## 2 Results and Discussion
### Examples
#### 2.2.3 Human urine samples  
Novel approaches to early diagnosis and therapy of kidney diseases are urgently needed from economical and from personal needs [22; 43]. For example, renal cell carcinoma (RCC) is a heterogeneous group of diseases with at least four well-defined histological types. The most common type of kidney malignancy is clear cell RCC (ccRCC), which is frequently associated with mutations of the von Hippel-Lindau gene. Another example is polycystic kidney disease (PKD), a genetic disorder characterized by the growth of numerous cysts in the kidneys. In the United States, more than half a million people have PKD, and cystic disease is the fourth leading cause of kidney failure. Due to the fact that the urine contains metabolic signatures of many biochemical pathways, urine is ideally suited for metabolomic analysis, especially involving diseases of the kidney and urinary system. We have found that HILIC-LC-ESI-MS technique was more informative than the other two technology platforms in analyzing human urine samples in biomarkers discovery protocol for ccRCC and PKD cases. Therefore we currently present HILIC LC-MS generated results. The reason that HILIC LC-MS method was superior may be based on fact that the most compounds in human urine are water-soluble and are more suitable for HILIC- than for RP-LC separations (Figure 5). For the ccRCC HILIC dataset, the PCA plot by MarkerView showed that groups 1 (red) and 2 (green) were well separated from groups 3 (cyan) and 4 (blue), suggesting that ccRCC patients had different metabolism from healthy controls (Figure 6). Figure 5: Comparison of LC-MS chromatograms of human urine sample acquired in RP and HILIC chromatography modes on LTQ (linear ion trap mass spectrometer). Unresolved peaks in RP at Rt 1.46 min (lower panel) are resolved at different Rt in HILIC (upper panel). The same components illustrated as peaks (arrows depicted) were identified with measured parent ion masses to charge ratios and MS/MS fragmentation pattern similarity in both modes of acquisition: positive and negative. Both GA and manual feature selection approaches were applied to peak tables (potent predictors) generated with MarkerView 1.1 for LC/MS data. It was established that different feature peaks were found from the two independent feature selection approaches, whereas both were able to differentiate nicely RCC patients from healthy controls (Figures 7, 8). Figure 6: PCA-DA plot of the ccRCC HILIC-LC–ESI–MS before feature selection. Groups were color-coded as: 1-red: female ccRCC, 2-green: male ccRCC, 3-blue: female control, 4-purple: male control. Total of 55 samples, 2,111 predictors. Predictors (metabolites) characterized on PCA loading plots as mass to charge ratio / retention time / peak registry number. Sample number having tx extension originated from Texas, USA. Others are from Sacramento area, CA, USA. It is true for all presented PCA plots. Figure 7: PCA-DA plot of the ccRCC HILIC-LC–ESI–MS after manual feature selection. Groups were color-coded as: 1-red: female ccRCC, 2-green: male ccRCC, 3- blue: female control, 4-purple: male control. 55 samples, 68 predictors. We have found that majority of GA selected predictors are minor and low abundant peaks, different from manually selected prominent predictors (Figure 9). Figure 8: PCA-DA plot of the ccRCC HILIC-LC–ESI–MS after GA feature selection. Groups were color-coded as: 1-red: female ccRCC, 2-green: male ccRCC, 3- blue: female control, 4-purple: male control. 55 samples, 22 predictors. Figure 9: GA and manual feature selection methods generated two unique small subsets of highly potential predictors found during processing HILIC LC-MS data (ccRCC case). This observation is somewhat in accordance with our previous results and those described for NMR based metabolomics study where GA was applied for feature selection [34, 44]. Similarly to describe above, PKD patients clearly differentiated from healthy controls on PCA-DA plots before and after GA or manual feature selection (Figures 10-12).