Cancer diagnosis marker extraction for soft tissue sarcomas based on gene expression profiling data by using projective adaptive resonance theory (PART) filtering method

BACKGROUND: Recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. To accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by DNA microarray analysis. RESULTS: Previously, we developed the projective adaptive resonance theory (PART) filtering method as a gene filtering method. This is one of the clustering methods that can select specific genes for each subtype. In this study, we applied the PART filtering method to analyze microarray data that were obtained from soft tissue sarcoma (STS) patients for the extraction of subtype-specific genes. The performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest shrunken centroids. In addition, various combinations of filtering and modeling methods were used to extract essential subtype-specific genes. The combination of the PART filtering method and boosting – the PART-BFCS method – showed the highest accuracy. Seven genes among the 15 genes that are frequently selected by this method – MIF, CYFIP2, HSPCB, TIMP3, LDHA, ABR, and RGS3 – are known prognostic marker genes for other tumors. These genes are candidate marker genes for the diagnosis of STS. Correlation analysis was performed to extract marker genes that were not selected by PART-BFCS. Sixteen genes among those extracted are also known prognostic marker genes for other tumors, and they could be candidate marker genes for the diagnosis of STS. CONCLUSION: The procedure that consisted of two steps, such as the PART-BFCS and the correlation analysis, was proposed. The results suggest that novel diagnostic and therapeutic targets for STS can be extracted by a procedure that includes the PART filtering method

Effects of verapamil and lidocaine on two components of the re-entry circuit of verapamil-sensitive idiopathic left ventricular tachycardia

AbstractOBJECTIVESWe characterized pharmacologically the slow conduction zone of verapamil-sensitive idiopathic left ventricular tachycardia (ILVT) with regard to the late diastolic potential (LDP).BACKGROUNDWe showed that the slow conduction zone of ILVT could be divided into two components by LDP; that is, the distal component with a tachycardia-dependent conduction delay property and the proximal one without it.METHODSElectrophysiologic studies were performed in eight consecutive patients. The LDP was recorded during left ventricular (LV) mapping during ILVT. Entrainment was performed from the right ventricular outflow tract while recording LDP. The effects of lidocaine (1 mg/kg body weight) and verapamil (0.5 or 1.0 mg) were examined during entrainment.RESULTSThe LDPs preceding the Purkinje potential (PP) were serially recorded from the upper third to the middle of the LV septum along the narrow longitudinal line. The ventricular tachycardia (VT) cycle length increased after lidocaine (p < 0.05), and further after verapamil (p < 0.05). The increments in the VT cycle length after administration of the drugs strongly correlated with those in LDP-PP (r > 0.9 for both drugs). The interval from the ventricular potential to LDP was unchanged after administration of the drugs. In one patient, verapamil terminated VT by local conduction block between LDP and PP. The LDP-PP measured during entrainment increased after lidocaine, and further after verapamil, whereas the interval from the stimulus to LDP remained unchanged.CONCLUSIONSThe component distal to LDP is mainly calcium channel-dependent and partly depressed sodium channel-dependent. The proximal component is considered to be sodium channel-dependent (normal)