Risk Factors, Prognosis, and a New Nomogram for Predicting Cancer-Specific Survival Among Lung Cancer Patients with Brain Metastasis: A Retrospective Study Based on SEER

Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PDAC) is a highly aggressive neoplasm characterized by limited therapeutic options, particularly in the realm of immunotherapy. This study aims to improve prognosis prediction to guide therapeutic decision-making, and to identify novel targets for immunotherapy of PDAC. We conducted Cox and LASSO regression analyses to develop immune-related gene signature and corresponding nomogram, and the robustness of these signatures was demonstrated using multiple approaches. Additionally, CIBERSORT, ESTIMATE, and xCell algorithms were utilized to assess immune cell infiltration, with experimental validation performed though qPCR. An immune-related gene signature consisting of 18 genes, and the prognostic nomogram was established with superior performance compared to the conventional staging system. Key parameters incorporated into the nomogram included the gene signature, tumor stage, and postoperative treatment. Patients identified as high-risk exhibited an anti-inflammatory tumor microenvironment, characterized by an increase in M2-like tumor-associated macrophages and heightened tumor purity. Notably, the expression of interleukin 6 receptor (IL6R) in PDAC was predominantly derived from macrophages and was significantly associated with patient survival outcomes. Furthermore, attenuated IL-6/IL-6R signaling was found to promote M2-like macrophage differentiation. This study successfully established an immune-related gene signature and a robust nomogram for predicting clinical outcomes in patients with PDAC. Furthermore, we identified IL6R as a promising target for future immunotherapeutic strategies.

Application of random survival forest to establish a nomogram combining clinlabomics-score and clinical data for predicting brain metastasis in primary lung cancer

PURPOSE

To establish a nomogram for predicting brain metastasis (BM) in primary lung cancer at 12, 18, and 24 months after initial diagnosis.

METHODS

In this study, we included 428 patients who were diagnosed with primary lung cancer at Harbin Medical University Cancer Hospital between January 2020 and January 2022. The endpoint event was BM. The patients were randomly categorized into two groups in a 7:3 ratio: training (n = 299) and validation (n = 129) sets. Least absolute shrinkage and selection operator was utilized to analyze the laboratory test results in the training set. Furthermore, clinlabomics-score was determined using regression coefficients. Then, clinlabomics-score was combined with clinical data to construct a nomogram using random survival forest (RSF) and Cox multivariate regression. Then, various methods were used to evaluate the performance of the nomogram.

RESULTS

Five independent predictive factors (pathological type, diameter, lymph node metastasis, non-lymph node metastasis and clinlabomics-score) were used to construct the nomogram. In the validation set, the bootstrap C-index was 0.7672 (95% CI 0.7092-0.8037), 12-month AUC was 0.787 (95% CI 0.708-0.865), 18-month AUC was 0.809 (95% CI 0.735-0.884), and 24-month AUC was 0.858 (95% CI 0.792-0.924). In addition, the calibration curve, decision curve analysis and Kaplan-Meier curves revealed a good performance of the nomogram.

CONCLUSIONS

Finally, we constructed and validated a nomogram to predict BM risk in primary lung cancer. Our nomogram can identify patients at high risk of BM and provide a reference for clinical decision-making at different disease time points.

Developing a predictive model and uncovering immune influences on prognosis for brain metastasis from lung carcinomas

Objective

Primary lung carcinomas (LCs) often metastasize to the brain, resulting in a grim prognosis for affected individuals. This population-based study aimed to investigate their survival period and immune status, while also establishing a predictive model.

Methods

The records of 86,763 primary LCs from the Surveillance, Epidemiology, and End Results (SEER) database were extracted, including 15,180 cases with brain metastasis (BM) and 71,583 without BM. Univariate and multivariate Cox regression were employed to construct a prediction model. Multiple machine learning methods were applied to validate the model. Flow cytometry and ELISA were used to explore the immune status in a real-world cohort.

Results

The research findings revealed a 17.49% prevalence of BM from LCs, with a median survival of 8 months, compared with 16 months for their counterparts (p <0.001). A nomogram was developed to predict survival at 1, 3, and 5 years on the basis of these variables, with the time-dependent area under the curve (AUC) of 0.857, 0.814, and 0.786, respectively. Moreover, several machine learning approaches have further verified the reliability of this model's performance. Flow cytometry and ELISA analysis suggested the prediction model was related the immune status.

Conclusions

BM from LCs have an inferior prognosis. Considering the substantial impact of these factors, the nomogram model is a valuable tool for guiding clinical decision-making in managing patients with this condition.

Evolving prognostic paradigms in lung adenocarcinoma with brain metastases: a web-based predictive model enhanced by machine learning

INTRODUCTION

Patients with lung adenocarcinoma (LUAD) who develop brain metastases (BM) face significantly poor prognoses. A well-crafted prognostic model could greatly assist clinicians in patient counseling and in devising tailored therapeutic strategies.

METHODS

The study cohort comprised LUAD patients with BM identified from the surveillance, epidemiology, and end results database between 2000 and 2018. We pinpointed independent prognostic features for overall survival (OS) using Lasso regression analyses. Predictive models were built using Random Forest, XGBoost, Decision Trees, and Artificial Neural Networks, with their performance evaluated via metrics including the area under the receiver operating characteristic curve (AUC), calibration plots, brier score, and decision curve analysis (DCA).

RESULTS

We extracted a total of 9121 eligible patients from the database, identifying eleven clinical parameters that significantly influenced OS prognostication. The XGBoost model exhibited superior discriminative power, achieving AUC values of 0.829 and 0.827 for 1- and 2-year survival, respectively, in the training cohort, and 0.816 and 0.809 in the validation cohort. In comparison to other models, the XGBoost model excelled in both training and validation phases, as demonstrated by substantial differences in AUC, DCA, calibration, and Brier score. This model has been made accessible via a web-based platform.

CONCLUSIONS

This study has developed an XGBoost-based machine learning model with an accompanying web-based application, providing a novel resource for clinicians to support personalized decision-making and enhance treatment outcomes for LUAD patients with BM.

Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer

Brain metastases (BMs) in extensive-stage small cell lung cancer (ES-SCLC) are often associated with poor survival rates and quality of life, making the timely identification of high-risk patients for BMs in ES-SCLC crucial. Patients diagnosed with ES-SCLC between 2010 and 2018 were screened from the Surveillance, Epidemiology, and End Results (SEER) database. Four different machine learning (ML) algorithms were used to create prediction models for BMs in ES-SCLC patients. The accuracy, sensitivity, specificity, AUROC, and AUPRC were compared among these models and traditional logistic regression (LR). The random forest (RF) model demonstrated the best performance and was chosen for further analysis. The AUROC and AUPRC were calculated and compared. The findings from the RF model were utilized to identify the risk factors linked to BMs in patients diagnosed with ES-SCLC. Examining 4,716 instances of ES-SCLC, the research conducted an analysis, with brain metastases arising in 1,900 cases. Through evaluation of the ROC curve and PRC concerning the RF Model, results depicted an AUROC of 0.896 (95% CI: 0.889-0.899) and AUPRC of 0.900 (95% CI: 0.895-0.904). Test accuracy measured at 0.810 (95% CI: 0.784-0.833), sensitivity at 0.797 (95% CI: 0.756-0.841), and specificity at 0.819 (95% CI: 0.754-0.879). Based on the SHAP analysis of the RF predictive model, the top 10 most relevant features were identified and ranked in order of relative importance: bone metastasis, liver metastasis, radiation, age, tumor size, primary tumor location, N-stage, race, T-stage, and chemotherapy. The research developed and validated a predictive RF model using clinical and pathological data to predict the risk of BMs in patients with ES-SCLC. This model may assist physicians in making clinical decisions that could delay the onset of BMs and improve patient survival rates.

The Impact of Time Interval on Prognosis in Patients with Non-Small Cell Lung Cancer Brain Metastases After Metastases Surgery

BACKGROUND

Non-small cell lung cancer (NSCLC) is a prominent malignancy often linked to the development of brain metastases (BM), which commonly appear at diverse time intervals (TI) following the lung cancer diagnosis. This study endeavors to determine the prognostic significance of the time interval in patients with NSCLC who undergo BM surgery. Through this investigation, we aim to improve our understanding of the factors impacting the prognosis of BM cases originating from NSCLC.

METHODS

We analyzed data from 74 patients (2011-2021) who underwent BM surgery at our institution. The relationship between various clinical, radiological, and histopathological factors, as well as TI and overall survival (OS), was examined.

RESULTS

The median TI from initial NSCLC diagnosis to BM surgery was 19 months (range: 9-36 months). Notably, a shorter TI of less than 23 months was found to be independently associated with postoperative survival (adjusted odds ratio [aOR] 2.87, 95% confidence interval [CI] 1.03-8.02, P = 0.045). Additionally, a shorter TI was independently correlated with the absence of adjuvant chemotherapy for NSCLC (aOR 0.25, 95% CI 0.07-0.83, P = 0.023) and lack of targeted therapy (aOR 0.02, 95% CI 0.00-0.16, P < 0.001). Late-onset BM (TI ≥ 36 months) was observed in 15 cases (20.3%), in this subgroup, patients aged 60 years or older at the time of lung cancer diagnosis exhibited a significant independent correlation with late-onset BM (aOR 7.24, 95% CI 1.59-32.95, P = 0.011). NSCLC patients who underwent adjuvant chemotherapy displayed a notable correlation with late-onset BM (aOR 6.46, 95% CI 1.52-27.43, P = 0.011), while those who received targeted therapy also exhibited an independent association (aOR 2.27, 95% CI 1.70-3.03, P < 0.001).

CONCLUSIONS

Multiple factors contribute to the variability in the onset interval of BM subsequent to NSCLC diagnosis. The occurrence of BM within TI < 23 months following the initial diagnosis of NSCLC was demonstrated as an independent factor associated with an unfavorable prognosis following BM surgery. Furthermore, patients with NSCLC who did not receive adjuvant chemotherapy and lacked targeted therapy were shown to have an elevated likelihood of developing BM after a long progression-free survival.

Prognostic factors and survival prediction of resected non-small cell lung cancer with ipsilateral pulmonary metastases: a study based on the Surveillance, Epidemiology, and End Results (SEER) database

BACKGROUND

Prognostic factors and survival outcomes of non-small cell lung cancer (NSCLC) with Ipsilateral pulmonary metastasis (IPM) are not well-defined. Thus, this study intended to identify the prognostic factors for these patients and construct a predictive nomogram model.

METHODS

One thousand, seven hundred thirty-two patients with IPM identified between 2000 to 2019 were from the Surveillance, Epidemiology, and End Results (SEER) database. Independent prognostic factors were identified using multivariate Cox regression analyses. Nomograms were constructed to predict the overall survival (OS), C-index, the area under the curve (AUC), and the calibration curve to determine the predictive accuracy and discrimination; the decision curve analysis was used to confirm the clinical utility.

RESULTS

Patients were randomly divided into training (n = 1213) and validation (n = 519) cohorts. In the training cohort, the multivariable analysis demonstrated that age, sex, primary tumor size, N status, number of regional lymph nodes removed, tumor grade, and chemotherapy were independent prognostic factors for IPM. We constructed a 1-year, 3-year, and 5-year OS prediction nomogram model using independent prognostic factors. The C-index of this model for OS prediction was 0.714 (95% confidence interval [CI], 0.692 to 0.773) in the training cohort and 0.695 (95% CI, 0.660 to 0.730) in the validation cohort. Based on the AUC of the receiver operating characteristic analysis, calibration plots, and decision curve analysis, we concluded that the prognosis model of IPM exhibited excellent performance. Patients with total nomogram points greater than 96 were considered high-risk.

CONCLUSION

We constructed and internally validated a nomogram to predict 1-year, 3-year, and 5-year OS for NSCLC patients with IPM according to independent prognostic factors. This nomogram demonstrated good calibration, discrimination, clinical utility, and practical decision-making effects for the prognosis of NSCLC patients with IPM.

The development and validation of a nomogram for predicting brain metastases after chemotherapy and radiotherapy in male small cell lung cancer patients with stage III

OBJECTIVE

The purpose of this research was to develop a model for brain metastasis (BM) in limited-stage small cell lung cancer (LS-SCLC) patients and to help in the early identification of high-risk patients and the selection of individualized therapies.

METHODS

Univariate and multivariate logic regression was applied to identify the independent risk factors of BM. A receiver operating curve (ROC) and nomogram for predicting the incidence of BM were then conducted based on the independent risk factors. The decision curve analysis (DCA) was performed to assess the clinical benefit of prediction model.

RESULTS

Univariate regression analysis showed that the CCRT, RT dose, PNI, LLR, and dNLR were the significant factors for the incidence of BM. Multivariate analysis showed that CCRT, RT dose, and PNI were independent risk factors of BM and were included in the nomogram model. The ROC curves revealed the area under the ROC (AUC) of the model was 0.764 (95% CI, 0.658-0.869), which was much higher than individual variable alone. The calibration curve revealed favorable consistency between the observed probability and predicted probability for BM in LS-SCLC patients. Finally, the DCA demonstrated that the nomogram provides a satisfactory positive net benefit across the majority of threshold probabilities.

CONCLUSIONS

In general, we established and verified a nomogram model that combines clinical variables and nutritional index characteristics to predict the incidence of BM in male SCLC patients with stage III. Since the model has high reliability and clinical applicability, it can provide clinicians with theoretical guidance and treatment strategy making.

Risk factors and a new nomogram for predicting brain metastasis from lung cancer: a retrospective study

Objective

This study aims to establish and validate a new nomogram for predicting brain metastasis from lung cancer by integrating data.

Methods

266 patients diagnosed as lung cancer between 2016 and 2018 were collected from Guangdong Academy of Medical Sciences. The first 70% of patients were designated as the primary cohort and the remaining patients were identified as the internal validation cohort. Univariate and multivariable logistics regression were applied to analyze the risk factors. Independent risk factors were used to construct nomogram. C-index was used to evaluate the prediction effect of nomogram.100 patients diagnosed as lung cancer between 2018 and 2019 were collected for external validation cohorts. The evaluation of nomogram was carried out through the distinction and calibration in the internal validation cohort and external validation cohort.

Results

166 patients were diagnosed with brain metastasis among the 266 patients. The gender, pathological type (PAT), leukocyte count (LCC) and Fibrinogen stage (FibS) were independent risk factors of brain metastasis. A novel nomogram has been developed in this study showed an effective discriminative ability to predict the probability of lung cancer patients with brain metastasis, the C-index was 0.811.

Conclusion

Our research provides a novel model that can be used for predicting brain metastasis of lung cancer patients, thus providing more credible evidence for clinical decision-making.

Slug Mediates MRP2 Expression in Non-Small Cell Lung Cancer Cells

Transcriptional factors, such as Snail, Slug, and Smuc, that cause epithelial-mesenchymal transition are thought to regulate the expression of Ezrin, Radixin, and Moesin (ERM proteins), which serve as anchors for efflux transporters on the plasma membrane surface. Our previous results using lung cancer clinical samples indicated a correlation between Slug and efflux transporter MRP2. In the current study, we aimed to evaluate the relationships between MRP2, ERM proteins, and Slug in lung cancer cells. HCC827 cells were transfected by Mock and Slug plasmid. Both mRNA expression levels and protein expression levels were measured. Then, the activity of MRP2 was evaluated using CDCF and SN-38 (MRP2 substrates). HCC827 cells transfected with the Slug plasmid showed significantly higher mRNA expression levels of MRP2 than the Mock-transfected cells. However, the mRNA expression levels of ERM proteins did not show a significant difference between Slug-transfected cells and Mock-transfected cells. Protein expression of MRP2 was increased in Slug-transfected cells. The uptake of both CDCF and SN-38 was significantly decreased after transfection with Slug. This change was abrogated by treatment with MK571, an MRP2 inhibitor. The viability of Slug-transfected cells, compared to Mock cells, significantly increased after incubation with SN-38. Thus, Slug may increase the mRNA and protein expression of MRP2 without regulation by ERM proteins in HCC827 cells, thereby enhancing MRP2 activity. Inhibition of Slug may reduce the efficacy of multidrug resistance in lung cancer.