An 8‑gene signature predicts the prognosis of cervical cancer following radiotherapy

Xie, Fei; Dong, Dan; Du, Na; Guo, Liang; Ni, Weihua; Yuan, Hongyan; Zhang, Nannan; Jiang, Jie; Liu, Guomu; Tai, Guihua

doi:10.3892/mmr.2019.10535

Cited by 12 publications

(11 citation statements)

References 64 publications

(67 reference statements)

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“…Using lung cancer data from TCGA, a seven-gene model was able to predict prognosis [ 26 ]. An eight-gene prognostic model was reported to have predictive power for cervical cancer prognosis [ 19 ]. Ding et al identified a potential three-gene signature (MSRB3, CENPM, ZIC2) that can predict the prognosis of cervical cancer through the TCGA dataset, which is expected to provide a basis for the prognostic evaluation of patients in the future [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

“…A recent study showed that a 70-gene signature can be used as a predictor of the therapeutic effect of individualized systemic treatment of advanced-stage cervical cancer, which can help prolong the survival time of patients [ 18 ]. Xie et al found that an 8-gene signature can predict the prognosis of patients with cervical cancer after radiotherapy [ 19 ]. Cai et al reported that a glycolysis-related six-gene signature can be used as a prognostic marker to accurately predict the prognosis of cervical cancer patients [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Therefore, parallel assessment and blindness are necessary during measurement procedures to ensure quality. Additionally, despite the increasing number of research on the predictive value of omics (35)(36)(37), the inclusion of omics into a conventional prediction model needs to be discussed considering the accessibility of these predictors and cost-effectiveness issues, especially in countries with middle or low income. Besides this, treatment is very important for the prognosis of cervical cancer patients, and different surgical methods may lead to different survival outcomes (38), but treatments were considered a predictor only in 18 models, which indicates that prognostic models tailored to different treatment modalities are needed.…”

Section: Discussionmentioning

confidence: 99%

Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal

Chen

Liu

et al. 2021

Front. Public Health

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Objective: This work aims to systematically identify, describe, and appraise all prognostic models for cervical cancer and provide a reference for clinical practice and future research.Methods: We systematically searched PubMed, EMBASE, and Cochrane library databases up to December 2020 and included studies developing, validating, or updating a prognostic model for cervical cancer. Two reviewers extracted information based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modeling Studies checklist and assessed the risk of bias using the Prediction model Risk Of Bias ASsessment Tool.Results: Fifty-six eligible articles were identified, describing the development of 77 prognostic models and 27 external validation efforts. The 77 prognostic models focused on three types of cervical cancer patients at different stages, i.e., patients with early-stage cervical cancer (n = 29; 38%), patients with locally advanced cervical cancer (n = 27; 35%), and all-stage cervical cancer patients (n = 21; 27%). Among the 77 models, the most frequently used predictors were lymph node status (n = 57; 74%), the International Federation of Gynecology and Obstetrics stage (n = 42; 55%), histological types (n = 38; 49%), and tumor size (n = 37; 48%). The number of models that applied internal validation, presented a full equation, and assessed model calibration was 52 (68%), 16 (21%), and 45 (58%), respectively. Twenty-four models were externally validated, among which three were validated twice. None of the models were assessed with an overall low risk of bias. The Prediction Model of Failure in Locally Advanced Cervical Cancer model was externally validated twice, with acceptable performance, and seemed to be the most reliable.Conclusions: Methodological details including internal validation, sample size, and handling of missing data need to be emphasized on, and external validation is needed to facilitate the application and generalization of models for cervical cancer.

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“…Three CSCC prognostic models (namely, the 8-, 5-and 3-gene signature) (36)(37)(38) reported in literature were selected, which were then compared with the 8-stemness gene signature constructed in our study. The corresponding Riskscore values of each prognostic model were calculated based on gene expression pro les for samples derived from TCGA-CSCC dataset, which were further converted to zscore values.…”

Section: Comparison Between Our 8-stemness Gene Signature Risk Model mentioning

confidence: 99%

Establishment of a Prognostic Stemness Signature for Cervical Squamous Cell Carcinoma

Sun

Wang

Zhao

et al. 2020

Preprint

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Background: The aim of this study was to construct a robust stemness-related gene signature for predicting prognosis of cervical squamous cell carcinoma (CSCC).Methods: Expression data for the PCBC database-derived pluripotent stem cell (PSC) samples were collected using the one-class logistic regression (OCLR) method to calculate stemness indexes (mRNAsi) of samples derived from the TCGA dataset. Functions of possible mRNAsi-related stemness genes extracted through WGCNA were then examined by enrichment analysis. Most representative stemness genes for prognosis prediction were screened to construct a stemness-related gene signature by shrinkage estimate and univariate analysis. Next, the TCGA dataset and the GSE44001 external dataset were incorporated into that model and classified to evaluate the model efficiency and stability in patient prognosis prediction and classification according to the Riskscore. The associations between the Riskscore and clinical characteristics as well as relevant signaling pathways were also explored. Moreover, the prognosis predicting efficiency of the stemness-related gene signature was compared with those of CSCC prognostic signatures reported in other studies.Results: According to the findings, mRNAsi showed significant correlation with key oncogene mutation degrees (including DMD, KMT2C, EP300 and MUC4), infiltrating stroma cells and the CIMP classification for CSCC cases. The 8-stemness gene signature in this study achieved high stability and accuracy in prognosis prediction for CSCC cases. In the meanwhile, the model provided diverse therapeutic targets to precisely treat CSCC in clinical practice based on various subtype-specific stemness genes.Conclusion: Our present study suggested that the 8 stemness gene signature can help to screen out novel stem-related diagnostic indicators, therapeutic targets and prognostic predictors in CSCC.

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An 8‑gene signature predicts the prognosis of cervical cancer following radiotherapy

Cited by 12 publications

References 64 publications

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

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

Prediction Models for Prognosis of Cervical Cancer: Systematic Review and Critical Appraisal

Establishment of a Prognostic Stemness Signature for Cervical Squamous Cell Carcinoma

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