Evaluation to Develop a Bioinformatics Pipeline that Utilizes DNA Barcoding Methods to Identify Novel Genetic or Epigenetic Cancer Biomarkers Across Various Cancer Types
Saptarshi Biswas*, Naresh Pratap
Department of Biochemistry, Shri Venkateshwara University, Gajraula, Uttar Pradesh, India
Received: 6th Jun, 2025; Revised: 11th Aug, 2025; Accepted: 19th Aug, 2025; Available Online: 25th Sep, 2025
ABSTRACT
This study examines transcriptional changes caused by treatment using differential gene expression and enrichment analysis. Gene Ontology (GO) enrichment revealed notable increase in biological processes connected to ribonucleoprotein complex biogenesis, ribosome biogenesis, and rRNA metabolic processes, as well as cellular components including the nucleolus and organelle lumen. Pathway enrichment with KEGG and Reactome databases showed increased activity in stress-response processes driven by EIF2AK4/GCN2, translation elongation, rRNA processing, and ribosome production. The increase of MYC-controlled gene sets also became clear, implying a key role for MYC in propelling transcriptional and translational activity. The notable enrichment of MYC targets and MYC-serum response genes was confirmed by Gene Set Enrichment Analysis (GSEA). With several genes exhibiting statistically significant expression changes, differential expression analysis between control and treated conditions revealed different gene regulation patterns, suggesting particular pathways altered by the treatment. These data combined demonstrate that the therapy activates MYC-driven transcriptional programs and increases ribosome and RNA processing activities, implying improved cellular biosynthetic and proliferative potential.
Keywords: Gene Ontology, RNA processing, Ribosome biogenesis, MYC transcription factor, KEGG pathways, Reactome, GSEA, Differential gene expression, Translational control, Biosynthetic activity
How to cite this article: Saptarshi Biswas, Naresh Pratap. Evaluation to Develop a Bioinformatics Pipeline that Utilizes DNA Barcoding Methods to Identify Novel Genetic or Epigenetic Cancer Biomarkers Across Various Cancer Types. International Journal of Drug Delivery Technology. 2025;15(3):1028-33. doi: 10.25258/ijddt.15.3.18
REFERENCES
- Huang CC, Du M, Wang L. Bioinformatics Analysis for Circulating Cell-Free DNA in Cancer. Cancers (Basel). 2019 Jun 11;11(6):805. doi: 10.3390/cancers11060805. PMID: 31212602; PMCID: PMC6627444.
- Clark, Alexis J., and James W. Lillard, Jr. 2024. "A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology" Genes15, no. 8: 1036. https://doi.org/10.3390/genes15081036
- Merkel A, Esteller M. Experimental and bioinformatic approaches to studying DNA methylation in cancer. Cancers. 2022 Jan 11;14(2):349.
- Ashok G, Ramaiah S. A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery. Medical Oncology. 2022 Sep 29;39(12):206.
- Dakal TC, Dhakar R, Beura A, Moar K, Maurya PK, Sharma NK, Ranga V, Kumar A. Emerging methods and techniques for cancer biomarker discovery. Pathology-Research and Practice. 2024 Aug 29:155567.
- Mäbert K, Cojoc M, Peitzsch C, Kurth I, Souchelnytskyi S, Dubrovska A. Cancer biomarker discovery: current status and future perspectives. International journal of radiation biology. 2014 Aug 1;90(8):659-77.
- Jiménez‐Santos MJ, García‐Martín S, Fustero‐Torre C, Di Domenico T, Gómez‐López G, Al‐Shahrour F. Bioinformatics roadmap for therapy selection in cancer genomics. Molecular Oncology. 2022 Nov;16(21):3881-908.
- García-Giménez JL, editor. Epigenetic biomarkers and diagnostics. Academic Press; 2015 Dec 7.
- Zamora Atenza, C.; Anguera, G.; Riudavets Melià, M.; Alserawan De Lamo, L.; Sullivan, I.; Barba Joaquin, A.; Serra Lopez, J.; Ortiz, M.A.; Mulet, M.; Vidal, S.; et al. The integration of systemic and tumor PD-L1 as a predictive biomarker of clinical outcomes in patients with advanced NSCLC treated with PD-(L)1blockade agents. Cancer Immunol. Immunother.2022, 71, 1823–1835.
- Nakagawa, H.; Fujita, M. Whole genome sequencing analysis for cancer genomics and precision medicine. Cancer Sci.2018, 109, 513–522.
- Tipu, H.N.; Shabbir, A. Evolution of DNA sequencing. Coll. Physicians Surg. Pak.2015, 25, 210–215
- Putri, G.H.; Anders, S.; Pyl, P.T.; Pimanda, J.E.; Zanini, F. Analysing high-throughput sequencing data in Python with HTSeq 2.0. Bioinformatics2022, 38, 2943–2945.
- Moncada, R.; Barkley, D.; Wagner, F.; Chiodin, M.; Devlin, J.C.; Baron, M.; Hajdu, C.H.; Simeone, D.M.; Yanai, I. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Biotechnol.2020, 38, 333–342.
- Perampalam, P.; Dick, F.A. BEAVR: A browser-based tool for the exploration and visualization of RNA-seq data. BMC Bioinform.2020, 21, 221.