapy in non‐small cell lung cancer

Tuo

Zheng

Zong

et al. 2020

Clinical & Translational Med

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“…326 Similar mechanisms have been further validated by various studies on breast cancer and NSCLC. 327,336 In short, these observations provide direct experimental evidence that EMT induction and PD-L1-mediated immunosuppressive TME are closely associated via miR-200/ZEB. 154 In addition, a retrospective study on colon cancer at the histological level has shown that the regulation of miR-200/ZEB axis, manifesting itself as upregulated expression of ZEB and downregulated expression of miR-200, occurs preferentially in regions of tumor budding at the invasive front where EMT is frequently accompanied by significantly elevated expression of PD-L1, rather than at the tumor center.…”

Section: Epithelial-mesenchymal Transition (Emt)mentioning

confidence: 70%

Emerging role of tumor cell plasticity in modifying therapeutic response

Qin

Jiang

Lü

et al. 2020

Sig Transduct Target Ther

169

122

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Resistance to cancer therapy is a major barrier to cancer management. Conventional views have proposed that acquisition of resistance may result from genetic mutations. However, accumulating evidence implicates a key role of non-mutational resistance mechanisms underlying drug tolerance, the latter of which is the focus that will be discussed here. Such non-mutational processes are largely driven by tumor cell plasticity, which renders tumor cells insusceptible to the drug-targeted pathway, thereby facilitating the tumor cell survival and growth. The concept of tumor cell plasticity highlights the significance of re-activation of developmental programs that are closely correlated with epithelial–mesenchymal transition, acquisition properties of cancer stem cells, and trans-differentiation potential during drug exposure. From observations in various cancers, this concept provides an opportunity for investigating the nature of anticancer drug resistance. Over the years, our understanding of the emerging role of phenotype switching in modifying therapeutic response has considerably increased. This expanded knowledge of tumor cell plasticity contributes to developing novel therapeutic strategies or combination therapy regimens using available anticancer drugs, which are likely to improve patient outcomes in clinical practice.

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“…The mesenchymal related genes are CDH2, FN1, MMP3, SNAI2, SOX10, and TWIST1. PD-L1 is regulated by signaling pathways, transcription factors and epigenetic factors, such as EMT ( Tuo et al, 2019 ). EMT is a crucial step in lung cancer progression, involving several morphological and phenotypical changes.…”

Section: Discussionmentioning

confidence: 99%

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

Shen

Wang

et al. 2021

Front. Cell Dev. Biol.

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BackgroundLung adenocarcinoma (LUAD) originates mainly from the mucous epithelium and glandular epithelium of the bronchi. It is the most common pathologic subtype of non-small cell lung cancer (NSCLC). At present, there is still a lack of clear criteria to predict the efficacy of immunotherapy. The 5-year survival rate for LUAD patients remains low.MethodsAll data were downloaded from The Cancer Genome Atlas (TCGA) database. We used Gene Set Enrichment Analysis (GSEA) database to obtain immune-related mRNAs. Immune-related lncRNAs were acquired by using the correlation test of the immune-related genes with R version 3.6.3 (Pearson correlation coefficient cor = 0.5, P < 0.05). The TCGA-LUAD dataset was divided into the testing set and the training set randomly. Based on the training set to perform univariate and multivariate Cox regression analyses, we screened prognostic immune-related lncRNAs and given a risk score to each sample. Samples were divided into the high-risk group and the low-risk group according to the median risk score. By the combination of Kaplan–Meier (KM) survival curve, the receiver operating characteristic (ROC) (AUC) curve, the independent risk factor analysis, and the clinical data of the samples, we assessed the accuracy of the risk model. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the differentially expressed mRNAs between the high-risk group and the low-risk group. The differentially expressed genes related to immune response between two risk groups were analyzed to evaluate the role of the model in predicting the efficacy and effects of immunotherapy. In order to explain the internal mechanism of the risk model in predicting the efficacy of immunotherapy, we analyzed the differentially expressed genes related to epithelial-mesenchymal transition (EMT) between two risk groups. We extracted RNA from normal bronchial epithelial cell and LUAD cells and verified the expression level of lncRNAs in the risk model by a quantitative real-time polymerase chain reaction (qRT-PCR) test. We compared our risk model with other published prognostic signatures with data from an independent cohort. We transfected LUAD cell with siRNA-LINC0253. Western blot analysis was performed to observed change of EMT-related marker in protein level.ResultsThrough univariate Cox regression analysis, 24 immune-related lncRNAs were found to be strongly associated with the survival of the TCGA-LUAD dataset. Utilizing multivariate Cox regression analysis, 10 lncRNAs were selected to establish the risk model. The K-M survival curves and the ROC (AUC) curves proved that the risk model has a fine predictive effect. The GO enrichment analysis indicated that the effect of the differentially expressed genes between high-risk and low-risk groups is mainly involved in immune response and intercellular interaction. The KEGG enrichment analysis indicated that the differentially expressed genes between high-risk and low-risk groups are mainly involved in endocytosis and the MAPK signaling pathway. The expression of genes related to the efficacy of immunotherapy was significantly different between the two groups. A qRT-PCR test verified the expression level of lncRNAs in LUAD cells in the risk model. The AUC of ROC of 5 years in the independent validation dataset showed that this model had superior accuracy. Western blot analysis verified the change of EMT-related marker in protein level.ConclusionThe immune lncRNA risk model established by us could better predict the prognosis of patients with LUAD.

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PD-L1 regulation by SDH5 via β-catenin/ZEB1 signaling

Cited by 14 publications

References 28 publications

HK3 is correlated with immune infiltrates and predicts response to immunotherapy in non‐small cell lung cancer

HK3 is correlated with immune infiltrates and predicts response to immunotherapy in non‐small cell lung cancer

Emerging role of tumor cell plasticity in modifying therapeutic response

Construction of a Prognostic Immune-Related LncRNA Risk Model for Lung Adenocarcinoma

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