Japanese version of The Cancer Genome Atlas, JCGA, established using fresh frozen tumors obtained from 5143 cancer patients

Nagashima, Takeshi; Yamaguchi, Ken; Urakami, Kenichi; Shimoda, Yuji; Ohnami, Sumiko; Ohshima, Keiichi; Tanabe, Tomoe; Naruoka, Akane; Kamada, Fukumi; Serizawa, Masakuni; Hatakeyama, Keiichi; Matsumura, Kenya; Ohnami, Shumpei; Maruyama, Koji; Mochizuki, Tohru; Kusuhara, Masatoshi; Shiomi, Akio; Ohde, Yasuhisa; Terashima, Masanori; Uesaka, Katsuhiko; Onitsuka, Tetsuro; Nishimura, Seiichiro; Hirashima, Yasuyuki; Hayashi, Nakamasa; Kiyohara, Yoshio; Tsubosa, Yasuhiro; Katagiri, Hirohisa; Niwakawa, Masashi; Takahashi, Kaoru; Kashiwagi, Hiroya; Nakagawa, Masahiro; Ishida, Yuji; Sugino, Takashi; Takahashi, Mitsuru; Akiyama, Yasuto

doi:10.1111/cas.14290

Cited by 68 publications

(131 citation statements)

References 40 publications

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“…Surgically resected tumor specimens and corresponding peripheral blood samples were obtained from 22 consecutive patients who underwent both colectomy and hepatectomy for CRC and synchronous CRLM between January 2014 and March 2015. All patients were enrolled in Project HOPE (High-tech Omics-based Patient Evaluation), a study launched at Shizuoka Cancer Center with the aim of evaluating the biological characteristics of cancer by multiomics-based analyses [10]. The clinicopathological data of patients were retrospectively reviewed.…”

Section: Methodsmentioning

confidence: 99%

“…Patients with multiple cancers such as synchronous or metachronous malignancy (within 5 years) other than carcinoma in situ, familial adenomatous polyposis, and appendiceal cancer were excluded. The median number of CRLM of patients was 2 (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14). The median size of CRLM was 30 (17-110) mm.…”

Section: Patients' Characteristicsmentioning

confidence: 99%

“…Genomic alterations contributing to tumorigenesis were analyzed using using an analysis pipeline called "Shizuoka Multi-omics Analysis Protocol (SMAP) [10]" developed in-house in primary CRC and CRLM.…”

Section: Gene Mutation Pro Lingmentioning

confidence: 99%

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Comprehensive genomic analysis contrasting primary colorectal cancer and matched liver metastases: a retrospective study

Shiomi

Kusuhara

Sugino

et al. 2020

Preprint

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(Background)Recent studies have revealed that colorectal cancer (CRC) displays intratumor genetic heterogeneity, and the cancer microenvironment play an important role in the proliferation, invasion, and metastasis of CRC.(Methods)We performed genomic analysis on 22 paired patients with primary CRC and synchronous colorectal liver metastasis (CRLM) using whole-exome sequencing, cancer gene panels, and microarray gene expression profiling. In addition, immunohistochemical analysis was used to confirm the protein expression of genes identified as highly expressed in CRLM by DNA microarray analysis.(Results)A total of 22 paired patients were enrolled in this study, including 13 (59.1%) colon and 9 (40.9%) rectal cancer patients. All patients had synchronous liver metastasis and did not have any other extrahepatic metastases. We identified 11 probes (10 genes) that were highly expressed in CRLM compared to CRC, from 36022 probes obtained from primary CRC, CRLM, and normal liver tissues, using gene expression analysis with DNA microarray. Of the 11 probes (10 genes), 5 genes classified as encoding “matricellular proteins” (osteopontin, periostin, thrombospondin-2, MGP, and GPNMB) were selected for immunohistochemical analysis.Osteopontin was strongly expressed in CRLM (6 of 22 cases: 27.3%) but not in CRC (0 of 22: 0%; p=0.02). Periostin also showed strong immunoreactivity in CRLM (17 of 22: 68.2%) compared to 7 of 22 (31.8%) in CRC (p=0.006). Thrombospondin-2 showed strong immunoreactivity both in CRC and CRLM (54.5% in CRC, 45.5% in CRLM; p=0.55). GPNMB and MGP were rarely positive for both CRC and CRLM. A comparison of immunoreactive positive factors for these 5 genes revealed the complexities of gene expression in CRLM. Of the cases, 16 (72.7%) CRC revealed zero or only one positive immunoreactivity. On the other hand, CRLM revealed more frequent multiple immunoreactivity; 16 cases (72.7%) shared 2 or more factors, which was statistically more frequent in CRLM than in CRC (p=0.007).(Conclusions)This study revealed the genomic heterogeneity between paired primary CRC and CRLM in terms of cancer cell microenvironment. This finding will lead to new diagnostic and therapeutic targets in the era of genome-guided personalized cancer treatment.(Trial registration)This study was retrospectively registered.

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Section: Methodsmentioning

confidence: 99%

Section: Patients' Characteristicsmentioning

confidence: 99%

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Comprehensive genomic analysis contrasting primary colorectal cancer and matched liver metastases: a retrospective study

Shiomi

Kusuhara

Sugino

et al. 2020

Preprint

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“…Cancer is a genetic disease where changes to genes cause malfunctions in the affected cells and further lead to various phenotypes of the disease [29]. Genetic changes, also referred to as mutations, can be classified into multiple categories (silent mutations, missense mutations, nonsense mutations, and frameshift mutations) and affect downstream biological functions by different mechanisms.…”

Section: Ranking Disease-associated Genesmentioning

confidence: 99%

MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery

2020

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The recent advancement of omic technologies provides researchers with the possibility to search for disease-associated biomarkers at the system level. The integrative analysis of data from a large number of molecules involved at various layers of the biological system offers a great opportunity to rank disease biomarker candidates. In this paper, we propose MOTA, a network-based method that uses data acquired at multiple layers to rank candidate disease biomarkers. The networks constructed by MOTA allow users to investigate the biological significance of the top-ranked biomarker candidates. We evaluated the performance of MOTA in ranking disease-associated molecules from three sets of multi-omic data representing three cohorts of hepatocellular carcinoma (HCC) cases and controls with liver cirrhosis. The results demonstrate that MOTA allows the identification of more top-ranked metabolite biomarker candidates that are shared by two different cohorts compared to traditional statistical methods. Moreover, the mRNA candidates top-ranked by MOTA comprise more cancer driver genes compared to those ranked by traditional differential expression methods.

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“…Based on the analysis of single omics data, researchers had found many factors related to PC from various aspects [4,5]. As a complex regulatory system, the occurrence of diseases usually involved genetic mutations, epigenetic changes, and abnormal gene expression.…”

Section: Introductionmentioning

confidence: 99%

Integrated analysis of multi-omics data to identification of prognostic genes for pancreatic cancer

Jiang

Zhang

Chen

et al. 2020

Preprint

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Background We aim to develop core modules related to pancreatic cancer (PC) to predict the prognosis of PC patients and explore their tumor microenvironment. Method: We merged 175 pancreatic cancer samples in the TCGA database with gene mutation expression, methylation level distribution, mRNA expression and pancreatic cancer-related genes into a public database, and then through weighted correlation network analysis (WGCNA), Two expression modules associated with pancreatic cancer are combined. Then, by integrating these selected genes identified from the first 10 genes of the two co-expression modules, a model risk score is established, and patients are divided into high-risk and low-risk subgroups. Kaplan-Meier survival analysis method is used to analyze differences, analyze the correlation of survival between subgroups, and analyze prognostic models. These selected core genes can divide early pancreatic cancer into two subgroups, compare the prognosis of these two groups, and screen for differentially expressed genes. Use GO and KEGG enrichment analysis to predict the function of differentially expressed genes. The differential expression level and immune cell infiltration level of these selected core genes were further analysis. Results Our findings shown nine core genes (MST1R, TMPRSS4, PTK6, KLF5, CGN, ABHD17C, MUC1, CAPN8, B3GNT3) were prognostic biomarkers of pancreatic cancer. These 9 genes could divide early pancreatic cancer into two subgroups, and the two subgroups had significant differences in prognosis, and were mainly different in functions such as digestion and extracellular cell adhesion. Further analysis revealed that the expression of these 9 genes were expressed at high levels in pancreatic cancer tissues. In addition, we validated pancreatic cancer cells and pancreatic epithelial cells through quantitative real-time PCR (qRT-PCR), suggesting that the MST1R, PTK6, ABHD17C and CGN expressed higher in PC cells. CIBERSORT analysis indicated that these genes expression were closely correlated with B-cell naive, CD8+ T cells, Macrophages M0 cells, suggesting that these genes may play a carcinogenic role in the preservation of immune-dominant status for tumor microenvironment. Conclusions Our research identified 9 key genes which may enhance our understanding of the molecular mechanisms associated with pancreatic cancer.

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Japanese version of The Cancer Genome Atlas, JCGA, established using fresh frozen tumors obtained from 5143 cancer patients

Abstract: This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Cited by 68 publications

References 40 publications

Comprehensive genomic analysis contrasting primary colorectal cancer and matched liver metastases: a retrospective study

Comprehensive genomic analysis contrasting primary colorectal cancer and matched liver metastases: a retrospective study

MOTA: Network-Based Multi-Omic Data Integration for Biomarker Discovery

Integrated analysis of multi-omics data to identification of prognostic genes for pancreatic cancer

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