Expression of FOXA1 gene regulates the proliferation and invasion of human gastric cancer cells

Dai, Yun; Yang, Guangming; Yang, Lie; Jiang, Li; Zheng, Guohua; Pan, Shuyin; Zhu, Changtai

doi:10.14715/cmb/2021.67.2.25

Cited by 11 publications

(6 citation statements)

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“…Despite the fact that dysregulated expression of certain FOX genes has been observed in a few cancers, 30 , 31 the expression pattern of the entire FOX gene family across cancer types remains unclear. Filtering for cancer types with at least 10 matched tumor‐normal samples, 16 cancer types were selected for further analysis.…”

Section: Resultsmentioning

confidence: 99%

Pan‐cancer analyses reveal multi‐omic signatures and clinical implementations of the forkhead‐box gene family

Zheng

Cai

et al. 2023

Cancer Medicine

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BackgroundForkhead box (FOX) proteins belong to one of the largest transcription factor families and play crucial roles in the initiation and progression of cancer. Prior research has linked several FOX genes, such as FOXA1 and FOXM1, to the crucial process of carcinogenesis. However, the overall picture of FOX gene family across human cancers is far from clear.MethodsTo investigate the broad molecular signatures of the FOX gene family, we conducted study on multi‐omics data (including genomics, epigenomics and transcriptomics) from over 11,000 patients with 33 different types of human cancers.ResultsPan‐cancer analysis reveals that FOX gene mutations were found in 17.4% of tumor patients with a substantial cancer type‐dependent pattern. Additionally, high expression heterogeneity of FOX genes across cancer types was discovered, which can be partially attributed to the genomic or epigenomic alteration. Co‐expression network analysis reveals that FOX genes may exert functions by regulating the expression of both their own and target genes. For a clinical standpoint, we provided 103 FOX gene‐drug target‐drug predictions and found FOX gene expression have potential survival predictive value. All of the results have been included in the FOX2Cancer database, which is freely accessible at http://hainmu‐biobigdata.com/FOX2Cancer.ConclusionOur findings may provide a better understanding of roles FOX genes played in the development of tumors, and help to offer new avenues for uncovering tumorigenesis and unprecedented therapeutic targets.

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Section: Resultsmentioning

confidence: 99%

Pan‐cancer analyses reveal multi‐omic signatures and clinical implementations of the forkhead‐box gene family

Zheng

Cai

et al. 2023

Cancer Medicine

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“…Among all transcription factors regulating the three hub genes, FOXA1 with the highest NES (NES = 5.142) was considered as the most important transcription factors ( Figure 5C ). Existing studies have shown that the expression of FOXA1 affects the proliferation and invasion of gastric cancer cells ( Lin et al, 2018 ; Dai Y. et al, 2021 ). The result verified the objectivity of the three hub genes in gastric cancer.…”

Section: Resultsmentioning

confidence: 99%

ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning

et al. 2022

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Based on multiple bioinformatics methods and machine learning techniques, this study was designed to explore potential hub genes of gastric cancer with a diagnostic value. The novel biomarkers were detected through multiple databases of gastric cancer–related genes. The NCBI Gene Expression Omnibus (GEO) database was used to obtain gene expression files. Three hub genes (ESRRG, ATP4A, and ATP4B) were detected through a combination of weighted gene co-expression network analysis (WGCNA), gene–gene interaction network analysis, and supervised feature selection method. GEPIA2 was used to verify the differences in the expression levels of the hub genes in normal and cancer tissues in the RNA-seq levels of Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases. The objectivity of potential hub genes was also verified by immunohistochemistry in the Human Protein Atlas (HPA) database and transcription factor–hub gene regulatory network. Machine learning (ML) methods including data pre-processing, model selection and cross-validation, and performance evaluation were examined on the hub-gene expression profiles in five Gene Expression Omnibus datasets and verified on a GEO external validation (EV) dataset. Six supervised learning models (support vector machine, random forest, k-nearest neighbors, neural network, decision tree, and eXtreme Gradient Boosting) and one semi-supervised learning model (label spreading) were established to evaluate the diagnostic value of biomarkers. Among the six supervised models, the support vector machine (SVM) algorithm was the most effective one according to calculated performance metrics, including 0.93 and 0.99 area under the curve (AUC) scores on the test and external validation datasets, respectively. Furthermore, the semi-supervised model could also successfully learn and predict sample types, achieving a 0.986 AUC score on the EV dataset, even when 10% samples in the five GEO datasets were labeled. In conclusion, three hub genes (ATP4A, ATP4B, and ESRRG) closely related to gastric cancer were mined, based on which the ML diagnostic model of gastric cancer was conducted.

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“…The anti-apoptotic protein BCL-2 binds to mitochondria and prevents the release of cytochrome c from mitochondria into the cytoplasm. In contrast, the pro-apoptotic protein BAX translocalizes to mitochondria and increases mitochondrial membrane permeability, thereby initiating apoptosis [ [27] , [28] , [29] , [30] , [31] ]. Inhibition of the PI3K/AKT/mTOR signaling pathway and promotion of E-cadherin expression can inhibit the proliferation and invasion of pancreatic cancer cells [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Mechanisms of vemurafenib-induced anti-tumor effects in ATC FRO cells

Xu,

Xue,

et al. 2024

Heliyon

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Expression of FOXA1 gene regulates the proliferation and invasion of human gastric cancer cells

Cited by 11 publications

References 0 publications

Pan‐cancer analyses reveal multi‐omic signatures and clinical implementations of the forkhead‐box gene family

Pan‐cancer analyses reveal multi‐omic signatures and clinical implementations of the forkhead‐box gene family

ESRRG, ATP4A, and ATP4B as Diagnostic Biomarkers for Gastric Cancer: A Bioinformatic Analysis Based on Machine Learning

Mechanisms of vemurafenib-induced anti-tumor effects in ATC FRO cells

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