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 A Novel Four-Gene Prognostic Signature for Prediction of Survival in Patients with Soft Tissue Sarcoma

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5837
Author(s):  
Changwu Wu ◽  
Siming Gong ◽  
Georg Osterhoff ◽  
Nikolas Schopow
Keyword(s):  
Soft Tissue ◽  
Risk Score ◽  
Cox Regression ◽  
Soft Tissue Sarcomas ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Prognostic Models ◽  
Malignant Tumours ◽  
Free Survival

Soft tissue sarcomas (STS), a group of rare malignant tumours with high tissue heterogeneity, still lack effective clinical stratification and prognostic models. Therefore, we conducted this study to establish a reliable prognostic gene signature. Using 189 STS patients’ data from The Cancer Genome Atlas database, a four-gene signature including DHRS3, JRK, TARDBP and TTC3 was established. A risk score based on this gene signature was able to divide STS patients into a low-risk and a high-risk group. The latter had significantly worse overall survival (OS) and relapse free survival (RFS), and Cox regression analyses showed that the risk score is an independent prognostic factor. Nomograms containing the four-gene signature have also been established and have been verified through calibration curves. In addition, the predictive ability of this four-gene signature for STS metastasis free survival was verified in an independent cohort (309 STS patients from the Gene Expression Omnibus database). Finally, Gene Set Enrichment Analysis indicated that the four-gene signature may be related to some pathways associated with tumorigenesis, growth, and metastasis. In conclusion, our study establishes a novel four-gene signature and clinically feasible nomograms to predict the OS and RFS. This can help personalized treatment decisions, long-term patient management, and possible future development of targeted therapy.

scholarly journals Identification of a novel glycolysis-related gene signature for predicting metastasis and survival in patients with lung adenocarcinoma

2019 ◽  
Vol 17 (1) ◽  
Author(s):  
Lei Zhang ◽  
Zhe Zhang ◽  
Zhenglun Yu
Keyword(s):  
Lung Cancer ◽  
Lung Adenocarcinoma ◽  
Cox Regression ◽  
Single Gene ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Test Series ◽  
Cox Regression Analysis ◽  
Incidence And Mortality

Abstract Background Lung cancer (LC) is one of the most lethal and most prevalent malignant tumors, and its incidence and mortality are increasing annually. Lung adenocarcinoma (LUAD) is the most common pathological type of lung cancer. Several biomarkers have been confirmed by data excavation to be related to metastasis, prognosis and survival. However, the moderate predictive effect of a single gene biomarker is not sufficient. Thus, we aimed to identify new gene signatures to better predict the possibility of LUAD. Methods Using an mRNA-mining approach, we performed mRNA expression profiling in large LUAD cohorts (n = 522) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and connections between genes and glycolysis were found in the Cox proportional regression model. Results We confirmed a set of nine genes (HMMR, B4GALT1, SLC16A3, ANGPTL4, EXT1, GPC1, RBCK1, SOD1, and AGRN) that were significantly associated with metastasis and overall survival (OS) in the test series. Based on this nine-gene signature, the patients in the test series could be divided into high-risk and low-risk groups. Additionally, multivariate Cox regression analysis revealed that the prognostic power of the nine-gene signature is independent of clinical factors. Conclusion Our study reveals a connection between the nine-gene signature and glycolysis. This research also provides novel insights into the mechanisms underlying glycolysis and offers a novel biomarker of a poor prognosis and metastasis for LUAD patients.

scholarly journals An energy metabolism-based eight-gene signature correlates with the clinical outcome of esophagus carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Weifeng Zheng ◽  
Chaoying Chen ◽  
Jianghao Yu ◽  
Chengfeng Jin ◽  
Tiemei Han
Keyword(s):  
Energy Metabolism ◽  
Cox Regression ◽  
Expression Profiles ◽  
Predictive Performance ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Prognostic Gene ◽  
Kaplan Meier ◽  
Esophagus Carcinoma

Abstract Background The essence of energy metabolism has spread to the field of esophageal cancer (ESC) cells. Herein, we tried to develop a prognostic prediction model for patients with ESC based on the expression profiles of energy metabolism associated genes. Materials and methods The overall survival (OS) predictive gene signature was developed, internally and externally validated based on ESC datasets including The Cancer Genome Atlas (TCGA), GSE54993 and GSE19417 datasets. Hub genes were identified in each energy metabolism related molecular subtypes by weighted gene correlation network analysis, and then enrolled for determination of prognostic genes. Univariate, LASSO and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. Kaplan-Meier curve, time-dependent receiver operating characteristic (ROC) curve, nomogram, decision curve analysis (DCA), and restricted mean survival time (EMST) were used to assess the performance of the gene signature. Results A novel energy metabolism based eight-gene signature (including UBE2Z, AMTN, AK1, CDCA4, TLE1, FXN, ZBTB6 and APLN) was established, which could dichotomize patients with significantly different OS in ESC. The eight-gene signature demonstrated independent prognostication potential in patient with ESC. The prognostic nomogram constructed based on the gene signature showed excellent predictive performance, whose robustness and clinical usability were higher than three previous reported prognostic gene signatures. Conclusions Our study established a novel energy metabolism based eight-gene signature and nomogram to predict the OS of ESC, which may help in precise clinical management.

scholarly journals Long Intergenic Non Coding RNA and MicroRNA Profiles of Pheochromocytoma and Paraganglioma as Prognostic Biomarkers

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A1024-A1024
Author(s):  
Suman Ghosal
Keyword(s):  
Multivariate Analysis ◽  
Risk Score ◽  
Cox Regression ◽  
Expression Profiles ◽  
Differential Expression Analysis ◽  
Hormone Secretion ◽  
Tumor Location ◽  
The Cancer Genome Atlas ◽  
Free Survival ◽  
Cox Regression Analysis

Abstract microRNAs (miRNAs) and long intergenic noncoding RNAs (lincRNAs) have been reported as important markers for many cancers. In search of new markers for the metastatic or aggressive phenotypes in the neuroendocrine tumor pheochromocytomas and paragangliomas (PCPG), we analyzed the non-coding transcriptome from patient gene expression data in The Cancer Genome Atlas. We used differential expression analysis and an elastic-net machine-learning model to identify miRNA and lincRNA transcriptomic signature specific to PCPG molecular subtypes. Similarly, miRNAs and lincRNAs specific to aggressive PCPGs were identified, and univariate and multivariate analysis were performed for identifying factors associated with metastasis-free survival. Upregulation of 13 lincRNAs and 4 miRNAs was found to be associated with aggressive/metastatic PCPGs. RT-PCR validation in tumor samples from PCPG patients confirmed the overexpression of 4 miRNAs and 4 lincRNAs in metastatic compared to non-metastatic PCPGs. Kaplan-Meier analysis identified 3 miRNAs and 5 lincRNAs as prognostic markers for metastasis-free survival of patients in PCPGs. In a multivariate Cox regression analysis combining these miRNA and lincRNA expression signatures with the previously identified clinically relevant parameters like SDHB germline mutation, ATRX somatic mutation, tumor location and hormone secretion phenotypes, we identified the miRNA miR-182 and lincRNA HIF1A-AS2 as independent predictors of poor metastasis-free survival. We formulated a risk-score model using multivariate analysis of lincRNA and miRNA expression profiles, presence of SDHB and ATRX mutations, tumor location, and hormone secretion phenotypes. Stratification of PCPG patients with this risk-score showed significant differences in metastasis-free survival.

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 Development and validation of 10-gene signature for predicting survival in patients with prostate cancer

2020 ◽  
Author(s):  
Nan Li ◽  
Kai Yu ◽  
Ling Zhong ◽  
Dingyuan Zeng
Keyword(s):  
Prostate Cancer ◽  
Cancer Patients ◽  
High Throughput Sequencing ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Sequencing Data

Abstract Background. The prognosis for prostate cancer patients remains poor. High-throughput sequencing data provide a solid basis for identifying genes associated with cancer prognosis, but genetic markers are needed to predict the clinical outcome of prostate cancer. Methods. The Cancer Genome Atlas (TCGA) database (N = 551) was adopted to estimate the prognostic value of immune genes. RNA-seq and clinical follow-up data were downloaded from TCGA. The samples were randomly divided into training and test. Cox regression analyses and least absolute shrinkage and selection operator (LASSO) were conducted to develop an immune risk score. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and single sample Gene Set Enrichment Analysis (ssGSEA) were used for functional Analysis. Tumor Immune Estimation Resource (TIMER) is used to analyze the immune score, and RMS curve and clinical decision curve analysis is used to analyze the superiority of the comparison with published models. Results. Survival analyses revealed that 19 genes significantly associated with the overall survival (OS). 10-genes signature was ultimately obtained through random forest feature selection. Riskscore effectively stratified samples in the training, test, and external verification sets and all TCGA sets. The 5-year survival AUC in the training, verification sets and all TCGA sets were around 0.7. Univariate and multivariate analysis showed that 10-genes signature has good predictive performance in clinical. TIMER analysis shows that immunosuppression may reduce the chances of survival for patients with prostate cancer. Compared with published models, our model has a higher C-index. Conclusion. We constructed a 10-gene signature as a new prognostic marker for predicting survival of prostate cancer patients.

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 of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Zi-Hao Wang ◽  
Yun-Zheng Zhang ◽  
Yu-Shan Wang ◽  
Xiao-Xin Ma
Keyword(s):  
Overall Survival ◽  
Endometrial Cancer ◽  
Mrna Expression ◽  
Cox Regression ◽  
Single Gene ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Rank Test ◽  
Cox Regression Analysis

Abstract Background Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC. Methods mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan–Meier survival estimates and the log‐rank test were used to validate the significance of risk parameters for prognosis prediction. Result Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors. Conclusion A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.

scholarly journals CACNA1C is a prognostic predictor for patients with ovarian cancer

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Xiaohan Chang ◽  
Yunxia Dong
Keyword(s):  
Ovarian Cancer ◽  
Cox Regression ◽  
Disease Free Survival ◽  
Cancer Genome ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Signal Pathways ◽  
Regression Analyses ◽  
Free Survival ◽  
Prognostic Predictor

Abstract Background CACNA1C, as a type of voltage-dependent calcium ion transmembrane channel, played regulatory roles in the development and progress of multiple tumors. This study was aimed to analyze the roles of CACNA1C in ovarian cancer (OC) of overall survival (OS) and to explore its relationships with immunity. Methods Single gene mRNA sequencing data and corresponding clinical information were obtained from The Cancer Genome Atlas Database (TCGA) and the International Cancer Genome Consortium (ICGC) datasets. Gene set enrichment analysis (GSEA) was used to identify CACNA1C-related signal pathways. Univariate and multivariate Cox regression analyses were applied to evaluate independent prognostic factors. Besides, associations between CACNA1C and immunity were also explored. Results CACNA1C had a lower expression in OC tumor tissues than in normal tissues (P < 0.001), with significant OS (P = 0.013) and a low diagnostic efficiency. We further validated the expression levels of CACNA1C in OC by means of the ICGC dataset (P = 0.01), qRT-PCR results (P < 0.001) and the HPA database. Univariate and multivariate Cox hazard regression analyses indicated that CACNA1C could be an independent risk factor of OS for OC patients (both P < 0.001). Five significant CACNA1C-related signaling pathways were identified by means of GSEA. As for genetic alteration analysis, altered CACNA1C groups were significantly associated with OS (P = 0.0169), progression-free survival (P = 0.0404), disease-free survival (P = 0.0417) and disease-specific survival (P = 9.280e-3), compared with unaltered groups in OC. Besides, CACNA1C was dramatically associated with microsatellite instability (MSI) and immunity. Conclusions Our results shed light on that CACNA1C could be a prognostic predictor of OS in OC and it was closely related to immunity.

scholarly journals Transcriptome Analyses Identify a Metabolic Gene Signature Indicative of Antitumor Immunosuppression of EGFR Wild Type Lung Cancers With Low PD-L1 Expression

2021 ◽  
Vol 11 ◽  
Author(s):  
Min Wang ◽  
Jie Zhu ◽  
Fang Zhao ◽  
Jiani Xiao
Keyword(s):  
High Risk ◽  
Cox Regression ◽  
Gene Signature ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Wild Type ◽  
Cox Regression Analysis ◽  
Metabolic Gene ◽  
Metabolic Genes ◽  
Low Expression

PurposeWith the development and application of targeted therapies like tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs), non-small cell lung cancer (NSCLC) patients have achieved remarkable survival benefits in recent years. However, epidermal growth factor receptor (EGFR) wild-type and low expression of programmed death-ligand 1 (PD-L1) NSCLCs remain unmanageable. Few treatments for these patients exist, and more side effects with combination therapies have been observed. We intended to generate a metabolic gene signature that could successfully identify high-risk patients and reveal its underlying molecular immunology characteristics.MethodsBy identifying the bottom 50% PD-L1 expression level as PD-L1 low expression and removing EGFR mutant samples, a total of 640 lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) tumor samples and 93 adjacent non-tumor samples were finally extracted from The Cancer Genome Atlas (TCGA). We identified differentially expressed metabolic genes (DEMGs) by R package limma and the prognostic genes by Univariate Cox proportional hazards regression analyses. The intersect genes between DEMGs and prognostic genes were put into the least absolute shrinkage and selection operator (LASSO) penalty Cox regression analysis. The metabolic gene signature contained 18 metabolic genes generated and successfully stratified LUAD and LUSC patients into the high-risk and low-risk groups, which was also validated by the Gene Expression Omnibus (GEO) database. Its accuracy was proved by the time-dependent Receiver Operating Characteristic (ROC) curve, Principal Components Analysis (PCA), and nomogram. Furthermore, the Single-sample Gene Set Enrichment Analysis (ssGSEA) and diverse acknowledged methods include XCELL, TIMER, QUANTISEQ, MCPcounter, EPIC, CIBERSORT-ABS, and CIBERSORT revealed its underlying antitumor immunosuppressive status. Besides, its relationship with somatic copy number alterations (SCNAs) and tumor mutational burden (TMB) was also discussed.ResultsIt is noteworthy that metabolism reprogramming is associated with the survival of the double-negative LUAD and LUSC patients. The SCNAs and TMB of critical metabolic genes can inhibit the antitumor immune process, which might be a promising therapeutic target.

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