{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T11:37:16Z","timestamp":1768563436373,"version":"3.49.0"},"reference-count":22,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T00:00:00Z","timestamp":1660867200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["por-journal.com"],"crossmark-restriction":true},"short-container-title":["Pathol. Oncol. Res."],"abstract":"<jats:p><jats:bold>Objectives:<\/jats:bold> This study aimed to identify a molecular marker associated with the prognosis of non-small-cell lung cancer (NSCLC).<\/jats:p><jats:p><jats:bold>Materials and Methods:<\/jats:bold> The RNA sequencing data and clinical information of NSCLC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The weighted gene co-expression network analysis (WGCNA) was used to identify the co-expression gene modules and differentially expressed genes (DEGs) by comparing gene expression between NSCLC tumor tissues and normal tissues. Subsequently, the functional enrichment analysis of the DEGs was performed. Kaplan-Meier survival analysis and the GEPIA2 online tool were performed to investigate the relationship between the expression of these genes of interest and the survival of NSCLC patients, and to validate one most survival-relevent hub gene, as well as validated the hub gene using independent datasets from the GEO database. Further analysis was carried out to characterize the relationship between the hub gene and tumor immune cell infiltration, tumor mutation burden (TMB), microsatellite instability (MSI), and other known biomarkers of lung cancer. The related genes were screened by analyzing the protein-protein interaction (PPI) network and the survival model was constructed. GEPIA2 was applied in the potential analysis of pan-cancer biomarker of hub gene.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> 57 hub genes were found to be involved in intercellular connectivity from the 779 identified differentially co-expressed genes. Myeloid-associated differentiation marker (<jats:italic>MYADM<\/jats:italic>) was strongly associated with overall survival (OS) and disease-free survival (DFS) of NSCLC patients, and high <jats:italic>MYADM<\/jats:italic> expression was associated with poor prognosis. Thus, <jats:italic>MYADM<\/jats:italic> was identified as a risk factor. Additionally, <jats:italic>MYADM<\/jats:italic> was validated as a survival risk factor in NSCLC patients in two independent datasets. Further analysis showed that <jats:italic>MYADM<\/jats:italic> was nagetively associated with TMB, and was positively correlated with macrophages, neutrophils, and dendritic cells, suggesting its role in regulating tumor immunity. The <jats:italic>MYADM<\/jats:italic> expression differed across many types of cancer and had the potential to serve as a pan-cancer marker.<\/jats:p><jats:p><jats:bold>Conclusion:<\/jats:bold><jats:italic>MYADM<\/jats:italic> is an independent prognostic factor for NSCLC patients, which can predict the progression of cancer and play a role in the tumor immune cell infiltration in NSCLC.<\/jats:p>","DOI":"10.3389\/pore.2022.1610504","type":"journal-article","created":{"date-parts":[[2022,8,19]],"date-time":"2022-08-19T04:11:47Z","timestamp":1660882307000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["A Comprehensive Bioinformatic Analysis for Identification of Myeloid-Associated Differentiation Marker as a Potential Negative Prognostic Biomarker in Non-Small-Cell Lung 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