Research Papers:

An integromic signature for lung cancer early detection

Qixin Leng, Yanli Lin, Min Zhan and Feng Jiang _

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Oncotarget. 2018; 9:24684-24692. https://doi.org/10.18632/oncotarget.25227

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Qixin Leng1,*, Yanli Lin1,*, Min Zhan2 and Feng Jiang1

1Department of Pathology, University of Maryland School of Medicine, Baltimore, MD 21201, USA

2Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA

*These authors have contributed equally to this work

Correspondence to:

Feng Jiang, email: [email protected]

Keywords: diagnosis; early stage; lung cancer; plasma; biomarkers

Received: March 24, 2018    Accepted: April 07, 2018    Published: May 15, 2018


We previously developed three microRNAs (miRs-21, 210, and 486-5p), two long noncoding RNAs (lncRNAs) (SNHG1 and RMRP), and two fucosyltransferase (FUT) genes (FUT8 and POFUT1) as potential plasma biomarkers for lung cancer. However, the diagnostic performance of the individual panels is not sufficient to be used in the clinics. Given the heterogeneity of lung tumors developed from multifactorial molecular aberrations, we determine whether integrating the different classes of molecular biomarkers can improve diagnosis of lung cancer. By using droplet digital PCR, we analyze expression of the seven genes in plasma of a development cohort of 64 lung cancer patients and 33 cancer-free individuals. The panels of three miRNAs (miRs-21, 210, and 486-5p), two lncRNAs (SNHG1 and RMRP), and two FUTs (FUT8 and POFUT1) have a sensitivity of 81-86% and a specificity of 84-87% for diagnosis of lung cancer. From the seven genes, an integromic plasma signature comprising miR-210, SNHG1, and FUT8 is developed that produces higher sensitivity (95.45%) and specificity (96.97%) compared with the individual biomarker panels (all p<0.05). The diagnostic value of the signature was confirmed in a validation cohort of 40 lung cancer patients and 29 controls, independent of stage and histological type of lung tumor, and patients’ age, sex, and smoking status (all p>0.05). The integration of the different categories of biomarkers might improve diagnosis of lung cancer.

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