Dataset Information

Integrative analysis of differential genes and identification of a "2-gene score" associated with survival in esophageal squamous cell carcinoma.

ABSTRACT: BACKGROUND:Developments in high-throughput genomic technologies have led to improved understanding of the molecular underpinnings of esophageal squamous cell carcinoma (ESCC). However, there is currently no model that combines the clinical features and gene expression signatures to predict outcomes. METHODS:We obtained data from the GSE53625 database of Chinese ESCC patients who had undergone surgical treatment. The R packages, Limma and WGCNA, were used to identify and construct a co-expression network of differentially expressed genes, respectively. The Cox regression model was used, and a nomogram prediction model was constructed. RESULTS:A total of 3654 differentially expressed genes were identified. Bioinformatics enrichment analysis was conducted. Multivariate analysis of the clinical cohort revealed that age and adjuvant therapy were independent factors for survival, and these were entered into the clinical nomogram. After integrating the gene expression profiles, we identified a "2-gene score" associated with overall survival. The combinational model is composed of clinical data and gene expression profiles. The C-index of the combined nomogram for predicting survival was statistically higher than the clinical nomogram. The calibration curve revealed that the combined nomogram and actual observation showed better prediction accuracy than the clinical nomogram alone. CONCLUSIONS:The integration of gene expression signatures and clinical variables produced a predictive model for ESCC that performed better than those based exclusively on clinical variables. This approach may provide a novel prediction model for ESCC patients after surgery.

SUBMITTER: Wang L

PROVIDER: S-EPMC6312844 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Integrative analysis of differential genes and identification of a "2-gene score" associated with survival in esophageal squamous cell carcinoma.

Wang Lin L Dong Gaochao G Xia Wenjie W Mao Qixing Q Wang Anpeng A Chen Bing B Ma Weidong W Wu Yaqin Y Xu Lin L Jiang Feng F

Thoracic cancer 20181112 1

<h4>Background</h4>Developments in high-throughput genomic technologies have led to improved understanding of the molecular underpinnings of esophageal squamous cell carcinoma (ESCC). However, there is currently no model that combines the clinical features and gene expression signatures to predict outcomes.<h4>Methods</h4>We obtained data from the GSE53625 database of Chinese ESCC patients who had undergone surgical treatment. The R packages, Limma and WGCNA, were used to identify and construct ...[more]

PMID: 30421504

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Project description:To uncover the genes associated with the development of esophageal squamous cell carcinoma (ESCC), an ESCC microarray dataset was used to identify genes differentially expressed between ESCC and normal control tissues. The dataset GSE17351 was downloaded from the Gene Expression Omnibus, containing 5 tumor esophageal mucosa samples and 5 adjacent normal esophageal mucosa samples from 5 male patients with ESCC. The differentially expressed genes (DEGs) were identified using the Linear Models for Microarray Data R package. Then, a co-expression network was constructed using the Weighted Correlation Network Analysis (WGCNA) package, and co-expression network modules were obtained with a hierarchical clustering algorithm. Additionally, functional enrichment analyses for DEGs in the top 2 modules with the highest significance were respectively conducted using the WGCNA package and the cluster Profiler package. In total, 487 upregulated and 468 downregulated DEGs were identified. A total of 24 modules were obtained from the co-expression network, and the top 2 modules with the highest significance, designated as 'blue4' and 'magenta', were further analyzed. In the module blue4, DEGs were significantly enriched in a number of Gene Ontology terms, including 'spindle organization' [e.g., ubiquitin conjugating enzyme E2 C (UBE2C) and SAC3 domain containing 1] and 'cell cycle process' [e.g., UBE2C, minichromosome maintenance complex component 6 (MCM6) and cell division cycle 20 (CDC20)]. Furthermore, a number of DEGs (e.g., UBE2C, CDC20 and MCM6) were enriched in the 'cell cycle' and 'ubiquitin mediated proteolysis' pathways. In the module 'magenta', a number of DEGs [e.g., transferrin receptor (TFRC) and TEA domain transcription factor 4 (TEAD4)] were enriched in the primary metabolic process and intracellular membrane-bounded organelle. Additionally, 308 upregulated genes and 215 downregulated genes were differentially expressed in the same pattern in another dataset, GSE20347, including UBE2C, CDC20, MCM6, TFRC, TEAD4, protein phosphatase 1 regulatory subunit 3C and MAL, T-cell differentiation protein. These DEGs may function in the development of ESCC.

Project description:BackgroundNaples prognostic score (NPS) serves as a new prognostic index based on nutritional and inflammatory status in recent years. The aim of the current study was to explore the prognostic effect of NPS and to develop and validate a reliable nomogram based on NPS for individual cancer-specific survival (CSS) prediction in patients with resected ESCC without neoadjuvant therapy.MethodsThe clinical data for 287 (Jan. 2010 to Jun. 2012, Training sets) and 118 (Jan. 2015 to Dec 2015, Validation sets) consecutive resected ESCC cases were retrospectively analyzed. Two NPS models based on the different cut-off values of parameters were compared. Cut-off values in model 1 were derived from previous published studies, while cut-off values in model 2 were obtained in this study based on receiver operating characteristic (ROC) curves. The relationships between NPS and clinical characteristics and CSS were analyzed. The prediction model of nomogram was developed with independent prognostic factors in the training sets and was validated in the validation sets.ResultsThe 5-year CSS for NPS 0, 1 and 2 were 61.9%, 34.6% and 13.4% in model 1 and 75.0%, 42.4% and 13.0% in model 2, respectively (P<0.001). Subgroup analyses revealed that NPS was also significantly associated with CSS in both model 1 and model 2 in different TNM stages. Multivariate analyses revealed that NPS was an independent prognostic marker regarding CSS in patients with resected ESCC (P<0.001). A predictive nomogram based on NPS was established and validated. The C-indexes of the nomogram in the training sets and validation sets were 0.68 and 0.72 in model 1 and 0.69 and 0.73 in model 2, respectively. These results confirmed that NPS-based nomogram was a more accurate and effective tool for predicting CSS in patients with resected ESCC.ConclusionThe current study confirmed that NPS was still a useful independent prognostic score in patients with resected ESCC. The NPS-based nomogram was successfully developed and validated, which may contribute to individual CSS prediction for resected ESCC patients.

Dataset Information

Integrative analysis of differential genes and identification of a "2-gene score" associated with survival in esophageal squamous cell carcinoma.

Publications

Integrative analysis of differential genes and identification of a "2-gene score" associated with survival in esophageal squamous cell carcinoma.

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