scholarly journals Identification and Validation of Three PDAC Subtypes and Individualized GSVA Immune Pathway-Related Prognostic Risk Score Formula in Pancreatic Ductal Adenocarcinoma Patients

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Deyu Zhang ◽  
Meiqi Wang ◽  
Lisi Peng ◽  
Xiaoli Yang ◽  
Keliang Li ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Potential Role ◽  
Ductal Adenocarcinoma ◽  
Immune Microenvironment ◽  
Hub Genes ◽  
Tumor Immune Microenvironment ◽  
Immune Pathway ◽  
Related Pathway

Background. With the progress of precision medicine treatment in pancreatic ductal adenocarcinoma (PDAC), individualized cancer-related medical examination and prediction are of great importance in this high malignant tumor and tumor-immune microenvironment with changed pathways highly enrolled in the carcinogenesis of PDAC. Methods. High-throughput data of pancreatic ductal adenocarcinoma were downloaded from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database. After batch normalization, the enrichment pathway and relevant scores were identified by the enrichment of immune-related pathway signature using gene set variation analysis (GSVA). Then, cancerous subtype in TCGA and GEO samples was defined through the NMF methods by cancertypes packages in R software, respectively. Subsequently, the significance between the characteristics of each TCGA sample and cancer type and the significant prognosis-related pathway with risk score formula is calculated through t-test and univariate Cox analysis. Next, the prognostic value of gained risk score formula and each significant prognosis-related pathway were validated in TCGA and GEO samples by survival analysis. The pivotal hub genes in the enriched significant prognosis-related pathway are identified and validated, and the TIMER database was used to identify the potential role of hub genes in the PDAC immune environment. The potential role of hub genes is promoting the transdifferentiation of cancer-associated fibroblasts. Results. The enrichment pathway and relevant scores were identified by GSVA, and 3 subtypes of pancreatic ductal adenocarcinoma were defined in TCGA and GEO samples. The clinical stage, tumor node metastasis classification, and tumor grade are strongly relative to the subtype above in TCGA samples. A risk formula about GSVA significant pathway “GSE45365_WT_VS_IFNAR_KO_CD11B_DC_MCMV_INFECTION_DN ∗ 0.80 + HALLMARK_GLYCOLYSIS ∗ 16.8 + GSE19888_CTRL_VS_T_CELL_MEMBRANES_ACT_MAST_CELL_DN ∗ 14.4” was identified and validated in TCGA and GEO samples through survival analysis with significance. DCN, VCAN, B4GALT7, SDC1, SDC2, B3GALT6, B3GAT3, SDC3, GPC1, and XYLT2 were identified as hub genes in these GSVA significant pathways and validated in silico. Conclusions. Three pancreatic ductal adenocarcinoma subtypes are identified, and an individualized GSVA immune pathway score-related prognostic risk score formula with 10 hub genes is identified and validated. The predicted function of the 10 upregulated hub genes in tumor-immune microenvironment was promoting the infiltration of cancer-associated fibroblasts. These findings will contribute to the precision medicine of pancreatic ductal adenocarcinoma treatment and tumor immune-related basic research.

scholarly journals Role of tumor mutation burden-related signatures in the prognosis and immune microenvironment of pancreatic ductal adenocarcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rong Tang ◽  
Xiaomeng Liu ◽  
Wei Wang ◽  
Jie Hua ◽  
Jin Xu ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Predictive Accuracy ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Immune Microenvironment ◽  
Tumor Mutation Burden ◽  
Oncogenic Pathways ◽  
Mutation Burden ◽  
Cancer Genome Atlas

Abstract Background High tumor mutation burden (TMB) has gradually become a sensitive biomarker for predicting the response to immunotherapy in many cancers, including lung, bladder and head and neck cancers. However, whether high TMB predicts the response to immunotherapy and prognosis in pancreatic ductal adenocarcinoma (PDAC) remained obscure. Hence, it is significant to investigate the role of genes related to TMB (TRGs) in PDAC. Methods The transcriptome and mutation data of PDAC was downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA). Five independent external datasets of PDAC were chosen to validate parts of our results. qRT-PCR and immunohistochemical staining were also performed to promote the reliability of this study. Results The median overall survival (OS) was significantly increased in TMB_low group compared with the counterpart with higher TMB score after tumor purity adjusted (P = 0.03). 718 differentially expressed TRGs were identified and functionally enriched in some oncogenic pathways. 67 TRGs were associated with OS in PDAC. A prognostic model for the OS was constructed and showed a high predictive accuracy (AUC = 0.849). We also found TMB score was associated with multiple immune components and signatures in tumor microenvironment. In addition, we identified a PDAC subgroup featured with TMBlowMicrosatellite instabilityhigh (MSIhigh) was associated with prolonged OS and a key molecule, ANKRD55, potentially mediating the survival benefits. Conclusion This study analyzed the biological function, prognosis value, implications for mutation landscape and potential influence on immune microenvironment of TRGs in PDAC, which contributed to get aware of the role of TMB in PDAC. Future studies are expected to investigate how these TRGs regulate the initiation, development or repression of PDAC.

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 The role of m6A RNA methylation-related lncRNAs in the prognosis and tumor immune microenvironment of papillary thyroid carcinoma

2021 ◽  
Author(s):  
WenLong Wang ◽  
Cong Shen ◽  
Yunzhe Zhao ◽  
Botao Sun ◽  
Xiangyuan Qiu ◽  
...  
Keyword(s):  
High Risk ◽  
Risk Score ◽  
Cox Regression ◽  
Consensus Clustering ◽  
Papillary Thyroid ◽  
Immune Microenvironment ◽  
Rna Methylation ◽  
Tumor Immune Microenvironment ◽  
M6a Rna Methylation

Abstract Background: Emerging evidence has indicated that N6-methylandenosine (m6A) RNA methylation plays a critical role in cancer development. However, the function of m6A RNA methylation-related long noncoding RNAs (m6A-lncRNAs) in papillary thyroid carcinoma (PTC) has never been reported. This study aimed to investigate the role of m6A-lncRNAs in the prognosis and tumor immune microenvironment of PTC.Methods: The gene expression data of lncRNAs and 20 m6A methylation regulators with corresponding clinicopathological information download from the Cancer Genome Atlas database. Based on consensus clustering analysis, LASSO Cox regression, nivariate and multivariate Cox regression analysis were used to determine the role of m6A-lncRNA in the prognosis and tumor immune microenvironment of PTC.Results: Three subgroups (clusters 1, 2, and 3) were identified by consensus clustering of 19 prognosis-related m6A-lncRNA regulators，of which cluster 1 preferentially related with unfavorable prognosis, lower immune scores, and distinct immune infiltrate level. A risk-score model was established based on 8 prognosis-related m6A-lncRNAs. Patients with a high-risk score had a worse prognosis and the ROC indicated a reliable prediction performance for patients with PTC (AUC=0.802). As expected, the immune scores, infiltration levels of immune cells and ESTIMATE scores in the low-risk subgroups were notably higher (p < 0.001) compared with those of high-risk subgroups. Furthermore, GSEA analysis showed that tumor associated pathways, hallmarks, and biological processes were remarkably enriched in the high-risk subgroup. Further analysis indicated that the risk score and age were independent prognostic factors for PTC. An integrated nomogram was constructed that accurately predicted the survival status (AUC = 0.963). Moreover, a lncRNA–miRNA–mRNA regulated network was established based on seven prognosis-related m6A-lncRNAs. Additional, 30 clinical samples and different PTC cells were validated. Conclusions: This is the first study to reveal that m6A-lncRNAs play a vital role in the prognosis and TME of PTC. To a certain degree, m6A-lncRNAs can be considered as new, promising prognostic biomarkers and treatment targets.

scholarly journals Clinical M2 macrophages-related genes to aid therapy in pancreatic ductal adenocarcinoma

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qianhui Xu ◽  
Shaohuai Chen ◽  
Yuanbo Hu ◽  
Wen Huang
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Risk Score ◽  
Roc Curves ◽  
R Package ◽  
Ductal Adenocarcinoma ◽  
M2 Macrophage ◽  
M2 Macrophages ◽  
Hub Genes ◽  
Kaplan Meier ◽  
Precision Therapy

Abstract Background Increasing evidence supports that infiltration M2 Macrophages act as pivotal player in tumor progression of pancreatic ductal adenocarcinoma (PDAC). Nonetheless, comprehensive analysis of M2 Macrophage infiltration and biological roles of hub genes (FAM53B) in clinical outcome and immunotherapy was lack. Method The multiomic data of PDAC samples were downloaded from distinct datasets. CIBERSORT algorithm was performed to uncover the landscape of TIME. Weighted gene co-expression network analysis (WGCNA) was performed to identify candidate module and significant genes associated with M2 Macrophages. Kaplan-Meier curve and receiver operating characteristic (ROC) curves were applied for prognosis value validation. Mutation data was analyzed by using “maftools” R package. Gene set variation analysis (GSVA) was employed to assign pathway activity estimates to individual sample. Immunophenoscore (IPS) was implemented to estimate immunotherapeutic significance of risk score. The half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs was predicted by using the pRRophetic algorithm. Finally, quantitative real-time polymerase chain reaction was used to determine FAM53B mRNA expression and TIMER database was utilized to uncover its possible role in immune infiltration of PDAC. Results Herein, 17,932 genes in 234 samples (214 tumor and 20 normal) were extracted from three platforms. Taking advantage of WGCNA, significant module (royalblue) and 135 candidate genes were considered as M2 Macrophages-related genes. Subsequently, risk signature including 5 hub genes was developed by multiple analysis, which exhibited excellent prognostic performance. Besides, comprehensive prognostic nomogram was constructed to quantitatively estimate risk. Then, intrinsic link between risk score with tumor mutation burden (TMB) was explored. Additionally, risk score significantly correlated with diversity of tumor immune microenvironment (TIME). PDAC samples within different risk presented diverse signaling pathways activity and experienced significantly distinct sensitivity to administering chemotherapeutic or immunotherapeutic agents. Finally, the biological roles of FAM53B were revealed in PDAC. Conclusions Taken together, comprehensive analyses of M2 Macrophages profiling will facilitate prognostic prediction, delineating complexity of TIME, and contribute insight into precision therapy for PDAC.

scholarly journals Analysis of m6A-Related Signatures in the Tumor Immune Microenvironment and Identification of Clinical Prognostic Regulators in Adrenocortical Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yi Jin ◽  
Zhanwang Wang ◽  
Dong He ◽  
Yuxing Zhu ◽  
Xueying Hu ◽  
...  
Keyword(s):  
Adrenocortical Carcinoma ◽  
Molecular Mechanisms ◽  
Potential Role ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Immune Microenvironment ◽  
Tumor Immune Microenvironment

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with a high rate of mortality and recurrence. N6-methyladenosine methylation (m6A) is the most common modification to affect cancer development, but to date, the potential role of m6A regulators in ACC prognosis is not well understood. In this study, we systematically analyzed 21 m6A regulators in ACC samples from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. We identified three m6A modification patterns with different clinical outcomes and discovered a significant relationship between diverse m6A clusters and the tumor immune microenvironment (immune cell types and ESTIMATE algorithm). Additionally, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) revealed that the m6A clusters were strongly associated with immune infiltration in the ACC. Next, to further explore the m6A prognostic signatures in ACC, we implemented Lasso (Least Absolute Shrinkage and Selection Operator) Cox regression to establish an eight-m6A-regulator prognostic model in the TCGA dataset, and the results showed that the model-based high-risk group was closely correlated with poor overall survival (OS) compared with the low-risk group. Subsequently, we validated the key modifications in the GEO datasets and found that high HNRNPA2B1 expression resulted in poor OS and event-free survival (EFS) in ACC. Moreover, to further decipher the molecular mechanisms, we constructed a competing endogenous RNA (ceRNA) network based on HNRNPA2B1, which consists of 12 long noncoding RNAs (lncRNAs) and 1 microRNA (miRNA). In conclusion, our findings indicate the potential role of m6A modification in ACC, providing novel insights into ACC prognosis and guiding effective immunotherapy.

scholarly journals Role of Tumor Mutation Burden-related Signatures in the Prognosis and Immune Microenvironment of Pancreatic Ductal Adenocarcinoma

2021 ◽  
Author(s):  
Rong Tang ◽  
Xiaomeng Liu ◽  
Wei Wang ◽  
Jie Hua ◽  
Jin Xu ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Predictive Accuracy ◽  
The Cancer Genome Atlas ◽  
Ductal Adenocarcinoma ◽  
Immune Microenvironment ◽  
Tumor Mutation Burden ◽  
Oncogenic Pathways ◽  
Mutation Burden ◽  
Cancer Genome Atlas

Abstract Background High tumor mutation burden (TMB) has gradually become a sensitive biomarker for predicting the response to immunotherapy in many cancers, including lung, bladder and head and neck cancers. Nonetheless, whether high TMB could predict the response to immunotherapy and prognosis in pancreatic ductal adenocarcinoma (PDAC), a classic “cold” tumor, remained obscure. Hence, it is significant to investigate the role of genes related to TMB (TRGs) in PDAC.Methods The transcriptome and mutation data of PDAC was downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA). Five independent external datasets of PDAC were chosen to validate parts of our results. qRT-PCR and immunohistochemical staining were also performed to promote the reliability of this study. Results The median overall survival (OS) was significantly increased in TMB_low group compared with the counterpart with higher TMB score after tumor purity adjusted (P = 0.03). 718 differentially expressed TRGs were identified and functionally enriched in some oncogenic pathways. 67 TRGs were associated with OS in PDAC. A prognostic model for the OS was constructed and showed a high predictive accuracy (AUC = 0.849). We also found TMB score was associated with multiple immune components and signatures in tumor microenvironment. In addition, we identified a PDAC subgroup featured with TMBlowMSIhigh was associated with prolonged OS and a key molecule, ANKRD55, potentially mediating the survival benefits.Conclusion This study analyzed the biological function, prognosis value, implications for mutation landscape and potential influence on immune microenvironment of TRGs in PDAC, which contributed to get aware of the role of TMB in PDAC. Future studies are expected to investigate how these TRGs regulate the initiation, development or repression of PDAC.

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