Lung cancer has a high mortality rate, but an early diagnosis can contribute to a favorable prognosis. A liquid biopsy that captures and detects tumor-related biomarkers in body fluids has great potential for early-stage diagnosis. Exosomes, nanosized extracellular vesicles found in blood, have been proposed as promising biomarkers for liquid biopsy. Here, we demonstrate an accurate diagnosis of early-stage lung cancer, using deep learning-based surface-enhanced Raman spectroscopy (SERS) of the exosomes. Our approach was to explore the features of cell exosomes through deep learning and figure out the similarity in human plasma exosomes, without learning insufficient human data. The deep learning model was trained with SERS signals of exosomes derived from normal and lung cancer cell lines and could classify them with an accuracy of 95%. In 43 patients, including stage I and II cancer patients, the deep learning model predicted that plasma exosomes of 90.7% patients had higher similarity to lung cancer cell exosomes than the average of the healthy controls. Such similarity was proportional to the progression of cancer. Notably, the model predicted lung cancer with an area under the curve (AUC) of 0.912 for the whole cohort and stage I patients with an AUC of 0.910. These results suggest the great potential of the combination of exosome analysis and deep learning as a method for early-stage liquid biopsy of lung cancer.
Activation of sonic hedgehog (Shh) signaling has been implicated in progression of a variety of tumors. In this study, we elucidated a role for Shh in the invasion of gastric tumors and determined the mechanism by which Shh is regulated. Immunohistochemical analysis of 178 primary human gastric tumor biopsies indicated that Shh expression was positively correlated with lymph node metastasis, high lymphatic vessel density, and poor prognosis. In mouse xenograft models of human gastric cancer, enforced expression of Shh significantly enhanced the incidence of lung metastasis compared with nonexpressing controls. Mechanistic investigations revealed that phosphoinositide 3-kinase (PI3K)/Akt inhibition blocked Shh-induced epithelial-mesenchyme transition, the activity of matrix metalloproteinase 9 (MMP-9), and lymphangiogenesis, reducing tumor invasiveness and metastasis. Taken together, our findings establish that Shh signaling promotes the metastasis of gastric cancer through activation of the PI3K/Akt pathway, which leads to mesenchymal transition and MMP-9 activation. These findings offer preclinical validation of Shh as a candidate therapeutic target for treatment of metastatic gastric cancers. Cancer Res; 71(22); 7061-70. Ó2011 AACR.
Owing to the role of exosome as a cargo for intercellular communication, especially in cancer metastasis, the evidence has been consistently accumulated that exosomes can be used as a noninvasive indicator of cancer. Consequently, several studies applying exosome have been proposed for cancer diagnostic methods such as ELISA assay. However, it has been still challenging to get reliable results due to the requirement of a labeling process and high concentration of exosome. Here, we demonstrate a label-free and highly sensitive classification method of exosome by combining surface-enhanced Raman scattering (SERS) and statistical pattern analysis. Unlike the conventional method to read different peak positions and amplitudes of a spectrum, whole SERS spectra of exosomes were analyzed by principal component analysis (PCA). By employing this pattern analysis, lung cancer cell derived exosomes were clearly distinguished from normal cell derived exosomes by 95.3% sensitivity and 97.3% specificity. Moreover, by analyzing the PCA result, we could suggest that this difference was induced by 11 different points in SERS signals from lung cancer cell derived exosomes. This result paved the way for new real-time diagnosis and classification of lung cancer by using exosome as a cancer marker.
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