Enoch_NISEB_Presentation_Yeah_Completed.pptx

NISEB Presentation

1. Identification of Hub Genes for Diagnostic and Prognostic Biomarkers and Potential Therapeutic Drugs for Pancreatic Ductal Adenocarcinoma (PDAC) using Bioinformatics Analysis Enoch Olanrewaju Akinleye 1,2 , E mmanuel Oladiran Amos 2 1 Department of Pharmacology and Toxicology , Faculty of Veterinary Medicine , University of Ilorin , Ilorin , Nigeria 2 D epartment of Biochemistry , Faculty of Life Sciences , University of Ilorin , Ilorin , Nigeria Presented by: Enoch Olanrewaju Akinleye Corresponding Author : E noch Olanrewaju Akinleye , E mail : [email protected] , [phone]

2. Outline Introduction and Literature Review Statement of the problem Justification for this Study Objective of the Study Materials and Methods Results and Discussion Conclusion 2

3. Introduction and Literature Review Most pancreatic neoplasms arise from the exocrine cells of the pancreas, and over 90 percent of pancreatic cancer diagnoses turn out to be pancreatic ductal adenocarcinoma (PDAC) ( Grossberg et al., 2020 ) Sadly, most cases of PDAC only show clear symptoms when the condition has progressed to an advanced stage and metastasis has already occurred, leading to a late diagnosis and diminished chances of survival (Wood et al., 2022 ). 3

4. Introduction and Literature Review There is therefore an urgent need for gene-based molecular markers to enable a better understanding of PDAC genetic triggers, progression patterns, and drug targets in order to improve early detection and diagnosis, prognosis, treatment outcomes and personalized treatment strategies. 4

5. Statement of the Problem The position of the pancreas deep in the abdomen and under the stomach makes early detection of PDAC using conventional cancer screening and diagnostic processes difficult (Halbrook et al., 2023 ). There is therefore a need to identify gene-based prognostic biomarkers to enhance early detection of PDAC. Also, the delicate nature of the organ renders the use of surgery unfeasible for over 80 percent of patients (Cao et al., 2023 ), making chemotherapy the only feasible option for most patients. There is, therefore, a need to identify more drugs for the treatment of PDAC. 5

6. Justification of the Study Mutations in a plethora of genes, most notably NF-kB, CXCR4, KRAS, CDK2NA, SMAD4, TP53, STK11 and TGF-ß, have been linked to PDAC development, progression, metastasis and acquisition of drug resistance (Grant et al., 2016; Quiñonero et al., 2019). However, there is a need to identify more biomarkers and hub genes to better understand PDAC progression. There are also limited studies which advance from identifying these hub genes to determining specific drug agents that can counter such consequences. et al., 2018; Shang et al., 2019; Tang et al., 2018) 6

7. Overall Objective of the Study The objective of this study was to identify hub genes associated with PDAC in order to establish their relevance in PDAC diagnosis and prognosis and to screen drug targets that will elicit potency against PDAC in silico . 7

8. Materials and Methods T hree gene expression profiles (GSE28735, GSE15471, and GSE183795) were acquired from the GEO database ( https://www.ncbi.nlm.nih.gov/geo / ) Differential expression analysis of genes (DEGs) was conducted using the GEO2R platform ( https://www.ncbi.nlm.nih.gov/geo/geo2r / ) to identify common DEGs (Barrett et al., 2012 ). To investigate the function of common DEGs, gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses was conducted. GO enrichment analysis encompassed biological processes (BP), cellular components (CC), and molecular functions (MF). 8

9. Materials and Methods The STRING ( https://string-db.org/ ) database (v 11.5) was used to assess the interrelationships among common DEGs by constructing Protein-protein Interaction ( PPI ) networks ( Szklarczyk et al., 2023 ). To identify these important nodes, the cytoHubba plugin in the Cytoscape tool was utilized, and the top 15 genes, ranked via the degree of connectivity and betweenness centrality, were designated as hub genes To identify hub genes that are significantly associated with patient survival, GEPIA ( http://gepia.cancer-pku.cn/ ) was employed to produce Kaplan-Meier estimates (Tang et al., 2017) 9

10. Materials and Methods The Drug-gene interactions for the hub genes was obtained from the Drug Gene Interaction database ( DGIdb ) (v4.2.0 ) and the connectivity map ( CMap ) database (https://clue.io/) was additionally ultilized to screen for additional novel potential therapeutic drugs for PDAC treatment (Musa et al., 2018 ). Molecular docking analyses were employed to predict the binding affinity of drugs with their respective targets, as suggested by the STITCH database. Additionally, this analysis identified crucial amino acids involved in drug-target interactions, providing valuable insights for future PDAC drug design. 10

11. Results and Discussion The intersection of the three datasets revealed 350 common DEGs, consisting of 258 upregulated and 92 downregulated genes , Figure 1 : Volcano plots of the differentially expressed genes (DEGs) from the three datasets ( GSE28735, GSE15471, and GSE183795) 11

12. Results and Discussion . Figure 2 : Common differentially expressed genes (DEGs) from the three datasets ( GSE28735, GSE15471, and GSE183795) 12

13. Results and Discussion GO functional enrichment analysis showed enrichment of extracellular matrix (ECM) organization, wound healing, cell adhesion, and collagen fibril organization. This underscores the importance of the tumor microenvironment and cell-matrix interactions in PDAC. These processes are integral to cancer invasion, metastasis, and resistance to therapies (Musa et al., 2018 ). Figure 3 : GO functional enrichment analysis of the common differentially expressed genes (DEGs) (Biological Processes) 13

14. Results and Discussion The KEGG pathway analysis highlighted three enriched pathways—ECM Receptor interaction, focal adhesion, and complement and coagulation cascades. These pathways are implicated in PDAC progression and align with the aggressive nature of the disease (Cao et al., 2023) ECM Receptor interaction and focal adhesion pathways are closely tied to cell adhesion, migration, and invasion, processes essential for cancer metastasis. Figure 4 : KEGG functional enrichment analysis of the common differentially expressed genes (DEGs) 14

15. Results and Discussion The hub genes identified in this analysis were FN1 (fibronectin-1 protein), ALB (albumin), COL1A1 (collagen type I alpha 1 chain), MMP2 (matrix metallopeptidase 2), EGF (epidermal growth factor), POSTN ( periostin ), COL3A1 (collagen type III alpha 1 chain), SPP1 (secreted phosphoprotein 1), LOX ( lysyl oxidase), THBS2 (thrombospondin 2), MMP1 (matrix metallopeptidase 1), BGN ( biglycan ), ITGB5 (integrin beta 5), FBN1 ( fibrillin 1), SPARC (secreted protein acidic and cysteine-rich ). The hub genes identified were genes majorly involved in ECM remodeling, growth factor signaling, and cell-matrix interactions. ECM interactions are vital for tumor invasion, metastasis, and microenvironmental crosstalk, emphasizing their potential as therapeutic targets 15

16. Results and Discussion Figure 5 : Hub genes identified using the cytoHubba plugin. Their annotation was provided by the stringApp plugin in cytoscape . 16

17. Results and Discussion After further validations of the hub genes using Kaplain Meyer survival analysis, strikingly, three hub genes, namely LOX , MMP1 , and ITGB5 , exhibited noteworthy correlations with overall survival (OS) in PDAC patients Figure 6: Overall survival curves based on LOX , MMP1 ITGB5 in 178 PDAC patient 17

18. Results and Discussion Using the DGIdb , 7 drugs were found that targeted specific hub genes while the CMap database led to the identification of additional 12 active small molecules with potential for PDAC therapy Among these small molecules, Epigallocatechin, Scriptaid , Apicidin , Trichostatin A, Panobinostat , Vorinostat , Cilengitide , Marimastat and BI 2536 have showed anti-tumor effect in some published researches ( Wood et al., 2022). 18

19. Results and Discussion Molecular docking analysis provided insights into the binding affinities of these drugs to proteins encoded by hub genes and predicted targets. Notably , Obovatal exhibited a high binding affinity against MMP2, Cochinchinenin C against Albumin, and Cilengitide , an ITGB5 inhibitor, displayed potential as a PDAC drug with a strong binding affinity. Additionally, Scriptaid , Apicidin , and Vorinostat displayed high binding affinities against HDAC1, HDAC2, HDAC6, and HDAC8 respectively, suggesting their potential as drugs targeting histone deacetylases. 19

20. Results and Discussion S/N Drug Target Name of Drug Database of Identification Binding Affinity (kcal/ mol ) 1 Matrix Metalloproteinase 2 (MMP2) Obovatal DGIdb -8.3 2 Integrin Subunit Beta 5 (ITGB5) Cilengitide DGIdb -9.2 3 Albumin (ALB) Cochinchinenin C DGIbd -8.3 4 Histone Deacetylase (HDAC1) Scriptaid CMap -6.8 5 Histone Deacetylase (HDAC2) Vorinostat CMap -8.2 6 Histone Deacetylase (HDAC6) Scriptaid CMap -7.7 7 Histone Deacetylase (HDAC8) Vorinostat CMap -7.7 8 Polo-like kinase 1 (PLK1) BI 2536 CMap -6.9 Table 1: Potential drugs for PDAC treatment from the DGIdb and CMap database, with binding affinity from Molecular Docking analysis 20

21. Thank you for listening 21

22. Conclusion In summary, in this study, LOX , MMP1 and ITGB5 were found as potential diagnosis and prognostic biomarker for PDAC. Additionally, Obovatal , Cochinchinenin C, Cilengitide , Scriptaid , Apicidin , and Vorinostat were identified as potential drugs for PDAC therapy . These findings expand the pool of potential therapeutic options and provide a foundation for further experimental validation and drug development efforts. 22