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Identification of Hub Genes Associated with Resistance to Prednisolone in Acute Lymphoblastic Leukemia Based on Weighted Gene Co-expression Network Analysis

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Abstract

Resistance against glucocorticoids which are used to reduce inflammation and treatment of a number of diseases, including leukemia, is known as the first stage of treatment failure in acute lymphoblastic leukemia. Since these drugs are the essential components of chemotherapy regimens for ALL and play an important role in stop of cell growth and induction of apoptosis, it is important to identify genes and the molecular mechanism that may affect glucocorticoid resistance. In this study, we used the GSE66705 dataset and weighted gene co-expression network analysis (WGCNA) to identify modules that correlated more strongly with prednisolone resistance in type B lymphoblastic leukemia patients. The PPI network was built using the DEGs key modules and the STRING database. Finally, we used the overlapping data to identify hub genes. out of a total of 12 identified modules by WGCNA, the blue module was find to have the most statistically significant correlation with prednisolone resistance and Nine genes including SOD1, CD82, FLT3, GART, HPRT1, ITSN1, TIAM1, MRPS6, MYC were recognized as hub genes Whose expression changes can be associated with prednisolone resistance. Enrichment analysis based on the MsigDB repository showed that the altered expressed genes of the blue module were mainly enriched in IL2_STAT5, KRAS, MTORC1, and IL6-JAK-STAT3 pathways, and their expression changes can be related to cell proliferation and survival. The analysis performed by the WGCNA method introduced new genes. The role of some of these genes was previously reported in the resistance to chemotherapy in other diseases. This can be used as clues to detect treatment-resistant (drug-resistant) cases in the early stages of diseases.

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Data Availability

Supporting and raw data are available upon a reasonable request to the corresponding author.

Abbreviations

ALL:

Acute lymphoblastic leukemia

GC:

Glucocorticoids

GR:

Glucocorticoid resistance

GEO:

Gene expression omnibus

WGCNA:

Weighted gene co-expression network analysis

PPI network:

Protein interaction protein network

DEG:

Differentially expressed gene

logFC:

Log2-fold change

FDR:

False discover rate

GEO:

Gene expression omnibus

SOD1:

Superoxide dismutase type 1

FLT3:

Fms-related receptor tyrosine kinase 3

GART:

Glycinamide ribonucleotide transformylase

HPRT1:

Hypoxanthine phosphoribosyl transferase 1

ITSN1:

Intersectin-1

TIAM1:

T-Cell lymphoma invasion and metastasis 1

MRPS6:

Mitochondrial ribosomal protein S6

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Acknowledgements

The authors would like to thank Dr. Behdokht Khani and Hussein Safin for helpful comments on the manuscript.

Funding

This work was supported by the Tehran University of Medical sciences.

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Authors and Affiliations

  1. Department of Molecular Medicine, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, No. 88, School of Advanced Technologies in Medicine, Italia st, Keshavarz Blvd, Tehran, 1417755469, Iran

    Shahram Nekoeian & Zahra Azizi

  2. Faculty of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran

    Shahram Nekoeian & Shaghayegh Ferdowsian

  3. Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, No. 88, School of Advanced Technologies in Medicine, Italia st, Keshavarz Blvd, Tehran, 1417755469, Iran

    Yazdan Asgari

  4. Department of Applied Cell Sciences, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Zahra Azizi

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Design and data mining was done by SN and YA, supervision and revision by ZA and YA, and writing of the draft of the manuscript by SN, SF. All authors have read and approved the final version.

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Correspondence to Yazdan Asgari or Zahra Azizi.

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Nekoeian, S., Ferdowsian, S., Asgari, Y. et al. Identification of Hub Genes Associated with Resistance to Prednisolone in Acute Lymphoblastic Leukemia Based on Weighted Gene Co-expression Network Analysis. Mol Biotechnol 65, 1913–1922 (2023). https://doi.org/10.1007/s12033-023-00707-0

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