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Expression of microRNAs (miRNAs) is characteristically altered in cancer, and they may play a role in cancer development and progression. The authors performed microarray and real-time quantitative reverse transcriptase-polymerase chain reaction (RT-PCR) analyses to determine the miRNA expression profile of primary small cell lung cancer. Here we show that at least 24 miRNAs are differentially expressed between normal lung and primary small cell lung cancer (SCLC) tumors. These include miR-301, miR-183/96/182, miR-126, and miR-223, which are microRNAs deregulated in other tumor types as well; and other miRNAs, such as miR-374 and miR-210, not previously reported in association with lung cancer. The aberrant miRNA profile of SCLC may offer new insights in the biology of this aggressive tumor, and could potentially provide novel diagnostic markers.

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A challenge in the treatment of lung cancer is the lack of early diagnostics. Here, we describe the application of monoclonal antibody proteomics for discovery of a panel of biomarkers for early detection (stage I) of non-small cell lung cancer (NSCLC). We produced large monoclonal antibody libraries directed against the natural form of protein antigens present in the plasma of NSCLC patients. Plasma biomarkers associated with the presence of lung cancer were detected via high throughput ELISA. Differential profiling of plasma proteomes of four clinical cohorts, totaling 301 patients with lung cancer and 235 healthy controls, identified 13 lung cancer-associated (p < 0.05) monoclonal antibodies. The monoclonal antibodies recognize five different cognate proteins identified using immunoprecipitation followed by mass spectrometry. Four of the five antigens were present in non-small cell lung cancer cells in situ. The approach is capable of generating independent antibodies against different epitopes of the same proteins, allowing fast translation to multiplexed sandwich assays. Based on these results, we have verified in two independent clinical collections a panel of five biomarkers for classifying patient disease status with a diagnostics performance of 77% sensitivity and 87% specificity. Combining CYFRA, an established cancer marker, with the panel resulted in a performance of 83% sensitivity at 95% specificity for stage I NSCLC.