Identification of Immune-Related Breast Cancer Chemotherapy Resistance Genes via Bioinformatics Approaches

Abstract

Chemotherapy resistance in breast cancer is an important factor affecting the prognosis of breast cancer patients. We computationally analyzed the differences in gene expression before and after chemotherapy in breast cancer patients, drug-sensitive groups, and drug-resistant groups. Through functional enrichment analysis, immune microenvironment analysis, and other computational analysis methods, we identified PRC1, GGTLC1, and IRS1 as genes that may mediate breast cancer chemoresistance through the immune pathway. After validation of certain other clinical datasets and in vitro cellular assays, we found that the above three genes influenced drug resistance in breast cancer patients and were closely related to the tumor immune microenvironment. Our finding that chemoresistance in breast cancer could be influenced by the mediation of tumor immunity expanded our knowledge of how to address this problem and could guide future research involving chemoresistance.

maGENEgerZ: An Efficient AI-Based Framework Can Extract More Expressed Genes and Biological Insights Underlying Breast Cancer Drug Response Mechanism

“…Although lasso was the fastest method, esvm had more expressed genes in breast cancer cell lines as well as breast tissues when compared to lasso. Therefore, the improvements in esvm are attributed to (1) superiority of computational efficiency when compared to the baseline svm; and (2) high performance results measured using the AUC when compared to lasso. Another advantage of esvm attributed to the computational efficiency is the computational feasibility to explicitly change the data representation and applying esvm to identify important genes.…”

“…For Dataset1, the treated groups were classified during phase II trial as (1) pathological complete response referring to the absence of invasive cancer in axilla and breast; and (2) residual disease referring to the presence of invasive cancer in breast and axilla. In terms of Dataset2, treated groups after neoadjuvant therapy within Phase II multicenter trial were classified as (1) sensitive referring to patients completely responding to the treatment; and (2) resistant referring to not completely responding to the treatment. Regarding Dataset3, the treated groups were categorized during neoadjuvant I-SPY2 trial as (1) complete response when responding to the treatment; and (2) failed complete response when not completely responding to the treatment.…”

“…In terms of Dataset2, treated groups after neoadjuvant therapy within Phase II multicenter trial were classified as (1) sensitive referring to patients completely responding to the treatment; and (2) resistant referring to not completely responding to the treatment. Regarding Dataset3, the treated groups were categorized during neoadjuvant I-SPY2 trial as (1) complete response when responding to the treatment; and (2) failed complete response when not completely responding to the treatment.…”

“…Future work includes (1) utilizing our framework to unveil various biological knowledge behind drug response mechanisms, related to different cancer types such as pancreatic cancer, liver cancer, and multiple myeloma; (2) collaborating with clinical research physicians to apply our tool to analyze drug response mechanism in the neoadjuvant setting; and (3) integrating different profiling data related to cancer drug response and performing an assessment from biological perspective.…”

“…Compared to existing baseline methods including deep learning, results demonstrate superiority and efficiency of esvm achieving high performance results and having more expressed genes in well-established breast cancer cell lines including MD-MB231, MCF7, and HS578T. Moreover, esvm is able to identify (1) various drugs including clinically approved ones (e.g., tamoxifen and erlotinib); (2) seventy-four unique genes (including tumor suppression genes such as TP53 and BRCA1); and (3) thirty-six unique TFs (including SP1 and RELA). These results have been reported to be linked to breast cancer drug response mechanism, progression, and metastasizing.…”