Identification of Key Pathways and Establishment of a Seven-Gene Prognostic Signature in Cervical Cancer

An, Ran; Meng, Silu; Hua, Qian

doi:10.1155/2022/4748796

Cited by 7 publications

(8 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As results showed in Supplementary Figure 4 , our eight gene signature performed better than the other then signature in 1, 3, 5 years OS prediction. but in ten year OS prediction, our signature showed less efficient than a seven gene signature (PLOD2, DSG2, SPP1, CXCL8, MCM5, HLTF, and KLF4) ( 32 ) and a six-gene signature (APOC1, GLTP, ISG20, SPP1, SLC24A3 and UPP1) ( 24 ). These results reveals that our signature may lead to a better treatment decisions comparison with other published prognostic signatures…”

Section: Resultsmentioning

confidence: 84%

“…Recent years the net reclassification index (NRI), integrated discrimination improvement (IDI) have become the two popularity alternatives assessment in risk evaluation or prediction between different models (21,22). We searched ten recently published CESC prognostic signatures, and used NRI, IDI method in "survIDINRI" package to compare the clinical benefits of our signature and published signatures (23)(24)(25)(26)(27)(28)(29)(30)(31)(32).…”

Section: Clinical Benefits Evaluation Of Cesc Os Signaturesmentioning

confidence: 99%

“…In a biological context, LASSO is widely used for gene expression and genomic data variable selection to identify a parsimonious model-a minimal subset of variables required to explain the data. There are relatively a few detailed reports of prognostic models based on gene expression profiles in cervical cancer (26,(28)(29)(30)32). In this study, we selected the CESC prognostic markers from HLA-G driven DEGs buy LASSO regression.…”

Section: Construction Of a Prognostic Model Based On Hla-g-driven Degsmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

Wang

Xing

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

Human leukocyte antigen G (HLA-G) is a potential checkpoint molecule that plays a key role in cervical carcinogenesis. The purpose of this study was to construct and validate a prognostic risk model to predict the overall survival (OS) of cervical cancer patients, providing a reference for individualized clinical treatment that may lead to better clinical outcomes. HLA-G-driven differentially expressed genes (DEGs) were obtained from two cervical carcinoma cell lines, namely, SiHa and HeLa, with stable overexpression of HLA-G by RNA sequencing (RNA-seq). The biological functions of these HLA-G-driven DEGs were analysed by GO enrichment and KEGG pathway using the “clusterProfiler” package. The protein-protein interactions (PPIs) were assessed using the STRING database. The prognostic relevance of each DEG was evaluated by univariate Cox regression using the TCGA-CESC dataset. After the TCGA-CESC cohort was randomly divided into training set and testing set, and a prognostic risk model was constructed by LASSO and stepwise multivariate Cox regression analysis in training set and validated in testing set or in different types of cervical cancer set. The predictive ability of the prognostic risk model or nomogram was evaluated by a series of bioinformatics methods. A total of 1108 candidate HLA-G-driven DEGs, including 391 upregulated and 717 downregulated genes, were obtained and were enriched mostly in the ErbB pathway, steroid biosynthesis, and MAPK pathway. Then, an HLA-G-driven DEG signature consisting of the eight most important prognostic genes CD46, LGALS9, PGM1, SPRY4, CACNB3, PLIN2, MSMO1, and DAGLB was identified as a key predictor of cervical cancer. Multivariate Cox regression analysis showed that this signature is an independent risk factor for the overall survival of CESC patients. Kaplan-Meier survival analysis showed that the 5-year overall survival rate is 23.0% and 84.6% for the high-risk and low-risk patients, respectively (P<0.001). The receiver operating characteristic (ROC) curve of this prognostic model with an area under the curve (AUC) was 0.896 for 5 years, which was better than that of other clinical traits. This prognostic risk model was also successfully validated in different subtypes of cervical cancer, including the keratinizing squamous cell carcinoma, non-keratinizing squamous cell carcinoma, squamous cell neoplasms, non-squamous cell neoplasms set. Single-sample gene set enrichment (ssGSEA) algorithm and Tumor Immune Dysfunction and Exclusion (TIDE) analysis confirmed that this signature influence tumour microenvironment and immune checkpoint blockade. A nomogram that integrated risk score, age, clinical stage, histological grade, and pathological type was then built to predict the overall survival of CESC patients and evaluated by calibration curves, AUC, concordance index (C-index) and decision curve analysis (DCA). To summarize, we developed and validated a novel prognostic risk model for cervical cancer based on HLA-G-driven DEGs, and the prognostic signature showed great ability in predicting the overall survival of patients with cervical cancer.

show abstract

Section: Resultsmentioning

confidence: 84%

Section: Clinical Benefits Evaluation Of Cesc Os Signaturesmentioning

confidence: 99%

Section: Construction Of a Prognostic Model Based On Hla-g-driven Degsmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

Wang

Xing

et al. 2022

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…Previously, a nine-lncRNA signature was established to predict the 1 year PFS in patients with CC; this model had an AUC of 0.793, 0.780, and 0.742 in two GEO test sets and one TCGA test set, respectively ( Mao et al, 2019 ). A seven-gene prognostic signature for CC had also been developed using GEO data, predicting 1-, 3-, and 5-year OS with AUC of 0.74, 0.76, and 0.81, respectively ( An et al, 2022 ). Another study established 11 immune-related gene signatures to assess OS in patients with CC, yielding 3- and 5-year AUC of 0.733 and 0.747 ( Yang S. et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Construction of a Six-Gene Prognostic Risk Model Related to Hypoxia and Angiogenesis for Cervical Cancer

Liu

Zhu²,

Wang

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

Background: The prognosis of cervical cancer (CC) is poor and not accurately reflected by the primary tumor node metastasis staging system. Our study aimed to develop a novel survival-prediction model.Methods: Hallmarks of CC were quantified using single-sample gene set enrichment analysis and univariate Cox proportional hazards analysis. We linked gene expression, hypoxia, and angiogenesis using weighted gene co-expression network analysis (WGCNA). Univariate and multivariate Cox regression was combined with the random forest algorithm to construct a prognostic model. We further evaluated the survival predictive power of the gene signature using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves.Results: Hypoxia and angiogenesis were the leading risk factors contributing to poor overall survival (OS) of patients with CC. We identified 109 candidate genes using WGCNA and univariate Cox regression. Our established prognostic model contained six genes (MOCSI, PPP1R14A, ESM1, DES, ITGA5, and SERPINF1). Kaplan-Meier analysis indicated that high-risk patients had worse OS (hazard ratio = 4.63, p < 0.001). Our model had high predictive power according to the ROC curve. The C-index indicated that the risk score was a better predictor of survival than other clinicopathological variables. Additionally, univariate and multivariate Cox regressions indicated that the risk score was the only independent risk factor for poor OS. The risk score was also an independent predictor in the validation set (GSE52903). Bivariate survival prediction suggested that patients exhibited poor prognosis if they had high z-scores for hypoxia or angiogenesis and high risk scores.Conclusions: We established a six-gene survival prediction model associated with hypoxia and angiogenesis. This novel model accurately predicts survival and also provides potential therapeutic targets.

show abstract

“…The entire PI3K/AKT signaling pathway regulates the cell physiology and pathology, including apoptosis, cell proliferation, invasion, and metastasis [ 62 , 63 , 64 ]. This pathway is abnormally activated in different tumors, including cervical cancer [ 65 , 66 ].…”

Section: Lncrnas-regulated Pathways In Cervical Cancermentioning

confidence: 99%

The Regulatory Functions and the Mechanisms of Long Non-Coding RNAs in Cervical Cancer

Yang

Al-Hendy

2022

Cells

View full text Add to dashboard Cite

Cervical cancer is one of the leading causes of death in gynecology cancer worldwide. High-risk human papillomaviruses (HPVs) are the major etiological agents for cervical cancer. Still, other factors also contribute to cervical cancer development because these cancers commonly arise decades after initial exposure to HPV. So far, the molecular mechanisms underlying the pathogenesis of cervical cancer are still quite limited, and a knowledge gap needs to be filled to help develop novel strategies that will ultimately facilitate the development of therapies and improve cervical cancer patient outcomes. Long non-coding RNAs (lncRNAs) have been increasingly shown to be involved in gene regulation, and the relevant role of lncRNAs in cervical cancer has recently been investigated. In this review, we summarize the recent progress of ascertaining the biological functions of lncRNAs in cervical cancer, from the perspective of cervical cancer proliferation, invasion, and metastasis. In addition, we provide the current state of knowledge by discussing the molecular mechanisms underlying the regulation and emerging role of lncRNAs in the pathogenesis of cervical cancer. Comprehensive and deeper insights into lncRNA-mediated alterations and interactions in cellular events will help develop novel strategies to treat patients with cervical cancer.

show abstract

Identification of Key Pathways and Establishment of a Seven-Gene Prognostic Signature in Cervical Cancer

Cited by 7 publications

References 71 publications

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

A Novel Prognostic Risk Model for Cervical Cancer Based on Immune Checkpoint HLA-G-Driven Differentially Expressed Genes

Construction of a Six-Gene Prognostic Risk Model Related to Hypoxia and Angiogenesis for Cervical Cancer

The Regulatory Functions and the Mechanisms of Long Non-Coding RNAs in Cervical Cancer

Contact Info

Product

Resources

About