Exploring and validating a guanylate-binding protein signature to predict the prognostic features of patients with lung adenocarcinoma

Yue Li¹Wei Tian²Chen Chen¹Hailin Liu¹Zhenfa Zhang¹Changli Wang^1*

• ¹National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Lung Cancer Center, Tianjin, China
• ²Department of General Surgery, The Second Affiliated Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China

Background: Lung adenocarcinoma (LUAD) exhibits rapid growth and high infiltration. Guanylatebinding proteins (GBPs) induced by interferons play a crucial role in inflammatory signaling. This study aimed to investigate their anti-cancer roles in LUAD.The TCGA-LUAD and GSE31210 datasets were sourced from the TCGA and GEO databases, and seven GBP genes were obtained. The GBP score was computed using ssGSEA for each patient, followed by performing weighted gene co-expression network analysis (WGCNA) using the WGCNA package. Using the Limma package, differentially expressed genes (DEGs) were identified and then candidate genes were selected by univariate and multivariate Cox regression analyses. The Survimer and timeROC packages were respectively employed to conduct Kaplan-Meier (KM) survival and receiver operating characteristic (ROC) analyses. Immune infiltration was assessed by calculating immune cell score with ESTIMATE and ssGSEA, and drug sensitivity was predicted applying pRRophetic package. The GSEA package was used to perform pathway enrichment analysis. Finally, wound healing and Transwell assays were carried out for functional validation.The GBP score was significantly higher in para-carcinoma tissues. The green-yellow module (β=7), which contained 422 genes, was closely associated with the GBP score. We identified 92 overlapping genes from the DEGs between the normal and tumor samples. A 4-gene risk model derived from Cox regression demonstrated high predictive accuracy for 1-, 3-, and 5-year survival.Patients were assigned by the median risk score into low-and high-risk groups. Notably, low-risk patients showed better prognostic outcomes and higher immune infiltration scores, particularly for 2 CD4+ T cells and activated B cells. CD19 influenced tumor cell migration and invasion. A nomogram combining multiple clinical factors displayed robust prognostic performance. We also predicted potential drugs for treating different risk groups of LUAD. Pathways such as reactive oxygen species (ROS) and Myc target V2 were enriched in the high-risk groups, indicating potential targets for immunotherapy. Inhibiting CD19 in A549 cells suppressed the cells' abilities to migrate and invade.We analyzed the GBP phenotype associated with LUAD progression and developed a useful risk score model for patient prognosis.