BackgroundLung cancer is a severe cancer with a high death rate. The 5-year survival rate for stage III lung cancer is much lower than stage I. Early detection and intervention of lung cancer patients can significantly increase their survival time. However, conventional lung cancer-screening methods, such as chest X-rays, sputum cytology, positron-emission tomography (PET), low-dose computed tomography (CT), magnetic resonance imaging, and gene-mutation, -methylation, and -expression biomarkers of lung tissue, are invasive, radiational, or expensive. Liquid biopsy is non-invasive and does little harm to the body. It can reflect early-stage dysfunctions of tumorigenesis and enable early detection and intervention.MethodsIn this study, we analyzed RNA-sequencing data of tumor-educated platelets (TEPs) in 402 non-small-cell lung cancer (NSCLC) patients and 231 healthy controls. A total of 48 biomarker genes were selected with advanced minimal-redundancy, maximal-relevance, and incremental feature-selection (IFS) methods.ResultsA support vector-machine (SVM) classifier based on the 48 biomarker genes accurately predicted NSCLC with leave-one-out cross-validation (LOOCV) sensitivity, specificity, accuracy, and Matthews correlation coefficients of 0.925, 0.827, 0.889, and 0.760, respectively. Network analysis of the 48 genes revealed that the WASF1 actin cytoskeleton module, PRKAB2 kinase module, RSRC1 ribosomal protein module, PDHB carbohydrate-metabolism module, and three intermodule hubs (TPM2, MYL9, and PPP1R12C) may play important roles in NSCLC tumorigenesis and progression.ConclusionThe 48-gene TEP liquid-biopsy biomarkers will facilitate early screening of NSCLC and prolong the survival of cancer patients.

Overexpression of Discoidin domain receptor 1 (DDR1) is known to enhance the malignancy of breast cancer considerably. This study reports the identification of a potent DDR1 inhibitor, Nilotinib, for the treatment of breast cancer. MTT assay was used to evaluate the inhibitory activity of Nilotinib and meantime we used flow cytometry to evaluate the pro-apoptotic activity of Nilotinib against MCF-7 and MDA-MB-231. Expression of DDR1 was manipulated in MDA-MB-231 and MCF-7 cell lines with low-level DDR1 expression by transfecting with plasmids containing shRNA. The effect of DDR1 or treatment with Nilotinib on cell migration was assayed. The expression of p-DDR1, DDR1, p-ERK1/2, ERK1/2 and E-cadherin, Vimentin, Snail1, and caspase 3 were detected by western blot and immunofluorescent staining. Nilotinib against MCF-7 -MBapoptotic cell death. After co-culturing with Nilotinib (500 nM), apoptosis rate is 29.60 % ± 2.19% and 18.75 % ± 2.30%, respectively. Moreover, Nilotinib effectually blocked the cellular migration of MCF-7. Interestingly, the knock-down DDR1 could significantly block the migration of breast cancer, meantime the sensitivity of MCF-7 and MDA-MB-231 to Nilotinib was reduced. Targeting DDR1 therapeutically could potentially affect survival and influence metabolism in breast cancer, and Nilotinib could be used as a candidate for the treatment of breast cancer.

Publicly available tumor gene expression datasets are widely reanalyzed, but it is unclear how representative they are of clinical populations. Estimations of molecular subtype classification and prognostic gene signatures were calculated for 16,130 patients from 70 breast cancer datasets. Collated patient demographics and clinical characteristics were sparse for many studies. Considerable variations were observed in dataset size, patient/tumor characteristics, and molecular composition. Results were compared with Surveillance, Epidemiology, and End Results Program (SEER) figures. The proportion of basal subtype tumors ranged from 4 to 59%. Date of diagnosis ranged from 1977 to 2013, originating from 20 countries across five continents although European ancestry dominated. Publicly available breast cancer gene expression datasets are a great resource, but caution is required as they tend to be enriched for high grade, ER-negative tumors from European-ancestry patients. These results emphasize the need to derive more representative and annotated molecular datasets from diverse populations.