School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China; College of Life Sciences and Chemistry, Hunan University of Technology, Zhuzhou, 412007, Hunan, China.
School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China.
Comput Biol Med. 2023 Sep;163:107137. doi: 10.1016/j.compbiomed.2023.107137. Epub 2023 Jun 12.
Cell-cell communication in a tumor microenvironment is vital to tumorigenesis, tumor progression and therapy. Intercellular communication inference helps understand molecular mechanisms of tumor growth, progression and metastasis.
Focusing on ligand-receptor co-expressions, in this study, we developed an ensemble deep learning framework, CellComNet, to decipher ligand-receptor-mediated cell-cell communication from single-cell transcriptomic data. First, credible LRIs are captured by integrating data arrangement, feature extraction, dimension reduction, and LRI classification based on an ensemble of heterogeneous Newton boosting machine and deep neural network. Next, known and identified LRIs are screened based on single-cell RNA sequencing (scRNA-seq) data in certain tissues. Finally, cell-cell communication is inferred by incorporating scRNA-seq data, the screened LRIs, a joint scoring strategy that combines expression thresholding and expression product of ligands and receptors.
The proposed CellComNet framework was compared with four competing protein-protein interaction prediction models (PIPR, XGBoost, DNNXGB, and OR-RCNN) and obtained the best AUCs and AUPRs on four LRI datasets, elucidating the optimal LRI classification ability. CellComNet was further applied to analyze intercellular communication in human melanoma and head and neck squamous cell carcinoma (HNSCC) tissues. The results demonstrate that cancer-associated fibroblasts highly communicate with melanoma cells and endothelial cells strong communicate with HNSCC cells.
The proposed CellComNet framework efficiently identified credible LRIs and significantly improved cell-cell communication inference performance. We anticipate that CellComNet can contribute to anticancer drug design and tumor-targeted therapy.
肿瘤微环境中的细胞间通讯对于肿瘤发生、肿瘤进展和治疗至关重要。细胞间通讯推断有助于理解肿瘤生长、进展和转移的分子机制。
本研究聚焦于配体-受体共表达，开发了一种集成深度学习框架 CellComNet，从单细胞转录组数据中破译配体-受体介导的细胞间通讯。首先，通过整合数据排列、特征提取、降维和基于异构牛顿提升机和深度神经网络集成的 LRI 分类，捕获可信的 LRI。接下来，基于特定组织的单细胞 RNA 测序（scRNA-seq）数据筛选已知和已识别的 LRI。最后，通过整合 scRNA-seq 数据、筛选的 LRI、结合配体和受体表达阈值以及表达产物的联合评分策略，推断细胞间通讯。
与四种竞争的蛋白质-蛋白质相互作用预测模型（PIPR、XGBoost、DNNXGB 和 OR-RCNN）相比，所提出的 CellComNet 框架在四个 LRI 数据集上获得了最佳的 AUC 和 AUPR，说明了最优的 LRI 分类能力。CellComNet 进一步应用于分析人类黑色素瘤和头颈部鳞状细胞癌（HNSCC）组织中的细胞间通讯。结果表明，癌相关成纤维细胞与黑色素瘤细胞高度通讯，内皮细胞与 HNSCC 细胞强烈通讯。
所提出的 CellComNet 框架有效地识别了可信的 LRI，并显著提高了细胞间通讯推断性能。我们预计 CellComNet 可以为抗癌药物设计和肿瘤靶向治疗做出贡献。
