Published online Dec 19, 2023. doi: 10.5498/wjp.v13.i12.1005
Peer-review started: August 31, 2023
First decision: September 14, 2023
Revised: October 13, 2023
Accepted: November 9, 2023
Article in press: November 9, 2023
Published online: December 19, 2023
Processing time: 110 Days and 5.1 Hours
Depression is a common life-threatening and disabling mental illness, and long-chain non-coding RNA (lncRNA) abnormal expression may affect the pathophysiological processes of depression. Our previous study reported that the single-nucleotide polymorphism (SNP) rs155979 GC in the promoter region of lncRNA NONHSAT102891 affects depression susceptibility in a Chinese population.
The complex interplay of species between major depressive disorder and lncRNA remains unclear.
To explored associations between two SNPs and haplotypes within lncRNA NONHSAT102891 promoter region and depression susceptibility in Chinese population.
We conducted a case-control study in a cohort of 480 patients with depression and 329 healthy controls, and performed genotyping by gene sequencing. The function of the two lncRNA NONHSAT102891 promoter G/C and A/T haplotypes was detected by dual-luciferase reporter assays of human embryonic kidney 293T transfected cells.
The degree of mild depressive episodes associated with the rs6230 TC/CC genotype increased by 1.59 times. The haploid analysis revealed linkage disequilibrium between rs3792747 and rs6230, and the double SNP CG haplotype was more common in the control group compared to case group, indicating that this haplotype significantly reduced the risk of depression (C/G vs T/A: odds ratio = 0.42, 95% confidence interval: 0.21-0.83, P = 0.01). There was no significant difference in the dual-luciferase reporter activity of the G/C and A/T haplotypes compared with the control group (P > 0.05).
The rs3792747 and rs6230 CG haplotypes of the lncRNA NONHSA T102891 promoter may be associated with a reduced risk of depression in the Chinese population. However, further studies with a larger sample size are required to determine the reference expression range of biomarkers.
This study provides insights into the early prediction and diagnosis of depression and important clues for development of tools that will facilitate more accurate diagnosis and treatment of depression in the clinic.