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

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.

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

10.21203/rs.3.rs-25596/v2 ◽

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.

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

10.21203/rs.3.rs-25596/v1 ◽

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.

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Clinical Characteristics and Overall Survival in Patients with Anaplastic Pancreatic Cancer

The American Surgeon ◽

10.1177/000313481408000218 ◽

2014 ◽

Vol 80 (2) ◽

pp. 117-123 ◽

Cited By ~ 3

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.

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Mining TCGA database for gene expression in ovarian serous cystadenocarcinoma microenvironment

PeerJ ◽

10.7717/peerj.11375 ◽

2021 ◽

Vol 9 ◽

pp. e11375

Author(s):

Youzheng Xu ◽

Yixin Xu ◽

Chun Wang ◽

Baoguo Xia ◽

Qingling Mu ◽

...

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Strong Predictor ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Poor Overall Survival ◽

Protein Protein Interaction ◽

Serous Cystadenocarcinoma ◽

Kaplan Meier ◽

Limma Package

Background Ovarian cancer is one of the leading causes of female deaths worldwide. Ovarian serous cystadenocarcinoma occupies about 90% of it. Effective and accurate biomarkers for diagnosis, outcome prediction and personalized treatment are needed urgently Methods Gene expression profile for OSC patients was obtained from the TCGA database. The ESTIMATE algorithm was used to calculate immune scores and stromal scores of expression data of ovarian serous cystadenocarcinoma samples. Survival results between high and low groups of immune and stromal score were compared and differentially expressed genes (DEGs) were screened out by limma package. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and the protein-protein interaction (PPI) network analysis were performed with the g:Profiler database, the Cytoscape and Search Tool for the Retrieval of Interacting Genes (STRING-DB). Survival results between high and low immune and stromal score groups were compared. Kaplan-Meier plots based on TCGA follow up information were generated to evaluate patients’ overall survival. Results Eighty-six upregulated DEGs and one downregulated DEG were identified. Three modules, which included 49 nodes were chosen as important networks. Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were considered to be correlated with poor overall survival. Conclusion Seven DEGs (VSIG4, TGFBI, DCN, F13A1, ALOX5AP, GPX3, SFRP4) were correlated with poor overall survival in our study. This new set of genes can become strong predictor of survival, individually or combined. Further investigation of these genes is needed to validate the conclusion to provide novel understanding of tumor microenvironment with ovarian serous cystadenocarcinoma prognosis and treatment.

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Bioinformatics analysis reveals the ceRNA co-expression network in tumor microenvironment and prognostic biomarkers in Sarcomas

10.21203/rs.3.rs-36486/v1 ◽

2020 ◽

Author(s):

Dandan Zou ◽

Yang Wang ◽

Meng Wang ◽

Bo Zhao ◽

Fei Hu ◽

...

Keyword(s):

Overall Survival ◽

Bioinformatics Analysis ◽

Immune Therapy ◽

The Cancer Genome Atlas ◽

Protein Protein Interaction ◽

Cerna Network ◽

Kaplan Meier ◽

Cancer Genome Atlas ◽

Tumor Microenviroment ◽

Prognostic Prediction

Abstract Background Sarcomas (SARCs) are rare, heterogeneous mesenchymal neoplasia. To understand the tumor microenviroment (TME) and identify potential biomarkers for prognosis that associated with TME of SARCs might provide effective clues for immune therapy. Methods We evaluated the immune scores and stromal scores by using the RNA sequencing dataset of SARCs patients from The Cancer Genome Atlas (TCGA) database and the ESTIMATE algorithm. Then the differentially expressed mRNAs (DEGs), miRNAs (DEMs) and lncRNAs (DELs) were filtered after comparing the two high- and low- scores groups. Next, based on these DERNAs, we established a competing endogenous RNA (ceRNA) network and explored the prognostic roles of biomarkers involved in the network with the help of bioinformatics analysis. Results High immune scores were significantly associated with favorable overall survival of SARCs patients. Next, a total of 328 DEGs, 18 DEMs and 67 DELs that commonly regulated in the immune and stromal scores groups were obtained. And for the DEGs, some of the Gene Ontology (GO) terms and pathways were mainly associated with immune processes. A ceRNA network were constructed with 142 nodes and 424 edges, in which hsa-miR-9-5p, hsa-miR-490-3p, hsa-miR-133a-3p, hsa-miR-133b and hsa-miR-129-5p were the top 5 nodes. Additionally, the protein-protein Interaction (PPI) network identified MMP9, TYROBP, CSF1, CXCR4, FBN1, FLNA, PDGFRB, CYBB, FCGR3A and MYH11 as hub nodes with considerable importance that functioned in the network. Finally, the Kaplan-Meier survival analysis demonstrated that 9 mRNAs (APOL1, EFEMP1, LYZ, MEDAG, MYH11, RARRES1, TNFAIP2, TNFSF10 and ZNF385A), 2 miRNAs (hsa-miR-9-5p and hsa-miR-183-5p) and 3 lncRNAs (CTD-2228K2.7, HOTAIRM1 and NCF1C) were closely associated with the overall survival of SARCs patients. Conclusions Taken together, our study confirmed that the prognosis value of immune scores for SARCs patients, also we identified a list of TME-related biomarkers which might contribute to prognostic prediction and help improve the efficacy of immune therapy.

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Bioinformatic analysis and identification of potential prognostic microRNAs and mRNAs in thyroid cancer

PeerJ ◽

10.7717/peerj.4674 ◽

2018 ◽

Vol 6 ◽

pp. e4674 ◽

Cited By ~ 13

Author(s):

Jianing Tang ◽

Deguang Kong ◽

Qiuxia Cui ◽

Kun Wang ◽

Dan Zhang ◽

...

Keyword(s):

Overall Survival ◽

Thyroid Cancer ◽

Interaction Network ◽

Bioinformatic Analysis ◽

Differentially Expressed ◽

Rank Test ◽

Protein Protein Interaction ◽

Kaplan Meier ◽

Independent Risk Factors ◽

Cancer Tissues

Thyroid cancer is one of the most common endocrine malignancies. Multiple evidences revealed that a large number of microRNAs and mRNAs were abnormally expressed in thyroid cancer tissues. These microRNAs and mRNAs play important roles in tumorigenesis. In the present study, we identified 72 microRNAs and 1,766 mRNAs differentially expressed between thyroid cancer tissues and normal thyroid tissues and evaluated their prognostic values using Kaplan-Meier survival curves by log-rank test. Seven microRNAs (miR-146b, miR-184, miR-767, miR-6730, miR-6860, miR-196a-2 and miR-509-3) were associated with the overall survival. Among them, three microRNAs were linked with six differentially expressed mRNAs (miR-767 was predicted to target COL10A1, PLAG1 and PPP1R1C; miR-146b was predicted to target MMP16; miR-196a-2 was predicted to target SYT9). To identify the key genes in the protein-protein interaction network , we screened out the top 10 hub genes (NPY, NMU, KNG1, LPAR5, CCR3, SST, PPY, GABBR2, ADCY8 and SAA1) with higher degrees. Only LPAR5 was associated with the overall survival. Multivariate analysis demonstrated that miR-184, miR-146b, miR-509-3 and LPAR5 were an independent risk factors for prognosis. Our results of the present study identified a series of prognostic microRNAs and mRNAs that have the potential to be the targets for treatment of thyroid cancer.

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

Scientific Reports ◽

10.1038/s41598-021-94005-x ◽

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.

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

World Journal of Surgical Oncology ◽

10.1186/s12957-021-02232-3 ◽

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.

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

Journal of International Medical Research ◽

10.1177/03000605211021043 ◽

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.

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

10.21203/rs.3.rs-125323/v1 ◽

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.

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