Bioinformatics analysis of the genes associated with co-occurrence of heart failure and lung cancer

A combination analysis based on bioinformatics tools reveals new signature genes related to maternal obesity and fetal programming

Background

Maternal obesity significantly influences fetal development and health later in life; however, the molecular mechanisms behind it remain unclear. This study aims to investigate signature genes related to maternal obesity and fetal programming based on a genomic-wide transcriptional placental study using a combination of different bioinformatics tools.

Methods

The dataset (GSE128381) was obtained from Gene Expression Omnibus (GEO). The data of 100 normal body mass index (BMI) and 27 obese mothers were included in the analysis. Differentially expressed genes (DEGs) were evaluated by limma package. Thereafter, functional enrichment analysis was implemented. Then, weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) analysis were used to further screening of signature genes. Simple linear regression analysis was used to assess the correlation between signature genes and newborn birth weight. Gene set enrichment analysis (GSEA) was implemented to study signaling pathways related to signature genes. The expression of the signature genes was also explored in 48 overweight mothers in the same dataset.

Results

A total of 167 DEGs were obtained, of which 122 were up-regulated while 45 were down-regulated. The dataset was then clustered into 11 modules by WGCNA, and the MEbrown was found as the most significant module related to maternal obesity and fetal programming (cor = 0.2, p = 0.03). The LASSO analysis showed that PTX3, NCF2, HOXB5, ABCA6, and C1orf162 are signature genes related to maternal obesity and fetal programming, which were increased in the placenta of obese mothers compared to those with normal BMI. The area under the curve (AUC) of the signature genes in the receiver operating characteristic curve (ROC) was 0.709, 0.660, 0.674, 0.667, and 0.717, respectively. Simple linear regression analysis showed that HOXB5 was associated with newborn birth weight. GSEA analysis revealed that these signature genes positively participate in various signaling pathways/functions in the placenta.

Conclusion

PTX3, NCF2, HOXB5, ABCA6, and C1orf162 are novel signature genes related to maternal obesity and fetal programming, of which HOXB5 is implicated in newborn birth weight.

CENPA and BRCA1 are potential biomarkers associated with immune infiltration in heart failure and pan-cancer

Heart failure (HF) and cancer are the two leading causes of death worldwide and affect one another in a bidirectional way. We aimed to identify hub therapeutic genes as potential biomarkers for the identification and treatment of HF and cancer. Gene expression data of heart samples from patients with ischemic HF (IHF) and healthy controls were retrieved from the GSE42955 and GSE57338 databases. Difference analysis and weighted gene co-expression network analysis (WGCNA) were used to identify key modules associated with IHF. The overlapping genes were subjected to gene and protein enrichment analyses to construct a protein-protein interaction (PPI) network, which was screened for hub genes among the overlapping genes. A total of eight hub genes were subjected to correlation, immune cell infiltration, and ROC analyses. Then we analyzed the roles of two significant genes in 33 tumor types to explore their potential as common targets in HF and cancer. A total of 85 genes were identified by WGCNA and differentially expressed gene (DEG) analyses. BRCA1, MED17, CENPA, RXRA, RXRB, SMARCA2, CDCA2, and PMS2 were identified as the hub genes with IHF. Finally, CENPA and BRCA1 were identified as potential common targets for IHF and cancer. These findings provide new perspectives for expanding our understanding of the etiology and underlying mechanisms of HF and cancer.