Analysis of the autophagy gene expression profile of pancreatic cancer based on autophagy-related protein microtubule-associated protein 1A/1B-light chain 3
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摘要
BACKGROUND Pancreatic cancer is a highly invasive malignant tumor. Expression levels of the autophagy-related protein microtubule-associated protein 1 A/1 B-light chain 3(LC3) and perineural invasion(PNI) are closely related to its occurrence and development. Our previous results showed that the high expression of LC3 was positively correlated with PNI in the patients with pancreatic cancer. In this study, we further searched for differential genes involved in autophagy of pancreatic cancer by gene expression profiling and analyzed their biological functions in pancreatic cancer, which provides a theoretical basis for elucidating the pathophysiological mechanism of autophagy in pancreatic cancer and PNI.AIM To identify differentially expressed genes involved in pancreatic cancer autophagy and explore the pathogenesis at the molecular level.METHODS Two sets of gene expression profiles of pancreatic cancer/normal tissue(GSE16515 and GSE15471) were collected from the Gene Expression Omnibus.Significance analysis of microarrays algorithm was used to screen differentially expressed genes related to pancreatic cancer. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were used to analyze the functional enrichment of the differentially expressed genes. Protein interaction data containing only differentially expressed genes was downloaded from String database and screened. Module mining was carried out by Cytoscape software and ClusterOne plug-in. The interaction relationship between the modules was analyzed and the pivot nodes between the functional modules were determined according to the information of the functional modules and the data of reliable protein interaction network.RESULTS Based on the above two data sets of pancreatic tissue total gene expression, 6098 and 12928 differentially expressed genes were obtained by analysis of genes with higher phenotypic correlation. After extracting the intersection of the two differential gene sets, 4870 genes were determined. GO analysis showed that 14 significant functional items including negative regulation of protein ubiquitination were closely related to autophagy. A total of 986 differentially expressed genes were enriched in these functional items. After eliminating the autophagy related genes of human cancer cells which had been defined, 347 differentially expressed genes were obtained. KEGG pathway analysis showed that the pathways hsa04144 and hsa04020 were related to autophagy. In addition,65 clustering modules were screened after the protein interaction network was constructed based on String database, and module 32 contains the LC3 gene,which interacts with multiple autophagy-related genes. Moreover, ubiquitin C acts as a pivot node in functional modules to connect multiple modules related to pancreatic cancer and autophagy.CONCLUSION Three hundred and forty-seven genes associated with autophagy in human pancreatic cancer were concentrated, and a key gene ubiquitin C which is closely related to the occurrence of PNI was determined, suggesting that LC3 may influence the PNI and prognosis of pancreatic cancer through ubiquitin C.
BACKGROUND Pancreatic cancer is a highly invasive malignant tumor. Expression levels of the autophagy-related protein microtubule-associated protein 1 A/1 B-light chain 3(LC3) and perineural invasion(PNI) are closely related to its occurrence and development. Our previous results showed that the high expression of LC3 was positively correlated with PNI in the patients with pancreatic cancer. In this study, we further searched for differential genes involved in autophagy of pancreatic cancer by gene expression profiling and analyzed their biological functions in pancreatic cancer, which provides a theoretical basis for elucidating the pathophysiological mechanism of autophagy in pancreatic cancer and PNI.AIM To identify differentially expressed genes involved in pancreatic cancer autophagy and explore the pathogenesis at the molecular level.METHODS Two sets of gene expression profiles of pancreatic cancer/normal tissue(GSE16515 and GSE15471) were collected from the Gene Expression Omnibus.Significance analysis of microarrays algorithm was used to screen differentially expressed genes related to pancreatic cancer. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were used to analyze the functional enrichment of the differentially expressed genes. Protein interaction data containing only differentially expressed genes was downloaded from String database and screened. Module mining was carried out by Cytoscape software and ClusterOne plug-in. The interaction relationship between the modules was analyzed and the pivot nodes between the functional modules were determined according to the information of the functional modules and the data of reliable protein interaction network.RESULTS Based on the above two data sets of pancreatic tissue total gene expression, 6098 and 12928 differentially expressed genes were obtained by analysis of genes with higher phenotypic correlation. After extracting the intersection of the two differential gene sets, 4870 genes were determined. GO analysis showed that 14 significant functional items including negative regulation of protein ubiquitination were closely related to autophagy. A total of 986 differentially expressed genes were enriched in these functional items. After eliminating the autophagy related genes of human cancer cells which had been defined, 347 differentially expressed genes were obtained. KEGG pathway analysis showed that the pathways hsa04144 and hsa04020 were related to autophagy. In addition,65 clustering modules were screened after the protein interaction network was constructed based on String database, and module 32 contains the LC3 gene,which interacts with multiple autophagy-related genes. Moreover, ubiquitin C acts as a pivot node in functional modules to connect multiple modules related to pancreatic cancer and autophagy.CONCLUSION Three hundred and forty-seven genes associated with autophagy in human pancreatic cancer were concentrated, and a key gene ubiquitin C which is closely related to the occurrence of PNI was determined, suggesting that LC3 may influence the PNI and prognosis of pancreatic cancer through ubiquitin C.
BACKGROUND Pancreatic cancer is a highly invasive malignant tumor. Expression levels of the autophagy-related protein microtubule-associated protein 1 A/1 B-light chain 3(LC3) and perineural invasion(PNI) are closely related to its occurrence and development. Our previous results showed that the high expression of LC3 was positively correlated with PNI in the patients with pancreatic cancer. In this study, we further searched for differential genes involved in autophagy of pancreatic cancer by gene expression profiling and analyzed their biological functions in pancreatic cancer, which provides a theoretical basis for elucidating the pathophysiological mechanism of autophagy in pancreatic cancer and PNI.AIM To identify differentially expressed genes involved in pancreatic cancer autophagy and explore the pathogenesis at the molecular level.METHODS Two sets of gene expression profiles of pancreatic cancer/normal tissue(GSE16515 and GSE15471) were collected from the Gene Expression Omnibus.Significance analysis of microarrays algorithm was used to screen differentially expressed genes related to pancreatic cancer. Gene Ontology(GO) analysis and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis were used to analyze the functional enrichment of the differentially expressed genes. Protein interaction data containing only differentially expressed genes was downloaded from String database and screened. Module mining was carried out by Cytoscape software and ClusterOne plug-in. The interaction relationship between the modules was analyzed and the pivot nodes between the functional modules were determined according to the information of the functional modules and the data of reliable protein interaction network.RESULTS Based on the above two data sets of pancreatic tissue total gene expression, 6098 and 12928 differentially expressed genes were obtained by analysis of genes with higher phenotypic correlation. After extracting the intersection of the two differential gene sets, 4870 genes were determined. GO analysis showed that 14 significant functional items including negative regulation of protein ubiquitination were closely related to autophagy. A total of 986 differentially expressed genes were enriched in these functional items. After eliminating the autophagy related genes of human cancer cells which had been defined, 347 differentially expressed genes were obtained. KEGG pathway analysis showed that the pathways hsa04144 and hsa04020 were related to autophagy. In addition,65 clustering modules were screened after the protein interaction network was constructed based on String database, and module 32 contains the LC3 gene,which interacts with multiple autophagy-related genes. Moreover, ubiquitin C acts as a pivot node in functional modules to connect multiple modules related to pancreatic cancer and autophagy.CONCLUSION Three hundred and forty-seven genes associated with autophagy in human pancreatic cancer were concentrated, and a key gene ubiquitin C which is closely related to the occurrence of PNI was determined, suggesting that LC3 may influence the PNI and prognosis of pancreatic cancer through ubiquitin C.
引文
1 Kamisawa T,Wood LD,Itoi T,Takaori K.Pancreatic cancer.Lancet 2016;388:73-85[PMID:26830752DOI:10.1016/S0140-6736(16)00141-0]
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3 Zhou Y,Zhou Q,Chen R.Pancreatic stellate cells promotes the perineural invasion in pancreatic cancer.Med Hypotheses 2012;78:811-813[PMID:22513235 DOI:10.1016/j.mehy.2012.03.017]
4 Tuloup-Minguez V,Greffard A,Codogno P,Botti J.Regulation of autophagy by extracellular matrix glycoproteins in HeLa cells.Autophagy 2011;7:27-39[PMID:20980830 DOI:10.4161/auto.7.1.13851]
5 Guo K,Ma Q,Li J,Wang Z,Shan T,Li W,Xu Q,Xie K.Interaction of the sympathetic nerve with pancreatic cancer cells promotes perineural invasion through the activation of STAT3 signaling.Mol Cancer Ther 2013;12:264-273[PMID:23288783 DOI:10.1158/1535-7163.MCT-12-0809]
6 Yang YH,Liu JB,Gui Y,Lei LL,Zhang SJ.Relationship between autophagy and perineural invasion,clinicopathological features,and prognosis in pancreatic cancer.World J Gastroenterol 2017;23:7232-7241[PMID:29142470 DOI:10.3748/wjg.v23.i40.7232]
7 Tsugata T,Nikoh N,Kin T,Saitoh I,Noguchi Y,Ueki H,Watanabe M,James Shapiro AM,Noguchi H.Potential Factors for the Differentiation of ESCs/iPSCs Into Insulin-Producing Cells.Cell Med 2014;7:83-93[PMID:26858897 DOI:10.3727/215517914X685178]
8 Artemov A,Aliper A,Korzinkin M,Lezhnina K,Jellen L,Zhukov N,Roumiantsev S,Gaifullin N,Zhavoronkov A,Borisov N,Buzdin A.A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.Oncotarget 2015;6:29347-29356[PMID:26320181 DOI:10.18632/oncotarget.5119]
9 Zhao LP,Li RH,Han DM,Zhang XQ,Nian GX,Wu MX,Feng Y,Zhang L,Sun ZG.Independent prognostic Factor of low-expressed LncRNA ZNF667-AS1 for cervical cancer and inhibitory function on the proliferation of cervical cancer.Eur Rev Med Pharmacol Sci 2017;21:5353-5360[PMID:29243775DOI:10.26355/eurrev_201712_13920]
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11 Available from:http://www.ncbi.nlm.nih.gov/Web/Genbank/[PMID:9847132]
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16 Wang J,Du Q,Li C.Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer.Oncol Lett 2017;14:7880-7888[PMID:29250180 DOI:10.3892/ol.2017.7256]
17 Huo T,Canepa R,Sura A,Modave F,Gong Y.Colorectal cancer stages transcriptome analysis.PLoS One2017;12:e0188697[PMID:29182684 DOI:10.1371/journal.pone.0188697]
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19 Zhao R,Wang Y,Zhang M,Gu X,Wang W,Tan J,Wei X,Jin N.Screening of potential therapy targets for prostate cancer using integrated analysis of two gene expression profiles.Oncol Lett 2017;14:5361-5369[PMID:29113170 DOI:10.3892/ol.2017.6879]
20 Franceschini A,Szklarczyk D,Frankild S,Kuhn M,Simonovic M,Roth A,Lin J,Minguez P,Bork P,von Mering C,Jensen LJ.STRING v9.1:protein-protein interaction networks,with increased coverage and integration.Nucleic Acids Res 2013;41:D808-D815[PMID:23203871 DOI:10.1093/nar/gks1094]
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24 Slebos RJ,Hoppin JA,Tolbert PE,Holly EA,Brock JW,Zhang RH,Bracci PM,Foley J,Stockton P,McGregor LM,Flake GP,Taylor JA.K-ras and p53 in pancreatic cancer:association with medical history,histopathology,and environmental exposures in a population-based study.Cancer Epidemiol Biomarkers Prev 2000;9:1223-1232[PMID:11097231 DOI:10.1023/A:1008907404366]
25 Scita G,Di Fiore PP.The endocytic matrix.Nature 2010;463:464-473[PMID:20110990 DOI:10.1038/nature08910]
26 Dixon SJ,Lemberg KM,Lamprecht MR,Skouta R,Zaitsev EM,Gleason CE,Patel DN,Bauer AJ,Cantley AM,Yang WS,Morrison B,Stockwell BR.Ferroptosis:an iron-dependent form of nonapoptotic cell death.Cell 2012;149:1060-1072[PMID:22632970 DOI:10.1016/j.cell.2012.03.042]
27 Kang R,Tang D.Autophagy and Ferroptosis-What's the Connection?Curr Pathobiol Rep 2017;5:153-159[PMID:29038744 DOI:10.1007/s40139-017-0139-5]
28 Shi D,Grossman SR.Ubiquitin becomes ubiquitous in cancer:emerging roles of ubiquitin ligases and deubiquitinases in tumorigenesis and as therapeutic targets.Cancer Biol Ther 2010;10:737-747[PMID:20930542 DOI:10.4161/cbt.10.8.13417]
29 Reyes-Turcu FE,Ventii KH,Wilkinson KD.Regulation and cellular roles of ubiquitin-specific deubiquitinating enzymes.Annu Rev Biochem 2009;78:363-397[PMID:19489724 DOI:10.1146/annurev.biochem.78.082307.091526]
30 Milani M,Rzymski T,Mellor HR,Pike L,Bottini A,Generali D,Harris AL.The role of ATF4stabilization and autophagy in resistance of breast cancer cells treated with Bortezomib.Cancer Res 2009;69:4415-4423[PMID:19417138 DOI:10.1158/0008-5472.CAN-08-2839]
31 Kaminskyy V,Zhivotovsky B.Proteases in autophagy.Biochim Biophys Acta 2012;1824:44-50[PMID:21640203 DOI:10.1016/j.bbapap.2011.05.013]
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33 Schreiber A,Peter M.Substrate recognition in selective autophagy and the ubiquitin-proteasome system.Biochim Biophys Acta 2014;1843:163-181[PMID:23545414 DOI:10.1016/j.bbamcr.2013.03.019]
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2 Brech A,Ahlquist T,Lothe RA,Stenmark H.Autophagy in tumour suppression and promotion.Mol Oncol 2009;3:366-375[PMID:19559660 DOI:10.1016/j.molonc.2009.05.007]
3 Zhou Y,Zhou Q,Chen R.Pancreatic stellate cells promotes the perineural invasion in pancreatic cancer.Med Hypotheses 2012;78:811-813[PMID:22513235 DOI:10.1016/j.mehy.2012.03.017]
4 Tuloup-Minguez V,Greffard A,Codogno P,Botti J.Regulation of autophagy by extracellular matrix glycoproteins in HeLa cells.Autophagy 2011;7:27-39[PMID:20980830 DOI:10.4161/auto.7.1.13851]
5 Guo K,Ma Q,Li J,Wang Z,Shan T,Li W,Xu Q,Xie K.Interaction of the sympathetic nerve with pancreatic cancer cells promotes perineural invasion through the activation of STAT3 signaling.Mol Cancer Ther 2013;12:264-273[PMID:23288783 DOI:10.1158/1535-7163.MCT-12-0809]
6 Yang YH,Liu JB,Gui Y,Lei LL,Zhang SJ.Relationship between autophagy and perineural invasion,clinicopathological features,and prognosis in pancreatic cancer.World J Gastroenterol 2017;23:7232-7241[PMID:29142470 DOI:10.3748/wjg.v23.i40.7232]
7 Tsugata T,Nikoh N,Kin T,Saitoh I,Noguchi Y,Ueki H,Watanabe M,James Shapiro AM,Noguchi H.Potential Factors for the Differentiation of ESCs/iPSCs Into Insulin-Producing Cells.Cell Med 2014;7:83-93[PMID:26858897 DOI:10.3727/215517914X685178]
8 Artemov A,Aliper A,Korzinkin M,Lezhnina K,Jellen L,Zhukov N,Roumiantsev S,Gaifullin N,Zhavoronkov A,Borisov N,Buzdin A.A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.Oncotarget 2015;6:29347-29356[PMID:26320181 DOI:10.18632/oncotarget.5119]
9 Zhao LP,Li RH,Han DM,Zhang XQ,Nian GX,Wu MX,Feng Y,Zhang L,Sun ZG.Independent prognostic Factor of low-expressed LncRNA ZNF667-AS1 for cervical cancer and inhibitory function on the proliferation of cervical cancer.Eur Rev Med Pharmacol Sci 2017;21:5353-5360[PMID:29243775DOI:10.26355/eurrev_201712_13920]
10 Fei HJ,Chen SC,Zhang JY,Li SY,Zhang LL,Chen YY,Chang CX,Xu CM.Identification of significant biomarkers and pathways associated with gastric carcinogenesis by whole genome-wide expression profiling analysis.Int J Oncol 2018;52:955-966[PMID:29328368 DOI:10.3892/ijo.2018.4243]
11 Available from:http://www.ncbi.nlm.nih.gov/Web/Genbank/[PMID:9847132]
12 Gautier L,Cope L,Bolstad BM,Irizarry RA.affy--analysis of Affymetrix GeneChip data at the probe level.Bioinformatics 2004;20:307-315[PMID:14960456 DOI:10.1093/bioinformatics/btg405]
13 Bolstad BM,Irizarry RA,Astrand M,Speed TP.A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.Bioinformatics 2003;19:185-193[PMID:12538238 DOI:10.1093/bioinformatics/19.2.185]
14 Tusher VG,Tibshirani R,Chu G.Significance analysis of microarrays applied to the ionizing radiation response.Proc Natl Acad Sci USA 2001;98:5116-5121[PMID:11309499 DOI:10.1073/pnas.091062498]
15 Amar D,Vizel A,Levy C,Shamir R.ADEPTUS:a discovery tool for disease prediction,enrichment and network analysis based on profiles from many diseases.Bioinformatics 2018;34:1959-1961[PMID:29360930 DOI:10.1093/bioinformatics/bty027]
16 Wang J,Du Q,Li C.Bioinformatics analysis of gene expression profiles to identify causal genes in luminal B2 breast cancer.Oncol Lett 2017;14:7880-7888[PMID:29250180 DOI:10.3892/ol.2017.7256]
17 Huo T,Canepa R,Sura A,Modave F,Gong Y.Colorectal cancer stages transcriptome analysis.PLoS One2017;12:e0188697[PMID:29182684 DOI:10.1371/journal.pone.0188697]
18 Tello-Ruiz MK,Naithani S,Stein JC,Gupta P,Campbell M,Olson A,Wei S,Preece J,Geniza MJ,Jiao Y,Lee YK,Wang B,Mulvaney J,Chougule K,Elser J,Al-Bader N,Kumari S,Thomason J,Kumar V,Bolser DM,Naamati G,Tapanari E,Fonseca N,Huerta L,Iqbal H,Keays M,Munoz-Pomer Fuentes A,Tang A,Fabregat A,D'Eustachio P,Weiser J,Stein LD,Petryszak R,Papatheodorou I,Kersey PJ,Lockhart P,Taylor C,Jaiswal P,Ware D.Gramene 2018:unifying comparative genomics and pathway resources for plant research.Nucleic Acids Res 2018;46:D1181-D1189[PMID:29165610 DOI:10.1093/nar/gkx1111]
19 Zhao R,Wang Y,Zhang M,Gu X,Wang W,Tan J,Wei X,Jin N.Screening of potential therapy targets for prostate cancer using integrated analysis of two gene expression profiles.Oncol Lett 2017;14:5361-5369[PMID:29113170 DOI:10.3892/ol.2017.6879]
20 Franceschini A,Szklarczyk D,Frankild S,Kuhn M,Simonovic M,Roth A,Lin J,Minguez P,Bork P,von Mering C,Jensen LJ.STRING v9.1:protein-protein interaction networks,with increased coverage and integration.Nucleic Acids Res 2013;41:D808-D815[PMID:23203871 DOI:10.1093/nar/gks1094]
21 Allemani C,Matsuda T,Di Carlo V,Harewood R,Matz M,Nik?i?M,Bonaventure A,Valkov M,Johnson CJ,Estève J,Ogunbiyi OJ,Azevedo E Silva G,Chen WQ,Eser S,Engholm G,Stiller CA,Monnereau A,Woods RR,Visser O,Lim GH,Aitken J,Weir HK,Coleman MP;CONCORD Working Group.Global surveillance of trends in cancer survival 2000-14(CONCORD-3):analysis of individual records for 37?513?025 patients diagnosed with one of 18 cancers from 322 population-based registries in71 countries.Lancet 2018;391:1023-1075[PMID:29395269 DOI:10.1016/S0140-6736(17)33326-3]
22 Chen W,Zheng R,Baade PD,Zhang S,Zeng H,Bray F,Jemal A,Yu XQ,He J.Cancer statistics in China,2015.CA Cancer J Clin 2016;66:115-132[PMID:26808342 DOI:10.3322/caac.21338]
23 Furukawa T,Horii A.Molecular pathology of pancreatic cancer:in quest of tumor suppressor genes.Pancreas 2004;28:253-256[PMID:15084966 DOI:10.1097/00006676-200404000-00007]
24 Slebos RJ,Hoppin JA,Tolbert PE,Holly EA,Brock JW,Zhang RH,Bracci PM,Foley J,Stockton P,McGregor LM,Flake GP,Taylor JA.K-ras and p53 in pancreatic cancer:association with medical history,histopathology,and environmental exposures in a population-based study.Cancer Epidemiol Biomarkers Prev 2000;9:1223-1232[PMID:11097231 DOI:10.1023/A:1008907404366]
25 Scita G,Di Fiore PP.The endocytic matrix.Nature 2010;463:464-473[PMID:20110990 DOI:10.1038/nature08910]
26 Dixon SJ,Lemberg KM,Lamprecht MR,Skouta R,Zaitsev EM,Gleason CE,Patel DN,Bauer AJ,Cantley AM,Yang WS,Morrison B,Stockwell BR.Ferroptosis:an iron-dependent form of nonapoptotic cell death.Cell 2012;149:1060-1072[PMID:22632970 DOI:10.1016/j.cell.2012.03.042]
27 Kang R,Tang D.Autophagy and Ferroptosis-What's the Connection?Curr Pathobiol Rep 2017;5:153-159[PMID:29038744 DOI:10.1007/s40139-017-0139-5]
28 Shi D,Grossman SR.Ubiquitin becomes ubiquitous in cancer:emerging roles of ubiquitin ligases and deubiquitinases in tumorigenesis and as therapeutic targets.Cancer Biol Ther 2010;10:737-747[PMID:20930542 DOI:10.4161/cbt.10.8.13417]
29 Reyes-Turcu FE,Ventii KH,Wilkinson KD.Regulation and cellular roles of ubiquitin-specific deubiquitinating enzymes.Annu Rev Biochem 2009;78:363-397[PMID:19489724 DOI:10.1146/annurev.biochem.78.082307.091526]
30 Milani M,Rzymski T,Mellor HR,Pike L,Bottini A,Generali D,Harris AL.The role of ATF4stabilization and autophagy in resistance of breast cancer cells treated with Bortezomib.Cancer Res 2009;69:4415-4423[PMID:19417138 DOI:10.1158/0008-5472.CAN-08-2839]
31 Kaminskyy V,Zhivotovsky B.Proteases in autophagy.Biochim Biophys Acta 2012;1824:44-50[PMID:21640203 DOI:10.1016/j.bbapap.2011.05.013]
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