echemi logo
Product
  • Product
  • Supplier
  • Inquiry
    Home > Active Ingredient News > Antitumor Therapy > Small databases come in handy

    Small databases come in handy

    • Last Update: 2021-04-14
    • Source: Internet
    • Author: User
    Search more information of high quality chemicals, good prices and reliable suppliers, visit www.echemi.com

    Today is April Fool's Day, and the editor reminds everyone not to believe other people's words.

    Unless the instructor tells you, don’t miss the sharing of Shengxinren today! Last year, I introduced a database ncRI, which collected a large amount of non-coding RNA in inflammation.

    The introduction and use of the database will not be repeated here.

    Why did you mention this today? Because so far, it seems that no one has been mining and posting based on the data in this database.

    By reading today's article, I found that this database can come in handy.

    Specifically how to use it effectively, let us first take a look at today's article on the study of genes related to inflammation.

    The article was published in Frontiers in Oncology (IF: 4.
    848) in March.

    An Inflammatory Response-Related Gene Signature Can Impact the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma An Inflammatory Response-Related Gene Signature Can Impact the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma Inflammatory Response-Related Gene Signature Can Impact the Immune Status and Predict the Prognosis of Hepatocellular Carcinoma Abstract: The link between inflammation and cancer is well known.

    The occurrence of inflammation and the role of cancer development have always been people's research.

    Inflammation can promote cancer or inhibit cancer.

    In this study, the author downloaded the mRNA expression profile of HCC patients and the corresponding clinical data from a public database.

    Then, the author constructed a prognostic model of differentially expressed genes (DEG) related to inflammation in the TCGA cohort, and verified the stability and reliability of the model through the ICGC cohort.

    The author explores its potential mechanism through further functional enrichment analysis.

    In addition, the authors analyzed the relationship between prognostic gene expression and the type of immune infiltration, and investigated the relationship between prognostic gene expression and tumor stemness and cancer chemoresistance.

    Finally, experiments verified the mRNA and protein expression of prognostic genes between liver cancer tissue and adjacent non-tumor tissues.

    Flow chart: 2.
    Material method: TCGA, ICGC, LASSO, PCA, t-SNE, GSEA, CellMiner, qRT-PCR, IHC 3.
    Results: 1.
    Identify the prognosis of the TCGA cohort and inflammation-related DEG Figure 1.
    Identify the potential inflammation in the TCGA cohort Response-related genes 2.
    Construction of prognostic models in the TCGA cohort and verification of the ICGC cohort Figure 2.
    Prognostic analysis of 8 gene models in the TCGA and ICGC cohort 3.
    Independent prognostic value of 8-gene characteristics Figure 3.
    Cox analysis and ROC 4.
    Prognostic model score and clinical Features Figure 4.
    Risk scores between different clinical characteristics groups 5.
    Immune status and tumor microenvironment analysis Figure 5.
    Immune status and tumor microenvironment between different risk score groups Figure 6.
    Immune checkpoint gene expression between different risk score groups 6.
    Biology Figure 7.
    Biological function and pathway gene set enrichment analysis.
    7.
    Prognostic gene expression and the sensitivity of cancer cells to chemotherapy.
    Figure 8.
    The correlation between prognostic gene expression and drug sensitivity.
    8.
    By qRT-PCR and IHC verified the prognostic gene expression between HCC tissues and adjacent non-tumor tissues.
    Figure 9.
    Experimental verification of the prognostic gene expression between HCC and adjacent non-tumor tissues.
    Summary: Based on the inflammation-related genes, a prognostic model was constructed and the prognostic genes were evaluated.
    The experimental verification.

    It stands to reason that it is a pity that a combination of dry and wet articles can easily reach 5 no matter what.

    Back to the topic, how to effectively use the ncRI database.

    In fact, it is a more appropriate idea to analyze the prognosis only by inflammatory response genes.

    However, if the analysis is based on the experimentally verified non-coding RNA collected in the database, the novelty can be improved.

    If the functional experiment is supplemented conditionally, the level of the article will never be low.

    Tumor inflammatory factor prognosis analysis ideas are interested and scan the code
    This article is an English version of an article which is originally in the Chinese language on echemi.com and is provided for information purposes only. This website makes no representation or warranty of any kind, either expressed or implied, as to the accuracy, completeness ownership or reliability of the article or any translations thereof. If you have any concerns or complaints relating to the article, please send an email, providing a detailed description of the concern or complaint, to service@echemi.com. A staff member will contact you within 5 working days. Once verified, infringing content will be removed immediately.

    Contact Us

    The source of this page with content of products and services is from Internet, which doesn't represent ECHEMI's opinion. If you have any queries, please write to service@echemi.com. It will be replied within 5 days.

    Moreover, if you find any instances of plagiarism from the page, please send email to service@echemi.com with relevant evidence.