scholarly journals Identification of Prognosis-Related Genes and Construction of Multi-regulatory Networks in Pancreatic Cancer Microenvironment by Bioinformatics Analysis

2020 ◽  
Author(s):  
Tong Li ◽  
Qiaofei Liu ◽  
Ronghua Zhang ◽  
Quan Liao ◽  
Yupei Zhao
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Global Gene Expression ◽  
The Cancer Genome Atlas ◽  
Poor Overall Survival ◽  
Protein Protein Interaction ◽  
Improve Treatment ◽  
Patient Prognosis

Abstract Background As one of the most lethal cancers, pancreatic cancer has been characterized by abundant supportive tumor-stromal cell microenvironment. Although the advent of tumor-targeted immune checkpoint blockers has brought light to patients with other cancers, its clinical efficacy in pancreatic cancer has been greatly limited due to the protective stroma. Thus, there is an urgent need to explore new targets and develop multi-regulatory networks to improve treatment and evaluate patient prognosis. Methods We followed a strategy based on mining the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm to obtain the immune scores and stromal scores. Differentially expressed genes (DEGs) associated with poor overall survival of pancreatic cancer were screened from a TCGA cohort. By comparing global gene expression with high vs. low immune scores and subsequent Kaplan-Meier analysis, DEGs that significantly correlate with poor overall survival of pancreatic cancer in TCGA cohort were extracted. After constructing the protein-protein interaction network using STRING and limiting the genes within the above DEGs, we utilized RAID 2.0, TRRUST v2 database and degree and betweenness analysis to obtain non-coding RNA (ncRNA)-pivotal nodes and TF-pivotal nodes. Finally, multi-regulatory networks have been constructed and pivotal drugs with potential benefit for pancreatic cancer patients were obtained by screening in the DrugBank. Results In this study, we obtained 246 DEGs that significantly correlate with poor overall survival of pancreatic cancer in the TCGA cohort. With the advent of 42 ncRNA-pivotal nodes and 7 TF-pivotal nodes, the multi-factor regulatory networks were constructed based on the above pivotal nodes. Prognosis-related genes and factors such as HCAR3, PPY, RFWD2, WSPAR and Amcinonide were screened and investigated. Conclusion The multi-regulatory networks constructed in this study are not only helpful to improve treatment and evaluate patient prognosis with pancreatic cancer, but also beneficial for implementing early diagnosis and personalized treatment. It is suggested that these factors may play an essential role in the progression of pancreatic cancer.

scholarly journals Identification of Prognosis-Related Genes and Construction of Multi-regulatory Networks in Pancreatic Cancer Microenvironment by Bioinformatics Analysis

2020 ◽  
Author(s):  
Tong Li ◽  
Qiaofei Liu ◽  
Ronghua Zhang ◽  
Quan Liao ◽  
Yupei Zhao
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Global Gene Expression ◽  
The Cancer Genome Atlas ◽  
Poor Overall Survival ◽  
Protein Protein Interaction ◽  
Improve Treatment ◽  
Patient Prognosis

Abstract Background: As one of the most lethal cancers, pancreatic cancer has been characterized by abundant supportive tumor-stromal cell microenvironment. Although the advent of tumor-targeted immune checkpoint blockers has brought light to patients with other cancers, its clinical efficacy in pancreatic cancer has been greatly limited due to the protective stroma. Thus, it is urgent to find potential new targets and establish multi-regulatory networks to predict patient prognosis and improve treatment.Methods: We followed a strategy based on mining the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm to obtain the immune scores and stromal scores. Differentially expressed genes (DEGs) associated with poor overall survival of pancreatic cancer were screened from a TCGA cohort. By comparing global gene expression with high vs. low immune scores and subsequent Kaplan-Meier analysis, DEGs that significantly correlate with poor overall survival of pancreatic cancer in TCGA cohort were extracted. After constructing the protein-protein interaction network using STRING and limiting the genes within the above DEGs, we utilized RAID 2.0, TRRUST v2 database and degree and betweenness analysis to obtain non-coding RNA (ncRNA)-pivotal nodes and TF-pivotal nodes. Finally, multi-regulatory networks have been constructed and pivotal drugs with potential benefit for pancreatic cancer patients were obtained by screening in the DrugBank.Results: In this study, we obtained 246 DEGs that significantly correlate with poor overall survival of pancreatic cancer in the TCGA cohort. With the advent of 42 ncRNA-pivotal nodes and 7 TF-pivotal nodes, the multi-factor regulatory networks were constructed based on the above pivotal nodes. Prognosis-related genes and factors such as HCAR3, PPY, RFWD2, WSPAR and Amcinonide were screened and investigated.Conclusion: The multi-regulatory networks constructed in this study are not only beneficial to improve treatment and evaluate patient prognosis with pancreatic cancer, but also favorable for implementing early diagnosis and personalized treatment. It is suggested that these factors may play an essential role in the progression of pancreatic cancer.

scholarly journals Identification of Prognosis-Related Genes and Construction of Multi-regulatory Networks in Pancreatic Cancer Microenvironment by Bioinformatics Analysis

2020 ◽  
Author(s):  
Tong Li ◽  
Qiaofei Liu ◽  
Ronghua Zhang ◽  
Quan Liao ◽  
Yupei Zhao
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Regulatory Networks ◽  
Interaction Network ◽  
Global Gene Expression ◽  
The Cancer Genome Atlas ◽  
Poor Overall Survival ◽  
Protein Protein Interaction ◽  
Kaplan Meier ◽  
Patient Prognosis

Abstract Background: As one of the most lethal cancers, pancreatic cancer has been characterized by abundant supportive tumor-stromal cell microenvironment. Although the advent of tumor-targeted immune checkpoint blockers has brought light to patients with other cancers, its clinical efficacy in pancreatic cancer has been greatly limited due to the protective stroma . Thus, it is urgent to find potential new targets and establish multi-regulatory networks to predict patient prognosis andimprove treatment. Methods: We followed a strategy based on mining the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm to obtain the immune scores and stromal scores. Differentially expressed genes (DEGs) associated with poor overall survival of pancreatic cancer were screened from a TCGA cohort. By comparing global gene expression with high vs. low immune scores and subsequent Kaplan-Meier analysis, DEGs that significantly correlate with poor overall survival of pancreatic cancer in TCGA cohort were extracted. After constructing the protein-protein interaction network using STRING and limiting the genes within the above DEGs, we utilized RAID 2.0, TRRUST v2 database and degree and betweenness analysis to obtain non-coding RNA (ncRNA)-pivotal nodes and TF-pivotal nodes. Finally, multi-regulatory networks have been constructed and pivotal drugs with potential benefit for pancreatic cancer patients were obtained by screening in the DrugBank. Results: In this study, we obtained 246 DEGs that significantly correlate with poor overall survival of pancreatic cancer in the TCGA cohort. With the advent of 38 ncRNA-pivotal nodes and 7 TF-pivotal nodes, the multi-factor regulatory networks were constructed based on the above pivotal nodes. Prognosis-related genes and factors such as HCAR3, PPY, RFWD2, WSPAR and Amcinonide were screened and investigated. Conclusion: The multi-regulatory networks constructed in this study are not only beneficial to improve treatment and evaluate patient prognosis with pancreatic cancer, but also favorable for implementing early diagnosis and personalized treatment. It is suggested that these factors may play an essential role in the progression of pancreatic cancer.

scholarly journals Transcriptional landscape of cellular networks reveal interactions driving the dormancy mechanisms in cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Dilara Uzuner ◽  
Yunus Akkoç ◽  
Nesibe Peker ◽  
Pınar Pir ◽  
Devrim Gözüaçık ◽  
...  
Keyword(s):  
Cancer Cells ◽  
Regulatory Networks ◽  
Metastatic Cancer ◽  
Interaction Network ◽  
Current Treatment ◽  
Cellular Mechanisms ◽  
Protein Protein Interaction ◽  
Cancer Dormancy ◽  
Protein Protein Interaction Networks ◽  
First Time

AbstractPrimary cancer cells exert unique capacity to disseminate and nestle in distant organs. Once seeded in secondary sites, cancer cells may enter a dormant state, becoming resistant to current treatment approaches, and they remain silent until they reactivate and cause overt metastases. To illuminate the complex mechanisms of cancer dormancy, 10 transcriptomic datasets from the literature enabling 21 dormancy–cancer comparisons were mapped on protein–protein interaction networks and gene-regulatory networks to extract subnetworks that are enriched in significantly deregulated genes. The genes appearing in the subnetworks and significantly upregulated in dormancy with respect to proliferative state were scored and filtered across all comparisons, leading to a dormancy–interaction network for the first time in the literature, which includes 139 genes and 1974 interactions. The dormancy interaction network will contribute to the elucidation of cellular mechanisms orchestrating cancer dormancy, paving the way for improvements in the diagnosis and treatment of metastatic cancer.

scholarly journals Long non-coding RNA PART1 predicts a poor prognosis and promotes the malignant progression of pancreatic cancer by sponging miR-122

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Xibao Hu ◽  
Lei Zhang ◽  
Jingjing Tian ◽  
Junhong Ma
Keyword(s):  
Pancreatic Cancer ◽  
Cell Proliferation ◽  
Overall Survival ◽  
Clinical Stage ◽  
Malignant Progression ◽  
Luciferase Reporter ◽  
Poor Overall Survival ◽  
Pancreatic Cancer Cells ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Abstract Background and objectives Long non-coding RNA (lncRNA) prostate androgen-regulated transcript 1 (PART1) was previously shown to exert an oncogenic role in several human cancers. However, whether PART1 is associated with the malignant progression of pancreatic cancer remains unclear. In the current study, we aimed to identify the role and potential mechanism of PART1 in pancreatic cancer. Methods qRT-PCR was applied to detect PART1 expression in 45 cases of pancreatic cancer patients. The chi-square test was performed to assess the association between PART1 expression and clinicopathologic features, and Kaplan-Meier method was applied to evaluate overall survival. In vitro CCK-8, transwell invasion, and flow cytometry assays were applied to detect the effects of PART1 on cell proliferation, invasion, and apoptosis, respectively. Luciferase reporter and RNA immunoprecipitation assays were used to identify the regulatory mechanism between PART1 and miR-122. Results PART1 expression was upregulated in pancreatic cancer tissues and cell lines. High PART1 expression was closely correlated with tumor size, T classification, clinical stage, and vascular invasion, and predicted a poor overall survival. PART1 knockdown significantly suppressed cell proliferation and invasion abilities of pancreatic cancer but promoted cell apoptosis. PART1 was found to serve as a molecular sponge of miR-122, and miR-122 inhibition partially reversed the inhibitory phenotypes of PART1 knockdown on pancreatic cancer cells. Conclusions PART1 promotes the malignant progression of pancreatic cancer by sponging miR-122. The PART1/miR-122 axis might be a promising target for anticancer therapy in patients with pancreatic cancer.

scholarly journals Hyaluronan-mediated motility receptor expression functions as a prognostic biomarker in uterine carcinosarcoma based on bioinformatics analysis

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110210
Author(s):  
Hui Sun ◽  
Li Ma ◽  
Jie Chen
Keyword(s):  
Interaction Network ◽  
Maximal Clique ◽  
Receptor Expression ◽  
The Cancer Genome Atlas ◽  
Uterine Carcinosarcoma ◽  
Sequencing Data ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Normal Tissues ◽  
Potential Biomarker

Objective Uterine carcinosarcoma (UCS) is a rare, aggressive tumour with a high metastasis rate and poor prognosis. This study aimed to explore potential key genes associated with the prognosis of UCS. Methods Transcriptional expression data were downloaded from the Gene Expression Profiling Interactive Analysis database and differentially expressed genes (DEGs) were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses using Metascape. A protein–protein interaction network was constructed using the STRING website and Cytoscape software, and the top 30 genes obtained through the Maximal Clique Centrality algorithm were selected as hub genes. These hub genes were validated by clinicopathological and sequencing data for 56 patients with UCS from The Cancer Genome Atlas database. Results A total of 1894 DEGs were identified, and the top 30 genes were considered as hub genes. Hyaluronan-mediated motility receptor (HMMR) expression was significantly higher in UCS tissues compared with normal tissues, and elevated expression of HMMR was identified as an independent prognostic factor for shorter survival in patients with UCS. Conclusions These results suggest that HMMR may be a potential biomarker for predicting the prognosis of patients with UCS.

scholarly journals Development and Validation of a Unique Gignature of Cancer Stemness-related Genes for Predicting the Overall survival of Colorectal Cancer Patients

2020 ◽  
Author(s):  
Ran Wei ◽  
Jichuan Quan ◽  
Shuofeng Li ◽  
Zhao Lu ◽  
Xu Guan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Cancer Stemness ◽  
Poor Overall Survival ◽  
Tissue Samples ◽  
Cox Regression Analysis ◽  
Rna Methylation ◽  
M6a Rna Methylation

Abstract Background: Cancer stem cells (CSCs), which are characterized by self-renewal and plasticity, are highly correlated with tumor metastasis and drug resistance. To fully understand the role of CSCs in colorectal cancer (CRC), we evaluated the stemness traits and prognostic value of stemness-related genes in CRC.Methods: In this study, the data from 616 CRC patients from The Cancer Genome Atlas (TCGA) were assessed and subtyped based on the mRNA expression-based stemness index (mRNAsi). The correlations of cancer stemness with the immune microenvironment, tumor mutational burden (TMB) and N6-methyladenosine (m6A) RNA methylation regulators were analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to identify the crucial stemness-related genes and modules. Furthermore, a prognostic expression signature was constructed using Lasso-penalized Cox regression analysis. The signature was validated via multiplex immunofluorescence staining of tissue samples in an independent cohort of 48 CRC patients.Results: This study suggests that high mRNAsi scores are associated with poor overall survival in stage Ⅳ CRC patients. Moreover, the levels of TMB and m6A RNA methylation regulators were positively correlated with mRNAsi scores, and low mRNAsi scores were characterized by increased immune activity in CRC. The analysis identified 2 key modules and 34 key genes as prognosis-related candidate biomarkers. Finally, a 3-gene prognostic signature (PARPBP, KNSTRN and KIF2C) was explored together with specific clinical features to construct a nomogram, which was successfully validated in an external cohort. Conclusions: There is a unique correlation between CSCs and the prognosis of CRC patients, and the novel biomarkers related to cell stemness could accurately predict the clinical outcomes of these patients.

scholarly journals Meta-analysis of the studies dedicated to the predictive significance of circulating tumor DNA in pancreatic cancer

2020 ◽  
Vol 22 (3) ◽  
pp. 127-132
Author(s):  
A. S. Popova ◽  
M. Yu. Fedyanin ◽  
I. A. Pokataev ◽  
S. A. Tyulyandin
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Confidence Interval ◽  
Meta Analysis ◽  
Progression Free Survival ◽  
Circulating Tumor Dna ◽  
Poor Overall Survival ◽  
Risk Of Mortality ◽  
Before And After ◽  
Tumor Dna

The method of liquid biopsy allows detection of circulating tumor DNA (ctDNA) in patient blood, but the clinical significance of this approach in pancreatic cancer is still unclear. In this regard, we have carried out a meta-analysis of the studies dedicated to the predictive significance of ctDNA in pancreatic cancer. Materials and methods.We carried out the search for the articles and abstracts in PubMed, ASCO and ESMO databases published before February 2020, containing data about the connection between ctDNA and the prognosis of pancreatic cancer. The exclusion criteria were the studies including 10 or less participating patients, absence of the data about the relative risk of mortality and/or progression, and the 95% confidence interval. The meta-analysis was carried out by using the Review Manager software (RevMan), Version 5.3. Results.There were no significant systematic errors associated with the publications. The presence of ctDNA in patient blood showed poor overall survival of patients (odds ratio OR 2.21, 95% confidence interval CI 1.35-3.33,p=0.001) regardless of the prevalence of the disease. In case of the resectable process, the detection of ctDNA in patient blood both before and after surgery was a factor of worse progression-free survival (OR 2.32, 95% CI 1.543.5,p0.001 and OR 3.06, 95% CI 1.635.76,р=0.0005 and overall survival (OR2.01, 95% CI 1.123.63,р=0,02 and OR 3.39, 95% CI 2.125.44,р0.00001, respectively). Conclusions.The detection of ctDNA in the bloodstream in pancreatic cancer patients is a factor of poor prognosis in both localized and advanced cancer. It is very important to make further prospective studies to develop the optimal protocol for detecting ctDNA in patient bloodstream.

scholarly journals Data mining of immune-related prognostic genes in metastatic melanoma microenvironment

2020 ◽  
Vol 40 (11) ◽  
Author(s):  
Wei Han ◽  
Biao Huang ◽  
Xiao-Yu Zhao ◽  
Guo-Liang Shen
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Metastatic Melanoma ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Protein Protein Interaction ◽  
Subtype Identification ◽  
Cancer Genome Atlas ◽  
Immune Related Genes

Abstract Skin cutaneous melanoma (SKCM) is one of the most deadly malignancies. Although immunotherapies showed the potential to improve the prognosis for metastatic melanoma patients, only a small group of patients can benefit from it. Therefore, it is urgent to investigate the tumor microenvironment in melanoma as well as to identify efficient biomarkers in the diagnosis and treatments of SKCM patients. A comprehensive analysis was performed based on metastatic melanoma samples from the Cancer Genome Atlas (TCGA) database and ESTIMATE algorithm, including gene expression, immune and stromal scores, prognostic immune-related genes, infiltrating immune cells analysis and immune subtype identification. Then, the differentially expressed genes (DEGs) were obtained based on the immune and stromal scores, and a list of prognostic immune-related genes was identified. Functional analysis and the protein–protein interaction network revealed that these genes enriched in multiple immune-related biological processes. Furthermore, prognostic genes were verified in the Gene Expression Omnibus (GEO) databases and used to predict immune infiltrating cells component. Our study revealed seven immune subtypes with different risk values and identified T cells as the most abundant cells in the immune microenvironment and closely associated with prognostic outcomes. In conclusion, the present study thoroughly analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for metastatic melanoma.

Clinical Characteristics and Overall Survival in Patients with Anaplastic Pancreatic Cancer

2014 ◽  
Vol 80 (2) ◽  
pp. 117-123 ◽  
Author(s):  
Clancy J. Clark ◽  
Janani S. Arun ◽  
Rondell P. Graham ◽  
Lizhi Zhang ◽  
Michael Farnell ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Tumor Stage ◽  
Ductal Adenocarcinoma ◽  
Rank Test ◽  
Poor Overall Survival ◽  
Perioperative Morbidity ◽  
Log Rank Test ◽  
Kaplan Meier

Anaplastic pancreatic cancer (APC) is a rare undifferentiated variant of pancreatic ductal adenocarcinoma with poor overall survival (OS). The aim of this study was to evaluate the clinical outcomes of APC compared with differentiated pancreatic ductal adenocarcinoma. We conducted a retrospective review of all patients treated at the Mayo Clinic with pathologically confirmed APC from 1987 to 2011. After matching with control subjects with pancreatic ductal adenocarcinoma, OS was evaluated using Kaplan-Meier estimates and log-rank test. Sixteen patients were identified with APC (56.3% male, median age 57 years). Ten patients underwent exploration of whom eight underwent pancreatectomy. Perioperative morbidity was 60 per cent with no mortality. The median OS was 12.8 months. However, patients with APC who underwent resection had longer OS compared with those who were not resected, 34.1 versus 3.3 months ( P = 0.001). After matching age, sex, tumor stage, and year of operation, the median OS was similar between patients with APC and those with ductal adenocarcinoma treated with pancreatic resection, 44.1 versus 39.9 months, ( P = 0.763). Overall survival for APC is poor; however, when resected, survival is similar to differentiated pancreatic ductal adenocarcinoma.

scholarly journals Pan-cancer analysis of non-oncogene addiction to DNA repair

2021 ◽  
Author(s):  
Luis Bermúdez-Guzmán
Keyword(s):  
Overall Survival ◽  
Dna Repair ◽  
Cancer Cells ◽  
Synthetic Lethality ◽  
The Cancer Genome Atlas ◽  
Molecular Signature ◽  
Systematic Evaluation ◽  
Oncogene Addiction ◽  
Poor Overall Survival ◽  
Pan Cancer

Abstract Cancer cells usually depend on the aberrant function of one or few driver genes to initiate and promote their malignancy, an attribute known as oncogene addiction. However, cancer cells might become dependent on the normal cellular functions of certain genes that are not oncogenes but ensure cell survival (non-oncogene addiction). The downregulation of DNA repair genes and the consequent genetic and epigenetic instability is key to promote malignancy, but the activation of the DNA-damage response (DDR) has been shown to become a type of non-oncogene addiction that critically supports tumour survival. While we know that different cancer types can become dependent on specific DDR genes for their survival, a systematic evaluation of DNA repair addiction at the pan-cancer level is missing. In the present study, this systematic evaluation was addressed using data derived from The Cancer Dependency Map and The Cancer Genome Atlas (TCGA). Following this approach, 59 DDR genes were identified as commonly essential in cancer cells with 14 genes being exclusively associated with better overall patient survival and 19 with worse overall survival. Notably, a specific molecular signature among the latter, characterized by DDR genes showing the weakest dependency scores, but significant upregulation was strongly associated with worse survival, supporting the presence and relevance of non-oncogenic addiction to DNA repair in cancer. Particularly, UBE2T, RFC4, POLQ, BRIP1, and H2AFX represent the best predictors of poor overall survival, and some might represent promising therapeutic targets, especially under the synthetic lethality approach.

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