Establishment of a Prognostic Nomogram for Lung Adenocarcinoma with Brain Metastases

Comprehensive transcriptomic analysis unveils macrophage-associated genes for establishing an abdominal aortic aneurysm diagnostic model and molecular therapeutic framework

BACKGROUND

Abdominal aortic aneurysm (AAA) is a highly lethal cardiovascular disease. The aim of this research is to identify new biomarkers and therapeutic targets for the treatment of such deadly diseases.

METHODS

Single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT algorithms were used to identify distinct immune cell infiltration types between AAA and normal abdominal aortas. Single-cell RNA sequencing data were used to analyse the hallmark genes of AAA-associated macrophage cell subsets. Six macrophage-related hub genes were identified through weighted gene co-expression network analysis (WGCNA) and validated for expression in clinical samples and AAA mouse models. We screened potential therapeutic drugs for AAA through online Connectivity Map databases (CMap). A network-based approach was used to explore the relationships between the candidate genes and transcription factors (TFs), lncRNAs, and miRNAs. Additionally, we also identified hub genes that can effectively identify AAA and atherosclerosis (AS) through a variety of machine learning algorithms.

RESULTS

We obtained six macrophage hub genes (IL-1B, CXCL1, SOCS3, SLC2A3, G0S2, and CCL3) that can effectively diagnose abdominal aortic aneurysm. The ROC curves and decision curve analysis (DCA) were combined to further confirm the good diagnostic efficacy of the hub genes. Further analysis revealed that the expression of the six hub genes mentioned above was significantly increased in AAA patients and mice. We also constructed TF regulatory networks and competing endogenous RNA networks (ceRNA) to reveal potential mechanisms of disease occurrence. We also obtained two key genes (ZNF652 and UBR5) through a variety of machine learning algorithms, which can effectively distinguish abdominal aortic aneurysm and atherosclerosis.

CONCLUSION

Our findings depict the molecular pharmaceutical network in AAA, providing new ideas for effective diagnosis and treatment of diseases.

BAG3 as a novel prognostic biomarker in kidney renal clear cell carcinoma correlating with immune infiltrates

PURPOSE

BCL-2-associated athanogene 3 (BAG3) is an anti-apoptotic protein that plays an essential role in the onset and progression of multiple cancer types. However, the clinical significance of BAG3 in kidney renal clear cell carcinoma (KIRC) remains unclear.

METHODS

Using Tumor IMmune Estimation Resource (TIMER), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) database, we explored the expression, prognostic value, and clinical correlations of BAG3 in KIRC. In addition, immunohistochemistry (IHC) of HKH cohort further validated the expression of BAG3 in KIRC and its impact on prognosis. Gene Set Cancer Analysis (GSCA) was utilized to scrutinize the prognostic value of BAG3 methylation. Gene Ontology (GO) term analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to identify potential biological functions of BAG3 in KIRC. Single-sample gene set enrichment analysis (ssGSEA) was performed to confirm the correlation between BAG3 expression and immune cell infiltration.

RESULTS

BAG3 mRNA expression and protein expression were significantly downregulated in KIRC tissues compared to normal kidney tissues, associated with adverse clinical-pathological factors and poor clinical prognosis. Multivariate Cox regression analysis indicated that low expression of BAG3 was an independent prognostic factor in KIRC patients. GSEA analysis showed that BAG3 is mainly involved in DNA methylation and the immune-related pathways in KIRC. In addition, the expression of BAG3 is closely related to immune cell infiltration and immune cell marker set.

CONCLUSION

BAG3 might be a potential therapeutic target and valuable prognostic biomarker of KIRC and is closely related to immune cell infiltration.

Development and validation of a nomogram model for individualized prediction of hypertension risk in patients with type 2 diabetes mellitus

Type 2 diabetes mellitus (T2DM) with hypertension (DH) is the most common diabetic comorbidity. Patients with DH have significantly higher rates of cardiovascular disease morbidity and mortality. The objective of this study was to develop and validate a nomogram model for the prediction of an individual's risk of developing DH. A total of 706 T2DM patients who met the criteria were selected and divided into a training set (n = 521) and a validation set (n = 185) according to the discharge time of patients. By using multivariate logistic regression analysis and stepwise regression, the DH nomogram prediction model was created. Calibration curves were used to evaluate the model's accuracy, while decision curve analysis (DCA) and receiver operating characteristic (ROC) curves were used to evaluate the model's clinical applicability and discriminatory power. Age, body mass index (BMI), diabetic nephropathy (DN), and diabetic retinopathy (DR) were all independent risk factors for DH (P < 0.05). Based on independent risk factors identified by multivariate logistic regression, the nomogram model was created. The model produces accurate predictions. If the total nomogram score is greater than 120, there is a 90% or higher chance of developing DH. In the training and validation sets, the model's ROC curves are 0.762 (95% CI 0.720-0.803) and 0.700 (95% CI 0.623-0.777), respectively. The calibration curve demonstrates that there is good agreement between the model's predictions and the actual outcomes. The decision curve analysis findings demonstrated that the nomogram model was clinically helpful throughout a broad threshold probability range. The DH risk prediction nomogram model constructed in this study can help clinicians identify individuals at high risk for DH at an early stage, which is a guideline for personalized prevention and treatments.

Diagnostic and prognostic nomograms for newly diagnosed intrahepatic cholangiocarcinoma with brain metastasis: A population-based analysis

Brain metastasis (BM) is one of the rare metastatic sites of intrahepatic cholangiocarcinoma (ICC). ICC with BM can seriously affect the quality of life of patients and lead to a poor prognosis. The aim of this study was to establish two nomograms to estimate the risk of BM in ICC patients and the prognosis of ICC patients with BM. Data on 19,166 individuals diagnosed with ICC were retrospectively collected from the Surveillance, Epidemiology, and End Results (SEER) database. Independent risk factors and prognostic factors were identified by the logistic and the Cox regression, respectively. Next, two nomograms were developed, and their discrimination was estimated by concordance index (C-index) and calibration plots, while the clinical benefits of the prognostic nomogram were evaluated using the receiver operating characteristic (ROC) curves, the decision curve analysis (DCA), and the Kaplan-Meier analyses. The independent risk factors for BM were T stage, N stage, surgery, alpha-fetoprotein (AFP) level, and tumor size. T stage, surgery, radiotherapy, and bone metastasis were prognostic factors for overall survival (OS). For the prognostic nomogram, the C-index was 0.759 (95% confidence interval (CI) = 0.745-0.773) and 0.764 (95% CI = 0.747-0.781) in the training and the validation cohort, respectively. The calibration curves revealed a robust agreement between predictions and actual observations probability. The area under curves (AUCs) for the 3-, 6-, and 9-month OS were 0.721, 0.727, and 0.790 in the training cohort and 0.702, 0.777, and 0.853 in the validation cohort, respectively. The DCA curves yielded remarkable positive net benefits over a wide range of threshold probabilities. The Kaplan-Meier analysis illustrated that the nomogram could significantly distinguish the population with different survival risks. We successfully established the two nomograms for predicting the incidence of BM and the prognosis of ICC patients with BM, which may assist clinicians in choosing more effective treatment strategies.

A Nomogram-Based Risk Classification System Predicting the Overall Survival of Childhood with Clear Cell Sarcoma of the Kidney Based on the SEER Database

Objective. Clear cell sarcoma of the kidney (CCSK) is a lethal pediatric renal malignancy with poor prognosis. A prognostic nomogram needs to be established for overall survival (OS) prediction of patients with CCSK. Methods. Eligible 2588 CCSK patients (age 0–19) diagnosed between 2000 and 2017 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Patients were randomized into training and validation cohorts (7 : 3). Independent prognostic factors were identified by univariate and multifactorial Cox regression analyses and used to construct a nomogram. Receiver operating characteristics (ROC) analysis, calibration curves, and decision curve analysis (DCA) were used to validate the nomogram. Moreover, a risk classification system was established based on the risk scores of the nomogram. Results. Cox analyses revealed that age, combined stage, and origin were most significant prognostic factors. Based on these prognostic factors, a nomogram was established for predicting 3- and 5-year OS of patients with CCSK. The area under the ROC curve (AUC) of 3- and 5-year OS was 0.733 and 0.728 in the training cohort, corresponding to 0.69 and 0.674 in the validation cohort. The C-index of calibration curves in the training and validation cohorts was 0.724 and 0.686. DCAs indicated the clinical utility of this nomogram. A risk classification system stratified CCSK patients into three different risk cohorts. The OS time of low-, intermediate-, and high-risk patients was 76, 68, and 65 months in the training cohort, corresponding to 69.5, 66, and 72 months in the validation cohort. Conclusion. A nomogram-based risk classification system has high accuracy for the prognostic prediction of CCSK.

The nomogram for the prediction of overall survival in patients with metastatic lung adenocarcinoma undergoing primary site surgery: A retrospective population-based study

Background

Lung adenocarcinoma (LUAD) is the most common type of Non-small-cell lung cancer (NSCLC). Distant metastasis of lung adenocarcinoma reduces the survival rate. we aim to develop a nomogram in order to predict the survival of patients with metastatic lung adenocarcinoma.

Methods

We retrospectively collected patients who were initially diagnosed as metastatic LUAD from 2010 to 2015 from SEER database. Based on the multivariate and univariate Cox regression analysis of the training cohorts, independent prognostic factors were assessed. The nomogram prediction model was then constructed based on these prognostic factors to predict the overall survival at 12, 24 and 36 months after surgery. Nomogram were identified and calibrated by c-index, time-dependent receiver operating characteristic curve (time-dependent AUC) and calibration curve. Decision curve analysis (DCA) was used to quantify the net benefit of the nomogram at different threshold probabilities, and to better compare with the TNM staging system, we calculated the c-index of this nomogram as well as the net reclassification improvement (NRI) and the integrated discrimination improvement (IDI).

Result

A total of 1102 patients with metastatic LUAD who met the requirements were included for analysis. They were randomly divided into 774 in the training cohorts and 328 in the validation cohorts. As can be seen from the calibration plots, the predicted nomogram and the actual observations in both of the training and validation cohorts were generally consistent. The time dependent AUC values of 12 months, 24 months and 36 months were 0.707, 0.674 and 0.686 in the training cohorts and 0.690, 0.680 and 0.688 in the verification cohorts, respectively. C-indexes for the training and validation cohorts were 0.653 (95%CI 0.626-0.68)and 0.663 (95%CI 0.626-1), respectively. NRI and IDI show that the model is more clinical applicable than the existing staging system. In addition, our risk scoring system based on Kaplan Meier (K-M) survival curve can accurately divide patients into three hierarchy risk groups.

Conclusion

This has led to the development and validation of a prognostic nomogram to assist clinicians in determining the prognosis of patients with metastatic lung adenocarcinoma after primary site surgery.

Development of a prognostic nomogram for metastatic pancreatic ductal adenocarcinoma integrating marital status

Previous studies have shown that marital status can affect the overall survival (OS) of cancer patients yet its role in metastatic pancreatic ductal adenocarcinoma (mPDAC) remains unclear. This study aimed to explore the impact of marital status on the OS of mPDAC patients and to construct a prognostic nomogram to predict OS outcomes. Data from patients diagnosed with mPDAC were obtained from the Surveillance, Epidemiology, and End Results database between 1973 and 2015. The patients were randomized into primary and validation cohorts. Kaplan-Meier survival analysis was performed to compare differences in survival depending on marital status. Univariate and multivariate analyses were conducted to identify independent prognostic factors and a nomogram was established based using Cox regression analyses. Validation of the prognostic nomogram was evaluated with a calibration curve and concordance index (C-index). Our data showed significant differences in the OS of mPDAC patients with different marital status by Kaplan-Meier analysis (P < 0.05). Univariate and multivariate analyses confirmed that marital status was an independent OS-related factor in mPDAC patients. Based on the multivariate models of the primary cohort, a nomogram was developed that combined marital status, age, grade, tumor size, surgery of primary site, surgery of lymph node and metastatic. The nomogram showed that marital status had a moderate influence on predicting the OS of mPDAC patients. Moreover, the internally and externally validated C-indexes were 0.633 and 0.619, respectively. A calibration curve confirmed favorable consistency between the observed and predicted outcomes. Marital status was identified as an independent prognostic factor for OS of mPDAC patients and is a reliable and valid parameter to predict the survival of patients with mPDAC. This prognostic model has value and may be integrated as a tool to inform decision-making in the clinic.

Relationship Between Neutrophil-To-Lymphocyte Ratio and Brain Metastasis in Non-Small Cell Lung Cancer Patients

Objective

To investigate the relationship between the neutrophil-to-lymphocyte ratio (NLR) of patients with non-small cell lung cancer (NSCLC) and their risk of developing brain metastases after adjusting for confounding factors.

Methods

A retrospective observational study of the general data of patients with NSCLC diagnosed from January 2016 to December 2020. Multivariate logistic regression was used to calculate the dominance ratio (OR) with 95% confidence interval (CI) for NLR and NSCLC brain metastases with subgroup analysis. Generalized summation models and smoothed curve fitting were used to identify whether there was a nonlinear relationship between them.

Results

In all 3 models, NLR levels were positively correlated with NSCLC brain metastasis (model 1: OR: 1.12, 95% CI: 1.01-1.23, P = .025; model 2: OR: 1.16, 95% CI: 1.04-1.29, P = .007; model 3: OR: 1.20, 95% CI: 1.05-1.37, P = .006). Stratified analysis showed that this positive correlation was present in patients with adenocarcinoma (LUAD) and female patients (LUAD: OR: 1.30, 95% CI: 1.10-1.54, P = .002; female: OR: 1.52, 95% CI: 1.05-2.20, P = .026), while there was no significant correlation in patients with squamous carcinoma (LUSC) and male patients (LUSC: OR:0.76,95% CI:0.38- 1.53, P = .443; male: OR:1.13, 95% CI:0.95-1.33, P = .159).

Conclusion

This study showed that elevated levels of NLR were independently associated with an increased risk of developing brain metastases in patients with NSCLC, and that this correlation varied by TYPE and SEX, with a significant correlation in female patients and patients with LUAD.

Identification of HCC Subtypes With Different Prognosis and Metabolic Patterns Based on Mitophagy

Background: Mitophagy is correlated with tumor initiation and development of malignancy. However, HCC heterogeneity with reference to mitophagy has yet not been systematically explored.

Materials and Methods: Mitophagy-related, glycolysis-related, and cholesterol biosynthesis-related gene sets were obtained from the Reactome database. Mitophagy-related and metabolism-related subtypes were identified using the ConsensusClusterPlus algorithm. Univariate Cox regression was analysis was performed to identify prognosis-related mitophagy regulators. Principal component analysis (PCA) was used to create composite measures of the prognosis-related mitophagy regulators (mitophagyscore). Individuals with a mitophagyscore higher or lower than the median value were classified in high- or low-risk groups. Kaplan-Meier survival and ROC curve analyses were utilized to evaluate the prognostic value of the mitophagyscore. The nomogram and calibration curves were plotted using the“rms” R package. The package “limma” was used for differential gene expression analysis. Differentially expressed genes (DEGs) between high- and low-risk groups were used as queries in the CMap database. R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database were used to determine the sensitivity of 21 previously reported anti-HCC drugs.

Results: Three distinct HCC subtypes with different mitophagic accumulation (low, high, and intermediate mitophagy subtypes) were identified. High mitophagy subtype had the worst outcome and highest glycolysis level. The lowest degree of hypoxia and highest cholesterol biosynthesis was observed in the low mitophagy subtype; oncogenic dedifferentiation level in the intermediate mitophagy subtype was the lowest. Mitophagyscore could serve as a novel prognostic indicator for HCC.High-risk patients had a poorer prognosis (log-rank test, p < 0.001). The area under the ROC curve for mitophagyscore in 1-year survival was 0.77 in the TCGA cohort and 0.75 in the ICGC cohort. Nine candidate small molecules which were potential drugs for HCC treatment were identified from the CMap database. A decline in the sensitivity towards 21 anti-HCC drugs was observed in low-risk patients by GDSC database. We also identified a novel key gene, SPP1, which was highly associated with different mitophagic subtypes.

Conclusion: Based on bioinformatic analyses, we systematically examined the HCC heterogeneity with reference to mitophagy and observed three distinct HCC subtypes having different prognoses and metabolic patterns.

Predictors for survival in patients with bone metastasis of small cell lung cancer: A population-based study

The objective of the current study is to analyze the clinical and demographic characteristics of patients with bone metastasis of small cell lung cancer (SCLC) and explore their survival predictors.We retrospectively extracted patients with bone metastasis of SCLC from the Surveillance, Epidemiology, and End Results database. We applied Cox regression analyses to identify independent survival predictor of overall survival (OS) and cancer-specific survival (CSS). Only significant predictors from univariable analysis were included for multivariable Cox analysis. Kaplan-Meier method was used to evaluate survival differences between groups by the log-rank test.A total of 5120 patients with bone metastasis of SCLC were identified and included for survival analysis. The 1-year OS and CSS rates of bone metastasis of SCLC were 19.8% and 21.4%, respectively. On multivariable analysis, gender, age, radiotherapy, chemotherapy, liver metastasis, brain metastasis, insurance status, and marital status independently predicted OS and CSS. There was no significant difference of OS and CSS in terms of race and tumor size.Independent predictors of survival were identified among patients with bone metastasis of SCLC, which could be valuable to clinicians in treatment decision. Patients with bone metastasis of SCLC may benefit from radiotherapy and chemotherapy.

The Clinical Characteristics and Prediction Nomograms for Primary Spine Malignancies

Background

Primary spine malignancies (PSMs) are relatively rare in bone tumors. Due to their rarity, the clinical characteristics and prognostic factors are still ambiguous. In this study, we aim to identify the clinical features and proposed prediction nomograms for patients with PSMs.

Methods

Patients diagnosed with PSMs including chordoma, osteosarcoma, chondrosarcoma, Ewing sarcoma, and malignant giant cell tumor of bone (GCTB) between 1975 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database. The patient and tumor characteristics were described based on clinical information. The significant prognostic factors of overall survival (OS) and cancer-specific survival (CSS) were identified by the univariate and multivariate Cox analysis. Then, the nomograms for OS and CSS were established based on the selected predictors and their accuracy was explored by the Cox–Snell residual plot, area under the curve (AUC) of receiver operator characteristic (ROC) and calibration curve.

Results

The clinical information of 1,096 patients with PSMs was selected from the SEER database between 1975 and 2016. A total of 395 patients were identified with full survival and treatment data between 2004 and 2016. Chordoma is the commonest tumor with 400 cases, along 172 cases with osteosarcoma, 240 cases with chondrosarcoma, 262 cases with Ewing sarcoma and 22 cases with malignant GCTB. The univariate and multivariate analyses revealed that older age (Age > 60), distant metastasis, chemotherapy, and Surgery were independent predictors for OS and/or CSS. Based on these results, the nomograms were established with a better applicability (AUC for CSS: 0.784; AUC for OS: 0.780).

Conclusions

This study provides the statistics evidence for the clinical characteristics and predictors for patients with PSMs based on a large size population. Additionally, precise prediction nomograms were also established with a well-applicability.