Transcription profiling identifies genes involved in severe asthma

  • Nor Ezleen Qistina Ahmad UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Centre, Jalan Ya’acob Latiff, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Malaysia
  • Norziha Zainul Abidin UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Centre, Jalan Ya’acob Latiff, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Malaysia
  • Husna Mohd Noor UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Centre, Jalan Ya’acob Latiff, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Malaysia
  • Roohaida Othman Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
  • Nursuhadah Mohamed Yusof UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Centre, Jalan Ya’acob Latiff, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Malaysia
  • Hasmawati Yahaya UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Centre, Jalan Ya’acob Latiff, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Malaysia
  • Rahman A Jamal A UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Centre, Jalan Ya’acob Latiff, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Malaysia
  • Roslan Harun UKM Medical Molecular Biology Institute, Universiti Kebangsaan Malaysia, UKM Medical Centre, Jalan Ya’acob Latiff, Bandar Tun Razak, 56000 Cheras, Kuala Lumpur, Malaysia

Abstract

Severe asthma is a chronic respiratory disease with heterogeneous symptoms. This study aimed to determine the gene expression pattern and pathways related to severe asthma and subsequently identify potential predictor for steroid-resistant asthma. Peripheral blood B lymphocytes were isolated from subjects with severe steroid-resistant (n=7) and severe steroid-dependent (n=7) asthma. Total RNA was extracted from the B lymphocytes and subjected to microarray experiment. Data were analyzed using GeneSpring GX software for differential gene expression analysis and gene set enrichment analysis (GSEA) was used to analyze disease pathways. The prediction model was generated using Prophet software and real-time polymerase chain reaction (PCR) was performed to validate the microarray gene expression. 307 genes were differentially expressed between both groups with p<0.001 using unpaired t-test. Six genes were selected as steroid-resistant predictor based on a particular selection criteria. Class predictors were identified with a predictive accuracy of 93%. This study has provided a better insight into the expression pattern and pathways of severe asthma and provided potential prognosis biomarkers to discriminate between severe steroid-resistant and steroid-sensitive asthma.

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Published
2018-12-28
How to Cite
QISTINA AHMAD, Nor Ezleen et al. Transcription profiling identifies genes involved in severe asthma. International Journal of Research in Pharmaceutical Sciences, [S.l.], v. 9, n. SPL2, p. 27-35, dec. 2018. ISSN 0975-7538. Available at: <https://pharmascope.org/index.php/ijrps/article/view/1736>. Date accessed: 24 jan. 2019. doi: https://doi.org/10.26452/ijrps.v9iSPL2.1736.
Section
Original Articles
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