Back

Selectively increased autofluorescence at certain regions of skin may become a novel diagnostic biomarker for lung cancer

Zhang, M.; Tao, Y.; Chang, Q.; Li, Y.; Chu, T.; Ying, W.

2020-03-12 cancer biology
10.1101/315440 bioRxiv
Show abstract

Early diagnosis is critical for improving the 5-year survival rate of lung cancer patients. Our current study tested our hypothesis that increased autofluorescence (AF) of skin and nails may become a novel diagnostic biomarker of lung cancer, which has generated the following findings: First, our study on a mouse model of lung cancer has shown that development of lung cancer led to a marked increase in the epidermal green AF of the mice. Second, the AF intensity of the untreated lung cancer patients was significantly higher than that of the healthy persons and the pulmonary infection patients at certain examined locations of the skin and fingernails. Third, the ‘Pattern of AF’ of healthy controls, pulmonary infection patients and untreated lung cancer patients was markedly different from each other. Fourth, when the number of the locations with increased AF was used as the sole diagnostic parameter, our ROC analysis showed that the AUC was 0.9067 for differentiating the healthy controls and the untreated lung cancer patients. Collectively, our study has indicated that development of lung cancer is sufficient to induce increases in the epidermal green AF of both mice and human subjects. Our study has also indicated that the ‘Pattern of AF’ of lung cancer patients could become a novel biomarker of lung cancer, which holds great promise for non-invasive, rapid and economic diagnosis and screening of lung cancer.Competing Interest StatementThe authors have declared no competing interest.View Full Text

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.