mrna sequencing was performed on a549 - bm5 cells and their parental counterparts. using a filtering threshold of a fold change greater than 2 and an adjusted p - value less than 0. 05, we identified 1, 371 differentially expressed genes ( degs ), including 589 upregulated and 782 downregulated genes ( fig. 5a ). principal component analysis ( pca ) revealed a clear separation between a549 - bm5 cells and their parental counterparts ( fig. 5b ). gene ontology ( go ) enrichment analysis identified multiple gene sets associated with epithelial - mesenchymal transition ( emt ), cell – cell adhesion, the wnt signaling pathway, the nf - κb signaling pathway, and the transforming growth factor ( tgf ) pathway ( fig. 5c ), all of which known to play key roles in bone metastasis. additionally, bone - related gene sets, including bone tissue morphogenesis and development, were significantly enriched ( fig. 5d ), which may indicate that a549 - bm5 cells acquire osteogenic - like transcriptional features. gene set enrichment analysis ( gsea ) further revealed enrichment of gene sets related to cell migration, adhesion, neuron differentiation, and cytokine signaling ( fig. 5e ). fig. 5analysis of the transcriptomic and proteomic characteristics of a549 - bm5. ( the volcano plot generated from transcriptome analyses of rna sample from a549 - parental and a549 - bm5 ( n = 3 ). ( principal component analysis ( pca ) of rna sequencing data from a549 - parental and a549 - bm5 cells. ( the bubble chart illustrates the gene ontology ( go ) enrichment analysis of differentially expressed genes ( degs ) between a549 - parental and a549 - bm5 cells. ( the bubble chart illustrates the enrichment of bone - related gene sets in the gene ontology ( go ) enrichment analysis of differentially expressed genes ( degs ) between a549 - parental and a549 - bm5 cells. ( gene set enrichment analysis ( gsea ) using the go database in the transcriptomic analysis. ( the volcano plot generated from proteomic analyses of a549 - parental and a549 - bm5 ( n = 3 ). ( principal component analysis ( pca ) of proteomic data from a549 - parental and a549 - bm5 cells. ( the bubble chart illustrates the gene ontology ( go ) enrichment analysis of differentially expressed proteins between a549 - parental and a549 - bm5 cells; ( i ) gene set enrichment analysis ( gsea ) using the go database in the proteomic analysis. ( the nine - square grid displays genes that have common changing trends between proteomic and transcriptomic analyses. ( k – l ) using the luad cohort from tcga, two random forest models were constructed to evaluate the contribution of genes to metastasis ( m1 ). the roc curves respectively display the classification performance of models established using commonly altered genes ( k ) and characteristic genes selected from the transcriptomic data ( l ). ( mean decrease gini of genes exhibiting concurrent changes in transcriptomic and proteomic profiles. ( mean decrease gini of characteristic genes in transcriptomic profiles. analysis of the transcriptomic and proteomic characteristics of a549 - bm5. ( the volcano plot generated from transcriptome analyses of rna sample from a549 - parental and a549 - bm5 ( n = 3 ). ( principal component analysis ( pca ) of rna sequencing data from a549 - parental and a549 - bm5 cells. ( the bubble chart illustrates the gene ontology ( go ) enrichment analysis of differentially expressed genes ( degs ) between a549 - parental and a549 - bm5 cells. ( the bubble chart illustrates the enrichment of bone - related gene sets in the gene ontology ( go ) enrichment analysis of differentially expressed genes ( degs ) between a549 - parental and a549 - bm5 cells. ( gene set enrichment analysis ( gsea ) using the go database in the transcriptomic analysis. ( the volcano plot generated from proteomic analyses of a549 - parental and a549 - bm5 ( n = 3 ). ( principal component analysis ( pca ) of proteomic data from a549 - parental and a549 - bm5 cells. ( the bubble chart illustrates the gene ontology ( go ) enrichment analysis of differentially expressed proteins between a549 - parental and a549 - bm5 cells; ( i ) gene set enrichment analysis ( gsea ) using the go database in the proteomic analysis. ( the nine - square grid displays genes that have common changing trends between proteomic and transcriptomic analyses. ( k – l ) using the luad cohort from tcga, two random forest models were constructed to evaluate the contribution of genes to metastasis ( m1 ). the roc curves respectively display the classification performance of models established using commonly altered genes ( k ) and characteristic genes selected from the transcriptomic data ( l ). ( mean decrease gini of genes exhibiting concurrent changes in transcriptomic and proteomic profiles. ( mean decrease gini of characteristic genes in transcriptomic profiles. to complement the transcriptomic data, we performed quantitative proteomics analysis on a549 - bm5 and parental cells. based on filtering criteria of a fold change greater than 1. 5 and an adjusted p - value less than 0. 05, we identified 144 upregulated proteins and 193 downregulated proteins ( fig. 5f ). pca of the proteomic data also demonstrated a clear separation between a549 - bm5 cells and their parental counterparts ( fig. 5g ). go enrichment analysis and gsea of the proteomic data revealed significant enrichment of pathways involved in cell migration, adhesion, ras signaling, mitochondrial function, histone modification, emt, and various metabolism − related pathways ( fig. 5h - i ). integrated analysis of transcriptomic and proteomic data identified 21 genes that were concurrently upregulated and 8 genes that were concurrently downregulated at both levels ( fig. 5h – j ). to explore the potential roles of these key genes in the distant metastasis of lung adenocarcinoma ( luad ) patients, we retrieved luad patient data from the tcga database. patients were stratified into m0 ( no distant metastasis ) and m1 ( distant metastasis ) groups according to their metastatic status. by utilizing a random forest model, we assessed the contribution of target genes to the metastatic status ( fig. 5k – l ). potential biomarkers for luad metastasis were identified based on their mean decrease gini scores obtained from the random forest model ( fig. 5m – n ). recent single - cell analyses have revealed substantial intra - line heterogeneity in gene expression and metastatic potential within cancer cell lines, supporting the rationale for selecting organ - tropic sublines via in vivo selection [,, ]. given the unique physicochemical properties and cytokine milieu of the bone and bone marrow microenvironment — characterized by hypoxia, high extracellular calcium levels, abundant matrix proteins such as collagen and osteopontin, and soluble factors like tgf - β, cxcl12, and rankl — in vivo circulation selection enables effective isolation and enrichment of tumor subpopulations with a propensity for bone metastasis. the a549 cell line is a widely used model for studying lung adenocarcinoma. however, our previous study revealed that although a549 cells possess the capacity to metastasize to bone after intracardiac injection, their metastatic efficiency is limited, with low and variable engraftment and a dispersed pattern of skeletal colonization. these features indicate the absence of strong bone - tropic preference and reduce their suitability for generating consistent and biologically relevant bone metastasis models. such characteristics make a549 suboptimal for quantitative studies specifically focused on bone metastasis. in the following analysis, we examined the key features of currently available lung cancer cell models with high bone - metastatic potential. hung et al. obtained results similar to our previous findings when establishing a bone metastasis model by injecting a549 cells via the left ventricle.