Genetic variants in <i>GHR</i> and <i>PLCE1</i> genes are associated with susceptibility to esophageal cancer

Wang, Rong; Si, Lining; Shen, Guo‐Ping; Long, Qifu; Zhao, Yanli

doi:10.1002/mgg3.1474

Cited by 2 publications

(1 citation statement)

References 39 publications

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“…Dysregulation of GHR signaling is associated with various diseases and chronic diseases, such as acromegaly, cancer, aging, metabolic diseases, fibrosis, inflammation, and autoimmunity ( Strous et al, 2020 ). Wang et al (2020) showed that GHR gene polymorphisms are associated with esophageal cancer in the general population, and GHR signaling can be applied to cancers and other therapeutic strategies ( Gao et al, 2020 ). The extracellular domain of GHR can be cleaved during shedding, reducing the number of cell-surface signaling receptors, which modulate the sensitivity of cells to GH ( Frank, 2001 ).…”

Section: Discussionmentioning

confidence: 99%

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Feng

Wang²,

Liu³

et al. 2022

Front. Pharmacol.

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Renal biopsy is the gold standard for defining renal fibrosis which causes calcium deposits in the kidneys. Persistent calcium deposition leads to kidney inflammation, cell necrosis, and is related to serious kidney diseases. However, it is invasive and involves the risk of complications such as bleeding, especially in patients with end-stage renal diseases. Therefore, it is necessary to identify specific diagnostic biomarkers for renal fibrosis. This study aimed to develop a predictive drug target signature to diagnose renal fibrosis based on m6A subtypes. We then performed an unsupervised consensus clustering analysis to identify three different m6A subtypes of renal fibrosis based on the expressions of 21 m6A regulators. We evaluated the immune infiltration characteristics and expression of canonical immune checkpoints and immune-related genes with distinct m6A modification patterns. Subsequently, we performed the WGCNA analysis using the expression data of 1,611 drug targets to identify 474 genes associated with the m6A modification. 92 overlapping drug targets between WGCNA and DEGs (renal fibrosis vs. normal samples) were defined as key drug targets. A five target gene predictive model was developed through the combination of LASSO regression and stepwise logistic regression (LASSO-SLR) to diagnose renal fibrosis. We further performed drug sensitivity analysis and extracellular matrix analysis on model genes. The ROC curve showed that the risk score (AUC = 0.863) performed well in diagnosing renal fibrosis in the training dataset. In addition, the external validation dataset further confirmed the outstanding predictive performance of the risk score (AUC = 0.755). These results indicate that the risk model has an excellent predictive performance for diagnosing the disease. Furthermore, our results show that this 5-target gene model is significantly associated with many drugs and extracellular matrix activities. Finally, the expression levels of both predictive signature genes EGR1 and PLA2G4A were validated in renal fibrosis and adjacent normal tissues by using qRT-PCR and Western blot method.

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Section: Discussionmentioning

confidence: 99%

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Feng

Wang²,

Liu³

et al. 2022

Front. Pharmacol.

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Single Nucleotide Polymorphisms (SNPs) in the Shadows: Uncovering their Function in Non-Coding Region of Esophageal Cancer

Saikia,

Postwala,

Athilingam

et al. 2024

CPB

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Esophageal cancer is a complex disease influenced by genetic and environmental factors. Single nucleotide polymorphisms [SNPs] in non-coding regions of the genome have emerged as crucial contributors to esophageal cancer susceptibility. This review provides a comprehensive overview of the role of SNPs in non-coding regions and their association with esophageal cancer. The accumulation of SNPs in the genome has been implicated in esophageal cancer risk. Various studies have identified specific locations in the genome where SNPs are more likely to occur, suggesting a location-specific response. Chromatin conformational studies have shed light on the localization of SNPs and their impact on gene transcription, posttranscriptional modifications, gene expression regulation, and histone modification. Furthermore, miRNA-related SNPs have been found to play a significant role in esophageal squamous cell carcinoma [ESCC]. These SNPs can affect miRNA binding sites, thereby altering target gene regulation and contributing to ESCC development. Additionally, the risk of ESCC has been linked to base excision repair, suggesting that SNPs in this pathway may influence disease susceptibility. Somatic DNA segment alterations and modified expression quantitative trait loci [eQTL] have also been associated with ESCC. These alterations can lead to disrupted gene expression and cellular processes, ultimately contributing to cancer development and progression. Moreover, SNPs have been found to be associated with the long non-coding RNA HOTAIR, which plays a crucial role in ESCC pathogenesis. This review concludes with a discussion of the current and future perspectives in the field of SNPs in non-coding regions and their relevance to esophageal cancer. Understanding the functional implications of these SNPs may lead to the identification of novel therapeutic targets and the development of personalized approaches for esophageal cancer prevention and treatment.

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Genetic variants in GHR and PLCE1 genes are associated with susceptibility to esophageal cancer

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 2 publications

References 39 publications

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Integrated bioinformatical analysis, machine learning and in vitro experiment-identified m6A subtype, and predictive drug target signatures for diagnosing renal fibrosis

Single Nucleotide Polymorphisms (SNPs) in the Shadows: Uncovering their Function in Non-Coding Region of Esophageal Cancer

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