Identification of Potential Biomarkers for Intervertebral Disc Degeneration Using the Genome-Wide Expression Analysis

Expression of ANGPTL4 in Nucleus Pulposus Tissues Is Associated with Intervertebral Disc Degeneration

Objective

Angiopoietin-like protein 4 (ANGPTL4), encoding a glycosylated secreted protein, has been reported to be closely related to many kinds of diseases, including diabetes, tumor, and some musculoskeletal pathologies, such as rheumatoid arthritis, osteoarthritis, and osteoporosis. The aim of the current study is to investigate the role of ANGPTL4 in intervertebral disc degeneration and analyze the association of ANGPTL4 expression with Pfirrmann grades.

Methods

A total of 162 nucleus pulposus tissues were collected from lumbar intervertebral disc herniation patients undergoing interforaminal endoscopic surgery. Real-time quantitative PCR and western blot were performed to determine the mRNA and protein expression of ANGPTL4 in nucleus pulposus samples. Statistical analysis was performed to analyze the association of ANGPTL4 expression with Pfirrmann grades.

Results

Based on the clinical data of 162 patients, results showed that Pfirrmann grades were significantly associated with patients' age (r = 0.162, P = 0.047) and were not significantly associated with patients' gender (P > 0.05). RT-qPCR and western blot results showed that the mRNA (r = 0.287, P < 0.05) and protein (r = 0.356, P < 0.05) expressions of ANGPTL4 were both closely associated with Pfirrmann grades. The expression of ANGPTL4 was remarkably increased in the groups of high IVDD Pfirrmann grades.

Conclusion

The results demonstrated that ANGPTL4 expression was positively associated with the Pfirrmann grades and the severity of intervertebral disc degeneration. ANGPTL4 may be served as a candidate biomarker for intervertebral disc degeneration.

Pan-Tissue Aging Clock Genes That Have Intimate Connections with the Immune System and Age-Related Disease

In our recent transcriptomic meta-analysis, we used random forest machine learning to accurately predict age in human blood, bone, brain, heart, and retina tissues given gene inputs. Although each tissue-specific model utilized a unique number of genes for age prediction, we found that the following six genes were prioritized in all five tissues: CHI3L2, CIDEC, FCGR3A, RPS4Y1, SLC11A1, and VTCN1. Since being selected for age prediction in multiple tissues is unique, we decided to explore these pan-tissue clock genes in greater detail. In the present study, we began by performing over-representation and network topology-based enrichment analyses in the Gene Ontology Biological Process database. These analyses revealed that the immunological terms "response to protozoan," "immune response," and "positive regulation of immune system process" were significantly enriched by these clock inputs. Expression analyses in mouse and human tissues identified that these inputs are frequently upregulated or downregulated with age. A detailed literature search showed that all six genes had noteworthy connections to age-related disease. For example, mice deficient in Cidec are protected against various metabolic defects, while suppressing VTCN1 inhibits age-related cancers in mouse models. Using a large multitissue transcriptomic dataset, we additionally generate a novel, minimalistic aging clock that can predict human age using just these six genes as inputs. Taken all together, these six genes are connected to diverse aspects of aging.