对不同吸烟史的癌症患者的综合研究,包括非吸烟者,前吸烟者,和目前的吸烟者,仍然难以捉摸。因此,我们进行了一项多组学分析,以探讨吸烟史对癌症患者的影响.从癌症基因组图谱数据库中筛选有吸烟史的患者,并下载了他们的多组学数据和临床信息。本研究共纳入2,317例患者,目前的吸烟者预后最差,其次是前吸烟者,而非吸烟者的预后最好。更重要的是,吸烟史是独立的预后因素。不同吸烟史的患者表现出不同的免疫含量,前吸烟者的免疫细胞和肿瘤免疫微环境最高。吸烟者的基因组不稳定性发生率较高,戒烟后某些变化可以逆转。我们还注意到吸烟会降低患者对化疗药物的敏感性,而戒烟可以扭转这种局面。竞争的内源性RNA网络显示mir-193b-3p,mir-301b,mir-205-5p,mir-132-3p,mir-212-3p,mir-1271-5p,mir-137可能在烟草介导的肿瘤形成中起重要作用。我们确定了11个甲基化驱动基因(包括EIF5A2,GBP6,HGD,HS6ST1,ITGA5,NR2F2,PLS1,PPP1R18,PTHLH,SLC6A15和YEATS2),一些基因的甲基化修饰尚未被报道与肿瘤相关。我们构建了一个46基因模型,预测总体生存率,具有良好的预测能力。接下来,我们绘制了每种癌症类型的列线图。有趣的是,校准图和一致性指数验证了列线图对患者预后的准确性。同时,我们发现,46基因模型对总生存期以及疾病特异性生存期和无进展间期具有良好的适用性.这项研究的结果为诊断提供了新的和有价值的见解,治疗,并对不同吸烟史的癌症患者进行随访。
Comprehensive studies on cancer patients with different smoking histories, including non-smokers, former smokers, and current smokers, remain elusive. Therefore, we conducted a multi-omics analysis to explore the effect of smoking history on cancer patients. Patients with smoking history were screened from The Cancer Genome Atlas database, and their multi-omics data and clinical information were downloaded. A total of 2,317 patients were included in this study, whereby current smokers presented the worst prognosis, followed by former smokers, while non-smokers showed the best prognosis. More importantly, smoking history was an independent prognosis factor. Patients with different smoking histories exhibited different immune content, and former smokers had the highest immune cells and tumor immune microenvironment. Smokers are under a higher incidence of genomic instability that can be reversed following smoking
cessation in some changes. We also noted that smoking reduced the sensitivity of patients to chemotherapeutic drugs, whereas smoking
cessation can reverse the situation. Competing endogenous RNA network revealed that mir-193b-3p, mir-301b, mir-205-5p, mir-132-3p, mir-212-3p, mir-1271-5p, and mir-137 may contribute significantly in tobacco-mediated tumor formation. We identified 11 methylation driver genes (including EIF5A2, GBP6, HGD, HS6ST1, ITGA5, NR2F2, PLS1, PPP1R18, PTHLH, SLC6A15, and YEATS2), and methylation modifications of some of these genes have not been reported to be associated with tumors. We constructed a 46-gene model that predicted overall survival with good predictive power. We next drew nomograms of each cancer type. Interestingly, calibration diagrams and concordance indexes are verified that the nomograms were highly accurate for the prognosis of patients. Meanwhile, we found that the 46-gene model has good applicability to the overall survival as well as to disease-specific survival and progression-free intervals. The results of this research provide new and valuable insights for the diagnosis, treatment, and follow-up of cancer patients with different smoking histories.