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pmid:27498782
Decreased LIPF expression is correlated with DGKA and predicts poor outcome of gastric cancer.
Yi Kong · Yan Zheng · Yanfei Jia · Ping Li · Yunshan Wang
Oncology reports · 2016 doi:10.3892/or.2016.4989
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Experiment
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Complete file
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Accordingly, exploring gene expression profile and identifying genes with analogical variance trend will bring new perspective into the diagnosis and treatment of GC. Herein, a RNA‑Seq dataset from Caucasian GC and their matched non‑cancerous samples [Gene Expression Omnibus (GEO): SRP049809] and datasets from four microarrays constituted with tumor and non‑tumor tissues (GEO: GSE13911, GSE19826, GSE29272, GSE33335) were analyzed to explore the differentially expressed genes (DGEs). As a result, we identified a core set of 373 DGEs. Among these genes, we found that most downregulated genes were related to lipid‑metabolic functions. Especially, the gastric lipase (LIPF) gene, which was connected with various lipid metabolism processes, was significantly decreased among all datasets. We then performed immunohistochemistry experiments using gastric tissue arrays to investigate the clinical effects, and the expression of a LIPF target gene, diacylglycerol kinase α (DGKA). Among the 90 samples of gastric adenocarcinoma, the LIPF and DGKA levels were both decreased in cancer tissues [LIPF, 59.1% (53/90); DGKA, 77.8% (70/90)] compared to normal tissues [LIPF, 94.4% (85/90); DGKA, 90% (81/90)]. The expression level of these two proteins in GC was associated with local invasion and disease stage. Cox regression identified high DGKA expression (HR, 0.49; 95% CI, 0.26‑0.94; P=0.03) as a predictor of good prognosis and LNM status (HR, 4.63; 95% CI, 1.39‑15.51; P=0.01) as a predictor of poor prognosis. Thus we speculated that LIPF‑DGKA might serve as a potential possible biomarkers for diagnosis of GC, and their downregulation may bring new perspective into the investigation of GC prognosis.", "claims": [{"id": "claim-method", "statement": "To assess the statistically significant DGEs between tumor and normal or adjacent non-tumor groups, we performed a paired t-test in LIMMA package (7–9).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:27498782", "locator": "sections[4] \"Microarray datasets of GC patients\" paragraph 7", "quote": "To assess the statistically significant DGEs between tumor and normal or adjacent non-tumor groups, we performed a paired t-test in LIMMA package (7–9)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "We investigated four distinct microarray datasets of GC retrieved from GEO dataset under accession nos. 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clinicopathological features of GC patients.", "structure": {"pmcid": "PMC5022960", "section_index": 10, "section_title": "Gene function annotation", "row_count": 22, "column_count": 9}, "table": {"label": "Table II", "caption": "Correlation between expression of LIPF, DGKA and clinicopathological features of GC patients.", "rows": [["Characteristics", "LIPF high expression", "LIPF low expression", "χ2 or Fisher's exact test", "P-value", "DGKA high expression", "DGKA low expression", "χ2 or Fisher's exact test", "P-value"], ["Age (years)", "", "", "2.18E-30", "1.0", "", "", "0.014", "0.91"], ["<60", "5", "26", "", "", "9", "22", "", ""], ["≥60", "10", "49", "", "", "15", "44", "", ""], ["Gender", "", "", "", "1.0", "", "", "2.94E-4", "0.99"], ["Male", "10", "52", "", "", "16", "46", "", ""], ["Female", "4", "24", "", "", "8", "20", "", ""], ["Local invasion", "", "", "10.03", "0.002", "", "", "", "0.0069"], ["T1+T2", "6", "5", "", "", "7", "4", "", ""], ["T3+T4", "9", "70", "", "", "17", "62", "", ""], ["Nodal status", "", "", "2.17", "0.14", "", "", "2.27", "0.13"], ["Positive", "8", "57", "", "", "14", "51", "", ""], ["Negative", "7", "18", "", "", "10", "15", "", ""], ["Stage of disease", "", "", "4.53", "0.033", "", "", "4.15", "0.041"], ["I+II", "10", "25", "", "", "14", "21", "", ""], ["III+IV", "5", "50", "", "", "10", "45", "", ""], ["Distant metastasis", "", "", "", "1.0", "", "", "", "1.0"], ["M0", "15", "74", "", "", "24", "65", "", ""], ["M1", "0", "1", "", "", "0", "1", "", ""], ["Depth of tumor invasion", "", "", "0.33", "0.031", "", "", "0.15", "0.70"], ["Mucosa, submucosa, muscularis propria, subserosa", "14", "47", "", "", "15", "46", "", ""], ["Penetration of serosa,adjacent structures", "1", "28", "", "", "9", "20", "", ""]], "row_count": 22, "column_count": 9, "numeric_cells": 84, "header_rows": 1, "locator": "section[10]/table-wrap[1]#tII-or-36-04-1852"}}, {"id": "asset:table:pmid:27498782:3", "role": "table", "format": "jats-table", 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["Other", "8", "8.8"], ["Tumor stage"], ["I", "4", "4.4"], ["II", "28", "31.1"], ["III", "52", "57.4"], ["IV", "1", "1.1"], ["Follow-up time (months)", "37 (1–78)", ""], ["Prognosis"], ["Alive", "34", "37.8"], ["Dead", "56", "62.2"], ["Patients lived for 5 years", "31", "34.4"], ["Nodal status"], ["Positive", "65", "72.2"], ["Negative", "25", "27.8"], ["Pathological type"], ["Adenocarcinoma", "90", "100"]], "row_count": 31, "column_count": 3, "numeric_cells": 48, "header_rows": 1, "locator": "section[11]/table-wrap[1]#tIII-or-36-04-1852"}}, {"id": "asset:table:pmid:27498782:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:752ea43133872626f4626fe8ff4a20361f08acd4e814fb4b5c669288e191cf87", "bytes": 818, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[2]#tIV-or-36-04-1852", "version": null, "note": "Multivariate Cox regression analysis of potential prognostic factors for survival in patients with GC.", "structure": {"pmcid": "PMC5022960", "section_index": 11, "section_title": "Survival analysis", "row_count": 9, "column_count": 5}, "table": {"label": "Table IV", "caption": "Multivariate Cox regression analysis of potential prognostic factors for survival in patients with GC.", "rows": [["Variables", "Univariate analysis", "Multivariate analysis"], ["HR (95% CI)", "P-value", "HR (95% CI)", "P-value"], ["Gender (male vs. female)", "0.51–1.55", "0.69", "", ""], ["Age (≥60 vs. <60 years)", "0.44–1.36", "0.40", "0.29–0.93", "0.02"], ["LNM status (yes vs. no)", "2.58–16.54", "7.19E-05", "1.39–15.51", "0.01"], ["DGKA (high vs. low)", "0.21–0.84", "0.01", "0.26–0.94", "0.03"], ["LIPF (high vs. low)", "0.15–0.95", "0.07", "", ""], ["Tumor status (T3+T4 vs. T1+T2)", "0.76–7.79", "0.13", "", ""], ["Stage (III+IV vs. I+II)", "2.19–8.31", "2.02E-05", "", ""]], "row_count": 9, "column_count": 5, "numeric_cells": 23, 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RNA-Seq and microarray data analysis of GC tissues B", "roarray data analysis of GC tissues Based on RNA-Seq data analyzed with TopHat and HTSeq, we stud"], "order_basis": "paper_text_order:2000", "id": "assay:2"}, {"role": "data_generation", "definition_id": "microarray", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ning DGEs was set to two times fold changes. Microarray datasets of GC patients We investigated four", "of GC patients We investigated four distinct microarray datasets of GC retrieved from GEO dataset un", "performed in R language package. RNA-Seq and microarray data analysis of GC tissues Based on RNA-Seq", "e in RNA-Seq dataset and adjusted P-value in microarray were employed to evaluate the significant le"], "order_basis": "paper_text_order:3316", "id": "assay:3"}, {"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": ["To assess the statistically significant DGEs between tumor and normal or adjacent non-tumor groups, we performed a paired t-test in LIMMA package (7–9)."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}