Lung cancer is the leading cause of cancer mortality, and early detection is key to improving survival. However, there are no reliable blood-based tests currently available for early-stage lung cancer diagnosis. Here, we performed single-cell RNA sequencing of different early-stage lung cancers and found that lipid metabolism was broadly dysregulated in different cell types, with glycerophospholipid metabolism as the most altered lipid metabolism–related pathway. Untargeted lipidomics was carried out in an exploratory cohort of 311 participants. Through support vector machine algorithm-based and mass spectrum–based feature selection, we identified nine lipids (lysophosphatidylcholines 16:0, 18:0, and 20:4; phosphatidylcholines 16:0–18:1, 16:0–18:2, 18:0–18:1, 18:0–18:2, and 16:0–22:6; and triglycerides 16:0–18:1–18:1) as the features most important for early-stage cancer detection. Using these nine features, we developed a liquid chromatography–mass spectrometry (MS)–based targeted assay using multiple reaction monitoring. This target assay achieved 100.00% specificity on an independent validation cohort. In a hospital-based lung cancer screening cohort of 1036 participants examined by low-dose computed tomography and a prospective clinical cohort containing 109 participants, the assay reached more than 90.00% sensitivity and 92.00% specificity. Accordingly, matrix-assisted laser desorption/ionization MS imaging confirmed that the selected lipids were differentially expressed in early-stage lung cancer tissues in situ. This method, designated as Lung Cancer Artificial Intelligence Detector, may be useful for early detection of lung cancer or large-scale screening of high-risk populations for cancer prevention.

Esophageal squamous cell carcinoma (ESCC) is one of the most prevalent and deadly types of cancer worldwide especially in Eastern Asia and the prognosis of ESCC remain poor. Recent evidence suggests that circular RNAs (circRNAs) play important roles in multiple diseases, including cancer. In this study, we characterized a novel circRNA termed hsa_circ_0067934 in ESCC tumor tissues and cell lines. We analyzed a cohort of 51 patients and found that hsa_circ_0067934 was significantly overexpressed in ESCC tissues compared with paired adjacent normal tissues. The high expression level of hsa_circ_0067934 was associated with poor differentiation (P = 0.025), I-II T stage (P = 0.04), and I-II TNM stage (P = 0.021). The in vitro silence of hsa_circ_0067934 by siRNA inhibited the proliferation and migration of ESCC cells and blocked cell cycle progression. Cell fraction analyses and fluorescence in situ hybridization detected that hsa_circ_0067934 was mostly located in the cytoplasm. Our findings suggest that hsa_circ_0067934 is upregulated in ESCC tumor tissue. Our data suggest that hsa_circ_0067934 represents a novel potential biomarker and therapeutic target of ESCC.

Somatic copy number variations (CNV) may drive cancer progression through both coding and noncoding transcripts. However, noncoding transcripts resulting from CNV are largely unknown, especially for circular RNAs. By integrating bioinformatics analyses of alerted circRNAs and focal CNV in lung adenocarcinoma, we identify a proto-oncogenic circular RNA (circPRKCI) from the 3q26.2 amplicon, one of the most frequent genomic aberrations in multiple cancers. circPRKCI was overexpressed in lung adenocarcinoma tissues, in part due to amplification of the 3q26.2 locus, and promoted proliferation and tumorigenesis of lung adenocarcinoma. circPRKCI functioned as a sponge for both miR-545 and miR-589 and abrogated their suppression of the protumorigenic transcription factor E2F7. Intratumor injection of cholesterol-conjugated siRNA specifically targeting circPRKCI inhibited tumor growth in a patient-derived lung adenocarcinoma xenograft model. In summary, circPRKCI is crucial for tumorigenesis and may serve as a potential therapeutic target in patients with lung adenocarcinoma. Significance: These findings reveal high expression of the circular RNA circPRKCI drives lung adenocarcinoma tumorigenesis. Cancer Res; 78(11); 2839–51. ©2018 AACR.

Background: Circulating tumor DNA (ctDNA) has offered a minimally invasive and feasible approach for detection of EGFR mutation for non–small cell lung cancer (NSCLC). This meta-analysis was designed to investigate the diagnostic value of ctDNA, compared with current “gold standard,” tumor tissues. Methods: We searched PubMed, EMBASE, Cochrane Library, and Web of Science to identify eligible studies that reported the sensitivity and specificity of ctDNA for detection of EGFR mutation status in NSCLC. Eligible studies were pooled to calculate the pooled sensitivity, specificity, and diagnostic odds ratio (DOR). The summary ROC curve (SROC) and area under SROC (AUSROC) were used to evaluate the overall diagnostic performance. Results: Twenty-seven eligible studies involving 3,110 participants were included and analyzed in our meta-analysis, and most studies were conducted among Asian population. The pooled sensitivity, specificity, and DOR were 0.620 [95% confidence intervals (CI), 0.513–0.716), 0.959 (95% CI, 0.929–0.977), and 38.270 (95% CI, 21.090–69.444), respectively. The AUSROC was 0.91 (95% CI, 0.89–0.94), indicating the high diagnostic performance of ctDNA. Conclusion: ctDNA is a highly specific and effective biomarker for the detection of EGFR mutation status. Impact: ctDNA analysis will be a key part of personalized cancer therapy of NSCLC. Cancer Epidemiol Biomarkers Prev; 24(1); 206–12. ©2014 AACR.