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Identification of Diagnostic Biomarkers Associated With M1 Macrophage in Lung Squamous Cell Carcinoma via Machine
Huiting Deng1, Zhenling Wang2, Qiangzhe Zhang3
1Tianjin Key Laboratory of Extracorporeal Life Support for Critical Diseases, Tianjin Institute of Hepatobiliary Disease, Tianjin Third Central Hospital affiliated to Nankai University, 300170 Tianjin, China.
Background:
Macrophage infiltration is prevalent in lung cancer tissues, significantly influencing disease progression and clinical outcomes. Lung squamous cell carcinoma (LUSC) is often diagnosed at advanced stages, resulting in poor prognosis. Identifying effective diagnostic biomarkers, particularly those associated with macrophage infiltration, is crucial for early detection and improved treatment outcomes. This study aims to identify diagnostic markers specifically linked to M1 macrophages in LUSC.
Methods:
Differential gene expression analysis and immune cell infiltration assessment were conducted using the limma and CIBERSORT packages. The WGCNA algorithm was then applied to identify genes in modules related to M1 macrophages. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to investigate the biological functions of M1 macrophage-related differentially expressed genes (DEGs). To identify M1 macrophage-associated biomarkers in LUSC, a diagnostic model was developed using four machine learning algorithms, with validation through nomogram visualization, calibration curves, and external datasets. Finally, immunohistochemical staining was performed to further confirm the expression of hub genes and the predictive accuracy of M1 macrophage-related biomarkers in LUSC.
Results:
A total of 143 M1 macrophage-related DEGs were identified, which were involved in regulating immune response pathways. The support vector machine (SVM) model based on these genes demonstrated exceptional performance, with area under the curve (AUC) values of 0.995 in the training cohort and 1.000 in three external validation datasets. Immunohistochemical analysis further confirmed the diagnostic accuracy of Matrix metalloproteinase-7 (MMP7), Reticulon-1 (RTN1), Zinc finger protein ZIC 2 (ZIC2), Killer cell lectin-like receptor subfamily B member 1 (KLRB1), and C-X-C motif chemokine 13 (CXCL13), yielding an AUC of 0.992. These results highlight the strong diagnostic capability of the 5 hub genes in LUSC.
Conclusion:
The study highlights the pivotal role of M1 macrophage-related DEGs in LUSC tumorigenesis. The newly identified 5 hub genes provide a highly accurate diagnostic tool for LUSC, offering potential improvements for both diagnostic and therapeutic strategies.
