Abstract：
Lung cancer is one of the most common malignant tumors around the world, which has the highest mortality rate among all cancers. Traditional Chinese medicine (TCM) has attracted increased attention in the field of lung cancer treatment. However, the abundance of ingredients in Chinese medicines presents a challenge in identifying promising ingredient candidates and exploring their mechanisms for lung cancer treatment. In this work, two network-based algorithms were combined to calculate the network relationships between ingredient targets and lung cancer targets in the human interactome. Based on the enrichment analysis of the constructed disease module, key targets of lung cancer were identified. In addition, molecular docking and enrichment analysis of the overlapping targets between lung cancer and ingredients were performed to investigate the potential mechanisms of ingredient candidates against lung cancer. Ten potential ingredients against lung cancer were identified and they may have similar effect on the development of lung cancer. The results obtained from this study offered valuable insights and provided potential avenues for the development of novel drugs aimed at treating lung cancer.
摘要：
肺癌是世界上最常见的恶性肿瘤之一，在所有癌症中死亡率最高。中医药在肺癌治疗领域受到越来越多的关注。然而,中药成分的丰富对确定有希望的候选成分和探索其治疗肺癌的机制提出了挑战。在这项工作中,将两种基于网络的算法结合起来,计算人体相互作用组中成分靶点和肺癌靶点之间的网络关系.在对构建的疾病模块进行富集分析的基础上,确定了肺癌的关键靶标。此外,进行了肺癌与成分之间重叠靶标的分子对接和富集分析,以研究候选成分抗肺癌的潜在机制.确定了10种潜在的抗肺癌成分，它们可能对肺癌的发展具有相似的作用。从这项研究中获得的结果提供了有价值的见解，并为开发旨在治疗肺癌的新药提供了潜在的途径。
