Purpose: Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths worldwide. Although tumor cell-T cell interactions are known to play a fundamental role in promoting tumor progression, these interactions have not been explored in LUAD. Methods: The 10x genomics single-cell RNA sequencing (scRNA-seq) and gene expression data of LUAD patients were obtained from ArrayExpress, TCGA, and GEO databases. scRNA-seq data were analyzed and infiltrating tumor cells, epithelial cells, and T cells were identified in the tumor microenvironment. Differentially expressed ligand-receptor pairs were identified in tumor cells/normal epithelial cells and tumor T cells/non-tumor T cells based on corresponding scRNA-seq and gene expression data, respectively. These important interactions inside/across cancer cells and T cells in LUAD were systematically analyzed. Furthermore, a valid prognostic machine-learning model based on ligand-receptor interactions was built to predict the prognosis of LUAD patients. Flow cytometry and qRT-PCR were performed to validate the significantly differently expressed ligand-receptor pairs. Results: Overall, 39,692 cells in scRNA-seq data were included in our study after quality filtering. A total of 65 ligand-receptor pairs (17 upregulated and 48 downregulated), including LAMB1-ITGB1, CD70-CD27, and HLA-B-LILRB2, and 96 ligand-receptor pairs (41 upregulated and 55 downregulated), including CCL5-CCR5, SELPLG-ITGB2, and CXCL13-CXCR5, were identified in LUAD cancer cells and T cells, respectively. To explore the crosstalk between cancer cells and T cells, 114 ligand-receptor pairs, including 11 ligand-receptor pair genes that could significantly affect survival outcomes, were identified in our research. A machine-learning model was established to accurately predict the prognosis of LUAD patients and ITGB4, CXCR5, and MET were found to play an important role in prognosis in our model. Flow cytometry and qRT-PCR analyses indicated the reliability of our study. Conclusion: Our study revealed functionally significant interactions within and between cancer cells and T cells. We believe these observations will improve our understanding of potential mechanisms of tumor microenvironment contributions to cancer progression and help identify potential targets for immunotherapy in the future.

中文翻译：

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肺腺癌中肿瘤细胞和 T 细胞内部和之间的配体-受体相互作用图谱。

目的：肺腺癌 (LUAD) 是全球癌症相关死亡的主要原因。尽管已知肿瘤细胞-T 细胞相互作用在促进肿瘤进展中起重要作用，但尚未在 LUAD 中探索这些相互作用。方法：LUAD患者的10x基因组单细胞RNA测序（scRNA-seq）和基因表达数据来自ArrayExpress、TCGA和GEO数据库。分析了 scRNA-seq 数据，并在肿瘤微环境中鉴定了浸润的肿瘤细胞、上皮细胞和 T 细胞。根据相应的 scRNA-seq 和基因表达数据，分别在肿瘤细胞/正常上皮细胞和肿瘤 T 细胞/非肿瘤 T 细胞中鉴定出差异表达的配体-受体对。系统分析了 LUAD 中癌细胞和 T 细胞内部/之间的这些重要相互作用。此外，建立了基于配体-受体相互作用的有效预后机器学习模型来预测 LUAD 患者的预后。进行流式细胞术和 qRT-PCR 以验证显着不同表达的配体 - 受体对。结果：总体而言，经过质量过滤后，我们的研究中包含了 scRNA-seq 数据中的 39,692 个细胞。总共 65 个配体-受体对（17 个上调和 48 个下调），包括 LAMB1-ITGB1、CD70-CD27 和 HLA-B-LILRB2，以及 96 个配体-受体对（41 个上调和 55 个下调），包括 CCL5-CCR5 、SELPLG-ITGB2 和 CXCL13-CXCR5 分别在 LUAD 癌细胞和 T 细胞中被鉴定。为了探索癌细胞和 T 细胞之间的串扰，我们的研究确定了 114 个配体-受体对，包括 11 个可显着影响生存结果的配体-受体对基因。建立机器学习模型以准确预测 LUAD 患者的预后，发现 ITGB4、CXCR5 和 MET 在我们的模型中在预后中起重要作用。流式细胞术和 qRT-PCR 分析表明我们研究的可靠性。结论：我们的研究揭示了癌细胞和 T 细胞内部和之间具有重要功能的相互作用。我们相信这些观察结果将提高我们对肿瘤微环境对癌症进展的潜在机制的理解，并有助于确定未来免疫治疗的潜在目标。在我们的研究中发现。建立机器学习模型以准确预测 LUAD 患者的预后，发现 ITGB4、CXCR5 和 MET 在我们的模型中在预后中起重要作用。流式细胞术和 qRT-PCR 分析表明我们研究的可靠性。结论：我们的研究揭示了癌细胞和 T 细胞内部和之间具有重要功能的相互作用。我们相信这些观察结果将提高我们对肿瘤微环境对癌症进展的潜在机制的理解，并有助于确定未来免疫治疗的潜在目标。在我们的研究中发现。建立机器学习模型以准确预测 LUAD 患者的预后，发现 ITGB4、CXCR5 和 MET 在我们的模型中在预后中起重要作用。流式细胞术和 qRT-PCR 分析表明我们研究的可靠性。结论：我们的研究揭示了癌细胞和 T 细胞内部和之间具有重要功能的相互作用。我们相信这些观察结果将提高我们对肿瘤微环境对癌症进展的潜在机制的理解，并有助于确定未来免疫治疗的潜在目标。流式细胞术和 qRT-PCR 分析表明我们研究的可靠性。结论：我们的研究揭示了癌细胞和 T 细胞内部和之间具有重要功能的相互作用。我们相信这些观察结果将提高我们对肿瘤微环境对癌症进展的潜在机制的理解，并有助于确定未来免疫治疗的潜在目标。流式细胞术和 qRT-PCR 分析表明我们研究的可靠性。结论：我们的研究揭示了癌细胞和 T 细胞内部和之间具有重要功能的相互作用。我们相信这些观察结果将提高我们对肿瘤微环境对癌症进展的潜在机制的理解，并有助于确定未来免疫治疗的潜在目标。