## 6 Analysis of the Protein-Protein Association Network  
Identifying protein-protein interactions (PPIs) is highly significant when verifying gene selection in the context of various biological and biomedical research areas because it places selected genes in a functional and biological context. It helps to confirm the relevance of chosen genes in various biological processes. To construct the PPI, we obtained the intersection of the lists of the genes that are obtained by each of the three feature selection methods (Chi square (7137 genes), RF (1656 genes), and LASSO (597 genes). The intersection yielded 301 genes that are commonly significantly associated with the 33 cancer types. The STRING database and the Cytoscape tool were used to build the protein-protein interaction network (PPI). Figure 4 illustrates the developed PPI network. The eccentricity of a node in a biological network is scored by how easily all other proteins in the network may functionally reach that node. To obtain the most significant genes and get a clear understanding of the top genes that can identify the cancer type we constructed the network for genes that have high degree (number of edges) using the two methods maximum neighborhood component (MCN) and maximal clique centrality (MCC). We used CytoHubba plugin by setting its parameters to the default values to select the to 10 genes from the PPI network. The 10 genes that are obtained by the two aforementioned methods are depicted in Figure 5. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classification Method** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Our proposed approach (33 cancer types + Normal) & 96.16\% & 94.11\% & 94.26\% & 94.14\% \\ [PERSON] et al. [12] & 95.00\% & 92.50\% & 93.17\% & 93.32\% \\ [PERSON] et al. [13] & 94.61\% & 92.29\% & 91.38\% & 91.53\% \\ [PERSON] et al. [36] & 95.65\% & 95.55\% & 95.69\% & 94.45\% \\ [PERSON] et al. [37] & 96.90\% & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation of proposed approach with existing techniques for classifying cancer types. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Features Selection** & **Classification** & **Cancer Types** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\
**Method** & **Method** & & & & & \\ \hline Proposed Features & & & & & & \\ Selection Methods & & & & & & \\ Grouping Genetic & & & & & & \\ Algorithm [38] & & 5 Cancer Types & 98.81\% & - & - & - \\ Genetic Algorithm [9] & KNN & 31 Cancer Types & 90\% & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: A comparison with recent methods of features selection. The six common hub genes can be obtained by taking the intersection of the MCC and MNC methods. These six genes are PAX8, KRT5, CDKN2A, EPCCAM, WT1, NKX2-1. To show the effectiveness of our features selection method we further studied the impact of these top 6 selected genes in cancer using previous studies. [PERSON] [PERSON] et al. [39] show that the organogenesis of the thyroid gland, kidney, neurological system, and Mullerian system depends on PAX8. Also, they demonstrate in earlier research that PAX8 is essential for thyroid differentiated epithelial cells' cell cycle progression and survival [40,41]. The tumors of gliomas, well-differentiated pancreatic neuroendocrine malignancies, renal, thyroid, ovarian, Wilms, and other cancers have all exhibited PAX8 positivity [42]. In addition, PAX8 is a helpful marker for primary or metastatic neoplasms diagnosis because it is not expressed in primary tumors of the breast, lung, or mesothelioma [43]. PAX8 was  
Figure 4: PPI – the dark blue indicates high eccentricity, while light blue indicates low eccentricity. Figure 5: The Top 10 genes that are obtained using maximum neighborhood component (MCN) and maximal clique centrality (MCC). (**a**) Top 10 hub genes using MCC method. (**b**) Top 10 hub genes using MNC method.