Non-small cell lung cancer (NSCLC) remains a leading cause of cancer-related mortality worldwide, yet its molecular drivers are not fully defined. Emerging evidence highlights the importance of tumor-stroma interactions mediated by secreted glycoproteins. However, the mechanisms by which cancer cells regulate the secretion of these protumorigenic proteins remain largely unknown. Endoplasmic reticulum–resident (ER-resident) N-glycan–processing enzymes regulate proper protein folding, a prerequisite for glycoproteins to exit the ER and undergo secretion. By evaluating their prognostic significance in lung tumors and conducting functional screening in lung cancer cells, we identify α-glucosidase II (α-Glc II) as a key regulator of NSCLC progression. α-Glc II promotes tumor growth and dissemination in a glucosidase activity–dependent manner in orthotopic mouse lung tumor model. Genetic disruption of α-Glc II induced ER stress and reduced cell proliferation and motility. Mechanistically, α-Glc II–mediated N-glycan modification regulated the ER-to-Golgi trafficking and secretion of specific oncogenic glycoproteins, including lysyl hydroxylase 2 (LH2), Tissue Inhibitor of Metalloproteinase 1 (TIMP1), and TGF-β, which are known to be associated with extracellular matrix remodeling. These findings uncover a role for ER glycosylation machinery in shaping the NSCLC secretome and highlight α-Glc II as a potential therapeutic target.
Shike Wang, Na Ding, Angelo Chen, Derrick Cardin, Yuting Xu, Kate Grimley, William K. Russell, Jun Xu, Jonathan M. Kurie, Guan-Yu Xiao, Xiaochao Tan
Usage data is cumulative from June 2026 through June 2026.
| Usage | JCI | PMC |
|---|---|---|
| Text version | 533 | 0 |
| 153 | 0 | |
| Figure | 291 | 0 |
| Supplemental data | 243 | 0 |
| Citation downloads | 93 | 0 |
| Totals | 1,313 | 0 |
| Total Views | 1,313 | |
Usage information is collected from two different sources: this site (JCI) and Pubmed Central (PMC). JCI information (compiled daily) shows human readership based on methods we employ to screen out robotic usage. PMC information (aggregated monthly) is also similarly screened of robotic usage.
Various methods are used to distinguish robotic usage. For example, Google automatically scans articles to add to its search index and identifies itself as robotic; other services might not clearly identify themselves as robotic, or they are new or unknown as robotic. Because this activity can be misinterpreted as human readership, data may be re-processed periodically to reflect an improved understanding of robotic activity. Because of these factors, readers should consider usage information illustrative but subject to change.