CT-Based Radiomics Score Can Accurately Predict Esophageal Variceal Rebleeding in Cirrhotic Patients

Purpose: This study aimed to develop a radiomics score (Rad-score) extracted from liver and spleen CT images in cirrhotic patients to predict the probability of esophageal variceal rebleeding.Methods: In total, 173 cirrhotic patients were enrolled in this retrospective study. A total of 2,264 radiomics features of the liver and spleen were extracted from CT images. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to select features and generate the Rad-score. Then, the Rad-score was evaluated by the concordance index (C-index), calibration curves, and decision curve analysis (DCA). Kaplan–Meier analysis was used to assess the risk stratification ability of the Rad-score.Results: Rad-scoreLiver, Rad-scoreSpleen, and Rad-scoreLiver−Spleen were independent risk factors for EV rebleeding. The Rad-scoreLiver−Spleen, which consisted of ten features, showed good discriminative performance, with C-indexes of 0.853 [95% confidence interval (CI), 0.776–0.904] and 0.822 (95% CI, 0.749–0.875) in the training and validation cohorts, respectively. The calibration curve showed that the predicted probability of rebleeding was very close to the actual probability. DCA verified the usefulness of the Rad-scoreLiver−Spleen in clinical practice. The Rad-scoreLiver−Spleen showed good performance in stratifying patients into high-, intermediate- and low-risk groups in both the training and validation cohorts. The C-index of the Rad-scoreLiver−Spleen in the hepatitis B virus (HBV) cohort was higher than that in the non-HBV cohort.Conclusion: The radiomics score extracted from liver and spleen CT images can predict the risk of esophageal variceal rebleeding and stratify cirrhotic patients accordingly.

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O6: EARLY ANASTOMOTIC BILIARY COMPLICATIONS AFTER LIVER TRANSPLANTATION

British Journal of Surgery ◽

10.1093/bjs/znab117.006 ◽

2021 ◽

Vol 108 (Supplement_1) ◽

Author(s):

SJ Tingle ◽

ER Thompson ◽

SS Ali ◽

IK Ibrahim ◽

E Irwin ◽

...

Keyword(s):

Risk Factors ◽

Liver Transplantation ◽

Hospital Stay ◽

Graft Survival ◽

Cox Regression ◽

Risk Groups ◽

Biliary Complications ◽

Anastomotic Strictures ◽

Selection Of Variables ◽

Independent Risk Factors

Abstract Introduction Biliary leaks and anastomotic strictures are common early biliary complications (EBC) following liver transplantation. However, their impact on outcomes remains controversial and poorly described. Method The NHS registry on adult liver transplantation between 2006 and 2017 was retrospectively reviewed (n=8304). Multiple imputations were performed to account for missing data. Adjusted regression models were used to assess predictors of EBC, and their impact on outcomes. 35 potential variables were included, and backwards stepwise selection enabled unbiased selection of variables for inclusion in final models. Result EBC occurred in 9.6% of patients. Adjusted cox regression revealed that EBCs have a significant and independent impact on graft survival (Leak HR=1.325; P=0.021, Stricture HR=1.514; P=0.002, Leak plus stricture HR=1.533; P=0.034) and patient survival (Leak HR=1.218; P=0.131, Stricture HR=1.578; P<0.001, Leak plus stricture HR=1.507; P=0.044). Patients with EBC had longer median hospital stay (23 versus 15 days; P<0.001) and increased chance for readmission within the first year (56% versus 32%; P<0.001). On adjusted logistic regression the following were identified as independent risk factors for development of EBC: donation following circulatory death (OR=1.280; P=0.009), accessory hepatic artery (OR=1.324; P=0.005), vascular anastomosis time in minutes (OR=1.005; P=0.032) and ethnicity ‘other’ (OR=1.838; P=0.011). Conclusion EBCs prolong hospital stay, increase readmission rates and are independent risk factors for diminished graft survival and increased mortality in liver transplantation. We have identified factors that increase the likelihood of EBC occurrence; further research into interventions to prevent EBCs in these at-risk groups is vital to improve liver transplantation outcomes. Take-home message Using a large registry database we have shown that early anastomotic biliary complications are independent risk factors for decreased graft survival and increased mortality after liver transplantation. Research into interventions to prevent biliary complications in high risk groups are essential to improve liver transplant outcomes.

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A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)

Frontiers in Genetics ◽

10.3389/fgene.2021.681867 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chunxia Zhao ◽

Yulu Wang ◽

Famei Tu ◽

Shuai Zhao ◽

Xiaoying Ye ◽

...

Keyword(s):

Cox Regression ◽

Survival Curve ◽

Pearson Correlation ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Kaplan Meier ◽

Non Coding Rna ◽

Novel Biomarkers ◽

Meier Survival Curve

BackgroundSome studies have proven that autophagy and lncRNA play important roles in AML. Several autophagy related lncRNA signatures have been shown to affect the survival of patients in some other cancers. However, the role of autophagy related lncRNA in AML has not been explored yet. Hence, this study aims to find an autophagy related lncRNA signature that can affect survival for AML patients.MethodA Pearson correlation analysis, a Kaplan–Meier survival curve, a univariate cox regression, and a multivariate cox regression were performed to establish an autophagy related lncRNA signature. A univariate cox regression, a multivariate cox regression, a Kaplan–Meier survival curve, and a ROC curve were applied to confirm if the signature is an independent prognosis for AML patients. The relationship between the signature and the clinical features was explored by using a T test. Gene Set Enrichment Analysis (GSEA) was used to investigate the potential tumor related pathways.ResultsA four-autophagy related lncRNA (MIR133A1HG, AL359715.1, MIRLET7BHG, and AL356752.1) signature was established. The high risk score based on signature was related to the short survival time of AML patients. The signature was an independent factor for the prognosis for AML patients (HR = 1.684, 95% CI = 1.324–2.142, P < 0.001). The signature was correlated with age, leukocyte numbers, and FAB (M3 or non-M3). The P53, IL6/JAK/STAT3, TNF-α, INF-γ, and IL2/STAT5 pathways might contribute to the differences between the risk groups based on signature in AML.ConclusionThe four autophagy related lncRNAs and their signature might be novel biomarkers for predicting the survival of AML patients. Some biological pathways might be the potential mechanisms of the signature for the survival of AML patients.

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Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.679133 ◽

2021 ◽

Vol 9 ◽

Author(s):

Bo Xiao ◽

Liyan Liu ◽

Zhuoyuan Chen ◽

Aoyu Li ◽

Pingxiao Wang ◽

...

Keyword(s):

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Enrichment Analysis ◽

Roc Curves ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

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Impact of metformin on outcomes in patients (pts) with metastatic renal cell carcinoma (mRCC): Results from a pooled clinical trials database.

Journal of Clinical Oncology ◽

10.1200/jco.2016.34.2_suppl.531 ◽

2016 ◽

Vol 34 (2_suppl) ◽

pp. 531-531

Author(s):

Lana Hamieh ◽

Rana R. McKay ◽

Suzanne S Mickey ◽

Xun Lin ◽

Ronit Simantov ◽

...

Keyword(s):

Clinical Trials ◽

Cox Regression ◽

Mapk Pathway ◽

Risk Groups ◽

Diabetic Patients ◽

First Line Therapy ◽

Kaplan Meier ◽

Tumor Types ◽

Phase Ii And Iii

531 Background: Metformin has been shown to confer anti-neoplastic properties in several tumor types. Its effect on outcomes in mRCC patients has not been completely characterized. In this study, we evaluated the role of metformin on survival outcomes in pts with mRCC. Methods: We conducted a retrospective study of pts with mRCC treated on several phase II and III clinical trials from 2003-2013. We analyzed overall survival (OS) in the metformin users versus non-users using the Cox regression model and the Kaplan-Meier method. Results: We identified 4,736 pts with mRCC including 486 diabetic pts of whom 218 (4.6%) were metformin users. The majority were <65 years of age (69%), male (71%), with clear-cell histology (89%) and prior nephrectomy (70%). With regard to IMDC risk groups, 14%, 42%, and 24% had favorable, intermediate, and poor-risk disease, respectively. Pts received treatment with sunitinib (n=1,059), sorafenib (n=772), axitinib (n=896), temsirolimus (TEM) (n=457), TEM + interferon (IFN)-α (n=208), bevacizumab (BEV) + TEM (n=393), BEV + IFN-α (n=391), or IFN-α (n=560); overall 3,044 (64%) received first-line therapy. In the total cohort, metformin use did not impact OS when compared to users of other anti-diabetic agents (p=0.17) or non-diabetics (p=0.69). In diabetic pts, metformin use did not confer a survival advantage when stratified by type of therapy and IMDC risk group. However, in the cohort of diabetic pts receiving sunitinib (n=128), metformin use was associated with an improvement in OS when compared to users of non-metformin anti-diabetic agents (29.3 versus 20.9 months, respectively, p=0.0008, HR 0.051, 95% CI 0.009, 0.292). Conclusions: This is the largest study to date investigating the role of metformin on outcomes in mRCC pts. In this analysis, we demonstrate that concomitant use of metformin may improve survival in diabetic pts with mRCC treated with sunitinib. Based on preclinical data, we hypothesize that the mechanism underlying this survival benefit may be related to synergistic inhibition of the MAPK pathway. However, the study is limited by the small number of diabetic patients. Larger prospective studies are warranted to validate these results.

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Identification of a N6-Methyladenosine (m6A)-Related lncRNA Signature for Predicting the Prognosis and Immune Landscape of Lung Squamous Cell Carcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.763027 ◽

2021 ◽

Vol 11 ◽

Author(s):

Chengyin Weng ◽

Lina Wang ◽

Guolong Liu ◽

Mingmei Guan ◽

Lin Lu

Keyword(s):

Squamous Cell Carcinoma ◽

Immune Checkpoint ◽

Immune Checkpoint Inhibitors ◽

Risk Model ◽

Cox Regression ◽

Checkpoint Inhibitors ◽

Lung Squamous Cell Carcinoma ◽

Risk Groups ◽

Immune Functions ◽

Kaplan Meier

Backgroundm6A-related lncRNAs emerged as potential targets for tumor diagnosis and treatment. This study aimed to identify m6A-regulated lncRNAs in lung squamous cell carcinoma (LUSC) patients.Materials and MethodsRNA sequencing and the clinical data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The m6A-related lncRNAs were identified by using Pearson correlation assay. Univariate and multivariate Cox regression analyses were utilized to construct a risk model. The performance of the risk model was validated using Kaplan–Meier survival analysis and receiver operating characteristics (ROC). Immune estimation of LUSC was downloaded from TIMER, and the correlations between the risk score and various immune cells infiltration were analyzed using various methods. Differences in immune functions and expression of immune checkpoint inhibitors and m6A regulators between high-risk and low-risk groups were further explored. Finally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were utilized to explore the biological functions of AL122125.1.ResultsA total of 351 m6A-related lncRNAs were obtained from TCGA. Seven lncRNAs demonstrated prognostic values. A further multivariate Cox regression assay constructed a risk model consisting of two lncRNAs (AL122125.1 and HORMAD2-AS1). The Kaplan–Meier analysis and area under the curve indicated that this risk model could be used to predict the prognosis of LUSC patients. The m6A-related lncRNAs were immune-associated. There were significant correlations between risk score and immune cell infiltration, immune functions, and expression of immune checkpoint inhibitors. Meanwhile, there were significant differences in the expression of m6A regulators between the high- and low-risk groups. Moreover, GO and KEGG analyses revealed that the upregulated expression of AL122125.1 was tumor-related.ConclusionIn this study, we constructed an m6A-related lncRNA risk model to predict the survival of LUSC patients. This study could provide a novel insight to the prognosis and treatment of LUSC patients.

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Construction and Validation of an Autophagy-Related Prognostic Model for Osteosarcoma Patients

Journal of Oncology ◽

10.1155/2021/9943465 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Hu Qian ◽

Ting Lei ◽

Pengfei Lei ◽

Yihe Hu

Keyword(s):

Prognostic Value ◽

Risk Model ◽

Cox Regression ◽

Bone Development ◽

Treatment Strategies ◽

Risk Groups ◽

Low Risk ◽

Lasso Regression ◽

Prognostic Accuracy ◽

Selection Operator

While the prognostic value of autophagy-related genes (ARGs) in OS patients remains scarcely known, increasing evidence is indicating that autophagy is closely associated with the development and progression of osteosarcoma (OS). Therefore, we explored the prognostic value of ARGs in OS patients and illuminate associated mechanisms in this study. When the OS patients in the training/validation cohort were stratified into high- and low-risk groups according to the risk model established using least absolute shrinkage and selection operator (LASSO) regression analysis, we observed that patients in the low-risk group possessed better prognosis ( P < 0.0001 ). Univariate/Multivariate COX regression and subgroup analysis demonstrated that the ARGs-based risk model was an independent survival indicator for OS patients. The nomogram incorporating the risk model and clinical features exhibited excellent prognostic accuracy. GO, KEGG, and GSVA analyses collectively indicated that bone development-associated pathway mediated the contribution of ARGs to the malignance of OS. Immune infiltration analysis suggested the potential pivotal role of macrophage in OS. In summary, the risk model based on 12 ARGs possessed potent capacity in predicting the prognosis of OS patients. Our work may assist clinicians to map out more reasonable treatment strategies and facilitate individual-targeted therapy in osteosarcoma.

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Prognostic value of the neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio in patients with colorectal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.4_suppl.551 ◽

2017 ◽

Vol 35 (4_suppl) ◽

pp. 551-551 ◽

Cited By ~ 1

Author(s):

Jae Hyun Kim ◽

Seun Ja Park

Keyword(s):

Colorectal Cancer ◽

Prognostic Value ◽

Cox Regression ◽

Neutrophil To Lymphocyte Ratio ◽

Platelet To Lymphocyte Ratio ◽

Poor Overall Survival ◽

Cox Regression Analysis ◽

Lymphocyte Ratio ◽

Kaplan Meier ◽

Independent Risk Factors

551 Background: Inflammatory response plays an important role in the pathogenesis of cancer. Some evidence has suggested that elevations in the neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) are associated with decreased survival in various types of cancer. In this study, we aimed to evaluate the prognostic value of the NLR and PLR in patients with colorectal cancer (CRC). Methods: Between August 1995 and December 2010, medical records from a total of 2,004 patients with CRC were retrospectively reviewed. The values of simple inflammatory markers including NLR and PLR in predicting the long-term outcomes of these patients were evaluated using Kaplan-Meier curves and multivariate Cox regression models. Results: The median follow-up duration was 42 months (interquartile range, 19 – 69). The estimation of NLR and PLR was based on the time of diagnosis. In multivariate Cox regression analysis, high NLR ( ≥ 2.6) [hazard ratio (HR) 2.251, 95% confidence interval (CI) 1.570-3.228, p < 0.001] and high PLR ( ≥ 155) [HR 1.473, 95% CI 1.019 – 2.128, p = 0.039] were independent risk factors predicting poor overall survival (OS) in CRC patients. Combined high NLR and PLR was also an independent risk factor predicting poor OS in patients with CRC [HR 2.316, 95% CI 1.529 – 3.508, p < 0.001]. Conclusions: In this study, we identified that high NLR ( ≥ 2.6), high PLR ( ≥ 155), and combined high NLR and PLR are useful prognostic factors to predict OS in CRC patients.

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Predicting dying from lung cancer: Urine metabolites predict the last weeks and days of life.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.12030 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 12030-12030

Author(s):

Seamus Coyle ◽

Elinor Chapman ◽

James Baker ◽

Hannah Coleman ◽

Brendan Norman ◽

...

Keyword(s):

Lung Cancer ◽

Cox Regression ◽

Urine Samples ◽

Lasso Regression ◽

Time Intervals ◽

Urine Metabolites ◽

Risk Of Death ◽

Kaplan Meier ◽

Selection Operator ◽

One Way Anova

12030 Background: Recognising dying is difficult. We believe there is a predictable biological process to dying and previously demonstrated that urinary volatile organic compounds change in the last weeks and days of life of patients with lung cancer. We further analysed our urine samples using a different metabolomic platform, Liquid Chromatography QTOF Mass Spectrometry (LC-QTOF-MS). Methods: We prospectively collected urine samples from people with lung cancer many of whom were in the last 4 weeks of life. The samples were analysed using a LC-QTOF-MS. Volcano plots identified metabolites that changed 2 fold for different time periods (0-28 days, 0-14 days, 0-7days, 0-5 days and 0-3 days). All metabolites were also grouped into weeks. A One-way ANOVA between the groups identified metabolites that changed significantly. Cox regression with Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression was used to analyse the data and create a statistical model. Results: 234 urine samples from 112 patients were analysed by LC-QTOF-MS. 90 metabolites were identified that increase or decrease in the last weeks or days. Pathway Analysis using MetaboAnalyst demonstrated a number of biochemical pathways affected during different time intervals; 0-2 weeks and 0-3 days before death. Cox LASSO regression analysis was performed for the last 28 days. A model using 21 metabolites, prognosticates for each day in the last 28 days with high AUC values (88-90%). Patients can be categorized into high, medium and low risk of death. A Kaplan-Meier survival analysis demonstrated the groups were well separated. Conclusions: The results confirm urine metabolites predict when people with lung cancer are in the last weeks and days of life. Our model, using 21 metabolites, prognosticates for each of the last 28 days of life and is approximately 88% -90% accurate. This is the only model able to prognosticate for the last week or days of life.

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Association of Comorbidities With Overall Survival in Myelodysplastic Syndrome: Development of a Prognostic Model

Journal of Clinical Oncology ◽

10.1200/jco.2010.31.3353 ◽

2011 ◽

Vol 29 (16) ◽

pp. 2240-2246 ◽

Cited By ~ 98

Author(s):

Kiran Naqvi ◽

Guillermo Garcia-Manero ◽

Sagar Sardesai ◽

Jeong Oh ◽

Carlos E. Vigil ◽

...

Keyword(s):

Myelodysplastic Syndrome ◽

Median Survival ◽

Prognostic Model ◽

Cox Regression ◽

Risk Groups ◽

International Prognostic Scoring System ◽

Cancer Center ◽

Kaplan Meier ◽

Severe Comorbidity ◽

Md Anderson

Purpose Patients with cancer often experience comorbidities that may affect their prognosis and outcome. The objective of this study was to determine the effect of comorbidities on the survival of patients with myelodysplastic syndrome (MDS). Patients and Methods We conducted a retrospective cohort study of 600 consecutive patients with MDS who presented to MD Anderson Cancer Center from January 2002 to December 2004. The Adult Comorbidity Evaluation-27 (ACE-27) scale was used to assess comorbidities. Data on demographics, International Prognostic Scoring System (IPSS), treatment, and outcome (leukemic transformation and survival) were collected. Kaplan-Meier methods and Cox regression were used to assess survival. A prognostic model incorporating baseline comorbidities with age and IPSS was developed to predict survival. Results Overall median survival was 18.6 months. According to the ACE-27 categories, median survival was 31.8, 16.8, 15.2, and 9.7 months for those with none, mild, moderate, and severe comorbidities, respectively (P < .001). Adjusted hazard ratios were 1.3, 1.6, and 2.3 for mild, moderate, and severe comorbidities, respectively, compared with no comorbidities (P < .001). A final pognostic model including age, IPSS, and comorbidity score predicted median survival of 43.0, 23.0, and 9.0 months for lower-, intermediate-, and high-risk groups, respectively (P < .001). Conclusion Comorbidities have a significant impact on the survival of patients with MDS. Patients with severe comorbidity had a 50% decrease in survival, independent of age and IPSS risk group. A comprehensive assessment of the severity of comorbidities helps predict survival in patients with MDS.

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N6-Methyladenosine-Regulated mRNAs: Potential Prognostic Biomarkers for Patients With Lung Adenocarcinoma

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.705962 ◽

2021 ◽

Vol 9 ◽

Author(s):

Junjun Sun ◽

Yili Ping ◽

Jingjuan Huang ◽

Bingjie Zeng ◽

Ping Ji ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Mrna Expression ◽

Cox Regression ◽

Interaction Network ◽

Prognostic Biomarkers ◽

Cox Regression Analysis ◽

Receiver Operating Characteristic Curves ◽

Network Analyses ◽

Kaplan Meier ◽

Selection Operator

Aberrant regulation of m6A mRNA modification can lead to changes in gene expression, thus contributing to tumorigenesis in several types of solid tumors. In this study, by integrating analyses of m6A methylation and mRNA expression, we identified 84 m6A-regulated mRNAs in lung adenocarcinoma (LUAD). Although the m6A methylation levels of total RNA in LUAD patient tumor tissue were reduced, the majority (75.2%) of m6A-regulated mRNAs were hypermethylated. The m6A-hypermethylated mRNAs were mainly enriched in terms related to transcription factor activity. We established a 10-m6A-regulated-mRNA signature score system through least absolute shrinkage and selection operator Cox regression analysis, with its predictive value validated by Kaplan–Meier curve and time-dependent receiver operating characteristic curves. RFXAP and KHDRBS2 from the signature also exhibited an independent prognostic value. The co-expression and interaction network analyses demonstrated the strong correlation between m6A regulators and the genes in the signature, further supporting the results of the m6A methylation modification patterns. These findings highlight the potential utility of integrating multi-omics data (m6A methylation level and mRNA expression) to accurately obtain potential prognostic biomarkers, which may provide important insights into developing novel and effective therapies for LUAD.

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