Abstract
No therapeutic targets have been identified for lung squamous cell cancer (SqCC) which is the second most prevalent lung cancer because its molecular profiles remain unclear. This study aimed to unveil disease-related protein networks by proteomic and bioinformatic assessment of laser-microdissected cancerous cells from seven SqCCs compared with eight representative lung adenocarcinomas. We identified three network modules significant to lung SqCC using weighted gene co-expression network analysis. One module was intrinsically annotated to keratinization and cell proliferation of SqCC, accompanied by hypoxia-induced aerobic glycolysis, in which key regulators were activated (HIF1A, ROCK2, EFNA1-5) and highly suppressed (KMT2D). The other two modules were significant for translational initiation, nonsense-mediated mRNA decay, inhibited cell death, and interestingly, eIF2 signaling, in which key regulators, MYC and MLXIPL, were highly activated. Another key regulator LARP1, the master regulator in cap-dependent translation, was highly suppressed although upregulations were observed for hub proteins including EIF3F and LARP1 targeted ribosomal proteins, among which PS25 is the key ribosomal protein in IRES-dependent translation. Our results suggest an underlying progression mechanism largely caused by switching to the cap-independent, IRES-dependent translation of mRNA subsets encoding oncogenic proteins. Our findings may help to develop therapeutic strategies to improve patient outcomes.
Highlights
No therapeutic targets have been identified for lung squamous cell cancer (SqCC) which is the second most prevalent lung cancer because its molecular profiles remain unclear
Potentially actionable oncogenic alterations have been identified in lung SqCC such as fibroblast growth factor receptor 1 (FGFR1) amplification, MET amplification, phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) mutation/amplification, and discoidin domain receptor tyrosine kinase 2 (DDR2) m utation[5,6,7]
The main aim of this study was to identify profiles of protein co-expression networks significantly associated with lung SqCC compared to lung papillary predominant adenocarcinoma (PPA), as representative lung adenocarcinoma
Summary
No therapeutic targets have been identified for lung squamous cell cancer (SqCC) which is the second most prevalent lung cancer because its molecular profiles remain unclear. Potentially actionable oncogenic alterations have been identified in lung SqCC such as fibroblast growth factor receptor 1 (FGFR1) amplification, MET amplification, phosphatidylinositol-4,5-bisphosphate 3-kinase, catalytic subunit alpha (PIK3CA) mutation/amplification, and discoidin domain receptor tyrosine kinase 2 (DDR2) m utation[5,6,7]. These oncogene aberrations might be useful as prognostic factors, no molecular targeted therapy has been established for SqCC. Weighted gene co-expression network analysis (WGCNA)[9], an unsupervised clustering method based on the correlation network of gene and/or protein expression, was performed to identify data-driven protein co-expression networks
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