Protein-protein interaction network of gene expression in the hydrocortisone-treated keloid

A Study on the Expression of Messenger RNAs and Long Noncoding RNA in Keloid Fibroblasts Based on Gene Expression Omnibus Microarray Data Mining

OBJECTIVE

The purpose of this study was to find the coding RNA [messenger RNA (mRNA)] and long noncoding RNA (lncRNA) expressed in keloid through the analysis of Gene Expression Omnibus microarray chip of keloid fibroblasts.

MATERIALS AND METHODS

Gene Expression Omnibus database GSE7890 database was downloaded with selection of keloids and normal scar group data. The data were analyzed by R language combined with online database. The log2FC>1, P value <0.01 was chosen as screening criteria, and the differentially expressed mRNAs were screened for GO and KEGG function analysis.

RESULTS

One hundred fifty-five mRNA expression in the keloid group was significantly different from that in the normal group, including 31 groups with upregulated mRNA expression and 124 groups with down-regulated mRNA expression. Meanwhile, 8 lncRNAs were changed in the keloid group, including 3 upregulated (Rp11-420a23.1, Rp11-522b15.3, and Rp11-706j10.1) and 5 down-regulated (LINC00511, LINC00327, Hoxb-as3, Rp11-385n17.1, and Rp3-428l16.2). Quantitative polymerase chain reaction analysis of DElncRNAs in keloid fibroblasts showed that the expression of all DElncRNAs except for RP11-385N17.1 was increased in the keloid group compared with the control group. Moreover, the differences in LINC00511 and RP11-706J10.1 were statistically significant.

CONCLUSION

The noncoding RNA information of Gene Expression Omnibus chip data can be deeply mined through bioinformatics, and the potential epigenomic mechanism affecting keloid formation can be found from the existing database.

Competing Endogenous RNA Network in Non-Keloid-Prone Individuals During Wound Healing

ABSTRACT

To study the interaction between differentially expressed long non-coding RNAs (lncRNAs), microRNAs, and messenger RNAs during wound healing in normal individuals. The GSE113621 dataset was downloaded from gene expression matrix, specimens regarding non-keloid-prone individuals were selected, including items before and 6 weeks after injury. A Pearson correlation coefficient of > 0.95 was selected as the index to screen targeting relationships among different RNAs. Cytoscape was used to construct a network diagram. The expression of 2547 lncRNAs was changed during the wound healing process-1479 were upregulated and 1068 were downregulated. After analyzing competitive endogenous RNA network, 4 upregulated (MEG8, MEG3, MIR181A1HG, MIR4435-2HG) lncRNAs were found expressed during wound healing. MEG8/MEG3 may regulate fibroblast proliferation, differentiation, and apoptosis through hsa-miR-296-3p/miR-6763-5p. In-depth mining of gene expression matrix data indicated that lncRNAs and a competitive endogenous RNA regulatory network participate in the wound healing process, possibly providing novel intervention targets and treatment options for delayed wound healing.

Secondary data mining of GEO database for long non-coding RNA and Competing endogenous RNA network in keloid-prone individuals

This study aimed to identify long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) differentially expressed (DE) during keloid formation, predict DElncRNA-DEmiRNA-DEmRNA interactions, and construct a competing endogenous RNA (ceRNA) network through secondary data mining of keloid-related sequencing and microarray data in the open-source Gene Expression Omnibus (GEO) database. The GSE113621 dataset was downloaded from the GEO database, |log₂FC|>1 and p<0.05 were set as screening criteria, genes expressed only in keloid-prone individuals were selected as research objects, and DEmRNAs, DElncRNAs, and DEmiRNAs before injury and 6 weeks after injury were screened. A Pearson correlation coefficient (PCC) of > 0.95 was selected as the index to predict the targeting relationships among lncRNAs, miRNAs, and mRNAs; and a network diagram was constructed using Cytoscape. The expression of 2356 lncRNAs was changed in the keloid-prone group—1306 were upregulated and 1050 were downregulated. Six lncRNAs, namely, 2 upregulated (DLEU2 and AP000317.2) and 4 downregulated (ADIRF-AS1, AC006333.2, AL137127.1 and LINC01725) lncRNAs, were expressed only in the keloid-prone group and were used to construct a ceRNA network. DLEU2 may regulate fibroblast proliferation, differentiation, and apoptosis through hsa-miR-30a-5p/hsa-miR-30b-5p. In-depth mining of GEO data indicated that lncRNAs and a ceRNA regulatory network participate in the wound healing process in keloid-prone individuals, possibly providing novel intervention targets and treatment options for keloid scars.

Gene expression profiling analysis: the effect of hydrocortisone on keloid fibroblasts by bioinformatics

BACKGROUND

We aimed to explore potential molecular basis of keloid formation and response mechanism of keloid to hydrocortisone (HC).

METHODS

Transcriptional profile of GSE7890 which contained five normal scars with no HC treatment (NNHC), four normal scars treated with HC (NHC), five keloids with no HC treatment (KNHC), and five keloids treated with HC (KHC) samples was downloaded to identify differentially expressed genes (DEGs). Based on DEGs, hierarchical cluster analysis and pathway enrichment analysis were performed. Then, identification of characteristic pathway was performed, followed by calculation of pathway deviation score.

RESULTS

Compared to NNHC group, total 1603 DEGs in NHC group, 895 DEGs in KHC group, and 832 DEGs in KNHC group were identified. Hierarchical cluster analysis revealed these four groups could be well distinguished. Total three pathways included cytokine-cytokine receptor interactions were significantly different between KNHC and NNHC groups. Besides, MAPK signaling pathway, endocytosis, and apoptosis were selected between KHC and KNHC groups. Genes of vascular endothelial growth factor C (VEGFC), tenascin C (TNC), and jun proto-oncogene (JUN) were selected as important DEGs in KHC, KNHC, and NHC groups, respectively.

CONCLUSIONS

VEGF and TNC were, respectively, involved in KHC and KNHC in the mechanism of focal adhesion. JUN might be a potential molecular marker related to normal scar.