{"id":"hum0111","version":1,"url":"https://humandbs.dbcls.jp/research/hum0111/v1","datePublished":"2017-07-31","versions":[{"version":1,"datePublished":"2017-07-31"},{"version":2,"datePublished":"2019-05-31"},{"version":3,"datePublished":"2021-02-19"}],"title":{"ja":"胃腫瘍のゲノム・遺伝子解析とその臨床病理学的意義の解明","en":"Analysis of gastric cancer genome and its clinicopathological significance"},"summary":{"aims":{"ja":"胃がんにおける腫瘍浸潤リンパ球の遺伝子解析","en":"Genomic analysis of tumor infiltrating lymphocytes in gastric cancer"},"methods":{"ja":"Illumina HiSeq 2000を使用したRNA-seqおよびIllumina MiSeqを使用したT/B細胞抗原受容体mRNAのターゲットシーケンシング","en":"RNA-seq by Illumina HiSeq 2000 and amplicon sequencing of T/B cell antigen receptors by Illumina MiSeq"},"targets":{"ja":"びまん性胃がん30症例（RNA-seqは腫瘍組織のみ。Amplicon-seqは腫瘍組織とその近傍の非腫瘍組織のペア）、胃がん60＋15症例（Amplicon-seq：腫瘍組織とその近傍の非腫瘍組織のペア）","en":"Specimens of 30 diffuse-type gastric carcinoma and 60 + 15 gastric carcinoma (matched tumor and normal tissue)"},"url":{"ja":null,"en":null}},"listingSummary":{},"releaseNote":{"ja":"びまん性胃がん30症例の腫瘍組織から抽出したRNAを用いたRNA-seq、および、腫瘍組織と非腫瘍組織から抽出したRNAを用いたAmplicon-seq（TCRα、TCRβ、IgVH、IgVκ/λ遺伝子のV領域からC領域）の結果を、テキストフィル（cuffnormの出力）およびfastqファイルにて提供する。TruSeq Stranded mRNA Sample Prep KitによるRNAライブラリ作製後、HiSeq 2000（Illumina）にて平均長100 bp x 2を読み（Paired-end）FPKM（Fragments Per Kilobase of transcript per Million mapped fragments）値を算出した。一方、Amplicon-seqでは、PCRによりライブラリ作製後、MiSeq（Illumina）にて平均長300 bp x 2を読んだ（Paired-end）。","en":"RNAs were extracted from tumor and normal tissues of 30 diffuse-type gastric carcinoma patients. Libraries for RNA-seq were prepared using TruSeq Stranded mRNA Sample Prep Kit and a deep sequencing was performed by using of Illumina HiSeq 2000 (100 bp x 2, Paired-end). The FPKM (Fragments Per Kilobase of transcript per Million mapped fragments) values are provided as a txt file. Libraries for Amplicon-seq (V region to C region of TCRα, TCRβ, IgVH and IgVκ/λ) were prepared by PCR and Amplicon-seq was performed by Illumina Miseq (300 bp x 2, Paired-end). The sequences are provided as fastq files."},"dataProviders":[{"name":{"ja":"石川 俊平","en":"Shumpei Ishikawa"},"organization":{"name":{"ja":"東京大学 医学系研究科 衛生学分野","en":"Department of Preventive Medicine, Graduate School of Medicine, The University of Tokyo"}}}],"researchProjects":[],"grants":[{"title":{"ja":"がん浸潤リンパ球の抗原受容体次世代シーケンス ～がん免疫システムの包括的理解～","en":"Immunogenetic Repertoire Analysis for Tumor-infiltrating Lymphocytes - toward the Understanding of Tumor Immunity -"},"agency":{"ja":"科学研究費助成事業 若手研究（A）","en":"KAKENHI Grant-in-Aid for Young Scientists (A)"},"grantIds":["26710009"]},{"title":{"ja":"胃癌における癌細胞と免疫細胞の統合ゲノミクス","en":"Integrated genomics of cancer cells and immune cells in gastric cancer"},"agency":{"ja":"日本医療研究開発機構（AMED） 次世代がん医療創生研究事業（P-CREATE）","en":"Project for Cancer Research and Therapeutic Evolution (P-CREATE), Japan Agency for Medical Research and Development (AMED)"},"grantIds":["15655817"]},{"title":{"ja":"臨床腫瘍特異的なシングルドメイン抗体機能複合体の取得技術に関する研究","en":"Technology development for obtaining functional and cancer-specific single-domain antigen receptor complex."},"agency":{"ja":"日本医療研究開発機構（AMED） 革新的バイオ医薬品創出基盤技術開発事業","en":"Basic Science and Platform Technology Program for Innovative Biological Medicine, Japan Agency for Medical Research and Development (AMED)"},"grantIds":["JP18am0301011"]},{"title":{"ja":"ゲノム情報科学と機械学習に基づく高機能抗体のデザイン開発技術に関する研究","en":"Study of designing technology of high performance antibodies based on clinical genome data science and machine learning"},"agency":{"ja":"日本医療研究開発機構（AMED） 創薬基盤推進研究事業","en":"Research on Development of New Drugs, Japan Agency for Medical Research and Development (AMED)"},"grantIds":["JP18ak0101096"]}],"relatedPublications":[{"title":"Immunogenetic Profiling for Gastric Cancers Identifies Sulfated Glycosaminoglycans as Major and Functional B-cell Antigens among Human Malignancies","doi":"https://doi.org/10.1016/j.celrep.2017.07.016","datasets":["JGAD000104","JGAD000105"]},{"title":"Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning.","doi":"https://doi.org/10.1186/s12859-019-2853-y","datasets":["JGAD000210"]},{"title":"Focal adhesion ribonucleoprotein complex proteins are major humoral cancer antigens and targets in autoimmune diseases","doi":"https://doi.org/10.1038/s42003-020-01305-5","datasets":["JGAD000342"]}],"datasets":["JGAD000105","JGAD000104"],"controlledAccessUsers":[]}