PKMYT1 knockdown inhibits cholesterol biosynthesis and promotes the drug sensitivity of triple-negative breast cancer cells to atorvastatin.

Triple negative breast cancer (TNBC) as the most aggressive molecular subtype of breast cancer is characterized by high cancer cell proliferation and poor patient prognosis. Abnormal lipid metabolism contributes to the malignant process of cancers. Study observed significantly enhanced cholesterol biosynthesis in TNBC. However, the mechanisms underlying the abnormal increase of cholesterol biosynthesis in TNBC are still unclear. Hence, we identified a member of the serine/threonine protein kinase family PKMYT1 as a key driver of cholesterol synthesis in TNBC cells. Aberrantly high-expressed PKMYT1 in TNBC was indicative of unfavorable prognostic outcomes. In addition, PKMYT1 promoted sterol regulatory element-binding protein 2 (SREBP2)-mediated expression of enzymes related to cholesterol biosynthesis through activating the TNF/ TNF receptor-associated factor 1 (TRAF1)/AKT pathway. Notably, downregulation of PKMYT1 significantly inhibited the feedback upregulation of statin-mediated cholesterol biosynthesis, whereas knockdown of PKMYT1 promoted the drug sensitivity of atorvastatin in TNBC cells. Overall, our study revealed a novel function of PKMYT1 in TNBC cholesterol biosynthesis, providing a new target for targeting tumor metabolic reprogramming in the cancer.

Exploring the resistance mechanism of triple-negative breast cancer to paclitaxel through the scRNA-seq analysis.

BACKGROUND: The triple negative breast cancer (TNBC) is the most malignant subtype of breast cancer with high aggressiveness. Although paclitaxel-based chemotherapy scenario present the mainstay in TNBC treatment, paclitaxel resistance is still a striking obstacle for cancer cure. So it is imperative to probe new therapeutic targets through illustrating the mechanisms underlying paclitaxel chemoresistance. METHODS: The Single cell RNA sequencing (scRNA-seq) data of TNBC cells treated with paclitaxel at different points were downloaded from the Gene Expression Omnibus (GEO) database. The Seurat R package was used to filter and integrate the scRNA-seq expression matrix. Cells were further clustered by the FindClusters function, and the gene marker of each subset was defined by FindAllMarkers function. Then, the hallmark score of each cell was calculated by AUCell R package, the biological function of the highly expressed interest genes was analyzed by the DAVID database. Subsequently, we performed pseudotime analysis to explore the change patterns of drug resistance genes and SCENIC analysis to identify the key transcription factors (TFs). Finally, the inhibitors of which were also analyzed by the CTD database. RESULTS: We finally obtained 6 cell subsets from 2798 cells, which were marked as AKR1C3+, WNT7A+, FAM72B+, RERG+, IDO1+ and HEY1+HCC1143 cell subsets, among which the AKR1C3+, IDO1+ and HEY1+ cell subsets proportions increased with increasing treatment time, and then were regarded as paclitaxel resistance subsets. Hallmark score and pseudotime analysis showed that these paclitaxel resistance subsets were associated with the inflammatory response, virus and interferon response activation. In addition, the gene regulatory networks (GRNs) indicated that 3 key TFs (STAT1, CEBPB and IRF7) played vital role in promoting resistance development, and five common inhibitors targeted these TFs as potential combination therapies of paclitaxel were identified. CONCLUSION: In this study, we identified 3 paclitaxel resistance relevant IFs and their inhibitors, which offers essential molecular basis for paclitaxel resistance and beneficial guidance for the combination of paclitaxel in clinical TNBC therapy.