scholarly journals A 4-gene signature predicts prognosis of uterine serous carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hui Chen ◽  
Lingjun Li ◽  
Ping Qin ◽  
Hanzhen Xiong ◽  
Ruichao Chen ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Serous Carcinoma ◽  
Regression Analyses ◽  
Uterine Serous Carcinoma ◽  
Clinical Records

Abstract Background Uterine serous carcinoma (USC) is an aggressive type of endometrial cancer that accounts for up to 40% of endometrial cancer deaths, creating an urgent need for prognostic biomarkers. Methods USC RNA-Seq data and corresponding patients’ clinical records were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Univariate cox, Lasso, and Multivariate cox regression analyses were conducted to forge a prognostic signature. Multivariable and univariable cox regression analysis and ROC curve evaluated the prediction efficiency both in the training and testing sets. Results We uncovered 1385 genes dysregulated in 110 cases of USC tissue relative to 113 cases of normal uterine tissue. Functional enrichment analysis of these genes revealed the involvement of various cancer-related pathways in USC. A novel 4-gene signature (KRT23, CXCL1, SOX9 and ABCA10) of USC prognosis was finally forged by serial regression analyses. Overall patient survival (OS) and recurrence-free survival (RFS) were significantly lower in the high-risk group relative to the low-risk group in both the training and testing sets. The area under the ROC curve of the 4-gene signature was highest among clinicopathological features in predicting OS and RFS. The 4-gene signature was found to be an independent prognostic indicator in USC and was a superior predictor of OS in early stage of USC. Conclusions Our findings highlight the potential of the 4-gene signature as a guide for personalized USC treatment.

scholarly journals A 4-gene signature predicts prognosis of uterine serous carcinoma

2020 ◽  
Author(s):  
Hui Chen ◽  
Lingjun Li ◽  
Ping Qin ◽  
Hanzhen Xiong ◽  
Ruichao Chen ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Serous Carcinoma ◽  
Regression Analyses ◽  
Uterine Serous Carcinoma ◽  
Clinical Records

Abstract Background: Uterine serous carcinoma (USC) is an aggressive type of endometrial cancer that accounts for up to 40% of endometrial cancer deaths, creating an urgent need for prognostic biomarkers. Methods: USC RNA-Seq data and corresponding patients’ clinical records were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Univariate cox, Lasso, and Multivariate cox regression analyses were conducted to forge a prognostic signature. Multivariable and univariable cox regression analysis and ROC curve evaluated the prediction efficiency both in the training and testing sets. Results: We uncovered 1385 genes dysregulated in 110 cases of USC tissue relative to 113 cases of normal uterine tissue. Functional enrichment analysis of these genes revealed the involvement of various cancer-related pathways in USC. A novel 4‐gene signature (KRT23, CXCL1, SOX9 and ABCA10) of USC prognosis was finally forged by serial regression analyses. Overall patient survival (OS) and recurrence-free survival (RFS) were significantly lower in the high-risk group relative to the low-risk group in both the training and testing sets. The area under the ROC curve of the 4-gene signature was highest among clinicopathological features in predicting OS and RFS. The 4-gene signature was found to be an independent prognostic indicator in USC and was a superior predictor of OS in early stage of USC. Conclusions: Our findings highlight the potential of the 4-gene signature as a guide for personalized USC treatment.

scholarly journals A 4-gene signature predicts prognosis of uterine serous carcinoma

2020 ◽  
Author(s):  
Hui Chen ◽  
Lingjun Li ◽  
Ping Qin ◽  
Hanzhen Xiong ◽  
Ruichao Chen ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Serous Carcinoma ◽  
Regression Analyses ◽  
Uterine Serous Carcinoma ◽  
Clinical Records

Abstract Background: Uterine serous carcinoma (USC) is an aggressive type of endometrial cancer that accounts for up to 40% of endometrial cancer deaths, creating an urgent need for prognostic biomarkers. Methods: USC RNA-Seq data and corresponding patients’ clinical records were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Univariate cox, Lasso, and Multivariate cox regression analyses were conducted to forge a prognostic signature. Multivariable and univariable cox regression analysis and ROC curve evaluated the prediction efficiency both in the training and testing sets. Results: We uncovered 1385 genes dysregulated in 110 cases of USC tissue relative to 113 cases of normal uterine tissue. Functional enrichment analysis of these genes revealed the involvement of various cancer-related pathways in USC. A novel 4‐gene signature (KRT23, CXCL1, SOX9 and ABCA10) of USC prognosis was finally forged by serial regression analyses. Overall patient survival (OS) and recurrence-free survival (RFS) were significantly lower in the high-risk group relative to the low-risk group in both the training and testing sets. The area under the ROC curve of the 4-gene signature was highest among clinicopathological features in predicting OS and RFS. The 4-gene signature was found to be an independent prognostic indicator in USC and was a superior predictor of OS in early stage of USC. Conclusions: Our findings highlight the potential of the 4-gene signature as a guide for personalized USC treatment.

scholarly journals A 4-gene Signature Predicts Prognosis of Uterine Serous Carcinoma

2020 ◽  
Author(s):  
Hui Chen ◽  
Lingjun Li ◽  
Ping Qin ◽  
Hanzhen Xiong ◽  
Ruichao Chen ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Functional Enrichment ◽  
Serous Carcinoma ◽  
Regression Analyses ◽  
Uterine Serous Carcinoma ◽  
Clinical Records

Abstract Background: Uterine serous carcinoma (USC) is a rare, aggressive variant of endometrial cancer that accounts for more than 50% of endometrial cancer deaths, creating an urgent need for prognostic biomarkers. Methods: USC RNA-Seq data and corresponding patients’ clinical records were obtained from The Cancer Genome Atlas and Genotype-Tissue Expression datasets. Univariate cox, Lasso, and Multivariate cox regression analyses were conducted to forge a prognostic signature. Multivariable and univariable cox regression analysis and ROC curve evaluated the prediction efficiency both in the training and testing sets. Results: We uncovered 1385 genes dysregulated in 110 cases of USC tissue relative to 113 cases of normal uterine tissue. Functional enrichment analysis of these genes revealed the involvement of various cancer-related pathways in USC. A novel 4‐gene signature (KRT23, CXCL1, SOX9 and ABCA10) of USC prognosis was finally forged by serial regression analyses. Overall patient survival (OS) and recurrence-free survival (RFS) were significantly lower in the high-risk group relative to the low-risk group in both the training and testing sets. The area under the ROC curve of the 4-gene signature was highest among clinicopathological features in predicting OS and RFS. The 4-gene signature was found to be an independent prognostic indicator in USC and was a superior predictor of OS in early stage of USC. Conclusions: Our findings highlight the potential of the 4-gene signature as a guide for personalized USC treatment.

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

scholarly journals A Novel Platelet-Related Gene Signature for Predicting the Prognosis of Triple-Negative Breast Cancer

2022 ◽  
Vol 9 ◽  
Author(s):  
Jindong Xie ◽  
Yutian Zou ◽  
Feng Ye ◽  
Wanzhen Zhao ◽  
Xinhua Xie ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Clinical Prognosis

Regarded as the most invasive subtype, triple-negative breast cancer (TNBC) lacks the expression of estrogen receptors (ERs), progesterone receptors (PRs), and human epidermal growth factor receptor 2 (HER2) proteins. Platelets have recently been shown to be associated with metastasis of malignant tumors. Nevertheless, the status of platelet-related genes in TNBC and their correlation with patient prognosis remain unknown. In this study, the expression and variation levels of platelet-related genes were identified and patients with TNBC were divided into three subtypes. We collected cohorts from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, we constructed a seven-gene signature which classified the two cohorts of patients with TNBC into low- or high-risk groups. Patients in the high-risk group were more likely to have lower survival rates than those in the low-risk group. The risk score, incorporated with the clinical features, was confirmed as an independent factor for predicting the overall survival (OS) time. Functional enrichment analyses revealed the involvement of a variety of vital biological processes and classical cancer-related pathways that could be important to the ultimate prognosis of TNBC. We then built a nomogram that performed well. Moreover, we tested the model in other cohorts and obtained positive outcomes. In conclusion, platelet-related genes were closely related to TNBC, and this novel signature could serve as a tool for the assessment of clinical prognosis.

scholarly journals Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer

2021 ◽  
Author(s):  
Jianxin Li ◽  
Ting Han ◽  
Xin Wang ◽  
Yinchun Wang ◽  
Qingqiang Yang
Keyword(s):  
Colorectal Cancer ◽  
Overall Survival ◽  
High Risk ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Regression Analyses

Abstract Background Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to accurately assess the prognosis of patients with colorectal cancer (CRC). Methods Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA), and the immune-related mRNAs were extracted from immunomodulatory gene datasets IMMUNE RESPONSE and IMMUNE SYSTEM PROCESS based on the Molecular Signatures Database (MSigDB). Then, the immune-related lncRNAs were identified by a correlation analysis between immune-related mRNAs and lncRNAs. Subsequently, univariate, lasso and multivariate Cox regression were used to identify an immune-related lncRNA signature in training cohort, and the predict ability of the signature was further confirmed in the testing cohort and the entire TCGA cohort. Finally, the lncRNA-mRNA co-expression network was established to explore the biological role of the immune-related lncRNA signature. Results In total, 272 Immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature based on univariate, lasso and multivariate Cox regression analyses. The signature divided patients with CRC into low- and high-risk groups, and patients with CRC in high-risk group had poorer overall survival than those in low-risk group. Univariate and multivariate Cox regression analyses confirmed that the signature could be an independent prognostic factor in human CRC. Furthermore, functional enrichment analysis revealed that the immune-related lncRNA signature was significantly enriched in immune process and tumor classical pathways. Conclusions The present study revealed that the novel immune-related lncRNA signature could be exploited as underlying molecular biomarkers and therapeutic targets for the patients with CRC.

scholarly journals Identification of a Four-Gene Signature With Prognostic Significance in Endometrial Cancer Using Weighted-Gene Correlation Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Shijin Huang ◽  
Lihong Pang ◽  
Changqiang Wei
Keyword(s):  
Endometrial Cancer ◽  
Network Analysis ◽  
Differentially Expressed Genes ◽  
Predictive Value ◽  
Cox Regression ◽  
Risk Group ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Hub Genes ◽  
Gene Correlation

Endometrial hyperplasia (EH) is a precursor for endometrial cancer (EC). However, biomarkers for the progression from EH to EC and standard prognostic biomarkers for EC have not been identified. In this study, we aimed to identify key genes with prognostic significance for the progression from EH to EC. Weighted-gene correlation network analysis (WGCNA) was used to identify hub genes utilizing microarray data (GSE106191) downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified from the Uterine Corpus Endometrial Carcinoma (UCEC) dataset of The Cancer Genome Atlas database. The Limma-Voom R package was applied to detect differentially expressed genes (DEGs; mRNAs) between cancer and normal samples. Genes with |log2 (fold change [FC])| > 1.0 and p < 0.05 were considered as DEGs. Univariate and multivariate Cox regression and survival analyses were performed to identify potential prognostic genes using hub genes overlapping in the two datasets. All analyses were conducted using R Bioconductor and related packages. Through WGCNA and overlapping genes in hub modules with DEGs in the UCEC dataset, we identified 42 hub genes. The results of the univariate and multivariate Cox regression analyses revealed that four hub genes, BUB1B, NDC80, TPX2, and TTK, were independently associated with the prognosis of EC (Hazard ratio [95% confidence interval]: 0.591 [0.382–0.912], p = 0.017; 0.605 [0.371–0.986], p = 0.044; 1.678 [1.132–2.488], p = 0.01; 2.428 [1.372–4.29], p = 0.02, respectively). A nomogram was established with a risk score calculated using the four genes’ coefficients in the multivariate analysis, and tumor grade and stage had a favorable predictive value for the prognosis of EC. The survival analysis showed that the high-risk group had an unfavorable prognosis compared with the low-risk group (p < 0.0001). The receiver operating characteristic curves also indicated that the risk model had a potential predictive value of prognosis with area under the curve 0.807 at 2 years, 0.783 at 3 years, and 0.786 at 5 years. We established a four-gene signature with prognostic significance in EC using WGCNA and established a nomogram to predict the prognosis of EC.

scholarly journals Idenfication of a novel signature based on mTORC1 pathway for hepatocellular carcinoma.

2020 ◽  
Author(s):  
Zhuomao Mo ◽  
Shaoju Luo ◽  
Zhirui Cao ◽  
Hao Hu ◽  
Ling Yu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Prognostic Factor ◽  
Roc Curve ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Signal Pathway ◽  
Cancer Genome ◽  
Cox Regression Analysis

Abstract Background mTORC1 signal pathway play a role in the initiation and progression of hepatocellular carcinoma (HCC), but no relevant gene signature was developed. This research aimed to explore the potential correlation between mTORC1 signal pathway and HCC and establish the related genes signature. Methods HCC cases were retrieved from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) databases. The genes to be included in mTORC1-assiociated signature were selected by performing univariate, multivariate Cox regression analysis and lasso regression analysis. Then, the signature was verified by survival analysis and multiple receiver operating characteristic (ROC) curve. Furthermore, a nomogram was established and evaluated by C-index, calibration plot and ROC curve. Results The signature was established with the six genes ( ETF1, GSR, SKAP2, HSPD1, CACYBP and PNP ). Under the grouping from signature, patients in the high- risk group showed worse survival than those in the low-risk group in both three datasets. The univariate and multivariate Cox regression analysis indicated that mTORC1 related signature can be the potential independent prognostic factor in HCC. Conclusion The mTORC1 associated gene signature established and validated in our research could be used as a potential prognostic factor in HCC.

scholarly journals Comprehensive Analysis of the Association Between the Glycolysis Prognostic Risk Signature and Immune Infiltration in Endometrial Cancer

2020 ◽  
Author(s):  
Xiao Yang ◽  
Xingchen Li ◽  
Boqiang Shen ◽  
Jingyi Zhou ◽  
Yuan Cheng ◽  
...  
Keyword(s):  
Overall Survival ◽  
Dendritic Cells ◽  
Endometrial Cancer ◽  
High Risk ◽  
Roc Curve ◽  
Risk Group ◽  
Gene Signature ◽  
Low Risk ◽  
Prognostic Signature ◽  
Immune Infiltration

Abstract Background: Glycolysis is the primary mechanism of energy metabolism in tumor cells. The purpose of this study is to develop a glycolysis-related risk signature for endometrial cancer and analyze its relationship with immune function.Methods: The mRNA expression profiling and clinical information of endometrial cancer were downloaded from TCGA database. GSEA was performed to screen out gene sets related to glycolysis, and the R software was used to screen DEGs. Univariate Cox and LASSO regression analyses were used to construct a glycolysis-related prognostic signature for endometrial cancer. WGCNA were performed to identify the potential mechanisms associated with the prognostic signature. Immune scores, stromal scores and ESTIMATE scores were calculated based on the R “ESTIMATE” algorithm. The “CIBERSORT” algorithm was used to evaluate the infiltration of immune cells. ROC curve, nomogram, gene alteration, coexpression analyses and PPIs were also performed. Results: We identified a glycolysis-related gene signature (PFKM, PSMC4, NUP85, PDHA1, CDK1, CLDN9, CENPA, GPI, NUP155 and GPCI) for predicting the prognosis of endometrial cancer. Based on this gene signature, the patients were divided into high- and low-risk subgroups. The overall survival of patients in the high-risk subgroup was significantly lower than that of the low-risk group. Multivariate Cox regression analysis results showed that the ten-gene signature was an independent prognostic factor for endometrial cancer. The ROC curve confirmed the accuracy of the prognostic signature (AUC=0.730). The immune scores and ESTIMATE scores of patients in the high-risk subgroup were lower than those of the low-risk subgroup, while the low immune score and ESTIMATE scores were closely related to the poor prognosis of patients. The high-risk group was associated with lower cellular immune infiltration of resting dendritic cells, resting memory T cells and Tregs, while the overall survival rate of resting dendritic cells and Tregs in the low-proportion group was significantly lower than that in the high-proportion group. Conclusions: we constructed a glycolysis-related ten-gene signature to predict the survival and prognosis of endometrial cancer. Our findings may help to elucidate the mechanism of glycolysis and provide new ideas for targeted therapy and prognosis of endometrial cancer.

scholarly journals Identification of a Four-Gene-Based SERM Signature for Prognostic and Drug Sensitivity Prediction in Gastric Cancer

2022 ◽  
Vol 11 ◽  
Author(s):  
Xiya Jia ◽  
Bing Chen ◽  
Ziteng Li ◽  
Shenglin Huang ◽  
Siyuan Chen ◽  
...  
Keyword(s):  
Poor Prognosis ◽  
Prognostic Value ◽  
Drug Sensitivity ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Prognostic Signature ◽  
Kaplan Meier ◽  
Drug Sensitivity Prediction

BackgroundGastric cancer (GC) is a highly molecular heterogeneous tumor with poor prognosis. Epithelial-mesenchymal transition (EMT) process and cancer stem cells (CSCs) are reported to share common signaling pathways and cause poor prognosis in GC. Considering about the close relationship between these two processes, we aimed to establish a gene signature based on both processes to achieve better prognostic prediction in GC.MethodsThe gene signature was constructed by univariate Cox and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses by using The Cancer Genome Atlas (TCGA) GC cohort. We performed enrichment analyses to explore the potential mechanisms of the gene signature. Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves were implemented to assess its prognostic value in TCGA cohort. The prognostic value of gene signature on overall survival (OS), disease-free survival (DFS), and drug sensitivity was validated in different cohorts. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) validation of the prognostic value of gene signature for OS and DFS prediction was performed in the Fudan cohort.ResultsA prognostic signature including SERPINE1, EDIL3, RGS4, and MATN3 (SERM signature) was constructed to predict OS, DFS, and drug sensitivity in GC. Enrichment analyses illustrated that the gene signature has tight connection with the CSC and EMT processes in GC. Patients were divided into two groups based on the risk score obtained from the formula. The Kaplan-Meier analyses indicated high-risk group yielded significantly poor prognosis compared with low-risk group. Pearson’s correlation analysis indicated that the risk score was positively correlated with carboplatin and 5-fluorouracil IC50 of GC cell lines. Multivariate Cox regression analyses showed that the gene signature was an independent prognostic factor for predicting GC patients’ OS, DFS, and susceptibility to adjuvant chemotherapy.ConclusionsOur SERM prognostic signature is of great value for OS, DFS, and drug sensitivity prediction in GC, which may give guidance to the development of targeted therapy for CSC- and EMT-related gene in the future.

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