Impact of the homogeneous and heterogeneous risk factors on the incidence and survival outcome of bone metastasis in NSCLC patients

Song, Qing; Shang, Jun; Zhang, Chufan; Zhang, Lanlin; Wu, Xianghua

doi:10.1007/s00432-018-02826-7

Cited by 22 publications

(17 citation statements)

References 15 publications

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“…T stage roughly classifies tumor size or depth of invasion, but it cannot reflect the specific character of NSCLC patients with BM and accurately predict the prognosis, because the tumor size of the same period varies greatly [23][24][25]. Age, tumor size, race, sex, histological types, grade, T stage, N stage, surgery, chemotherapy, liver metastases, and radiotherapy were related to prognosis, which was similar to the previous research [26][27][28][29][30]. The presence of liver metastases significantly decreased survival in lung cancer patients with BM [17].…”

Section: Discussionsupporting

confidence: 69%

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

Huang

Chi

et al. 2020

BioMed Research International

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Non-small-cell lung cancer (NSCLC) patients often develop bone metastases (BM), and the overall survival for these patients is usually perishing. However, a model with high accuracy for predicting the survival of NSCLC with BM is still lacking. Here, we aimed to establish a model based on artificial intelligence for predicting the 1-year survival rate of NSCLC with BM by using extreme gradient boosting (XGBoost), a large-scale machine learning algorithm. We selected NSCLC patients with BM between 2010 and 2015 from the Surveillance, Epidemiology, and End Results database. In total, 5973 cases were enrolled and divided into the training (n=4183) and validation (n=1790) sets. XGBoost, random forest, support vector machine, and logistic algorithms were used to generate predictive models. Receiver operating characteristic curves were used to evaluate and compare the predictive performance of each model. The parameters including tumor size, age, race, sex, primary site, histological subtype, grade, laterality, T stage, N stage, surgery, radiotherapy, chemotherapy, distant metastases to other sites (lung, brain, and liver), and marital status were selected to construct all predictive models. The XGBoost model had a better performance in both training and validation sets as compared with other models in terms of accuracy. Our data suggested that the XGBoost model is the most precise and personalized tool for predicting the 1-year survival rate for NSCLC patients with BM. This model can help the clinicians to design more rational and effective therapeutic strategies.

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Section: Discussionsupporting

confidence: 69%

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

Huang

Chi

et al. 2020

BioMed Research International

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“…Homogeneous risk factors for morbidity and survival outcomes in NSCLC patients with bone metastasis included male (OR 0.815, 95% CI [0.771–0.861], P < 0.001) and higher T-stage (OR 1.287, 95% CI [1.251–1.324], P < 0.001). In addition, these clinical factors were able to be combined with biomarkers to more accurately predict bone metastasis in NSCLC patients, contributing to the early diagnosis and early intervention of bone metastasis (Song et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Relationship between tumor size and metastatic site in patients with stage IV non-small cell lung cancer: A large SEER-based study

Shan

Fan

Guo

et al. 2019

PeerJ

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Objective To analyze the relationship between tumor size and metastatic site in stage IV NSCLC patients. Methods A total of 40,196 stage IV NSCLC patients from 2010 to 2015 were screened by SEER database. Chi-square test was used to compare the characteristics of clinical variables. At the same time, multivariate Logistic regression analysis was used to evaluate the relationship between tumor size and organ metastasis. Results Regardless of tumor size, the proportion of bone metastasis and lung metastasis was higher and similar in patients with squamous cell carcinoma, while in patients with adenocarcinoma, bone metastasis accounted for the highest proportion. We found that whether the metastatic site was bone, brain, liver or lung, the proportion of patients with a tumor size of 3–7 cm was the highest. Multivariate regression analysis demonstrated that patients with a tumor size of 3–7 cm and a tumor size ≥7 cm were more likely to develop brain metastasis and lung metastasis compared with patients with a tumor size ≤3 cm (all P < 0.001), which meant the larger the tumor, the greater the risk of brain or lung metastasis. At the same time, the results indicated that patients with a tumor size of 3–7 cm had a tendency to develop liver metastasis (P = 0.004), while the statistical significance was not found for patients with a tumor size ≥7 cm (P = 0.524). The results also revealed that patients with a tumor size of 3–7cm had no significant difference to develop bone metastasis (P = 0.116), while the statistical significance was found for patients with a tumor size ≥7 cm (P < 0.001). Conclusions There was statistical significance between tumor size and metastatic site in patients with stage IV NSCLC. For brain or lung metastasis, the larger the tumor, the higher the risk of brain or lung metastasis. For liver metastasis, patients with a tumor size of 3–7 cm were more prone to develop liver metastasis. For bone metastasis, patients with a tumor size ≥7 cm were more likely to have bone metastasis.

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“…In addition to the induction of EMT to promote the metastasis of tumor cells into liver tissue, brain metastasis and bone metastasis of NSCLC patients are also common clinical sites of advanced lung cancer metastasis (Song et al, 2019). This study only observed the formation of metastatic nodules in liver tissues after 8 weeks of establishment of transplantation tumor model in nude mice, but no tumor formation occurred in the brain or bone tissue regions.…”

Section: Discussionmentioning

confidence: 74%

“…In addition, the occurrence of bone metastases in lung cancer patients has been found to be associated with the abnormal expression of multiple circulating miRNAs (Lei and Dian-Ming, 2017;Song et al, 2019), and plasma miR-142-5p has been reported to be of great significance in the early diagnosis of colorectal cancer in previous study (Ghanbari et al, 2015), suggesting that we can further explore the role and significance of YY1 and miR-142-5p in bone metastasis and early diagnosis of NSCLC.…”

Section: Discussionmentioning

confidence: 99%

MiR-142-5p Suppresses Lung Cancer Cell Metastasis by Targeting Yin Yang 1 to Regulate Epithelial–Mesenchymal Transition

Zhang

Liu

Zhang³

et al. 2020

Cellular Reprogramming

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This study aimed to investigate the mechanism of miR-142-5p and Yin Yang 1 (YY1) on regulating epithelialmesenchymal transition (EMT) in lung cancer cell metastasis. The expressions of YY1 and miR-142-5p in different lung cancer cell lines were negatively correlated. The results of the dual-luciferase reporter assay further validated that miR-142-5p directly targeted YY1. Subsequently, transwell assays, wound-healing assay, and transplantation tumor model in nude mice proved that YY1 could promote the metastasis of lung cancer cells, whereas miR-142-5p impaired the stimulating effect of YY1 on the metastasis ability of lung cancer cells in vitro and in vivo. Western blot and quantitative real-time polymerase chain reaction analysis of the EMTrelated proteins indicated that YY1 could enhance the metastasis ability of lung cancer cells by promoting EMT. On the contrary, miR-142-5p constrained the expression of mesenchymal markers by targeting YY1, reversed the differentiation of cells into mesenchymal cells, and weakened the metastasis ability of tumor cells in vitro and in vivo. In summary, miR-142-5p may regulate the expressions of EMT-related proteins by targeting YY1, thereby inhibiting lung cancer metastasis, which provides a promising therapeutic target for lung cancer.

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Impact of the homogeneous and heterogeneous risk factors on the incidence and survival outcome of bone metastasis in NSCLC patients

Cited by 22 publications

References 15 publications

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

An Artificial Intelligence Model for Predicting 1-Year Survival of Bone Metastases in Non-Small-Cell Lung Cancer Patients Based on XGBoost Algorithm

Relationship between tumor size and metastatic site in patients with stage IV non-small cell lung cancer: A large SEER-based study

MiR-142-5p Suppresses Lung Cancer Cell Metastasis by Targeting Yin Yang 1 to Regulate Epithelial–Mesenchymal Transition

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