Biology Reference
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more than one pathological condition. Decipher-
ing the critical molecules is not an easy task. A
single
differing sample preparation and experimental
procedures. Another aspect is how to examine
the data. The following examples can illustrate
this point. Sreekumar et al., 43 in a study pub-
lished in the journal Nature,
protein may be associated
with multiple cancers and diseases. For example,
a urine proteomic study revealed 26 proteins that
were overexpressed in bladder cancer. 40 A search
using Ingenuity Pathway Systems indicated that
each of these proteins is involved in multiple
cancers and diseases, suggesting that any of these
proteins would result in a biomarker with
low sensitivity and speci
biomarker
ed the
metabolite sarcosine as a potential biomarker
for prostate cancer. In a following study, Jentz-
mik et al. 44 stated that
identi
Our study diminish[es]
the hope that the ratio of sarcosine to creatinine
will become a successful indicator for prostate
cancer management.
city. As an example,
annexin A1 is involved in cardiovascular disease,
endocrine system disorders, gastrointestinal
disease, hematological disease, immunological
disease, metabolic disease, organismal injury
and abnormalities, reproductive system disease,
and respiratory disease, in addition to cancer.
Annexin A1 is reported to be downregulated in
ductal 41 and squamous cell carcinoma 42 and
upregulated in bladder cancer. 40 In human laryn-
geal tumors, annexin A1 was upregulated in the
nuclei and cytoplasmic granule matrix from
larynx mast cells and downregulated in larynx
epithelial cells. 43
Another example is carcinoembryonic antigen
(CEA), which is used mainly to monitor the
treatment of cancer patients, especially those
with colon cancer. A PubMed search using
That outcome might be
thecaseifthecomparisonofboth
findings
was accurate. Sreekumar et al. 43 compared the
ratio of sarcosine to alanine, and Jentzmik
et al. 44 comparedthera ioofsarcosineto
creatinine.
ST ATISTICAL DATA ANALY SIS
Multivariate statistical analysis is generally
employed to analyze NMR or MS data to discrim-
inate between different data sets. 45 Metabolomic,
as well as proteomic, analysis of biological
systems using NMR, GC/MS, CE/MS, and
HPLC/MS, as with genomics, transcriptomics,
and proteomics, results in a wealth of information
that can be overwhelming. The sizes of these data
sets make them virtually impossible to analyze
manually. For any meaningful interpretation of
the data, the appropriate statistical tools must
be employed to manipulate the large raw data
sets to provide a useful, understandable, and
workable format. Different multidimensional
and multivariate statistical analyses and pattern-
recognition programs have been developed to
distill the large amounts of data in an effort to
interpret the complex metabolic pathway infor-
mation from the measurements and to search
for the discriminating features between two
data sets. 46 The most popular multivariate statis-
tical methods are principal component analysis
(PCA), 47 partial least square discriminate analysis
(PLS-DA
indicates that CEA is used
as a marker for cancers of the lung, breast,
rectum, liver, pancreas, stomach, and ovary.
Also, not all cancers produce CEA. Increased
CEA levels can indicate some non-cancer-related
conditions such as in
CEA and cancer
ammation, cirrhosis, rectal
polyps, emphysema, ulcerative colitis, peptic
ulcer, and benign breast disease. CEA is not rec-
ommended for screening a general population.
These results indicate that selecting a protein
that functions as a biomarker for a unique path-
ological condition is not an easy task.
Published Results Comparison
As mentioned earlier, comparison of results
from different sources is challenging due to
, 48
)
and support vector machines
. 49 Mehadevan et al. 49 compared PLS-DA
(SVM
)
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