Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer

Chen, Hengyu; Deng, Qingchun; Wang, Wenwen; Tao, Huishan; Gao, Ying

doi:10.1186/s13048-020-00730-8

Cited by 30 publications

(21 citation statements)

References 35 publications

(36 reference statements)

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“…Recently, Zhao S et al (2020) have constructed a 4-gene prognostic risk score model in CESC by identifying DEGs [ 52 ]. Beyond the above-mentioned studies, there are also many studies about the prognostic model [ 53 , 54 ]. Compared with the previous studies, our prognostic model could assecss immune-genomic profiles.…”

Section: Discussionmentioning

confidence: 99%

Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Wang

Vattai

Vilsmaier

et al. 2021

IJMS

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Cervical cancer is primarily caused by the infection of high-risk human papillomavirus (hrHPV). Moreover, tumor immune microenvironment plays a significant role in the tumorigenesis of cervical cancer. Therefore, it is necessary to comprehensively identify predictive biomarkers from immunogenomics associated with cervical cancer prognosis. The Cancer Genome Atlas (TCGA) public database has stored abundant sequencing or microarray data, and clinical data, offering a feasible and reliable approach for this study. In the present study, gene profile and clinical data were downloaded from TCGA, and the Immunology Database and Analysis Portal (ImmPort) database. Wilcoxon-test was used to compare the difference in gene expression. Univariate analysis was adopted to identify immune-related genes (IRGs) and transcription factors (TFs) correlated with survival. A prognostic prediction model was established by multivariate cox analysis. The regulatory network was constructed and visualized by correlation analysis and Cytoscape, respectively. Gene functional enrichment analysis was performed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A total of 204 differentially expressed IRGs were identified, and 22 of them were significantly associated with the survival of cervical cancer. These 22 IRGs were actively involved in the JAK-STAT pathway. A prognostic model based on 10 IRGs (APOD, TFRC, GRN, CSK, HDAC1, NFATC4, BMP6, IL17RD, IL3RA, and LEPR) performed moderately and steadily in squamous cell carcinoma (SCC) patients with FIGO stage I, regardless of the age and grade. Taken together, a risk score model consisting of 10 novel genes capable of predicting survival in SCC patients was identified. Moreover, the regulatory network of IRGs associated with survival (SIRGs) and their TFs provided potential molecular targets.

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Section: Discussionmentioning

confidence: 99%

Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Wang

Vattai

Vilsmaier

et al. 2021

IJMS

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“…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

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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.

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“…The correlation between autophagic gene and cervical cancer has been validated by great quantity of studies [17,[32][33][34]. CDKN2A (aliase p16) is a famous autophagic gene which regulates the cellular cycle process of G1/S.…”

Section: Discussionmentioning

confidence: 99%

Expression profiling analysis of autophagy-related genes in cervical cancer

Huang

Wang

Liu

et al. 2021

Preprint

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Background: It has been demonstrated by studies globally that autophagy took part in the development of cervical cancer (CC). Few studies concentrated on the correlation between overall survival and CC patients. We retrieved significant autophagy-related genes (ARGs) correlated to the process of cervical cancer. They may be used as prognosis marker or treatment target for clinical application.Methods: Expressions level of genes in cervical cancer and normal tissue samples were obtained from GTEx and TCGA database. Autophagy-related genes (ARGs) were retrieved accroding to the gene list from HaDB. Differentially expressed autophagy related genes (DE-ARGs) related to cervical cancer were identified by Wilcoxon signed-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 DE-ARGs 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 constructed accroding to multivariate cox regression. Correlations between Differentially expressed autophagy related genes (DE-ARGs) and other clinical features were investigated by t test or Cruskal wallis analysis. Correlation between Immune and autophagy in cervical cancer was investigated by ssGSEA and TIMER database. Results: Fifty-six differentially expressed ARGs (DE-ARGs) were retrieved from cervical cancer tissue and normal tissue samples. GO enrichment analysis showed that these ARGs involved in autophagy, ubiquitination of protein and apoptosis. Cox regression medel showed that there were six ARGs significantly associated with overall survival of cervical caner patients. VAMP7 (HR = 0.599, P= 0.033) and TP73 (HR = 0.671, P= 0.014) played protective roles in survival among these six genes. Stage (Stage IV vs Stage I HR = 3.985, P<0.001) and risk score (HR = 1.353, 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 six predictor ARGs were correlated significantly in statistic with tumor grade or stage, clinical T stage, clinical N stage, pathology or risk score (all P< 0.05). The 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 autophagy genes involved in the progress of cervical cancer. Many autophagy-related genes could probably serve as prognostic biomarkers and accelerate the discovery of treatment targets for CC patients.

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Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer

Cited by 30 publications

References 35 publications

Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

Immunogenomic Identification for Predicting the Prognosis of Cervical Cancer Patients

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

Expression profiling analysis of autophagy-related genes in cervical cancer

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