scholarly journals A Novel Four-Gene Prognostic Signature as a Risk Biomarker in Cervical Cancer

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Jun Wang ◽  
Hua Zheng ◽  
Yatian Han ◽  
Geng Wang ◽  
Yanbin Li
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Treatment Options ◽  
Treatment Strategies ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
Prognostic Signature ◽  
Tissue Samples ◽  
Kaplan Meier ◽  
Cox Analysis

Background. Cervical cancer (CC) is a major malignancy affecting women worldwide, with limited treatment options for patients with advanced disease. The aim of this study was to identify novel prognostic biomarkers for CC. Methods. RNA-Seq data from four Gene Expression Omnibus datasets (GSE5787, GSE6791, GSE26511, and GSE63514) were used to identify differentially expressed genes (DEGs) between CC and normal cervical tissues. Functional and enrichment analyses of the DEGs were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Oncomine database, Cytoscape software, and Kaplan-Meier survival analyses were used for in-depth screening of hub DEGs. The Cox regression was then used to develop a prognostic signature, which was in turn used to create a nomogram. Results. A total of 207 DEGs were identified in the tissue samples, eight of which were prognostically significant in terms of overall survival (OS). Thereafter, a novel four-gene signature consisting of DSG2, MMP1, SPP1, and MCM2 was developed and validated using stepwise Cox analysis. The area under the receiver operating characteristic (ROC) curve (AUC) values were 0.785, 0.609, and 0.686 in the training, verification, and combination groups, respectively. The protein expression levels of the four genes were well validated by the western blotting. Moreover, the nomogram analysis showed that a combination of this four-gene signature plus lymph node metastasis (LNM) status effectively predicted the 1- and 3-year OS probabilities of CC patients with accuracies of 69.01% and 83.93%, respectively. Conclusions. We developed a four-gene signature that can accurately predict the prognosis in terms of OS, of CC patients, and could be a valuable tool for designing treatment strategies.

scholarly journals A novel four-gene prognostic signature as a risk biomarker in cervical cancer

2020 ◽  
Author(s):  
Jun Wang ◽  
Hua Zheng ◽  
Yatian Han ◽  
Geng Wang ◽  
Yanbin Li
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Treatment Options ◽  
Treatment Strategies ◽  
Gene Signature ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
Tissue Samples ◽  
Cox Analysis

Abstract Background: Cervical cancer (CC) is a major malignancy affecting women worldwide, with limited treatment options for patients with advanced disease. The aim of this study was to identify novel prognostic biomarkers for CC by a bioinformatics-based analysis using the Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)-CC cohort. Methods: RNA-Seq data from four GEO datasets (GSE5787, GSE6791, GSE26511, and GSE63514) were used to identify differentially expressed genes (DEGs) between CC and normal cervical tissues. Functional and enrichment analyses of the DEGs were performed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and the Database for Annotation, Visualization and Integrated Discovery (DAVID). The Oncomine database, Cytoscape software, and Kaplan–Meier survival analysis were used for in-depth screening for hub DEGs. Cox regression was then used to develop prognostic signature, which was in turn used to create a nomogram. Results: A total of 207 DEGs were identified in the tissue samples, eight of which were prognostically significant in terms of overall survival (OS). Thereafter, a novel four-gene signature consisting of DSG2, MMP1, SPP1, and MCM2 was developed and validated using stepwise Cox analysis. The area under the receiver operating characteristic (ROC) curve (AUC) values of 0.785, 0.609, and 0.686 in the training, verification, and combination groups, respectively. Moreover, the nomogram analysis showed that a combination of this four-gene signature plus lymph node metastasis (LNM) status effectively predicted the 1- and 3-year OS probabilities of CC patients with accuracies of 69.01% and 83.93%, respectively. Conclusions: We developed a four-gene signature that can accurately predict the prognosis, in terms of OS, of CC patients, and could be a valuable tool for designing treatment strategies.

scholarly journals Development and validation of an immune-related prognostic signature in cervical cancer

2021 ◽  
Author(s):  
Rongjia Su ◽  
Chengwen Jin ◽  
Hualei Bu ◽  
Xiaoyun Wang ◽  
Menghua Kuang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Clinical Information ◽  
Risk Groups ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Gynecological Malignancy ◽  
Immune Infiltration

Abstract Background Cervical cancer is the fourth most frequently gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods Raw data and clinical information of cervical cancer samples were download from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results There were 8 immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing PD-L1 expression and PD-1 checkpoint pathway differences between high risk and low risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusion The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.

Utility of a metabolic-associated nomogram to predict the recurrence-free survival of stage I cervical cancer

2021 ◽  
Vol 17 (11) ◽  
pp. 1325-1337
Author(s):  
Yan Zhang ◽  
Huan Lu ◽  
Jinjin Zhang ◽  
Shixuan Wang
Keyword(s):  
Cervical Cancer ◽  
Risk Score ◽  
Cox Regression ◽  
Area Under The Curve ◽  
Gene Expression Omnibus ◽  
Stage I ◽  
Recurrence Free Survival ◽  
Free Survival ◽  
Cox Regression Analysis ◽  
Cox Analysis

Aims: To identify metabolism-associated genes (MAGs) that serve as biomarkers to predict prognosis associated with recurrence-free survival (RFS) for stage I cervical cancer (CC). Patients & methods: By analyzing the Gene Expression Omnibus (GEO) database for 258 cases of stage I CC via univariate Cox analysis, LASSO and multivariate Cox regression analysis, we unveiled 11 MAGs as a signature that was also validated using Kaplan–Meier and receiver operating characteristic analyses. In addition, a metabolism-related nomogram was developed. Results: High accuracy of this signature for prediction was observed (area under the curve at 1, 3 and 5 years was 0.964, 0.929 and 0.852 for the internal dataset and 0.759, 0.719 and 0.757 for the external dataset). The high-risk score group displayed markedly worse RFS than did the low-risk score group. The indicators performed well in our nomogram. Conclusions: We identified a novel signature as a biomarker for predicting prognosis and a nomogram to facilitate the individual management of stage I CC patients.

scholarly journals Identification of a tumor microenvironment-related gene signature to improve the prediction of cervical cancer prognosis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qian Chen ◽  
Bingqing Qiu ◽  
Xiaoyun Zeng ◽  
Lang Hu ◽  
Dongping Huang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Tumor Microenvironment ◽  
Prognostic Model ◽  
Immune Cell ◽  
Gene Signature ◽  
The Cancer Genome Atlas ◽  
Cancer Prognosis ◽  
Risk Scores ◽  
Kaplan Meier ◽  
Cox Analysis

Abstract Background Previous studies have found that the microenvironment of cervical cancer (CESC) affects the progression and treatment of this disease. Thus, we constructed a multigene model to assess the survival of patients with cervical cancer. Methods We scored 307 CESC samples from The Cancer Genome Atlas (TCGA) and divided them into high and low matrix and immune scores using the ESTIMATE algorithm for differential gene analysis. Cervical cancer patients were randomly divided into a training group, testing group and combined group. The multigene signature prognostic model was constructed by Cox analyses. Multivariate Cox analysis was applied to evaluate the significance of the multigene signature for cervical cancer prognosis. Prognosis was assessed by Kaplan–Meier curves comparing the different groups, and the accuracy of the prognostic model was analyzed by receiver operating characteristic-area under the curve (ROC-AUC) analysis and calibration curve. The Tumor Immune Estimation Resource (TIMER) database was used to analyze the relationship between the multigene signature and immune cell infiltration. Results We obtained 420 differentially expressed genes in the tumor microenvironment from 307 patients with cervical cancer. A three-gene signature (SLAMF1, CD27, SELL) model related to the tumor microenvironment was constructed to assess patient survival. Kaplan–Meier analysis showed that patients with high risk scores had a poor prognosis. The ROC-AUC value indicated that the model was an accurate predictor of cervical cancer prognosis. Multivariate cox analysis showed the three-gene signature to be an independent risk factor for the prognosis of cervical cancer. A nomogram combining the three-gene signature and clinical features was constructed, and calibration plots showed that the nomogram resulted in an accurate prognosis for patients. The three-gene signature was associated with T stage, M stage and degree of immune infiltration in patients with cervical cancer. Conclusions This research suggests that the developed three-gene signature may be applied as a biomarker to predict the prognosis of and personalized therapy for CESC.

scholarly journals Development and Validation of an Immune-related Prognostic Signature in Cervical Cancer

2021 ◽  
Author(s):  
Rongjia Su ◽  
Chengwen Jin ◽  
Hualei Bu ◽  
Xiaoyun Wang ◽  
Menghua Kuang ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Cox Regression ◽  
Clinical Information ◽  
Risk Groups ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Prognostic Signature ◽  
Cox Regression Analysis ◽  
Gynecological Malignancy ◽  
Immune Infiltration

Abstract Background: Cervical cancer is the fourth most frequently gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods: Raw data and clinical information of cervical cancer samples were download from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results: There were 8 immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing PD-L1 expression and PD-1 checkpoint pathway differences between high risk and low risk groups. Conclusions: The 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Furthermore, the 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical 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.

scholarly journals Identification of Transcription Factor-Related Gene Signature and Risk Score Model for Colon Adenocarcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianwei Lin ◽  
Zichao Cao ◽  
Dingye Yu ◽  
Wei Cai
Keyword(s):  
Risk Model ◽  
Cox Regression ◽  
Characteristic Curve ◽  
Differential Expression Analysis ◽  
Colon Adenocarcinoma ◽  
Gene Signature ◽  
Cox Regression Analysis ◽  
Diagnosis And Prognosis ◽  
Kaplan Meier ◽  
Cox Analysis

The prognosis of colon adenocarcinoma (COAD) remains poor. However, the specific and sensitive biomarkers for diagnosis and prognosis of COAD are absent. Transcription factors (TFs) are involved in many biological processes in cells. As the molecule of the signal pathway of the terminal effectors, TFs play important roles in tumorigenesis and development. A growing body of research suggests that aberrant TFs contribute to the development of COAD, as well as to its clinicopathological features and prognosis. In consequence, a few studies have investigated the relationship between the TF-related risk model and the prognosis of COAD. Therefore, in this article, we hope to develop a prognostic risk model based on TFs to predict the prognosis of patients with COAD. The mRNA transcription data and corresponding clinical data were downloaded from TCGA and GEO. Then, 141 differentially expressed genes, validated by the GEPIA2 database, were identified by differential expression analysis between normal and tumor samples. Univariate, multivariate and Lasso Cox regression analysis were performed to identify seven prognostic genes (E2F3, ETS2, HLF, HSF4, KLF4, MEIS2, and TCF7L1). The Kaplan–Meier curve and the receiver operating characteristic curve (ROC, 1-year AUC: 0.723, 3-year AUC: 0.775, 5-year AUC: 0.786) showed that our model could be used to predict the prognosis of patients with COAD. Multivariate Cox analysis also reported that the risk model is an independent prognostic factor of COAD. The external cohort (GSE17536 and GSE39582) was used to validate our risk model, which indicated that our risk model may be a reliable predictive model for COAD patients. Finally, based on the model and the clinicopathological factors, we constructed a nomogram with a C-index of 0.802. In conclusion, we emphasize the clinical significance of TFs in COAD and construct a prognostic model of TFs, which could provide a novel and reliable model for the prognosis of COAD.

scholarly journals A 10-gene prognostic signature points to LIMCH1 and HLA-DQB1 as important players in aggressive cervical cancer disease

2021 ◽  
Author(s):  
Mari K. Halle ◽  
Marte Sødal ◽  
David Forsse ◽  
Hilde Engerud ◽  
Kathrine Woie ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Primary Tumour ◽  
Treatment Options ◽  
Prognostic Significance ◽  
Gene Signature ◽  
Prognostic Impact ◽  
Cancer Disease ◽  
Gene Set ◽  
Patient Cohort ◽  
Protein Levels

Abstract Background Advanced cervical cancer carries a particularly poor prognosis, and few treatment options exist. Identification of effective molecular markers is vital to improve the individualisation of treatment. We investigated transcriptional data from cervical carcinomas related to patient survival and recurrence to identify potential molecular drivers for aggressive disease. Methods Primary tumour RNA-sequencing profiles from 20 patients with recurrence and 53 patients with cured disease were compared. Protein levels and prognostic impact for selected markers were identified by immunohistochemistry in a population-based patient cohort. Results Comparison of tumours relative to recurrence status revealed 121 differentially expressed genes. From this gene set, a 10-gene signature with high prognostic significance (p = 0.001) was identified and validated in an independent patient cohort (p = 0.004). Protein levels of two signature genes, HLA-DQB1 (n = 389) and LIMCH1 (LIM and calponin homology domain 1) (n = 410), were independent predictors of survival (hazard ratio 2.50, p = 0.007 for HLA-DQB1 and 3.19, p = 0.007 for LIMCH1) when adjusting for established prognostic markers. HLA-DQB1 protein expression associated with programmed death ligand 1 positivity (p < 0.001). In gene set enrichment analyses, HLA-DQB1high tumours associated with immune activation and response to interferon-γ (IFN-γ). Conclusions This study revealed a 10-gene signature with high prognostic power in cervical cancer. HLA-DQB1 and LIMCH1 are potential biomarkers guiding cervical cancer treatment.

scholarly journals Development and validation of a 13-m6a related lncRNA prognostic signature for lung adenocarcinoma

2021 ◽  
Author(s):  
Pingfan Wu ◽  
Xiaowen Zhao ◽  
Ling Xue ◽  
Xiaojing Yang ◽  
Yuxiang Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Treatment Strategies ◽  
Risk Groups ◽  
Gene Expression Omnibus ◽  
High Risk Group ◽  
The Cancer Genome Atlas ◽  
Prognostic Signature ◽  
P53 Signaling ◽  
Selection Operator

Abstract Considerable evidence suggests that N6-methyladenosine (m6A) is involved in the regulation of long non-coding RNA (lncRNA), whichparticipates in the occurrence, development and prognosis of tumorscancerBut the relationship between m6A regulators-related lncRNA (mRlncRNA) and lung adenocarcinoma (LUAD) remains unclear. This study aims to determine a feature based on mRlncRNA for prognostic evaluation of LUAD patients. By integrating the gene expression data of LUAD and normal samples from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, the m6A gene and mRlncRNA with imbalanced expression were screened out. Then we used the least absolute shrinkage and selection operator (LASSO) to obtain the 13-lncRNA prognostic signature in the TCGA training cohort. Patients were divided into two risk groups based on the risk score of lncRNAs characteristics, and their overall survival (OS) was significantly different. The predictive power of this signature was verified in TCGA testing cohort and entire TCGA cohort. These landmark lncRNAs were involved in several biologiocal processes and pathways related to cell cycle, DNA replication, P53 signaling pathway and mismatch repair. Besides, the high-risk group was low-response to cisplatin, while high-response to mitomycin, docetaxel and immunotherapy. In conclusion, we identified a 13-mRlncRNA model associated with prognosis and treatment sensitivity in LUAD, which may provide clues about the influence of m6A on lncRNA in LUAD and promote the further improvement of LUAD individualized treatment strategies.

scholarly journals Effectiveness comparisons of various therapies for FIGO stage IB2/IIA2 cervical cancer: a Bayesian network meta-analysis

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Cheng ◽  
Beibei Liu ◽  
Biao Wang ◽  
Xicui Long ◽  
Zhihong Li ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Bayesian Network ◽  
Cancer Patients ◽  
Radical Surgery ◽  
Meta Analysis ◽  
Treatment Options ◽  
Female Genital Tract ◽  
Treatment Strategies ◽  
Final Analysis ◽  
Phase Iii

Abstract Background Cervical cancer is a common malignancy of the female genital tract. Treatment options for cervical cancer patients diagnosed at FIGO (2009) stage IB2 and IIA2 remains controversial. Methods We perform a Bayesian network meta-analysis to directly or indirectly compare various interventions for FIGO (2009) IB2 and IIA2 disease, in order to improve our understand of the optimal treatment strategy for these women. Three databases were searched for articles published between 1971 and 2020. Data on included study characteristics, outcomes, and risk of bias were abstracted by two reviewers. Results Seven thousand four hundred eighty-six articles were identified. Thirteen randomized controlled trials of FIGO (2009) IB2 and IIA2 cervical cancer patients were included in the final analysis. These trials used six different interventions: concomitant chemoradiotherapy (CCRT), radical surgery (RS), radical surgery following chemoradiotherapy (CCRT+RS), neoadjuvant chemotherapy followed by radical surgery (NACT+RS), adjuvant radiotherapy followed by Radical surgery (RT + RS), radiotherapy alone (RT).SUCRA ranking of OS and Relapse identified CCRT+RS and CCRT as the best interventions, respectively. Systematic clustering analysis identified the CCRT group as a unique cluster. Conclusion These data suggest that CCRT may be the best approach for improving the clinical outcome of cervical cancer patients diagnosed at FIGO (2009) stage IB2/IIA2. Phase III randomized trials should be performed in order to robustly assess the relative efficacy of available treatment strategies in this disease context.

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