Lu Wei, Yang Zhenyu, Wang Mengjie, Li Shiqi, Bi Hui, Yang Xiaonan
Department of Hemangioma and Vascular Malformation, Plastic Surgery Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100144, China.
Chinese Academy of Medical Sciences & Peking Union Medical College, 4+4 M.D. Program, Beijing, 100144, China.
Heliyon. 2024 Mar 15;10(7):e27357. doi: 10.1016/j.heliyon.2024.e27357. eCollection 2024 Apr 15.
Breast cancer (BC) remains the most common cancer among women, and novel post-surgical reconstruction techniques, including autologous fat transplantation, have emerged. While Adipose-derived stem cells (ADSCs) are known to impact the viability of fat grafts, their influence on breast cancer progression remains unclear. This study aims to elucidate the genetic interplay between ADSCs and breast cancer, focusing on potential therapeutic targets.
Using the GEO and TCGA databases, we pinpointed differentially expressed (DE) mRNAs, miRNAs, lncRNAs, and pseudogenes of ADSCs and BC. We performed functional enrichment analysis and constructed protein-protein interaction (PPI), RNA binding protein (RBP)-pseudogene-mRNA, and lncRNA-miRNA-transcription factor (TF)-gene networks. Our study delved into the correlation of AK4 expression with 33 different malignancies and examined its impact on prognostic outcomes across a pan-cancer cohort. Additionally, we scrutinized immune infiltration, microsatellite instability, and tumor mutational burden, and conducted single-cell analysis to further understand the implications of AK4 expression. We identified novel sample subtypes based on hub genes using the ConsensusClusterPlus package and examined their association with immune infiltration. The random forest algorithm was used to screen DE mRNAs between subtypes to validate the powerful prognostic prediction ability of the artificial neural network.
Our analysis identified 395 DE mRNAs, 3 DE miRNAs, 84 DE lncRNAs, and 26 DE pseudogenes associated with ADSCs and BC. Of these, 173 mRNAs were commonly regulated in both ADSCs and breast cancer, and 222 exhibited differential regulation. The PPI, RBP-pseudogene-mRNA, and lncRNA-miRNA-TF-gene networks suggested AK4 as a key regulator. Our findings support AK4 as a promising immune-related therapeutic target for a wide range of malignancies. We identified 14 characteristic genes based on the AK4-related cluster using the random forest algorithm. Our artificial neural network yielded excellent diagnostic performance in the testing cohort with AUC values of 0.994, 0.973, and 0.995, indicating its ability to distinguish between breast cancer and non-breast cancer cases.
Our research sheds light on the dual role of ADSCs in BC at the genetic level and identifies AK4 as a key protective mRNA in breast cancer. We found that AK4 significantly predicts cancer prognosis and immunotherapy, indicating its potential as a therapeutic target.
乳腺癌（BC）仍是女性中最常见的癌症，包括自体脂肪移植在内的新型术后重建技术已出现。虽然已知脂肪来源干细胞（ADSCs）会影响脂肪移植的存活率，但其对乳腺癌进展的影响仍不清楚。本研究旨在阐明ADSCs与乳腺癌之间的基因相互作用，重点关注潜在的治疗靶点。
利用GEO和TCGA数据库，我们确定了ADSCs和BC中差异表达的（DE）mRNA、miRNA、lncRNA和假基因。我们进行了功能富集分析，并构建了蛋白质-蛋白质相互作用（PPI）、RNA结合蛋白（RBP）-假基因-mRNA和lncRNA-miRNA-转录因子（TF）-基因网络。我们的研究深入探讨了AK4表达与33种不同恶性肿瘤的相关性，并在泛癌队列中研究了其对预后结果的影响。此外，我们仔细研究了免疫浸润、微卫星不稳定性和肿瘤突变负担，并进行了单细胞分析以进一步了解AK4表达的意义。我们使用ConsensusClusterPlus软件包基于枢纽基因鉴定了新的样本亚型，并研究了它们与免疫浸润的关联。使用随机森林算法筛选亚型之间的DE mRNA，以验证人工神经网络强大的预后预测能力。
我们的分析确定了395个与ADSCs和BC相关的DE mRNA、3个DE miRNA、84个DE lncRNA和26个DE假基因。其中，173个mRNA在ADSCs和乳腺癌中均受到共同调控，222个表现出差异调控。PPI、RBP-假基因-mRNA和lncRNA-miRNA-TF-基因网络表明AK4是关键调节因子。我们的研究结果支持AK4作为多种恶性肿瘤有前景的免疫相关治疗靶点。我们使用随机森林算法基于与AK4相关的聚类确定了14个特征基因。我们的人工神经网络在测试队列中表现出优异的诊断性能，AUC值分别为0.994、0.973和0.995，表明其能够区分乳腺癌和非乳腺癌病例。
我们的研究揭示了ADSCs在乳腺癌遗传水平上的双重作用，并确定AK4是乳腺癌中的关键保护性mRNA。我们发现AK4显著预测癌症预后和免疫治疗，表明其作为治疗靶点的潜力。
