Identification of Key Genes and Pathways Associated With Irradiation in Breast Cancer Tissue and Breast Cancer Cell Lines

Robust Identification of Differential Gene Expression Patterns from Multiple Transcriptomics Datasets for Early Diagnosis, Prognosis, and Therapies for Breast Cancer

Background and Objectives: Breast cancer (BC) is one of the major causes of cancer-related death in women globally. Proper identification of BC-causing hub genes (HubGs) for prognosis, diagnosis, and therapies at an earlier stage may reduce such death rates. However, most of the previous studies detected HubGs through non-robust statistical approaches that are sensitive to outlying observations. Therefore, the main objectives of this study were to explore BC-causing potential HubGs from robustness viewpoints, highlighting their early prognostic, diagnostic, and therapeutic performance. Materials and Methods: Integrated robust statistics and bioinformatics methods and databases were used to obtain the required results. Results: We robustly identified 46 common differentially expressed genes (cDEGs) between BC and control samples from three microarrays (GSE26910, GSE42568, and GSE65194) and one scRNA-seq (GSE235168) dataset. Then, we identified eight cDEGs (COL11A1, COL10A1, CD36, ACACB, CD24, PLK1, UBE2C, and PDK4) as the BC-causing HubGs by the protein-protein interaction (PPI) network analysis of cDEGs. The performance of BC and survival probability prediction models with the expressions of HubGs from two independent datasets (GSE45827 and GSE54002) and the TCGA (The Cancer Genome Atlas) database showed that our proposed HubGs might be considered as diagnostic and prognostic biomarkers, where two genes, COL11A1 and CD24, exhibit better performance. The expression analysis of HubGs by Box plots with the TCGA database in different stages of BC progression indicated their early diagnosis and prognosis ability. The HubGs set enrichment analysis with GO (Gene ontology) terms and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways disclosed some BC-causing biological processes, molecular functions, and pathways. Finally, we suggested the top-ranked six drug molecules (Suramin, Rifaximin, Telmisartan, Tukysa Tucatinib, Lynparza Olaparib, and TG.02) for the treatment of BC by molecular docking analysis with the proposed HubGs-mediated receptors. Molecular docking analysis results also showed that these drug molecules may inhibit cancer-related post-translational modification (PTM) sites (Succinylation, phosphorylation, and ubiquitination) of hub proteins. Conclusions: This study's findings might be valuable resources for diagnosis, prognosis, and therapies at an earlier stage of BC.

Lift the Veil of Breast Cancers Using 4 or Fewer Critical Genes

Known genes in the breast cancer study literature could not be confirmed whether they are vital to breast cancer formations due to lack of convincing accuracy, although they may be biologically directly related to breast cancer based on present biological knowledge. It is hoped vital genes can be identified with the highest possible accuracy, for example, 100% accuracy and convincing causal patterns beyond what has been known in breast cancer. One hope is that finding gene-gene interaction signatures and functional effects may solve the puzzle. This research uses a recently developed competing linear factor analysis method in differentially expressed gene detection to advance the study of breast cancer formation. Surprisingly, 3 genes are detected to be differentially expressed in TNBC and non-TNBC (Her2, Luminal A, Luminal B) samples with 100% sensitivity and 100% specificity in 1 study of triple-negative breast cancers (TNBC, with 54 675 genes and 265 samples). These 3 genes show a clear signature pattern of how TNBC patients can be grouped. For another TNBC study (with 54 673 genes and 66 samples), 4 genes bring the same accuracy of 100% sensitivity and 100% specificity. Four genes are found to have the same accuracy of 100% sensitivity and 100% specificity in 1 breast cancer study (with 54 675 genes and 121 samples), and the same 4 genes bring an accuracy of 100% sensitivity and 96.5% specificity in the fourth breast cancer study (with 60 483 genes and 1217 samples). These results show the 4-gene-based classifiers are robust and accurate. The detected genes naturally classify patients into subtypes, for example, 7 subtypes. These findings demonstrate the clearest gene-gene interaction patterns and functional effects with the smallest numbers of genes and the highest accuracy compared with findings reported in the literature. The 4 genes are considered to be essential for breast cancer studies and practice. They can provide focused, targeted researches and precision medicine for each subtype of breast cancer. New breast cancer disease types may be detected using the classified subtypes, and hence new effective therapies can be developed.

Transcriptome Profile Analysis of Triple-Negative Breast Cancer Cells in Response to a Novel Cytostatic Tetrahydroisoquinoline Compared to Paclitaxel

The absence of chemotherapeutic target hormone receptors in breast cancer is descriptive of the commonly known triple-negative breast cancer (TNBC) subtype. TNBC remains one of the most aggressive invasive breast cancers, with the highest mortality rates in African American women. Therefore, new drug therapies are continually being explored. Microtubule-targeting agents such as paclitaxel (Taxol) interfere with microtubules dynamics, induce mitotic arrest, and remain a first-in-class adjunct drug to treat TNBC. Recently, we synthesized a series of small molecules of substituted tetrahydroisoquinolines (THIQs). The lead compound of this series, with the most potent cytostatic effect, was identified as 4-Ethyl-N-(7-hydroxy-3,4-dihydroisoquinolin-2(1H)-yl) benzamide (GM-4-53). In our previous work, GM-4-53 was similar to paclitaxel in its capacity to completely abrogate cell cycle in MDA-MB-231 TNBC cells, with the former not impairing tubulin depolymerization. Given that GM-4-53 is a cytostatic agent, and little is known about its mechanism of action, here, we elucidate differences and similarities to paclitaxel by evaluating whole-transcriptome microarray data in MDA-MB-231 cells. The data obtained show that both drugs were cytostatic at non-toxic concentrations and caused deformed morphological cytoskeletal enlargement in 2D cultures. In 3D cultures, the data show greater core penetration, observed by GM-4-53, than paclitaxel. In concentrations where the drugs entirely blocked the cell cycle, the transcriptome profile of the 48,226 genes analyzed (selection criteria: (p-value, FDR p-value < 0.05, fold change −2< and >2)), paclitaxel evoked 153 differentially expressed genes (DEGs), GM-4-53 evoked 243 DEGs, and, of these changes, 52/153 paclitaxel DEGs were also observed by GM-4-53, constituting a 34% overlap. The 52 DEGS analysis by String database indicates that these changes involve transcripts that influence microtubule spindle formation, chromosome segregation, mitosis/cell cycle, and transforming growth factor-β (TGF-β) signaling. Of interest, both drugs effectively downregulated “inhibitor of DNA binding, dominant negative helix-loop-helix” (ID) transcripts; ID1, ID3 and ID4, and amphiregulin (AREG) and epiregulin (EREG) transcripts, which play a formidable role in cell division. Given the efficient solubility of GM-4-53, its low molecular weight (MW; 296), and capacity to penetrate a small solid tumor mass and effectively block the cell cycle, this drug may have future therapeutic value in treating TNBC or other cancers. Future studies will be required to evaluate this drug in preclinical models.