scholarly journals Comprehensive pathway-related genes signature for prognosis and recurrence of ovarian cancer

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10437
Author(s):  
Xinnan Zhao ◽  
Miao He
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Time Dependent ◽  
Prognostic Indicators ◽  
High Recurrence Rate ◽  
Cox Regression Analysis ◽  
Ovarian Tumorigenesis

Background Ovarian cancer (OC) is a highly malignant disease with a poor prognosis and high recurrence rate. At present, there is no accurate strategy to predict the prognosis and recurrence of OC. The aim of this study was to identify gene-based signatures to predict OC prognosis and recurrence. Methods mRNA expression profiles and corresponding clinical information regarding OC were collected from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) and LASSO analysis were performed, and Kaplan–Meier curves, time-dependent ROC curves, and nomograms were constructed using R software and GraphPad Prism7. Results We first identified several key signalling pathways that affected ovarian tumorigenesis by GSEA. We then established a nine-gene-based signature for overall survival (OS) and a five-gene-based-signature for relapse-free survival (RFS) using LASSO Cox regression analysis of the TCGA dataset and validated the prognostic value of these signatures in independent GEO datasets. We also confirmed that these signatures were independent risk factors for OS and RFS by multivariate Cox analysis. Time-dependent ROC analysis showed that the AUC values for OS and RFS were 0.640, 0.663, 0.758, and 0.891, and 0.638, 0.722, 0.813, and 0.972 at 1, 3, 5, and 10 years, respectively. The results of the nomogram analysis demonstrated that combining two signatures with the TNM staging system and tumour status yielded better predictive ability. Conclusion In conclusion, the two-gene-based signatures established in this study may serve as novel and independent prognostic indicators for OS and RFS.

scholarly journals Integrated analysis of lymphocyte infiltration-associated lncRNA for ovarian cancer via TCGA, GTEx and GEO datasets

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8961 ◽  
Author(s):  
Meijing Wu ◽  
Xiaobin Shang ◽  
Yue Sun ◽  
Jing Wu ◽  
Guoyan Liu
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Integrated Analysis ◽  
Lymphocyte Infiltration ◽  
Gene Expressions ◽  
Gene Markers ◽  
Cox Regression Analysis ◽  
Cerna Network

Background Abnormal expression of long non-coding RNAs (lncRNA) play a significant role in the incidence and progression of high-grade serous ovarian cancer (HGSOC), which is a leading cause of mortality among gynecologic malignant tumor patients. In this study, our aim is to identify lncRNA-associated competing endogenous RNA (ceRNA ) axes that could define more reliable prognostic parameters of HGSOC, and to investigate the lncRNAs’ potential mechanism of in lymphocyte infiltration. Methods The RNA-seq and miRNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) database; while for obtaining the differentially expressed lncRNAs (DELs), miRNAs (DEMs), and genes (DEGs), we used edgeR, limma and DESeq2. After validating the RNA, miRNA and gene expressions, using integrated three RNA expression profiles (GSE18520, GSE27651, GSE54388) and miRNA profile (GSE47841) from the Gene Expression Omnibus (GEO) database, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analyses through ClusterProfiler. The prognostic value of these genes was determined with Kaplan–Meier survival analysis and Cox regression analysis. The ceRNA network was constructed using Cytoscape. The correlation between lncRNAs in ceRNA network and immune infiltrating cells was analyzed by using Tumor IMmune Estimation Resource (TIMER), and gene markers of tumor-infiltrating immune cells were identified using Spearman’s correlation after removing the influence of tumor purity. Results A total of 33 DELs (25 upregulated and eight downregulated), 134 DEMs (76 upregulated and 58 downregulated), and 1,612 DEGs (949 upregulated and 663 downregulated) were detected that could be positively correlated with overall survival (OS) of HGSOC. With the 1,612 analyzed genes, we constructed a ceRNA network, which indicated a pre-dominant involvement of the immune-related pathways. Furthermore, our data revealed that LINC00665 influenced the infiltration level of macrophages and dendritic cells (DCs). On the other hand, FTX and LINC00665, which may play their possible roles through the ceRNA axis, demonstrated a potential to inhibit Tregs and prevent T-cell exhaustion. Conclusion We defined several prognostic biomarkers for the incidence and progression of HGSOC and constructed a network for ceRNA axes; among which three were indicated to have a positive correlation with lymphocyte infiltration, namely: FTX-hsa-miR-150-5p-STK11, LINC00665-hsa-miR449b-5p-VAV3 and LINC00665-hsa-miR449b-5p-RRAGD.

scholarly journals Identification of a Ubiquitination-Related Gene Risk Model for Predicting Survival in Patients With Pancreatic Cancer

2020 ◽  
Vol 11 ◽  
Author(s):  
Hao Zuo ◽  
Luojun Chen ◽  
Na Li ◽  
Qibin Song
Keyword(s):  
Pancreatic Cancer ◽  
Regression Analysis ◽  
Cancer Patients ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Transcriptional Level ◽  
Cox Regression Analysis

Pancreatic cancer is known as “the king of cancer,” and ubiquitination/deubiquitination-related genes are key contributors to its development. Our study aimed to identify ubiquitination/deubiquitination-related genes associated with the prognosis of pancreatic cancer patients by the bioinformatics method and then construct a risk model. In this study, the gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas (TCGA) database and the Genotype-tissue Expression (GTEx) database. Ubiquitination/deubiquitination-related genes were obtained from the gene set enrichment analysis (GSEA). Univariate Cox regression analysis was used to identify differentially expressed ubiquitination-related genes selected from GSEA which were associated with the prognosis of pancreatic cancer patients. Using multivariate Cox regression analysis, we detected eight optimal ubiquitination-related genes (RNF7, NPEPPS, NCCRP1, BRCA1, TRIM37, RNF25, CDC27, and UBE2H) and then used them to construct a risk model to predict the prognosis of pancreatic cancer patients. Finally, the eight risk genes were validated by the Human Protein Atlas (HPA) database, the results showed that the protein expression level of the eight genes was generally consistent with those at the transcriptional level. Our findings suggest the risk model constructed from these eight ubiquitination-related genes can accurately and reliably predict the prognosis of pancreatic cancer patients. These eight genes have the potential to be further studied as new biomarkers or therapeutic targets for pancreatic cancer.

scholarly journals Identification and Validation of a PPP1R12A-Related Five-Gene Signature Associated With Metabolism to Predict the Prognosis of Patients With Prostate Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Zhihao Zou ◽  
Ren Liu ◽  
Yingke Liang ◽  
Rui Zhou ◽  
Qishan Dai ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Cox Regression ◽  
Expression Profiles ◽  
Affinity Purification ◽  
Disease Free Survival ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Cox Regression Analysis

BackgroundProstate cancer (PCa) is the most common malignant male neoplasm in the American male population. Our prior studies have demonstrated that protein phosphatase 1 regulatory subunit 12A (PPP1R12A) could be an efficient prognostic factor in patients with PCa, promoting further investigation. The present study attempted to construct a gene signature based on PPP1R12A and metabolism-related genes to predict the prognosis of PCa patients.MethodsThe mRNA expression profiles of 499 tumor and 52 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. We selected differentially expressed PPP1R12A-related genes among these mRNAs. Tandem affinity purification-mass spectrometry was used to identify the proteins that directly interact with PPP1R12A. Gene set enrichment analysis (GSEA) was used to extract metabolism-related genes. Univariate Cox regression analysis and a random survival forest algorithm were used to confirm optimal genes to build a prognostic risk model.ResultsWe identified a five-gene signature (PPP1R12A, PTGS2, GGCT, AOX1, and NT5E) that was associated with PPP1R12A and metabolism in PCa, which effectively predicted disease-free survival (DFS) and biochemical relapse-free survival (BRFS). Moreover, the signature was validated by two internal datasets from TCGA and one external dataset from the Gene Expression Omnibus (GEO).ConclusionThe five-gene signature is an effective potential factor to predict the prognosis of PCa, classifying PCa patients into high- and low-risk groups, which might provide potential novel treatment strategies for these patients.

scholarly journals Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

2021 ◽  
Vol 27 ◽  
Author(s):  
Aoshuang Qi ◽  
Mingyi Ju ◽  
Yinfeng Liu ◽  
Jia Bi ◽  
Qian Wei ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Antigen Processing ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Indicators ◽  
Gene Set ◽  
Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

scholarly journals Mining novel cell glycolysis related gene markers that can predict the survival of colon adenocarcinoma patients

2020 ◽  
Vol 40 (8) ◽  
Author(s):  
Sihan Chen ◽  
Guodong Cao ◽  
Wei Wu ◽  
Yida Lu ◽  
Xiaobo He ◽  
...  
Keyword(s):  
Cox Regression ◽  
Mechanistic Model ◽  
Single Gene ◽  
Colon Adenocarcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Risk Scores ◽  
Gene Markers ◽  
Cox Regression Analysis

Abstract Colon adenocarcinoma (COAD) is a malignant gastrointestinal tumor, often occurring in the left colon, which is regulated by glycolysis-related processes. In past studies, multiple genes that influence the prognosis for survival have been discovered through bioinformatics analysis. However, the prediction of disease prognosis using a single gene is not an accurate method. In the present study, a mechanistic model was established to achieve better prediction for the prognosis of COAD. COAD-related data downloaded from The Cancer Genome Atlas (TCGA) were correlated with the glycolysis process using gene set enrichment analysis (GSEA) to determine the glycolysis-related genes that regulate COAD. Using COX regression analysis, glycolysis-related genes associated with the prognosis of COAD were identified, and the genes screened to establish a predictive model. The risk scores of this model were correlated with relevant clinical data to obtain a connection diagram between the model and survival rate, tumor characteristic data, etc. Finally, genes in the model were correlated with cells in the tumor microenvironment, finding that they affected specific immune cells in the model. Seven genes related to glycolysis were identified (PPARGC1A, DLAT, 6PC2, P4HA1, STC2, ANKZF1, and GPC1), which affect the prognosis of patients with COAD and constitute the model for prediction of survival of COAD patients.

scholarly journals Development and Validation of a Prognostic Nomogram Based on DNA Methylation-Driven Genes for Patients With Ovarian Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Min Zhou ◽  
Shasha Hong ◽  
Bingshu Li ◽  
Cheng Liu ◽  
Ming Hu ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Dna Methylation ◽  
Mrna Expression ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Figo Stage ◽  
Prognostic Indicator ◽  
Tissue Expression ◽  
The Cancer Genome Atlas

Background: DNA methylation affects the development, progression, and prognosis of various cancers. This study aimed to identify DNA methylated-differentially expressed genes (DEGs) and develop a methylation-driven gene model to evaluate the prognosis of ovarian cancer (OC).Methods: DNA methylation and mRNA expression profiles of OC patients were downloaded from The Cancer Genome Atlas, Genotype-Tissue Expression, and Gene Expression Omnibus databases. We used the R package MethylMix to identify DNA methylation-regulated DEGs and built a prognostic signature using LASSO Cox regression. A quantitative nomogram was then drawn based on the risk score and clinicopathological features.Results: We identified 56 methylation-related DEGs and constructed a prognostic risk signature with four genes according to the LASSO Cox regression algorithm. A higher risk score not only predicted poor prognosis, but also was an independent poor prognostic indicator, which was validated by receiver operating characteristic (ROC) curves and the validation cohort. A nomogram consisting of the risk score, age, FIGO stage, and tumor status was generated to predict 3- and 5-year overall survival (OS) in the training cohort. The joint survival analysis of DNA methylation and mRNA expression demonstrated that the two genes may serve as independent prognostic biomarkers for OS in OC.Conclusion: The established qualitative risk score model was found to be robust for evaluating individualized prognosis of OC and in guiding therapy.

scholarly journals Identification of a Glycolysis-Related Gene Signature for Survival Prediction of Ovarian Cancer Patients

2020 ◽  
Author(s):  
Dai Zhang ◽  
Si Yang ◽  
Yiche Li ◽  
Meng Wang ◽  
Jia Yao ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
Gynecological Cancer ◽  
Predictive Ability ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Survival Prediction ◽  
Test Sets

Abstract Background: Ovarian cancer (OV) is deemed as the most lethal gynecological cancer in women. The aim of this study was construct an effective gene prognostic model for OV patients.Methods: The expression profiles of glycolysis-related genes (GRGs) and clinical data of patients with OV were extracted from The Cancer Genome Atlas (TCGA) database. Univariate, multivariate, and least absolute shrinkage and selection operator Cox regression analyses were conducted, and a prognostic signature based on GRGs was constructed. The predictive ability of the signature was analyzed in training and test sets.Results: Based on nine GRGs (ISG20, CITED2, PYGB, IRS2, ANGPTL4, TGFBI, LHX9, PC, and DDIT4), a gene risk signature was identified to predict the outcome of patients with OV. The signature showed a good prognostic ability for OV, particularly high-grade OV, in the TCGA dataset, with areas under the curve of 0.709, 0.762, and 0.808 for 3-, 5- and 10-year survival, respectively. Similar results were found in the test sets, and the signature was also an independent prognostic factor. Moreover, a nomogram combining the prediction model and clinical factors was constructed.Conclusion: Our study established a nine-GRG risk model and a nomogram to better perform on OV patients’ survival prediction. The risk model represents a promising and independent prognostic predictor for OV patients. Moreover, our study of GRGs could offer guidances for underlying mechanisms explorations in the future.

scholarly journals Clinical Value of lncRNA LUCAT1 Expression in Liver Cancer and its Potential Pathways

2019 ◽  
Vol 28 (4) ◽  
pp. 439-447 ◽  
Author(s):  
Yan Jiao ◽  
Yanqing Li ◽  
Bai Ji ◽  
Hongqiao Cai ◽  
Yahui Liu
Keyword(s):  
Liver Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Rank Test ◽  
P Value ◽  
Rna Seq ◽  
Cox Regression Analysis

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

scholarly journals Identification of a prognostic long non-coding RNA signature in breast cancer

2020 ◽  
Author(s):  
Gaochen Lan ◽  
Xiaoling Yu ◽  
Yanna Zhao ◽  
Jinjian Lan ◽  
Wan Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cox Regression ◽  
Prognostic Biomarker ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Cox Regression Analysis

Abstract Background: Breast cancer is the most common malignant disease among women. At present, more and more attention has been paid to long non-coding RNAs (lncRNAs) in the field of breast cancer research. We aimed to investigate the expression profiles of lncRNAs and construct a prognostic lncRNA for predicting the overall survival (OS) of breast cancer.Methods: The expression profiles of lncRNAs and clinical data with breast cancer were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were screened out by R package (limma). The survival probability was estimated by the Kaplan‑Meier Test. The Cox Regression Model was performed for univariate and multivariate analysis. The risk score (RS) was established on the basis of the lncRNAs’ expression level (exp) multiplied regression coefficient (β) from the multivariate cox regression analysis with the following formula: RS=exp a1 * β a1 + exp a2 * β a2 +……+ exp an * β an. Functional enrichment analysis was performed by Metascape.Results: A total of 3404 differentially expressed lncRNAs were identified. Among them, CYTOR, MIR4458HG and MAPT-AS1 were significantly associated with the survival of breast cancer. Finally, The RS could predict OS of breast cancer (RS=exp CYTOR * β CYTOR + exp MIR4458HG * β MIR4458HG + exp MAPT-AS1 * β MAPT-AS1). Moreover, it was confirmed that the three-lncRNA signature could be an independent prognostic biomarker for breast cancer (HR=3.040, P=0.000).Conclusions: This study established a three-lncRNA signature, which might be a novel prognostic biomarker for breast cancer.

scholarly journals Identification of Hub Prognosis-Associated Oxidative Stress Genes in Pancreatic Cancer Using Integrated Bioinformatics Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Xin Qiu ◽  
Qin-Han Hou ◽  
Qiu-Yue Shi ◽  
Hai-Xing Jiang ◽  
Shan-Yu Qin
Keyword(s):  
Oxidative Stress ◽  
Gene Expression ◽  
Pancreatic Cancer ◽  
Cancer Progression ◽  
Risk Model ◽  
Cox Regression ◽  
Expression Profiles ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Cox Regression Analysis

BackgroundIntratumoral oxidative stress (OS) has been associated with the progression of various tumors. However, OS has not been considered a candidate therapeutic target for pancreatic cancer (PC) owing to the lack of validated biomarkers.MethodsWe compared gene expression profiles of PC samples and the transcriptome data of normal pancreas tissues from The Cancer Genome Atlas (TCGA) and Genome Tissue Expression (GTEx) databases to identify differentially expressed OS genes in PC. PC patients’ gene profile from the Gene Expression Omnibus (GEO) database was used as a validation cohort.ResultsA total of 148 differentially expressed OS-related genes in PC were used to construct a protein-protein interaction network. Univariate Cox regression analysis, least absolute shrinkage, selection operator analysis revealed seven hub prognosis-associated OS genes that served to construct a prognostic risk model. Based on integrated bioinformatics analyses, our prognostic model, whose diagnostic accuracy was validated in both cohorts, reliably predicted the overall survival of patients with PC and cancer progression. Further analysis revealed significant associations between seven hub gene expression levels and patient outcomes, which were validated at the protein level using the Human Protein Atlas database. A nomogram based on the expression of these seven hub genes exhibited prognostic value in PC.ConclusionOur study provides novel insights into PC pathogenesis and provides new genetic markers for prognosis prediction and clinical treatment personalization for PC patients.

Sign in / Sign up

Export Citation Format

Share Document