Blood Biomarkers of Bone Metastasis in Digestive Tract Malignant Tumors

Ma, Xiaoting; Fan, Y L; Chen, Zhaoxin; Zhang, Yujian; Wang, Shan; Yu, Jing

doi:10.2217/fon-2020-0509

Cited by 6 publications

(5 citation statements)

References 28 publications

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“…The reason may be that in the tumor microenvironment, the glycolysis of malignant tumor tissues is higher than that of normal tissues, and tumor cells preferentially use lactic acid as an energy source, which reduces the pH value in vivo and promotes the invasion and metastasis of tumor cells [ 23 ]. Scholars studied that elevated serum LDH has the potential to predict early metastasis, according to an analysis of serum biomarkers in 267 patients with gastrointestinal cancer [ 24 ]. As we all know, lymph node metastasis is associated with poor prognosis, which indirectly suggests that elevated lymph node metastasis may be associated with poor prognosis.…”

Section: Discussionmentioning

confidence: 99%

Prognostic value of serum α-HBDH levels in patients with lung cancer

Zhang¹,

Wang

Chen

2023

World J Surg Onc

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Background The purpose of our study is to investigate the expression level and prognostic value of serum α-hydroxybutyrate dehydrogenase (α-HBDH) in lung cancer (LC) patients. Method LC patients treated in the Department of Oncology, Shaanxi Provincial Cancer Hospital from January 2014 to December 2016 were included in this study, all of whom underwent serological detection of α-HBDH prior to admission, and were enrolled in follow-up 5-year survival. Comparing the differences between high group and normal groups based on α-HBDH and LDH expression via clinicopathological parameters and laboratory data. Univariate and multivariate regression and overall survival (OS) were analyzed to explore whether elevated α-HBDH was an independent risk factor for LC, compared to LDH. Results Multivariate regression analysis showed that age (P = 0.018), liver metastasis (P = 0.011), α-HBDH (P = 0.015), and neutrophil-to-lymphocyte ratio (NLR) (P = 0.031) were independent prognostic factors affecting OS in LC patients. The overall diagnostic efficacy of α-HBDH (AUC = 0.887) was higher than that of LDH (AUC = 0.709) in the ROC curve. The sensitivity was significantly higher of α-HBDH (sensitivity: 76.06%, specificity: 94.87%) compared with LDH (sensitivity: 49.30%, specificity: 94.87%). The median of OS was more significant in the high-α-HBDH group (6.4 months) than in the normal-α-HBDH group (12.7 months) (P = 0.023). The median of OS was significant in the high-LDH (> 245 U/L) group at 5.8 months and 12.0 months in the normal-LDH (≤ 245 U/L) group (P = 0.068). Conclusions Elevated expression of α-HBDH may indicate a poor prognosis of LC patients. It has a higher sensitivity than LDH and can be used as a potential early biomarker and an independent risk factor predicting the prognosis of LC survival.

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Section: Discussionmentioning

confidence: 99%

Prognostic value of serum α-HBDH levels in patients with lung cancer

Zhang¹,

Wang

Chen

2023

World J Surg Onc

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“…In previous studies ( 34 – 36 ,) T stage, N stage and grade are considered risk factors for BM of cancer. Jie et al.…”

Section: Discussionmentioning

confidence: 99%

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Liu

Sun

et al. 2023

Front. Oncol.

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ObjectiveThe purpose of this paper was to develop a machine learning algorithm with good performance in predicting bone metastasis (BM) in non-small cell lung cancer (NSCLC) and establish a simple web predictor based on the algorithm.MethodsPatients who diagnosed with NSCLC between 2010 and 2018 in the Surveillance, Epidemiology and End Results (SEER) database were involved. To increase the extensibility of the research, data of patients who first diagnosed with NSCLC at the First Affiliated Hospital of Nanchang University between January 2007 and December 2016 were also included in this study. Independent risk factors for BM in NSCLC were screened by univariate and multivariate logistic regression. At this basis, we chose six commonly machine learning algorithms to build predictive models, including Logistic Regression (LR), Decision tree (DT), Random Forest (RF), Gradient Boosting Machine (GBM), Naive Bayes classifiers (NBC) and eXtreme gradient boosting (XGB). Then, the best model was identified to build the web-predictor for predicting BM of NSCLC patients. Finally, area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity were used to evaluate the performance of these models.ResultsA total of 50581 NSCLC patients were included in this study, and 5087(10.06%) of them developed BM. The sex, grade, laterality, histology, T stage, N stage, and chemotherapy were independent risk factors for NSCLC. Of these six models, the machine learning model built by the XGB algorithm performed best in both internal and external data setting validation, with AUC scores of 0.808 and 0.841, respectively. Then, the XGB algorithm was used to build a web predictor of BM from NSCLC.ConclusionThis study developed a web predictor based XGB algorithm for predicting the risk of BM in NSCLC patients, which may assist doctors for clinical decision making

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“…Subsequent analysis of peripheral blood indicators, such as monocyte count and percentage, eosinophil count and percentage, platelet level, D-D dimer level, LDH level, ALP level, and Hsp90α level, was performed, identifying monocyte percentage, LDH level, and ALP level as independent risk factors for lung cancer BM. Elevated LDH reflects tumor burden [19], predicting BM status in digestive system tumors [20]. High ALP levels are recognized as independent risk factors in various solid tumors [21][22][23][24][25].…”

Section: Discussionmentioning

confidence: 99%

Predictive nomogram for bone metastases in lung cancer based on monocyte infiltration

2024

Am J Cancer Res

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The presence of bone metastases (BM) in patients with lung cancer is indicative of a worse prognosis. The present study aims to investigate the risk factors associated with BM in patients with lung cancer. Patients with lung cancer admitted to the First Affiliated Hospital of Anhui Medical University between June 2019 and September 2021 were enrolled in this study. A nomogram was constructed based on the outcomes derived from univariate and multivariate analyses. Concordance index, calibration plots, receiver operating characteristic curves, and decision curve analysis were used to evaluate the nomogram. To substantiate the influence of monocytes on lung cancer BM, various assays, including cell co-culture, Transwell, wound-healing assays, and immunohistochemistry and immunofluorescence staining, were conducted. Statistical analyses were performed using SPSS 22.0 software and GraphPad Prism 7.0. A total of 462 eligible patients were enrolled, comprising 220 with BM and 242 without. Multivariate analysis revealed that histological type, medical history, monocyte percentage, and LDH (Lactate Dehydrogenase) and ALP (Alkaline Phosphatase) levels were independent risk factors for BM in lung cancer. Transwell and woundhealing assays indicated that co-culture with monocytes significantly enhanced the migration and invasion capabilities of A549 cells in vitro. Immunohistochemistry and immunofluorescence analyses demonstrated a noteworthy increase in monocyte infiltration in the primary lesions of patients with lung cancer with BM. In conclusion, this study successfully constructed and validated a precise, straightforward, and cost-effective prognostic nomogram for patients with lung cancer with BM.

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Blood Biomarkers of Bone Metastasis in Digestive Tract Malignant Tumors

Cited by 6 publications

References 28 publications

Prognostic value of serum α-HBDH levels in patients with lung cancer

Prognostic value of serum α-HBDH levels in patients with lung cancer

Prediction of bone metastasis in non-small cell lung cancer based on machine learning

Predictive nomogram for bone metastases in lung cancer based on monocyte infiltration

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