Dataset Information

A Comprehensive RNA Expression Signature for Cervical Squamous Cell Carcinoma Prognosis.

ABSTRACT: Clinicopathological characteristics alone are not enough to predict the survival of patients with cervical squamous cell carcinoma (CESC) due to clinical heterogeneity. In recent years, many genes and non-coding RNAs have been shown to be oncogenes or tumor-suppressors in CESC cells. This study aimed to develop a comprehensive transcriptomic signature for CESC patient prognosis. Univariate, multivariate, and Least Absolute Shrinkage and Selection Operator penalized Cox regression were used to identify prognostic signatures for CESC patients from transcriptomic data of The Cancer Genome Atlas. A normalized prognostic index (NPI) was formulated as a synthetical index for CESC prognosis. Time-dependent receiver operating characteristic curve analysis was used to compare prognostic signatures. A prognostic transcriptomic signature was identified, including 1 microRNA, 1 long non-coding RNA, and 6 messenger RNAs. Decreased survival was associated with CESC patients being in the high-risk group stratified by NPI. The NPI was an independent predictor for CESC patient prognosis and it outperformed the known clinicopathological characteristics, microRNA-only signature, gene-only signature, and previously identified microRNA and gene signatures. Function and pathway enrichment analysis revealed that the identified prognostic RNAs were mainly involved in angiogenesis. In conclusion, we proposed a transcriptomic signature for CESC prognosis and it may be useful for effective clinical risk management of CESC patients. Moreover, RNAs in the transcriptomic signature provided clues for downstream experimental validation and mechanism exploration.

SUBMITTER: Xiong J

PROVIDER: S-EPMC6328499 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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A Comprehensive RNA Expression Signature for Cervical Squamous Cell Carcinoma Prognosis.

Xiong Jie J Guo Shengyu S Bing Zhitong Z Su Yanlin Y Guo Le L

Frontiers in genetics 20190104

Clinicopathological characteristics alone are not enough to predict the survival of patients with cervical squamous cell carcinoma (CESC) due to clinical heterogeneity. In recent years, many genes and non-coding RNAs have been shown to be oncogenes or tumor-suppressors in CESC cells. This study aimed to develop a comprehensive transcriptomic signature for CESC patient prognosis. Univariate, multivariate, and Least Absolute Shrinkage and Selection Operator penalized Cox regression were used to id ...[more]

PMID: 30662454

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Project description:BackgroundCervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) are among the most prevalent gynecologic malignancies globally. The prognosis is abysmal once cervical cancer progresses to lymphatic metastasis. Anoikis, a specialized form of apoptosis induced by loss of cell adhesion to the extracellular matrix, plays a critical role. The prediction model based on anoikis-related genes (ARGs) expression and clinical data could greatly aid clinical decision-making. However, the relationship between ARGs and CESC remains unclear.MethodsARGs curated from the GeneCards and Harmonizome portals were instrumental in delineating CESC subtypes and in developing a prognostic framework for patients afflicted with this condition. We further delved into the intricacies of the immune microenvironment and pathway enrichment across the identified subtypes. Finally, our efforts culminated in the creation of an innovative nomogram that integrates ARGs. The utility of this prognostic tool was underscored by Decision Curve Analysis (DCA), which illuminate its prospective benefits in guiding clinical interventions.ResultsIn our study, We discerned a set of 17 survival-pertinent, anoikis-related differentially expressed genes (DEGs) in CESC, from which nine were meticulously selected for the construction of prognostic models. The derived prognostic risk score was subsequently validated as an autonomous prognostic determinant. Through comprehensive functional analyses, we observed distinct immune profiles and drug response patterns among divergent prognostic stratifications. Further, we integrated the risk scores with the clinicopathological characteristics of CESC to develop a robust nomogram. DCA corroborated the utility of our model, demonstrating its potential to enhance patient outcomes through tailored clinical treatment strategies.ConclusionThe predictive signature, encompassing nine pivotal genes, alongside the meticulously constructed nomogram developed in this research, furnishes clinicians with a sophisticated tool for tailoring treatment strategies to individual patients diagnosed with CESC.

Project description:BackgroundIt has been reported that tumor immune microenvironment performs a vital role in tumor progress. However, acting mechanism of immune cell related genes (IRGs) in esophageal squamous cell carcinoma (ESCC) is uncertain.MethodsTCGA-ESCC, GSE23400, GSE26886, GSE75241, and GSE196756 datasets were gained via public databases. First, differentially expressed genes (DEGs) between ESCC and control samples from GSE23400, GSE26886, and GSE75241 were screened out by differential expression analysis, and overlapping DEGs were identified. Single-cell transcriptome data of GSE196756 were applied to explore immune cells that might be involved in regulation of ESCC. Then, weighted gene co-expression network analysis was applied to screen IRGs. Next, differentially expressed IRGs (DE-IRGs) were identified by overlapping IRGs and DEGs, and were incorporated into univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox to acquire prognosis-related genes, and ESCC samples were grouped into high-/low-risk groups on the basis of median risk score. Finally, the role of prognosis model in immunotherapy was analyzed.ResultsTotally 248 DEGs were yielded by overlapping 3,915 DEGs in GSE26886, 459 DEGs in GSE23400, and 1,641 DEGs in GSE75241. Single-cell analysis found that B cells, dendritic cells, monocytes, neutrophils, natural killer cells, and T cells were involved in ESCC development. Besides, MEred, MEblack, MEpink, MEblue and MEbrown modules were considered as key modules because of their highest correlations with immune cell subtypes. A total of 154 DE-IRGs were yielded by taking intersection of DEGs and genes in key modules. Moreover, CTSC, ALOX12, and RMND5B were identified as prognosis-related genes in ESCC. Obviously, Exclusion and TIDE scores were notably lower in high-risk group than in the other one, indicating that high-risk group was more responsive to immunotherapy.ConclusionsThrough bioinformatic analysis, we identified a prognosis model consisting of IRGs (CTSC, ALOX12, and RMND5B) in ESCC, providing new ideas for studies related to treatment and prognosis of ESCC.

Dataset Information

A Comprehensive RNA Expression Signature for Cervical Squamous Cell Carcinoma Prognosis.

Publications

A Comprehensive RNA Expression Signature for Cervical Squamous Cell Carcinoma Prognosis.

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