Project description:The traditional Chinese medicine Jinfukang (JFK) has been shown as a valuable drug for the non-small cell lung cancer (NSCLC) patients. Although clinically effective, the underlining mechanism remains unclear. Here, we performed RNA-seq assays for study the antitumor mechanisms of JFK ethanol extract on lung cancer cell line A549.
Project description:Lung cancer remains the leading cause of mortality from malignant tumors, non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer cases, and individualized diagnosis and treatment in traditional Chinese Medicine (TCM) is an effective trend. In this study, a proteomics research with DIA mode and a non-targeted lipidomics research were developed in NSCLC patients with Qi deficiency and Yin deficiency (QDYD) and Qi deficiency of lung-spleen (QDLS) syndromes.
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 microbiota of non small-cell cancer patients
Project description:One of the most fertile applications of next generation sequencing will be in the field of cancer genomics. Here, we report a high-throughput multi-dimensional sequencing study of primary non-small cell lung adenocarcinoma tumors and adjacent normal tissues of 6 never-smoker Korean female patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations including 47 somatic mutations and 20 fusion transcripts. We also characterized gene expression profiles which we sought to integrate with genomic aberrations and epigenetic regulations into functional networks. Importantly, among others the gene network module governing G2/M cell check point emerged as the primary source of disturbance in these patients. In addition, our study strongly suggests that microRNAs make key regulatory inputs into this gene network module. Our study offers a paradigm for integrative genomics analysis and proposes potential target pathways for the control of non-small cell lung adenocarcinoma. Study of primary non-small cell lung adenocarcinoma tumors and normal tissues of 6 patients.
Project description:One of the most fertile applications of next generation sequencing will be in the field of cancer genomics. Here, we report a high-throughput multi-dimensional sequencing study of primary non-small cell lung adenocarcinoma tumors and adjacent normal tissues of 6 never-smoker Korean female patients. Our data encompass results from exome-seq, RNA-seq, small RNA-seq, and MeDIP-seq. We identified and validated novel genetic aberrations including 47 somatic mutations and 20 fusion transcripts. We also characterized gene expression profiles which we sought to integrate with genomic aberrations and epigenetic regulations into functional networks. Importantly, among others the gene network module governing G2/M cell check point emerged as the primary source of disturbance in these patients. In addition, our study strongly suggests that microRNAs make key regulatory inputs into this gene network module. Our study offers a paradigm for integrative genomics analysis and proposes potential target pathways for the control of non-small cell lung adenocarcinoma. Study of primary non-small cell lung adenocarcinoma tumors and normal tissues of 6 patients.
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
