A Database of Lung Cancer-Related Genes for the Identification of Subtype-Specific Prognostic Biomarkers

A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks

Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer's disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

Applications and advancements of CRISPR-Cas in the treatment of lung cancer

Lung cancer is one of the most malignant diseases and a major contributor to cancer-related deaths worldwide due to the deficiency of early diagnosis and effective therapy that are of great importance for patient prognosis and quality of life. Over the past decade, the advent of clustered regularly interspaced short palindromic repeats/CRISPR associated protein (CRISPR/Cas) system has significantly propelled the progress of both fundamental research and clinical trials of lung cancer. In this review, we review the current applications of the CRISPR/Cas system in diagnosis, target identification, and treatment resistance of lung cancer. Furthermore, we summarize the development of lung cancer animal models and delivery methods based on CRISPR system, providing novel insights into clinical diagnosis and treatment strategies of lung cancer.