7154 results for "canc*"

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• Research for biological features by gene expression analysis and biomarkers at diagnosis/therapeutics in lung cancer

  WGS, WES, ATAC-seq and methylation analysis with DNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients. RNA-seq analysis with RNA harvested from lung cancer organoids established from surgery, biopsy, pleural effusion, sputum and blood samples of patients.

  Study JGAS000609

• ATAC-seq and ChIP-seq analysis of patient-derived normal pancreas and pancreas neoplasm organoids

  Pancreatic cancers arise from two different precursors; intraductal papillary mucinous neoplasms (IPMN) and pancreatic intraepithelial neoplasm (PanIN), while biological differences in cancers originated from them remain obscure. Here, we analyzed their epigenomic landscape by ATAC-seq and ChIP-seq using patient-derived organoids.

  Study JGAS000264

• Comprehensive analyses of genetic aberrations in cholangiolocarcinoma

  The nature of CLC is still unclear and its pathological classification still remains controversial. To clarify the positioning of CLC among primary liver cancers, and to find characteristics that could distinguish CLC from other liver cancers.

  Study JGAS000597

• Whole-genome sequencing on hepatocellular carcinoma with nodule-in-nodule appearance reveals stepwise cancer evolution

  We examined five cases with hypovascular early HCC with hypervascular foci, so-called nodule-in-nodule HCC. Multiregional sampling was performed from both the inner hypervascular region and outer hypovascular region, together with noncancerous liver tissue and lymphocytes as controls. Comprehensive analysis of genomic aberrations, such as single nucleotide variants (SNVs) and structural variations (STVs), was performed by whole-genome sequencing.

  Study JGAS000190

• CAGE analysis for non-small cell lung carcinoma

  Targeted therapies based on the molecular and histological features of cancer types are becoming standard practice. The most effective regimen in lung cancers is different between squamous cell carcinoma (SCC) and adenocarcinoma (AD). Therefore a precise diagnosis is crucial, but this has been difficult, particularly for poorly differentiated SCC (PDSCC) and AD without a lepidic growth component (non-lepidic AD). Biomarkers enabling a precise diagnosis are therefore urgently needed. To achieve this goal, we performed CAGE (Cap Analysis of Gene Expression) to quantify promoter activities on 97 frozen tissues from surgically resected lung cancers (22 SCC and 75 AD). The result was followed up by by immunohistochemical analysis (IHC) in an independent group.

  Study JGAS000070

• Integrated Exome and RNA Sequencing of Dedifferentiated Liposarcoma

  Multiomics analysis of rare cancers to identify genetic alterations related with pathogenesis and prognosis. To identification of genetics alterations of Dedifferentiated liposarcoma, DNAs and RNAs were extracted from dedifferentiated liposarcoma and paired non-cancer (normal) tissues. NGS libaraies were prepared by using Agilent SureSelect XT Human All Exon V5 + IncRNA kit for whole exome sequencing (WES) and TruSeq Stranded mRNA Prep kit or TruSeq RNA Access Library Prep kit (RNA seq). Sequencing was perfomred by Illumina HiSeq 2500.

  Study JGAS000177

• Comprehensive analyses of clinicopathological features and genomic mutations of combined hepatocellular-cholangiocarcinoma

  Combined hepatocellular and cholangiocarcinoma (cHCC-CCA) is a rare primary liver cancer. We performed whole exome sequencing to clarify the relationship between genetic mutations and clinicopathological features in cHCC-CCA.

  Study JGAS000599

• The RNA, ChIP and whole exome sequencing analysis of human colorectal cancer organoids and normal colon organoids treated with (+)-JQ1

  To reveal the mechanism of anti-tumor effect of (+)-JQ1, we performed RNA, ChIP and whole exome sequencing using human colorectal cancer and normal colon organoids.

  Study JGAS000378

• Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation

  Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expressions in unmeasured regions and tissues can enhance biologists��� histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region that is difficult to identify using raw data of spatial transcriptome alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.

  Study JGAS000290

• Whole genome analysis of liver cancer by long-read sequencing

  Aberrant integration of Hepatitis B virus DNA promotes major restructuring of human hepatocellular carcinoma genome architecture

  Study JGAS000361