Dry and wet experiments reveal diagnostic clustering and immune landscapes of cuproptosis patterns in patients with ankylosing spondylitis

Zhang, Peng; Chen, Honglin; Zhang, You; Liu, Yu; Zhu, Guangye; Zhao, Wenhua; Shang, Qi; He, Jiahui; Zhou, Zelin; Shen, Gengyang; Yu, Xiang; Zhang, Zhida; Chen, Guifeng; Yu, Fuyong; Liang, De; Tang, Jingjing; Liu, Zhixiang; Cui, Jianchao; Jiang, Xiaobing; Ren, Hui

doi:10.1016/j.intimp.2023.111326

Cited by 1 publication

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“…These findings underscore the potential of disulfidptosis patterns as diagnostic markers for OP, offering insights into tailored immunotherapeutic interventions. Methodological approaches employed in this study align with previous published literature [16][17][18]. The flow chart of research process is described in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

RNA sequencing-based approaches to identifying disulfidptosis-related diagnostic clusters and immune landscapes in osteoporosis

Zhang,

Li,

Chen

et al. 2024

Aging

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Disulfidptosis, a newly recognized cell death triggered by disulfide stress, has garnered attention for its potential role in osteoporosis (OP) pathogenesis. Although sulfide-related proteins are reported to regulate the balance of bone metabolism in OP, the precise involvement of disulfidptosis regulators remains elusive. Herein, leveraging the GSE56815 dataset, we conducted an analysis to delineate disulfidptosis-associated diagnostic clusters and immune landscapes in OP. Subsequently, vertebral bone tissues obtained from OP patients and controls were subjected to RNA sequencing (RNA-seq) for the validation of key disulfidptosis gene expression. Our analysis unveiled seven significant disulfidptosis regulators, including FLNA, ACTB, PRDX1, SLC7A11, NUBPL, OXSM, and RAC1, distinguishing OP samples from controls. Furthermore, employing a random forest model, we identified four diagnostic disulfidptosis regulators including FLNA, SLC7A11, NUBPL, and RAC1 potentially predictive of OP risk. A nomogram model integrating these four regulators was constructed and validated using the GSE35956 dataset, demonstrating promising utility in clinical decision-making, as affirmed by decision curve analysis. Subsequent consensus clustering analysis stratified OP samples into two different disulfidptosis subgroups (clusters A and B) using significant disulfidptosis regulators, with cluster B exhibiting higher disulfidptosis scores and implicating monocyte immunity, closely linked to osteoclastogenesis. Notably, RNA-seq analysis corroborated the expression patterns of two disulfidptosis modulators, PRDX1 and OXSM, consistent with bioinformatics predictions. Collectively, our study sheds light on disulfidptosis patterns, offering potential markers and immunotherapeutic avenues for future OP management.

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Section: Introductionmentioning

confidence: 99%