An integrated multi-algorithm analysis to decipher senescence and metabolic characteristics in nucleus pulposus cells during disc degeneration.

Accumulating evidence has suggested that intervertebral disc degeneration (IVDD) serves as a health problem deserving attention from worldwide, owing to the low back pain and even disability it causes. Cellular senescence, an irreversible process of terminal cell cycle arrest, plays an essential role in the degeneration of nucleus pulposus (NP) cells and IVDD with an unknown underlying mechanism. Here, we have explored the senescence signature in IVDD through high dimensional weighted gene co-expression network analysis (hdWGCNA) algorithm in two single cell datasets (PRJCA014236 and GSE244889) to construct a novel signature called NP_Senescence, which accurately identifies the patients with IVDD. The efficacy and accuracy were validated in various external datasets, including two expression array datasets (GSE70362 and GSE34095) and one single cell dataset (GSE230809). Additionally, the accuracy of the specific model was also verified with the senescent cell identification (SenCID) algorithm. IVDD patients with a high NP_Senescence score exhibited the characteristics of compensated activation of hyaluronic acid monomers synthesis and abnormal status of DNA damage repair through single-cell Flux Estimation Analysis (scFEA) algorithm. Furthermore, the NP_Senescence was negatively associated with the level of AMP and Glucose-1-phosphate, while positively associated with the level of β-alanine and UDP-glucuronic acid. In conclusion, we constructed a novel disc-specific scoring model NP_Senescence by comprehensively analyzing the senescence-related genes of IVDD, which can be used for patient early diagnosis and treatment, as well as the identification of metabolic characteristics.