Development and Validation of an m6A RNA Methylation Regulator-Based Signature for Prognostic Prediction in Cervical Squamous Cell Carcinoma

Pan, Jianji; Xu, Lichao; Pan, Hongda

doi:10.3389/fonc.2020.01444

Cited by 45 publications

(42 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In our study, taken together, we believe that this model can be used to evaluate the prognostic risk in cervical cancer patients. And our model is better than other cervical cancer models [ 23 , 24 ].…”

Section: Discussionmentioning

confidence: 86%

Constructe a novel 5 hypoxia genes signature for cervical cancer

Yang

et al. 2021

Cancer Cell Int

View full text Add to dashboard Cite

Background Hypoxia, which affects the development, metastasis and prognosis of cancer, represents a key feature of cancer. This study describe a hypoxia risk factor model, with predicting the prognosis of cervical cancer. Methods Based on hypoxia pathway related genes, we divided cervical cancer samples into high and low expression groups. A cox analysis was then performed. Genes from these cervical cancer samples showing a significant impact on OS were selected for cluster analysis to obtain two subtypes. The TPM dataset of TCGA was divided into training and validation sets. For the training set, a lasso analysis was conducted as based on cox analysis of meaningful genes and a risk factor model was constructed. The constructed model was verified in internal and external data sets. Finally, RT-PCR, immunohistochemistry were used to detect the expression of relative genes or proteins and functional assays were used to evaluate the biological function of signature genes. Results Two molecular subtypes were obtained, Cluster2 vs Cluster1.These subtypes were obtained by clustering with a total of 149 DEGs (Differential expressed genes) being in line with this standard, of which 27 were up-regulated and 122 were down-regulated. The five genes with lambda = 0.0571 were selected to construct the model, the RiskScore = AK4*0.042 + HK2*0.021 + P4HA1*0.22 + TGFBI*0.1 + VEGFA*0.077. Further, in order to verify the signature, we used TCGA-test and GSE44001 chip datasets to test, and finally got a good risk prediction effect in those datasets. Moreover, the result of RT-PCR and immunohistochemistry demonstrated that AK4, HK2, P4HA1, TGFBI and VEGFA were all highly expressed in these cervical cancer tissue samples. The functional study shown that expression of AK4, HK2, P4HA1, TGFBI and VEGFA can regulate the proliferation, migration, and invasion ability of cervical cancer cells in vitro. Conclusions In summary, we developed a 5-gene signature prognostic hierarchical system based on the hypoxic pathway of cervical cancer, which is independent of clinical characteristics. And also conducted experimental verifications on these signature gene. Therefore, we propose that use of this classifier as a molecular diagnostic test can provide an effective means for evaluating the prognostic risk of cervical cancer patients, and provide potential targets for the treatment of cervical cancer patients.

show abstract

Section: Discussionmentioning

confidence: 86%

Constructe a novel 5 hypoxia genes signature for cervical cancer

Yang

et al. 2021

Cancer Cell Int

View full text Add to dashboard Cite

show abstract

“…To compare the 6-gene model with existing signatures, we selected several recently reported cervical cancer-related prognostic signatures, such as the 6-gene signature of Chen et al 11 , the 4-gene signature of mei et al 12 , and Pan et al 13 . We used the same method to calculate the risk score of each patient in the TCGA data set, and compared the RiskScore with the 1, 3, and 5-year ROC of these three models, such as Figure S3A–D .…”

Section: Resultsmentioning

confidence: 99%

Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients

Huo¹,

Zhou²,

Peng³

et al. 2021

OTT

View full text Add to dashboard Cite

Background Although the incidence of cervical cancer has decreased in recent decades with the development of human papillomavirus vaccines and cancer screening, cervical cancer remains one of the leading causes of cancer-related death worldwide. Identifying potential biomarkers for cervical cancer treatment and prognosis prediction is necessary. Methods Samples with mRNA sequencing, copy number variant, single nucleotide polymorphism and clinical follow-up data were downloaded from The Cancer Genome Atlas database and randomly divided into a training dataset (N=146) and a test dataset (N=147). We selected and identified a prognostic gene set and mutated gene set and then integrated the two gene sets with the random survival forest algorithm and constructed a prognostic signature. External validation and immunohistochemical staining were also performed. Results We obtained 1416 differentially expressed prognosis-related genes, 624 genes with copy number amplification, 1038 genes with copy number deletion, and 163 significantly mutated genes. A total of 75 candidate genes were obtained after overlapping the differentially expressed genes and the genes with genomic variations. Subsequently, we obtained six characteristic genes through the random survival forest algorithm. The results showed that high expression of SLC19A3, FURIN, SLC22A3 , and DPAGT1 and low expression of CCL17 and DES were associated with a poor prognosis in cervical cancer patients. We constructed a six-gene signature that can separate cervical cancer patients according to their different overall survival rates, and it showed robust performance for predicting survival (training set: p ˂ 0.001, AUC = 0.82; testing set: p ˂ 0.01, AUC = 0.59). Conclusion Our study identified a novel six-gene signature and nomogram for predicting the overall survival of cervical cancer patients, which may be beneficial for clinical decision-making for individualized treatment.

show abstract

“…For example, in the eld of correlation between cervical cancer and RNA methylation. Jingxin Pan et al developed a prognostic prediction model for cervical cancer patients based on m6A RNA methylation regulator [37]. While, most of research concentrate on the methylation of protein or DNA such as the promoters of genes instead of RNA.…”

Section: Discussionmentioning

confidence: 99%

Expression Profile of RNA Binding Protein in Cervical Cancer

Huang¹,

Li²,

Li³

2021

Preprint

View full text Add to dashboard Cite

Background: It has been demonstrated by studies globally that RNA binding proteins (RBPs) took part in the development of cervical cancer (CC). Few studies concentrated on the correlation between RBPs and overall survival of CC patients. We retrieved significant DEGs (differently expressed genes, RNA binding proteins) correlated to the process of cervical cancer development. Methods: Expressions level of genes in cervical cancer and normal tissue samples were obtained from GTEx and TCGA database. Differently expressed RNA binding proteins (DEGs) were retrieved by Wilcoxon sum-rank test. ClusterProfiler package worked in R software was used to perform GO and KEGG enrichment analyses. Univariate propotional hazard cox regression and multivariate propotional hazard cox regressions were applied to identify DEGs equipped with prognostic value and other clinical independent risk factors. ROC curve was drawn for comparing the survival predict feasibility of risk score with other risk factors in CC patients. Nomogram was drawn to exhibit the prediction model and validated by C-index and calibration curve. Correlations between Differentially expressed RNA binding proteins (DEGs) and other clinical features were investigated by t test or Cruskal wallis analysis. Correlation between Immune and DEGs in cervical cancer was investigated by ssGSEA. Results: 347 differentially expressed RBPs (DEGs) were retrieved from cervical cancer tissue and normal tissue samples. GO enrichment analysis showed that these DEGs involved in RNA splicing, catabolic process and metabolism. Cox regression medel showed that there were ten DEGs significantly associated with overall survival of cervical caner patients. WDR43 (HR = 0.423, P=0.008), RBM38 (HR = 0.533, P<0.001), RNASEH2A (HR=0.474, P=0.002) and HENMT1 (HR=0.720, P=0.071) played protective roles in survival among these ten genes. Stage (Stage IV vs Stage I HR = 3.434, P<0.001) and risk score (HR = 1.214, P< 0.001) were sorted as independent prognostic risk factors based on multivariate cox regression. ROC curve validated that risk score was preferable to predict survival of CC patients than other risk factors. Additionally, we found some of these ten predictor DEGs were correlated significantly in statistic with tumor grade or stage, clinical T stage, clinical N stage, pathology or risk score (all P< 0.05). Part of immune cells and immune functions showed a lower activity in high risk group than low risk group which is distincted by median risk score. Conclusion: Our discovery showed that many RNA binding proteins involved in the progress of cervical cancer, which could probably serve as prognostic biomarkers and accelerate the discovery of treatment targets for CC patients.

show abstract

Development and Validation of an m6A RNA Methylation Regulator-Based Signature for Prognostic Prediction in Cervical Squamous Cell Carcinoma

Cited by 45 publications

References 24 publications

Constructe a novel 5 hypoxia genes signature for cervical cancer

Constructe a novel 5 hypoxia genes signature for cervical cancer

Identification of a Six-Gene Signature for Predicting the Overall Survival of Cervical Cancer Patients

Expression Profile of RNA Binding Protein in Cervical Cancer

Contact Info

Product

Resources

About