Smoking-related epigenetic changes have been linked to lung cancer, but the contribution of epigenetic alterations unrelated to smoking remains unclear. We sought for a sparse set of CpG sites predicting lung cancer and explored the role of smoking in these associations. We analysed CpGs in relation to lung cancer in participants from two nested case–control studies, using (LASSO)-penalised regression. We accounted for the effects of smoking using known smoking-related CpGs, and through conditional-independence network. We identified 29 CpGs (8 smoking-related, 21 smoking-unrelated) associated with lung cancer. Models additionally adjusted for Comprehensive Smoking Index-(CSI) selected 1 smoking-related and 49 smoking-unrelated CpGs. Selected CpGs yielded excellent discriminatory performances, outperforming information provided by CSI only. Of the 8 selected smoking-related CpGs, two captured lung cancer-relevant effects of smoking that were missed by CSI. Further, the 50 CpGs identified in the CSI-adjusted model complementarily explained lung cancer risk. These markers may provide further insight into lung cancer carcinogenesis and help improving early identification of high-risk patients.

与吸烟相关的表观遗传变化与肺癌有关，但与吸烟无关的表观遗传变化的贡献仍不清楚。我们寻找一组稀疏的 CpG 位点来预测肺癌，并探讨了吸烟在这些关联中的作用。我们使用 (LASSO) 惩罚回归分析了两项巢式病例对照研究参与者中与肺癌相​​关的 CpG。我们使用已知的与吸烟相关的 CpG 并通过条件独立网络来解释吸烟的影响。我们确定了 29 个与肺癌相关的 CpG（8 个与吸烟相关，21 个与吸烟无关）。模型还针对综合吸烟指数 (CSI) 进行了调整，选择了 1 个与吸烟相关的 CpG 和 49 个与吸烟无关的 CpG。选定的 CpG 产生了出色的区分性能，优于仅由 CSI 提供的信息。在选定的 8 个与吸烟相关的 CpG 中，有两个捕获了 CSI 遗漏的吸烟对肺癌的相关影响。此外，CSI 调整模型中确定的 50 个 CpG 补充解释了肺癌风险。这些标志物可以进一步了解肺癌的致癌作用，并有助于提高高危患者的早期识别。