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Identification of Key Genes and Associated Pathways In bioRxiv preprint Identification of key genes and associated pathways in neuroendocrine tumors through bioinformatics analysis and predictions of small drug molecules (which wasnotcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. 1 2 3 4 5 Praveenkumar Devarbhavi , Basavaraj Vastrad , Anandkumar Tengli , Chanabasayya Vastrad* Iranna Kotturshetti doi: https://doi.org/10.1101/2020.12.23.424165 1. Department of Endocrinology and Metabolism, Subbaiah Institute of Medical Sciences and Research Centre, Shimoga 577222, Karnataka, India 2. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 3. Department of Pharmaceutical Chemistry, JSS College of Pharmacy, Mysuru and JSS Academy of Higher Education & Research, Mysuru- 570015, Karnataka, India. 4. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karanataka, India. ; 5. Department of Ayurveda, Rajiv Gandhi Education Society`s Ayurvedic Medical College, Ron 562209, Karanataka, India. this versionpostedDecember24,2020. The copyrightholderforthispreprint * Chanabasayya Vastrad Email: [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, bioRxiv preprint Dharwad 580001 , Karanataka, India (which wasnotcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. Abstract doi: https://doi.org/10.1101/2020.12.23.424165 Neuroendocrine tumor (NET) is one of malignant cancer and is identified with high morbidity and mortality rates around the world. With indigent clinical outcomes, potential biomarkers for diagnosis, prognosis and drug target are crucial to explore. The aim of this study is to examine the gene expression module of NET and to identify potential diagnostic and prognostic biomarkers as well as to find out new drug target. The differentially expressed genes (DEGs) identified from GSE65286 dataset was used for pathway enrichment analyses and gene ontology (GO) enrichment analyses and protein - protein interaction (PPI) analysis and module analysis. Moreover, miRNAs and transcription factors (TFs) that regulated the up and down regulated genes were predicted. Furthermore, validation of hub genes was performed. Finally, molecular docking studies were performed. DEGs were identified, including 453 down regulated and 459 up regulated genes. Pathway ; and GO enrichment analysis revealed that DEGs were enriched in sucrose degradation, creatine biosynthesis, anion this versionpostedDecember24,2020. transport and modulation of chemical synaptic transmission. Important hub genes and target genes were identified through PPI network, modules, target gene - miRNA network and target gene - TF network. Finally, survival analyses, receiver operating characteristic (ROC) curve and RT-PCR validated the significant difference of ATP1A1, LGALS3, LDHA, SYK, VDR, OBSL1, KRT40, WWOX, NINL and PPP2R2B between metastatic NET and normal controls. In conclusion, the DEGs and hub genes with their regulatory elements identified in this study will help us understand the molecular mechanisms underlying NET and provide candidate targets for future research. Keywords: differentially expressed genes; neuroendocrine tumor; bioinformatics; hub gene; protein–protein interaction The copyrightholderforthispreprint bioRxiv preprint Introduction A small intestine neuroendocrine tumors (NET) is common cancer of small bowel [1]. The absence of early diagnostic and (which wasnotcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. biomarkers are the main reasons for death caused by NET. The 5‑year survival rate across all stages of NET is only 70- doi: 100% [2]. In the past several decades, numerous attempts have been taken to disclose the molecular pathogenesis of NET, https://doi.org/10.1101/2020.12.23.424165 and to advance the patient prognosis through many therapeutic strategies. However, narrow progress has been made to lengthen the survival and to diminish the mortality. For patients, surgery, trans arterial embolisation and chemotherapy offer only limited options for NET treatment [3-4]. However, due to surgical therapy limitations, progressive tumors can reappear even after complete surgical resection; maximum patients with NET are not suitable for surgical resection and fewer than 23.8% of patients respond to conventional chemotherapy [5]. In addition, the clinical success of NET always compromised due to initial metastasis and chemo resistance. Consequently, extensive investigations of potential diagnosis biomarkers and identification of new therapeutic targets for NET patients are urgently needed. ; Recently, the gene expression profile chip, a high throughput and efficient technique, has been extensively used in an this versionpostedDecember24,2020. array of disease research fields to explain the association between disease and genes and pathways, and provide the important clues for the progression of the NET [6]. For example, alteration in CDKN1B was linked with development of NET [7]. Genes such as NAP1L1, MAGE-D2, MTA1, MIB-1, p53, and bcl-2 were responsible for pathogenesis of NET [8-9]. High expression of CDX-2 was liable for development of NET [10]. Loss of tumor suppressor gene TCEB3C was linked with development of NET [11]. Pathways such as mTOR signaling pathway [12], notch signaling [13], phosphatidylinositol 3-kinase/Akt signaling [14], sonic hedgehog-gli1 signaling pathway [15] and GPCR pathways [16] were responsible for pathogenesis of NET. Thus, more work is needed to discover the underlying molecular mechanisms in NET. The copyrightholderforthispreprint In this study, we diagnosed important genes in NET by combining bioinformatics analyses. In this study, we diagnosed important genes in NET by combining bioinformatics analyses. Differentially expressed genes (DEGs), pathways, and gene ontology (GO) terms associated with NET were investigated. Subsequently, the identification of hub genes from protein–protein interactions (PPIs) network, modules analysis, construction of target gene ‐ miRNA network and target gene ‐ TF network. Furthermore, survival analyses, receiver operating characteristic (ROC) curve and RT-PCR were bioRxiv preprint carried out for validation of hub genes. Finally, molecular docking studies was performed. We investigated the potential candidate biomarkers for their utility in diagnosis, prognosis, and drug targeting in NET. (which wasnotcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. Materials and methods doi: https://doi.org/10.1101/2020.12.23.424165 Microarray data Gene expression profile GSE65286 was downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo) [17] database. GSE65286 included 33 patients with metastatic NET and on 10 normal controls. The expression profile was provided on GPL4133 Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature Number version). DEG screening The original txt files were downloaded and classified as patients with metastatic NET and normal controls. The limma ; this versionpostedDecember24,2020. package of Bioconductor (http://www.bioconductor.org/) [18] was used for data standardization and transforming raw data into expression values. The significance analysis of the empirical Bayes methods within limma package was applied to diagnose DEGs between patients with metastatic NET and on normal controls. The genes with the following cutoff criteria were considered as the significant DEGs: p < 0.05, and |logFC|> 3.01 for up regulated genes and |logFC|> -2.75 for down regulated genes. Pathway enrichment analysis Pathway enrichment analysis was used to diagnose the potential functional and metabolic pathways associated with DEGs. BIOCYC (https://biocyc.org/) [19], Kyoto Encyclopedia of Genes and Genomes (KEGG) The copyrightholderforthispreprint (http://www.genome.jp/kegg/pathway.html) [20], Pathway Interaction Database (PID) (https://wiki.nci.nih.gov/pages/viewpage.action?pageId=315491760) [21], REACTOME (https://reactome.org/) [22], GenMAPP (http://www.genmapp.org/) [23], MSigDB C2 BIOCARTA (v6.0) (http://software.broadinstitute.org/gsea/msigdb/collections.jsp) [24], PantherDB (http://www.pantherdb.org/) [25], Pathway Ontology (http://www.obofoundry.org/ontology/pw.html) [26] and Small Molecule Pathway Database (SMPDB) bioRxiv preprint (http://smpdb.ca/) [27] were a collection of databases that store a large number of information about genomes, biological pathways, diseases, chemical substances, and drugs. We performed pathway enrichment analysis by ToppCluster (which wasnotcertifiedbypeerreview)istheauthor/funder.Allrightsreserved.Noreuseallowedwithoutpermission. (https://toppcluster.cchmc.org/) [28]. ToppCluster is a commonly used online biological information database that provides doi: comprehensive pathway interpretations. p < 0.05 was considered statistically significant. https://doi.org/10.1101/2020.12.23.424165 Gene ontology (GO) enrichment analysis The GO (http://www.geneontology.org) [29] database associated three categories such as biological process (BP), cellular component (CC), and molecular function (MF). ToppCluster (https://toppcluster.cchmc.org/) [28] provides a set of functional annotation tools to analyze the biological roles of genes. In this study, GO terms were analyzed using the DAVID online tool with the enrichment threshold of P<0.05. Protein–protein interaction (PPI) network construction
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