In silico analysis of genes and molecular pathways involved in the pathogenesis of follicular lymphoma

Al-Hasnawi Rasool Riyadh  Abdulwahid; Mohammed H.  Mahdi; Bahareh Shateri Amiri; Eman  Koosehlar; Niloufar  Kazemi; Fatemeh  Ghiasi; Shaghayegh  Ghobadi; Hadi  Rezaeeyan

doi:10.18502/ijpho.v14i3.15990

Al-Hasnawi Rasool Riyadh Abdulwahid Xian Jiaotong University Medical Campus, Xian Jiaotong University, Xian, Shaanxi Province, China.
Mohammed H. Mahdi College of pharmacy, Ahl Al Bayt University Kerbala, Iraq.
Bahareh Shateri Amiri Department of Internal Medicine, School of Medicine Hazrat-e Rasool General Hospital, Iran University of Medical Sciences, Tehran, Iran.
Eman Koosehlar Department of Biotechnology, Faculty of Biological Science and Technology, University of Isfahan, Isfahan, Iran.
Niloufar Kazemi Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Iranian Blood Transfusion Organization (IBTO), Tehran, Iran
Fatemeh Ghiasi Instructor of critical care nursing, Department of Anesthesiology, School of Allied Medical Sciences, Ayatollah Taleghani Hospital, Ilam University of Medical Sciences, Ilam, Iran.
Shaghayegh Ghobadi Department of internal medicine, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Hadi Rezaeeyan Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Iranian Blood Transfusion Organization (IBTO), Tehran, Iran.

DOI: https://doi.org/10.18502/ijpho.v14i3.15990

Keywords: Follicular Lymphoma; Molecular Genetics; Bioinformatics

Abstract

Background: Follicular lymphoma (FL) is a common form of non-Hodgkin lymphoma, characterized by abnormal B-cell growth within the germinal center. Research has shown the role of genes and molecular pathways in the pathogenesis of FL. However, the main factor of pathogenesis has not been determined. Therefore, in this study, the genes and molecular pathways related to the pathogenesis of FL were evaluated using a systems biology approach.

Materials and Methods: In this study (bioinformatics analysis), the GSE32018 database was used for data analysis. This database was extracted from Gene Expression Omnibus (GEO). The sample of this database was 36, which included normal and FL samples. For this purpose, 23 cases were FL and 13 were healthy samples. Protein-protein interaction (PPI) is performed to show the interaction between DEGs. STRING software is used for this purpose. Associations between the hub genes, transcription factors, and microRNAs were assessed using the miRTarBase and TRRUST databases. The criteria used for data analysis included log fold change greater than one and p < 0.05.

Results: After evaluating and analyzing the data, the results showed that 866 DEGs were identified between the control and FL samples. Of this population, 231 cases of UP regulation and 635 cases of downregulation were in FL samples compared to control samples. PPI network and hub gene analyses identified 7 hub genes, including RPL37A, MRPS7, RPS14, RPS28, RPL34, RPS20, and RPS3. According to the results, hsa-miR-191-5p has the highest interactions with hub genes among miRNAs, and KDM5A has the most interactions among TFs.

Conclusion: Identifying genes and molecular pathways can be effective in designing therapeutic strategies and preventing the proliferation of FL cells, thereby increasing patients’ survival.