ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysis

AbstractPancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer with an abysmal prognosis rate over the last few decades. Early diagnosis and prevention could effectively combat this malignancy. Therefore, it is crucial to discover potential biomarkers to identify asymptomatic premalignant or early malignant tumors of PDAC. Gene expression analysis is a powerful technique to identify candidate biomarkers involved in disease progression. In the present study, five independent gene expression datasets, including 321 PDAC tissues and 208 adjacent non-cancerous tissue samples, were subjected to statistical and bioinformatics analysis. A total of 20 differentially expressed genes (DEGs) were identified in PDAC tissues compared to non-cancerous tissue samples. Gene ontology and pathway enrichment analysis showed that DEGs were mainly enriched in extracellular matrix (ECM), cell adhesion, ECM–receptor interaction, and focal adhesion signaling. The protein–protein interaction network was constructed, and the hub genes were evaluated. Collagen type XII alpha 1 chain (COL12A1), fibronectin 1 (FN1), integrin subunit alpha 2 (ITGA2), laminin subunit beta 3 (LAMB3), laminin subunit gamma 2 (LAMC2), thrombospondin 2 (THBS2), and versican (VCAN) were identified as hub genes. The correlation analysis revealed that identified hub genes were significantly interconnected. Wherein COL12A1, FN1, ITGA2, LAMB3, LAMC2, and THBS2 were significantly associated with PDAC pathological stages. The Kaplan–Meier survival plots revealed that ITGA2, LAMB3, and LAMC2 expression were inversely correlated with a prolonged patient survival period. Furthermore, the Human Protein Atlas database was used to validate the expression and cellular origins of hub genes encoded proteins. The protein expression of hub genes was higher in pancreatic cancer tissue than in normal pancreatic tissue samples, wherein ITGA2, LAMB3, and LAMC2 were exclusively expressed in pancreatic cancer cells. Pancreatic cancer cell-specific expression of these three proteins may play pleiotropic roles in cancer progression. Our results collectively suggest that ITGA2, LAMB3, and LAMC2 could provide deep insights into pancreatic carcinogenesis molecular mechanisms and provide attractive therapeutic targets.

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ITGA2, LAMB3, and LAMC2 May Be the Potential Therapeutic Targets in Pancreatic Ductal Adenocarcinoma: an Integrated Bioinformatics Analysis

10.21203/rs.3.rs-379158/v1 ◽

2021 ◽

Author(s):

Shajedul Islam ◽

Takao Kitagawa ◽

Byron Baron ◽

Yoshihiro Abiko ◽

Itsuo Chiba ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Pancreatic Ductal Adenocarcinoma ◽

Bioinformatics Analysis ◽

Therapeutic Targets ◽

Ductal Adenocarcinoma ◽

Integrin Subunit ◽

Hub Genes ◽

Cancerous Tissue ◽

Tissue Samples

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the most common form of pancreatic cancer with an abysmal prognosis rate over the last few decades. Early diagnosis and prevention could effectively combat this malignancy. Therefore, it is crucial to discover potential biomarkers to identify asymptomatic premalignant or early malignant tumors of PDAC. Gene expression analysis is a powerful technique to identify candidate biomarkers involved in disease progression. In the present study, five independent gene expression datasets, including 321 PDAC tissues and 208 adjacent non-cancerous tissue samples, were subjected to statistical and bioinformatics analysis. A total of 20 differentially expressed genes (DEGs) were identified in PDAC tissues compared to non-cancerous tissue samples. Gene ontology and pathway enrichment analysis showed that DEGs were mainly enriched in extracellular matrix (ECM), cell adhesion, ECM-receptor interaction, and focal adhesion signaling. The protein protein interaction network was constructed, and the hub genes were evaluated. Collagen type XII alpha 1 chain (COL12A1), fibronectin 1 (FN1), integrin subunit alpha 2 (ITGA2), laminin subunit beta 3 (LAMB3), laminin subunit gamma 2 (LAMC2), thrombospondin 2 (THBS2), and versican (VCAN) were identified as hub genes. The correlation analysis revealed that identified hub genes were significantly interconnected. Wherein COL12A1, FN1, ITGA2, LAMB3, LAMC2, and THBS2 were significantly associated with PDAC pathological stages. The Kaplan-Meier survival plots revealed that ITGA2, LAMB3, and LAMC2 expression were inversely correlated with a prolonged patient survival period. Furthermore, the Human Protein Atlas database was used to validate the expression and cellular origins of hub genes encoded proteins. The protein expression of hub genes was higher in pancreatic cancer tissue than in normal pancreatic tissue samples, wherein ITGA2, LAMB3, and LAMC2 were exclusively expressed in pancreatic cancer cells. Pancreatic cancer cell-specific expression of these three proteins may play pleiotropic roles in cancer progression. Our results collectively suggest that ITGA2, LAMB3, and LAMC2 could provide deep insights into pancreatic carcinogenesis molecular mechanisms and provide attractive therapeutic targets.

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Identify key genes and pathways in pancreatic ductal adenocarcinoma via bioinformatics analysis

10.21203/rs.3.rs-16813/v1 ◽

2020 ◽

Author(s):

Huatian Luo ◽

Da-qiu Chen ◽

Jing-jing Pan ◽

Zhang-wei Wu ◽

Can Yang ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Pancreatic Ductal Adenocarcinoma ◽

Bioinformatics Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Ductal Adenocarcinoma ◽

Hub Genes ◽

Expression Levels

Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes. Methods: Three data sets containing the gene expression profiles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Last but not least, UALCAN analysis online tool was implemented to analyze the overall survival. Results: The 376 DEGs were highly enriched in biological processes including signal transduction, apoptotic process and several pathways, mainly associated with Protein digestion and absorption and Pancreatic secretion pathway. The expression levels of nucleolar and spindle associated protein 1 (NUSAP1) and SHC binding and spindle associated 1 (SHCBP1) were discovered highly expressed in pancreatic ductal adenocarcinoma tissues. NUSAP1 and SHCBP1 had a high correlation with prognosis. Conclusions: The findings of this bioinformatics analysis indicate that NUSAP1 and SHCBP1 may be key factors in the prognosis and treatment of pancreatic cancer.

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Identify key genes and pathways in pancreatic ductal adenocarcinoma via bioinformatics analysis

10.21203/rs.3.rs-19826/v1 ◽

2020 ◽

Author(s):

Huatian Luo ◽

Da-qiu Chen ◽

Jing-jing Pan ◽

Zhang-wei Wu ◽

Can Yang ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Pancreatic Ductal Adenocarcinoma ◽

Bioinformatics Analysis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Gene Expression Omnibus ◽

Ductal Adenocarcinoma ◽

Hub Genes ◽

Expression Levels

Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes.Methods: Three data sets containing the gene expression profiles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Last but not least, UALCAN analysis online tool was implemented to analyze the overall survival.Results: The 376 DEGs were highly enriched in biological processes including signal transduction, apoptotic process and several pathways, mainly associated with Protein digestion and absorption and Pancreatic secretion pathway. The expression levels of nucleolar and spindle associated protein 1 (NUSAP1) and SHC binding and spindle associated 1 (SHCBP1) were discovered highly expressed in pancreatic ductal adenocarcinoma tissues. NUSAP1 and SHCBP1 had a high correlation with prognosis.Conclusions: The findings of this bioinformatics analysis indicate that NUSAP1 and SHCBP1 may be key factors in the prognosis and treatment of pancreatic cancer.

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Integrated Bioinformatics Analysis of Gene Expression Profiles for Potential Biomarker Identification Towards Early Therapeutic Intervention in Pancreatitis and Pancreatic Ductal Adenocarcinoma

10.21203/rs.3.rs-879048/v1 ◽

2021 ◽

Author(s):

Manoj M Wagle ◽

Ananya Rao Kedige ◽

Shama P Kabekkodu ◽

Sandeep Mallya

Keyword(s):

Gene Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Bioinformatics Analysis ◽

Ductal Adenocarcinoma ◽

Rapid Progression ◽

Hub Genes ◽

Altered Expression ◽

Biochemical Pathways ◽

Potential Biomarker ◽

Key Genes

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a malignancy associated with rapid progression and an abysmal prognosis. It has been reported that chronic pancreatitis can increase the risk of developing PDAC by 16-fold. Our study aims to identify the key genes and biochemical pathways mediating pancreatitis and PDAC. The gene expression datasets were retrieved from the EMBL-EBI ArrayExpress and NCBI GEO database. A total of 172 samples of normal pancreatic tissue, 68 samples of pancreatitis, and 306 samples of PDAC were used in this study. The differentially expressed genes (DEGs) identified were used to perform downstream analysis for ontology, interaction, and associated pathways. Furthermore, hub gene expression was validated using the GEPIA2 tool and survival analysis using the Kaplan-Meier (KM) plotter. The potential druggability of the hub genes identified was determined using the Drug-Gene Interaction Database (DGIdb). Our study identified a total of 45 genes found to have altered expression levels in both PDAC and pancreatitis. Over-representation analysis revealed that protein digestion and absorption pathway, ECM-receptor interaction pathway, PI3k-Akt signaling pathway, and proteoglycans in cancer pathways as significantly enriched. Module analysis revealed 15 hub genes with 92 edges, of which 14 were found to be in the druggable genome category. Through bioinformatics analysis, we identified key genes and biochemical pathways disrupted in pancreatitis and PDAC. The results can provide new insights into targeted therapy and intervening therapeutically at an earlier stage can be used as an effective strategy to decrease the incidence and severity of PDAC.

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Identification of novel genes associated with a poor prognosis in pancreatic ductal adenocarcinoma via a bioinformatics analysis

Bioscience Reports ◽

10.1042/bsr20190625 ◽

2019 ◽

Vol 39 (8) ◽

Cited By ~ 7

Author(s):

Jun Zhou ◽

Xiaoliang Hui ◽

Ying Mao ◽

Liya Fan

Keyword(s):

Pancreatic Ductal Adenocarcinoma ◽

Bioinformatics Analysis ◽

Functional Enrichment ◽

Differentially Expressed ◽

Ductal Adenocarcinoma ◽

Peroxisome Proliferator ◽

Integrin Subunit ◽

Hub Genes ◽

Potential Biomarker ◽

Km Plotter

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a class of the commonest malignant carcinomas. The present study aimed to elucidate the potential biomarker and prognostic targets in PDAC. The array data of GSE41368, GSE43795, GSE55643, and GSE41369 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and differentially expressed microRNAs (DEmiRNAs) in PDAC were obtained by using GEO2R, and overlapped DEGs were acquired with Venn Diagrams. Functional enrichment analysis of overlapped DEGs and DEmiRNAs was conducted with Metascape and FunRich, respectively. The protein–protein interaction (PPI) network of overlapped DEGs was constructed by STRING and visualized with Cytoscape. Overall survival (OS) of DEmiRNAs and hub genes were investigated by Kaplan–Meier (KM) plotter (KM plotter). Transcriptional data and correlation analyses among hub genes were verified through GEPIA and Human Protein Atlas (HPA). Additionally, miRNA targets were searched using miRTarBase, then miRNA–DEG regulatory network was visualized with Cytoscape. A total of 32 DEmiRNAs and 150 overlapped DEGs were identified, and Metascape showed that DEGs were significantly enriched in cellular chemical homeostasis and pathways in cancer, while DEmiRNAs were mainly enriched in signal transduction and Glypican pathway. Moreover, seven hub genes with a high degree, namely, V-myc avian myelocytomatosis viral oncogene homolog (MYC), solute carrier family 2 member 1 (SLC2A1), PKM, plasminogen activator, urokinase (PLAU), peroxisome proliferator activated receptor γ (PPARG), MET proto-oncogene, receptor tyrosine kinase (MET), and integrin subunit α 3 (ITGA3), were identified and found to be up-regulated between PDAC and normal tissues. miR-135b, miR-221, miR-21, miR-27a, miR-199b-5p, miR-143, miR-196a, miR-655, miR-455-3p, miR-744 and hub genes predicted poor OS of PDAC. An integrative bioinformatics analysis identified several hub genes that may serve as potential biomarkers or targets for early diagnosis and precision target treatment of PDAC.

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Identifying MMP14 and COL12A1 as a potential combination of prognostic biomarkers in pancreatic ductal adenocarcinoma using integrated bioinformatics analysis

PeerJ ◽

10.7717/peerj.10419 ◽

2020 ◽

Vol 8 ◽

pp. e10419

Author(s):

Jingyi Ding ◽

Yanxi Liu ◽

Yu Lai

Keyword(s):

Gene Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Prognostic Biomarkers ◽

Ductal Adenocarcinoma ◽

Receptor Interaction ◽

Analysis Tool ◽

Ppi Network ◽

Hub Genes

Background Pancreatic ductal adenocarcinoma (PDAC) is a fatal malignant neoplasm. It is necessary to improve the understanding of the underlying molecular mechanisms and identify the key genes and signaling pathways involved in PDAC. Methods The microarray datasets GSE28735, GSE62165, and GSE91035 were downloaded from the Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified by integrated bioinformatics analysis, including protein–protein interaction (PPI) network, Gene Ontology (GO) enrichment, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The PPI network was established using the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. GO functional annotation and KEGG pathway analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery. Hub genes were validated via the Gene Expression Profiling Interactive Analysis tool (GEPIA) and the Human Protein Atlas (HPA) website. Results A total of 263 DEGs (167 upregulated and 96 downregulated) were common to the three datasets. We used STRING and Cytoscape software to establish the PPI network and then identified key modules. From the PPI network, 225 nodes and 803 edges were selected. The most significant module, which comprised 11 DEGs, was identified using the Molecular Complex Detection plugin. The top 20 hub genes, which were filtered by the CytoHubba plugin, comprised FN1, COL1A1, COL3A1, BGN, POSTN, FBN1, COL5A2, COL12A1, THBS2, COL6A3, VCAN, CDH11, MMP14, LTBP1, IGFBP5, ALB, CXCL12, FAP, MATN3, and COL8A1. These genes were validated using The Cancer Genome Atlas (TCGA) and Genotype–Tissue Expression (GTEx) databases, and the encoded proteins were subsequently validated using the HPA website. The GO analysis results showed that the most significantly enriched biological process, cellular component, and molecular function terms among the 20 hub genes were cell adhesion, proteinaceous extracellular matrix, and calcium ion binding, respectively. The KEGG pathway analysis showed that the 20 hub genes were mainly enriched in ECM–receptor interaction, focal adhesion, PI3K-Akt signaling pathway, and protein digestion and absorption. These findings indicated that FBN1 and COL8A1 appear to be involved in the progression of PDAC. Moreover, patient survival analysis performed via the GEPIA using TCGA and GTEx databases demonstrated that the expression levels of COL12A1 and MMP14 were correlated with a poor prognosis in PDAC patients (p < 0.05). Conclusions The results demonstrated that upregulation of MMP14 and COL12A1 is associated with poor overall survival, and these might be a combination of prognostic biomarkers in PDAC.

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Meta-analysis of 1,200 transcriptomic profiles identifies a prognostic model for pancreatic ductal adenocarcinoma

10.1101/355602 ◽

2018 ◽

Cited By ~ 2

Author(s):

Vandana Sandhu ◽

Knut Jorgen Labori ◽

Ayelet Borgida ◽

Ilinca Lungu ◽

John Bartlett ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Overall Survival ◽

Pancreatic Ductal Adenocarcinoma ◽

Prognostic Model ◽

Meta Analysis ◽

Early Death ◽

Receiver Operating Curve ◽

Ductal Adenocarcinoma ◽

Survival Predictor

ABSTRACTBackgroundWith a dismal 8% median 5-year overall survival (OS), pancreatic ductal adenocarcinoma (PDAC) is highly lethal. Only 10-20% of patients are eligible for surgery, and over 50% of these will die within a year of surgery. Identify molecular predictors of early death would enable the selection of PDAC patients at high risk.MethodsWe developed the Pancreatic Cancer Overall Survival Predictor (PCOSP), a prognostic model built from a unique set of 89 PDAC tumors where gene expression was profiled using both microarray and sequencing platforms. We used a meta-analysis framework based on the binary gene pair method to create gene expression barcodes robust to biases arising from heterogeneous profiling platforms and batch effects. Leveraging the largest compendium of PDAC transcriptomic datasets to date, we show that PCOSP is a robust single-sample predictor of early death (≤1 yr) after surgery in a subset of 823 samples with available transcriptomics and survival data.ResultsThe PCOSP model was strongly and significantly prognostic with a meta-estimate of the area under the receiver operating curve (AUROC) of 0.70 (P=1.9e-18) and hazard ratio (HR) of 1.95(1.6-2.3) (P=2.6e-16) for binary and survival predictions, respectively. The prognostic value of PCOSP was independent of clinicopathological parameters and molecular subtypes. Over-representation analysis of the PCOSP 2619 gene-pairs (1070 unique genes) unveiled pathways associated with Hedgehog signalling, epithelial mesenchymal transition (EMT) and extracellular matrix (ECM) signalling.ConclusionPCOSP could improve treatment decision by identifying patients who will not benefit from standard surgery/chemotherapy and may benefit from alternate approaches.AbbreviationsAUROCArea under the receiver operating curveGOGene annotationOSOverall survivalPCOSPPancreatic cancer overall survival predictorPDACPancreatic ductal adenocarcinomaTSPTop scoring pairs.

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Identification of Key Prognostic Biomarker and Its Correlation with Immune Infiltrates in Pancreatic Ductal Adenocarcinoma

Disease Markers ◽

10.1155/2020/8825997 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12 ◽

Cited By ~ 1

Author(s):

Hong Luan ◽

Chuang Zhang ◽

Tuo Zhang ◽

Ye He ◽

Yanna Su ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Poor Prognosis ◽

Immune Therapy ◽

The Cancer Genome Atlas ◽

Ductal Adenocarcinoma ◽

Immune Checkpoints ◽

Hub Genes ◽

Immune Infiltration ◽

The Relationship

Pancreatic ductal adenocarcinoma (PDAC) is an extremely malignant tumor. The immune profile of PDAC and the immunologic milieu of its tumor microenvironment (TME) are unique; however, the mechanism of how the TME engineers the carcinogenesis of PDAC is not fully understood. This study is aimed at better understanding the relationship between the immune infiltration of the TME and gene expression and identifying potential prognostic and immunotherapeutic biomarkers for PDAC. Analysis of data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases identified differentially expressed genes (DEGs), including 159 upregulated and 53 downregulated genes. Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes enrichment were performed and showed that the DEGs were mainly enriched for the PI3K-Akt signaling pathway and extracellular matrix organization. We used the cytoHubba plugin of Cytoscape to screen out the most significant ten hub genes by four different models (Degree, MCC, DMNC, and MNC). The expression and clinical relevance of these ten hub genes were validated using Gene Expression Profiling Interactive Analysis (GEPIA) and the Human Protein Atlas, respectively. High expression of nine of the hub genes was positively correlated with poor prognosis. Finally, the relationship between these hub genes and tumor immunity was analyzed using the Tumor Immune Estimation Resource. We found that the expression of SPARC, COL6A3, and FBN1 correlated positively with infiltration levels of six immune cells in the tumors. In addition, these three genes had a strong coexpression relationship with the immune checkpoints. In conclusion, our results suggest that nine upregulated biomarkers are related to poor prognosis in PDAC and may serve as potential prognostic biomarkers for PDAC therapy. Furthermore, SPARC, COL6A3, and FBN1 play an important role in tumor-related immune infiltration and may be ideal targets for immune therapy against PDAC.

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Local and Systemic Cytokine Profiling for Pancreatic Ductal Adenocarcinoma to Study Cancer Cachexia in an Era of Precision Medicine

International Journal of Molecular Sciences ◽

10.3390/ijms19123836 ◽

2018 ◽

Vol 19 (12) ◽

pp. 3836 ◽

Cited By ~ 6

Author(s):

Michael H. Gerber ◽

Patrick W. Underwood ◽

Sarah M. Judge ◽

Daniel Delitto ◽

Andrea E. Delitto ◽

...

Keyword(s):

Gene Expression ◽

Pancreatic Cancer ◽

Pancreatic Ductal Adenocarcinoma ◽

Cancer Cachexia ◽

Soluble Proteins ◽

Triceps Surae ◽

Human Pancreatic Cancer ◽

Ductal Adenocarcinoma ◽

Related Gene ◽

Pdx Models

Cancer cachexia is a debilitating condition seen frequently in patients with pancreatic ductal adenocarcinoma (PDAC). The underlying mechanisms driving cancer cachexia are not fully understood but are related, at least in part, to the immune response to the tumor both locally and systemically. We hypothesize that there are unique differences in cytokine levels in the tumor microenvironment and systemic circulation between PDAC tumors and that these varying profiles affect the degree of cancer cachexia observed. Patient demographics, operative factors, oncologic factors, and perioperative data were collected for the two patients in the patient derived xenograft (PDX) model. Human pancreatic cancer PDX were created by implanting fresh surgical pancreatic cancer tissues directly into immunodeficient mice. At PDX end point, mouse tumor, spleen and muscle tissues were collected and weighed, muscle atrophy related gene expression measured, and tumor and splenic soluble proteins were analyzed. PDX models were created from surgically resected patients who presented with different degrees of cachexia. Tumor free body weight and triceps surae weight differed significantly between the PDX models and control (P < 0.05). Both PDX groups had increased atrophy related gene expression in muscle compared to control (FoxO1, Socs3, STAT3, Acvr2b, Atrogin-1, MuRF1; P < 0.05). Significant differences were noted in splenic soluble protein concentrations in 14 of 15 detected proteins in tumor bearing mice when compared to controls. Eight splenic soluble proteins were significantly different between PDX groups (P < 0.05). Tumor soluble proteins were significantly different between the two PDX groups in 15 of 24 detected proteins (P < 0.05). PDX models preserve the cachectic heterogeneity found in patients and are associated with unique cytokine profiles in both the spleen and tumor between different PDX. These data support the use of PDX as a strategy to study soluble cachexia protein markers and also further efforts to elucidate which cytokines are most related to cachexia in order to provide potential targets for immunotherapy.

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Comprehensive Analysis of the Transcriptional Expressions and Prognostic Value of S100A Family in Pancreatic Ductal Adenocarcinoma

10.21203/rs.3.rs-149466/v1 ◽

2021 ◽

Author(s):

Hong-Bin Li ◽

Jian-Li Wang ◽

Xiao-Dong Jin ◽

Lei Zhao ◽

Hui-Li Ye ◽

...

Keyword(s):

Pancreatic Cancer ◽

Pancreatic Ductal Adenocarcinoma ◽

Prognostic Value ◽

Pancreatic Cancer Cell ◽

Expression Patterns ◽

Ductal Adenocarcinoma ◽

Tissue Samples ◽

Cancer Types ◽

Cancer Tissues ◽

Refractory Malignancy

Abstract Background Pancreatic ductal adenocarcinoma (PDAC) remains a treatment-refractory malignancy with poor prognosis. It is urgent to identify novel and valid biomarkers to predict the progress and prognosis of PDAC. The S100A family have been identified as being involved in cell proliferation, migration and differentiation progression of various cancer types. However, the expression patterns and prognostic values of S100As in PDAC remain to be analyzed. Methods We investigated the transcriptional expressions, methylation level and prognostic value of S100As in PDAC patients from the Oncomine, GEPIA2, Linkedomics, and cBioPortal databases. Real-time PCR was used to detect the expressions of S100A2/4/6/10/14/16 in four pancreatic cancer cell lines and pancreatic cancer tissues from PDAC patients undergoing surgery. To verify the results further, immunohistochemistry was used to measure the expression of S100A2/4/6/10/14/16 in 43 PDAC patients’ tissue samples. The drug relations of S100As were also analyzed by using Drugbank. Results The results suggested that, the expression levels of S100A2/4/6/10/14/16 were elevated to PDAC tissues than in normal pancreatic tissues, and the promoter methylation levels of S100A S100A2/4/6/10/14/16 in PDAC (n = 10) were lower compared with normal tissue (n = 184) (P < 0.05). In addition, their expressions were negatively correlated with PDAC patient survival. Conclusions Taken together, these results suggest that S100A2/4/6/10/14/16 might be served as prognostic biomarkers for survivals of PDAC patients.

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