Prognostic Factors in a Large Nationwide Cohort of Histologically Confirmed Primary and Secondary Angiosarcomas

Angiosarcoma (AS) is a rare sarcoma of endothelial origin, arising spontaneously (primary AS) or after external damage such as radiation therapy or UV exposure (secondary AS). To date, reliable assessment of prognostic factors has proven difficult, due to disease rarity and heterogeneity of study cohorts. Although large registries provide relatively large AS patient series, these cases often lack histological confirmation. This study aimed to analyze AS prognostic factors in a large nationwide cohort of histologically confirmed cases, established through linkage of clinical data from the Netherlands Cancer Registry and pathology data from the Dutch pathology registry (PALGA). All cases were reviewed by an expert pathologist, showing a 16% discordance rate. Multivariable Cox regression survival analysis among 479 confirmed AS patients revealed remarkably poorer overall survival (OS) for primary AS compared to secondary AS (7 vs 21 months, Hazard ratio (HR) = 1.5; 95% confidence interval (CI) = 1.2–1.9). Age above 65 years, male gender, and no surgical treatment also significantly correlated to worse OS. Overall, OS was relatively poor, with a median of 13 months (95% CI = 10–16 months) and 22% five-year survival rate. With this study, we illustrate AS heterogeneity in clinical behavior and show for the first time better survival for secondary AS compared to primary AS.

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Incidence, Risk Factors, and Prognosis of Meningiomas With Distant Metastases at Presentation

10.21203/rs.3.rs-409950/v1 ◽

2021 ◽

Author(s):

Huy Gia Vuong ◽

Tam N.M. Ngo ◽

Ian F. Dunn

Keyword(s):

Prognostic Factors ◽

Cox Regression ◽

Rare Event ◽

Distant Metastases ◽

Extent Of Resection ◽

Male Gender ◽

Standards Of Care ◽

Rank Test ◽

Large Tumor ◽

Who Grade

Abstract IntroductionDistant metastases (DM) at presentation in meningiomas is a very rare event, and the incidence and factors predicting this are uncertain. This population-based study also aimed to investigate the prognostic implication of DM at presentation and clinical parameters to prognosticate the overall survival (OS) of meningiomas presenting with DM (M1).MethodsWe accessed the Surveillance, Epidemiology, and End Results program to search for patients who were diagnosed with meningioma between 2004-2016. The log-rank test was used to compare Kaplan-Meier survival curves and multivariate Cox regression model was utilized to evaluate the prognostic parameters of meningiomas with DM at presentation.ResultsThe incidence of DM at presentation among all meningiomas was 0.18%. Clinical variables associated with this event were male gender, large tumor size, and WHO grade III. The presence of DM at diagnosis conferred a shorter survival in comparison to those without DM (HR = 2.015; 95% CI = 1.600-2.536). Older patient age, male gender, malignant histology, and the lesser extent of resection were independent prognostic factors that could negatively impact OS of M1 meningiomas. Radiotherapy and chemotherapy were not associated with an improved outcome for these patients.ConclusionOur study highlighted the clinical and prognostic factors of M1 meningiomas. These data suggest that a greater extent of resection is associated with increased OS across a nationwide analysis and emphasize the need to establish the standards of care in these patients.

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Incidence, risk factors, and prognosis of meningiomas with distant metastases at presentation

Neuro-Oncology Advances ◽

10.1093/noajnl/vdab084 ◽

2021 ◽

Author(s):

Huy Gia Vuong ◽

Tam N M Ngo ◽

Ian F Dunn

Keyword(s):

Prognostic Factors ◽

Cox Regression ◽

Rare Event ◽

Distant Metastases ◽

Extent Of Resection ◽

Male Gender ◽

Standards Of Care ◽

Rank Test ◽

Large Tumor ◽

Who Grade

Abstract Background Distant metastases (DM) at presentation in meningiomas is a very rare event, and the incidence and factors predicting this are uncertain. This population-based study also aimed to investigate the prognostic implication of DM at presentation and clinical parameters to prognosticate the overall survival (OS) of meningiomas presenting with DM (M1). Methods We accessed the Surveillance, Epidemiology, and End Results program to search for patients who were diagnosed with meningioma between 2004-2016. The log-rank test was used to compare Kaplan-Meier survival curves and multivariate Cox regression model was utilized to evaluate the prognostic parameters of meningiomas with DM at presentation. Results The incidence of DM at presentation among all meningiomas was 0.18%. Clinical variables associated with this event were male gender, large tumor size, and WHO grade III. The presence of DM at diagnosis conferred a shorter survival in comparison to those without DM (HR = 2.015; 95% CI = 1.600-2.536). Older patient age, male gender, malignant histology, and the lesser extent of resection were independent prognostic factors that could negatively impact OS of M1 meningiomas. Radiotherapy and chemotherapy were not associated with an improved outcome for these patients. Conclusion Our study highlighted the clinical and prognostic factors of M1 meningiomas. These data suggest that a greater extent of resection is associated with increased OS across a nationwide analysis and emphasize the need to establish the standards of care in these patients.

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(Pro)renin Receptor Is a Novel Independent Prognostic Marker in Invasive Urothelial Carcinoma of the Bladder

Cancers ◽

10.3390/cancers13225642 ◽

2021 ◽

Vol 13 (22) ◽

pp. 5642

Author(s):

Gorka Larrinaga ◽

Julio Calvete-Candenas ◽

Jon Danel Solano-Iturri ◽

Ana M. Martín ◽

Angel Pueyo ◽

...

Keyword(s):

Prognostic Factors ◽

Cox Regression ◽

Prognostic Role ◽

Primary Tumors ◽

P53 Expression ◽

Erk Activation ◽

Independent Prognostic Marker ◽

Comorbidity Index ◽

Carcinoma Of The Bladder ◽

First Time

(Pro)renin receptor (PRR) is being investigated in several malignancies as it activates pathogenic pathways that contribute to cell proliferation, immunosuppressive microenvironments, and acquisition of aggressive neoplastic phenotypes. Its implication in urothelial cancer (UC) has not been evaluated so far. We retrospectively evaluate the prognostic role of PRR expression in a series of patients with invasive UC treated with radical cystectomy and other clinical and histopathological parameters including p53, markers of immune-checkpoint inhibition, and basal and luminal phenotypes evaluated by tissue microarray. Cox regression analyses using stepwise selection evaluated candidate prognostic factors and disease-specific survival. PRR was expressed in 77.3% of the primary tumors and in 70% of positive lymph nodes. PRR expression correlated with age (p = 0.006) and was associated with lower preoperatively hemoglobin levels. No other statistical association was evidenced with clinical and pathological variables (gender, ASA score, Charlson comorbidity index, grade, pT, pN) or immunohistochemical expressions evaluated (CK20, GA-TA3, CK5/6, CD44, PD-L1, PD-1, B7-H3, VISTA, and p53). PRR expression in primary tumors was associated with worse survival (log-rank, p = 0.008). Cox regression revealed that PRR expression (HR 1.85, 95% CI 1.22–2.8), pT (HR 7.02, 95% CI 2.68–18.39), pN (HR 2.3, 95% CI 1.27–4.19), and p53 expression (HR 1.95, 95% CI 1.1–3.45) were independent prognostic factors in this series. In conclusion, we describe PRR protein and its prognostic role in invasive UC for the first time. Likely mechanisms involved are MAPK/ERK activation, Wnt/β-catenin signaling, and v-ATPAse function.

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Predictors of renal damage in systemic lupus erythematous patients: data from a multiethnic, multinational Latin American lupus cohort (GLADEL)

RMD Open ◽

10.1136/rmdopen-2020-001299 ◽

2020 ◽

Vol 6 (3) ◽

pp. e001299

Author(s):

Cristina Reátegui-Sokolova ◽

Manuel F Ugarte-Gil ◽

Guillermina B Harvey ◽

Daniel Wojdyla ◽

Guillermo J Pons-Estel ◽

...

Keyword(s):

Risk Factors ◽

Systemic Lupus Erythematosus ◽

Latin American ◽

Lupus Erythematosus ◽

Renal Damage ◽

Cox Regression ◽

Damage Index ◽

Early Response ◽

Male Gender ◽

Systemic Lupus

AimA decrease in proteinuria has been considered protective from renal damage in lupus nephritis (LN), but a cut-off point has yet to be established. The aim of this study was to identify the predictors of renal damage in patients with LN and to determine the best cut-off point for a decrease in proteinuria.MethodsWe included patients with LN defined clinically or histologically. Possible predictors of renal damage at the time of LN diagnosis were examined: proteinuria, low complement, anti-double-stranded DNA antibodies, red cell casts, creatinine level, hypertension, renal activity (assessed by the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI)), prednisone dose, immunosuppressive drugs and antimalarial use. Sociodemographic variables were included at baseline. Proteinuria was assessed at baseline and at 12 months, to determine if early response (proteinuria <0.8 g/day within 12 months since LN diagnosis) is protective of renal damage occurrence. Renal damage was defined as an increase of one or more points in the renal domain of The Systemic Lupus International Collaborating Clinics (SLICC)/American College of Rheumatology (ACR) Damage Index (SDI). Cox regression models using a backward selection method were performed.ResultsFive hundred and two patients with systemic lupus erythematosus patients were included; 120 patients (23.9%) accrued renal damage during their follow-up. Early response to treatment (HR=0.58), antimalarial use (HR=0.54) and a high SES (HR=0.25) were protective of renal damage occurrence, whereas male gender (HR=1.83), hypertension (HR=1.86) and the renal component of the SLEDAI (HR=2.02) were risk factors for its occurrence.ConclusionsEarly response, antimalarial use and high SES were protective of renal damage, while male gender, hypertension and higher renal activity were risk factors for its occurrence in patients with LN.

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Comprehensive analysis of competitive endogenous RNA associated with immune infiltration in lung adenocarcinoma

Scientific Reports ◽

10.1038/s41598-021-90755-w ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Wenjie Chen ◽

Wen Li ◽

Zhenkun Liu ◽

Guangzhi Ma ◽

Yunfu Deng ◽

...

Keyword(s):

Prognostic Factors ◽

Mast Cells ◽

Correlation Analysis ◽

Lung Adenocarcinoma ◽

Molecular Mechanisms ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Cerna Network ◽

Monocytes And Macrophages ◽

Competitive Endogenous Rna

AbstractTo identify the prognostic biomarker of the competitive endogenous RNA (ceRNA) and explore the tumor infiltrating immune cells (TIICs) which might be the potential prognostic factors in lung adenocarcinoma. In addition, we also try to explain the crosstalk between the ceRNA and TIICs to explore the molecular mechanisms involved in lung adenocarcinoma. The transcriptome data of lung adenocarcinoma were obtained from The Cancer Genome Atlas (TCGA) database, and the hypergeometric correlation of the differently expressed miRNA-lncRNA and miRNA-mRNA were analyzed based on the starBase. In addition, the Kaplan–Meier survival and Cox regression model analysis were used to identify the prognostic ceRNA network and TIICs. Correlation analysis was performed to analysis the correlation between the ceRNA network and TIICs. In the differently expressed RNAs between tumor and normal tissue, a total of 190 miRNAs, 224 lncRNAs and 3024 mRNAs were detected, and the constructed ceRNA network contained 5 lncRNAs, 92 mRNAs and 10 miRNAs. Then, six prognostic RNAs (FKBP3, GPI, LOXL2, IL22RA1, GPR37, and has-miR-148a-3p) were viewed as the key members for constructing the prognostic prediction model in the ceRNA network, and three kinds of TIICs (Monocytes, Macrophages M1, activated mast cells) were identified to be significantly related with the prognosis in lung adenocarcinoma. Correlation analysis suggested that the FKBP3 was associated with Monocytes and Macrophages M1, and the GPI was obviously related with Monocytes and Macrophages M1. Besides, the LOXL2 was associated with Monocytes and Activated mast cells, and the IL22RA1 was significantly associated with Monocytes and Macrophages M1, while the GPR37 and Macrophages M1 was closely related. The constructed ceRNA network and identified Monocytes, Macrophages M1 and activated Mast cells are all prognostic factors for lung adenocarcinoma. Moreover, the crosstalk between the ceRNA network and TIICs might be a potential molecular mechanism involved.

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Integrated analysis of methylation-driven genes and pretreatment prognostic factors in patients with hepatocellular carcinoma

BMC Cancer ◽

10.1186/s12885-021-08314-5 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dongsheng He ◽

Shengyin Liao ◽

Lifang Cai ◽

Weiming Huang ◽

Xuehua Xie ◽

...

Keyword(s):

Hepatocellular Carcinoma ◽

Prognostic Factors ◽

Risk Score ◽

Prognostic Model ◽

Cox Regression ◽

The Cancer Genome Atlas ◽

Integrated Analysis ◽

Calibration Plot ◽

Clinical Parameters ◽

T Stage

Abstract Background The potential reversibility of aberrant DNA methylation indicates an opportunity for oncotherapy. This study aimed to integrate methylation-driven genes and pretreatment prognostic factors and then construct a new individual prognostic model in hepatocellular carcinoma (HCC) patients. Methods The gene methylation, gene expression dataset and clinical information of HCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Methylation-driven genes were screened with a Pearson’s correlation coefficient less than − 0.3 and a P value less than 0.05. Univariable and multivariable Cox regression analyses were performed to construct a risk score model and identify independent prognostic factors from the clinical parameters of HCC patients. The least absolute shrinkage and selection operator (LASSO) technique was used to construct a nomogram that might act to predict an individual’s OS, and then C-index, ROC curve and calibration plot were used to test the practicability. The correlation between clinical parameters and core methylation-driven genes of HCC patients was explored with Student’s t-test. Results In this study, 44 methylation-driven genes were discovered, and three prognostic signatures (LCAT, RPS6KA6, and C5orf58) were screened to construct a prognostic risk model of HCC patients. Five clinical factors, including T stage, risk score, cancer status, surgical method and new tumor events, were identified from 13 clinical parameters as pretreatment-independent prognostic factors. To avoid overfitting, LASSO analysis was used to construct a nomogram that could be used to calculate the OS in HCC patients. The C-index was superior to that from previous studies (0.75 vs 0.717, 0.676). Furthermore, LCAT was found to be correlated with T stage and new tumor events, and RPS6KA6 was found to be correlated with T stage. Conclusion We identified novel therapeutic targets and constructed an individual prognostic model that can be used to guide personalized treatment in HCC patients.

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Identification of Prognostic RBPs in Osteosarcoma

Technology in Cancer Research & Treatment ◽

10.1177/15330338211004918 ◽

2021 ◽

Vol 20 ◽

pp. 153303382110049

Author(s):

Bei Li ◽

Long Fang ◽

Baolong Wang ◽

Zengkun Yang ◽

Tingbao Zhao

Keyword(s):

Regression Analysis ◽

Prognostic Factors ◽

Differential Expression ◽

Ribosome Biogenesis ◽

Rna Binding ◽

Cox Regression ◽

Rna Binding Proteins ◽

Malignant Tumors ◽

Differential Expression Analysis ◽

Cox Regression Analysis

Osteosarcoma often occurs in children and adolescents and causes poor prognosis. The role of RNA-binding proteins (RBPs) in malignant tumors has been elucidated in recent years. Our study aims to identify key RBPs in osteosarcoma that could be prognostic factors and treatment targets. GSE33382 dataset was downloaded from Gene Expression Omnibus (GEO) database. RBPs extraction and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological function of differential expression RBPs. Moreover, we constructed Protein-protein interaction (PPI) network and obtained key modules. Key RBPs were identified by univariate Cox regression analysis and multiple stepwise Cox regression analysis combined with the clinical information from Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Risk score model was generated and validated by GSE16091 dataset. A total of 38 differential expression RBPs was identified. Go and KEGG results indicated these RBPs were significantly involved in ribosome biogenesis and mRNA surveillance pathway. COX regression analysis showed DDX24, DDX21, WARS and IGF2BP2 could be prognostic factors in osteosarcoma. Spearman’s correlation analysis suggested that WARS might be important in osteosarcoma immune infiltration. In conclusion, DDX24, DDX21, WARS and IGF2BP2 might play key role in osteosarcoma, which could be therapuetic targets for osteosarcoma treatment.

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FRI0248 PROGNOSTIC FACTORS FOR STEROID-FREE REMISSION IN PATIENTS WITH IDIOPATHIC INFLAMMATORY MYOPATHIES: IMPORTANCE OF ANTHROPOMETRIC MEASUREMENTS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2020-eular.3010 ◽

2020 ◽

Vol 79 (Suppl 1) ◽

pp. 708.1-708

Author(s):

J. S. Lee ◽

S. H. Nam ◽

S. J. Choi ◽

W. J. Seo ◽

S. Hong ◽

...

Keyword(s):

Prognostic Factors ◽

Cox Regression ◽

Idiopathic Inflammatory Myopathies ◽

Inflammatory Myopathies ◽

Immunosuppressant Therapy ◽

Muscle Enzymes ◽

Cox Regression Analysis ◽

Factors Associated ◽

Early Use

Background:Several studies have been conducted on factors associated with mortality in idiopathic inflammatory myopathies (IIM), but few studies have assessed prognostic factors for steroid-free remission in IIM.Objectives:We investigated the various clinical factors, including body measurements, that affect IIM treatment outcomes.Methods:Patients who were newly diagnosed with IIM between 2000 and 2018 were included. Steroid-free remission was defined as at least three months of normalisation of muscle enzymes and no detectable clinical disease activity. The factors associated with steroid-free remission were evaluated by a Cox regression analysis.Results:Of the 106 IIM patients, 35 displayed steroid-free remission during follow-up periods. In the multivariable Cox regression analyses, immunosuppressants’ early use within one month after diagnosis [hazard ratio (HR) 6.21, 95% confidence interval (CI) 2.61–14.74, p < 0.001] and sex-specific height quartiles (second and third quartiles versus first quartile, HR 3.65, 95% CI 1.40–9.51, p = 0.008 and HR 2.88, 95% CI 1.13–7.32, p = 0.027, respectively) were positively associated with steroid-free remission. Polymyositis versus dermatomyositis (HR 0.21, 95% CI 0.09–0.53, p = 0.001), presence of dysphagia (HR 0.15, CI 0.05–0.50, p = 0.002) and highest versus lowest quartile of waist circumference (WC) (HR 0.24, 95% CI 0.07–0.85, p = 0.027) were negatively associated with steroid-free remission.Conclusion:The early initiation of immunosuppressant therapy, type of myositis and presence of dysphagia are strong predictors of steroid-free remission in IIM; moreover, height and WC measurements at baseline may provide additional important prognostic value.Disclosure of Interests:None declared

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A nomogram based on pretreatment levels of serum bilirubin and total bile acid levels predicts survival in colorectal cancer patients

BMC Cancer ◽

10.1186/s12885-021-07805-9 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Yinghao Cao ◽

Shenghe Deng ◽

Lizhao Yan ◽

Junnan Gu ◽

Jia Yang ◽

...

Keyword(s):

Colorectal Cancer ◽

Prognostic Factors ◽

Bile Acid ◽

Bile Acids ◽

Cancer Patients ◽

Serum Bilirubin ◽

Cox Regression ◽

Total Bile Acid ◽

Cox Regression Analysis ◽

Colorectal Cancer Patients

Abstract Background Serum bilirubin and total bile acid (TBA) levels have been reported to be strongly associated with the risk and prognosis of certain cancers. Here, we aimed to investigate the effects of pretreatment levels of serum bilirubin and bile acids on the prognosis of patients with colorectal cancer (CRC). Methods A retrospective cohort of 1474 patients with CRC who underwent surgical resection between January 2015 and December 2017 was included in the study. Survival analysis was used to evaluate the predictive value of pretreatment levels of bilirubin and bile acids. X-Tile software was used to identify optimal cut-off values for total bilirubin (TBIL), direct bilirubin (DBIL) and TBA in terms of overall survival (OS) and disease-free survival (DFS). Results DBIL, TBIL, and TBA were validated as significant prognostic factors by univariate Cox regression analysis for both 3-year OS and DFS. Multivariate Cox regression analyses confirmed that high DBIL, TBIL and TBA levels were independent prognostic factors for both OS (HR: 0.435, 95% CI: 0.299–0.637, P < 0.001; HR: 0.436, 95% CI: 0.329–0.578, P < 0.001; HR: 0.206, 95% CI: 0.124–0.341, P < 0.001, respectively) and DFS (HR: 0.583, 95% CI: 0.391–0.871, P = 0.008; HR:0.437,95% CI: 0.292–0.655, P <0.001; HR: 0.634, 95% CI: 0.465–0.865, P = 0.004, respectively). In addition, nomograms for OS and DFS were established according to all significant factors, and the c-indexes were 0.819 (95% CI: 0.806–0.832) and 0.835 (95% CI: 0.822–0.849), respectively. Conclusions TBIL, DBIL and TBA levels are independent prognostic factors in colorectal cancer patients. The nomograms based on OS and DFS can be used as a practical model for evaluating the prognosis of CRC patients.

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Explainable machine learning can outperform Cox regression predictions and provide insights in breast cancer survival

Scientific Reports ◽

10.1038/s41598-021-86327-7 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Arturo Moncada-Torres ◽

Marissa C. van Maaren ◽

Mathijs P. Hendriks ◽

Sabine Siesling ◽

Gijs Geleijnse

Keyword(s):

Breast Cancer ◽

Machine Learning ◽

Explicit Knowledge ◽

Cox Regression ◽

Metastatic Breast ◽

Gradient Boosting ◽

Support Vector ◽

Netherlands Cancer Registry ◽

Extreme Gradient Boosting ◽

The Impact

AbstractCox Proportional Hazards (CPH) analysis is the standard for survival analysis in oncology. Recently, several machine learning (ML) techniques have been adapted for this task. Although they have shown to yield results at least as good as classical methods, they are often disregarded because of their lack of transparency and little to no explainability, which are key for their adoption in clinical settings. In this paper, we used data from the Netherlands Cancer Registry of 36,658 non-metastatic breast cancer patients to compare the performance of CPH with ML techniques (Random Survival Forests, Survival Support Vector Machines, and Extreme Gradient Boosting [XGB]) in predicting survival using the $$c$$ c -index. We demonstrated that in our dataset, ML-based models can perform at least as good as the classical CPH regression ($$c$$ c -index $$\sim \,0.63$$ ∼ 0.63 ), and in the case of XGB even better ($$c$$ c -index $$\sim 0.73$$ ∼ 0.73 ). Furthermore, we used Shapley Additive Explanation (SHAP) values to explain the models’ predictions. We concluded that the difference in performance can be attributed to XGB’s ability to model nonlinearities and complex interactions. We also investigated the impact of specific features on the models’ predictions as well as their corresponding insights. Lastly, we showed that explainable ML can generate explicit knowledge of how models make their predictions, which is crucial in increasing the trust and adoption of innovative ML techniques in oncology and healthcare overall.

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