Dataset Information

CAFET algorithm reveals Wnt/PCP signature in lung squamous cell carcinoma.

ABSTRACT: We analyzed the gene expression patterns of 138 Non-Small Cell Lung Cancer (NSCLC) samples and developed a new algorithm called Coverage Analysis with Fisher's Exact Test (CAFET) to identify molecular pathways that are differentially activated in squamous cell carcinoma (SCC) and adenocarcinoma (AC) subtypes. Analysis of the lung cancer samples demonstrated hierarchical clustering according to the histological subtype and revealed a strong enrichment for the Wnt signaling pathway components in the cluster consisting predominantly of SCC samples. The specific gene expression pattern observed correlated with enhanced activation of the Wnt Planar Cell Polarity (PCP) pathway and inhibition of the canonical Wnt signaling branch. Further real time RT-PCR follow-up with additional primary tumor samples and lung cancer cell lines confirmed enrichment of Wnt/PCP pathway associated genes in the SCC subtype. Dysregulation of the canonical Wnt pathway, characterized by increased levels of ?-catenin and epigenetic silencing of negative regulators, has been reported in adenocarcinoma of the lung. Our results suggest that SCC and AC utilize different branches of the Wnt pathway during oncogenesis.

SUBMITTER: Hu Y

PROVIDER: S-EPMC3189939 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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CAFET algorithm reveals Wnt/PCP signature in lung squamous cell carcinoma.

Hu Yue Y Galkin Anna V AV Wu Chunlei C Reddy Venkateshwar V Su Andrew I AI

PloS one 20111010 10

We analyzed the gene expression patterns of 138 Non-Small Cell Lung Cancer (NSCLC) samples and developed a new algorithm called Coverage Analysis with Fisher's Exact Test (CAFET) to identify molecular pathways that are differentially activated in squamous cell carcinoma (SCC) and adenocarcinoma (AC) subtypes. Analysis of the lung cancer samples demonstrated hierarchical clustering according to the histological subtype and revealed a strong enrichment for the Wnt signaling pathway components in t ...[more]

PMID: 22016777

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Project description:BackgroundLung squamous cell carcinoma (LUSC), as the second frequent subtype of lung cancer, causes lots of mortalities primarily due to a lack of precise prognostic markers and timely treatment intervention. Previous studies have constructed several risk prognostic models based on DNA methylation sites in multiple tumors, whereas, DNA methylation signature of LUSC remains to be built, and its predictive value need to be evaluated.MethodsThe genome-wide DNA methylation data of LUSC samples was obtained from The Cancer Genome Atlas dataset. Univariate Cox analysis and the least absolute shrinkage and selection operator (LASSO) were implemented to identify DNA methylation sites related to overall survival of LUSC patients. Thus, we performed multivariate Cox regression to establish a DNA methylation signature. The Kaplan-Meier (K-M) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted to estimate the prognostic power of the signature. Comparison with other known prognostic biomarkers, our DNA methylation signature showed higher predictive specificity and sensitivity. In addition, multivariate Cox regression screened out independent prognostic factors and constructed a nomogram.ResultsSeveral statistical methods were performed to construct an 11-DNA methylation signature. LUSC patients were divided into low- and high-risk group based on risk score, and high-risk group had a shorter survival time. According to the results of K-M and ROC analyses, the 11-DNA methylation signature showed significant sensitivity and specificity in predicting the LUSC patients’ overall survival. Finally, we integrated some independent prognostic factors (risk score, metastasis stage, and tobacco smoking history) to construct a nomogram, which has excellent prognostic power and may provide guidance for the therapeutic strategies.ConclusionsWe constructed the first risk prognosis model based on DNA methylation site in LUSC, which showed better predictive ability. In addition, a nomogram integrating the DNA methylation signature, metastasis stage, and tobacco smoking history was developed.

Project description:BackgroundDiagnosis of early stage lung squamous cell carcinoma (LUSC) has improved; however, a comprehensive analysis of prognostic signatures is needed.PurposeTo identify, establish, and validate a signature model based on pyroptosis-related genes for prognostic predictions of early stage LUSC.Patients and methodsTwo independent cohorts were included. RNA-seq transcriptome data from patients with early stage LUSC were obtained from The Cancer Genome Atlas (TCGA) database. Thirty-three pyroptosis-related genes were analyzed between early stage LUSC and normal lung tissues. Cox regression analysis, random survival forest, and least absolute shrinkage and selection operator algorithms established a three-gene signature. Kaplan-Meier survival and receiver-operating characteristic curves assessed the prognostic efficacy of the model. Single-sample gene set enrichment analysis (ssGSEA) assessed the relationship between pyroptosis and immune cells. Patients with early stage LUSC from the GSE74777 dataset were used for validation. Pyroptosis-related genes were verified by RT-qPCR and Western blotting.ResultsTwenty-three differentially expressed pyroptosis-related genes were identified in the LUSC and adjacent normal tissues. Three differentially expressed pyroptosis-related genes were identified as hub genes in early stage LUSC. Patients with early stage LUSC in the TCGA cohort were classified into low- and high-risk subgroups according to the risk score. Overall survival (OS) was significantly short in the high-risk subgroup versus the low-risk subgroup. A similar result was found for the GSE74777 dataset. ssGSEA of immune cells and immune-related pathways between the low- and high-risk subgroups may explain the different OS for patients with early-stage LUSC. IL-6 expression was upregulated, which was inconsistent with the bioinformatic analysis. NOD1 and CASP4 were downregulated in LUSC (all P < 0.05) versus normal lung tissues.ConclusionDifferentially expressed pyroptosis-related genes may be involved in early stage LUSC. Pyroptosis-related genes are important in tumor immunity and may be potential prognostic predictors for early stage LUSC.

Dataset Information

CAFET algorithm reveals Wnt/PCP signature in lung squamous cell carcinoma.

Publications

CAFET algorithm reveals Wnt/PCP signature in lung squamous cell carcinoma.

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