# A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning
## 4 Discussion  
### Outcome Prediction for Patients with Ovarian Cancer  
For the outcome prediction based on the gene expression profile, it can be demonstrated that high performance is achieved with common machine learning methods. Apart from the K-means clustering method, all machine learning models had a decent prediction performance. As shown in Figure 6, the models were able to predict good and bad outcomes very well. This is probably due to the data preprocessing steps and filtering of the genes based on their differential expression. With 149 genes from originally 20,429, the data size has decreased significantly, and also, the genes with no or low information value have been removed. Furthermore, the separation of the patients between those that have a very short survival of 2 years or less after their treatment and those with long survival of 5 years or longer probably increased the differences in gene expression as well. In Kaplan-Meier plots (Figure 9), it was possible to identify genes that are significant for the OS of ovarian cancer patients using the SHAP algorithm. TMEFF2 is the gene with the highest median Shapley value. It has been shown that high expression of TMEFF2 in endometrial cancer is correlated with advanced cancer stage, poor differentiation, and lymph node metastasis [45]. Expression is also correlated with the recurrence of the tumor after successful therapy [46]. ADIPOR2 is in the top 20 of the selected genes by SHAP and is correlated, as shown in the Kaplan-Meier plot (Figure 8), with shorter OS survival when highly expressed. It has been shown in chicken ovarian cancer cell lines that ADIPOR2 protein expression is higher in cancerous ovaries than in normal ovaries [47]. ADIPOR2 expression is positively associated with proliferation and lethality in prostate cancer [48]. ACSM3 is number four of the median Shapley values. High expression of this gene, on the other hand, is correlated with inhibited cell proliferation, migration, and invasion of ovarian cancer cells. Overexpression of the gene even led to suppression in cell migration [49]. Moreover, in high-grade serous ovarian carcinoma (HGSOC), ACSM3 can suppress tumor growth in vitro and in vivo [50]. Even though the biological implications of ALIPPL2 remains unclear, high expression of the gene is correlated with good outcome in patients, as shown in Figure 8, and it has been reported as a true tumor-specific antigen [51]. For GMPB, the increased expression is correlated with favorable outcomes. There is little information about its role in cancer, but two studies identified the gene as a predictive marker in ovarian cancer as well [52; 53]. The last gene shown is C2 orf88 which is upregulated in patients with longer OS. Regarding the biological function C2 orf88, it is only predicted that it enables protein kinase A regulatory subunit binding activity [54]. But it is a prognostic factor for ovarian cancer patients. Furthermore, the pathway and annotation analysis in Appendix B Table 14 shows TNFR8 and TIGAR in multiple pathways. For the pathway analysis, genes with a SHAP value up to 0.01 have been considered. The highly expressed genes from the bad outcome group have been used in the analysis. The TNFR8 and TIGAR are associated with pathways for positive regulation of the apoptotic process and programmed cell death, which is interesting since both were shown in studies to be associated with bad outcomes in patients. TNFR8 upregulation has been linked to several downregulated genes in pathways for cell death, cell-mediated immune response, and inflammatory response [55]. It has also been exploited as a therapeutic target using monoclonal antibody monotherapy [56]. For TIGAR, it has been shown that its higher expression is associated with poor overall survival in ovarian cancer as well as the knockdown of the gene enhances sensitivity to the PARP inhibitors in cancer cells [57]. PARP Inhibitors are used to treat advanced BRCA-mutated ovarian cancer in adults [58].