Project description:No effective treatments against lung cancer for early or advanced stages have been found. Our aim is to assess whether cancer stem cells (CSC) do show over expression of genes, which were previously identified for adenocarcinoma recurrence, in patients with early and locally advanced non-small cell lung cancer (NSCLC) stages.
Project description:Immunotherapy has improved the prognosis of patients with advanced non-small cell lung
cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC
Project description:We present data of global proteomes for non-small cell lung cancer for squamous cell and adenocarcinoma, and for normal adjacent tissue.
Project description:We used microarrays to detail the differentially expressed miRNA before and after down-regulation of miR-744 in lung cancer cell lines by genome-wide expression microarray To gain insights into the molecular mechanism of miR-744 in the promotion of malignant phenotype of lung cancer cells, we analyzed the mRNAs differentially expressed before and after miR-744 down-regulation.
Project description:This series contain 36 samples obtained from human lung tissue and includes the following: 7 Adenocarcinoma samples. 16 Squamous cell carcinoma samples. 1 AdenoSquamous sample. 2 Renal Metastasis. 1 Colon metastasis. 7 normal lung tissue adjacent to the tumors. 2 commercial normal lung RNA. Keywords = Lung Keywords = Non Small Cell Lung Cancer Keywords = Adenocarcinoma Keywords = Squamous Cell Carcinoma Keywords = Normal Lung. Keywords: other