There are very few prognostic studies that combine both clinicopathologic and genomic data. Most of the studies use only clinicopathologic factors without taking into consideration the tumour biology and molecular information, while some studies use genomic markers or microarray information only without the clinicopathologic parameters. Thus, these studies may not be able to prognoses a patient effectively. Previous studies have shown that prognosis results are more accurate when using both clinicopathologic and genomic data. The objectives of this research were to apply hybrid artificial intelligent techniques in the prognosis of oral cancer based on the correlation of clinicopathologic and genomic markers and to prove that the prognosis is better with both markers. The proposed hybrid model consisting of two stages, where stage one with ReliefF-GA feature selection method to find an optimal feature of subset and stage two with ANFIS classification to classify either the patients alive or dead after certain years of diagnosis. The proposed prognostic model was experimented on two groups of oral cancer dataset collected locally here in Malaysia, Group 1 with clinicopathologic markers only and Group 2 with both clinicopathologic and genomic markers. The results proved that the proposed model with optimum features selected is more accurate with the use of both clinicopathologic and genomic markers and outperformed the other methods of artificial neural network, support vector machine and logistic regression. This prognostic model is feasible to aid the clinicians in the decision support stage and to identify the high risk markers to better predict the survival rate for each oral cancer patient.
Introduction: One of the risk factors for cancer is the habit of smoking. Some carcinogenic substances in ciga-rettes are nicotine and nitrosamine. In cigarette smoke there are free radical molecules or Reactive Oxygen Species (ROS) that can cause DNA mutations that can disrupt the balance of cell metabolism. One of them is the apoptosis, apoptosis is a programmed cell death mechanism. In cancer conditions there are apoptotic disorders and excessive proliferation of cells. The process of apoptosis is influenced by the death receptor, Tumor Necrosis Factor apoptosis inducing ligand R1 (TRAIL R1). This study aims to determine the effect of smoke exposure to expression of TRAIL R1 on the mucosal epithelium of the tongue of the Wistar rat (Rattus Novergicus). Methods: The subjects of this study were 24 male Rattus Novergicus with the age range of 12-14 weeks and weighing ± 170 grams. Divided into 4 groups with 2 control groups 4 weeks (K4), 8 weeks (K8) and 2 treatment groups each given 2 cigarettes / day ex-posure to cigarette smoke for each rat for 4 weeks (P4) and 8 weeks (P8). Results: The results showed that exposure to cigarette smoke can cause interference with TRAIL R1 expression. There was a significant difference in TRAIL R1 expression between the control and treatment groups and there was a significant difference in TRAIL R1 expression between the duration of cigarette smoke exposure (P4 and P8). Conclusion: Exposure to cigarette smoke can interfere with the process of apoptosis.