Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.
ObjectiveTo analyze the correlation between the molecular biological information of SMARCA4-deficient non-small cell lung cancer (SMARCA4-dNSCLC) and its clinical prognosis, and to explore the spatial features and molecular mechanisms of interactions between cells in the tumor microenvironment (TME) of SMARCA4-dNSCLC. MethodsUsing data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), this study conducted functional enrichment analysis on differentially expressed genes (DEGs) in SMARCA4-dNSCLC and depicted its genomic variation landscape. Through weighted gene co-expression network analysis (WGCNA) and a combination of 10 different machine learning algorithms, patients in the training group were divided into a low-risk group and a high-risk group based on a median risk score (RiskScore). A corresponding prognostic prediction model was established, and on this basis, a nomogram was constructed to predict the 1, 3, and 5-year survival rates of patients. K-M survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model. External datasets from GEO further validated the prognostic value of the prediction model. In addition, we also evaluated the immunological characteristics of the TME of the prognostic model. Finally, using single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST), we explored the spatial features of interactions between cells in the TME of SMARCA4-dNSCLC, intercellular communication, and molecular mechanisms. ResultsA total of 56 patients were included in the training group, including 38 males and 18 females, with a median age of 62 (56-70) years. There were 28 patients in both the low-risk and high-risk groups. A total of 474 patients were included in the training group, including 265 males and 209 females, with a median age of 65 (58-70) years. A risk score model composed of 8 prognostic feature genes (ELANE, FSIP2, GFI1B, GPR37, KRT81, RHOV, RP1, SPIC) was established. Compared with patients in the low-risk group, those in the high-risk group showed a more unfavorable prognostic outcome. Immunological feature analysis revealed differences in the infiltration of various immune cells between the low-risk and high-risk groups. ScRNA-seq and ST analyses found that interactions between cells were mainly through macrophage migration inhibitory factor (MIF) signaling pathways (MIF-CD74+CXCR4 and MIF-CD74+CD44) via ligand-receptor pairs, while also describing the niche interactions of the MIF signaling pathway in tissue regions. ConclusionThe 8-gene prognostic model constructed in this study has certain predictive accuracy in predicting the survival of SMARCA4-dNSCLC. Combining the ScRNA-seq and ST analyses, cell-to-cell crosstalk and spatial niche interaction may occur between cells in the TME via the MIF signaling pathway (MIF-CD74+CXCR4 and MIF-CD74+CD44).