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Fig 1.

The study flowchart.

Abbreviations:GEO, gene expression omnibus; BPD, Bronchopulmonary dysplasia; PPI, protein-protein interaction; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; DMNC, Maximum Neighborhood Component; EPC, EcCentricity, Edge Percolated Component; MCC, Maximal Clique Centrality; MNC, Maximum Neighborhood Component; SVM-RFE, Support Vector Machine-Recursive Feature Elimination; ssGSEA, Single-sample gene set enrichment analysis; CCNB1, Cyclin B1; CXCL10, the C-X-C motif chemokine 10; IL7R, IL-7 receptor; DEFA4, the Defensin Alpha4; PRTN3, Proteinase 3; NCAPG, non-SMC condensin I complex subunit G.

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Fig 2.

DEGs.

(A) The volcano plot shows all DEGs, of which red and blue dots refer to significant DEGs. (B) Visualization of the PPI network genes by Cytoscape. (C) Venn diagram shows that 38 genes are identified from the Cytoscape using 12 topological analysis methods. (D) The heatmap displays the 38 upregulated and downregulated DEGs identified from Cytoscape, and each column represents one of BPD cases or controls. Red and blue represent upregulated and downregulated gene expression. DEGs differentially expressed genes.

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Fig 3.

Enrichment analysis.

(A-C) GO analysis of the DEGs, including biological process (BP), cellular component (CC), and molecular function (MF), respectively. The y-axis represents different GO terms, the x-axis represents gene ratio enriched in relative GO terms, the circle size refers to gene numbers, and the color represents p-value. (D) KEGG pathway analysis of the intersection of genes. Different colors represent various significant pathways and related enriched genes.

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Fig 4.

Machine learning.

(A-B) Biomarkers was using the SVMs through 5-fold cross-validation. (C-D) LASSO logistic regression algorithm to screen diagnostic markers. (E) Based on RF algorithm to screen biomarkers. (F) Venn diagram shows that 6 genes are identified from three ML algorithms. SVM-RFE support vector machine-recursive feature elimination, RF random forest, LASSO least absolute shrinkage and selection operator. ML Machine Learning.

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Fig 5.

Immuno-infiltration analysis of the BPD dataset.

(A) Histo-gram displays the results of 20 immune cell infiltrations. (B) Box plot displays the results differentially immune cell infiltrations between the BPD and non‐BPD group. An asterisk (*) signifies a P-value of < 0.05, indicating statistical significance. (C) Heat map displays the correlation analyses of immune infiltrated cells and immune infiltrated cells between six hub genes, red indicates a positive correlation, while blue represents a negative correlation.

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Fig 6.

The enrichplot of ssGSEA for CCNB1, CXCL10, DEFA4, IL7R, NCAPG and PRTN3.

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Fig 7.

The diagnostic value evaluation and nomogram construction of the discovery cohort.

(A-F)The Box plot showed expression of hub genes in BPDs and non-BPD groups. (G) The ROC curve of 6 hub genes in BPD. The number in the parentheses represents the AUC (Area Under the Curve).

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Fig 8.

Molecular docking diagram of the six diagnostic bmarkers with resveratrol.

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Fig 9.

Molecular docking diagram of the six diagnostic bmarkers with progesterone.

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