Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
91 tokens/sec
GPT-4o
12 tokens/sec
Gemini 2.5 Pro Pro
o3 Pro
5 tokens/sec
GPT-4.1 Pro
15 tokens/sec
DeepSeek R1 via Azure Pro
33 tokens/sec
Gemini 2.5 Flash Deprecated
12 tokens/sec
2000 character limit reached

Exploring Gene Regulatory Interaction Networks and predicting therapeutic molecules for Hypopharyngeal Cancer and EGFR-mutated lung adenocarcinoma (2402.17807v1)

Published 27 Feb 2024 in q-bio.GN and cs.LG

Abstract: With the advent of Information technology, the Bioinformatics research field is becoming increasingly attractive to researchers and academicians. The recent development of various Bioinformatics toolkits has facilitated the rapid processing and analysis of vast quantities of biological data for human perception. Most studies focus on locating two connected diseases and making some observations to construct diverse gene regulatory interaction networks, a forerunner to general drug design for curing illness. For instance, Hypopharyngeal cancer is a disease that is associated with EGFR-mutated lung adenocarcinoma. In this study, we select EGFR-mutated lung adenocarcinoma and Hypopharyngeal cancer by finding the Lung metastases in hypopharyngeal cancer. To conduct this study, we collect Mircorarray datasets from GEO (Gene Expression Omnibus), an online database controlled by NCBI. Differentially expressed genes, common genes, and hub genes between the selected two diseases are detected for the succeeding move. Our research findings have suggested common therapeutic molecules for the selected diseases based on 10 hub genes with the highest interactions according to the degree topology method and the maximum clique centrality (MCC). Our suggested therapeutic molecules will be fruitful for patients with those two diseases simultaneously.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (99)
  1. Phoebe Chen YP, Chen F. Identifying targets for drug discovery using bioinformatics. Expert Opinion on Therapeutic Targets 2008;12(4):383–389.
  2. Hanif MA. Head and Neck Cancer-Bangladesh Perspective. Bangladesh Journal of Otorhinolaryngology 2018;24(1):1–2.
  3. An efficient deep learning model to categorize brain tumor using reconstruction and fine-tuning. Expert Systems with Applications 2023;p. 120534.
  4. Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture. Computers in Biology and Medicine 2023;p. 107789.
  5. Cancer Classification Utilizing Voting Classifier with Ensemble Feature Selection Method and Transcriptomic Data. Genes 2023;14(9):1802.
  6. MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs. arXiv preprint arXiv:240213277 2024;.
  7. Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction. Journal of Big Data 2024;11(1):1–44.
  8. Sanders O, Pathak S. Hypopharyngeal Cancer 2022;.
  9. Human papillomaviruses in head and neck cancer: 8 year-survival-analysis of 73 patients. Cancer letters 2005;218(2):199–206.
  10. Prevalence of human papillomavirus in laryngeal and hypopharyngeal squamous cell carcinomas in northern Spain. Cancer epidemiology 2015;39(1):37–41.
  11. Human papillomavirus is a favourable prognostic factor in tonsillar cancer and its oncogenic role is supported by the expression of E6 and E7. Molecular oncology 2007;1(3):350–355.
  12. EGFR-mutated squamous cell lung cancer and its association with outcomes. Frontiers in oncology 2021;11:2262.
  13. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 2018;68(6):394–424.
  14. Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Systems with Applications 2022;205:117695.
  15. World cancer report: Cancer research for cancer prevention. International Agency for Research on Cancer. World Health Organization: Geneva, Switzerland 2020;.
  16. Score based risk assessment of lung cancer and its evaluation for Bangladeshi people. Asian Pacific Journal of Cancer Prevention 2014;15(17):7021–7027.
  17. Incidence of human papilloma virus in lung cancer. Lung Cancer 2009;65(1):13–18.
  18. Differentiating head and neck carcinoma from lung carcinoma with an electronic nose: a proof of concept study. European Archives of Oto-Rhino-Laryngology 2016;273(11):3897–3903.
  19. Indications and approach to surgical resection of lung metastases. Journal of surgical oncology 2010;102(2):187–195.
  20. Surgical intervention for pulmonary metastases. Deutsches Ärzteblatt international 2012;109(40):652.
  21. Surgical treatment for metastatic malignancies. Pulmonary metastasis: indications and outcomes. International journal of clinical oncology 2005;10(2):81–85.
  22. Surgical treatment of pulmonary metastasis: report from a tertiary care center. Asian Cardiovascular and Thoracic Annals 2018;26(4):296–301.
  23. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer 2015;136(5):E359–E386.
  24. Lung metastases in newly diagnosed hypopharyngeal cancer: a population-based study. European Archives of Oto-Rhino-Laryngology 2021;p. 1–8.
  25. Hypopharyngeal Cancer Information;. https://cancer.ca/en/cancer-information/cancer-types/hypopharyngeal/if-cancer-spreads.
  26. Genesis: cluster analysis of microarray data. Bioinformatics 2002;18(1):207–208.
  27. Importance of replication in microarray gene expression studies: statistical methods and evidence from repetitive cDNA hybridizations. Proceedings of the National Academy of Sciences 2000;97(18):9834–9839.
  28. Identification of molecular biomarkers and key pathways for esophageal carcinoma (EsC): a bioinformatics approach. BioMed Research International 2022;2022.
  29. Network-based identification genetic effect of SARS-CoV-2 infections to Idiopathic pulmonary fibrosis (IPF) patients. Briefings in bioinformatics 2021;22(2):1254–1266.
  30. Identification of biomarkers and pathways for the SARS-CoV-2 infections that make complexities in pulmonary arterial hypertension patients. Briefings in Bioinformatics 2021;22(2):1451–1465.
  31. Screening and identification of potential target genes in head and neck cancer using bioinformatics analysis. Oncology Letters 2019;18(3):2955–2966.
  32. Identification of key genes for HNSCC from public databases using bioinformatics analysis. Cancer Cell International 2021;21:1–13.
  33. Jin Y, Yang Y. Identification and analysis of genes associated with head and neck squamous cell carcinoma by integrated bioinformatics methods. Molecular Genetics & Genomic Medicine 2019;7(8):e857.
  34. Identification of hub genes associated with development of head and neck squamous cell carcinoma by integrated bioinformatics analysis. Frontiers in oncology 2020;10:681.
  35. Identification of potential biomarkers and survival analysis for head and neck squamous cell carcinoma using bioinformatics strategy: a study based on TCGA and GEO datasets. BioMed research international 2019;2019.
  36. Exploration of prognostic biomarkers for lung adenocarcinoma through bioinformatics analysis. Frontiers in Genetics 2021;12:647521.
  37. Ni KW, Sun GZ. The identification of key biomarkers in patients with lung adenocarcinoma based on bioinformatics. Math Biosci Eng 2019;16(6):7671–7687.
  38. Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma. PeerJ 2019;7:e7313.
  39. Elevated mRNA Levels of AURKA, CDC20 and TPX2 are associated with poor prognosis of smoking related lung adenocarcinoma using bioinformatics analysis. International Journal of Medical Sciences 2018;15(14):1676.
  40. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic acids research 2002;30(1):207–210.
  41. Davis S, Meltzer PS. GEOquery: a bridge between the Gene Expression Omnibus (GEO) and BioConductor. Bioinformatics 2007;23(14):1846–1847.
  42. Clough E, Barrett T. The gene expression omnibus database 2016;p. 93–110.
  43. Smyth GK. Limma: linear models for microarray data 2005;p. 397–420.
  44. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 1995;57(1):289–300.
  45. Oliveros JC. VENNY. An interactive tool for comparing lists with Venn Diagrams. http://bioinfogp cnb csic es/tools/venny/index html 2007;.
  46. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 2005;102(43):15545–15550.
  47. Doms A, Schroeder M. GoPubMed: exploring PubMed with the gene ontology. Nucleic acids research 2005;33(suppl_2):W783–W786.
  48. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic acids research 2000;28(1):27–30.
  49. The reactome pathway knowledgebase. Nucleic acids research 2018;46(D1):D649–D655.
  50. Nishimura D. BioCarta. Biotech Software & Internet Report: The Computer Software Journal for Scient 2001;2(3):117–120.
  51. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic acids research 2018;46(D1):D661–D667.
  52. Detection of molecular signatures and pathways shared in inflammatory bowel disease and colorectal cancer: A bioinformatics and systems biology approach. Genomics 2020;112(5):3416–3426.
  53. miRTarBase: a database curates experimentally validated microRNA–target interactions. Nucleic acids research 2011;39(suppl_1):D163–D169.
  54. RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse. Database 2015;2015.
  55. NetworkAnalyst 3.0: a visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic acids research 2019;47(W1):W234–W241.
  56. NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data. Nature protocols 2015;10(6):823–844.
  57. Large-scale mapping of human protein–protein interactions by mass spectrometry. Molecular systems biology 2007;3(1):89.
  58. The MIPS mammalian protein–protein interaction database. Bioinformatics 2005;21(6):832–834.
  59. Network-based identification of genetic factors in ageing, lifestyle and type 2 diabetes that influence to the progression of Alzheimer’s disease. Informatics in Medicine Unlocked 2020;19:100309.
  60. Ben-Hur A, Noble WS. Kernel methods for predicting protein–protein interactions. Bioinformatics 2005;21(suppl_1):i38–i46.
  61. STRING: a database of predicted functional associations between proteins. Nucleic acids research 2003;31(1):258–261.
  62. The STRING database in 2011: functional interaction networks of proteins, globally integrated and scored. Nucleic acids research 2010;39(suppl_1):D561–D568.
  63. InnateDB: systems biology of innate immunity and beyond—recent updates and continuing curation. Nucleic acids research 2013;41(D1):D1228–D1233.
  64. The use of Gene Ontology terms for predicting highly-connected’hub’nodes in protein-protein interaction networks. BMC systems biology 2008;2(1):1–14.
  65. cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC systems biology 2014;8(4):1–7.
  66. Identification of hub genes associated with tuberculous pleurisy by integrated bioinformatics analysis. Frontiers in Genetics 2021;12:730491.
  67. Identification and validation of hub genes associated with hepatocellular carcinoma via integrated bioinformatics analysis. Frontiers in Oncology 2021;11:614531.
  68. Identifying hubs in protein interaction networks. PloS one 2009;4(4):e5344.
  69. Habib N. Drug design and analysis for bipolar disorder and associated diseases: A bioinformatics approach. Network Biology 2017;7(2):41.
  70. GeneMANIA prediction server 2013 update. Nucleic acids research 2013;41(W1):W115–W122.
  71. Frishman D, Valencia A. Modern genome annotation. The BioSapiens Network 2009;p. 213–38.
  72. Network inference and reconstruction in bioinformatics. In: Encyclopedia of Bioinformatics and Computational Biology Elsevier; 2019.p. 805–813.
  73. A general co-expression network-based approach to gene expression analysis: comparison and applications. BMC systems biology 2010;4(1):1–21.
  74. Design protein-protein interaction network and protein-drug interaction network for common cancer diseases: A bioinformatics approach. Informatics in Medicine Unlocked 2020;18:100311.
  75. Reconstruction of gene co-expression network from microarray data using local expression patterns. BMC bioinformatics 2014;15:1–14.
  76. Identification of drug and protein-protein interaction network among stress and depression: A bioinformatics approach. Informatics in Medicine Unlocked 2023;37:101174.
  77. GeneMANIA update 2018. Nucleic acids research 2018;46(W1):W60–W64.
  78. Bioinformatic identification of candidate biomarkers and related transcription factors in nasopharyngeal carcinoma. World journal of surgical oncology 2019;17(1):1–10.
  79. Integrative analysis of transcription factor combinatorial interactions using a bayesian tensor factorization approach. Frontiers in genetics 2017;8:140.
  80. A System Biology and Bioinformatics Approach to Determine the Molecular Signature, Core Ontologies, Functional Pathways, Drug Compounds in Between Stress and Type 2 Diabetes. In: International Work-Conference on Bioinformatics and Biomedical Engineering Springer; 2023. p. 320–331.
  81. Trimming of mammalian transcriptional networks using network component analysis. BMC bioinformatics 2010;11:1–11.
  82. Widespread evidence of cooperative DNA binding by transcription factors in Drosophila development. Nucleic acids research 2013;41(17):8237–8252.
  83. NetworkAnalyst-integrative approaches for protein–protein interaction network analysis and visual exploration. Nucleic acids research 2014;42(W1):W167–W174.
  84. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 2010;26(19):2438–2444.
  85. León LE, Calligaris SD. Visualization and Analysis of MiRNA–Targets Interactions Networks 2017;p. 209–220.
  86. Experimental validation of miRNA targets. Methods 2008;44(1):47–54.
  87. The roles of microRNA in cancer and apoptosis. Biological Reviews 2009;84(1):55–71.
  88. Profiling of regulatory microRNA transcriptomes in various biological processes: a review. Journal of applied genetics 2010;51:501–507.
  89. Carthew RW. Gene regulation by microRNAs. Current opinion in genetics & development 2006;16(2):203–208.
  90. DIANA-TarBase v8: a decade-long collection of experimentally supported miRNA–gene interactions. Nucleic acids research 2018;46(D1):D239–D245.
  91. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic acids research 2016;p. gkw943.
  92. Gene-disease network analysis reveals functional modules in mendelian, complex and environmental diseases. PloS one 2011;6(6):e20284.
  93. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC bioinformatics 2013;14(1):1–14.
  94. DSigDB: drug signatures database for gene set analysis. Bioinformatics 2015;31(18):3069–3071.
  95. Consortium GO. The Gene Ontology (GO) database and informatics resource. Nucleic acids research 2004;32(suppl_1):D258–D261.
  96. KEGG for linking genomes to life and the environment. Nucleic acids research 2007;36(suppl_1):D480–D484.
  97. WikiPathways: connecting communities. Nucleic acids research 2021;49(D1):D613–D621.
  98. The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic acids research 2010;38(suppl_2):W214–W220.
  99. Transcription factor and microRNA co-regulatory loops: important regulatory motifs in biological processes and diseases. Briefings in bioinformatics 2015;16(1):45–58.
Citations (4)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets