Gene and miRNA expression changes in squamous cell carcinoma of larynx and hypopharynx.

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Despite the presence of a large body of information, molecular biomarkers are not currently used in the diagnosis, treatment and management of patients for this group of cancer. Here, we have profiled expression of genes and microRNAs of larynx and hypopharynx tumors using high-throughput sequencing experiments. We found that matrix metalloproteinases along with SCEL, CRNN, KRT4, SPINK5, and TGM3 among others have significantly altered expression in these tumors. Alongside gene expression, the microRNAs hsa-miR-139, hsa-miR-203 and the hsa-miR-424/503 cluster have aberrant expression in these cancers. Using target genes for these microRNAs, we found the involvement of pathways linked to cell cycle, p53 signaling, and viral carcinogenesis significant (P-values 10(-13), 10(-9) and 10(-7) respectively). Finally, using an ensemble machine-learning tool, we discovered a unique 8-gene signature for this group of cancers that differentiates the group from the other tumor subsites of head and neck region. We investigated the role of promoter methylation in one of these genes, WIF1, and found no correlation between DNA methylation and down-regulation of WIF1. We validated our findings of gene expression, 8-gene signature and promoter methylation using q-PCR, data from TCGA and q-MSP respectively. Data presented in this manuscript has been submitted to the NCBI Geo database with the accession number GSE67994.", "claims": [{"id": "claim-method", "statement": "Normalization of the raw counts was done using the DESeq R package (R-3.1.0, DESeq-1.16.0) [76] and tested for differential expression with the following combinations: 1) all tumors vs their matched normal samples (referred as TN_P), 2) all tumor vs normal samples (matched & unmatched, TN_UP), and 3) pair-wise analyses for the individual tumor-normal pairs (T_P).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:26413216", "locator": "sections[15] \"Differential gene expression analysis\" paragraph 2", "quote": "Normalization of the raw counts was done using the DESeq R package (R-3.1.0, DESeq-1.16.0) [76] and tested for differential expression with the following combinations: 1) all tumors vs their matched normal samples (referred as TN_P), 2) all tumor vs normal samples (matched & unmatched, TN_UP), and 3) pair-wise analyses for the individual tumor-normal pairs (T_P)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data presented in this manuscript has been submitted to the NCBI Geo database with the accession number GSE67994.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:26413216", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Data presented in this manuscript has been submitted to the NCBI Geo database with the accession number GSE67994."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study used random-forest ensemble machine learning on sequencing-derived gene-expression data, with TCGA data used for validation, and identified an 8-gene signature distinguishing larynx and hypopharynx carcinoma from other head-and-neck tumor subsites.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:26413216", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Finally, using an ensemble machine-learning tool, we discovered a unique 8-gene signature for this group of cancers that differentiates the group from the other tumor subsites of head and neck region."}, {"asset": "source:pmid:26413216", "locator": "sections[1] \"INTRODUCTION\" paragraph 4", "quote": "We used computational pipeline with sequencing data from the discovery set to identify the significantly over- and under-expressed genes in SCCL and SCCHP."}, {"asset": "source:pmid:26413216", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "We validated our findings of gene expression, 8-gene signature and promoter methylation using q-PCR, data from TCGA and q-MSP respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study used random-forest ensemble machine learning on sequencing-derived gene-expression data, with TCGA data used for validation, and identified an 8-gene signature distinguishing larynx and hypopharynx carcinoma from other head-and-neck tumor subsites.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:26413216"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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We designed qPCR primers (Supplementary Table 2) and measured", "ynx and hypopharynx tumors Quantitative PCR (qPCR) validation of gene expression changes, Red:"], "order_basis": "paper_text_order:3190", "id": "assay:1"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Normalization of the raw counts was done using the DESeq R package (R-3.1.0, DESeq-1.16.0) [76] and tested for differential expression with the following combinations: 1) all tumors vs their matched normal samples (referred as TN_P), 2) all tumor vs normal samples (matched & unmatched, TN_UP), and 3) pair-wise analyses for the individual tumor-normal pairs (T_P)."], "order_basis": "consumes_earlier_output", "id": "assay:2"}]}