Dataset Information

TSPAN1, TMPRSS4, SDR16C5, and CTSE as Novel Panel for Pancreatic Cancer: A Bioinformatics Analysis and Experiments Validation.

ABSTRACT: Pancreatic cancer is a lethal malignancy with a poor prognosis. This study aims to identify pancreatic cancer-related genes and develop a robust diagnostic model to detect this disease. Weighted gene co-expression network analysis (WGCNA) was used to determine potential hub genes for pancreatic cancer. Their mRNA and protein expression levels were validated through reverse transcription PCR (RT-PCR) and immunohistochemical (IHC). Diagnostic models were developed by eight machine learning algorithms and ten-fold cross-validation. Four hub genes (TSPAN1, TMPRSS4, SDR16C5, and CTSE) were identified based on bioinformatics. RT-PCR showed that the four hub genes were expressed at medium to high levels, IHC revealed that their protein expression levels were higher in pancreatic cancer tissues. For the panel of these four genes, eight models performed with 0.87-0.92 area under the curve value (AUC), 0.91-0.94 sensitivity, and 0.84-0.86 specificity in the validation cohort. In the external validation set, these models also showed good performance (0.86-0.98 AUC, 0.84-1.00 sensitivity, and 0.86-1.00 specificity). In conclusion, this study has identified four hub genes that might be closely related to pancreatic cancer: TSPAN1, TMPRSS4, SDR16C5, and CTSE. Four-gene panels might provide a theoretical basis for the diagnosis of pancreatic cancer.

SUBMITTER: Ye H

PROVIDER: S-EPMC8015801 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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<i>TSPAN1, TMPRSS4, SDR16C5</i>, and <i>CTSE</i> as Novel Panel for Pancreatic Cancer: A Bioinformatics Analysis and Experiments Validation.

Ye Hua H Li Tiandong T Wang Hua H Wu Jinyu J Yi Chuncheng C Shi Jianxiang J Wang Peng P Song Chunhua C Dai Liping L Jiang Guozhong G Huang Yuxin Y Yu Yongwei Y Li Jitian J

Frontiers in immunology 20210318

Pancreatic cancer is a lethal malignancy with a poor prognosis. This study aims to identify pancreatic cancer-related genes and develop a robust diagnostic model to detect this disease. Weighted gene co-expression network analysis (WGCNA) was used to determine potential hub genes for pancreatic cancer. Their mRNA and protein expression levels were validated through reverse transcription PCR (RT-PCR) and immunohistochemical (IHC). Diagnostic models were developed by eight machine learning algorit ...[more]

PMID: 33815409

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Project description:BackgroundPancreatic cancer is a common malignant tumor of the digestive tract. It has a high degree of malignancy and poor prognosis. Finding effective molecular markers has great significance for pancreatic cancer diagnosis and treatment. This study aimed to investigate DLGAP5 expression in pancreatic cancer and explore the possible mechanisms and clinical value of DLGAP5 in tumorigenesis and tumor development.MethodsDifferentially expressed genes were screened using the Gene Expression Omnibus (GEO) data set GSE16515. Gene Ontology (GO)-based functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analysis were performed on the corresponding proteins of the above genes using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Kaplan-Meier Plotter database was used to analyze the relationship between differentially expressed genes and pancreatic cancer prognosis. The most prognostic gene, DLGAP5, was screened out, and the Oncomine and gene expression profiling interactive analysis (GEPIA) databases were used to analyze its expression in pancreatic cancer and other cancer tissues. The Cancer Genome Atlas (TCGA) database was used to analyze the overall survival of DLGAP5. Gene set enrichment analysis (GSEA) was performed to explore its possible molecular mechanisms in pancreatic cancer. Furthermore, the biological behavior of DLGAP5 in pancreatic cancer was verified by cell function experiments.ResultsA total of 201 significant upregulated differentially expressed genes and 79 downregulated genes were selected. The biological processes with significant enrichment of differential genes included cell adhesion, apoptosis, wound healing, leukocyte migration, angiogenesis. Pathways were mainly enriched in tumor-related signaling pathways such as cancer pathways, the extracellular matrix-receptor interaction pathway, and the p53 signaling pathway. DLGAP5 was significantly expressed in pancreatic cancer, and its expression level had a significant effect on patients' survival time and progression-free survival. GSEA results indicated that DLGAP5 had significantly enriched into signaling pathways such as the cell cycle, the p53 signaling pathway, and oocyte meiosis. The experimental results showed that when we knocked down the expression of DLGAP5 in pancreatic cancer cells, their proliferation ability was significantly inhibited, and their invasion and migration ability significantly decreased.ConclusionsDLGAP5 can be used as a prognostic indicator for pancreatic cancer and affect the occurrence and development of pancreatic cancer.

Project description:Pancreatic cancer is one of the most aggressive tumors associated with a poor clinical prognosis, weakly effective therapeutic options. Therefore, there is a strong impetus to discover new therapeutic targets in pancreatic cancer. In the present study, we first demonstrated that TSPAN1 is upregulated in pancreatic cancer and that TSPAN1 depletion decreases pancreatic cancer cell proliferation in vitro and in vivo. TSPAN1 expression was correlated with poor overall survival of pancreatic cancer patients. Moreover, we demonstrated that TSPAN1 is a novel positive regulator of macroautophagy/autophagy characterized by decreased LC3-II and SQSTM1/p62 expressions, inhibited puncta formation of GFP-LC3 and autophagic vacuoles. We also demonstrated that tspan1 mutation impaired autophagy in the zebrafish model. Furthermore, we showed that TSPAN1 promoted autophagy maturation via direct binding to LC3 by two conserved LIR motifs. Mutations in the LIR motifs of TSPAN1 resulted in a loss of the ability to induce autophagy and promote pancreatic cancer proliferation. Second, we discovered two conservative TCF/LEF binding elements present in the promoter region of the TSPAN1 gene, which was further verified through luciferase activity and ChIP assays. Furthermore, TSPAN1 was upregulated by FAM83A through the canonical WNT-CTNNB1 signaling pathway. We further demonstrated that both TSPAN1 and FAM83A are both direct targets of MIR454 (microRNA 454). Additionally, we revealed the role of MIR454-FAM83A-TSPAN1 in the proliferation of pancreatic cancer cells in vitro and in vivo. Our findings suggest that components of the MIR454-FAM83A-TSPAN1 axis may be valuable prognosis markers or therapeutic targets for pancreatic cancer.Abbreviations: AMPK: adenosine 5'-monophosphate (AMP)-activated protein kinase; APC: APC regulator of WNT signaling pathway; ATG: autophagy related; AXIN2: axin 2; BECN1: beclin 1; CCND1: cyclin D1; CSNK1A1/CK1α: casein kinase 1 alpha 1; CTNNB1/β-catenin: catenin beta 1; DAPI: 4'6-diamino-2-phenylindole; EBSS: Earle's balanced salt solution; EdU: 5-ethynyl-20-deoxyuridine; FAM83A: family with sequence similarity 83 member A; GAPDH: glyceraldehyde-3-phosphate dehydrogenase; GFP: green fluorescent protein; GSEA: gene set enrichment analysis; GSK3B: glycogen synthase kinase 3 beta; IHC: immunohistochemical; LAMP1: lysosomal associated membrane protein 1; LIR: LC3-interacting region; MAP1LC3/LC3, microtubule associated protein 1 light chain 3; MIR454: microRNA 454; miRNA: microRNA; MKI67: antigen identified by monoclonal antibody Ki 67; MTOR: mechanistic target of rapamycin kinase; MTT: 3-(4,5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide; MYC: MYC proto-oncogene, bHLH transcription factor; OS: overall survival; PDAC: pancreatic ductal adenocarcinoma; RAB7A: RAB7A, member RAS oncogene family; shRNA: short hairpin RNA; SQSTM1: sequestosome 1; TBE: TCF/LEF binding element; TCGA: The Cancer Genome Atlas; TCF/LEF: transcription factor/lymphoid enhancer binding factor; TCF4: transcription factor 4; TSPAN1: tetraspanin 1; TUNEL: terminal deoxynucleotidyl transferase mediated dUTP nick end labeling; UTR: untranslated region; WT: wild type.

Project description:IntroductionNew biomarkers are urgently needed to detect pancreatic ductal adenocarcinoma (PDAC) at an earlier stage for individualized treatment strategies and to improve outcomes. Autoantibodies (AAbs) in principle make attractive biomarkers as they arise early in disease, report on disease-associated perturbations in cellular proteomes, and are static in response to other common stimuli, yet are measurable in the periphery, potentially well in advance of the onset of clinical symptoms.MethodsHere, we used high-throughput, custom cancer antigen microarrays to identify a clinically relevant autoantibody biomarker combination able to differentially detect PDAC. Specifically, we quantified the serological AAb profiles of 94 PDAC, chronic pancreatitis (CP), other pancreatic- (PC) and prostate cancers (PRC), non-ulcer dyspepsia patients (DYS), and healthy controls (HC).ResultsCombinatorial ROC curve analysis on the training cohort data from the cancer antigen microarrays identified the most effective biomarker combination as CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Additionally, differential expression analysis on the samples run on the iOme™ array identified 4 biomarkers (ALX1-GPA33-LIP1-SUB1) upregulated in PDAC against diseased and healthy controls. Identified AAbs were validated in silico using public immunohistochemistry datasets and experimentally using a custom PDAC protein microarray comprising the 11 optimal AAb biomarker panel. The clinical utility of the biomarker panel was tested in an independent cohort comprising 223 PDAC, PC, PRC, colorectal cancer (CRC), and HC samples. Combinatorial ROC curve analysis on the validation data identified the most effective biomarker combination to be CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Subsequently, the specificity of the 11-biomarker panel was validated against other cancers (PDAC vs PC: AUC = 70·3%; PDAC vs CRC: AUC = 84·3%; PDAC vs PRC: AUC = 80·2%) and healthy controls (PDAC vs HC: AUC = 80·9%), confirming that this novel AAb biomarker panel is able to selectively detect PDAC amongst other confounding diseases.ConclusionThis AAb panel may therefore have the potential to form the basis of a novel diagnostic test for PDAC.

Project description:Background: Dendrobium nobile (D. nobile), a traditional Chinese medicine, has received attention as an anti-tumor drug, but its mechanism is still unclear. In this study, we applied network pharmacology, bioinformatics, and in vitro experiments to explore the effect and mechanism of Dendrobin A, the active ingredient of D. nobile, against pancreatic ductal adenocarcinoma (PDAC). Methods: The databases of SwissTargetPrediction and PharmMapper were used to obtain the potential targets of Dendrobin A, and the differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression databases. The protein-protein interaction (PPI) network for Dendrobin A anti-PDAC targets was constructed based on the STRING database. Molecular docking was used to assess Dendrobin A anti-PDAC targets. PLAU, one of the key targets of Dendrobin A anti-PDAC, was immunohistochemically stained in clinical tissue arrays. Finally, in vitro experiments were used to validate the effects of Dendrobin A on PLAU expression and the proliferation, apoptosis, cell cycle, migration, and invasion of PDAC cells. Results: A total of 90 genes for Dendrobin A anti-PDAC were screened, and a PPI network for Dendrobin A anti-PDAC targets was constructed. Notably, a scale-free module with 19 genes in the PPI indicated that the PPI is highly credible. Among these 19 genes, PLAU was positively correlated with the cachexia status while negatively correlated with the overall survival of PDAC patients. Through molecular docking, Dendrobin A was found to bind to PLAU, and the Dendrobin A treatment led to an attenuated PLAU expression in PDAC cells. Based on clinical tissue arrays, PLAU protein was highly expressed in PDAC cells compared to normal controls, and PLAU protein levels were associated with the differentiation and lymph node metastatic status of PDAC. In vitro experiments further showed that Dendrobin A treatment significantly inhibited the proliferation, migration, and invasion, inducing apoptosis and arresting the cell cycle of PDAC cells at the G2/M phase. Conclusion: Dendrobin A, a representative active ingredient of D. nobile, can effectively fight against PDAC by targeting PLAU. Our results provide the foundation for future PDAC treatment based on D. nobile.

Dataset Information

TSPAN1, TMPRSS4, SDR16C5, and CTSE as Novel Panel for Pancreatic Cancer: A Bioinformatics Analysis and Experiments Validation.

Publications

<i>TSPAN1, TMPRSS4, SDR16C5</i>, and <i>CTSE</i> as Novel Panel for Pancreatic Cancer: A Bioinformatics Analysis and Experiments Validation.

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