Differential expression and effect analysis of lncRNA-mRNA in congenital pseudarthrosis of the tibia

Abstract

Background: To analyze the lncRNA-mRNA differential expression and co-expression network of periosteal stem cells (PSCs) from congenital pseudarthrosis of the tibia (CPT) and normal patients, and to explore the role of key lncRNAs. Methods: Differentially expressed lncRNAs and mRNAs in PSCs were obtained by sequencing, and biological functions of differentially expressed mRNAs were detected by gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG) pathway and protein -protein interaction (PPI) analysis. The co-expression network of lncRNA-mRNA was constructed by correlation analysis of differentially expressed lncRNAs and mRNAs, and the key lncRNAs were screened according to the connectivity degree. After that, the cis-regulated target genes of differential expressed lncRNAs and mRNAs were predicted. Results: A total of 194 differentially expressed lncRNAs were identified, including 73 upregulated and 121 downregulated genes. A total of 822 differentially expressed mRNAs were identified, including 311 upregulated and 511 downregulated genes. GO, KEGG and PPI enrichment analysis showed that the regulatory function of differentially expressed mRNAs were mainly gathered in skeletal system development and tissue morphogenesis. The co-expression network with 226 nodes and 3,390 edges was constructed based on correlation analysis. A total of 10 key lncRNAs, including FAM227B, POM121L9P, AF165147 and AC103702, were screened according to connectivity degree. Prediction of target genes indicated that FAM227B-FGF7 and AC103702-HOXB4/5/6 may play an important role in the pathogenesis of CPT. Conclusion: A total of 10 key lncRNAs, including FAM227B, POM121L9P, AF165147, and AC103702, occupy the core position in the co-expression network, suggesting that these lncRNAs and their target genes may play an important role in the pathogenesis of CPT.

Keywords: bioinformatic analysis; congenital pseudarthrosis of the tibia; long non-coding RNA; messenger RNA; periosteal stem cell.

FIGURE 1

The schematic diagram of sequencing.

FIGURE 2

Flow cytometry was used to detect the positive ratio of molecular markers CD31, CD34, CD44 and CD90 of purified PSCs derived from different periosteal tissues. PSCs, periosteal stem cells.

FIGURE 3

(A), the first 100 lncRNAs with the smallest p-value were plotted by heat map. (B), the first 100 mRNAs with the smallest p-value were plotted by heat map. (C), the expression difference of lncRNA in normal and CPT PSCs was analyzed by volcanic map. (D), the expression difference of mRNA in normal and CPT PSCs was analyzed by volcanic map. CPT, congenital pseudarthrosis of the tibia; PSCs, periosteal stem cells.

FIGURE 4

(A), GO analysis revealed gene enrichment in different cellular biological processes. (B), the top 20 significant GO terms were hierarchically clustered into a tree based on Kappa-statistical similarities among their gene memberships. (C), the representative terms from the full cluster were selected and converted into a network layout. Each term was represented by a circle node, where its size is proportional to the number of genes fall under that term, and its color represent its cluster identity (nodes of the same color belong to the same cluster). Terms with a similarity score >0.3 were linked by an edge (the thickness of the edge represents the similarity score). GO, Gene ontology.

FIGURE 5

(A), the top 20 significant signaling pathways were hierarchically clustered into a tree, which showed that the pathways with the most significant differences were mainly concentrated in pathways in cancer and PI3K-Akt signaling pathway. (B), the regulatory changes of related mRNAs involved in the pathways of cancer. Red represented upregulation and blue represented downregulation. (C), the regulatory changes of related mRNAs involved in PI3K-Akt signaling pathway. Red represented upregulation and blue represented downregulation.

FIGURE 6

(A), all protein-protein interactions among differentially expressed mRNAs were extracted from PPI data source and formed a PPI network. (B), MCODE algorithm was applied to identify neighborhoods where proteins were densely connected. Five MCODE modules were enriched. PPI, protein-protein interaction; MCODE, Molecular Complex Detection.

FIGURE 7

(A), a lncRNA-mRNA co-expression network with 226 nodes and 3,390 edges was constructed, and the top 10 lncRNAs ranked by connectivity degree in the network were highlighted as squares. The larger the square, the higher connectivity degree. Blue squares represented lncRNAs and yellow circles represented mRNAs. (B), cis-regulation predictive analysis on the differentially expressed lncRNAs and mRNAs was performed. The top five lncRNAs with significant differences were listed separately, with green diamonds represented lncRNAs and yellow circles represented mRNAs.

FIGURE 8

Five differentially expressed lncRNAs were selected for qRT-PCR. qRT-PCR, quantitative reverse transcription-polymerase chain reaction.