A novel multimodal prediction model based on DNA methylation biomarkers and low-dose computed tomography images for identifying early-stage lung cancer

Objective:DNA methylation alterations are early events in carcinogenesis and immune signalling in lung cancer.This study aimed to develop a model based on short stature homeobox 2 gene (SHOX2)/prostaglandin E receptor 4gene (PTGER4) DNA methylation in plasma,appearance subtype of pulmonary nodules (PNs) and low-dose computed tomography (LDCT) images to distinguish early-stage lung cancers.Methods:We developed a multimodal prediction model with a training set of 257 individuals.The performance of the multimodal prediction model was further validated in an independent validation set of 42 subjects.In addition,we explored the association between SHOX2/PTGER4 DNA methylation and driver gene mutations in lung cancer based on data from The Cancer Genome Atlas (TCGA) portal.Results:There were significant differences between the early-stage lung cancers and benign groups in the methylation levels.The area under a receiver operator characteristic curve (AUC) of SHOX2 in patients with solid nodules,mixed ground-glass opacity nodules and pure ground-glass opacity nodules were 0.693,0.497 and 0.864,respectively,while the AUCs of PTGER4 were 0.559,0.739 and 0.619,respectively.With the highest AUC of0.894,the novel multimodal prediction model outperformed the Mayo Clinic model (0.519) and LDCT-based deep learning model (0.842) in the independent validation set.Database analysis demonstrated that patients with SHOX2/PTGER4 DNA hypermethylation were enriched in TP53 mutations.Conclusions:The present multimodal prediction model could more efficiently distinguish early-stage lung cancer from benign PNs.A prognostic index based on DNA methylation and lung cancer driver gene alterations may separate the patients into groups with good or poor prognosis.

Genetic susceptibility loci of lung cancer are associated with malignant risk of pulmonary nodules and improve malignancy diagnosis based on CEA levels

Objective:The heightened prevalence of pulmonary nodules(PN)has escalated its significance as a public health concern.While the precise identification of high-risk PN carriers for malignancy remains an ongoing challenge,genetic variants hold potentials as determinants of disease susceptibility that can aid in diagnosis.Yet,current understanding of the genetic loci associated with malignant PN(MPN)risk is limited.Methods:A frequency-matched case-control study was performed,comprising 247 MPN cases and 412 benign NP(BNP)controls.We genotyped 11 established susceptibility loci for lung cancer in a Chinese cohort.Loci associated with MPN risk were utilized to compute a polygenic risk score(PRS).This PRS was subsequently incorporated into the diagnostic evaluation of MPNs,with emphasis on serum tumor biomarkers.Results:Loci rs10429489G>A,rs17038564A>G,and rs12265047A>G were identified as being associated with an increased risk of MPNs.The PRS,formulated from the cumulative risk effects of these loci,correlated with the malignant risk of PNs in a dose-dependent fashion.A high PRS was found to amplify the MPN risk by 156%in comparison to a low PRS[odds ratio(OR)=2.56,95%confidence interval(95%CI),1.40−4.67].Notably,the PRS was observed to enhance the diagnostic accuracy of serum carcinoembryonic antigen(CEA)in distinguishing MPNs from BPNs,with diagnostic values rising from 0.716 to 0.861 across low-to high-PRS categories.Further bioinformatics investigations pinpointed rs10429489G>A as an expression quantitative trait locus.Conclusions:Loci rs10429489G>A,rs17038564A>G,and rs12265047A>G contribute to MPN risk and augment the diagnostic precision for MPNs based on serum CEA concentrations.