Distinct signatures of lung cancer types: aberrant mucin O-glycosylation and compromised immune response.

ABSTRACT:

Background

Genomic initiatives such as The Cancer Genome Atlas (TCGA) contain data from -omics profiling of thousands of tumor samples, which may be used to decipher cancer signaling, and related alterations. Managing and analyzing data from large-scale projects, such as TCGA, is a demanding task. It is difficult to dissect the high complexity hidden in genomic data and to account for inter-tumor heterogeneity adequately.

Methods

In this study, we used a robust statistical framework along with the integration of diverse bioinformatic tools to analyze next-generation sequencing data from more than 1000 patients from two different lung cancer subtypes, i.e., the lung adenocarcinoma (LUAD) and the squamous cell carcinoma (LUSC).

Results

We used the gene expression data t

SUBMITTER: Lucchetta M 

PROVIDER: S-EPMC6702745 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature