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hepatocellular_carcinoma [2020/12/07 22:29] – [**Sources of Human HCC RNA-seq Data**] adminhepatocellular_carcinoma [2020/12/16 00:03] (current) – [Project 3:] admin
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 ====== Hepatocellular carcinoma ====== ====== Hepatocellular carcinoma ======
 +
 ===== Objectives ===== ===== Objectives =====
  
 \\ \\
 Hepatocellular carcinoma (HCC) is a the most common type of liver cancer and the third leading cause of cancer deaths in the world. [[https://www.merckmanuals.com/home/liver-and-gallbladder-disorders/fibrosis-and-cirrhosis-of-the-liver/cirrhosis-of-the-liver#v28485447|Cirrhosis]] of the liver is a major risk and contributing factor for HCC. The goal of project one is to identify the genes that are associated with regressing tumors (regardless of type of treatment) vs. those that are growing from the C3HeB/FeJ mouse model. Normal liver is used as a control. The results will be useful in identifying new therapeutic targets and potential drug combinations which could lead to more efficient treatments. Project two is related to knockdown of a specific protein that results in HCC in a different mouse model. The goal in the second project is to identify the genes and pathways that correlate with this protein. Hepatocellular carcinoma (HCC) is a the most common type of liver cancer and the third leading cause of cancer deaths in the world. [[https://www.merckmanuals.com/home/liver-and-gallbladder-disorders/fibrosis-and-cirrhosis-of-the-liver/cirrhosis-of-the-liver#v28485447|Cirrhosis]] of the liver is a major risk and contributing factor for HCC. The goal of project one is to identify the genes that are associated with regressing tumors (regardless of type of treatment) vs. those that are growing from the C3HeB/FeJ mouse model. Normal liver is used as a control. The results will be useful in identifying new therapeutic targets and potential drug combinations which could lead to more efficient treatments. Project two is related to knockdown of a specific protein that results in HCC in a different mouse model. The goal in the second project is to identify the genes and pathways that correlate with this protein.
- 
- 
-===== Related software ===== 
- 
-  - [[https://ccb.jhu.edu/software/tophat/index.shtml|TopHat]], useful for aligning RNAseq data to a genome. 
-  - [[http://www.nature.com.libproxy.uthscsa.edu/nbt/journal/v33/n3/full/nbt.3122.html|StringTie]], reconstructs transcriptom from RNAseq data (2015). 
-  - John Garbe has tutorials ([[https://www.msi.umn.edu/sites/default/files/RNA-Seq Module 1.pdf|1]], [[https://www.msi.umn.edu/sites/default/files/RNA_seq_Lecture2_2014_v2.pdf|2]], and [[https://www.yumpu.com/en/document/view/6745921/rna-seq-module-3|3]]) on design and analysis of RNAseq. 
-  - [[http://homer.salk.edu/homer/basicTutorial/mapping.html|Homer's]] quick tutorial on mapping NGS data using several tools including bowtie2, bwa, TopHAt, etc. with command line examples. 
-  - [[http://gqinnovationcenter.com/documents/bioinformatics/RNAseq_Cuba_OMICS_2013.pdf|Lefebvre's]] quick tutorial on RNA-Seq data analysis. 
-  - Schiffthaler's ~1 hour video on RNA Seq data [[https://www.youtube.com/watch?v=1rNEkWSxB5s|preprocessing]] including FastQC, sortmerna to exclude rRNA, trimmomatic to trim the adaptors and low quality bps, STAR to map reads to the genome, samtools to index the bam file, IGV to visualize the reads on the genome, and HTSeq to count the number of reads mapped to each gene (coverage). These are all steps we need to do before differential analysis using, say DESeq2. [[http://www.epigenesys.eu/images/stories/protocols/pdf/20150303161357_p67.pdf|This]] is a textual version explaining the same steps. 
- 
-[[:hepatocellular_carcinoma|Drafts]], [[:hepatocellular_carcinoma|Next steps]] 
  
  
 ===== Sources of Human HCC RNA-seq Data ===== ===== Sources of Human HCC RNA-seq Data =====
  
-  - [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25599|GSE25599]] +  - [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE25599|GSE25599]] 10 match-paired HBV-related Chinese HCC and non-cancerous adjacent tissues. Identified 1,378 significantly DE genes 
- - 10 match-paired HBV-related Chinese HCC and non-cancerous adjacent tissues. Identified 1,378 significantly DE genes. +  - [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33294|GSE33294]] Chinese HBV-related hepatocellular carcinoma, paired tumor and non-cancerous adjacent tissues from 3 patients. 
-  - [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33294|GSE33294]] +  - [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE59259|GSE59259]] Alcohol-related HCC 8 paired samples of liver and HCC 
- - Chinese HBV-related hepatocellular carcinoma, paired tumor and non-cancerous adjacent tissues from 3 patients. +  - [[https://trace.ddbj.nig.ac.jp/DRASearch/submission?acc=SRA074279|SRA074279]] 9 Chinese patients: paired HCC and adjacent non-cancerous tissues; [[http://www.sciencedirect.com/science/article/pii/S0888754314002341#bb0080|Publication]] 
-  - [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE59259|GSE59259]] +  - [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55759|GSE55759]] Paired HCC and non-cancerous adjacent tissue; median of 7019 DE genes per set, 93 DE genes shared by 6/8 patients
- - Alcohol-related HCC 8 paired samples of liver and HCC +
-  - [[https://trace.ddbj.nig.ac.jp/DRASearch/submission?acc=SRA074279|SRA074279]] +
- - 9 Chinese patients: paired HCC and adjacent non-cancerous tissues; [[http://www.sciencedirect.com/science/article/pii/S0888754314002341#bb0080|Publication]] +
-  - [[http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55759|GSE55759]] +
- - Paired HCC and non-cancerous adjacent tissue; median of 7019 DE genes per set, 93 DE genes shared by 6/8 patients+
  
 |Sex/Age|Viral Infection|Tumor Differentiation|No. of Tumors|Vascular Invasion|TNM* Stage| |Sex/Age|Viral Infection|Tumor Differentiation|No. of Tumors|Vascular Invasion|TNM* Stage|
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 ===== Collaborators ===== ===== Collaborators =====
-Drs. [[http://uthscsa.edu/csb/faculty/walter.asp|Christi Walter]] and [[http://uthscsa.edu/csa/faculty/Dong.asp|Lily Dong]] from the Department of Structural and Cellular Biology at UT Health Science Center in San Antonio.\\ + 
 +Drs. [[http://uthscsa.edu/csb/faculty/walter.asp|Christi Walter]] and [[http://uthscsa.edu/csa/faculty/Dong.asp|Lily Dong]] from the Department of Structural and Cellular Biology at UT Health Science Center in San Antonio. 
 =====   ===== =====   =====
 +
 ===== Analysis ===== ===== Analysis =====
 +
 ==== Project 1: ==== ==== Project 1: ====
  
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   - PCA: Jessica's lab notebook, 2016/5/31.   - PCA: Jessica's lab notebook, 2016/5/31.
   - Habil inferred the eigengene in TCGA data on [[http://oncinfo.org/habils_lab_notebook#section20190514|2019/05/14]].   - Habil inferred the eigengene in TCGA data on [[http://oncinfo.org/habils_lab_notebook#section20190514|2019/05/14]].
- 
  
 ==== Publication: ==== ==== Publication: ====
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 {{:wiki:public:jessica_compare.png?direct&400}} {{:wiki:public:jessica_compare.png?direct&400}}
- 
  
 ==== Project 2: ==== ==== Project 2: ====
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 ==== Project 3: ==== ==== Project 3: ====
  
-**Protemoe:**  Data-independent analysis Mass spectrometry (DIA-MS) done on 3 HCC cell lines and an immortalized hepatocyte line, each with 3 biological replicates. Our goal is to understand if the APE1 interactome is 1) different in HCC cell lines vs non tumor cells, 2) different between HCC cell lines with overexpressed APEX1 (SNU398 vs Huh7), 3) and how it compares to that described in [[https://www.nature.com/articles/s41598-019-56981-z|Ayyildiz 2020]]. Christi sent these data to Habil in an email entitled: "M2021-026 Scaffold DIA and Excel files".+**Protemoe and the APEX1 interactome:** Data-independent analysis Mass spectrometry (DIA-MS) done on 3 biological replicates of 2 HCC cell lines (SNU398 and Huh7) and an immortalized hepatocyte line (THLE2). Also, we have 1 biological replicate of a primary hepatocyte organoid derived from a patient (UTHSS-28T). 
 + 
 +**Our goal** is to understand if the APE1 interactome is 1) different in HCC cell lines vs non tumor cells, 2) different between two HCC cell lines with {{:apex1_analysis_of_dia-ms_dc_139-12-1-20.pptx|overexpressed}}  APEX1 (SNU398 vs Huh7), 3) and how it compares to that described in [[https://www.nature.com/articles/s41598-019-56981-z|Ayyildiz 2020]]. Christi sent these data to Habil on 2020-12-02 in an email entitled: "M2021-026 {{:hcc-in-vitro-proteome-scaffold-dia-2020-12-02.7z|Scaffold}}  DIA and Excel files".
  
  
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   - Dr. Sukeshi Arora's {{:sukeshi_arora_hcc_update_3.18.20.pptx|slides}}  presented in the HCC meeting on 2020-04-18, which summarizes statistics on the prognosis, the current clinical practice, and response to different treatments.   - Dr. Sukeshi Arora's {{:sukeshi_arora_hcc_update_3.18.20.pptx|slides}}  presented in the HCC meeting on 2020-04-18, which summarizes statistics on the prognosis, the current clinical practice, and response to different treatments.
  
- +===== Related software =====
-=====   Related software   =====+
  
   - [[https://ccb.jhu.edu/software/tophat/index.shtml|TopHat]], useful for aligning RNAseq data to a genome.   - [[https://ccb.jhu.edu/software/tophat/index.shtml|TopHat]], useful for aligning RNAseq data to a genome.
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   - [[http://homer.salk.edu/homer/basicTutorial/mapping.html|Homer's]] quick tutorial on mapping NGS data using several tools including bowtie2, bwa, TopHAt, etc. with command line examples.   - [[http://homer.salk.edu/homer/basicTutorial/mapping.html|Homer's]] quick tutorial on mapping NGS data using several tools including bowtie2, bwa, TopHAt, etc. with command line examples.
   - [[http://gqinnovationcenter.com/documents/bioinformatics/RNAseq_Cuba_OMICS_2013.pdf|Lefebvre's]] quick tutorial on RNA-Seq data analysis.   - [[http://gqinnovationcenter.com/documents/bioinformatics/RNAseq_Cuba_OMICS_2013.pdf|Lefebvre's]] quick tutorial on RNA-Seq data analysis.
-  - Schiffthaler's ~1 hour video on RNA Seq data [[https://www.youtube.com/watch?v=1rNEkWSxB5s|preprocessing]] including FastQC, sortmerna to exclude rRNA, trimmomatic to trim the adaptors and low quality bps, STAR to map reads to the genome, samtools to index the bam file, IGV to visualize the reads on the genome, and HTSeq to count the number of reads mapped to each gene (coverage). These are all steps we need to do before differential analysis using, say DESeq2. [[http://www.epigenesys.eu/images/stories/protocols/pdf/20150303161357_p67.pdf|This]] is a textual version explaining the same steps. \\+  - Schiffthaler's ~1 hour video on RNA Seq data [[https://www.youtube.com/watch?v=1rNEkWSxB5s|preprocessing]] including FastQC, sortmerna to exclude rRNA, trimmomatic to trim the adaptors and low quality bps, STAR to map reads to the genome, samtools to index the bam file, IGV to visualize the reads on the genome, and HTSeq to count the number of reads mapped to each gene (coverage). These are all steps we need to do before differential analysis using, say DESeq2. [[http://www.epigenesys.eu/images/stories/protocols/pdf/20150303161357_p67.pdf|This]] is a textual version explaining the same steps.
  
 [[:hepatocellular_carcinoma|Drafts]], [[:hepatocellular_carcinoma|Next steps]] [[:hepatocellular_carcinoma|Drafts]], [[:hepatocellular_carcinoma|Next steps]]
 +
 +\\