The role of microRNAs (miRNAs) in tumor diagnosis and patients’ prognosis has recently gained extensive research attention. This study was designed to analyze miRNA in lung adenocarcinoma (LUAD) using bioinformatics analysis and to identify novel biomarkers to predict overall survival (OS) for LUAD patients. Differential miRNA expression analysis was performed on LUAD, and normal tissues were extracted from The Cancer Genome Atlas (TCGA). Univariate Cox risk regression and least absolute shrinkage and selection operator (LASSO) Cox analysis were used to screen prognostic miRNAs and develop a risk score model. The prognostic performance of the system was examined utilizing the Kaplan–Meier and receiver operating characteristic (ROC) curves. Independent prognostic factors of LUAD were determined by multivariate Cox regression analysis. Nomogram was constructed according to the independent prognostic factors to evaluate the patients’ one-, three- and five-year OS. A 7-miRNA signature based on miR-584-5p, miR-31-3p, miR-490-3, miR-4661-5p, miR-30e-5p, miR-582-5p, and miR-148a-3p was established. To categorize patients into high- and low-risk groups, the risk score was computed. The OS of the low-risk group was significantly longer than the high-risk group, and the signature showed high sensitivity and specificity in anticipating the one-, three- and five-year OS. The system was an independent factor in predicting the OS of LUAD patients and performed better when combined with the N stage in nomogram. A 7-miRNA signature developed in this study could accurately predict LUAD survival.

Background: Sleeve lobectomy is recognized as an alternative surgical operation to pneumonectomy because it preserves the most pulmonary function and has a considerable prognosis. In this study, we aimed to investigate the implications of residual status for patients after sleeve lobectomy. Methods: In this retrospective cohort study, we summarized 58 242 patients who underwent surgeries from 2015 to 2018 in Shanghai Chest Hospital and found 456 eligible patients meeting the criteria. The status of R2 was excluded. The outcomes were overall survival (OS) and recurrence-free survival (RFS). We performed a subgroup analysis to further our investigation. Results: After the propensity score match, the baseline characteristic was balanced between two groups. The survival analysis showed no significant difference of overall survival and recurrence-free survival between R0 and R1 groups (OS: p = 0.053; RFS: p = 0.14). In the multivariate Cox analysis, we found that the margin status was not a dependent risk factor to RFS (p = 0.119) and OS (p = 0.093). In the patients of R1, N stage and age were closely related to OS, but we did not find any significant risk variable in RFS for R1 status. In the subgroup analysis, R1 status may have a worse prognosis on patients with more lymph nodes examination. On further investigation, we demonstrated no differences among the four histological types of margin status. Conclusion: In our study, we confirmed that the margin status after sleeve lobectomies was not the risk factor to prognosis. However, patients with more lymph nodes resection should pay attention to the margin status.

No abstract

Low-dose computed tomography (LDCT) is recommended for early lung cancer screening in high-risk populations. Small lung nodules are identified by these CT scans in up to 50% of high-risk patients. While over 50% of lung nodules less than 1 cm are benign, discriminating benign nodules from malignant nodules remains extremely challenging without an invasive lung biopsy. Taking advantage of methylation signatures in circulating tumor DNA (ctDNA), we developed a non-invasive screening assay to identify patients with malignant lung nodules from those with benign lung nodules. Methods: We first adopted Singlera's MONOD+ assay as a marker screening technology to interrogate the methylation state of over 4,000,000 CpG sites across more than 200,000 methylation haplotype blocks in the human genome from as little as 10 ng of cell-free or tissue DNA. We first processed 129 fresh frozen and FFPE tissue samples from healthy lung, benign lung nodules, and lung cancer at various stages of malignancy. We identified a preliminary set of methylation markers capable of segregating lung nodules by malignancy and invasiveness. To examine the reproducibility of these markers in plasma, we applied the same screening assay to plasma cell-free DNA from 46 healthy patients and 175 lung nodule patients with either benign or malignant nodules (most at an early stage of Ia1/Ia2). We refined the marker set by removing methylation markers that were unstable in plasma samples. We next applied Singlera's MethylTitan assay to deeply interrogate approximately 1,000 methylation haplotype blocks enriched in malignant lung nodules in plasma samples, as MethylTitan's higher conversion rate allowed us to better handle the limited amount of DNA in plasma samples. We ran the MethylTitan assay on both healthy and lung nodule plasma samples; using this method, we confirmed that tissue-derived methylation signatures could be rapidly screened in plasma samples in a high-throughput and highly sensitive manner. Results: Using the MONOD+ marker screening assay on plasma samples, we were able to utilize cell-free DNA methylation to identify early-stage malignant lung nodule patients with a sensitivity of 80.25% and a specificity of 83.08%. We were able to further improve upon these results using the MethylTitan assay, as we could more efficiently and more accurately screen the methylation markers in a large number of plasma samples. Conclusion: In summary, we have applied Singlera's MONOD+ and MethylTitan methylation assays to non-invasively identify early stage lung cancer in patients with small lung nodules. We are currently expanding this study to a much larger cohort of lung nodule patients in order to clinically validate our early lung cancer screening assay. The approach of using MONOD+ to identify markers and MethylTitan to analyze plasma samples will allow us to noninvasively screen for additional cancer types in a high-throughput clinical setting. Citation Format: Ruijun Liu, Xiaojie Li, Athurva Gore, Zi Qin, Jeff Gole, Qiye He, Rui Liu, Shun Lu. Detection of early-stage malignant lung cancer using methylation signatures in circulating tumor DNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3249.