Health Medical Homework Help

Abnormal immune responses can trigger a range of autoimmune diseases, in
which an individual’s immune system is attacking normal tissues in the
body. Well-known examples of autoimmune diseases are type 1 diabetes
mellitus, lupus, and multiple sclerosis.

Ideally, one would like to harness the immune system to attack abnormal
substances or tissues like cancer, while sparing the normal (unaffected)
tissue. Many tumor cells produce antigens (proteins) that theoretically
ought to trigger an immune response: that is, one’s immune system ought
to recognize cancer cells as somehow foreign or abnormal, and
thereafter eliminate these cells from the body. The field of cancer
immunotherapy is actively pursuing this study.

Tumor antigens may also be useful for diagnostic tests; high levels of
tumor antigens could be taken as markers or indicators of cancer. In
this assignment, you will be examining levels of tumor-associated
antigens (TAAs) as determined from immunoassays (i.e., biochemical tests
that measure the concentrations of the tumor-associated antigens in
serum samples).

• Download the Excel file MHA610_assignment_3_data.xls, and open it.
• The spreadsheet contains data on 250 individuals: 90 normal
  individuals from San Diego (the controls), and 160 individuals from
  Korea and China, all of whom were diagnosed with hepatocellular
  carcinoma (HCC).
  • Serum samples were taken from the controls and from the
    cases at time of diagnosis of HCC. Levels of a panel of 12
    tumor-associated antigens (TAAs) were assessed via immunoassays in all
    individuals;
    • The levels are given in the columns with headings Ab14,
      HCC1, IMP1, KOC, MDM2, NPM1, P16, P53, P90, RaIA, and Survivin. (These
      are the designations of the 12 TAAs, all of which were thought to be
      potentially predictive of cancer.)
• The underlying question is whether we can effectively
  discriminate between the cases and controls on the basis of the levels
  of these TAAs. This is sometimes termed a classification problem in the
  statistics and biostatistics literature: we wish to classify individuals
  as normal or cancer patients on the basis of their TAA levels.
• We will examine these data in Statdisk. Use the MHA610_Week 3_Assignment_Data.CSV file to upload this information into Statdisk.
  • If you choose the latter option, Start Statdisk, then choose
    File>Open and select the .csv file you created (unless you changed
    the name, it ought to be MHA610_assignment_3_data.csv)
  • Check the box that specifies the data contains column titles
    or headers, select Comma separated for how the data are delimited,
    click finish, and the dataset will have been successfully imported into
    Statdisk.
  • NOTE: you may want to read through the remainder of the
    assignment first, before proceeding with this step. This may save you
    some work afterwards!
• Note that Statdisk operates on columns of data, and that both
  cases and controls are contained in each column of TAA levels. It will
  be necessary to separate the cases and controls for further analyses.
  This can be accomplished either by copying within Statdisk or by
  reverting to the original Excel workbook, copying in Excel, exporting as
  a .csv file, and then importing into Statdisk. (Don’t say you weren’t
  warned!)
• Explain if you would characterize any or all of the TAA levels
  as approximately normally distributed for the controls and for the
  cases.
  • Provide plots and statistics in support of your conclusions.
• Explain if any of the TAAs are useful for discriminating between the cases and controls.
  • Provide plots and statistics in support of your conclusions.
• All writing assignments should be at least 250-500 words in APA format supported by scholarly sources.

BONUS. In the above, we pooled all cases together. 
Summarize whether you think this is legitimate or whether the levels of
any of the TAAs appear to differ significantly between the cases from
China and the cases from Korea. Provide evidence in support of your
conclusion.