Department of Oncology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
Day Surgery Center, Dalian Municipal Central Hospital, Dalian, China.
PLoS One. 2024 Jan 16;19(1):e0297260. doi: 10.1371/journal.pone.0297260. eCollection 2024.
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.
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.
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.
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.
三阴性乳腺癌（TNBC）是乳腺癌中侵袭性最强的恶性亚型。尽管紫杉醇为基础的化疗方案是 TNBC 治疗的主要手段，但紫杉醇耐药仍然是癌症治愈的一个突出障碍。因此，通过阐明紫杉醇耐药的机制，探索新的治疗靶点是当务之急。
从基因表达综合数据库（GEO）下载了 TNBC 细胞在不同时间点用紫杉醇处理的单细胞 RNA 测序（scRNA-seq）数据。使用 Seurat R 包过滤和整合 scRNA-seq 表达矩阵。通过 FindClusters 函数对细胞进行进一步聚类，并通过 FindAllMarkers 函数定义每个子集的基因标记。然后，通过 AUCell R 包计算每个细胞的标志性评分，通过 DAVID 数据库分析高表达感兴趣基因的生物学功能。随后，我们进行了伪时间分析，以探索耐药基因的变化模式，并进行 SCENIC 分析，以识别关键转录因子（TF）。最后，还通过 CTD 数据库分析了其抑制剂。
我们最终从 2798 个细胞中获得了 6 个细胞亚群，分别标记为 AKR1C3+、WNT7A+、FAM72B+、RERG+、IDO1+和 HEY1+HCC1143 细胞亚群，其中 AKR1C3+、IDO1+和 HEY1+细胞亚群的比例随着治疗时间的增加而增加，然后被视为紫杉醇耐药亚群。标志性评分和伪时间分析表明，这些紫杉醇耐药亚群与炎症反应、病毒和干扰素反应激活有关。此外，基因调控网络（GRNs）表明 3 个关键 TF（STAT1、CEBPB 和 IRF7）在促进耐药发展中起着至关重要的作用，鉴定出了 5 种针对这些 TF 的共同抑制剂，作为紫杉醇联合治疗的潜在组合疗法。
在这项研究中，我们鉴定了 3 个与紫杉醇耐药相关的 IFs 及其抑制剂，为紫杉醇耐药提供了重要的分子基础，并为临床 TNBC 治疗中紫杉醇的联合应用提供了有益的指导。
