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

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.

scholarly journals Prognostic values, ceRNA network, and immune regulation function of SDPR in KRAS-mutant lung cancer

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Xiaoqing Luo ◽  
Shunli Peng ◽  
Sijie Ding ◽  
Qin Zeng ◽  
Rong Wang ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Immune Checkpoint ◽  
Cox Regression ◽  
Tissue Array ◽  
Lung Cancers ◽  
Cox Regression Analysis ◽  
Immune Checkpoint Molecules ◽  
Cerna Network ◽  
Kaplan Meier ◽  
Infiltration Models

Abstract Background Serum Deprivation Protein Response (SDPR) plays an important role in formation of pulmonary alveoli. However, the functions and values of SDPR in lung cancer remain unknown. We explored prognostic value, expression pattern, and biological function of SDPR in non-small cell lung cancer (NSCLC) and KRAS-mutant lung cancers. Methods SDPR expression was evaluated by quantitative real-time PCR (RT-qPCR), immunohistochemistry (IHC), and Western blot on human NSCLC cells, lung adenocarcinoma tissue array, KRAS-mutant transgenic mice, TCGA and GEO datasets. Prognostic values of SDPR were evaluated by Kaplan–Meier and Cox regression analysis. Bioinformatics implications of SDPR including SDPR-combined transcription factors (TFs) and microRNAs were predicted. In addition, correlations between SDPR, immune checkpoint molecules, and tumor infiltration models were illustrated. Results SDPR expression was downregulated in tumor cells and tissues. Low SDPR expression was an independent factor that correlated with shorter overall survival of patients both in lung cancer and KRAS-mutant subgroups. Meanwhile, ceRNA network was constructed to clarify the regulatory and biological functions of SDPR. Negative correlations were found between SDPR and immune checkpoint molecules (PD-L1, TNFRSF18, TNFRSF9, and TDO2). Moreover, diversity immune infiltration models were observed in NSCLC with different SDPR expression and copy number variation (CNV) patterns. Conclusions This study elucidated regulation network of SDPR in KRAS-mutant NSCLC, and it illustrated correlations between low SDPR expression and suppressed immune system, unfolding a prognostic factor and potential target for the treatment of lung cancer, especially for KRAS-mutant NSCLC.

scholarly journals Fast-Track Programs in Total Hip and Knee Replacement at Swedish Hospitals—Influence on 2-Year Risk of Revision and Mortality

2021 ◽  
Vol 10 (8) ◽  
pp. 1680
Author(s):  
Urban Berg ◽  
Annette W-Dahl ◽  
Anna Nilsdotter ◽  
Emma Nauclér ◽  
Martin Sundberg ◽  
...  
Keyword(s):  
Fast Track ◽  
Cox Regression ◽  
Cox Regression Analysis ◽  
Total Hip ◽  
Total Knee Replacements ◽  
Risk Of Death ◽  
Kaplan Meier ◽  
Increased Risk ◽  
Hip And Knee Arthroplasty ◽  
Total Knee

Purpose: We aimed to study the influence of fast-track care programs in total hip and total knee replacements (THR and TKR) at Swedish hospitals on the risk of revision and mortality within 2 years after the operation. Methods: Data were collected from the Swedish Hip and Knee Arthroplasty Registers (SHAR and SKAR), including 67,913 THR and 59,268 TKR operations from 2011 to 2015 on patients with osteoarthritis. Operations from 2011 to 2015 Revision and mortality in the fast-track group were compared with non-fast-track using Kaplan–Meier survival analysis and Cox regression analysis with adjustments. Results: The hazard ratio (HR) for revision within 2 years after THR with fast-track was 1.19 (CI: 1.03–1.39), indicating increased risk, whereas no increased risk was found in TKR (HR 0.91; CI: 0.79–1.06). The risk of death within 2 years was estimated with a HR of 0.85 (CI: 0.74–0.97) for TKR and 0.96 (CI: 0.85–1.09) for THR in fast-track hospitals compared to non-fast-track. Conclusions: Fast-track programs at Swedish hospitals were associated with an increased risk of revision in THR but not in TKR, while we found the mortality to be lower (TKR) or similar (THR) as compared to non-fast track.

Down-regulation of miR-219-5p increase the risk of cancer-related mortality in patients with prostate cancer

2021 ◽  
pp. postgradmedj-2021-139981
Author(s):  
Shimin Tang ◽  
Hao Jiang ◽  
Zhijun Cao ◽  
Qiang Zhou
Keyword(s):  
Prostate Cancer ◽  
Prediction Model ◽  
Cancer Patients ◽  
Cox Regression ◽  
Cox Model ◽  
Risk Of Death ◽  
Kaplan Meier ◽  
Risk Of Progression ◽  
Patient Prognosis ◽  
Risk Of Cancer

IntroductionProstate cancer is a common malignancy in men that is difficult to treat and carries a high risk of death. miR-219-5p is expressed in reduced amounts in many malignancies. However, the prognostic value of miR-219-5p for patients with prostate cancer remains unclear.MethodsWe retrospectively analysed data from 213 prostate cancer patients from 10 June 2012 to 9 May 2015. Overall survival was assessed by Kaplan-Meier analysis and Cox regression models. Besides, a prediction model was constructed, and calibration curves evaluated the model’s accuracy.ResultsOf the 213 patients, a total of 72 (33.8%) died and the median survival time was 60.0 months. We found by multifactorial analysis that miR-219-5p deficiency increased the risk of death by nearly fourfold (HR: 3.86, 95% CI): 2.01 to 7.44, p<0.001) and the risk of progression by twofold (HR: 2.79, 95% CI: 1.68 to 4.64, p<0.001). To quantify each covariate’s weight on prognosis, we screened variables by cox model to construct a predictive model. The Nomogram showed excellent accuracy in estimating death’s risk, with a corrected C-index of 0.778.ConclusionsmiR-219-5p can be used as a biomarker to predict death risk in prostate cancer patients. The mortality risk prediction model constructed based on miR-219-5p has good consistency and validity in assessing patient prognosis.

Time from stereotactic body radiotherapy to immunotherapy as a predictor for outcome in metastatic non small cell lung cancer.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 9024-9024
Author(s):  
Rodney E Wegner ◽  
Stephen Abel ◽  
Shaakir Hasan ◽  
Richard White ◽  
Gene Grant Finley ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Overall Survival ◽  
Small Cell Lung Cancer ◽  
Median Survival ◽  
Stereotactic Body Radiotherapy ◽  
Cox Regression ◽  
Stage Iv ◽  
Small Cell ◽  
Small Cell Lung ◽  
Kaplan Meier

9024 Background: Immunotherapy has changed the face of treatment for stage IV non small cell lung cancer (NSCLC), quickly becoming the standard of care. The appropriate timing of immunotherapy in the setting of other ablative therapies, namely stereotactic radiosurgery (SRS) and stereotactic body radiotherapy (SBRT), remains to be determined. We sought to use the National Cancer Database to examine trends in immunotherapy use as well as timing as it relates to SBRT/SRS in stage IV NSCLC patients. Methods: We queried the NCDB for patients with Stage IV NSCLC diagnosed between 2004-2015 that were treated with SRS or SBRT techniques (to any site) and had at least three months of follow up. Multivariable logistic regression was used to identify predictors of immunotherapy use. Receiver operator curve analysis was used to identify the optimal timepoint between SBRT and immunotherapy correlating with overall survival. Kaplan-meier curves were generated to determine overall survival. Multivariable cox regression was used to determine factors predictive of survival. A propensity score was generated and incorporated into Kaplan-meier and cox regressions to account for indication bias. Results: We identified 13,862 patients meeting the above eligibility criteria, 371 being treated with immunotherapy. The vast majority (75%) had chemotherapy as well. Patients with adenocarcinoma, treatment with chemotherapy, and more recent year of treatment were more likely to receive immunotherapy. Univariable Kaplan-meier analysis showed improved median survival with immunotherapy, 17 months vs. 13 months, p < 0.0001. On multivariable propensity-adjusted cox regression significant predictors for improved overall survival were younger age, lower comorbidity score, lower grade, private insurance, and female gender. Using a cutoff of 21 days after start of SBRT, patients treated thereafter were more likely to survive longer, median survival of 19 months vs 15 months, p = 0.0335. Conclusions: Immunotherapy use in Stage IV NSCLC after SBRT has increased over time, mostly in patients with adenocarcinoma and in the setting of chemotherapy. In this analysis, outcomes were improved when immunotherapy was given at least three weeks after start of SBRT.

scholarly journals Marital Status Independently Predicts Survival in Patients with Small Cell Lung Cancer: A SEER Database Analysis

2021 ◽  
Author(s):  
Pei Luo ◽  
Yan Mao ◽  
Liping Yang ◽  
Chao Pan ◽  
Jun Guo
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Marital Status ◽  
Cell Lung Cancer ◽  
Cox Regression ◽  
Small Cell ◽  
Rank Test ◽  
Seer Database ◽  
Small Cell Lung ◽  
Kaplan Meier

Abstract Purpose This study will investigate the relationship between marital status and prognosis in small cell lung cancer patients. Methods Patients of SCLC was selected from the SEER database (1973-2013) and the patient sinformation. Kaplan-Meier analysis, log-rank test and Cox regression model were used for studying patientprognosis. Result 27069 SCLC patients eligible for inclusion were screened from the SEER database. Kaplan-meier test showed that the median OS values were 8, 7, 6 months in married, single and SDW patients, respectively. Conclusion This study shows that marital status is an independent prognostic factor for overall survival in SCLC patients. Married patients with small cell lung cancer have better prognosis than those who were divorced/separated, widowed and single.

scholarly journals Integrated Analysis of Cell Cycle–Related and Immunity-Related Biomarker Signatures to Improve the Prognosis Prediction of Lung Adenocarcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Fangyu Chen ◽  
Jiahang Song ◽  
Ziqi Ye ◽  
Bing Xu ◽  
Hongyan Cheng ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Lung Adenocarcinoma ◽  
Risk Model ◽  
Cox Regression ◽  
Differentially Expressed ◽  
Integrated Analysis ◽  
Survival Prediction ◽  
Lasso Regression ◽  
Kaplan Meier ◽  
Geo Database

BackgroundLung adenocarcinoma (LUAD) is a leading malignancy and has a poor prognosis over the decades. LUAD is characterized by dysregulation of cell cycle. Immunotherapy has emerged as an ideal option for treating LUAD. Nevertheless, optimal biomarkers to predict outcomes of immunotherapy is still ill-defined and little is known about the interaction of cell cycle-related genes (CCRGs) and immunity-related genes (IRGs).MethodsWe downloaded gene expression and clinical data from TCGA and GEO database. LASSO regression and Cox regression were used to construct a differentially expressed CCRGs and IRGs signature. We used Kaplan-Meier analysis to compare survival of LUAD patients. We constructed a nomogram to predict the survival and calibration curves were used to evaluate the accuracy.ResultsA total of 61 differentially expressed CCRGs and IRGs were screened out. We constructed a new risk model based on 8 genes, including ACVR1B, BIRC5, NR2E1, INSR, TGFA, BMP7, CD28, NUDT6. Subgroup analysis revealed the risk model accurately predicted the overall survival in LUAD patients with different clinical features and was correlated with immune cells infiltration. A nomogram based on the risk model exhibited excellent performance in survival prediction of LUAD.ConclusionsThe 8 gene survival signature and nomogram in our study are effective and have potential clinical application to predict prognosis of LUAD.

scholarly journals Proportions of Beneficial Factors in MLR, NLR, PLR and D-dimer in Preoperative Peripheral Blood of Patients With Early Stage Lung Cancer as Predictors of Patient Survival After Surgery

2021 ◽  
Author(s):  
jun wang ◽  
huawei li ◽  
ran xu ◽  
tong lu ◽  
jiaying zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Regression Analysis ◽  
Cox Regression ◽  
Early Stage ◽  
Progression Free Survival ◽  
Early Stage Lung Cancer ◽  
Cox Regression Analysis ◽  
Lymphocyte Ratio ◽  
Kaplan Meier ◽  
D Dimer

Abstract ObjectiveThe purpose of this paper is to predict the following items. preoperative baseline monocyte-to-lymphocyte ratio (MLR)、neutrophil-to-lymphocyte ratio (NLR) Platura-to-lymphocyte ratio (PLR) and dimeric fibrin fragment D (D-dimer) associated with clinical outcome in patients with Early Lung Cancer (LC).MethodsWe performed a retrospective analysis of 376 patients with LC. Progression-free survival (PFS) and overall survival (OS) were assessed by Kaplan-Meier, and univariate and multivariate Cox regression analyses were performed to identify prognostic factors. Finally, multivariate Cox regression analysis was used to evaluate the influence of favorable factors on patients’ OS and PFS combined with the basic clinical characteristics of the patient ResultsAmong the variables screened by univariate Cox regression, MLR < 0.22, NLR < 1.99, PLR < 130.55 and D-Dimer < 70.5 (ng/ml) were significantly associated with both better OS and PFS. In multivariate Cox regression analysis, it was determined that MLR and D-Dimer had a better independent correlation with OS (p = 0.009, p = 0.05, respectively), while MLR was only better independently associated with PFS (P = 0.005). Furthermore, according to the number of favorable factors, patients with none of these factors had a significantly worse prognosis than patients with at least one of these factors.ConclusionBaseline characteristics of low MLR, low NLR, low PLR and low D-dimer were associated with better outcomes.

scholarly journals Prognostic value of Ki-67 in patients with hypertension and prostate cancer: A real-world study in a Chinese population

2020 ◽  
Author(s):  
Zhijun Cao ◽  
Mengqi Xiang ◽  
Zhiyu Zhang ◽  
Jianglei Zhang ◽  
Minjun Jiang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Value ◽  
Cox Regression ◽  
Cox Model ◽  
Prognostic Nutritional Index ◽  
Fold Increase ◽  
Lasso Regression ◽  
Ki 67 ◽  
Risk Of Death ◽  
Risk Of Progression

Abstract Background Prostate cancer is the second most common malignancy in males worldwide, with high mortality, especially when combined with hypertension. Ki-67 is one of the most reliable markers of growth for neoplastic human cell populations. However, the prognostic value of Ki-67 in patients with hypertension and prostate cancer remains unclear.Methods We retrospectively analyzed 296 patients with hypertension and prostate cancer from May 1, 2012, to October 1, 2015. The overall survival was evaluated by Cox regression models and Kaplan-Meier analysis. In addition, a nomogram was established, and the accuracy of the model was assessed by a calibration curve.Results A total of 101 (34.1%) patients died. In the multivariate analysis, being Ki-67(+) was associated with a >5-fold increase in the risk of death (hazard ratio [HR] 5.83, 95% confidence interval [CI] 3.35-10.14, p<0.001) and a 2-fold increase in the risk of progression (HR 2.06, 95% CI 1.37-3.10, p<0.001). Multivariate Lasso regression showed that smoking, heart failure, ACS, Ki-67 expression, serum albumin, prognostic nutritional index, surgery, Gealson score, and stage were positively associated with prognosis in patients with prostate cancer. To quantify the contribution of each covariate to the prognosis, a nomogram of the Cox model was generated. The nomogram demonstrated excellent accuracy in estimating the risk of death, with a bootstrap-corrected C index of 0.829. There was also a suitable calibration curve for risk estimation.Conclusions The presence of Ki-67 predicts worsened outcomes for overall mortality. A cross-validated multivariate score including Ki-67 had excellent concordance and efficacy for predicting prostate cancer.

scholarly journals Association of Diabetes Status With Survival After Immune Checkpoint Inhibitor Treatment for Advanced Lung Cancer: a Retrospective Study in Japan

2020 ◽  
Author(s):  
Kaori Hisanaga ◽  
hiroshi uchino ◽  
Naoko Kakisu ◽  
Masahiko Miyagi ◽  
Fukumi Yoshikawa ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Propensity Score ◽  
Immune Checkpoint ◽  
Cox Regression ◽  
Tnm Staging ◽  
Wilcoxon Test ◽  
Advanced Lung Cancer ◽  
Kaplan Meier ◽  
Diabetes Status ◽  
Organ Systems

Abstract Introduction: Although immune checkpoint inhibitors (ICIs) are promising in the treatment of advanced cancer, they may lead to immune-related adverse events (irAEs), which can affect endocrine organ systems. However, development of the irAEs was associated with improved cancer-specific survival, the risk for years have not been elucidated. We investigated the association of pre-ICI comorbidities—including diabetes—with years and overall survival (OS) and progression-free survival (PFS) in advanced lung cancer. Research design and methods: Patients with lung cancer who were treated with ICIs at the period from September 1, 2015 through July 31, 2018 were retrospectively enrolled. All data were collected from the university patient NEPTUNE database. Hazard ratios were estimated by using Cox regression weighted for propensity scores. Odds ratios were calculated by logistic regression and adjusted for unbalanced variables. The Kaplan–Meier method was used to compare OS, and the generalized Wilcoxon test was used to compare median survival. The results: Among the 88 patients identified, 22 (25.0%) had diabetes (DM) before ICI treatment and 57 (75.0%) did not (non-DM). Iris developed in 12.2% of patients with DM and in 9.1% of patients in non-DM (p=0.87). Diabetes status was not associated with auras risk in relation to baseline characteristics (age, sex, TNM staging, thyroid and renal function) or after propensity score matching analysis (age, TNM staging). During a mean follow-up of 30 months, OS and cancer-specific PFS were significantly higher in patients who developed iris (Kaplan–Meier estimates, p=0·04 and 0·03, respectively). In propensity score–matched analysis, diabetes was significantly associated with lower OS (multivariate hazard ratio, 0·36; 95% CI, 0·13–0·98). Irrespective of eras, PFS was lower among patients with DM than among non-DM (Kaplan–Meier estimate, p=0·04). Conclusions: Pre-existing diabetes was associated with higher mortality in advanced lung cancer, regardless of irAEs development after treatment with ICIs.

scholarly journals Objective quantitation of EGFR protein levels using Quantitative Dot Blot (QDB) method for prognosis of gastric cancer patients

2021 ◽  
Author(s):  
Lei Xin ◽  
Fangrong Tang ◽  
Bo Song ◽  
Maozhou Yang ◽  
Jiandi Zhang
Keyword(s):  
Gastric Cancer ◽  
Lung Cancer ◽  
Overall Survival ◽  
Clinical Trials ◽  
Cancer Patients ◽  
Cox Regression ◽  
Dot Blot ◽  
Protein Levels ◽  
Kaplan Meier ◽  
Gastric Cancer Patients

Background: One causing factor underlying failures of several clinical trials of anti-EGFR therapies is the lack of effective method to select patients overexpressing EGFR protein. Quantitative Dot Blot method (QDB) is proposed here to measure EGFR protein levels objectively and quantitatively. Its feasibility was evaluated for prognosis of overall survival (OS) of gastric cancer patients. Methods: FFPE slices of 2X5 microM from gastric and Lung cancer specimens were used to extract total tissue lysates for QDB measurement. Absolutely quantitated EGFR protein levels were used for Kaplan-Meier Overall Survival (OS) analysis of gastric cancer patients. Results: EGFR protein levels ranged from 0 to 772 pmole/g for gastric cancer specimens (n=246), and from 0 to 2695 pmole/g for lung cancer patients (n=81). Poor correlation was observed between quantitated EGFR levels and IHC scores with r=0.018, p=0.786 from Spearman correlation analysis. EGFR was identified as an independent negative prognostic biomarker for gastric patients only through absolute quantitation, with HR at 2.29 (95%CI:1.23-4.26, p=0.0089) from multivariate cox regression OS analysis. A cutoff of 207.7 pmole/g was proposed to stratify gastric cancer patients, with 5-year survival probability at 37% for those whose EGFR levels were above the cutoff, and at 64% those below the cutoff based on Kaplan-Meier OS analysis. p=0.0057 from Log Rank test. Conclusion: A QDB-based assay was developed for both gastric and Lung cancer specimens to measure EGFR protein levels absolutely, quantitatively and objectively. This assay should facilitate clinical trials aiming to evaluate anti-EGFR therapies retrospectively and prospectively.

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