The extent, regulation and enzymatic basis of RNA editing by cytidine deamination are incompletely understood. Here we show that transcripts of hundreds of genes undergo site-specific C>U RNA editing in macrophages during M1 polarization and in monocytes in response to hypoxia and interferons. This editing alters the amino acid sequences for scores of proteins, including many that are involved in pathogenesis of viral diseases. APOBEC3A, which is known to deaminate cytidines of single-stranded DNA and to inhibit viruses and retrotransposons, mediates this RNA editing. Amino acid residues of APOBEC3A that are known to be required for its DNA deamination and anti-retrotransposition activities were also found to affect its RNA deamination activity. Our study demonstrates the cellular RNA editing activity of a member of the APOBEC3 family of innate restriction factors and expands the understanding of C>U RNA editing in mammals.

Prognostic markers that can predict the relapse of localized non-small cell lung cancer (NSCLC) have yet to be defined. We surveyed expression profiles of microRNA (miRNA) in stage I NSCLC to identify patterns that might predict recurrence after surgical resection of this common deadly cancer. Small RNAs extracted from formalin-fixed and paraffin-embedded tissues were hybridized to locked nucleic acid probes against 752 human miRNAs (representing 82% of the miRNAs in the miRBase 13.0 database) to obtain expression profiles for 37 cases with recurrence and 40 cases without recurrence (with clinical follow-up for at least 32 months). Differential expression between the two case groups was detected for 49% of the miRNAs (Wilcoxon rank sum test; P < 0.01). The performance of expression profiles at differentiating the two case groups was assessed by leave-one-out and Monte Carlo cross-validations. In leave-one-out cross-validation using support vector machines-or top-scoring gene pair classifier methods, which looked for six-or two-miRNA-based classifiers, the identified miRNA expression pattern predicted recurrence with an accuracy of 70% and 83%, and hazard ratio of 3.6 [95% confidence interval (95% CI), 1.8-7.1] and 9.0 (95% CI, 4.4-18.2), respectively. Mean accuracy in Monte Carlo cross-validation using 1,000 random 60-17 splits was 69% (95% CI, 68-70) and 72% (95% CI, 71-72), respectively. The specific miRNAs mir-200b*, mir-30c-1*, mir-510, mir-630, mir-657, and mir146b-3p and mir-124*, mir-585, and mir-708, respectively, represented most commonly among the 1,000 classifiers identified in Monte Carlo cross-validation by the two methods. MiRNAs mir-488, mir-503, and mir-647 were identified as potential reference miRNAs for future studies, based on the stability of their expression patterns across the 77 cases and the two case-groups. Our findings reinforce efforts to profile miRNA expression patterns for better prognostication of stage I NSCLC. Cancer Res; 70(1); 36-45. ©2010 AACR.

The association of lung cancer with changes in microRNAs in plasma shown in multiple studies suggests a utility for circulating microRNA biomarkers in non-invasive detection of the disease. We examined if presence of lung cancer is reflected in whole blood microRNA expression as well, possibly because of a systemic response. Locked nucleic acid microarrays were used to quantify the global expression of microRNAs in whole blood of 22 patients with lung adenocarcinoma and 23 controls, ten of whom had a radiographically detected non-cancerous lung nodule and the other 13 were at high risk for developing lung cancer because of a smoking history of >20 pack-years. Cases and controls differed significantly for age with a mean difference of 10.7 years, but not for gender, race, smoking history, blood hemoglobin, platelet count, or white blood cell count. Of 1282 quantified human microRNAs, 395 (31%) were identified as expressed in the study’s subjects, with 96 (24%) differentially expressed between cases and controls. Classification analyses of microRNA expression data were performed using linear kernel support vector machines (SVM) and top-scoring pairs (TSP) methods, and classifiers to identify presence of lung adenocarcinoma were internally cross-validated. In leave-one-out cross-validation, the TSP classifiers had sensitivity and specificity of 91% and 100%, respectively. The values with SVM were both 91%. In a Monte Carlo cross-validation, average sensitivity and specificity values were 86% and 97%, respectively, with TSP, and 88% and 89%, respectively, with SVM. MicroRNAs miR-190b, miR-630, miR-942, and miR-1284 were the most frequent constituents of the classifiers generated during the analyses. These results suggest that whole blood microRNA expression profiles can be used to distinguish lung cancer cases from clinically relevant controls. Further studies are needed to validate this observation, including in non-adenocarcinomatous lung cancers, and to clarify upon the confounding effect of age.

Tumor-derived exosomes (TEXs) play instrumental roles in tumor growth, angiogenesis, immune modulation, metastasis, and drug resistance. TEX RNAs are a new class of noninvasive biomarkers for cancer. Neither current techniques, such as quantitative reverse transcription polymerase chain reaction (qRT-PCR) and next-generation sequencing, nor new ones, such as electrochemical or surface plasmon resonance-based biosensors, are able to selectively capture and separate TEXs from normal cell-derived exosomes, making TEX RNAs potentially less sensitive biomarkers. We developed an immuno-biochip that selectively captures TEXs using antibodies against tumor-associated proteins and quantifies in situ TEX RNAs using cationic lipoplexes containing molecular beacons. We used the immuno-biochip to measure the expression of miR-21 microRNA and TTF-1 mRNA in EGFR-or PD-L1-bearing exosomes from human sera and achieved absolute sensitivity and specificity in distinguishing normal controls from non-small cell lung cancer patients. Our results demonstrated that the effective separation of TEXs from other exosomes greatly improved the detection sensitivity and specificity. Compared with the traditional immunomagnetic separation−RNA isolation−qRT-PCR workflow, the immuno-biochip showed superior lung cancer diagnostic performance, consumed less samples (∼30 μL), and shortened assay time from ∼24 to 4 h.