**4. References**

316 Biomarker

These conclusions are based on computer simulations. In the computer simulations, the *steady-state* variation and the rate of tumour increase (λ = slope) are based on clinical data from the literature and the simulations are based on random counts generated from a Gaussian distribution from the computer multiplied by the parameters borrowed from publications. Furthermore, the cost-price for the clinical investigations compared with computer simulation is enormous and the computer simulation is a convenient, easy and quick method to compare algorithm performances based on the same simulated data-points. Thus, computer simulation should be a tool to select the "right" algorithm before a clinical investigation regarding for example low number of false positive (FP) signals. Computer simulations are thus not a substitute for clinical investigations, but a supplementary tool in helping to interpret biomarker variations and challenge the algorithms with extreme

Thus, the advantage of computer simulations is that it is relatively easy to vary the parameters in the simulation model and examine the impact on the performances of the algorithms. In this investigation we have investigated these performances under standard conditions as well as under extremes with conditions of varied CVB in *steady-state* and varying slopes of tumour growth. In addition, we have tested the robustness of the algorithms by using extreme values for CVB and we have tested for variation in the

Parameters which interestingly could also be varied are sampling intervals or the starting

In this study we have chosen a sampling interval of every two months, which is a relevant time schedule for monitoring of patients with breast cancer during follow-up after treatment (Söletormos et al., 2000b). Obviously, a sampling interval of one month could give earlier detection of tumour growth progression. However, in many of the algorithms the number of FP signals will simultaneously increase, and, conversely, longer sampling intervals will

We have chosen arbitrarily the starting point of exponential tumour growth to be 1% of cutoff. The impact on the performances of the algorithms when varying this starting point for the contribution from the growing tumour may be comparable for all the algorithms. If, for example, a starting point of 50% of the cut-off concentration was selected - the time for crossing cut-off would be shortened, so the progression detection time would possibly be

In this investigation, we have investigated and challenged the seven algorithms, but the effect of sampling interval and of the start value of the contribution of marker from the tumour has not been studied. Furthermore, this computer model for simulations can be used for evaluation of other algorithms which can be tested and compared to the existing

earlier, whereas the percentages of FP would be unchanged for all algorithms.

algorithms, before they are published or introduced in the clinic.

b. Some algorithms are more robust against increased biological variation than others. c. Variation in tumour growth has only limit impact on the performances of the

algorithms.

parameters in the model.

**3.2 Future research** 

exponential slopes of tumour growth.

points of the exponential tumour growth.

reduce FP signals, but true signals will be delayed.


**Using miRNA as** 

*2Department of Pharmacology,* 

*4Department of Biochemistry,* 

*USA* 

**Biomarkers to Evaluate the** 

*3Department of Molecular Biophysics and Physiology,* 

*Rush University Medical Center, Chicago IL,* 

**Alcohol-Induced Oxidative Stress** 

Yueming Tang1,\*, Christopher B. Forsyth1,4 and Ali Keshavarzian1,2,3 *1Division of Digestive Disease and Nutrition, Department of Internal Medicine,* 

Oxidative stress is responsible for a variety of degenerative processes in many human diseases, as either cause or effect. At present, some biomarkers of oxidative stress have been used to determine an individual's oxidative status in relation to disease conditions. However, their accuracy, sensitivity, or specificity needs to be improved. The development

Micro RNAs (miRNAs) are highly conserved regulatory molecules expressed in eukaryotic cells. They are short non-coding RNAs that regulate gene expression by binding to target mRNAs, which leads to reduced protein synthesis and sometimes decreased steady-state mRNA levels. Although hundreds of miRNAs have been identified, much less is known about their biological function. There are evidences that miRNAs affect pathways fundamental to metabolic control in higher organisms such as adipocyte and skeletal muscle differentiation. Also, some miRNAs are implicated in lipid, amino acid, and glucose homeostasis. Thus, miRNA abnormalities may contribute to common metabolic and systemic diseases where oxidative stress plays a key role in their pathogenesis. Indeed, there are evidences indicated that miRNAs are able to modulate the cellular response to oxidative stress both *in vitro* and *in vivo*. Therefore, miRNA may be novel biomarkers for oxidative

We hypothesize that miRNAs may be biomarkers for oxidative stress because: (1) since miRNA are post-transcriptional gene regulators, they may be able to function as 'quick responders' to oxidative stress. For example, upon exposure to stress, miRNA may rapidly localize to P-bodies or stress granules to regulate key genes involved in the oxidative stress response. After the stress is mitigated, miRNA inhibition may be promptly abated, allowing

**1. Introduction** 

stress.

 \*

Correponding Author

of novel biomarkers for oxidative stress is urgent.

Tondini, C. & Hayes DF. (1989) Circulating tumor markers in breast cancer. *Hematol Oncol Clin N Am*, 3, pp. 653-74 **15** 
