Identification of Diagnostic Markers for Breast Cancer Based on Differential Gene Expression and Pathway Network

Abstract

Background: Breast cancer is the second largest cancer in the world, the incidence of breast cancer continues to rise worldwide, and women’s health is seriously threatened. Therefore, it is very important to explore the characteristic changes of breast cancer from the gene level, including the screening of differentially expressed genes and the identification of diagnostic markers.Methods: The gene expression profiles of breast cancer were obtained from the TCGA database. The edgeR R software package was used to screen the differentially expressed genes between breast cancer patients and normal samples. The function and pathway enrichment analysis of these genes revealed significant enrichment of functions and pathways. Next, download these pathways from KEGG website, extract the gene interaction relations, construct the KEGG pathway gene interaction network. The potential diagnostic markers of breast cancer were obtained by combining the differentially expressed genes with the key genes in the network. Finally, these markers were used to construct the diagnostic prediction model of breast cancer, and the predictive ability of the model and the diagnostic ability of the markers were verified by internal and external data.Results: 1060 differentially expressed genes were identified between breast cancer patients and normal controls. Enrichment analysis revealed 28 significantly enriched pathways (p < 0.05). They were downloaded from KEGG website, and the gene interaction relations were extracted to construct the gene interaction network of KEGG pathway, which contained 1277 nodes and 7345 edges. The key nodes with a degree greater than 30 were extracted from the network, containing 154 genes. These 154 key genes shared 23 genes with differentially expressed genes, which serve as potential diagnostic markers for breast cancer. The 23 genes were used as features to construct the SVM classification model, and the model had good predictive ability in both the training dataset and the validation dataset (AUC = 0.960 and 0.907, respectively).Conclusion: This study showed that the difference of gene expression level is important for the diagnosis of breast cancer, and identified 23 breast cancer diagnostic markers, which provides valuable information for clinical diagnosis and basic treatment experiments.

A Focus on the Synergy of Radiomics and RNA Sequencing in Breast Cancer

“…Additional differential expressed genes in TNBC have been identified by Zhang and colleagues through the integration of several bioinformatics research methods [ 40 ]. In this study, out of 1060 genes screened in both breast and healthy samples, 544 genes were up-regulated and 516 were down-regulated in the cancer tissue.…”

“…These genes are involved in many cancer-related biological processes and pathways, such as chemical carcinogenesis, drug metabolism, xenobiotic metabolic process, metabolic pathways, and oxygen binding. Some of these 23 genes have been confirmed to be related to BC in literature, including ADH1A, ADH1C, AKR1C4, ALDH3A1, CYP1A2, CYP2B6, CYP2C18, CYP2C19, CYP3A4, CYP3A7, GSTA1 , and the RXRG gene [ 40 , 41 ].…”

Unveiling the cytotoxic and anti-proliferative potential of green-synthesized silver nanoparticles mediated by Colletotrichum gloeosporioides

“…38 Thus, the dysregulation in gene expression related to apoptosis could be considered as signicant diagnostic markers in chemotherapy. 39 Many reports have demonstrated the role of fungal endophytes against breast cancer progression and thus have been emerged as an effective therapeutic approach over other treatment strategies. [40][41][42] Our previous ndings showed ethyl acetate extract of fungal endophyte C. gloeosporioides isolated from O. indicum exhibits unique biochemical prole and potential cytotoxic activity.…”

Quenched Electrochemical Biosensor Based on Adsorption Between GO and Aptamer for the Detection of ERα

“…Breast cancer is the most common malignancy in women and the leading cause of cancerrelated death in women. 2,3 However, studies have shown that breast cancer patients diagnosed at an early stage have a 5 year survival rate of more than 90%, which means that early diagnosis can effectively improve the survival rate of patients. 4 Estrogen receptor alpha (ERα), which is positive in more than 75% of breast cancers, is an important biomarker of breast cancer and plays an important role in the early diagnosis of breast cancer.…”