Screening of genes related to survival prognosis of cervical squamous cell carcinoma and construction of prognosis prediction model

2021 ◽  
Author(s):  
Rui Qin ◽  
Lu Cao ◽  
Cong Ye ◽  
Junrong Wang ◽  
Ziqian Sun
Keyword(s):  
Squamous Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cervical Squamous Cell Carcinoma ◽  
Survival Prognosis ◽  
Prognosis Prediction ◽  
Prognosis Prediction Model

scholarly journals A characterization and prognosis prediction model for primary squamous cell carcinoma of the thyroid

Gland Surgery ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 1325-1338
Author(s):  
Xingyu Zhang ◽  
Gangcai Zhu ◽  
Bin Tang ◽  
Huimei Huang ◽  
Changhan Chen ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Primary Squamous Cell Carcinoma ◽  
Prognosis Prediction ◽  
Prognosis Prediction Model

scholarly journals Establishment of a Prognostic Stemness Signature For Cervical Squamous Cell Carcinoma

2021 ◽  
Author(s):  
Xiwen Sun ◽  
Fang Wang ◽  
Jiayu Shen ◽  
Jianwei Zhou
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Univariate Analysis ◽  
Therapeutic Targets ◽  
Cervical Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Related Gene ◽  
Prognosis Prediction ◽  
Tcga Dataset

Abstract 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.

scholarly journals Prognostic Signatures Based on Ferroptosis- and Immune-Related Genes for Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma

2022 ◽  
Vol 11 ◽  
Author(s):  
Chaoqun Xing ◽  
Huiming Yin ◽  
Zhi-Yong Yao ◽  
Xiao-Liang Xing
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Prediction Model ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Female Genital Tract ◽  
Cervical Squamous Cell Carcinoma ◽  
Assessment Model ◽  
Endocervical Adenocarcinoma ◽  
Immune Related Genes

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most common malignancies of the female genital tract. Ferroptosis and immunity regulate each other and play important roles in the progression of CESC. The present study aimed to screen ferroptosis- and immune-related differentially expressed genes (FI-DEGs) to identify suitable prognostic signatures for patients with CESC. We downloaded the RNAseq count data and corresponding clinical information of CESC patients from The Cancer Genome Atlas database; obtained recognized ferroptosis- and immune-related genes from the FerrDb and ImmPort databases, respectively; and screened for suitable prognostic signatures using a series of bioinformatics analyses. We identified eight FI-DEGs (CALCRL, CHIT1, DES, DUOX1, FLT1, HELLS, SCD, and SDC1) that were independently correlated with the overall survival of patients with CESC. The prediction model constructed using these eight FI-DEGs was also independently correlated with overall survival. Both the sensitivity and specificity of the prediction model constructed using these eight signatures were over 60%. The comprehensive index of ferroptosis and immune status was significantly correlated with the immunity of patients with CESC. In conclusion, the risk assessment model constructed with these eight FI-DEGs predicted the CESC outcomes. Therefore, these eight FI-DEGs could serve as prognostic signatures for CESC.

scholarly journals Establishment of a Prognostic Stemness Signature for Cervical Squamous Cell Carcinoma

2020 ◽  
Author(s):  
Xiwen Sun ◽  
Fang Wang ◽  
Jiayao Zhao ◽  
Jianwei Zhou
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Univariate Analysis ◽  
Therapeutic Targets ◽  
Cervical Squamous Cell Carcinoma ◽  
Gene Signature ◽  
Related Gene ◽  
Prognosis Prediction ◽  
Tcga Dataset

Abstract 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.

scholarly journals Enhancing prognosis prediction using pre-treatment nodal SUVmax and HPV status in cervical squamous cell carcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Chae Moon Hong ◽  
Shin-Hyung Park ◽  
Gun Oh Chong ◽  
Yoon Hee Lee ◽  
Ju Hye Jeong ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Cervical Squamous Cell Carcinoma ◽  
Prognosis Prediction ◽  
Hpv Status ◽  
Pre Treatment
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