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PRPF40A As a Potential Diagnostic and Prognostic Marker Is Upregulated in Pancreatic Cancer Tissues and Cell Lines: an Integrated Bioinformatics Data Analysis

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OncoTargets and Therapy Dovepress open access to scientific and medical research Open Access Full Text Article ORIGINAL RESEARCH PRPF40A as a potential diagnostic and prognostic marker is upregulated in pancreatic cancer tissues and cell lines: an integrated bioinformatics data analysis This article was published in the following Dove Press journal: OncoTargets and Therapy Zhen Huo* Background: Pre-mRNA processing factor 40 homolog A (PRPF40A) is an important Shuyu Zhai* protein involved in pre-mRNA splicing and is expressed in a variety of cell types. Yuanchi Weng* However, the function of PRPF40A in pancreatic cancer remains unclear. Therefore, our Hao Qian study is to investigate the role of PRPF40A in the pathogenesis of pancreatic cancer. Xiaomei Tang Materials and methods: We extracted expression data and clinical information of Yusheng Shi PRPF40A from different online databases, including the Cancer Genome Atlas (TCGA), Oncomine and the Gene Expression Omnibus (GEO). Subsequently, samples were collected Xiaxing Deng from patients to validate gene expression using qPCR, Western blotting and immunohisto- Yue Wang chemical (IHC) analyses. Receiver operating characteristic (ROC) and Kaplan-Meier curve Baiyong Shen were used to evaluate the diagnostic and prognostic potential. Colony formation assays and Department of General Surgery, Ruijin CCK-8 assays were performed to measure the proliferative capacity of pancreatic cancer. Hospital, Shanghai Jiao Tong University Finally, gene ontology (GO) and pathway enrichment analyses of co-expressed genes of School of Medicine, Shanghai 200025, People’s Republic of China PRPF40A were conducted using the Database for Annotation, Visualization and Integrated Discovery (DAVID). *These authors contributed equally to Results: We found that PRPF40A was upregulated based on data from both the online this work databases and our samples. PRPF40A possessed a significant diagnostic value, and its overexpression was associated with poor prognosis. PRPF40A knockdown inhibited cell proliferation in pancreatic cancer. GO and pathway analysis showed that the co-expressed genes were mainly involved in viral processing, mRNA splicing and the AMPK signaling pathway. Conclusion: The results suggest that PRPF40A is an oncogene and can serve as a diagnostic and prognostic biomarker for pancreatic cancer. However, the underlying mechanisms remain to be elucidated. Keywords: PRPF40A, pancreatic cancer, diagnosis, prognosis, biomarker Introduction Correspondence: Yue Wang; Baiyong Pancreatic cancer is often not diagnosed until advanced stage due to its anatomical Shen Department of General Surgery, Ruijin location and aggressive nature, which results in a high mortality rate. The prognosis Hospital, Shanghai Jiao Tong University of pancreatic cancer is typically worse than that of other digestive tumors, and the School of Medicine, 197 Ruijin 2nd Road, 1 Shanghai 200025, People’s Republic of 5-year survival rate is less than 5%. Chemotherapy is not always effective and China radical resection is currently the only curative treatment for pancreatic cancer.2,3 Email [email protected]; [email protected] Therefore, more effective methods of making early diagnosis and indicating submit your manuscript | www.dovepress.com OncoTargets and Therapy 2019:12 5037–5051 5037 DovePress © 2019 Huo et al. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work http://doi.org/10.2147/OTT.S206039 you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php). Huo et al Dovepress prognosis are required to improve pre-operative condition index.html),21 which is based on the Cancer Genome assessment and the personalized medical treatment for Atlas (TCGA) and the Genotype-Tissue Expression patients with pancreatic cancer. (GTEx) project and contains 171 normal and 179 cancer At present, the use of targeted therapy for the treatment samples in total. Overall survival and disease-free survival of pancreatic cancer has become a topic of increased curves were also generated based on this database. interest.4 Previous studies have discovered several clini- Differential expression analysis was performed using the cally useful markers of pancreatic cancer. For instance, Oncomine database (http://www.oncomine.com)22 with CA19-9 and CA125 are well-established diagnostic and data from three datasets that were uploaded by Segara (6 prognostic markers for pancreatic cancer.5,6 Moreover, normal and 11 cancer tissues),23 Pei (16 normal tissues and MIC-1, PAM4, S100A6 and OPN can serve as diagnostic 36 cancer tissues)24 and Badea (39 tumor tissues and markers for pancreatic cancer, while SPARC, CISD2 and paired normal tissues).25 In addition, three datasets SMAD4 are identified as promising prognostic (GSE74629, GSE71729 and GSE62452) were obtained markers.7–13 from the Gene Expression Omnibus (GEO) database PRPF40A is one of the two putative homologs of Pre- (https://www.ncbi.nlm.nih.gov/geo/).26 GSE74629 con- mRNA processing protein 40, which plays a vital role in tains 14 normal and 36 tumor samples, GSE71729 con- the initiation of pre-mRNA splicing.14,15 It has been tains of 46 normal and 145 tumor samples, and GSE62452 reported that these two homologs are associated with is comprised of 69 tumor and paired normal samples. Six genetic diseases such as Rett syndrome and Huntington’s GEO datasets were used to determine the diagnostic power disease.16,17 Besides, PRPF40A may be one of the poten- of PRPF40A, including GSE1542 (24 tumor vs 25 adja- tial target genes of p53, which can cause malignant human cent normal tissues), GSE15471 (39 tumor and paired cancer when it is mutated.18 Recent studies found that adjacent normal tissues), GSE16515 (36 tumor vs 16 adja- PRPF40A is associated with hypoxia response in lung cent normal tissues), GSE28735 (45 tumor and paired cancer and is overexpressed in pancreatic ductal adjacent normal tissues), GSE62452 (69 tumor vs 61 adja- adenocarcinoma.19,20 However, the role of PRPF40A in cent normal tissues) and GSE71729 (145 tumor vs 46 pancreatic cancer and its underlying molecular mechan- adjacent normal tissues). isms remain unclear. To study the expression pattern of PRPF40A and the Gene ontology (GO) and pathway enrichment analysis biological mechanisms involved in tumorigenesis and the The top 100 co-expressed genes targeted by PRPF40A in progression of pancreatic cancer, we extracted genetic pancreatic cancer were screened using GEPIA and expression data and clinical information from online data- imported into the Database for Annotation, Visualization bases (TCGA, Oncomine and GEO) and obtained co- and Integrated Discovery (DAVID) (http://david.abcc. expressed genes of PRPF40A from GEPIA. ncifcrf.gov/)27 to perform the GO and pathway analysis. Subsequently, gene ontology and pathway analysis were GO analysis is a bioinformatics method that is mainly used performed using DAVID and a co-expression network was to annotate genes and categorize them according to biolo- constructed using GeneMANIA and Cytoscape. Colony gical process (BP), cellular component (CC) and molecu- formation assays and CCK-8 assays were also performed. lar function (MF).28 Pathway analysis was performed In addition, specimens and clinicopathological data were using the Kyoto Encyclopedia of Genes and Genomes collected from patients treated at our hospital and analyzed (KEGG), which is a database that contains various kinds to validate the results of the bioinformatic analysis. of data from large molecular datasets generated using high-throughput experimental technologies.29 The results Materials and methods were visualized using GraphPad Prism 7. Bioinformatic analysis Expression and survival data extraction Protein-protein interaction (PPI) network construction The results of the differential expression analysis per- and analysis formed on tumor and normal tissues from patients with The data of PRPF40A and its co-expressed genes was cancer, including pancreatic cancer, were obtained and uploaded into GeneMANIA (http://genemania.org/), which analyzed using the Gene Expression Profiling Interactive is an online database to predict protein-protein interaction Analysis (GEPIA) server (http://gepia.cancer-pku.cn/ based on data collected from GEO, BioGRID, IRefIndex 5038 submit your manuscript | www.dovepress.com OncoTargets and Therapy 2019:12 DovePress Dovepress Huo et al and I2D,30 and then imported into Cytoscape31 to generate were acquired from Cell bank of Chinese Academy of a PPI network. GO analysis of these genes was performed Sciences and were cultured in RPMI 1640, DMEM, and visualized using the BiNGO plugin in Cytoscape.32 IMDM supplemented with 10% fetal bovine serum (FBS) and antibiotics. Western blotting was used to ana- Clinical samples lyze protein expression, the results of which were quanti- Forty-four surgical specimens from pancreatic cancers and fied using ImageJ. Briefly, each type of cell was collected adjacent normal tissues that were located at least 2 cm in cold PBS and lysed using RIPA buffer (Sigma-Aldrich;
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  • Supplemental Fig. S1. Evidence of Post-Transcriptional Cleavage from CAGE Tag Analysis

    Supplemental Fig. S1. Evidence of Post-Transcriptional Cleavage from CAGE Tag Analysis

    A. ~20 nt exon-exon junction (EEJ) ~20 nt exon 1 3’ 5’ exon 2 CAGE tag B. 3000 CAGE tags mapping to genome CAGE tags mapping across exon-exon junction 2000 1000 CAGE tags (total count) tags (total CAGE -50 nt 3’ 5’ +30 nt CAGE tag 5’ termini distribution relative to exon-exon junctions Supplemental Fig. S1. Evidence of post-transcriptional cleavage from CAGE tag analysis. (A) Schematic diagram illustrating the mapping strategy employed to discern CAGE tags that span exon-exon junctions (EEJs). (B) Distribution of mouse CAGE tags mapping proximal to RefSeq EEJs. The frequency of CAGE tags that map uniquely and exactly to the genome but do not span EEJs (blue) are under-represented adjacent to exon boundaries. This under-representation can be reconciled with CAGE tags that map uniquely and exactly across EEJs (red). The distribution also shows a distinct peak of enrichment of CAGE tags immediately downstream of EEJs (green arrow). A. H3K4me1 (human CD4+ T cells) B. H3K4me2 (human CD4+T cells) C. H3K4me3 (human CD4+ T cells) exon CAGE promoter CAGE 0.05 0.25 0.04 average tag frequency average -2000 nt +1 +2000 nt -2000 nt +1 +2000 nt -2000 nt +1 +2000 nt position relative to CAGE tag (nucleotides; nt) position relative to CAGE tag (nucleotides; nt) position relative to CAGE tag (nucleotides; nt) D. H3K9ac (human CD4+ T cells) E. H2Az (human CD4+ T cells) F. RNA Polymerase 2 (human CD4+ T cells) 0.1 0.06 0.04 average tag frequency average -2000 nt +1 +2000 nt -2000 nt +1 +2000 nt -2000 nt +1 +2000 nt position relative to CAGE tag (nucleotides; nt) position relative to CAGE tag (nucleotides; nt) position relative to CAGE tag (nucleotides; nt) G.