Project description:This cohort included NGS data of a panel on 1021 cancer related gene from paired cancer-normal samples of 1794 Chinese lung cancer patients, including 44 SCLC patients and 1750 non-small cell lung cancer patients. According to ACMG 2015 guideline, pathogenic germline mutations were annotated and manually verified.
2022-07-06 | PRJEB41076 | EVA
Project description:Lung microbiome of non-small cell lung cancer
| PRJNA647170 | ENA
Project description:Choice Study of Sequencing Chinese Non-Small Cell Lung Cancer Patients
Project description:Normal appearing airway samples from non-small cell lung (NSCLC) cancer patients were profiled using illumina sequencing arrays. Allelic imbalance was detected in normal-appearing large and small airway samples and affected known lung cancer driver genes.
Project description:Lung tumors, as well as normal tumor-adjacent (NTA) tissue of non-small cell lung cancer (NSCLC) patients, were collected and subjected label-free quantitation shotgun proteomics in data-independent mode to identify differences between the tumors and adjacent tissue. By employing in-depth proteomics, we identified several pathways that are up- or downregulated in the tumors of non-small cell lung cancer patients.
2023-11-27 | PXD046998 | Pride
Project description:Non-small Cell Lung Cancer (qRT-PCR)
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
