A predictive model for preterm infants with bronchopulmonary dysplasia based on ferroptosis-related lncRNAs

Abstract

Background: Bronchopulmonary dysplasia (BPD) is the most challenging chronic lung disease for prematurity, with difficulties in early identification. Given lncRNA emerging as a novel biomarker and the regulator of ferroptosis, this study aims to develop a BPD predictive model based on ferroptosis-related lncRNAs (FRLs).

Methods: Using a rat model, we firstly explored mRNA levels of ferroptosis-related genes and ferrous iron accumulation in BPD rat lungs. Subsequently, a microarray dataset of umbilical cord tissue from 20 preterm infants with BPD and 34 preterm infants without BPD were downloaded from the Gene Expression Omnibus databases. Random forest and LASSO regression were conducted to identify diagnostic FRLs. Nomogram was used to construct a predictive BPD model based on the FRLs. Finally, umbilical cord blood lymphocytes of preterm infants born before 32 weeks gestational age and term infants were collected and determined the expression level of diagnostic FRLs by RT-qPCR.

Results: Increased iron accumulation and several dysregulated ferroptosis-associated genes were found in BPD rat lung tissues, indicating that ferroptosis was participating in the development of BPD. By exploring the microarray dataset of preterm infants with BPD, 6 FRLs, namely LINC00348, POT1-AS1, LINC01103, TTTY8, PACRG-AS1, LINC00691, were determined as diagnostic FRLs for modeling. The area under the receiver operator characteristic curve of the model was 0.932, showing good discrimination of BPD. In accordance with our analysis of microarray dataset, the mRNA levels of FRLs were significantly upregulated in umbilical cord blood lymphocytes from preterm infants who had high risk of BPD.

Conclusion: The incorporation of FRLs into a predictive model offers a non-invasive approach to show promise in improving early detection and management of this challenging chronic lung disease in premature infant, enabling timely intervention and personalized treatment strategies.

Keywords: Bronchopulmonary dysplasia; Diagnosis; Ferroptosis; LncRNA; Preterm infant.

Fig. 1

The flow diagram of this study. The study was divided into four parts: identification of elevated iron accumulation in BPD animal model, identification of biomarkers in GEO database associated with BPD, construction of a BPD predictive model, validation of FRLs expression level in preterm infants

Fig. 2

Elevated iron deposition and overactive ferroptosis in lungs from BPD rats. A Establishment of a rat BPD model. B Relative Fe²⁺ level in lungs of BPD and control rats. C Relative MDA level in lungs of BPD and control rats. D Relative mRNA expression level of ferroptosis-related genes in lungs of BPD rats and control rats. MDA, malondialdehyde, ACSL4, acyl-CoA synthase long-chain family member 4. HMOX1, heme oxygenase 1. FTH, ferritin heavy chain. ATM, ataxia telangiectasia mutated. ATF3, activating transcription factor 3. PRKAA1, protein kinase AMP-activated catalytic submit alpha-1. GSS, Glutathione. All data throughout the figure was presented as mean ± SEM (n ≥ 7). Statistical comparison by unpaired t-test, * p < 0.05, ** p < 0.01

Fig. 3

Differentially expressed genes in preterm infants with BPD. A Volcano plot of differentially expressed genes associated with BPD. B Heatmap of differentially expressed genes associated with BPD. C BP (D) CC (E) MF enrichment analysis of differentially expressed genes. F Enrichment analysis of KEGG pathway for differentially expressed genes. G GSEA analysis of differentially expressed genes. BP, biological process. CC, cellular component. MF, molecular function. KEGG, Kyoto encyclopedia of genes and genomes. GSEA, gene set enrichment analysis

Fig. 4

Differentially expressed ferroptosis-related lncRNAs (FRLs) in preterm infants with BPD. A Venn diagram to identify 3 differentially expressed ferroptosis-related genes. B Venn diagram to explore 16 differentially expressed lncRNAs. C Expression analysis of 3 differentially expressed ferroptosis-related genes in dataset. D Correlation analysis to determine 12 FRLs

Fig. 5

Diagnostic FRLs in preterm infants with BPD. A-B 6 diagnostic FRLs were determined using the random forest classifier Gini coefficients algorithm. C 8 diagnostic FRLs were obtained using LASSO regression. D Optimal λ selection in the LASSO regression. E Intersection results of random forest and LASSO regression, with a total of 6 diagnostic FRLs finally screened out. LASSO, least absolute shrinkage and selection operator

Fig. 6

Independent predictive potential of diagnostic FRLs. A Expression analysis of 6 diagnostic FRLs between BPD group and control group. Data was presented as mean ± SEM. Statistical comparison by unpaired t-test, * p < 0.05, ** p < 0.01. B Correlation analysis between 6 diagnostic FRLs. Red and blue indicated positive and negative correlations respectively. C ROC curve of LINC00348 (AUC = 0.796). D ROC curve of POT1-AS1 (AUC = 0.736). E ROC curve of LINC01103 (AUC = 0.706). F ROC curve of TTTY8 (AUC = 0.721). G ROC curve of LINC00691 (AUC = 0.697). H ROC curve of PACRG-AS1 (AUC = 0.71). ROC, receiver operating characteristic. AUC, area under the ROC curve

Fig. 7

Constructing a predictive model based on diagnostic FRLs. A The predicting nomogram of 6 diagnostic FRLs for BPD incidence. B ROC curve for the training set (AUC = 0.932). C Calibration curve for the training set (mean absolute error = 0.053 mean squared error = 0.00393). D DCA curve for the training set. E ROC curve for internal validation (AUC = 0.923). F Calibration curve for internal validation (mean absolute error = 0.046 mean squared error = 0.00328). G DCA curve for internal validation. ROC, Receiver operating characteristic. AUC, area under the ROC curve. DCA, decision curve analysis

Fig. 8

Biological functional and pathway enrichment analysis of high FRL socre group and low FRL score group. A Differences in GSVA scores of metabolism-related KEGG signaling pathway between high FRL score group and low FRL score group. B Differences in GSVA scores of immune-related KEGG signaling pathways between the high FRL score group and low FRL score group. C Differences in hallmarks between high FRL score group and low FRL score group. GSVA, gene set variation analysis. FRL score, scores of ferroptosis-related lncRNAs

Fig. 9

Expression levels of 6 diagnostic FRLs in umbilical cord blood lymphocytes from preterm infants and term infants. A Relative mRNA expression of LINC00348 in umbilical cord blood lymphocytes of preterm infants born before 32 weeks gestational age versus term infants. B Relative mRNA expression of LINC00691 in umbilical cord blood lymphocytes of preterm infants born before 32 weeks gestational age versus term infants. C Relative mRNA expression of LINC01003 in umbilical cord blood lymphocytes from preterm infants born before 32 weeks gestational age versus term infants. D Relative mRNA expression of POT1-AS1 in umbilical cord blood lymphocytes from preterm infants born before 32 weeks gestational age versus term infants. E Relative mRNA expression of PACRG-AS1 in umbilical cord blood lymphocytes from preterm infants born before 32 weeks gestational age versus term infants. F Relative mRNA expression of TTTY8 in umbilical cord blood lymphocytes from preterm infants born before 32 weeks gestational age versus term infants. G Relative mRNA expression of LINC00348, LINC00691, LINC01003, POT1-AS1, PACRG-AS1, TTTY8 in umbilical cord blood lymphocytes from preterm infants with BPD versus preterm infants without BPD. All data throughout the figure was presented as mean ± SEM (n = 3 to 20). Statistical comparison by unpaired t-test, * p < 0.05, ** p < 0.01, ns = no significance