Dataset Information

C-Reactive Protein to Prealbumin Ratio (CPR): A Novel Inflammatory-Nutritional Prognostic Factor for Predicting Cancer-Specific Survival (CSS) and Overall Survival (OS) in Patients with Resectable Esophageal Squamous Cell Carcinoma.

ABSTRACT:

Background

The inflammation and nutrition play an important role in prognosis. A novel index combined with inflammatory and nutritional biomarkers, named C-reactive protein (CRP) to prealbumin (PALB) ratio (CPR), was initially reported to predict the prognosis in resectable esophageal squamous cell carcinoma (ESCC).

Patients and methods

A retrospective study was conducted including 346 resectable ESCC patients. The X-tile program was used to confirm the optimal cut-off value. The Kaplan-Meier methods and Cox regression analyses were performed to analyze the cancer-specific survival (CSS) and overall survival (OS).

Results

The optimum cut-off point was 0.03 for CPR. Patients with a high level of CPR (> 0.03) were associated with poor CSS (12.0% vs. 43.0%, P<0.001) and OS (11.2% vs. 40.7%, P<0.001). Multivariate analyses revealed that CPR was an independent predictor in resectable ESCC patients (CSS, P=0.008; OS, P=0.007).

Conclusion

This study, to the best of our knowledge, is the first to investigate prognostic role of CPR in patients with ESCC. Our retrospective observations indicate that CPR, with the optimal cut-off value of 0.03, is a useful potential predictor in resectable ESCC patients.

SUBMITTER: Feng JF

PROVIDER: S-EPMC6662452 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

C-Reactive Protein to Prealbumin Ratio (CPR): A Novel Inflammatory-Nutritional Prognostic Factor for Predicting Cancer-Specific Survival (CSS) and Overall Survival (OS) in Patients with Resectable Esophageal Squamous Cell Carcinoma.

Feng Ji-Feng JF Wang Liang L Jiang You-Hua YH Yang Xun X

Journal of oncology 20190714

<h4>Background</h4>The inflammation and nutrition play an important role in prognosis. A novel index combined with inflammatory and nutritional biomarkers, named C-reactive protein (CRP) to prealbumin (PALB) ratio (CPR), was initially reported to predict the prognosis in resectable esophageal squamous cell carcinoma (ESCC).<h4>Patients and methods</h4>A retrospective study was conducted including 346 resectable ESCC patients. The X-tile program was used to confirm the optimal cut-off value. The ...[more]

PMID: 31379941

Similar Datasets

Project description:IntroductionLongitudinal tumor measurements (TM) are commonly recorded in cancer clinical trials of solid tumors. To define patient response to treatment, the Response Evaluation Criteria in Solid Tumors (RECIST) categorizes the otherwise continuous measurements, which results in substantial information loss. We investigated two modeling approaches to incorporate all available cycle-by-cycle (continuous) TM to predict overall survival (OS) and compare the predictive accuracy of these two approaches to RECIST.Material and methodsJoint modeling (JM) for longitudinal TM and OS and two-stage modeling with potential time-varying coefficients were utilized to predict OS using data from three trials with cycle-by-cycle TM. The JM approach incorporates TM data collected throughout the course of the clinical trial. The two-stage modeling approach incorporates information from early assessments (before 12 weeks) to predict subsequent OS outcome. The predictive accuracy was quantified by c-indices.ResultsData from 577, 337, and 126 patients were included for the analysis (from two stage IV colorectal cancer trials (N9741, N9841) and an advanced non-small cell lung cancer trial (N0026), respectively). Both the JM and two-stage modeling reached a similar conclusion, i.e. the baseline covariates (age, gender, and race) were mostly not predictive of OS (p-value > 0.05). Quantities derived from TM were strong predictors of OS in the two colorectal cancer trials (p < 0.001 for both association in JM and two-stage modeling parameters); but less so in the lung cancer trial (p = 0.053 for association in JM and p = 0.024 and 0.160 for two-stage modeling parameters). The c-indices from the two-stage modeling were higher than those from a model using RECIST (range: 0.611-0.633 versus 0.586-0.590). The dynamic c-indices from the JM were in the range of 0.627-0.683 indicating good predictive accuracy.ConclusionBoth modeling approaches provide highly interpretable and clinical meaningful results; the improved predictive performance compared with RECIST indicates the possibility of deriving better trial endpoints from these approaches.

Project description:BackgroundPancreatic carcinoma is one of the most lethal human cancers. In patients with resectable tumors, surgery followed by adjuvant chemotherapy is the only curative treatment. However, the 5-year survival is 20%. Because of a strong metastatic propensity, neoadjuvant chemotherapy is being tested in randomized clinical trials. In this context, improving the selection of patients for immediate surgery or neoadjuvant chemotherapy is crucial, and high-throughput molecular analyses may help; the present study aims to address this.MethodsClinicopathological and gene expression data of 695 pancreatic carcinoma samples were collected from nine datasets and supervised analysis was applied to search for a gene expression signature predictive for overall survival (OS) in the 601 informative operated patients. The signature was identified in a learning set of patients and tested for its robustness in a large independent validation set.ResultsSupervised analysis identified 1400 genes differentially expressed between two selected patient groups in the learning set, namely 17 long-term survivors (LTS; ≥ 36 months after surgery) and 22 short-term survivors (STS; dead of disease between 2 and 6 months after surgery). From these, a 25-gene prognostic classifier was developed, which identified two classes ("STS-like" and "LTS-like") in the independent validation set (n = 562), with a 25% (95% CI 18-33) and 48% (95% CI 42-54) 2-year OS (P = 4.33 × 10-9), respectively. Importantly, the prognostic value of this classifier was independent from both clinicopathological prognostic features and molecular subtypes in multivariate analysis, and existed in each of the nine datasets separately. The generation of 100,000 random gene signatures by a resampling scheme showed the non-random nature of our prognostic classifier.ConclusionThis study, the largest prognostic study of gene expression profiles in pancreatic carcinoma, reports a 25-gene signature associated with post-operative OS independently of classical factors and molecular subtypes. This classifier may help select patients with resectable disease for either immediate surgery (the LTS-like class) or neoadjuvant chemotherapy (the STS-like class). Its assessment in the current prospective trials of adjuvant and neoadjuvant chemotherapy trials is warranted, as well as the functional analysis of the classifier genes, which may provide new therapeutic targets.

Project description:Background Pancreatic ductal adenocarcinoma (PDAC) displays a typical mucin expression pattern which is characterized by MUC1 positive, MUC2 negative, and MUC5AC positive. More and more evidences show that mucins are involved in the development of pancreatic diseases. However, the relationship between mucin expression and prognosis of PDAC patients has been controversial in the past decades; therefore, we aim to figure out the association of mucin expression with survival in PDAC patients who underwent radical resection. Methods We performed immunohistochemistry (IHC) to detect the expression of MUC1, MUC2, and MUC5AC in resected PDAC specimens from Shanghai Cancer Center, Fudan University (FUSCC, n = 427) and obtained the corresponding clinical statistical data for retrospective study. Kaplan-Meier methods and Mantel-Cox tests were used to compare the survival curves, and the Cox regression model was employed for multivariate analyses to determine the independent risk factors. Survival analysis was also performed in the Queensland Centre for Medical Genomics (QCMG, n = 70) PDAC cohort to verify the conclusion. Results Both the FUSCC cohort and the QCMG cohort demonstrated that MUC1 absence was significantly correlated with worse overall survival (OS). The presence of MUC2 showed marginal significance in predicting shorter OS of PDAC patients, while MUC5AC had no prognostic value. In the FUSCC cohort, MUC1 absence was associated with increased proportion of stage III PDAC (p = 0.011), and MUC1 absence and MUC2 presence were associated with tumour perineural aggression (p = 0.011 and p = 0.030, respectively). Multivariable adjusted hazard ratios (HRs) for mortality of MUC1 and MUC2 were 0.492 (95% CI: 0.274-0.883, p = 0.017) and 1.596 (95% CI: 1.061-2.401, p = 0.025), respectively. Conclusions MUC1 absence or MUC2 presence is independently associated with poor OS among patients with resectable PDAC. MUC5AC absence tended to be associated with short-term death.

Dataset Information

C-Reactive Protein to Prealbumin Ratio (CPR): A Novel Inflammatory-Nutritional Prognostic Factor for Predicting Cancer-Specific Survival (CSS) and Overall Survival (OS) in Patients with Resectable Esophageal Squamous Cell Carcinoma.

Background

Patients and methods

Results

Conclusion

Publications

C-Reactive Protein to Prealbumin Ratio (CPR): A Novel Inflammatory-Nutritional Prognostic Factor for Predicting Cancer-Specific Survival (CSS) and Overall Survival (OS) in Patients with Resectable Esophageal Squamous Cell Carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets