Prognostic factors and survival of patients undergoing surgical intervention for breast cancer bone metastases

Association of Socioeconomic Status With Worse Overall Survival in Patients With Bone and Joint Cancer

BACKGROUND

The effect of socioeconomic status (SES) on the outcomes of patients with metastatic cancer to bone has not been adequately studied. We analyzed the association between the Yost Index, a composite geocoded SES score, and overall survival among patients who underwent nonprimary surgical resection for bone metastases.

METHODS

This population-based study used data from the National Cancer Institute's Surveillance, Epidemiology, and End Results database (2010 to 2018). We categorized bone and joint sites using International Classification of Disease-O-3 recodes. The Yost Index was geocoded using a factor analysis and categorized into quintiles using census tract-level American Community Service 5-year estimates and seven measures: median household income, median house value, median rent, percent below 150% of the poverty line, education index, percent working class, and percent unemployed. Multivariate Cox regression models were used to calculate adjusted hazard ratios of overall survival and 95% confidence intervals.

RESULTS

A total of 138,158 patients were included. Patients with the lowest SES had 34% higher risk of mortality compared with those with the highest SES (adjusted hazard ratio of 1.34, 95% confidence interval: 1.32 to 1.37, P < 0.001). Among patients who underwent nonprimary surgery of the distant bone tumor (n = 11,984), the age-adjusted mortality rate was 31.3% higher in the lowest SES patients compared with the highest SES patients (9.9 versus 6.8 per 100,000, P < 0.001). Patients in the lowest SES group showed more racial heterogeneity (63.0% White, 33.5% Black, 3.1% AAPI) compared with the highest SES group (83.9% White, 4.0% Black, 11.8% AAPI, P < 0.001). Higher SES patients are more likely to be married (77.5% versus 59.0%, P < 0.0001) and to live in metropolitan areas (99.6% versus 73.6%, P < 0.0001) compared with lower SES patients.

DISCUSSION

Our results may have implications for developing interventions to improve access and quality of care for patients from lower SES backgrounds, ultimately reducing disparities in orthopaedic surgery.

A machine learning-Based model to predict early death among bone metastatic breast cancer patients: A large cohort of 16,189 patients

Purpose: This study aims to develop a prediction model to categorize the risk of early death among breast cancer patients with bone metastases using machine learning models. Methods: This study examined 16,189 bone metastatic breast cancer patients between 2010 and 2019 from a large oncological database in the United States. The patients were divided into two groups at random in a 90:10 ratio. The majority of patients (n = 14,582, 90%) were served as the training group to train and optimize prediction models, whereas patients in the validation group (n = 1,607, 10%) were utilized to validate the prediction models. Four models were introduced in the study: the logistic regression model, gradient boosting tree model, decision tree model, and random forest model. Results: Early death accounted for 17.4% of all included patients. Multivariate analysis demonstrated that older age; a separated, divorced, or widowed marital status; nonmetropolitan counties; brain metastasis; liver metastasis; lung metastasis; and histologic type of unspecified neoplasms were significantly associated with more early death, whereas a lower grade, a positive estrogen receptor (ER) status, cancer-directed surgery, radiation, and chemotherapy were significantly the protective factors. For the purpose of developing prediction models, the 12 variables were used. Among all the four models, the gradient boosting tree had the greatest AUC [0.829, 95% confident interval (CI): 0.802-0.856], and the random forest (0.828, 95% CI: 0.801-0.855) and logistic regression (0.819, 95% CI: 0.791-0.847) models came in second and third, respectively. The discrimination slopes for the three models were 0.258, 0.223, and 0.240, respectively, and the corresponding accuracy rates were 0.801, 0.770, and 0.762, respectively. The Brier score of gradient boosting tree was the lowest (0.109), followed by the random forest (0.111) and logistic regression (0.112) models. Risk stratification showed that patients in the high-risk group (46.31%) had a greater six-fold chance of early death than those in the low-risk group (7.50%). Conclusion: The gradient boosting tree model demonstrates promising performance with favorable discrimination and calibration in the study, and this model can stratify the risk probability of early death among bone metastatic breast cancer patients.

Anterior Gradient 2 is a Significant Prognostic Biomarker in Bone Metastasis of Breast Cancer

Background: The study aimed to detect DEGs associated with BRCA bone metastasis, filter prognosis biomarkers, and explore possible pathways.

Methods: GSE175692 dataset was used to detect DEGs between BRCA bone metastatic cases and non-bone metastatic cases, followed by the construction of a PPI network among DEGs. The main module among the PPI network was then determined and pathway analysis on genes within the module was performed. Through performing Cox regression, Kaplan-Meier, nomogram, and ROC curve analyses using GSE175692 and GSE124647 datasets at the same time, the most significant prognostic biomarker was gradually filtered. Finally, important pathways associated with prognostic biomarkers were explored by GSEA analysis.

Results: The 74 DEGs were detected between bone metastasis and non-bone metastasis groups. A total of 15 nodes were included in the main module among the whole PPI network and they mainly correlated with the IL-17 signaling pathway. We then performed Cox analysis on 15 genes using two datasets and only enrolled the genes with p < 0.05 in Cox analysis into the further analyses. Kaplan-Meier analyses using two datasets showed that the common biomarker AGR2 expression was related to the survival time of BRCA metastatic cases. Further, the nomogram determined the greatest contribution of AGR2 on the survival probability and the ROC curve revealed its optimal prognostic performance. More importantly, high expression of AGR2 prolonged the survival time of BRCA bone metastatic patients. These results all suggested the importance of AGR2 in metastatic BRCA. Finally, we performed the GSEA analysis and found that AGR2 was negatively related to IL-17 and NF-kβ signaling pathways.

Conclusion: AGR2 was finally determined as the most important prognostic biomarker in BRCA bone metastasis, and it may play a vital role in cancer progression by regulating IL-17 and NF-kB signaling pathways.

Outcomes and prognostic factors after surgery for bone metastases in the extremities and pelvis: A retrospective analysis of 140 patients

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Pathological fracture, visceral metastasis and lung cancer were negative prognostic factors for patients with bone metastases in the extremities and pelvis.

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Complications occurred in every fourth patient within the first 30 postoperative days.

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No significant differences in short- and long-term outcomes were observed between endoprosthetic replacement and internal fixation.

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