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PLoS One. 2014 Sep 4;9(9):e106801. doi: 10.1371/journal.pone.0106801. eCollection 2014.

Analysis of gene expression profiles of soft tissue sarcoma using a combination of knowledge-based filtering with integration of multiple statistics.

Author information

1
Plant Biology Research Center, Chubu University, Kasugai, Aichi, Japan.
2
Division of Genetics, National Cancer Center Research Institute, Tokyo, Japan; Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Japan.
3
Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya, Aichi, Japan.
4
Division of Genetics, National Cancer Center Research Institute, Tokyo, Japan; Faculty of Horticulture, Chiba University, Matsudo, Chiba, Japan.
5
Graduate School of Bioscience and Biotechnology, Chubu University, Kasugai, Aichi, Japan.
6
Department of Surgical Pathology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, Japan.
7
Division of Genetics, National Cancer Center Research Institute, Tokyo, Japan.
8
Department of Surgical Pathology, Sapporo Medical University School of Medicine, Sapporo, Hokkaido, Japan; Pathology Division, National Cancer Center Hospital, Tokyo, Japan.
9
Plant Biology Research Center, Chubu University, Kasugai, Aichi, Japan; Division of Genetics, National Cancer Center Research Institute, Tokyo, Japan; Graduate School of Horticulture, Chiba University, Matsudo, Chiba, Japan.

Abstract

The diagnosis and treatment of soft tissue sarcomas (STS) have been difficult. Of the diverse histological subtypes, undifferentiated pleomorphic sarcoma (UPS) is particularly difficult to diagnose accurately, and its classification per se is still controversial. Recent advances in genomic technologies provide an excellent way to address such problems. However, it is often difficult, if not impossible, to identify definitive disease-associated genes using genome-wide analysis alone, primarily because of multiple testing problems. In the present study, we analyzed microarray data from 88 STS patients using a combination method that used knowledge-based filtering and a simulation based on the integration of multiple statistics to reduce multiple testing problems. We identified 25 genes, including hypoxia-related genes (e.g., MIF, SCD1, P4HA1, ENO1, and STAT1) and cell cycle- and DNA repair-related genes (e.g., TACC3, PRDX1, PRKDC, and H2AFY). These genes showed significant differential expression among histological subtypes, including UPS, and showed associations with overall survival. STAT1 showed a strong association with overall survival in UPS patients (logrank p = 1.84 × 10(-6) and adjusted p value 2.99 × 10(-3) after the permutation test). According to the literature, the 25 genes selected are useful not only as markers of differential diagnosis but also as prognostic/predictive markers and/or therapeutic targets for STS. Our combination method can identify genes that are potential prognostic/predictive factors and/or therapeutic targets in STS and possibly in other cancers. These disease-associated genes deserve further preclinical and clinical validation.

PMID:
25188299
PMCID:
PMC4154757
DOI:
10.1371/journal.pone.0106801
[Indexed for MEDLINE]
Free PMC Article

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