Abstract
BackgroundGenomic 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.MethodsIn 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).ResultsWe used the gene expression data to identify co-expression modules and differentially expressed genes to discriminate between LUAD and LUSC. We identified a group of genes which could act as specific oncogenes or tumor suppressor genes in one of the two lung cancer types, along with two dual role genes. Our results have been validated against other transcriptomics data of lung cancer patients.ConclusionsOur integrative approach allowed us to identify two key features: a substantial up-regulation of genes involved in O-glycosylation of mucins in LUAD, and a compromised immune response in LUSC. The immune-profile associated with LUSC might be linked to the activation of three oncogenic pathways, which promote the evasion of the antitumor immune response. Collectively, our results provide new future directions for the design of target therapies in lung cancer.
Highlights
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
The hyaluronan binding protein 2 (HABP2) is an extracellular serine protease, which is up-regulated in lung adenocarcinoma (LUAD) and down-regulated in LUSC has been associated with lung cancer [83]
Our analyses showed that LUAD and LUSC differentiate for the biological processes that are altered
Summary
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. It is difficult to dissect the high complexity hidden in genomic data and to account for inter-tumor heterogeneity adequately. The main subtypes of NSCLC are divided into adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Despite the staining strategy to separate lung tumors into different classes, cases that are ambiguous at the immunohistochemical level are often reported and difficult to resolve. A proper differentiation between LUAD and LUSC determines eligibility for certain types of therapeutic strategies [5]. Some drugs are contraindicated for one of the two lung cancer types, such as Bevacizumab (Avastin) in LUSC [6]. It becomes crucial to discriminate among the two lung cancer types in a precise way
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