Predicting Lung Metastasis Research Articles

Establishment of a nomogram for potential prediction of lung metastasis in patients with primary limb bone tumors: a study based on the SEER database.

The prognosis of lung metastasis in primary limb bone tumors represents a pivotal yet challenging aspect of oncological management. Despite advancements in diagnostic modalities, the predictive accuracy for metastatic spread remains suboptimal. This study aims to bridge this gap by leveraging the Surveillance, Epidemiology, and End Results (SEER) database to construct a nomogram that forecasts the risk of lung metastasis, thereby enhancing clinical decision-making processes. A retrospective cohort, including 1,822 patients with primary limb bony tumors from 2010 to 2015 in the SEER database, was extracted. Using precise inclusion and exclusion criteria, variables essential for predicting lung metastasis were identified through univariate and multivariate analyses, along with least absolute shrinkage and selection operator (LASSO) regression. These variables provided a solid basis for creating the multivariable nomogram, of which the discriminating power and utility were verified using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis. The model incorporated seven key predicting variables, including age, histological type, surgery, radiation, chemotherapy, T stage, and N stage. The nomogram emerged as a cohesive whole with good discriminative power. The area under the curve (AUC) was 0.806 in the training cohort and 0.767 in the validation cohort. The calibration curves demonstrated the model's validity by showing a good match between the actual outcomes and the model-predicted probabilities of lung metastasis. This study showed for the first time the reliability of the predictive model in translating the hard-to-interpret demographic, clinical, and pathologic data into a very usable predictive model. Thus, it represents a significant step toward demystifying the risk of lung metastasis in primary limb bone tumors. It is an invitation for a paradigm shift of oncology, to evidence-based, person-based oncology that is taking a new metric for cancer prognosis.

Nomograms to predict lung metastasis in malignant primary osseous spinal neoplasms and cancer-specific survival in lung metastasis subgroup.

To construct and validate nomograms for predicting lung metastasis probability in patients with malignant primary osseous spinal neoplasms (MPOSN) at initial diagnosis and predicting cancer-specific survival (CSS) in the lung metastasis subgroup. A total of 1,298 patients with spinal primary osteosarcoma, chondrosarcoma, Ewing sarcoma, and chordoma were retrospectively collected. Least absolute shrinkage and selection operator (LASSO) and multivariate logistic analysis were used to identify the predictors for lung metastasis. LASSO and multivariate Cox analysis were used to identify the prognostic factors for 3- and 5-year CSS in the lung metastasis subgroup. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCA) were used to estimate the accuracy and net benefits of nomograms. Histologic type, grade, lymph node involvement, tumor size, tumor extension, and other site metastasis were identified as predictors for lung metastasis. The area under the curve (AUC) for the training and validating cohorts were 0.825 and 0.827, respectively. Age, histologic type, surgery at primary site, and grade were identified as the prognostic factors for the CSS. The AUC for the 3- and 5-year CSS were 0.790 and 0.740, respectively. Calibration curves revealed good agreements, and the Hosmer and Lemeshow test identified the models to be well fitted. DCA curves demonstrated that nomograms were clinically useful. The nomograms constructed and validated by us could provide clinicians with a rapid and user-friendly tool to predict lung metastasis probability in patients with MPOSN at initial diagnosis and make a personalized CSS evaluation for the lung metastasis subgroup.

Predictive factors for lung metastasis in pediatric differentiated thyroid cancer: a clinical prediction study.

The objective of this study was to develop and evaluate the efficacy of a nomogram for predicting lung metastasis in pediatric differentiated thyroid cancer. The SEER database was utilized to collect a dataset consisting of 1,590 patients who were diagnosed between January 2000 and December 2019. This dataset was subsequently utilized for the purpose of constructing a predictive model. The model was constructed utilizing a multivariate logistic regression analysis, incorporating a combination of least absolute shrinkage feature selection and selection operator regression models. The differentiation and calibration of the model were assessed using the C-index, calibration plot, and ROC curve analysis, respectively. Internal validation was performed using a bootstrap validation technique. The results of the study revealed that the nomogram incorporated several predictive variables, namely age, T staging, and positive nodes. The C-index had an excellent calibration value of 0.911 (95 % confidence interval: 0.876-0.946), and a notable C-index value of 0.884 was achieved during interval validation. The area under the ROC curve was determined to be 0.890, indicating its practicality and usefulness in this context. This study has successfully developed a novel nomogram for predicting lung metastasis in children and adolescent patients diagnosed with thyroid cancer. Clinical decision-making can be enhanced by assessing clinicopathological variables that have a significant predictive value for the probability of lung metastasis in this particular population.

Abstract A039: Metabolic evolution in breast cancer predicts organ-specific metastasis

Abstract The metastatic cascade is a critical component of cancer evolution. Understanding the rewired metabolism in organ-specific metastasis in breast cancer could help identify strategies to improve the treatment and prevention of metastatic disease. Here, we used a systems biology approach to assess the evolution of metabolic fluxes from parental breast cancer cells to their brain- and lung-homing metastatic derivatives. We found that divergent lineages had distinct, heritable metabolic fluxes. Lung-homing cells maintained adaptations for high glycolytic flux despite low levels of glycolytic intermediates, constitutively activating a pathway sink into lactate. This exacerbated Warburg effect –– stronger than the Warburg effect in the primary tumor –– was associated with a high ratio of lactate dehydrogenase (LDH) to pyruvate dehydrogenase (PDH) expression, a signature which correlated with lung metastasis in patients with breast cancer. While feature classification models trained on clinical characteristics alone were unable to predict tropism, the LDH/PDH ratio was a significant predictor of a patient's future metastasis to the lung but not to other organs, independent of other transcriptomic signatures. Lung- and brain-homing lineages had differential selection patterns in acidic metabolic microenvironments. High lactate efflux was also a trait in lung-homing metastatic pancreatic cancer cells, suggesting that lactate production may be a convergent phenotype in lung metastasis. Together, these analyses highlight the essential role that metabolic evolution plays in organ-specific cancer metastasis and identify a putative biomarker for predicting lung metastasis in breast cancer patients. Citation Format: Deepti Mathur, Chen Liao, Wendy Lin, Alessandro La Ferlita, Salvatore Alaimo, Samuel Taylor, Yi Zhong, Christine Iacobuzio-Donahue, Alfredo Ferro, Joao Xavier. Metabolic evolution in breast cancer predicts organ-specific metastasis [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr A039.

Development and validation of a nomogram to predict lung metastasis in patients with testicular germ cell tumors

BackgroundLung metastatic tumor (LM) is one of testicular germ cell tumors' most common metastatic sites. Our study aimed to develop a nomogram for predicting the risk of LM among patients with testicular germ cell tumors (TGCTs). MethodsClinicopathological information of 4078 patients with TGCT between 2010 and 2015 was obtained from SEER. Univariate and multivariate logistic regression analyses were performed to identify risk factors for LM, and a nomogram was developed based on these factors. Calibration curves, area under the receiver operating curve (AUC), and decision curve analysis (DCA) were used to evaluate the accuracy and discrimination of the model. ResultsStudy participants included 4078 people with TGCTs, including 305 people with LM. They were randomly divided into two groups (training cohort = 2854 and validation cohort = 1224) at a ratio of 7:3. The following variables were incorporated in the nomogram: marital status, tumor histological type, T stage, brain metastasis, liver metastasis, lactate dehydrogenase (LDH), and chemotherapy. Besides, the AUC of it was 0.922 in the training cohort, while was 0.930 in the validation cohort. Training and validation cohort calibrations showed that the nomogram had excellent predictive abilities. DCA suggested it was more clinically relevant than the traditional TN staging. ConclusionWe have established a nomogram to predict the risk of LM in patients with TGCTs. Doctors and patients can use this nomogram to monitor and identify lung metastasis of tumors through active monitoring and follow-up.

Development and External Validation of Machine Learning-Based Models for Predicting Lung Metastasis in Kidney Cancer: A Large Population-Based Study.

The accuracy of indices widely used to evaluate lung metastasis (LM) in patients with kidney cancer (KC) is insufficient. Therefore, we aimed at developing a model to estimate the risk of developing LM in KC based on a large population size and machine learning algorithms. Demographic and clinicopathologic variables of patients with KC diagnosed between 2004 and 2017 were retrospectively analyzed. We performed a univariate logistic regression analysis to identify risk factors for LM in patients with KC. Six machine learning (ML) classifiers were established and tuned using the ten-fold cross-validation method. External validation was performed using clinicopathologic information from 492 patients from the Southwest Hospital, Chongqing, China. Algorithm performance was estimated by analyzing the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, precision, recall, F1 score, clinical decision analysis (DCA), and clinical utility curve (CUC). A total of 52,714 eligible patients diagnosed with KC were enrolled, of whom 2,618 developed LM. Variables of age, sex, race, T stage, N stage, tumor size, histology, and grade were identified as important for the prediction of LM. The extreme gradient boosting (XGB) algorithm performed better than other models in both the internal validation (AUC: 0.913, sensitivity: 0.873, specificity: 0.809, and F1 score: 0.325) and the external validation (AUC: 0.904, sensitivity: 0.750, specificity: 0.878, and F1 score: 0.364). This study established a predictive model for LM in KC patients based on ML algorithms which showed high accuracy and applicative value. A web-based predictor was built using the XGB model to help clinicians make more rational and personalized decisions.

A High-Quality Nomogram for Predicting Lung Metastasis in Newly Diagnosed Stage IV Thyroid Cancer: A Population-Based Study.

Introduction: Lung metastasis (LM) implies a very dismal event in patients with thyroid cancer. We aimed to construct a nomogram to predict LM for newly diagnosed stage IV thyroid cancer. Methods: A total of 1407 stage IV thyroid cancer patients were gathered from the surveillance, epidemiology, and end results (SEER) database. Pearson's Chi-squared test or Fisher's exact test was used to identify LM-related factors, and logistic regression analysis was employed to identify independent risk parameters of LM, which were included to establish a nomogram model by R software. The discriminative ability and predictive accuracy of the nomogram were assessed using the area under the curve (AUC) and calibration plots. Cox regression analysis and Kaplan-Meier analysis were applied to evaluate the clinical utility of this model. A simulation trial was conducted to verify the health economic value of this nomogram in predicting TCLM. Results: Five variables were found to be independent risk predictors of LM, including grade, histology, N stage, bone metastasis, and liver metastasis. The results of the AUC and calibration curves demonstrated that the nomogram exhibited outstanding performance for predicting the risk of LM patients both internally and externally. The LM prediction risk was an independent prognostic factor for stage IV thyroid cancer patients [P = .009, hazard ratio (HR): 1.812, 95% CI: 1.163-2.824]. Conclusion: We successfully developed a predictive model for stage IV thyroid cancer, which provides important information for identifying patients at high risk of LM and implementing early preventive interventions to improve their outcomes.

The role of neutrophil to lymphocyte ratio in predicting lung metastasis in giant cell tumor of the extremities.

Inflammation has a vital role in tumor development and metastasis. Changes in blood count parameters have been associated with tumor prognosis. We aimed to evaluate the prognostic significance of neutrophil to lymphocyte ratio (NLR) in predicting lung metastasis of giant cell tumors of the bone (GCTB) of the extremities. 34 GCTB patients (22 males and 12 females) were included in the study. Patients were divided into two groups. The metastasis group (n = 7) included GCTB patients with lung metastasis, while the non-metastasis group (n = 27) included those without lung metastasis. Descriptive statistics and frequency distribution were calculated [age, white blood cell (WBC), neutrophil, lymphocyte, platelets, neutrophil to lymphocyte ratio (NLR), and platelets to lymphocytes ratio (PLR)]. Continuous normal variables were expressed as mean ± standard deviation and compared using Student's t-tests. The receiver operating characteristic (ROC) curve analysis was used to evaluate the ability of NLR and PLR to predict lung metastasis. The factors were considered to be statistically significant at p < 0.05. There were no significant differences between the lymphocyte count (1.81 vs. 2.23 103/mm3), platelet count (436 vs. 364 103/mm3), and PLR values (247 vs. 190) of the two groups (p > 0.05). The WBC count (11.8 vs. 8.95 103/mm3), neutrophil count (8.78 vs. 5.69 103/mm3), and NLR levels (5.45 vs. 2.81) (p < 0.05) were significantly higher in the metastasis group. The presence of an NLR cut-off value of 3.7 significantly predicted the existence of lung metastasis (AUC = 0.857 [95%CI = 0.714-1], p = 0.004) with a sensitivity of 85% and specificity of 82%. NLR may serve as a promising prognostic marker for predicting lung metastasis in GCTB patients.

A deep belief network-based clinical decision system for patients with osteosarcoma.

Osteosarcoma was the most frequent type of malignant primary bone tumor with a poor survival rate mainly occurring in children and adolescents. For precision treatment, an accurate individualized prognosis for Osteosarcoma patients is highly desired. In recent years, many machine learning-based approaches have been used to predict distant metastasis and overall survival based on available individual information. In this study, we compared the performance of the deep belief networks (DBN) algorithm with six other machine learning algorithms, including Random Forest, XGBoost, Decision Tree, Gradient Boosting Machine, Logistic Regression, and Naive Bayes Classifier, to predict lung metastasis for Osteosarcoma patients. Therefore the DBN-based lung metastasis prediction model was integrated as a parameter into the Cox proportional hazards model to predict the overall survival of Osteosarcoma patients. The accuracy, precision, recall, and F1 score of the DBN algorithm were 0.917/0.888, 0.896/0.643, 0.956/0.900, and 0.925/0.750 in the training/validation sets, respectively, which were better than the other six machine-learning algorithms. For the performance of the DBN survival Cox model, the areas under the curve (AUCs) for the 1-, 3- and 5-year survival in the training set were 0.851, 0.806 and 0.793, respectively, indicating good discrimination, and the calibration curves showed good agreement between the prediction and actual observations. The DBN survival Cox model also demonstrated promising performance in the validation set. In addition, a nomogram integrating the DBN output was designed as a tool to aid clinical decision-making.

A new nomogram for predicting lung metastasis in newly diagnosed endometrial carcinoma patients: A study based on SEER.

BackgroundLung metastasis (LM) is an independent risk factor for survival in patients with endometrial cancer (EC).MethodsWe reviewed data on patients diagnosed with EC between 2010 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. The independent predictors of LM in patients with EC were identified using univariate and multivariate logistic regression analyses. A nomogram for predicting LM in patients with EC was developed, and the predictive model was evaluated using calibration and receiver operating characteristic (ROC) curves.ResultsUnivariate and multivariate logistic regression analyses showed that high grade; specific histological type; high tumor and node stages; larger tumor size; and liver, brain, and bone metastases were positively associated with LM risk. A new nomogram was developed by combining these factors to predict LM in patients newly diagnosed with EC. Internal and external verification of the calibration charts showed that the nomogram was well calibrated. The areas under the ROC curves for the training and validation cohorts were 0.924 and 0.913, respectively.ConclusionWe performed a retrospective analysis of 42,073 patients with EC using the SEER database, established a new nomogram for predicting LM based on eight independent risk factors, and visualized the model using a nomogram for the first time.

Association of high cellular expression and plasma concentration of angiopoietin-like 4 with tongue cancer lung metastasis and poor prognosis.

Angiopoietin-like 4 (ANGPTL4) promotes cancer cell migration through vessels and has been implicated in cancer metastasis. Our previous study identified a robust increase in ANGPTL4 mRNA expression in lung-metastasized tongue cancer (TC) cells. Therefore, the present study investigated the association of ANGPTL4 with lung metastasis and outcomes of patient with TC. ANGPTL4 expression in TC cells was investigated by immunohistochemical staining. Patients were classified into ‘low (0-30%)’ and ‘high (>30%)’ ANGPTL4-expression groups based on the proportion of ANGPTL4-positive TC cells. The high ANGPTL4-expression group included 15 of 48 patients with TC. Notably, a significantly greater proportion of patients with lung metastasis exhibited a high rate of ANGPTL4-expressing cancer cells compared with patients without lung metastasis (P=0.029). The overall 5-year survival rate was lower in the high (27%) ANGPTL4-expression group compared with the low (68%) ANGPTL4-expression group. Univariate and multivariate analyses revealed that patients with high ANGPTL4 expression in TC cells exhibited significantly lower overall survival (OS) rates [hazard ratio (HR), 2.99; 95% confidence interval (95% CI), 1.34-6.69; P=0.008 and HR, 2.72; 95% CI, 1.14-6.51; P=0.024, respectively]. High plasma ANGPTL4 concentrations as measured by ELISA were associated with lung metastasis (P<0.001). The optimal cut-point for prediction of TC lung metastasis was 9.1 ng/ml (P<0.001; 95% CI, 7.2-10.9). The OS of patients with plasma ANPTL4 above the cut-point was significantly lower than that of patients with plasma ANGPTL4 ≤9.1 ng/ml (P<0.001). These results suggest that a high level of ANGPTL4 in cancer cells and plasma may predict lung metastasis and/or a poor prognosis of patients with TC.

An External-Validated Prediction Model to Predict Lung Metastasis among Osteosarcoma: A Multicenter Analysis Based on Machine Learning.

Background Lung metastasis greatly affects medical therapeutic strategies in osteosarcoma. This study aimed to develop and validate a clinical prediction model to predict the risk of lung metastasis among osteosarcoma patients based on machine learning (ML) algorithms. Methods We retrospectively collected osteosarcoma patients from the Surveillance Epidemiology and End Results (SEER) database and from four hospitals in China. Six ML algorithms, including logistic regression (LR), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), and multilayer perceptron (MLP), were applied to build predictive models for predicting lung metastasis using patient's demographics, clinical characteristics, and therapeutic variables from the SEER database. The model was internally validated using 10-fold cross-validation to calculate the mean area under the curve (AUC) and the model was externally validated using the Chinese multicenter osteosarcoma data. Relative importance ranking of predictors was plotted to understand the importance of each predictor in different ML algorithms. The correlation heat map of predictors was plotted to understand the correlation of each predictor, selecting the 10-fold cross-validation with the highest AUC value in the external validation ROC curve to build a web calculator. Results Of all enrolled patients from the SEER database, 17.73% (194/1094) developed lung metastasis. The multiple logistic regression analysis showed that sex, N stage, T stage, surgery, and bone metastasis were all independent risk factors for lung metastasis. In predicting lung metastasis, the mean AUCs of the six ML algorithms ranged from 0.711 to 0.738 in internal validation and 0.697 to 0.729 in external validation. Among the six ML algorithms, the extreme gradient boosting (XGBoost) model had the highest AUC value with an average internal AUC of 0.738 and an external AUC of 0.729. The best performing ML algorithm model was used to build a web calculator to facilitate clinicians to calculate the risk of lung metastasis for each patient. Conclusions The XGBoost model may have the best prediction effect and the online calculator based on this model can help doctors to determine the lung metastasis risk of osteosarcoma patients and help to make individualized medical strategies.

Prediction of lung metastases in thyroid cancer using machine learning based on SEER database.

PurposeLung metastasis (LM) is one of the most frequent distant metastases of thyroid cancer (TC). This study aimed to develop a machine learning algorithm model to predict lung metastasis of thyroid cancer for providing relative information in clinical decision‐making.MethodsData comprising of demographic and clinicopathological characteristics of patients with thyroid cancer were extracted from the National Institutes of Health (NIH)’s Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015, which is employed to develop six machine learning algorithm models support vector machine (SVM), logistic regression (LR), eXtreme gradient boosting (XGBoost), decision tree (DT), random forest (RF), and k‐nearest neighbor (KNN). Compared and evaluated models by the following indicators: accuracy, precision, recall rate, F1‐score, the area under the ROC curve (AUC) value and Brier score, and interpreted the association between clinicopathological characteristics and target variables based on the best model.ResultsNine thousand nine hundred and fifty patients were selected, which including 212 patients (2.1%) with lung metastasis, and 9738 patients without lung metastasis (97.9%). Multivariate logistic regression showed that age, T stage, N stage, and histological type were independent factors in TC with LM. Evaluation indicators of the best model‐ RF were as following: accuracy (0.99), recall rate (0.88), precision (0.61), F1‐score (0.72), AUC value (0.99), and the Brier score (0.016).ConclusionRF learning model performed better and can be applied to forecast lung metastasis of thyroid cancer, and offer valuable and significant reference for clinicians' decision‐making in advance.

A Hypercoagulable Hematological Metastasis Breast Cancer Model.

Background The hypercoagulable status, which forms a vicious cycle with hematogenous metastasis, is a common systemic alteration in cancers. As modeling is a key approach in research, a model which is suitable for studying how the hypercoagulable status promotes hematogenous metastasis in breast cancer is urgently needed. Methods Based on the tumor-bearing period (TBP) and postoperative incubation period (PIP), 4T1-breast cancer models were constructed to evaluate coagulation and tumor burden to generate multiple linear regression-based lung metastasis prediction formula. Platelets and 4T1 cells were cocultured for 30 min or 24 h in vitro to evaluate the early and late phases of their crosstalk, and then the physical characteristics (concentration and size) and procoagulant activity of the coculture supernatants were assayed. Results The multiple linear regression model was constructed as log10 (photon number) = 0.147 TBP + 0.14 PIP + 3.303 (TBP ≤ 25 and PIP ≤ 17) to predict lung metastasis. Coculture of platelets and 4T1 cells contributed to the release of extracellular vesicles (EVs) and the development of the hypercoagulable status. ConclusionsIn vivo and in vitro hypercoagulable status models were developed to explore the mechanism of hypercoagulable status which is characterized by platelet activation and promotes hematogenous metastasis in breast cancer.

Analysis of ADAM12-Mediated Ephrin-A1 Cleavage and Its Biological Functions.

Accumulating evidence indicates that an elevated ephrin-A1 expression is positively correlated with a worse prognosis in some cancers such as colon and liver cancer. The detailed mechanism of an elevated ephrin-A1 expression in a worse prognosis still remains to be fully elucidated. We previously reported that ADAM12-cleaved ephrin-A1 enhanced lung vascular permeability and thereby induced lung metastasis. However, it is still unclear whether or not cleaved forms of ephrin-A1 are derived from primary tumors and have biological activities. We identified the ADAM12-mediated cleavage site of ephrin-A1 by a Matrix-assisted laser desorption ionization mass spectrometry and checked levels of ephrin-A1 in the serum and the urine derived from the primary tumors by using a mouse model. We found elevated levels of tumor-derived ephrin-A1 in the serum and the urine in the tumor-bearing mice. Moreover, inhibition of ADAM-mediated cleavage of ephrin-A1 or antagonization of the EphA receptors resulted in a significant reduction of lung metastasis. The results suggest that tumor-derived ephrin-A1 is not only a potential biomarker to predict lung metastasis from the primary tumor highly expressing ephrin-A1 but also a therapeutic target of lung metastasis.

Nomograms to predict lung metastasis probability and lung metastasis subgroup survival in malignant bone tumors.

The aim of this study was to construct and validate nomograms for predicting lung metastasis and lung metastasis subgroup overall survival in malignant primary osseous neoplasms. Least absolute shrinkage and selection operator, logistic and Cox analyses were used to identify risk factors for lung metastasis in malignant primary osseous neoplasms and prognostic factors for overall survival in the lung metastasis subgroup. Further, nomograms were established and validated. A total of 3184 patients were collected. Variables including age, histology type, American Joint Committee on Cancer T and N stage, other site metastasis, tumor extension and surgery were extracted for the nomograms. The authors found that nomograms could provide an effective approach for clinicians to identify patients with a high risk of lung metastasis in malignant primary osseous neoplasms and perform a personalized overall survival evaluation for the lung metastasis subgroup.

Construction and validation of a 6-gene nomogram discriminating lung metastasis risk of breast cancer patients.

Breast cancer is the most common malignant disease in women. Metastasis is the foremost cause of death. Breast tumor cells have a proclivity to metastasize to specific organs. The lung is one of the most common sites of breast cancer metastasis. Therefore, we aimed to build a useful and convenient prediction tool based on several genes that may affect lung metastasis-free survival (LMFS). We preliminarily identified 319 genes associated with lung metastasis in the training set GSE5327 (n = 58). Enrichment analysis of GO functions and KEGG pathways was conducted based on these genes. The best genes for modeling were selected using a robust likelihood-based survival modeling approach: GOLGB1, TMEM158, CXCL8, MCM5, HIF1AN, and TSPAN31. A prognostic nomogram for predicting lung metastasis in breast cancer was developed based on these six genes. The effectiveness of the nomogram was evaluated in the training set GSE5327 and the validation set GSE2603. Both the internal validation and the external validation manifested the effectiveness of our 6-gene prognostic nomogram in predicting the lung metastasis risk of breast cancer patients. On the other hand, in the validation set GSE2603, we found that neither the six genes in the nomogram nor the risk predicted by the nomogram were associated with bone metastasis of breast cancer, preliminarily suggesting that these genes and nomogram were specifically associated with lung metastasis of breast cancer. What’s more, five genes in the nomogram were significantly differentially expressed between breast cancer and normal breast tissues in the TIMER database. In conclusion, we constructed a new and convenient prediction model based on 6 genes that showed practical value in predicting the lung metastasis risk for clinical breast cancer patients. In addition, some of these genes could be treated as potential metastasis biomarkers for antimetastatic therapy in breast cancer. The evolution of this nomogram will provide a good reference for the prediction of tumor metastasis to other specific organs.

Development and validation of a nomogram to predict synchronous lung metastases in patients with ovarian cancer: a large cohort study.

Purpose: Lung metastasis is an independent risk factor affecting the prognosis of ovarian cancer patients. We developed and validated a nomogram to predict the risk of synchronous lung metastases in newly diagnosed ovarian cancer patients.Methods: Data of ovarian cancer patients from the Surveillance, Epidemiology, and Final Results (SEER) database between 2010 and 2015 were retrospectively collected. The model nomogram was built on the basis of logistic regression. The consistency index (C-index) was used to evaluate the discernment of the synchronous lung metastasis nomogram. Calibration plots were drawn to analyze the consistency between the observed probability and predicted probability of synchronous lung metastases. The Kaplan–Meier method was used to estimate overall survival rate, and influencing factors were included in multivariate Cox regression analysis (P<0.05) to determine the independent prognostic factors of synchronous lung metastases.Results: Overall, 16059 eligible patients were randomly divided into training (n=11242) and validation cohorts (n=4817). AJCC T, N stage, bone metastases, brain metastases, and liver metastases were evaluated as predictors of synchronous lung metastases. Finally, a nomogram was constructed. The nomogram based on independent predictors was calibrated and showed good discriminative ability. Mixed histological types, chemotherapy, and primary site surgery were factors affecting the overall survival of patients with synchronous lung metastases.Conclusion: The clinical prediction model has high accuracy and can be used to predict lung metastasis risk in newly diagnosed ovarian cancer patients, which can guide the treatment of patients with synchronous lung metastases.

Fusion of FDG-PET Image and Clinical Features for Prediction of Lung Metastasis in Soft Tissue Sarcomas.

Extracting massive features from images to quantify tumors provides a new insight to solve the problem that tumor heterogeneity is difficult to assess quantitatively. However, quantification of tumors by single-mode methods often has defects such as difficulty in features extraction and high computational complexity. The multimodal approach has shown effective application prospects in solving these problems. In this paper, we propose a feature fusion method based on positron emission tomography (PET) images and clinical information, which is used to obtain features for lung metastasis prediction of soft tissue sarcomas (STSs). Random forest method was adopted to select effective features by eliminating irrelevant or redundant features, and then they were used for the prediction of the lung metastasis combined with back propagation (BP) neural network. The results show that the prediction ability of the proposed model using fusion features is better than that of the model using an image or clinical feature alone. Furthermore, a good performance can be obtained using 3 standard uptake value (SUV) features of PET image and 7 clinical features, and its average accuracy, sensitivity, and specificity on all the sets can reach 92%, 91%, and 92%, respectively. Therefore, the fusing features have the potential to predict lung metastasis for STSs.

The role of osteosarcoma cell-derived exosomes in the promotion of lung metastasis

Abstract Osteosarcoma (OS) is the most common primary tumor of bone and typically occurs in children and young adults. 30–40% of OS patients develop lung metastases after initial diagnosis, with only 20% of these patients surviving 4 years post-relapse. For over 30 years, there have been no improvements in the treatment of OS, and more importantly, no diagnostic tools that help identify those patients at high risk for lung metastases. Recent data has shown that in other tumor types, tumor-secreted extracellular vesicles (EVs) deliver biological cargo to distant sites, resulting in priming of non-malignant host cells for promotion of metastasis. The aim of this project is to understand how OS EVs influence the response of lung fibroblasts (LFs) and alveolar macrophages (AMs), and if their uptake of EVs results in pre-metastatic priming that can be detected via AM transcriptomic changes. Hypothesis LFs are primary targets of OS EVs, which will increase LF IL-6 production and cause paracrine STAT3 activation of AM’s into an M2 tumor-promoting phenotype. Results Flow cytometry and confocal microscopy demonstrated in vitro uptake of OS EVs by LF cells. In vitro ‘education’ of LFs with OS EVs induced significant production of IL-6, IL-8, and CCL2, with the greatest mean difference between EV-educated and naïve LFs observed with IL-6 secretion. Conclusion This data suggest that OS EVs efficiently prime LFs to induce IL-6 secretion, and this response may be associated with promotion of OS lung metastasis. Further data is being collected to determine the role of IL-6 in OS cell survival and lung metastatic colonization. RNA sequencing of AMs will be used to characterize phenotype which could serve as an effective cellular biomarker to predict lung metastasis.