Highlights d Discovery of prognosis-associated proteins and pathways at early stage of LUAD d Proteomics revealed three subtypes related to clinical and molecular features d Identification of subtype-specific kinases and cancerassociated phosphoproteins d Identification of potential prognostic biomarkers and drug targets in LUAD

Purpose:The extracellular matrix (ECM) molecule osteopontin is implicated in many pathologic processes, including inflammation, cell proliferation, ECM invasion, tumor progression, and metastasis. The present study evaluated the clinical and biological importance of osteopontin in human lung cancer. Experimental Design and Results: Tissue microarrays derived from non^small cell lung cancer (NSCLC) patients were analyzed immunohistochemically. Osteopontin protein expression was observed in 64.5% (205 of 318) of primary tumors and 75.5% (108 of 143) of lymph node metastases, but in only 27.9% (12 of 43) of normal-appearing bronchial epithelial and pulmonary tissues. Osteopontin expression was associated with tumor growth, tumor staging, and lymph node invasion. In vitro osteopontin enhanced ECM invasion of NSCLC cells, and an osteopontin antibody abolished this effect. We further analyzed osteopontin levels in circulating plasma derived from 158 patients with NSCLC, 54 patients of benign pulmonary disease, and 25 healthy donors, and found that the median osteopontin levels for the three groups were 319.1, 161.6, and 17.9 ng/mL, respectively. Conclusions: Overexpression of osteopontin is common in primary NSCLC and may be important in the development and progression of the cancer. Osteopontin levels in the plasma may serve as a biomarker for diagnosing or monitoring patients with NSCLC.Lung cancer is the leading cause of cancer-related deaths in industrialized countries. It claims >150,000 lives each year in the U.S. alone, exceeding the combined mortality from breast, prostate, and colorectal cancer (1, 2). Despite recent advances in understanding lung cancer biology, the 5-year survival rate for the patients remains <15% (3). For the patients diagnosed with stage IV disease, this figure drops to a mere 1% due to local relapses and distant metastases. Predicting the metastatic behavior of the tumor and eradicating or controlling dissemination of the malignancy remain major clinical challenges to oncologists.Cancer progression depends on an accumulation of metastasis-supporting genetic modifications and physiologic alterations regulated by cell signaling molecules such as extracellular matrix (ECM) proteins. The latter contribute to interaction among cancer cells and endothelial cells, which play a critical role in the development of local invasion and distant metastasis (4, 5). One such ECM protein is osteopontin. Previous research suggests that osteopontin is up-regulated in a variety of cancers, such as breast, gastric, and colorectal cancers (6, 7). Reports also suggest that some highly metastatic cancer cell lines synthesize abundant osteopontin. For example, the metastatic cell Ca2-5-LT1 expresses osteopontin mRNA at a level nine times higher than that expressed by the nonmetastatic parental cell Rama 37 (8). These findings suggest that osteopontin is a key extracellular molecule involved in tumor development and progression. However, it has not been extensively evaluated as such in lung cancer. Evid...

Early stage lung cancer detection is the first step toward successful clinical therapy and increased patient survival. Clinicians monitor cancer progression by profiling tumor cell proteins in the blood plasma of afflicted patients. Blood plasma, however, is a difficult cancer protein assessment medium because it is rich in albumins and heterogeneous protein species. We report herein a method to detect the proteins released into the circulatory system by tumor cells. Initially we analyzed the protein components in the conditioned medium (CM) of lung cancer primary cell or organ cultures and in the adjacent normal bronchus using one-dimensional PAGE and nano-ESI-MS/MS. We identified 299 proteins involved in key cellular process such as cell growth, organogenesis, and signal transduction. We selected 13 interesting proteins from this list and analyzed them in 628 blood plasma samples using ELISA. We detected 11 of these 13 proteins in the plasma of lung cancer patients and non-patient controls. Our results showed that plasma matrix metalloproteinase 1 levels were elevated significantly in late stage lung cancer patients and that the plasma levels of 14-3-3 , ␤, and in the lung cancer patients were significantly lower than those in the control subjects. To our knowledge, this is the first time that fascin, ezrin, CD98, annexin A4, 14-3-3 , 14-3-3 ␤, and 14-3-3 proteins have been detected in human plasma by ELISA. The preliminary results showed that a combination of CD98, fascin, polymeric immunoglobulin receptor/secretory component and 14-3-3 had a higher sensitivity and specificity than any single marker. In conclusion, we report a method to detect proteins released into blood by lung cancer. This pilot approach may lead to the identification of novel protein markers in blood and provide a new method of identifying tumor biomarker profiles for guiding both early detection and

BackgroundNeutrophil extracellular traps (NETs) were originally thought to be formed by neutrophils to trap invading microorganisms as a defense mechanism. Increasing studies have shown that NETs play a pivotal role in tumor progression and diffusion. In this case, transcriptome analysis provides an opportunity to unearth the association between NETs and clinical outcomes of patients with pan-cancer.MethodsThe transcriptome sequencing data of The Cancer Genome Atlas pan-cancer primary focus was obtained from UCSC Xena, and a 19-gene NETs score was then constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression model based on the expression levels of 69 NETs initial biomarkers we collected from multistudies. In addition, multiple datasets covering multiple cancer types from other databases were collected and used to validate the signature. Gene ontology enrichment analyses were used to annotate the functions of NETs-related pathways. Immunohistochemistry (IHC) was implemented to evaluate the role of NETs-related genes in clinical patients across types of tumors, including lung adenocarcinoma (n=58), colorectal carcinoma (n=93), kidney renal clear cell carcinoma (n=90), and triple-negative breast cancer (n=80).ResultsThe NETs score was calculated based on 19-NETs related genes according to the LASSO Cox model. The NETs score was considered a hazardous factor in most cancer types, with a higher score indicating a more adverse outcome. In addition, we found that NETs were significantly correlated to various malignant biological processes, such as the epithelial to mesenchymal transition (R=0.7444, p<0.0001), angiogenesis (R=0.5369, p<0.0001), and tumor cell proliferation (R=0.3835, p<0.0001). Furthermore, in IHC cohorts of a variety of tumors, myeloperoxidase, a gene involved in the model and a classical delegate of NETs formation, was associated with poor clinical outcomes.ConclusionsCollectively, these constitutive and complementary biomarkers represented the ability of NETs formation to predict the development of patients’ progression. Integrative transcriptome analyses plus clinical sample validation may facilitate the biomarker discovery and clinical transformation.

This research aims to address the problem of discriminating benign cysts from malignant masses in breast ultrasound (BUS) images based on Convolutional Neural Networks (CNNs). The biopsy-proven benchmarking dataset was built from 1422 patient cases containing a total of 2058 breast ultrasound masses, comprising 1370 benign and 688 malignant lesions. Three transferred models, InceptionV3, ResNet50, and Xception, a CNN model with three convolutional layers (CNN3), and traditional machine learning-based model with hand-crafted features were developed for differentiating benign and malignant tumors from BUS data. Cross-validation results have demonstrated that the transfer learning method outperformed the traditional machine learning model and the CNN3 model, where the transferred InceptionV3 achieved the best performance with an accuracy of 85.13% and an AUC of 0.91. Moreover, classification models based on deep features extracted from the transferred models were also built, where the model with combined features extracted from all three transferred models achieved the best performance with an accuracy of 89.44% and an AUC of 0.93 on an independent test set.