[Retracted] The Hub Genes Related to Osteoporosis Were Identified by Bioinformatics Analysis

Hu, Mengdie; Ding, Hong; Chao, Rui; Cao, Zhidong

doi:10.1155/2023/6726038

Cited by 3 publications

(3 citation statements)

References 44 publications

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“…CCL7 belongs to the CC chemokine family and its role in osteoporosis is currently under study [ 64 ]. RANKL induces the expression of many chemokines including CCL7, to enhance osteoclast formation.…”

Section: Discussionmentioning

confidence: 99%

Effect of immunology biomarkers associated with hip fracture and fracture risk in older adults

Cedeno-Veloz,

Lozano-Vicario,

Zambom-Ferraresi

et al. 2023

Immun Ageing

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Osteoporosis is a skeletal disease that can increase the risk of fractures, leading to adverse health and socioeconomic consequences. However, current clinical methods have limitations in accurately estimating fracture risk, particularly in older adults. Thus, new technologies are necessary to improve the accuracy of fracture risk estimation. In this observational study, we aimed to explore the association between serum cytokines and hip fracture status in older adults, and their associations with fracture risk using the FRAX reference tool. We investigated the use of a proximity extension assay (PEA) with Olink. We compared the characteristics of the population, functional status and detailed body composition (determined using densitometry) between groups. We enrolled 40 participants, including 20 with hip fracture and 20 without fracture, and studied 46 cytokines in their serum. After conducting a score plot and two unpaired t-tests using the Benjamini-Hochberg method, we found that Interleukin 6 (IL-6), Lymphotoxin-alpha (LT-α), Fms-related tyrosine kinase 3 ligand (FLT3LG), Colony stimulating factor 1 (CSF1), and Chemokine (C-C motif) ligand 7 (CCL7) were significantly different between fracture and non-fracture patients (p < 0.05). IL-6 had a moderate correlation with FRAX (R2 = 0.409, p < 0.001), while CSF1 and CCL7 had weak correlations with FRAX. LT-α and FLT3LG exhibited a negative correlation with the risk of fracture. Our results suggest that targeted proteomic tools have the capability to identify differentially regulated proteins and may serve as potential markers for estimating fracture risk. However, longitudinal studies will be necessary to validate these results and determine the temporal patterns of changes in cytokine profiles.

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Section: Discussionmentioning

confidence: 99%

Effect of immunology biomarkers associated with hip fracture and fracture risk in older adults

Cedeno-Veloz,

Lozano-Vicario,

Zambom-Ferraresi

et al. 2023

Immun Ageing

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“…Another bioinformatics technique, xCell, can evaluate immune cell composition in diseases [ 21 ]. While some studies have identified hub genes associated with primary osteoporosis (PMOP) [ 22 , 23 ], few have combined WGCNA and Machine Learning to select hub genes and focus on immune infiltration levels and related biomarkers.…”

Section: Introductionmentioning

confidence: 99%

Identification of ROCK1 as a novel biomarker for postmenopausal osteoporosis and pan-cancer analysis

Lai,

Jiang,

Gao

et al. 2023

Aging

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Background: Postmenopausal osteoporosis (PMOP) is a prevalent bone disorder with significant global impact. The elevated risk of osteoporotic fracture in elderly women poses a substantial burden on individuals and society. Unfortunately, the current lack of dependable diagnostic markers and precise therapeutic targets for PMOP remains a major challenge. Methods: PMOP-related datasets GSE7429, GSE56814, GSE56815, and GSE147287, were downloaded from the GEO database. The DEGs were identified by “limma” packages. WGCNA and Machine Learning were used to choose key module genes highly related to PMOP. GSEA, DO, GO, and KEGG enrichment analysis was performed on all DEGs and the selected key hub genes. The PPI network was constructed through the GeneMANIA database. ROC curves and AUC values validated the diagnostic values of the hub genes in both training and validation datasets. xCell immune infiltration and single-cell analysis identified the hub genes’ function on immune reaction in PMOP. Pan-cancer analysis revealed the role of the hub genes in cancers. Results: A total of 1278 DEGs were identified between PMOP patients and the healthy controls. The purple module and cyan module were selected as the key modules and 112 common genes were selected after combining the DEGs and module genes. Five Machine Learning algorithms screened three hub genes (KCNJ2, HIPK1, and ROCK1), and a PPI network was constructed for the hub genes. ROC curves validate the diagnostic values of ROCK1 in both the training (AUC = 0.73) and validation datasets of PMOP (AUC = 0.81). GSEA was performed for the low-ROCK1 patients, and the top enriched field included protein binding and immune reaction. DCs and NKT cells were highly expressed in PMOP. Pan-cancer analysis showed a correlation between low ROCK1 expression and SKCM as well as renal tumors (KIRP, KICH, and KIRC). Conclusions: ROCK1 was significantly associated with the pathogenesis and immune infiltration of PMOP, and influenced cancer development, progression, and prognosis, which provided a potential therapy target for PMOP and tumors. However, further laboratory and clinical evidence is required before the clinical application of ROCK1 as a therapeutic target.

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“…This article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. This investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”

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confidence: 99%