Gene co-expression analysis and Network biology studies in Indian population reveals functional similarities between Gastric cancer and other metabolic disorders

Authors

  • Blessantoli Mohandas Department of Biochemistry, School of Agricultural and Biosciences, Karunya Institute of Technology & Sciences, Coimbatore-614114, India
  • J Jannet Vennila MCC MRF Innovation Park, Madras Christian College, Chennai-600059, India
  • Nikhil Ruban Department of Humanities and Social sciences, Birla Institute of Technology and Sciences, Pilani Campus, Rajasthan-333 031, India

DOI:

https://doi.org/10.3329/bjms.v21i3.59586

Keywords:

Gastric Cancer; Network biology; Microarray Data Analysis; Metabolic disorders and Gastric Cancer; Gene expression profiling

Abstract

Objective: Gastric cancer (GC) is a multifactorial disease and known to have been associated with metabolic disorders. Gene expression profiling among various GC populations will help to strategize diagnosis and treatment. The current study employed microarray data analysis (MDA) and network biology methods to understand the significant genes in a GC Indian population and its association with other metabolic disorders.

Materials and Methods: The microarray datasets of GC Indian population (Bangalore) was retrieved from Gene Expression Omnibus (GEO), normalized and analyzed using GeneSpring. With the fold change of ± 1.5, the differentially expressed genes (DEG) were identified. An interactome was built to study interactions and generate gene clusters. Statistical (centrality) parameters were applied to identify highly connected clusters followed by functional enrichment to identify significant pathways associated with the GC genes.

Results and Discussion: MDA identified 7181 DEGs (3984 up regulated genes and 3197 down regulated) and the interactome yielded 16552 interactions and two sub clusters. Cluster 1 was found to be statistically fit. The functional characteristics of the significant genes in this cluster revealed their association with adrenal cortex hormone insufficiency, thyroid disorders and deficiencies in kidney water resorption.

Conclusion: It is inferred from our study that, deficiency in Thyroid, Adrenal hormones and Antidiuretic hormone (ADH) functions has fair share in the prognosis and pathogenesis of GC Indian population. Henceforth, GC should not be viewed as separate entity in the series of cancers and gene expression profiling will help in improvising personalized medicine.

Bangladesh Journal of Medical Science Vol. 21 No. 03 July’22 Page: 688-693

Downloads

Download data is not yet available.
Abstract
2
PDF
6

Downloads

Published

2022-05-21

How to Cite

Mohandas, B. ., Vennila, J. J. ., & Ruban, N. . (2022). Gene co-expression analysis and Network biology studies in Indian population reveals functional similarities between Gastric cancer and other metabolic disorders. Bangladesh Journal of Medical Science, 21(3), 688–693. https://doi.org/10.3329/bjms.v21i3.59586

Issue

Vol. 21 No. 3 (2022)

Section

Original Articles

License

Copyright (c) 2022 Blessantoli Mohandas, J Jannet Vennila, Nikhil Ruban

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish in the Bangladesh Journal of Medical Science agree to the following terms that:

  1. Authors retain copyright and grant Bangladesh Journal of Medical Science the right of first publication of the work.

  2. Creative Commons Licence
    Articles in Bangladesh Journal of Medical Science are licensed under a Creative Commons Attribution 4.0 International License CC BY-4.0.This license permits use, distribution and reproduction in any medium, provided the original work is properly cited.
  3. Authors are able to enter into separate, additional contractual arrangements for the distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  4. Authors are permitted to post their work online (e.g., in institutional repositories or on their website) as it can lead to productive exchanges, as well as greater citation of published work.