COL1A2 is a TBX3 target that mediates its impact on fibrosarcoma and chondrosarcoma cell migration

Omar, Rehana; Cooper, Aretha; Maranyane, Hapiloe Mabaruti; Zerbini, Luiz F.; Prince, Sharon

doi:10.1016/j.canlet.2019.06.004

Cited by 15 publications

(10 citation statements)

References 72 publications

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“…Interestingly, miR-25 silencing increased the expression of COL1A2 and inhibited the expression of E-cadherin, revealing the inhibitory effect of mir-25 on diffuse gastric cancer 44 . Notably, COL1A2 was highly expressed in pancreatic cancer 45 , sarcoma 46 , and gastric cancer 47 tissues and promoted cell proliferation, migration, and invasion, which provided insights for mechanistic studies and clinical trials. COL3A1 was found to be the main structural component of hollow organs such as the great vessels, the uterus, and the intestines.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatic Identification of Potential Hub Genes in Muscle-Invasive Bladder Urothelial Carcinoma

et al. 2020

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Despite aggressive treatment approaches, muscle-invasive bladder urothelial carcinoma (MIBC) patients still have a 50% chance of developing general incurable metastases. Therefore, there is an urgent need for candidate markers to enhance diagnosis and generate effective treatments for this disease. We evaluated four mRNA microarray datasets to find differences between non-MIBC (NMIBC) and MIBC tissues. Through a gene expression profile analysis via the Gene Expression Omnibus database, we identified 56 differentially expressed genes (DEGs). Enrichment analysis of gene ontology, Kyoto Encyclopedia of Genes and Genomes, and Reactome pathways revealed the interactions between these DEGs. Next, we established a protein-protein interaction network to determine the interrelationship between the DEGs and selected 10 hub genes accordingly. Bladder urothelial carcinoma (BLCA) patients with COL1A2, COL5A1, and COL5A2 alterations showed poor disease-free survival rates, while BLCA patients with COL1A1 and LUM alterations showed poor overall survival rates. Oncomine analysis of MIBC versus NMIBC tissues showed that COL1A1, COL5A2, COL1A2, and COL3A1 were consistently among the top 20 overexpressed genes in different studies. Using the TCGAportal, we noted that the high expression of each of the four genes led to shorter BLCA patient overall survival. It was evident that BLCA patients with an elevated high combined gene expression had significantly shorter overall survival and relapse-free survival than those with low combined gene expression using PROGgeneV2. Using Gene Expression Profiling Interactive Analysis, we noted that COL1A1, COL1A2, COL3A1, and COL5A2 were positively correlated with each other in BLCA. These genes are considered as clinically relevant genes, suggesting that they may play an important role in the carcinogenesis, development, invasion, and metastasis of MIBC. However, considering we adopted a bioinformatic approach, more research is crucial to confirm our results. Nonetheless, our findings may have important prospective clinical implementations.

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Section: Discussionmentioning

confidence: 99%

Bioinformatic Identification of Potential Hub Genes in Muscle-Invasive Bladder Urothelial Carcinoma

et al. 2020

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“…For instance, COL1A2 belongs to TBX3 target gene that exerts an influence on the migration of chondrosarcoma and fibrosarcoma cells through the AKT1/TBX3/COL1A2 axis. 44 Apart from this, frequent 7q21-22 genomic region amplification and its intronic miR-25 is able to suppress a massive number of genes that are co-expressed with COL1A2, played a vital role in the angiogenesis and epithelial to mesenchymal transition (EMT), which are the representative characteristics in diffuse type of gastric carcinoma. 45 Additionally, it is found that COL1A2, COL6A3, and THBS2 were all highly expressed in stomach cancer, and suppressing COL1A2, COL6A3, and THBS2 exhibited inhibited proliferation, invasion, and migration via the PI3K-Akt signaling pathway.…”

Section: Discussionmentioning

confidence: 99%

Predicting functional circular RNA-based competitive endogenous RNA network in gastric carcinoma using novel bioinformatics analysis

Zhu

Huang

2021

Exp Biol Med (Maywood)

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Gastric cancer (GC) remains one of the most prevalent types of malignancies worldwide, and also one of the most reported lethal tumor-related diseases. Circular RNAs (circRNAs) have been certified to be trapped in multiple aspects of GC pathogenesis. Yet, the mechanism of this regulation is mostly undefined. This research is designed to discover the vital circRNA-microRNA (miRNA)-messenger RNA (mRNA) regulatory network in GC. Expression profiles with diverse levels including circRNAs, miRNAs, and mRNAs were all determined using microarray public datasets from Gene Expression Ominous (GEO). The differential circRNAs expressions were recognized against the published robust rank aggregation algorithm. Besides, a circRNA-based competitive endogenous RNA (ceRNA) interaction network was visualized via Cytoscape software (version 3.8.0). Functional and pathway enrichment analysis associated with differentially expressed targeted mRNAs were conducted using Cytoscape and an online bioinformatics database. Furthermore, an interconnected protein–protein interaction association network which consisted of 51 mRNAs was predicted, and hub genes were screened using STRING and CytoHubba. Then, several hub genes were chosen to explore their expression associated with survival rate and clinical stage in GEPIA and Kaplan-Meier Plotter databases. Finally, a carefully designed circRNA-related ceRNA regulatory subnetwork including four circRNAs, six miRNAs, and eight key hub genes was structured using the online bioinformatics tool.

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“…Another study demonstrated that COL1A2 may affect proliferation, migration, and invasion of colorectal cancer cells (Yu et al, 2018). Omar Ret al, reported that COL1A2 affects cell migration of fibrosarcoma and chondrosarcoma by acting on TBX3 (Omar et al, 2019). Several studies have shown that COL1A1/2 plays a huge role in osteogenesis (Pollitt et al, 2006;Sato et al, 2016;Wang et al, 2019;Zhytnik et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis

2020

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Purpose: Stomach adenocarcinoma (STAD) is one of the most frequently diagnosed cancer in the world with both high mortality and high metastatic capacity. Therefore, the present study aimed to investigate novel therapeutic targets and prognostic biomarkers that can be used for STAD treatment. Materials and Methods: We acquired four original gene chip profiles, namely GSE13911, GSE19826, GSE54129, and GSE65801 from the Gene Expression Omnibus (GEO). The datasets included a total of 114 STAD tissues and 110 adjacent normal tissues. The GEO2R online tool and Venn diagram software were used to discriminate differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) enriched pathways were also performed for annotation and visualization with DEGs. The STRING online database was used to identify the functional interactions of DEGs. Subsequently, we selected the most significant DEGs to construct the protein-protein interaction (PPI) network and to reveal the core genes involved. Finally, the Kaplan-Meier Plotter online database and Gene Expression Profiling Interactive Analysis (GEPIA) were used to analyze the prognostic information of the core DEGs.

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COL1A2 is a TBX3 target that mediates its impact on fibrosarcoma and chondrosarcoma cell migration

Cited by 15 publications

References 72 publications

Bioinformatic Identification of Potential Hub Genes in Muscle-Invasive Bladder Urothelial Carcinoma

Bioinformatic Identification of Potential Hub Genes in Muscle-Invasive Bladder Urothelial Carcinoma

Predicting functional circular RNA-based competitive endogenous RNA network in gastric carcinoma using novel bioinformatics analysis

Identification of Potential Core Genes Associated With the Progression of Stomach Adenocarcinoma Using Bioinformatic Analysis

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