TLR7: A Key Prognostic Biomarker and Immunotherapeutic Target in Lung Adenocarcinoma

<b>Background:</b> The tumor microenvironment (TME) plays a crucial role in the progression of lung adenocarcinoma (LUAD). However, understanding its dynamic immune and stromal modulation remains a complex challenge. <b>Methods:</b> We utilized the ESTIMATE algorithm to evaluate the immune and stromal components of the LUAD TME from the TCGA database. Correlations between these components and clinical characteristics and patient prognosis were analyzed. Toll-like receptor 7 (TLR7) was identified as a key prognostic biomarker through PPI network and COX regression analysis. Validation of TLR7 expression was conducted using GEO data, qPCR, WB, and IHC. A prognostic model was developed using a nomogram, incorporating TLR7 expression. Enrichment analysis, the Tumor Immune Estimation Resource database, and single-sample gene set enrichment analysis were used to explore TLR7's potential function. The response of the TLR7 subgroup to immunotherapy and drug sensitivity was observed. <b>Results:</b> We found significant associations between the immune and stromal components of LUAD TME and clinical features and prognosis. Specifically, TLR7 was identified as a prognostic biomarker, where lower expression in tumor tissues was linked to worse outcomes. This finding was further confirmed by comparing TLR7 expression in LUAD cells to normal bronchial epithelial cells, revealing lower expression in the tumor cells. Incorporating TLR7 into a nomogram prognostic model resulted in a good predictor of patient survival. Additionally, TLR7 was associated with immune function and positively correlated with various immune cells. Importantly, patients with high TLR7 expression were more likely to benefit from anti-PD-1 checkpoint blockade therapy. We also identified four treatment candidates for patients with high TLR7 expression. <b>Conclusion:</b> TLR7 is a powerful clinical feature that predicts patient prognosis, immunotherapeutic response, and drug candidates, providing additional insights for the treatment of LUAD.