Volume 27, Issue 1 (1-2023)
IBJ 2023, 27(1): 23-33 |
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Abstract:
Background: Hypoxic tumor microenvironment is one of the important impediments for conventional cancer therapy. This study aimed to computationally identify hypoxia-related messenger RNA (mRNA) signatures in nine hypoxic-conditioned cancer cell lines and investigate their role during hypoxia.
Methods: Nine RNA sequencing (RNA-Seq) expression data sets were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in each cancer cell line. Then 23 common DEGs were selected by comparing the gene lists across the nine cancer cell lines. Reverse transcription-quantitative PCR (qRT-PCR) was performed to validate the identified DEGs.
Results: By comparing the data sets, GAPDH, LRP1, ALDOA, EFEMP2, PLOD2, CA9, EGLN3, HK, PDK1, KDM3A, UBC, and P4HA1 were identified as hub genes. In addition, miR-335-5p, miR-122-5p, miR-6807-5p, miR-1915-3p, miR-6764-5p, miR-92-3p, miR-23b-3p, miR-615-3p, miR-124-3p, miR-484, and miR-455-3p were determined as common micro RNAs. Four DEGs were selected for mRNA expression validation in cancer cells under normoxic and hypoxic conditions with qRT-PCR. The results also showed that the expression levels determined by qRT-PCR were consistent with RNA-Seq data.
Conclusion: The identified protein-protein interaction network of common DEGs could serve as potential hypoxia biomarkers and might be helpful for improving therapeutic strategies.
Methods: Nine RNA sequencing (RNA-Seq) expression data sets were retrieved from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified in each cancer cell line. Then 23 common DEGs were selected by comparing the gene lists across the nine cancer cell lines. Reverse transcription-quantitative PCR (qRT-PCR) was performed to validate the identified DEGs.
Results: By comparing the data sets, GAPDH, LRP1, ALDOA, EFEMP2, PLOD2, CA9, EGLN3, HK, PDK1, KDM3A, UBC, and P4HA1 were identified as hub genes. In addition, miR-335-5p, miR-122-5p, miR-6807-5p, miR-1915-3p, miR-6764-5p, miR-92-3p, miR-23b-3p, miR-615-3p, miR-124-3p, miR-484, and miR-455-3p were determined as common micro RNAs. Four DEGs were selected for mRNA expression validation in cancer cells under normoxic and hypoxic conditions with qRT-PCR. The results also showed that the expression levels determined by qRT-PCR were consistent with RNA-Seq data.
Conclusion: The identified protein-protein interaction network of common DEGs could serve as potential hypoxia biomarkers and might be helpful for improving therapeutic strategies.
Type of Study: Full Length/Original Article |
Subject:
Molecular Genetics & Genomics
References
1. Jing X, Yang F, Shao C, Wei K, Xie M, Shen H, Shu Y. Role of hypoxia in cancer therapy by regulating the tumor microenvironment. Molecular cancer 2019; 18(1): 157. [DOI:10.1186/s12943-019-1089-9]
2. Challapalli A, Carroll L, Aboagye EO. Molecular mechanisms of hypoxia in cancer. Clinical and translational imaging 2017; 5(3): 225-253. [DOI:10.1007/s40336-017-0231-1]
3. Makhijani RK, Raut SA, Purohit HJ. Identification of common key genes in breast, lung and prostate cancer and exploration of their heterogeneous expression. Oncology letters 2018; 15(2): 1680-1690. [DOI:10.3892/ol.2017.7508]
4. Kulshrestha A, Suman S, Ranjan R. Network analysis reveals potential markers for pediatric adrenocortical carcinoma. Oncotargets and therapy 2016; 9: 4569-4581. [DOI:10.2147/OTT.S108485]
5. Mao Y, Nie Q, Yang Y, Mao G. Identification of co‑expression modules and hub genes of retinoblastoma via co‑expression analysis and protein‑protein interaction networks. Molecular medicine reports 2020; 22(2): 1155-1168. [DOI:10.3892/mmr.2020.11189]
6. Mirabelli P, Coppola L, Salvatore M. Cancer cell lines are useful model systems for medical rsearch. Cancers (Basel). 2019; 11(8): 1098. [DOI:10.3390/cancers11081098]
7. Oliveto S, Mancino M, Manfrini N, Biffo S. Role of microRNAs in translation regulation and cancer. World journal of biological chemistry 2017; 8(1): 45-56. [DOI:10.4331/wjbc.v8.i1.45]
8. Kulshreshtha R, Ferracin M, Wojcik SE, Garzon R, Alder H, Agosto-Perez FJ, Davuluri R, Liu CG, Croce CM, Negrini M, Calin GA, Ivan M. A microRNA signature of hypoxia. Molecular and cellular biology 2007; 27(5): 1859-1867. [DOI:10.1128/MCB.01395-06]
9. Lan H, Lu H, Wang X, Jin H. MicroRNAs as Potential Biomarkers in Cancer: Opportunities and challenges. BioMed research international 2015; 2015: 125094. [DOI:10.1155/2015/125094]
10. Soneson C, Love MI, Robinson MD. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000research 2015; 4: 1521. [DOI:10.12688/f1000research.7563.1]
11. Conway JR, Lex A, Gehlenborg N. UpSetR: an R package for the visualization of intersecting sets and their properties. Bioinformatics 2017; 33(18): 2938-2940. [DOI:10.1093/bioinformatics/btx364]
12. Li Z, Zhao K, Tian H. Integrated analysis of differential expression and alternative splicing of non-small cell lung cancer based on RNA sequencing. Oncology letters 2017; 14(2): 1519-1525. [DOI:10.3892/ol.2017.6300]
13. Carbon S, Ireland A, Mungall CJ, Shu S, Marshall B, Lewis S, Hub W, Presence Working Group. AmiGO: online access to ontology and annotation data. Bioinformatics 2009; 25(2): 288-289. [DOI:10.1093/bioinformatics/btn615]
14. Kanehisa M, Araki M, Goto S, Hattori M, Hirakawa M, Itoh M, Katayama T, Kawashima S, Okuda S, Tokimatsu T, Yamanishi Y. KEGG for linking genomes to life and the environment. Nucleic acids research 2008; 36(Database issue): D480-484. [DOI:10.1093/nar/gkm882]
15. Mi H, Muruganujan A, Thomas PD. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic acids research 2013; 41(Database issue): D377-386. [DOI:10.1093/nar/gks1118]
16. Xia J, Gill EE, Hancock REW. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nature protocols 2015;10(6):823-44. [DOI:10.1038/nprot.2015.052]
17. Chen SJ, Liao DL, Chen CH, Wang TY, Chen KC. Construction and analysis of protein-protein interaction network of heroin use disorder. Scientific reports 2019; 9(1): 4980. [DOI:10.1038/s41598-019-41552-z]
18. Soofi A, Taghizadeh M, Tabatabaei SM, Rezaei Tavirani M, Shakib H, Namaki S, Safari Alighiarloo N. Centrality analysis of protein-protein interaction networks and molecular docking prioritize potential drug-targets in type 1 diabetes. Iranian journal of pharmaceutical research 2020; 19(4): 121-134.
19. Li CY, Cai JH, Tsai JJP, Wang CCN. Identification of hub genes associated with development of head and neck squamous cell carcinoma by integrated bioinformatics analysis. Frontiers in oncology 2020; 10: 681. [DOI:10.3389/fonc.2020.00681]
20. Hsu SD, Lin FM, Wu WY, Liang C, Huang WC, Chan WL, Tsai WT, Chen GZ, Lee CJ, Chiu CM, Chien CH, Wu NH, Huang CY, Tsou AP, Huang HD. miRTarBase: a database curates experimentally validated microRNA-target interactions. Nucleic acids research 2011; 39(Database issue): D163-169. [DOI:10.1093/nar/gkq1107]
21. Sethupathy P, Corda B, Hatzigeorgiou AG. TarBase: A comprehensive database of experimentally supported animal microRNA targets. RNA 2006; 12(2): 192-197. [DOI:10.1261/rna.2239606]
22. Shayan S, Arashkia A, Bahramali G, Abdoli A, Nosrati MSS, Azadmanesh K. Cell type-specific response of colon cancer tumor cell lines to oncolytic HSV-1 virotherapy in hypoxia. Cancer cell international 2022; 22(1): 164. [DOI:10.1186/s12935-022-02564-4]
23. Bakhashab S, Lary S, Ahmed F, Schulten HJ, Bashir A, Ahmed FW, Al-Malki AL, Jamal HS, Gari MA, Weaver JU. Reference genes for expression studies in hypoxia and hyperglycemia models in human umbilical vein endothelial cells. G3 (Bethesda, Md) 2014; 4(11): 2159-2165. [DOI:10.1534/g3.114.013102]
24. Si W, Shen J, Zheng H, Fan W. The role and mechanisms of action of microRNAs in cancer drug resistance. Clinical epigenetics 2019; 11(1): 25. [DOI:10.1186/s13148-018-0587-8]
25. He X, Zhang J. Why Do Hubs Tend to Be Essential in Protein Networks? PLOS genetics 2006; 2(6): e88. [DOI:10.1371/journal.pgen.0020088]
26. Ardila DC, Aggarwal V, Singh M, Chattopadhyay A, Chaparala S, Sant S. Identifying molecular signatures of distinct modes of collective migration in response to the microenvironment using three-dimensional breast cancer models. Cancers (Basel) 2021; 13(6). 1429. [DOI:10.3390/cancers13061429]
27. Cal R, Castellano J, Revuelta-López E, Aledo R, Barriga M, Farré J, Vilahur G, Nasarre L, Hove-Madsen L, Badimon L, Llorente-Cortés V. Low-density lipoprotein receptor-related protein 1 mediates hypoxia-induced very low density lipoprotein-cholester