Prognostic Biomarkers and Immunotherapeutic Targets Among CXC Chemokines in Pancreatic Adenocarcinoma

BackgroundPancreatic cancer is one of the principal causes of tumor-related death worldwide. CXC chemokines, a subfamily of functional chemotactic peptides, affect the initiation of tumor cells and clinical outcomes in several human malignant tumors. However, the specific biological functions and clinical significance of CXC chemokines in pancreatic cancer have not been clarified.MethodsBioinformatics analysis tools and databases, including ONCOMINE, GEPIA2, the Human Protein Atlas, DAVID, GeneMANIA, cBioPortal, STRING, DGidb, MethSurv, TRRUST, SurvExpress, SurvivalMeth, and TIMER, were utilized to clarify the clinical significance and biological functions of CXC chemokine in pancreatic cancer.ResultsExcept for CXCL11/12, the transcriptional levels of other CXC chemokines in PAAD tissues were significantly elevated, and the expression level of CXCL16 was the highest among these CXC chemokines. Our findings also suggested that all of the CXC chemokines were linked to tumor-immune dysfunction involving the abundance of immune cell infiltration, and the Cox proportional hazard model confirmed that dendritic and CXCL3/5/7/8/11/17 were significantly associated with the clinical outcome of PAAD patients. Furthermore, increasing expressions of CXCL5/9/10/11/17 were related to unfavorable overall survival (OS), and only CXCL17 was a prognostic factor for disease-free survival (DFS) in PAAD patients. The expression pattern and prognostic power of CXC chemokines were further validated in the independent GSE62452 dataset. For the prognostic value of single CpG of DNA methylation of CXC chemokines in patients with PAAD, we identified 3 CpGs of CXCL1, 2 CpGs of CXCL2, 2 CpGs of CXCL3, 3 CpGs of CXCL4, 10 CpGs of CXCL5, 1 CpG of CXCL6, 1 CpG of CXCL7, 3 CpGs of CXCL12, 3 CpGs of CXCL14, and 5 CpGs of CXCL17 that were significantly associated with prognosis in PAAD patients. Moreover, the prognostic value of CXC chemokine signature in PAAD was explored and tested in two independent cohort, and results indicated that the patients in the low-risk group had a better OS compared with the high-risk group. Survival analysis of the DNA methylation of CXC chemokine signature demonstrated that PAAD patients in the high-risk group had longer survival times.ConclusionsThese findings reveal the novel insights into CXC chemokine expression and their biological functions in the pancreatic cancers, which might serve as accurate prognostic biomarkers and suitable immunotherapeutic targets for patients with pancreatic cancer.

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Identifying an immune-related gene-pair for prognosis prediction of metastatic colorectal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.e15565 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. e15565-e15565

Author(s):

Qiqi Zhu ◽

Du Cai ◽

Wei Wang ◽

Min-Er Zhong ◽

Dejun Fan ◽

...

Keyword(s):

Colorectal Cancer ◽

High Risk ◽

Prognostic Value ◽

Validation Cohort ◽

Risk Group ◽

High Risk Group ◽

Related Gene ◽

Training Cohort ◽

Gene Pairs ◽

Immune Related Gene

e15565 Background: Few robust predictive biomarkers have been applied in clinical practice due to the heterogeneity of metastatic colorectal cancer (mCRC) . Using the gene pair method, the absolute expression value of genes can be converted into the relative order of genes, which can minimize the influence of the sequencing platform difference and batch effects, and improve the robustness of the model. The main objective of this study was to establish an immune-related gene pairs signature (IRGPs) and evaluate the impact of the IRGPs in predicting the prognosis in mCRC. Methods: A total of 205 mCRC patients containing overall survival (OS) information from the training cohort ( n = 119) and validation cohort ( n = 86) were enrolled in this study. LASSO algorithm was used to select prognosis related gene pairs. Univariate and multivariate analyses were used to validate the prognostic value of the IRGPs. Gene sets enrichment analysis (GSEA) and immune infiltration analysis were used to explore the underlying biological mechanism. Results: An IRGPs signature containing 22 gene pairs was constructed, which could significantly separate patients of the training cohort ( n = 119) and validation cohort ( n = 86) into the low-risk and high-risk group with different outcomes. Multivariate analysis with clinical factors confirmed the independent prognostic value of IRGPs that higher IRGPs was associated with worse prognosis (training cohort: hazard ratio (HR) = 10.54[4.99-22.32], P < 0.001; validation cohort: HR = 3.53[1.24-10.08], P = 0.012). GSEA showed that several metastasis and immune-related pathway including angiogenesis, TGF-β-signaling, epithelial-mesenchymal transition and inflammatory response were enriched in the high-risk group. Through further analysis of the immune factors, we found that the proportions of CD4+ memory T cell, regulatory T cell, and Myeloid dendritic cell were significantly higher in the low-risk group, while the infiltrations of the Macrophage (M0) and Neutrophil were significantly higher in the high-risk group. Conclusions: The IRGPs signature could predict the prognosis of mCRC patients. Further prospective validations are needed to confirm the clinical utility of IRGPs in the treatment decision.

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Gene signature for prognosis in comparison of pancreatic cancer patients with diabetes and non-diabetes

PeerJ ◽

10.7717/peerj.10297 ◽

2020 ◽

Vol 8 ◽

pp. e10297

Author(s):

Mingjun Yang ◽

Boni Song ◽

Juxiang Liu ◽

Zhitong Bing ◽

Yonggang Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

High Risk ◽

Prognostic Value ◽

Prognostic Biomarker ◽

Risk Group ◽

Gene Signature ◽

Low Risk ◽

Risk Genes ◽

Genetic Biomarkers ◽

Patients With Diabetes

Background Pancreatic cancer (PC) has much weaker prognosis, which can be divided into diabetes and non-diabetes. PC patients with diabetes mellitus will have more opportunities for physical examination due to diabetes, while pancreatic cancer patients without diabetes tend to have higher risk. Identification of prognostic markers for diabetic and non-diabetic pancreatic cancer can improve the prognosis of patients with both types of pancreatic cancer. Methods Both types of PC patients perform differently at the clinical and molecular levels. The Cancer Genome Atlas (TCGA) is employed in this study. The gene expression of the PC with diabetes and non-diabetes is used for predicting their prognosis by LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression. Furthermore, the results are validated by exchanging gene biomarker with each other and verified by the independent Gene Expression Omnibus (GEO) and the International Cancer Genome Consortium (ICGC). The prognostic index (PI) is generated by a combination of genetic biomarkers that are used to rank the patient’s risk ratio. Survival analysis is applied to test significant difference between high-risk group and low-risk group. Results An integrated gene prognostic biomarker consisted by 14 low-risk genes and six high-risk genes in PC with non-diabetes. Meanwhile, and another integrated gene prognostic biomarker consisted by five low-risk genes and three high-risk genes in PC with diabetes. Therefore, the prognostic value of gene biomarker in PC with non-diabetes and diabetes are all greater than clinical traits (HR = 1.102, P-value < 0.0001; HR = 1.212, P-value < 0.0001). Gene signature in PC with non-diabetes was validated in two independent datasets. Conclusions The conclusion of this study indicated that the prognostic value of genetic biomarkers in PCs with non-diabetes and diabetes. The gene signature was validated in two independent databases. Therefore, this study is expected to provide a novel gene biomarker for predicting prognosis of PC with non-diabetes and diabetes and improving clinical decision.

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Prognostic Value of Drug Targets Predicted using Deep Bioinformatic Analysis of M6A-associated lncRNA-based Pancreatic Cancer Model Characteristics and its Tumour Microenvironment

10.21203/rs.3.rs-1200367/v1 ◽

2021 ◽

Author(s):

Peng-wei Cao ◽

Lei Liu ◽

Zi-Han Li ◽

Feng Cao ◽

Fu-Bao Liu

Keyword(s):

Pancreatic Cancer ◽

High Risk ◽

Immune Function ◽

Immune Evasion ◽

Drug Sensitivity ◽

Risk Group ◽

Risk Groups ◽

Tumour Microenvironment ◽

High Risk Group ◽

Low Risk

Abstract Background: The role of N6-methyladenosine (m6A)-associated long-stranded non-coding RNA (lncRNA) in pancreatic cancer is unclear. Therefore, we analysed the characteristics and tumour microenvironment in pancreatic cancer and determined the value of m6A-related lncRNAs for prognosis and drug target prediction.Methods: An m6A-lncRNA co-expression network was constructed using The Cancer Genome Atlas database to screen m6A-related lncRNAs. Prognosis-related lncRNAs were screened using univariate Cox regression; patients were divided into high- and low-risk groups and randomised into training and test groups. In the training group, least absolute shrinkage and selection operator (LASSO) was used for regression analysis and to construct a prognostic model, which was validated in the test group. Tumour mutational burden (TMB), immune evasion, and immune function of risk genes were analysed using R; drug sensitivity and potential drugs were examined using the Genomics of Drug Sensitivity in Cancer database.Results: We screened 129 m6A-related lncRNAs; 17 prognosis-related m6A-related lncRNAs were obtained using multivariate analysis and three m6A-related lncRNAs (AC092171.5, MEG9, AC002091.1) were screened using LASSO regression. Survival rates were significantly higher (P < 0.05) in the low-risk than in the high-risk group. Risk score was an independent predictor affecting survival (P < 0.001), with the highest risk score being obtained by calculating the c-index. The TMB significantly differed between the high- and low-risk groups (P < 0.05). In the high- and low-risk groups, mutations were detected in 61 of 70 samples and 49 of 71 samples, respectively, with KRAS, TP53, and SMAD4 showing the highest mutation frequencies in both groups. A lower survival rate was observed in patients with a high versus low TMB. Immune function HLA, Cytolytic activity, and Inflammation-promoting, T cell co-inhibition, Check-point, and T cell co-stimulation significantly differed in different subgroups (P < 0.05). Immune evasion scores were significantly higher in the high-risk group than in the low-risk group. Eight sensitive drugs were screened: ABT.888, ATRA, AP.24534, AG.014699, ABT.263, axitinib, A.443654, and A.770041.Conclusions: We screened m6A-related lncRNAs using bioinformatics, constructed a prognosis-related model, explored TMB and immune function differences in pancreatic cancer, and identified potential therapeutic agents, providing a foundation for further studies of pancreatic cancer diagnosis and treatment.

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Identification of N6-Methyladenosine-Associated Long Non-coding RNAs for Immunotherapeutic Response and Prognosis in Patients With Pancreatic Cancer

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.748442 ◽

2021 ◽

Vol 9 ◽

Author(s):

Xinshuang Yu ◽

Peng Dong ◽

Yu Yan ◽

Fengjun Liu ◽

Hui Wang ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Messenger Rna ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Risk Scores ◽

Significant Difference ◽

Selection Operator

Pancreatic cancer is a highly aggressive disease with poor prognosis. N6-methyladenosine (m6A) is critical for post-transcriptional modification of messenger RNA (mRNA) and long non-coding RNA (lncRNA). However, the m6A-associated lncRNAs (m6A-lncRNA) and their values in predicting clinical outcomes and immune microenvironmental status in pancreatic cancer patients remain largely unexplored. This study aimed to evaluate the importance of m6A-lncRNA and established a m6A-lncRNA signature for predicting immunotherapeutic response and prognosis of pancreatic cancer. The m6A-lncRNA co-expression networks were constructed using data from the TCGA and GTEx database. Based on the least absolute shrinkage and selection operator (LASSO) analysis, we constructed an 8 m6A-lncRNA signature risk model, and selection operator (LASSO) analysis, and stratified patients into the high- and low-risk groups with significant difference in overall survival (OS) (HR = 2.68, 95% CI = 1.74–4.14, P < 0.0001). Patients in the high-risk group showed significantly reduced OS compared to patients in the low-risk group (P < 0.001). The clinical characteristics and m6A-lncRNA risk scores were used to construct a nomogram which accurately predicted the OS in pancreatic cancer. TIMER 2.0 were used to investigate tumor immune infiltrating cells and its relationship with pancreatic cancer. CIBERSORT analysis revealed increased higher infiltration proportions of M0 and M2 macrophages, and lower infiltration of naive B cell, CD8+ T cell and Treg cells in the high-risk group. Compared to the low-risk group, functional annotation using ssGSEA showed that T cell infiltration and the differential immune-related check-point genes are expressed at low level in the high-risk group (P < 0.05). In summary, our study constructed a novel m6A-associated lncRNAs signature to predict immunotherapeutic responses and provided a novel nomogram for the prognosis prediction of pancreatic cancer.

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Development and validation of a ferroptosis-related prognostic model in pancreatic cancer

10.21203/rs.3.rs-139597/v2 ◽

2021 ◽

Author(s):

Chen-jie Qiu ◽

Xue-bing Wang ◽

Zi-ruo Zheng ◽

Chao-zhi Yang ◽

Kai Lin ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

High Risk Group ◽

Low Risk ◽

Immune Cell Infiltration

Abstract Background: The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas database, we established the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus datasets and tissue samples obtained from our center were utilized to validate the model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The prognosis of the low-risk group was significantly better than that of the high-risk group. In the high-risk group, patients treated with chemotherapy had a better prognosis. The nomogram showed that the model was the most important element. Gene set enrichment analysis identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

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Development and Validation of a Ferroptosis-related Prognostic Model in Pancreatic Cancer

10.21203/rs.3.rs-139597/v1 ◽

2021 ◽

Author(s):

Chen-jie Qiu ◽

Xue-bing Wang ◽

Zi-ruo Zheng ◽

Chao-zhi Yang ◽

Kai Lin ◽

...

Keyword(s):

Pancreatic Cancer ◽

T Cell ◽

High Risk ◽

Risk Score ◽

Prognostic Model ◽

Immune Cell ◽

Risk Group ◽

High Risk Group ◽

Immune Cell Infiltration ◽

Cox Analysis

Abstract Background: With the development of genomics, ferroptosis has been determined to be highly important in cancer. The purpose of this study was to identify ferroptosis-related genes (FRGs) associated with the prognosis of pancreatic cancer and to construct a prognostic model based on FRGs. Methods: Based on pancreatic cancer data obtained from The Cancer Genome Atlas (TCGA) database, we employed univariate Cox analysis, least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox analysis to establish the prognostic model from 232 FRGs. A nomogram was constructed by combining the prognostic model and clinicopathological features. Gene Expression Omnibus (GEO) datasets and tissue samples obtained from our center were utilized to validate the prognostic model. Relationship between risk score and immune cell infiltration was explored by CIBERSORT and TIMER.Results: The prognostic model was established based on four FRGs (ENPP2, ATG4D, SLC2A1 and MAP3K5) and can be an independent risk factor in pancreatic cancer (HR 1.648, 95% CI 1.335-2.035, p < 0.001). Based on the median risk score, patients were divided into a high-risk group and a low-risk group. The KM curve indicated that the overall survival (OS) of the low-risk group was significantly better than that of the high-risk group. The nomogram showed that the prognostic model was the most important element. Gene set enrichment analysis (GSEA) identified three key pathways, namely, TGFβ signaling, HIF signaling pathway and adherens junction. GSE57495, GSE62452 and 88 pancreatic cancer tissues from our center were utilized to validate the prognostic model. The prognostic model can also affect the immune cell infiltration, such as macrophages M0, M1, CD4+T cell and CD8+T cell. Conclusion: A ferroptosis-related prognostic model can be employed to predict the prognosis of pancreatic cancer. Ferroptosis can be an important marker and immunotherapy can be a potential therapeutic target for pancreatic cancer.

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Distribution of Aluminium between Plasma and Erythrocytes

Human Toxicology ◽

10.1177/096032718500400613 ◽

1985 ◽

Vol 4 (6) ◽

pp. 643-648 ◽

Cited By ~ 11

Author(s):

G.B. van der Voet ◽

F.A. de Wolff

Keyword(s):

High Risk ◽

Whole Blood ◽

Blood Cells ◽

Prognostic Value ◽

Risk Group ◽

Body Burden ◽

High Risk Group ◽

Cell Compartment ◽

Clinical Laboratories ◽

Toxic Trace Metals

1 It is common use to monitor body burdens of toxic trace metals by measuring concentrations in whole blood. To monitor aluminium (Al) body burden in renal patients on haemodialysis, which is a high-risk group for Al poisoning, the concentrations of Al in plasma (AIP) or serum (AlS) are determined rather than Al in whole blood (AlB). 2 To evaluate this custom, which exists in clinical laboratories, an investigation was made into the distribution of Al between the plasma and the blood-cell compartment and on the extent of binding of Al to the blood cells both in rats and in dialysed patients. 3 The results show that Al is distributed between plasma and blood cells with only very small quantitative differences, that binding of Al to blood cells is very weak and that AlP and AIB have similar prognostic value for toxicity.

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New-onset type 2 diabetes mellitus – A high-risk group suitable for the screening of pancreatic cancer?

Pancreatology ◽

10.1016/j.pan.2015.12.005 ◽

2016 ◽

Vol 16 (2) ◽

pp. 266-271 ◽

Cited By ~ 24

Author(s):

Dóra Illés ◽

Viktória Terzin ◽

Gábor Holzinger ◽

Klára Kosár ◽

Richárd Róka ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Pancreatic Cancer ◽

Type 2 Diabetes Mellitus ◽

High Risk ◽

Risk Group ◽

High Risk Group ◽

Onset Type ◽

New Onset

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Obesity and menopause modify the epigenomic profile of breast cancer

Endocrine Related Cancer ◽

10.1530/erc-16-0565 ◽

2017 ◽

pp. 351-363 ◽

Cited By ~ 16

Author(s):

Ana B Crujeiras ◽

Angel Diaz-Lagares ◽

Olafur A Stefansson ◽

Manuel Macias-Gonzalez ◽

Juan Sandoval ◽

...

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

High Risk ◽

Risk Group ◽

Menopausal Status ◽

Breast Tumors ◽

Methylation Pattern ◽

High Risk Group ◽

Cpg Sites ◽

The Impact

Obesity is a high risk factor for breast cancer. This relationship could be marked by a specific methylome. The current work was aimed to explore the impact of obesity and menopausal status on variation in breast cancer methylomes. Data from Infinium 450K array-based methylomes of 64 breast tumors were coupled with information on BMI and menopausal status. Additionally, DNA methylation results were validated in 18 non-tumor and 81 tumor breast samples. Breast tumors arising in either pre- or postmenopausal women stratified by BMI or menopausal status alone were not associated with a specific DNA methylation pattern. Intriguingly, the DNA methylation pattern identified in association with the high-risk group (postmenopausal women with high BMI (>25) and premenopausal women with normal or low BMI < 25) exclusively characterized by hypermethylation of 1287 CpG sites as compared with the low-risk group. These CpG sites included the promoter region of fourteen protein-coding genes of which CpG methylation over the ZNF577 promoter region represents the top scoring associated event. In an independent cohort, the ZNF577 promoter methylation remained statistically significant in association with the high-risk group. Additionally, the impact of ZNF577 promoter methylation on mRNA expression levels was demonstrated in breast cancer cell lines after treatment with a demethylating agent (5-azacytidine). In conclusion, the epigenome of breast tumors is affected by a complex interaction between BMI and menopausal status. The ZNF577 methylation quantification is clearly relevant for the development of novel biomarkers of precision therapy in breast cancer.

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N6-Methylandenosine-Related lncRNAs Play an Important Role in the Prognosis and Immune Microenvironment of Pancreatic Ductal Adenocarcinoma

10.21203/rs.3.rs-274618/v1 ◽

2021 ◽

Author(s):

YuHai Hu ◽

YiPing Chen

Keyword(s):

High Risk ◽

Tumor Immunity ◽

Prognostic Value ◽

Risk Group ◽

Epigenetic Modification ◽

Cohort Analysis ◽

High Risk Group ◽

Cancer Genome ◽

Ductal Adenocarcinoma ◽

Training Cohort

Abstract BackgroundPancreatic ductal adenocarcinoma (PDAC) is a highly aggressive, fatal tumor. N6-methylandenosine (m6A) methylation is the major epigenetic modification of RNA including lncRNAs. The roles of m6A-related lncRNAs in PDAC have not been fully clarified. The aim of this study is to assess gene signatures and prognostic value of m6A-related lncRNAs in PDAC.MethodsThe Cancer Genome Atlas (TCGA) dataset and the International Cancer Genome Consortium (ICGC) dataset were explored to identify m6A-related lncRNAs. Univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were performed to construct the m6A-related lncRNAs prognostic riskscore (m6A-LPR) model to predict the overall survival (OS) in the TCGA training cohort. Kaplan–Meier curve with log-rank test and receiver operating characteristic (ROC) curve were used to evaluate the prognostic value of the m6A-LPR. Furthermore, the robustness of the m6A-LPR was further validated in the ICGC cohort. Tumor immunity was evaluated using ESTIMATE and CIBERSORT algorithms.ResultsA total of 262 m6A-related lncRNAs were identified in two datasets. In the TCGA training cohort, 28 prognostic m6A-related lncRNAs were identified and the m6A-LPR including four m6A-related lncRNAs was constructed. The m6A-LPR was able to identify high-risk patients with significantly poorer OS and accurately predict OS in both the TCGA training cohort and the ICGC validation cohort. Analysis of tumor immunity revealed that high-risk group had remarkably lower stromal, immune, and ESTIMATE scores. Moreover, high-risk group was associated with significantly higher levels of plasma B cells and resting NK cells infiltration, and lower levels of infiltrating resting memory CD4 T cells, monocytes and resting mast cells.ConclusionsOur study proposed a robust m6A-related prognostic signature of lncRNAs for predicting OS in PDAC, which provides some clues for further studies focusing on the mechanism process underlying m6A modification of lncRNAs.

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