Dataset Information

Identification of Biomarker for Cutaneous Squamous Cell Carcinoma Using Microarray Data Analysis.

ABSTRACT: Cutaneous squamous cell carcinoma (CSCC) is one of the most malignant tumors worldwide. We aimed to explore the molecular mechanism of this CSCC and screen feature genes that can function as the biomarker of CSCC and thus provide a theoretical basis for the pathogenesis research and development of medicine. The method of microarray data analysis was used in this study to explore the differentially expressed genes between tissues of normal specimens and tissues of patients with CSCC. Besides, functional enrichment analysis and signal pathway were performed on these genes to screen the feature genes that are closely associated with CSCC can function as the potential biomarkers of CSCC.A total of 53 samples from two datasets, GSE45216 and GSE45164, were used in the differentially expressed analysis. And as a result, a total of 833 genes were screened out, including 465 up-regulated genes and 215 down-regulated genes. Candidate genes, including up-regulated genes like S100A12, MMP1, DEFB4B/DEFB4A, KRT16 and PI3, and down-regulated genes like EGR3, LRP4, C14orf132, PAMR1, CCL27, and KRT2 were screened out. All these genes were testified in the dataset of GSE66359. The result showed that only three genes, KRT16, PI3 and EGR3, were mostly differentially expressed and only EGR3 had the same expression pattern with both datasets, GSE45216 and GSE45164.Of note, EGR3 gene was found to be the most differentially expressed gene in cutaneous squamous cell carcinoma, which had the potential to function as the candidate genes and help in the diagnosis and prognostic treatments of CSCC.

SUBMITTER: Wei W

PROVIDER: S-EPMC5771347 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Identification of Biomarker for Cutaneous Squamous Cell Carcinoma Using Microarray Data Analysis.

Wei Wei W Chen Yan Y Xu Jie J Zhou Yu Y Bai Xinping X Yang Ming M Zhu Ju J

Journal of Cancer 20180101 2

Cutaneous squamous cell carcinoma (CSCC) is one of the most malignant tumors worldwide. We aimed to explore the molecular mechanism of this CSCC and screen feature genes that can function as the biomarker of CSCC and thus provide a theoretical basis for the pathogenesis research and development of medicine. The method of microarray data analysis was used in this study to explore the differentially expressed genes between tissues of normal specimens and tissues of patients with CSCC. Besides, fun ...[more]

PMID: 29344286

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Project description:BackgroundCutaneous squamous cell carcinoma (cSCC) is the second most common cancer worldwide with relatively low metastatic potential (2-5%). Developments in therapeutic options have highlighted the need to better identify high-risk patients who could benefit from closer surveillance, adjuvant therapies and baseline/follow-up imaging, while at the same time safely omitting low-risk patients from further follow-up. Controversy remains regarding the predictive performance of current cSCC staging systems and which methodology to adopt.ObjectivesTo validate the performance of four cSCC staging systems [American Joint Committee on Cancer 8th edition (AJCC8), Brigham and Women's Hospital (BWH), Tübingen and Salamanca T3 refinement] in predicting metastasis using a nationwide cohort.MethodsA nested case-control study using data from the National Disease Registration Service, England, 2013-2015 was conducted. Metastatic cSCC cases were identified using an algorithm to identify all potential cases for manual review. These were 1 : 1 matched on follow-up time to nonmetastatic controls randomly selected from 2013. Staging systems were analysed for distinctiveness, homogeneity, monotonicity, specificity, positive predictive value (PPV), negative predictive value (NPV) and c-index.ResultsWe included 887 metastatic cSCC cases and 887 nonmetastatic cSCC controls. The BWH system showed the highest specificity [92.8%, 95% confidence interval (CI) 90.8-94.3%, PPV (13.2%, 95% CI 10.6-16.2) and c-index (0.84, 95% CI 0.82-0.86). The AJCC8 showed superior NPV (99.2%, 95% CI 99.2-99.3), homogeneity and monotonicity compared with the BWH and Tübingen diameter and thickness classifications (P < 0.001). Salamanca refinement did not show any improvement in AJCC8 T3 cSCC staging.ConclusionsWe validated four cSCC staging systems using the largest nationwide dataset of metastatic cSCC so far. Although the BWH system showed the highest overall discriminative ability, PPV was low for all staging systems, which shows the need for further improvement and refining of current cSCC staging systems.

Project description:To identify candidate key genes and miRNAs associated with esophageal squamous cell carcinoma (ESCC) development and prognosis, the gene expression profiles and miRNA microarray data including GSE20347, GSE38129, GSE23400, and GSE55856 were downloaded from the Gene Expression Omnibus (GEO) database. Clinical and survival data were retrieved from The Cancer Genome Atlas (TCGA). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of differentially expressed genes (DEGs) was analyzed via DAVID, while the DEG-associated protein-protein interaction network (PPI) was constructed using the STRING database. Additionally, the miRNA target gene regulatory network and miRNA coregulatory network were constructed, using the Cytoscape software. Survival analysis and prognostic model construction were performed via the survival (version 2.42-6) and rbsurv R packages, respectively. The results showed a total of 2575, 2111, and 1205 DEGs, and 226 differentially expressed miRNAs (DEMs) were identified. Pathway enrichment analyses revealed that DEGs were mainly enriched in 36 pathways, such as the proteasome, p53, and beta-alanine metabolism pathways. Furthermore, 448 nodes and 1144 interactions were identified in the PPI network, with MYC having the highest random walk score. In addition, 7 DEMs in the microarray data, including miR-196a, miR-21, miR-205, miR-194, miR-103, miR-223, and miR-375, were found in the regulatory network. Moreover, several reported disease-related miRNAs, including miR-198a, miR-103, miR-223, miR-21, miR-194, and miR-375, were found to have common target genes with other DEMs. Survival analysis revealed that 85 DEMs were related to prognosis, among which hsa-miR-1248, hsa-miR-1291, hsa-miR-421, and hsa-miR-7-5p were used for a prognostic survival model. Taken together, this study revealed the important roles of DEGs and DEMs in ESCC development, as well as DEMs in the prognosis of ESCC. This will provide potential therapeutic targets and prognostic predictors for ESCC.

Project description:MicroRNAs (miRNAs) are a novel class of short RNAs which have shown to be dysregulated in a variety of cancers including squamous cell carcinoma (SCC) of the head&neck. Microarray based miRNA expression profiles of cutaneous SCC (cSCC) however have not been investigated so far. Seven patients with cutaneous SCC were enrolled in the study. Tumor biopsies (n=7) were taken from the center of the tumor. Adjacent healthy skin (n=7) was biopsied as a control (intraindividual control). miRNA expression profiles of all specimen were detected by mircroarray miRNA expression profiling based on miRBAse 16 and compared. In 7 patients biopsy specimens of cutaneous SCC and control specimens from an adjacent healthy skin site near the tumor border (intraindividual control) were obtained during surgical removal of the tumor. Specimes were immediately stored in RNAlater (Qiagen, HIlden, Germany) at -80 M-BM-0C. Total RNA including miRNAs were isolated with miRNeasy Mini Kit (Quiagen, Hilden, Germany) according to the manufacturerM-bM-^@M-^Ys protocol. For RNA quality control purposes we determined RNA concentration, purity and RNA integrity number (RIN) with Agilent 2100 Bioanalyzer, RNA 6000 NanoLabCHip Kits (both Agilent Technologies, Santa Clara, USA) and the NanoDrop ND-1000 spectral photometer (Peqlab, Erlangen, Germany). All of the following steps described were carried out according to the manufacturerM-BM-4s protocol. In order to enable assessment of labeling and hybridization efficiencies total RNA samples were spiked with MicroRNA Spike-In Kit (Agilent Technologies, Santa Clara, USA). After treatment with calf intestine phosphatase (CIP), a labeling reaction was started with 100 ng total-RNA per sample. For labeling dephosphorylated RNA, T4 RNA ligase, incorporating Cyanine 3-Cytidine biphosphate (miRNA Complete Labeling and Hyb Kit, Agilent Technologies, Santa Clara, USA) was used. The Cyanine-3-labeled miRNA samples were then prepared for One-Color based hybridization (Complete miRNA Labeling and Hyb Kit, Agilent Technologies, Santa Clara, USA). Hybridization was performed at 55M-BM-0C for 20 hrs with Human miRNA Microarrays Release 16.0, 8x60K format (Agilent Technologies, Santa Clara, USA). Microarray slides were washed (Gene Expression Wash Buffers, Agilent Technologies, Santa Clara) and dried with acetonitrile (Sigma-Aldich, St.Louis, USA). Fluorescent signal intensities were detected with Agilent DNA Microarray Scanner (Agilent Technologies, Santa Clara, USA) and Scan Control A.8.4.1 Software (Agilent Technologies, Santa Clara, USA). Extraction of data was done by using the Feature Extraction 10.7.3.1 Software (Agilent Technologies, Santa Clara, USA).

Project description:Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

Dataset Information

Identification of Biomarker for Cutaneous Squamous Cell Carcinoma Using Microarray Data Analysis.

Publications

Identification of Biomarker for Cutaneous Squamous Cell Carcinoma Using Microarray Data Analysis.

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