## 1 Introduction  
Cancer is one of the continually-developing non-communicable diseases (NCDs) that affects many people worldwide. The World Health Organization (WHO) reported that NCDs are a leading cause of death and responsible for 71% of global deaths [1]. Among these, cancer is the most common and deadliest in the world [2]. The International Cancer Statistics indicates that about 19.3 million new cancer cases occurred in 2020 alone, with close to 10 million deaths of 36 cancers in 185 countries [2]. Breast cancer (BC) is the most incident cancer among women, with an estimated 2.3 million new cases, followed by lung, colorectal, prostate, and stomach cancers. BC is a heterogeneous cancer with different characteristics, such as morphological appearance, profile, response to therapy, tumour-node-metastasis (TNM) staging, and histological grade [3, 4]. Currently, breast cancer early detection using morphological features poses a significant challenge for physicians [5]. For this reason, researchers have recently made tremendous efforts to develop models that allow early detection or prevention of breast cancer and increase the likelihood of a cure [6; 7]. Next-generation sequencing (NGS) technologies that detect the expression of thousands of genes in a single assay have proven to be an effective tool for early cancer diagnosis. These technologies also produce massive gene expression data, facilitating cancer diagnoses and prognoses. Although numerous efforts have been made, the predictive and prognostic factors utilized for breast cancer therapy are insufficient. Therefore, new markers need to be identified to help targeted treatment [3; 8]. Gene expression data allows determining the key biomarkers that might help in the early and efficient detection of breast cancer and can potentially increase the patient's survival rates [9]. Moreover, gene expression data helps identify the pathways and molecular information driving the disease from early to late stage. Such information can aid in understanding the disease mechanism more clearly. For example, some genes could be linked to faster disease progression, and some could be linked to slow progression. Combining gene expression data with existing proteomic data (i.e., PPI) may help us gain a deeper knowledge of prevalent human disorders because PPIs exhibit one of the clearest functional links between genes. In order to identify protein complexes and signaling pathways in cancer, it is crucial to construct PPI networks. Several studies on analysing microarray and RNASeq gene expression data use pathway tools to infer meaningful pathways out of these gene expression data. These studies include the work of [PERSON] et al. [10], who integrated gene expression data based on microarray and RNASeq technologies to find the DEGs and age-related macular degeneration (AMD) associated pathways. Their results showed two novel pathways associated with high enrichment in DEGs in AMD, namely the neuroactive-ligand receptor interaction pathway and the extracellular matrix (ECM) receptor interaction pathway. Moreover, [PERSON] et al. conducted a protein-protein interaction (PPI) network to identify the hub genes, which revealed _HDAC1_ and _CDK1_ as two central hub genes connected with the control of cell proliferation/differentiation processes. [PERSON] et al. [11] integrated microarray and RNASeq gene expression data for pancreatic cancer (PaCa) to find the DEGs. Additionally, they conducted PPI network and pathway analyses. Their results indicated that _ITGA1_, _ITGA2_, _ITGB1_, _ITGB3_, _MET_, _LAMB1_, _VEGFA_, _PTK2_, and _TGFB1_ are important hub genes. In addition, their results also identified two important pathways, which are the ECM-receptor interaction and focal adhesion pathways. [PERSON] et al. [12] integrated four microarray gene expression datasets of colorectal cancer (CRC) downloaded from the GEO database. They conducted DEGs, GO terms, and Kyoto Encyclopaedia of Genes and Genomes pathway (KEGG) enrichment analyses.