Identification of key ferroptosis genes in paraspinal muscle degeneration based on RNA sequencing and bioinformatics analysis

doi:10.11958/20240587

Abstract

Abstract:

Objective To explore the gene expression profile in paraspinal muscle degeneration (PMD) and identify key ferroptosis genes. Methods RNA sequencing was performed on paraspinal muscle tissue of 3 normal and 3 PMD patients respectively to obtain differentially expressed genes. Through protein-protein interaction (PPI) and gene functional enrichment analysis, the intersection of ferroptosis genes was identified to identify key hub genes associated with ferroptosis. The diagnostic value for PMD disease was analyzed by receiver operating characteristic (ROC) curves. Results A total of 292 differentially expressed genes were identified in PMD. Among them, 125 genes were significantly downregulated and 167 genes were significantly upregulated. Bioinformatics analysis revealed that 14 differentially expressed genes were associated with ferroptosis. Among them, ferroptosis genes MUC1, ATF3 and CDKN1A were key hub genes with good specificity and sensitivity for diagnosing PMD. Functional enrichment analysis revealed that they may mediate the occurrence and progression of PMD by regulating cell apoptosis, ferroptosis and skeletal muscle tissue development and differentiation. Conclusion Ferroptosis genes MUC1, ATF3 and CDKN1A can serve as biomarkers for diagnosing PMD, providing theoretical basis for decoding the pathological mechanism of PMD and developing new drugs.

Key words: sequence analysis, RNA, ferroptosis, computational biology, paraspinal muscles degeneration

CLC Number:

R685

Figures/Tables 6

Fig.1 Identification of the gene expression profile in PMD by RNA-seq

Fig.2 Identification of the ferroptosis-related DEGs in PMD

Tab.1 The expression profile of 14 ferroptosis-related DEGs in PMD

基因名称	log₂（FC）	FDR	基因名称	log₂（FC）	FDR
CHAC1	-1.454 4	0.001 0	SESN2	1.338 2	0.006 8
GPT2	-1.262 4	0.026 2	MUC1	1.427 3	0.032 1
SLC40A1	-1.207 3	0.013 2	STING1	1.442 5	0.000 2
NR1D2	-1.102 5	<0.000 1	ATF3	1.700 5	0.000 5
TFRC	-1.072 2	0.004 0	GCH1	1.826 3	0.013 1
HILPDA	1.116 3	0.040 3	CDKN1A	2.147 8	<0.000 1
NDRG1	1.197 2	0.000 2	SLC2A1	2.717 8	<0.000 1

Fig.3 PPI identification of key hub DEGs in PMD

Fig.4 GO functional annotation and KEGG pathway enrichment analysis

Fig.5 Key FRGs used to evaluate the ROC curve of PMD

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