Integrated Center for Oncology

Breast Cancer Gene-Expression Miner v5.2
(bc-GenExMiner v5.2)

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Gene correlation targeted analysis Tutorial

Enter input genes and choose one of the following options:

  • GENES EXPRESSION DATA Open
    • First, choose your gene expression data. Information about cohorts : microarrays or RNA-seq

    Then fill the textbox with actualised* gene symbol.
    For more than two genes, identifiers must be validated one by one (either by choosing from the drop-down list or by pressing the "enter" key).

    *: see actualised web databases (e.g.: Ensembl, GeneCards, HGNC, NCBI Gene...)

  • DNA microarrays
    		• (n = 10 872)
    Clear allClear
  • RNA-seq
    		• (n = 4 421)

    		Open
    The uploaded file must comply with all following criteria:
    • file with tabulation, space, comma or newline separated values,
    • file type from among the following extensions: .csv, .tsv or .txt,
    • file containing only gene symbols (not full names or descriptions),
    • file with a maximum items of 20 gene symbols,
    • file with a maximum size of 10 Kbytes.

  • POPULATION Open
    • Choose one of the radio button to select which population you want to explore.

    This option allows to explore triple-negative breast cancer (TNBC) (IHC) subtypes (LAR, MLIA, BLIA and BLIS):
    • LAR: luminal androgen receptor,
    • MLIA: mesenchymal-like immune-altered,
    • BLIA: basal-like immune-activated,
    • BLIS: basal-like immune-suppressed.
    More details about TNBC subtypes classification : Jézéquel et al. Breast Cancer. 2024 May 22.

  • OUTPUT CORRELATION FIGURE Open
    • Choose one of the radio button to select which output figure you want to get as a result plot.
    Have a sneak peak of each figure by clicking on following options available:

  • Type of plot:


    • Legend Open
     IHC:immunohistochemistry
     PAM50:Parker's instrinsic molecular subtypes
     RIMSPC:robust intrinsic molecular subtype predictors classification based on patients classified in the same subtype with the six molecular subtype predictors (3 SSPs + 3 SCMs)
     SCM:Subtype clustering model (SCMOD1, SCMOD2 or SCMGENE)
     SSP:single sample predictor (Sorlie's, Hu's or PAM50)
     TNBC:triple-negative breast cancer; tumours negative for oestrogen and progesterone receptors and epidermal growth factor receptor 2 (HER2), by means of IHC


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