PLEK2 mediates metastasis and vascular invasion via the ubiquitin‐dependent degradation of SHIP2 in non‐small cell lung cancer

Wu, Dongming; Deng, Shi‐Hua; Zhou, Jin; Han, Rong; Li, Teng; Zhang, Ting; Li, Jing; Chen, Jian‐ping; Xu, Ying

doi:10.1002/ijc.32675

Cited by 46 publications

(60 citation statements)

References 28 publications

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“…Supporting an EndoMT interpretation, we also observed enrichment for genes imparting a mesenchymal phenotype, which could reflect the increase in cancer-associated fibroblasts that accompanies EndoMT [54]. We note that although an EndoMT process underpinning cancer invasion and metastasis has been suggested previously [52,53,61], our novel analysis reveals DNAm changes in endothelial cells that could underpin this transformation, if not causally, at least as a useful associative EndoMT marker.…”

Section: Discussionsupporting

confidence: 73%

EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data

Teschendorff

Zhu²,

Breeze

et al. 2020

Genome Biol

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Cell type heterogeneity presents a challenge to the interpretation of epigenome data, compounded by the difficulty in generating reliable single-cell DNA methylomes for large numbers of cells and samples. We present EPISCORE, a computational algorithm that performs virtual microdissection of bulk tissue DNA methylation data at single cell-type resolution for any solid tissue. EPISCORE applies a probabilistic epigenetic model of gene regulation to a single-cell RNA-seq tissue atlas to generate a tissue-specific DNA methylation reference matrix, allowing quantification of cell-type proportions and cell-type-specific differential methylation signals in bulk tissue data. We validate EPISCORE in multiple epigenome studies and tissue types.

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Section: Discussionsupporting

confidence: 73%

EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data

Teschendorff

Zhu²,

Breeze

et al. 2020

Genome Biol

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“…Enrichment of The OOCYTEMEIOSIS pathway was found in bladder cancer and cervical cancer (Gao et al, 2018;Wu et al, 2019). UBIQUITINMEDIATESPROTEOLYSIS related pathways plays an important role in the disease progression of colorectal cancer and non-small cell lung cancer (Lu et al, 2018;Wu et al, 2020). These three pathways were enriched in our highrisk patient group.…”

Section: Discussionmentioning

confidence: 72%

Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma

Bai

Liu

et al. 2021

Front. Genet.

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BackgroundHepatocellular carcinoma (HCC) is ranked fifth among the most common cancer worldwide. Hypoxia can induce tumor growth, but the relationship with HCC prognosis remains unclear. Our study aims to construct a hypoxia-related multigene model to predict the prognosis of HCC.MethodsRNA-seq expression data and related clinical information were download from TCGA database and ICGC database, respectively. Univariate/multivariate Cox regression analysis was used to construct prognostic models. KM curve analysis, and ROC curve were used to evaluate the prognostic models, which were further verified in the clinical traits and ICGC database. GSEA analyzed pathway enrichment in high-risk groups. Nomogram was constructed to predict the personalized treatment of patients. Finally, real-time fluorescence quantitative PCR (RT-qPCR) was used to detect the expressions of KDELR3 and SCARB1 in normal hepatocytes and 4 HCC cells. The expressions of SCARB1 in hepatocellular carcinoma tissue in 46 patients were detected by immunohistochemistry, and the correlation between its expressions and disease free survival of patient was calculated.ResultsThrough a series of analyses, seven prognostic markers related to HCC survival were constructed. HCC patients were divided into the high and low risk group, and the results of KM curve showed that there was a significant difference between the two groups. Stratified analysis, found that there were significant differences in risk values of different ages, genders, stages and grades, which could be used as independent predictors. In addition, we assessed the risk value in the clinical traits analysis and found that it could accelerate the progression of cancer, while the results of GSEA enrichment analysis showed that the high-risk group patients were mainly distributed in the cell cycle and other pathways. Then, Nomogram was constructed to predict the overall survival of patients. Finally, RT-qPCR showed that KDELR3 and SCARB1 were highly expressed in HepG2 and L02, respectively. Results of IHC staining showed that SCARB1 was highly expressed in cancer tissues compared to adjacent normal liver tissues and its expression was related to hepatocellular carcinoma differentiation status. The Kaplan-Meier survival showed a poor percent survival in the SCARB1 high group compared to that in the SCARB1 low group.ConclusionThis study provides a potential diagnostic indicator for HCC patients, and help clinicians to deepen the comprehension in HCC pathogenesis so as to make personalized medical decisions.

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“…In addition, it is also closely associated with cancer invasion and migration [20]. Besides, PLEK2 mediates metastasis and vascular invasion via the ubiquitin-dependent degradation of SHIP2 in NSCLC [21]. S100P is related to the proliferation and migration of nasopharyngeal carcinoma cells.…”

Section: Discussionmentioning

confidence: 99%

A ten-gene signature-based risk assessment model predicts the prognosis of lung adenocarcinoma

Jiang

Chen

2020

BMC Cancer

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Background: Lung adenocarcinoma (LUAD) is a major cause of cancer death. Therefore, identifying potential prognostic risk factors is critical to improve the survival of patients with LUAD. Methods: Here, relevant datasets were downloaded from TCGA and GEO databases to screen the differentially expressed genes (DEGs). Univariate Cox analysis, LASSO regression analysis and multivariate Cox analysis were conducted on the DEGs combined with TCGA clinical data, and finally a risk assessment model based on 10 feature genes was constructed. Results: The prognosis of patients was evaluated after the patients were grouped based on the median risk score and the results showed that the survival time of patients in the high-risk group was significantly shorter than that in the low-risk group. ROC analysis showed that the AUC values of the 1, 3, 5-year survival were 0.753, 0.724, and 0.73, respectively, indicating that the model was precise in predicting the prognosis, which was also verified in the external dataset GSE72094. In addition, a significant correlation was found between the risk score and the clinical stages of LUAD, that is, a later stage always corresponded to a higher risk score. Then, we performed survival analysis on the 10 feature genes independently in the TCGA-LUAD dataset through the GEPIA database, finding that the high expression of 6 genes (COL5A2, PLEK2, BAIAP2L2, S100P, ZIC2, SFXN1) was associated with the poor prognosis of LUAD patients. Conclusion: To sum, this study established a 10-gene risk assessment model and further evaluated its value in predicting LUAD prognosis, which provided a new method for the prognosis prediction of LUAD.

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PLEK2 mediates metastasis and vascular invasion via the ubiquitin‐dependent degradation of SHIP2 in non‐small cell lung cancer

Cited by 46 publications

References 28 publications

EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data

EPISCORE: cell type deconvolution of bulk tissue DNA methylomes from single-cell RNA-Seq data

Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma

A ten-gene signature-based risk assessment model predicts the prognosis of lung adenocarcinoma

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