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

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
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.

scholarly journals High JAK2 Protein Expression Predicts a Poor Prognosis in Patients with Resectable Pancreatic Ductal Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yang Song ◽  
Mei-Yue Tang ◽  
Wei Chen ◽  
Zhe Wang ◽  
Si-Liang Wang
Keyword(s):  
Protein Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Poor Prognosis ◽  
Prognostic Value ◽  
Survival Curve ◽  
Clinicopathological Features ◽  
Ductal Adenocarcinoma ◽  
Clinicopathological Analysis ◽  
Primary Focus ◽  
The Relationship

Background. Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. The JAK/STAT signaling pathway is involved in pancreatic cancer tumorigenesis. However, the prognostic value of JAK2 expression in resectable PDAC is unclear. Method. In this study, we performed a clinicopathological analysis of 62 resectable PDAC cases with a primary focus on survival. JAK2 expression was examined by immunohistochemistry. The relationship between JAK2 expression and clinicopathological features and prognosis was analyzed. Results. Survival curve analyses revealed that high levels of JAK2 expression predict a poor prognosis in resectable PDAC patients. Multivariate analysis confirmed that JAK2 expression can predict the prognosis of PDAC. Conclusions. Assessment of JAK2 protein expression may be a promising method to predict prognosis in patients with resectable PDAC.

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

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.

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

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.

scholarly journals Network analyses of circular RNAs expression profiles and their hub genes in pancreatic ductal adenocarcinoma

2019 ◽  
Author(s):  
Yanyan Tang ◽  
Ping Zhang
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Signaling Pathway ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Ductal Adenocarcinoma ◽  
Ppi Network ◽  
Hub Genes ◽  
Kegg Pathway Analysis

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignant tumor in digestive system. CircRNAs involve in lots of biological processes through interacting with miRNAs and their targeted mRNA. We obtained the circRNA gene expression profiles from Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) between PDAC samples and paracancerous tissues. Bioinformatics analyses, including GO analysis, KEGG pathway analysis and PPI network analysis, were conducted for further investigation. We also constructed circRNA‑microRNA-mRNA co-expression network. A total 291 differentially expressed circRNAs were screened out. The GO enrichment analysis revealed that up-regulated DEGs were mainly involved metabolic process, biological regulation, and gene expression, and down-regulated DEGs were involved in cell communication, single-organism process, and signal transduction. The KEGG pathway analysis, the upregulated circRNAs were enriched cGMP-PKG signaling pathway, and HTLV-I infection, while the downregulated circRNAs were enriched in protein processing in endoplasmic reticulum, insulin signaling pathway, regulation of actin cytoskeleton, etc. Four genes were identified from PPI network as both hub genes and module genes, and their circRNA‑miRNA-mRNA regulatory network also be constructed. Our study indicated possible involvement of dysregulated circRNAs in the development of PDAC and promoted our understanding of the underlying molecular mechanisms.

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

PeerJ ◽  
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.

scholarly journals Identification of FEZ2 as a potential oncogene in pancreatic ductal adenocarcinoma

PeerJ ◽  
2022 ◽  
Vol 9 ◽  
pp. e12736
Author(s):  
Chaozhi Yang ◽  
Xuebing Wang ◽  
Chenjie Qiu ◽  
Ziruo Zheng ◽  
Kai Lin ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Cancer Progression ◽  
Poor Prognosis ◽  
Malignant Tumors ◽  
Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Pathway Enrichment Analysis ◽  
Conserved Gene

Pancreatic ductal adenocarcinoma (PDAC) is one of the common malignant tumors with high lethal rate and poor prognosis. Dysregulation of many genes have been reported to be involved in the occurrence and development of PDAC. However, as a highly conserved gene in eukaryotes, the role of Fasciculation and Elongation protein Zeta 2 (FEZ2) in pancreatic cancer progression is not clear. In this study, we identified the oncogenic effect of FEZ2 on PDAC. By mining of The Cancer Genome Atlas (TCGA) database, we found that FEZ2 was upregulated in PDAC tissues and FEZ2 expression was negatively regulated by its methylation. Moreover, high expression and low methylation of FEZ2 correlated with poor prognosis in PDAC patients. Besides, we found that FEZ2 could promote PDAC cells proliferation, migration and 5-FU resistance in vitro. Furthermore, Gene pathway enrichment analysis demonstrated a positive correlation between Wnt signaling activation and FEZ2 expression in PDAC patients. Western blot showed that FEZ2 knockdown significantly suppressed β-catenin expression. Collectively, our finding revealed that FEZ2 functioned as a potential oncogene on PDAC progression and migration, and the expression of FEZ2 had guidance value for the treatment and chemotherapy program of PDAC patients.

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

2021 ◽  
Vol 11 (1) ◽  
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

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.

scholarly journals Comprehensive analysis of abnormal expression, prognostic value and oncogenic role of the hub gene FN1 in pancreatic ductal adenocarcinoma via bioinformatic analysis and in vitro experiments

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12141
Author(s):  
Xiaohua Lei ◽  
Guodong Chen ◽  
Jiangtao Li ◽  
Wu Wen ◽  
Jian Gong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Bioinformatic Analysis ◽  
Ductal Adenocarcinoma ◽  
Therapeutic Effects ◽  
Hub Genes ◽  
Poor Overall Survival ◽  
Geo Dataset

Background Pancreatic ductal adenocarcinoma (PDAC) is one of the most commonly diagnosed cancers with a poor prognosis worldwide. Although the treatment of PDAC has made great progress in recent years, the therapeutic effects are still unsatisfactory. Methods. In this study, we identified differentially expressed genes (DEGs) between PDAC and normal pancreatic tissues based on four Gene Expression Omnibus (GEO) datasets (GSE15471, GSE16515, GSE28735 and GSE71729). A protein–protein interaction (PPI) network was established to evaluate the relationship between the DEGs and to screen hub genes. The expression levels of the hub genes were further validated through the Gene Expression Profiling Interactive Analysis (GEPIA), ONCOMINE and Human Protein Atlas (HPA) databases, as well as the validation GEO dataset GSE62452. Additionally, the prognostic values of the hub genes were evaluated by Kaplan–Meier plotter and the validation GEO dataset GSE62452. Finally, the mechanistic roles of the most remarkable hub genes in PDAC were examined through in vitro experiments. Results We identified the following nine hub genes by performing an integrated bioinformatics analysis: COL1A1, COL1A2, FN1, ITGA2, KRT19, LCN2, MMP9, MUC1 and VCAN. All of the hub genes were significantly upregulated in PDAC tissues compared with normal pancreatic tissues. Two hub genes (FN1 and ITGA2) were associated with poor overall survival (OS) rates in PDAC patients. Finally, in vitro experiments indicated that FN1 plays vital roles in PDAC cell proliferation, colony formation, apoptosis and the cell cycle. Conclusions In summary, we identified two hub genes that are associated with the expression and prognosis of PDAC. The oncogenic role of FN1 in PDAC was first illustrated by performing an integrated bioinformatic analysis and in vitro experiments. Our results provide a fundamental contribution for further research aimed finding novel therapeutic targets for overcoming PDAC.

scholarly journals RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Benchen Rao ◽  
Jianhao Li ◽  
Tong Ren ◽  
Jing Yang ◽  
Guizhen Zhang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Network Analysis ◽  
Tumor Progression ◽  
Poor Prognosis ◽  
Molecular Mechanisms ◽  
The Cancer Genome Atlas ◽  
Bile Acid Metabolism ◽  
Hub Genes ◽  
Immune Infiltration ◽  
Tumor Tissues

BackgroundHepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.MethodsThe differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.ResultsA total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p &lt; 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.ConclusionOur study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

scholarly journals High expression of PARD3 predicts poor prognosis in hepatocellular carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Songwei Li ◽  
Jian Huang ◽  
Fan Yang ◽  
Haiping Zeng ◽  
Yuyun Tong ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Dendritic Cells ◽  
Poor Prognosis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Predictive Capacity ◽  
Immune Infiltration ◽  
Cancer Genome Atlas ◽  
Liver Hepatocellular Carcinoma ◽  
The Relationship

AbstractHepatocellular carcinoma (HCC) is one of the most commonly cancers with poor prognosis and drug response. Identifying accurate therapeutic targets would facilitate precision treatment and prolong survival for HCC. In this study, we analyzed liver hepatocellular carcinoma (LIHC) RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA), and identified PARD3 as one of the most significantly differentially expressed genes (DEGs). Then, we investigated the relationship between PARD3 and outcomes of HCC, and assessed predictive capacity. Moreover, we performed functional enrichment and immune infiltration analysis to evaluate functional networks related to PARD3 in HCC and explore its role in tumor immunity. PARD3 expression levels in 371 HCC tissues were dramatically higher than those in 50 paired adjacent liver tissues (p < 0.001). High PARD3 expression was associated with poor clinicopathologic feathers, such as advanced pathologic stage (p = 0.002), vascular invasion (p = 0.012) and TP53 mutation (p = 0.009). Elevated PARD3 expression also correlated with lower overall survival (OS, HR = 2.08, 95% CI = 1.45–2.98, p < 0.001) and disease-specific survival (DSS, HR = 2.00, 95% CI = 1.27–3.16, p = 0.003). 242 up-regulated and 71 down-regulated genes showed significant association with PARD3 expression, which were involved in genomic instability, response to metal ions, and metabolisms. PARD3 is involved in diverse immune infiltration levels in HCC, especially negatively related to dendritic cells (DCs), cytotoxic cells, and plasmacytoid dendritic cells (pDCs). Altogether, PARD3 could be a potential prognostic biomarker and therapeutic target of HCC.

Sign in / Sign up

Export Citation Format

Share Document