Meta-Analyses of Microarray Datasets Identifies ANO1 and FADD as Prognostic Markers of Head and Neck Cancer
Publication Date
January 25, 2016
Journal
PLOS ONE
Authors
Ram Bhupal Reddy, Anupama Rajan Bhat, Bonney Lee James, Sindhu Valiyaveedan Govindan, et al
Volume
11
Issue
1
Pages
e0147409
DOI
https://dx.plos.org/10.1371/journal.pone.0147409
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0147409
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26808319
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4726811
Europe PMC
http://europepmc.org/abstract/MED/26808319
Web of Science
000369527800090
Scopus
84983684580
Mendeley
http://www.mendeley.com/research/metaanalyses-microarray-datasets-identifies-ano1-fadd-prognostic-markers-head-neck-cancer
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Mendeley | Further Information

{"title"=>"Meta-Analyses of Microarray Datasets Identifies ANO1 and FADD as Prognostic Markers of Head and Neck Cancer", "type"=>"journal", "authors"=>[{"first_name"=>"Ram Bhupal", "last_name"=>"Reddy", "scopus_author_id"=>"56998142600"}, {"first_name"=>"Anupama Rajan", "last_name"=>"Bhat", "scopus_author_id"=>"57192418753"}, {"first_name"=>"Bonney Lee", "last_name"=>"James", "scopus_author_id"=>"57194482333"}, {"first_name"=>"Sindhu Valiyaveedan", "last_name"=>"Govindan", "scopus_author_id"=>"56979040600"}, {"first_name"=>"Rohit", "last_name"=>"Mathew", "scopus_author_id"=>"57194479771"}, {"first_name"=>"D. R.", "last_name"=>"Ravindra", "scopus_author_id"=>"57190752157"}, {"first_name"=>"Naveen", "last_name"=>"Hedne", "scopus_author_id"=>"54790885300"}, {"first_name"=>"Jeyaram", "last_name"=>"Illiayaraja", "scopus_author_id"=>"36489563100"}, {"first_name"=>"Vikram", "last_name"=>"Kekatpure", "scopus_author_id"=>"24390854000"}, {"first_name"=>"Samanta S.", "last_name"=>"Khora", "scopus_author_id"=>"24393876900"}, {"first_name"=>"Wesley", "last_name"=>"Hicks", "scopus_author_id"=>"7005822298"}, {"first_name"=>"Pramila", "last_name"=>"Tata", "scopus_author_id"=>"37098192300"}, {"first_name"=>"Moni A.", "last_name"=>"Kuriakose", "scopus_author_id"=>"14019825000"}, {"first_name"=>"Amritha", "last_name"=>"Suresh", "scopus_author_id"=>"38863138600"}], "year"=>2016, "source"=>"PloS one", "identifiers"=>{"doi"=>"10.1371/journal.pone.0147409", "scopus"=>"2-s2.0-84983684580", "pui"=>"615997444", "issn"=>"19326203", "pmid"=>"26808319", "sgr"=>"84983684580", "isbn"=>"1932-6203"}, "id"=>"f15eaebe-e5f2-30a3-a3cb-ba11c3c13a16", "abstract"=>"The head and neck squamous cell carcinoma (HNSCC) transcriptome has been profiled extensively, nevertheless, identifying biomarkers that are clinically relevant and thereby with translational benefit, has been a major challenge. The objective of this study was to use a meta-analysis based approach to catalog candidate biomarkers with high potential for clinical application in HNSCC. Data from publically available microarray series (N = 20) profiled using Agilent (4X44K G4112F) and Affymetrix (HGU133A, U133A_2, U133Plus 2) platforms was downloaded and analyzed in a platform/chip-specific manner (GeneSpring software v12.5, Agilent, USA). Principal Component Analysis (PCA) and clustering analysis was carried out iteratively for segregating outliers; 140 normal and 277 tumor samples from 15 series were included in the final analysis. The analyses identified 181 differentially expressed, concordant and statistically significant genes; STRING analysis revealed interactions between 122 of them, with two major gene clusters connected by multiple nodes (MYC, FOS and HSPA4). Validation in the HNSCC-specific database (N = 528) in The Cancer Genome Atlas (TCGA) identified a panel (ECT2, ANO1, TP63, FADD, EXT1, NCBP2) that was altered in 30% of the samples. Validation in treatment naïve (Group I; N = 12) and post treatment (Group II; N = 12) patients identified 8 genes significantly associated with the disease (Area under curve>0.6). Correlation with recurrence/re-recurrence showed ANO1 had highest efficacy (sensitivity: 0.8, specificity: 0.6) to predict failure in Group I. UBE2V2, PLAC8, FADD and TTK showed high sensitivity (1.00) in Group I while UBE2V2 and CRYM were highly sensitive (>0.8) in predicting re-recurrence in Group II. Further, TCGA analysis showed that ANO1 and FADD, located at 11q13, were co-expressed at transcript level and significantly associated with overall and disease-free survival (p<0.05). The meta-analysis approach adopted in this study has identified candidate markers correlated with disease outcome in HNSCC; further validation in a larger cohort of patients will establish their clinical relevance.", "link"=>"http://www.mendeley.com/research/metaanalyses-microarray-datasets-identifies-ano1-fadd-prognostic-markers-head-neck-cancer", "reader_count"=>21, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>5, "Student > Ph. D. Student"=>4, "Student > Postgraduate"=>1, "Other"=>2, "Student > Master"=>3, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>5, "Student > Ph. D. 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Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2633435"], "description"=>"<p>The publically available raw microarray data of Head and Neck Squamous Cell Carcinoma (HNSCC) series were downloaded and grouped and analyzed in Genespring statistical software. Normalization was carried out on the samples, which were then grouped into tumor and normal prior to performing the gene level experiment. Principal Component Analysis (PCA) was performed to remove the discordant samples. Fold change and <i>p</i> value were calculated to obtain the significant gene entities. These statistically significant entities were used for further analysis; database annotations (Gene Ontology, STRING and miRWALK) and patient validation (Experimental validation by Quantitative Real Time PCR (qPCR) and The Cancer Genome Atlas (TCGA)).</p>", "links"=>[], "tags"=>["Microarray Datasets Identifies ANO 1", "ect", "44k", "catalog candidate biomarkers", "agilent", "platform", "4112F", "principal component analysis", "myc", "2V", "validation", "133a", "EXT", "ncbp", "series", "sensitivity", "hspa", "pca", "UBE", "approach", "fos", "gene", "fadd", "PLAC", "Cancer Genome Atlas", "TCGA", "usa", "group II", "ttk", "tp", "277 tumor samples", "hgu", "CRYM", "hnscc", "Ano 1", "analysis"], "article_id"=>1641713, "categories"=>["Biological Sciences"], "users"=>["Ram Bhupal Reddy", "Anupama Rajan Bhat", "Bonney Lee James", "Sindhu Valiyaveedan Govindan", "Rohit Mathew", "Ravindra DR", "Naveen Hedne", "Jeyaram Illiayaraja", "Vikram Kekatpure", "Samanta S. Khora", "Wesley Hicks", "Pramila Tata", "Moni A. Kuriakose", "Amritha Suresh"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0147409.g001", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Meta_analysis_work_flow_/1641713", "title"=>"Meta-analysis work flow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:41:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/2633464", "https://ndownloader.figshare.com/files/2633465", "https://ndownloader.figshare.com/files/2633466", "https://ndownloader.figshare.com/files/2633467", "https://ndownloader.figshare.com/files/2633468", "https://ndownloader.figshare.com/files/2633469", "https://ndownloader.figshare.com/files/2633470", "https://ndownloader.figshare.com/files/2633471", "https://ndownloader.figshare.com/files/2633472", "https://ndownloader.figshare.com/files/2633473", "https://ndownloader.figshare.com/files/2633474"], "description"=>"<div><p>The head and neck squamous cell carcinoma (HNSCC) transcriptome has been profiled extensively, nevertheless, identifying biomarkers that are clinically relevant and thereby with translational benefit, has been a major challenge. The objective of this study was to use a meta-analysis based approach to catalog candidate biomarkers with high potential for clinical application in HNSCC. Data from publically available microarray series (N = 20) profiled using Agilent (4X44K G4112F) and Affymetrix (HGU133A, U133A_2, U133Plus 2) platforms was downloaded and analyzed in a platform/chip-specific manner (GeneSpring software v12.5, Agilent, USA). Principal Component Analysis (PCA) and clustering analysis was carried out iteratively for segregating outliers; 140 normal and 277 tumor samples from 15 series were included in the final analysis. The analyses identified 181 differentially expressed, concordant and statistically significant genes; STRING analysis revealed interactions between 122 of them, with two major gene clusters connected by multiple nodes (MYC, FOS and HSPA4). Validation in the HNSCC-specific database (N = 528) in The Cancer Genome Atlas (TCGA) identified a panel (<i>ECT2</i>, <i>ANO1</i>, <i>TP63</i>, <i>FADD</i>, <i>EXT1</i>, <i>NCBP2</i>) that was altered in 30% of the samples. Validation in treatment naïve (Group I; N = 12) and post treatment (Group II; N = 12) patients identified 8 genes significantly associated with the disease (Area under curve>0.6). Correlation with recurrence/re-recurrence showed <i>ANO1</i> had highest efficacy (sensitivity: 0.8, specificity: 0.6) to predict failure in Group I. <i>UBE2V2</i>, <i>PLAC8</i>, <i>FADD</i> and <i>TTK</i> showed high sensitivity (1.00) in Group I while <i>UBE2V2</i> and <i>CRYM</i> were highly sensitive (>0.8) in predicting re-recurrence in Group II. Further, TCGA analysis showed that <i>ANO1</i> and <i>FADD</i>, located at 11q13, were co-expressed at transcript level and significantly associated with overall and disease-free survival (<i>p</i><0.05). The meta-analysis approach adopted in this study has identified candidate markers correlated with disease outcome in HNSCC; further validation in a larger cohort of patients will establish their clinical relevance.</p></div>", "links"=>[], "tags"=>["Microarray Datasets Identifies ANO 1", "ect", "44k", "catalog candidate biomarkers", "agilent", "platform", "4112F", "principal component analysis", "myc", "2V", "validation", "133a", "EXT", "ncbp", "series", "sensitivity", "hspa", "pca", "UBE", "approach", "fos", "gene", "fadd", "PLAC", "Cancer Genome Atlas", "TCGA", "usa", "group II", "ttk", "tp", "277 tumor samples", "hgu", "CRYM", "hnscc", "Ano 1", "analysis"], "article_id"=>1641731, "categories"=>["Uncategorised"], "users"=>["Ram Bhupal Reddy", "Anupama Rajan Bhat", "Bonney Lee James", "Sindhu Valiyaveedan Govindan", "Rohit Mathew", "Ravindra DR", "Naveen Hedne", "Jeyaram Illiayaraja", "Vikram Kekatpure", "Samanta S. Khora", "Wesley Hicks", "Pramila Tata", "Moni A. Kuriakose", "Amritha Suresh"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0147409.s001", "https://dx.doi.org/10.1371/journal.pone.0147409.s002", "https://dx.doi.org/10.1371/journal.pone.0147409.s003", "https://dx.doi.org/10.1371/journal.pone.0147409.s004", "https://dx.doi.org/10.1371/journal.pone.0147409.s005", "https://dx.doi.org/10.1371/journal.pone.0147409.s006", "https://dx.doi.org/10.1371/journal.pone.0147409.s007", "https://dx.doi.org/10.1371/journal.pone.0147409.s008", "https://dx.doi.org/10.1371/journal.pone.0147409.s009", "https://dx.doi.org/10.1371/journal.pone.0147409.s010", "https://dx.doi.org/10.1371/journal.pone.0147409.s011"], "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Meta_Analyses_of_Microarray_Datasets_Identifies_ANO1_and_FADD_as_Prognostic_Markers_of_Head_and_Neck_Cancer/1641731", "title"=>"Meta-Analyses of Microarray Datasets Identifies <i>ANO1</i> and <i>FADD</i> as Prognostic Markers of Head and Neck Cancer", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2016-01-28 12:41:11"}
  • {"files"=>["https://ndownloader.figshare.com/files/2633438"], "description"=>"<p>Analysis for protein-protein interaction by STRING network identified two major interconnecting clusters with high degree interactions between the genes (N = 122). These 2 major clusters were interconnected by the nodes MYC, FN1, FOS and HSPA4. The number of lines represent the levels of evidence as indicated in the color legend. The different sizes of the node are based on the extent of protein structural information available for each gene while the colors of the node are a visual aid used for better representation. The markers from this analysis selected for patient validation are encircled.</p>", "links"=>[], "tags"=>["Microarray Datasets Identifies ANO 1", "ect", "44k", "catalog candidate biomarkers", "agilent", "platform", "4112F", "principal component analysis", "myc", "2V", "validation", "133a", "EXT", "ncbp", "series", "sensitivity", "hspa", "pca", "UBE", "approach", "fos", "gene", "fadd", "PLAC", "Cancer Genome Atlas", "TCGA", "usa", "group II", "ttk", "tp", "277 tumor samples", "hgu", "CRYM", "hnscc", "Ano 1", "analysis"], "article_id"=>1641716, "categories"=>["Biological Sciences"], "users"=>["Ram Bhupal Reddy", "Anupama Rajan Bhat", "Bonney Lee James", "Sindhu Valiyaveedan Govindan", "Rohit Mathew", "Ravindra DR", "Naveen Hedne", "Jeyaram Illiayaraja", "Vikram Kekatpure", "Samanta S. Khora", "Wesley Hicks", "Pramila Tata", "Moni A. Kuriakose", "Amritha Suresh"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0147409.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Identification_of_Protein_Protein_Interaction_/1641716", "title"=>"Identification of Protein-Protein Interaction.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:41:10"}
  • {"files"=>["https://ndownloader.figshare.com/files/2633439"], "description"=>"<p>Quantitative gene expression profiling of the selected markers was carried out in Group I (primary; A) and the Group II (recurrent; B) cohort. <i>PLAC8</i> and <i>UBE2V2</i> were validated in all the samples (100%) of Group I with regard to regulation trends whereas other genes showed similar trend in >60% of the samples. In Group II, >60% of the patients showed concordant regulation trends for four genes. Based on the patient follow-up, the Group I was sub-categorized into non-recurrent (C) and recurrent (D) and the expression was further evaluated. ROC curve analysis in the Group I patients showed that <i>PLAC8</i> (E), <i>FOS</i> (F), <i>ANO1</i> (G) and <i>UBE2V2</i> (H) had highest association with the disease (AUC >0.8). Bar represents the median fold change of Normals.</p>", "links"=>[], "tags"=>["Microarray Datasets Identifies ANO 1", "ect", "44k", "catalog candidate biomarkers", "agilent", "platform", "4112F", "principal component analysis", "myc", "2V", "validation", "133a", "EXT", "ncbp", "series", "sensitivity", "hspa", "pca", "UBE", "approach", "fos", "gene", "fadd", "PLAC", "Cancer Genome Atlas", "TCGA", "usa", "group II", "ttk", "tp", "277 tumor samples", "hgu", "CRYM", "hnscc", "Ano 1", "analysis"], "article_id"=>1641717, "categories"=>["Biological Sciences"], "users"=>["Ram Bhupal Reddy", "Anupama Rajan Bhat", "Bonney Lee James", "Sindhu Valiyaveedan Govindan", "Rohit Mathew", "Ravindra DR", "Naveen Hedne", "Jeyaram Illiayaraja", "Vikram Kekatpure", "Samanta S. Khora", "Wesley Hicks", "Pramila Tata", "Moni A. Kuriakose", "Amritha Suresh"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0147409.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validation_of_the_markers_in_patients_/1641717", "title"=>"Validation of the markers in patients.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:41:09"}
  • {"files"=>["https://ndownloader.figshare.com/files/2633440"], "description"=>"<p>The selected markers were analyzed in the TCGA database for the co-expression, overall survival and disease free survival for their significance in the HNSCC TCGA provisional study. <i>ANO1</i> and <i>FADD</i> showed highest correlation in the co-expression analysis (A) with Pearson’s and Spearman’s correlation (0.68). <i>ANO1</i> and <i>FADD</i> were further analyzed for their overall survival (OS) (B and D) and Disease free survival (DFS) (<i>ANO1</i>; C). Patients with <i>ANO1</i> over-expression showed low median survival (18.96 vs 56.44 months; p = 0.0003) and low DFS (20.04 vs 53.09 months; <i>p</i> = 0.02) when compared with the cohort without alterations (B and C). <i>FADD</i> showed association with OS wherein low median survival (21.48 vs 57.42; <i>p</i> = 0.002) was observed in patients with an upregulation of the gene (D). Both <i>ANO1</i> and <i>FADD</i> when assessed in combination, were associated with low median survival (21.48 vs 57.88; <i>p</i> = 0.0007) (E) and disease free survival (25.72 vs 53.09; <i>p</i> = 0.04) (F) in altered cases when compared to cases without alterations.</p>", "links"=>[], "tags"=>["Microarray Datasets Identifies ANO 1", "ect", "44k", "catalog candidate biomarkers", "agilent", "platform", "4112F", "principal component analysis", "myc", "2V", "validation", "133a", "EXT", "ncbp", "series", "sensitivity", "hspa", "pca", "UBE", "approach", "fos", "gene", "fadd", "PLAC", "Cancer Genome Atlas", "TCGA", "usa", "group II", "ttk", "tp", "277 tumor samples", "hgu", "CRYM", "hnscc", "Ano 1", "analysis"], "article_id"=>1641718, "categories"=>["Biological Sciences"], "users"=>["Ram Bhupal Reddy", "Anupama Rajan Bhat", "Bonney Lee James", "Sindhu Valiyaveedan Govindan", "Rohit Mathew", "Ravindra DR", "Naveen Hedne", "Jeyaram Illiayaraja", "Vikram Kekatpure", "Samanta S. Khora", "Wesley Hicks", "Pramila Tata", "Moni A. Kuriakose", "Amritha Suresh"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0147409.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Validation_with_the_TCGA_database_/1641718", "title"=>"Validation with the TCGA database.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 12:41:09"}

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  • {"unique-ip"=>"17", "full-text"=>"18", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"11", "full-text"=>"13", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"3", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}

Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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