TGM2, HMGA2, FXYD3, and LGALS4 genes as biomarkers in acquired oxaliplatin resistance of human colorectal cancer: A systems biology approach

Cheraghi-Shavi, Tayebeh; Jalal, Razieh; Minuchehr, Zarrin

doi:10.1371/journal.pone.0289535

PLoS ONE

2023

DOI: 10.1371/journal.pone.0289535

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TGM2, HMGA2, FXYD3, and LGALS4 genes as biomarkers in acquired oxaliplatin resistance of human colorectal cancer: A systems biology approach

Tayebeh Cheraghi-Shavi

Razieh Jalal

Zarrin Minuchehr

Abstract: Acquired resistance to oxaliplatin is considered as the primary reason for failure in colorectal cancer (CRC) therapy. Identifying the underlying resistance mechanisms may improve CRC treatment. The present study aims to identify the key genes involved in acquired oxaliplatin-resistant in CRC by confirming the oxaliplatin resistance index (OX-RI). To this aim, two public microarray datasets regarding oxaliplatin-resistant CRC cells with different OX-RI, GSE42387, and GSE76092 were downloaded from GEO database … Show more

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Cited by 4 publications

References 108 publications

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Decoding high mobility group A2 protein expression regulation and implications in human cancers

Khazem,

Zetoune

2024

Discov Onc

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High Mobility Group A2 (HMGA2) oncofetal proteins are a distinct category of Transcription Factors (TFs) known as “architectural factors” due to their lack of direct transcriptional activity. Instead, they modulate the three-dimensional structure of chromatin by binding to AT-rich regions in the minor grooves of DNA through their AT-hooks. This binding allows HMGA2 to interact with other proteins and different regions of DNA, thereby regulating the expression of numerous genes involved in carcinogenesis. Consequently, multiple mechanisms exist to finely control HMGA2 protein expression at various transcriptional levels, ensuring precise concentration adjustments to maintain cellular homeostasis. During embryonic development, HMGA2 protein is highly expressed but becomes absent in adult tissues. However, recent studies have revealed its re-elevation in various cancer types. Extensive research has demonstrated the involvement of HMGA2 protein in carcinogenesis at multiple levels. It intervenes in crucial processes such as cell cycle regulation, apoptosis, angiogenesis, epithelial-to-mesenchymal transition, cancer cell stemness, and DNA damage repair mechanisms, ultimately promoting cancer cell survival. This comprehensive review provides insights into the HMGA2 protein, spanning from the genetic regulation to functional protein behavior. It highlights the significant mechanisms governing HMGA2 gene expression and elucidates the molecular roles of HMGA2 in the carcinogenesis process. Graphical Abstract

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Decoding high mobility group A2 protein expression regulation and implications in human cancers

Khazem,

Zetoune

2024

Discov Onc

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Identification and validation of new fatty acid metabolism–related mechanisms and biomarkers for erectile dysfunction

He,

Liu,

Zheng

et al. 2024

Sexual Medicine

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Background Erectile dysfunction (ED) is a common condition affecting middle-aged and elderly men. Aim The study sought to investigate differentially expressed fatty acid metabolism–related genes and the molecular mechanisms of ED. Methods The expression profiles of GSE2457 and GSE31247 were downloaded from the Gene Expression Omnibus database and merged. Differentially expressed genes (DEGs) between ED and normal samples were obtained using the R package limma. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses of DEGs were conducted using the R package clusterProfiler. Fatty acid metabolism–related DEGs (FAMDEGs) were further identified and analyzed. Machine learning algorithms, including Lasso (least absolute shrinkage and selection operator), support vector machine, and random forest algorithms, were utilized to identify hub FAMDEGs with the ability to predict ED occurrence. Coexpression analysis and gene set enrichment analysis of hub FAMDEGs were performed. Outcome Fatty acid metabolism–related functions (such as fatty acid metabolism and degradation) may play a vital role in ED. Results In total, 5 hub FAMDEGs (Aldh2, Eci2, Acat1, Acadl, and Hadha) were identified and found to be differentially expressed between ED and normal samples. Gene set enrichment analysis identified key pathways associated with these genes. The area under the curve values of the 5 hub FAMDEGs for predicting ED occurrence were all >0.8. Clinical Translation Our results suggest that these 5 key FAMDEGs may serve as biomarkers for the diagnosis and treatment of ED. Strengths and Limitations The strengths of our study include the use of multiple datasets and machine learning algorithms to identify key FAMDEGs. However, limitations include the lack of validation in animal models and human tissues, as well as research on the mechanisms of these FAMDEGs. Conclusion Five hub FAMDEGs were identified as potential biomarkers for ED progression. Our work may prove that fatty acid metabolism–related genes are worth further investigation in ED.

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Identification of key lncRNAs associated with oxaliplatin resistance in colorectal cancer cells and isolated exosomes: From In-Silico prediction to In-Vitro validation

Sahebnasagh,

Deli,

Shadboorestan

et al. 2024

PLoS ONE

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One of the critical challenges in managing colorectal cancer (CRC) is the development of oxaliplatin (OXP) resistance. Long non-coding RNAs (lncRNAs) have a crucial role in CRC progression and chemotherapy resistance, with exosomal lncRNAs emerging as potential biomarkers. This study aimed to predict key lncRNAs involved in OXP-resistance using in-silico methods and validate them using RT-qPCR methods in CRC cells and their isolated exosomes. Two public datasets, GSE42387 and GSE119481, were downloaded from the GEO database to identify differentially expressed genes (DEGs) and miRNAs (DEmiRNAs) associated with OXP-resistance in the HCT116 cell line. The analysis of GSE42387 revealed 210 DEGs, and GSE119481 identified 73 DEmiRNAs. A protein-protein interaction (PPI) network analysis of the DEGs identified 133 interconnected genes, from which the top ten genes with the highest degree scores were selected. By intersecting predicted miRNAs targeting these genes with the DEmiRNAs, 38 common miRNAs were found. Subsequently, 224 lncRNAs targeting these common miRNAs were predicted. LncRNA-miRNA-mRNA network were constructed and the top five lncRNAs with the highest degree scores were identified. Analysis using the Kaplan-Meier plotter database revealed that the key lncRNAs NEAT1, OIP5-AS1, and MALAT1 are significantly associated with the overall survival of CRC patients. To validate these lncRNAs, OXP-resistant HCT116 sub-cell line (HCT116/OXR) was developed by exposing parental HCT116 cells to gradually increasing concentrations of OXP. Exosomes derived from both HCT116 and HCT116/OXR cells were isolated and characterized utilizing dynamic light scattering (DLS), transmission electron microscopy (TEM), and Western blotting. RT-qPCR confirmed elevated levels of NEAT1, OIP5-AS1, and MALAT1 in HCT116/OXR cells and their exosomes compared to parental HCT116 cells and their exosomes. This study concludes that NEAT1, OIP5-AS1, and MALAT1 are associated with the OXP-resistance in CRC. The high levels of these lncRNAs in exosomes of resistant cells suggest their involvement in intercellular communication and resistance propagation. This positioning makes them promising biomarkers for OXP-resistance in CRC.

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TGM2, HMGA2, FXYD3, and LGALS4 genes as biomarkers in acquired oxaliplatin resistance of human colorectal cancer: A systems biology approach

Cited by 4 publications

References 108 publications

Decoding high mobility group A2 protein expression regulation and implications in human cancers

Decoding high mobility group A2 protein expression regulation and implications in human cancers

Identification and validation of new fatty acid metabolism–related mechanisms and biomarkers for erectile dysfunction

Identification of key lncRNAs associated with oxaliplatin resistance in colorectal cancer cells and isolated exosomes: From In-Silico prediction to In-Vitro validation

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