How to purify lung cancer samples for microarray?

bingyin83 bingyin83 at pubem.cicams.ac.cn
Thu Nov 16 07:36:46 EST 2000


Hi, all scientist,

I am a PhD student. I am going to perform study with cDNA microarray. 

I am planning to investigate expression profiles of lung cancer using
surgical dissected samples. A major concern when using this type of
tumor samples for quantification has been the inherent cellular
heterogeneity of lung cancer.  In addition to neoplastic cells, a
significant volume of a tumor mass may be composed of normal epithelia,
endothelia, stromal and inflammatory cells.  And another problem about
this study is the insufficient normal epithelia of bronchi.  In order to
collect the sufficient qualified samples, I have four strategies to
chose.

1. Purify the tumor cells and pick up the normal bronchi epithelia using
laser capture microdissection.  After the reverse transcription,
linearly amplify the RNA with the T7 RNA polymerase.  It can provide
enough RNA for the following analysis, but it is time-consuming and it
may bring artifact during the in vitro transcription.
2. Purify cells by primary culturing both the cancer tissues and
corresponding normal epithelia at the same time.  I have primarily
cultured 5 pairs of samples and it proved this method can give enough
cells and the purity is satisfactory, but it will sure bring artifacts
during culture.
3. Purify cancer cells by LCM and using the normal lung tissue without
purify the epithelia like most of the former studies on the differential
expression between the tumor and normal tissue, such as mRNA-DD, SAGE,
and so on.  And eliminate the artifacts caused by the
tissue-specific-expression by the in situ hybridization.

I have collected about 80 pairs of lung cancer tissues and corresponding
normal lung tissues and 5 pairs of primarily cultured cells.  But the
strategies mentioned above all have its advantages and disadvantages.
Could you be so kind to tell me which I should chose or is there any
other better choice?

Thank you very much for your kind help.


J Zhang.


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