# A Bioinformatics Analysis of Ovarian Cancer Data Using Machine Learning
## 4 Discussion
### Prediction of Platinum Response Status  
It was possible to create multiple well-performing machine-learning models to predict whether a patient is platinum-sensitive or resistant. The highest score reached the random forest model with an f1-score of 0.91. The principal components from the PCA have been used as input. In the studies reported by [PERSON] et al. [21], they used a similar approach and trained a deep learning model with a much bigger patient cohort with 2616 samples. The best performance of their model in predicting the platinum resistance status of patients had an f1-score of 83.1. The higher performance of the random forest model used here could be due to the different data preprocessing or the smaller sample size [21]. In Figure 13 the Kaplan-Meier plots of three genes that are upregulated in patients with platinum sensitivity and three genes that are upregulated in patients with resistance are depicted. In comparison to the outcome prediction, the plots in Figure 13 show the PFS of ovarian cancer patients instead. The reason for it is that patients that have a recurrence within 6 months after treatment are considered resistant, and patients that have no recurrences or one after 6 months are considered sensitive. Therefore, the PFS is a better fit to identify genes that are associated with platinum response. SLC4A1 (SW) has the highest median Shapley value, and its increased expression is correlated with low PFS. The gene is upregulated in patients that are resistant to platinum. As stated in another paper, the gene is an independent prognostic factor for poor OS in grade 3 or 4 serous ovarian cancer [59]. The protein AE1 is a chloride/bicarbonate transporter that is encoded by SLC4A1. AEs are important in regulating intracellular pH [60]. Alterations in pH are frequently altered in different types of cancer, like ovarian cancer [61; 62]. ALDH4A1 has the fourth-highest median Shapley value, and its high expression of it is highly correlated with platinum resistance and poor PFS. The gene has been associated by other studies with chemoresistance and might mediate carboplatin resistance [63; 64]. MITD1 is the last one of the platinum-resistant group, and the high expression of this gene is associated with low PFS [65]. The protein coded by MITD1 is recruited by ESCRT-III to middbodies and participates afterward in cytokinesis abscission [66]. Alterations in MITD1 expression may affect ESCRT-III function in cytokinesis abscission, aneuploidy, and response to platinum. High expression of CAMK1G is associated with longer PFS survival, and it is upregulated in patients that are platinum sensitive. The gene belongs to the protein kinase I family, and these enzymes control a wide range of functions in cancer and might be potential therapeutic targets [67]. GPR15 has the same prognostic attribution as CAMK1G. GPR15 expression is higher in normal tissue than in tumors in colon and lung adenocarcinomas and has the potential, with its natural ligand, to inhibit cancer cell growth [68]. High gene expression of PPFIA2 is highly correlated with longer PFS in ovarian cancer patients. Not too much research has been performed to identify the correlation between PPFIA2 and cancer types but the protein it encodes binds to calcium/calmodulin-dependent kinases [69]. It has already been suggested that Ca\({}^{2+}\) signaling is important in cancer cell function so there might be a correlation between Ca\({}^{2+}\) pathways and acquiring platinum resistance. The pathway analysis for the higher expressed genes in the resistant patients showed enhancement of cell-cell adhesion and cell-cell adhesion via plasma-membrane adhesion molecules, as can be seen in Appendix B Table A2. The \(p\)-value \(<0.01\) cutoff has resulted in 20 genes upregulated in the resistant patients. The two genes in the named pathways are NLGN1 and PCDHB15. Studies show that NLGN1 is highly expressed in clusters of aggressive migrating single tumor cells and promotes trans-endothelial migration [70].