ObjectiveTo find the hub genes related to bone metastasis of breast cancer by weighted geneco-expression network analysis (WGCNA) method, and provide theoretical support for the development of new targeted therapeutic drugs.MethodsThe basic clinical features of 286 breast cancer patients and the gene expression information of tumor specimens were downloaded from the GSE2034 dataset from the Gene Expression Omnibus. R software was used to analyze the gene microarray. The WGCNA package embedded in the R software was used for various analysis in weighted correlation network analysis. Cox proportional hazard regression was performed by using SPSS software.ResultsThe top one quarter genes with the greatest variance variability were selected by WGCNA, and a total of 5 000 genes were used for further enrichment analysis. Finally, 15 gene co-expression modules were constructed, and the magenta module (r=0.94, P<0.001) was significantly positively correlated with bone metastasis of breast cancer. It was further found that six hub genes highly associated with bone metastasis in the magenta module were: Ral GTPase-activating protein subunitalpha-1 (RALGAPA1), B-cell antigen receptor complex-associated protein alpha chain (CD79A), immunoglobulin kappa chain C region (IGKC), arrestin beta 2 (ARRB2), differentially expressed in FDCP 6 homolog (DEF6), and immunoglobulin lambda variable 2 (IGLV2).ConclusionWe found that RALGAPA1, CD79A, IGKC, ARRB2. DEF6, and IGLV2 may play an important role in bone metastasis of breast cancer.
ObjectiveTo identify the risk factors of bone metastasis in breast cancer and construct a predictive model. MethodsThe data of breast cancer patients met inclusion and exclusion criteria from 2010 to 2015 were obtained from the SEER*Stat database. Additionally, the data of breast cancer patients diagnosed with distant metastasis in the Affiliated Hospital of Southwest Medical University from 2021 to 2023 were collected. The patients from the SEER database were randomly divided into training (70%) and validation (30%) sets using R software, and the breast cancer patients from the Affiliated Hospital of Southwest Medical University were included in the validation set. The univariate and multivariate logistic regressions were used to identify risk factors of breast cancer bone metastasis. A nomogram predictive model was then constructed based on these factors. The predictive effect of the nomogram was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. ResultsThe study included 8 637 breast cancer patients, with 5 998 in the training set and 2 639 (including 68 patients in the Affiliated Hospital of Southwest Medical University) in the validation set. The statistical differences in the race and N stage were observed between the training and validation sets (P<0.05). The multivariate logistic regression analysis revealed that being of white race, having a low histological grade (Ⅰ–Ⅱ), positive estrogen and progesterone receptors status, negative human epidermal growth factor receptor 2 status, and non-undergoing surgery for the primary breast cancer site increased the risk of breast cancer bone metastasis (P<0.05). The nomogram based on these risk factors showed that the AUC (95% CI) of the training and validation sets was 0.676 (0.533, 0.744) and 0.690 (0.549, 0.739), respectively. The internal calibration using 1 000 Bootstrap samples demonstrated that the calibration curves for both sets closely approximated the ideal 45-degree reference line. The decision curve analysis indicated a stronger clinical utility within a certain probability threshold range. ConclusionsThis study constructs a nomogram predictive model based on factors related to the risk of breast cancer bone metastasis, which demonstrates a good consistency between actual and predicted outcomes in both training and validation sets. The nomogram shows a stronger clinical utility, but further analysis is needed to understand the reasons of the lower differentiation of nomogram in both sets.
Lung cancer is the highest morbidity of malignant tumor in China, and bone metastasis is one of the common sites. With the development of imaging and nuclear medicine technology, the level of early diagnosis of bone metastasis has been improved. There are also many evidence-based evidences and advances in systemic therapy (chemotherapy, targeted therapy, immunotherapy) and bone modification drugs for treatment of bone metastases from lung cancer. The comprehensive treatment model under the guidance of multiple disciplines (including medical oncology, surgery, radiotherapy, interventional medicine, nuclear medicine, psychological rehabilitation, etc.) has been widely implemented in clinical practice. Therefore, Lung Cancer Medical Education Committee of China Medicine Education Association, Youth Specialists Committee of Lung Cancer, Beijing Medical Award Foundation and Lung Cancer Specialty Committee of Chinese Elderly Health Care Association have written the "Chinese Clinical Guidelines on Diagnosis and Treatment of Lung Cancer Bone Metastasis (Version 2024)", based on the "Expert Consensus on Diagnosis and Treatment of Lung Cancer Bone Metastasis (Version 2019)". The aim is to enhance the comprehensive treatment level of lung cancer bone metastasis in China.
Objective To predict the patients who can benefit from local surgery for bone-only metastatic breast cancer (bMBC). Methods Patients newly diagnosed with bMBC between 2010 and 2019 in SEER database were randomly divided into a training set and a validation set at a ratio of 7∶3. The Cox proportional hazards model was used to analyze the independent prognostic factors of overall survival in the training set, and the variables were screened and the prognostic prediction model was constructed. The concordance index (C-index), time-dependent clinical receiver operating characteristic curve and area under the curve (AUC), calibration curve and decision curve analysis (DCA) were used to evaluate the discrimination, calibration and clinical applicability of the model in the training set and validation set, respectively. The model was used to calculate the patient risk score and classify the patients into low-, medium- and high-risk groups. Survival analysis was used to compare the survival difference between surgical and non-surgical patients in different risk groups. Results A total of 2057 patients were enrolled with a median age of 45 years (interquartile range 47-62 years) and a median follow-up of 32 months (interquartile range 16-53 months). Totally 865 patients (42.1%) died. Multivariate Cox proportional hazards model analysis showed that the overall survival of patients with surgery was better than that of patients without surgery [hazard ratio=0.51, 95% confidence interval (0.43, 0.60), P<0.001]. Chemotherapy, marital status, molecular subtype, age, pathological type and histological grade were independent prognostic factors for overall survival (P<0.05), and a prognostic prediction model was constructed based on the independent prognostic factors. The C-index was 0.702 in the training set and 0.703 in the validation set. The 1-, 3-, and 5-year AUCs of the training set and validation set were 0.734, 0.727, 0.731 and 0.755, 0.737, 0.708, respectively. The calibration curve showed that the predicted survival rates of 1, 3, and 5 years in the training set and the validation set were highly consistent with the actual survival rates. DCA showed that the prediction model had certain clinical applicability in the training set and the validation set. Patients were divided into low-, medium- and high-risk subgroups according to their risk scores. The results of log-rank test showed that local surgery improved overall survival in the low-risk group (training set: P=0.013; validation set: P=0.024), but local surgery did not improve overall survival in the medium-risk group (training set: P=0.45; validation set: P=0.77) or high-risk group (training set: P=0.56; validation set: P=0.94). Conclusions Local surgery can improve the overall survival of some patients with newly diagnosed bMBC. The prognostic stratification model based on clinicopathological features can evaluate the benefit of local surgery in patients with newly diagnosed bMBC.