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      A Novel HOXA10-Associated 5-Gene–Based Prognostic Signature for Stratification of Short-term Survivors of Pancreatic Ductal Adenocarcinoma

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          Abstract

          Purpose:

          Despite the significant association of molecular subtypes with poor prognosis in patients with pancreatic ductal adenocarcinoma (PDAC), few efforts have been made to identify the underlying pathway(s) responsible for this prognosis. Identifying a clinically relevant prognosis-based gene signature may be the key to improving patient outcomes.

          Experimental Design:

          We analyzed the transcriptomic profiles of treatment-naïve surgically resected short-term survivor (STS) and long-term survivor (LTS) tumors (GSE62452) for expression and survival, followed by validation in several datasets. These results were corroborated by IHC analysis of PDAC-resected STS and LTS tumors. The mechanism of this differential survival was investigated using CIBERSORT and pathway analyses.

          Results:

          We identified a short-surviving prognostic subtype of PDAC with a high degree of significance (P = 0.018). One hundred thirty genes in this novel subtype were found to be regulated by a master regulator, homeobox gene HOXA10, and a 5-gene signature derived from these genes, including BANF1, EIF4G1, MRPS10, PDIA4, and TYMS, exhibited differential expression in STSs and a strong association with poor survival. This signature was further associated with the proportion of T cells and macrophages found in STSs and LTSs, demonstrating a potential role in PDAC immunosuppression. Pathway analyses corroborated these findings, revealing that this HOXA10-driven prognostic signature is associated with immune suppression and enhanced tumorigenesis.

          Conclusions:

          Overall, these findings reveal the presence of a HOXA10-associated prognostic subtype that can be used to differentiate between STS and LTS patients of PDAC and inform on the molecular interactions that play a role in this poor prognosis.

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          Most cited references80

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          Gene Ontology: tool for the unification of biology

          Genomic sequencing has made it clear that a large fraction of the genes specifying the core biological functions are shared by all eukaryotes. Knowledge of the biological role of such shared proteins in one organism can often be transferred to other organisms. The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing. To this end, three independent ontologies accessible on the World-Wide Web (http://www.geneontology.org) are being constructed: biological process, molecular function and cellular component.
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            Cytoscape: a software environment for integrated models of biomolecular interaction networks.

            Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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              KEGG: kyoto encyclopedia of genes and genomes.

              M Kanehisa (2000)
              KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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                Author and article information

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                Journal
                Clinical Cancer Research
                American Association for Cancer Research (AACR)
                1078-0432
                1557-3265
                September 15 2023
                July 11 2023
                September 15 2023
                July 11 2023
                : 29
                : 18
                : 3759-3770
                Article
                10.1158/1078-0432.CCR-23-0825
                37432996
                1ef01a1d-71b9-4661-bb2d-beecb6f618e3
                © 2023
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