Molecular prognostic prediction for locally advanced nasopharyngeal carcinoma by support vector machine integrated approach

Distant Metastasis Risk Definition by Tumor Biomarkers Integrated Nomogram Approach for Locally Advanced Nasopharyngeal Carcinoma

Identifying metastasis remains a challenge for death control and tailored therapy for nasopharyngeal carcinoma (NPC). Here, we addressed this by designing a nomogram-based Cox proportional regression model through integrating a panel of tumor biomarkers. A total of 147 locally patients with advanced NPC, derived from a randomized phase III clinical trial, were enrolled. We constructed the model by selecting the variables from 31 tumor biomarkers, including 6 pathological signaling pathway molecules and 3 Epstein-Barr virus-related serological variables. Through the least absolute shrinkage and selection operator (LASSO) Cox proportional regression analysis, a nomogram was designed to refine the metastasis risk of each NPC individuals. Using the LASSO Cox regression model, we constructed a 9 biomarkers-based prognostic nomogram: Beclin 1, Aurora-A, Cyclin D1, Ki-67, P27, Bcl-2, MMP-9, 14-3-3σ, and VCA-IgA. The time-dependence receiver operating characteristic analysis at 1, 3, and 5 years showed an appealing prognostic accuracy with the area under the curve of 0.830, 0.827, and 0.817, respectively. In the validation subset, the concordance index of this nomogram reached to 0.64 to identify the individual metastasis pattern. Supporting by this nomogram algorithm, the individual metastasis risk might be refined personally and potentially guiding the treatment decisions and target therapy against the related signaling pathways for patients with locally advanced NPC.

Evaluation of Prognosis in Nasopharyngeal Cancer Using Machine Learning

BACKGROUND AND AIM

Although the prognosis of nasopharyngeal cancer largely depends on a classification based on the tumor-lymph node metastasis staging system, patients at the same stage may have different clinical outcomes. This study aimed to evaluate the survival prognosis of nasopharyngeal cancer using machine learning.

SETTINGS AND DESIGN

Original, retrospective.

MATERIALS AND METHODS

A total of 72 patients with a diagnosis of nasopharyngeal cancer who received radiotherapy ± chemotherapy were included in the study. The contribution of patient, tumor, and treatment characteristics to the survival prognosis was evaluated by machine learning using the following techniques: logistic regression, artificial neural network, XGBoost, support-vector clustering, random forest, and Gaussian Naive Bayes.

RESULTS

In the analysis of the data set, correlation analysis, and binary logistic regression analyses were applied. Of the 18 independent variables, 10 were found to be effective in predicting nasopharyngeal cancer-related mortality: age, weight loss, initial neutrophil/lymphocyte ratio, initial lactate dehydrogenase, initial hemoglobin, radiotherapy duration, tumor diameter, number of concurrent chemotherapy cycles, and T and N stages. Gaussian Naive Bayes was determined as the best algorithm to evaluate the prognosis of machine learning techniques (accuracy rate: 88%, area under the curve score: 0.91, confidence interval: 0.68-1, sensitivity: 75%, specificity: 100%).

CONCLUSION

Many factors affect prognosis in cancer, and machine learning algorithms can be used to determine which factors have a greater effect on survival prognosis, which then allows further research into these factors. In the current study, Gaussian Naive Bayes was identified as the best algorithm for the evaluation of prognosis of nasopharyngeal cancer.

Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma

Background: Recurrence remains one of the key reasons of relapse after the radical radiation for locally advanced nasopharyngeal carcinoma (NPC). Here, the multiple molecular and clinical variables integrated decision tree algorithms were designed to predict individual recurrence patterns (with VS without recurrence) for locally advanced NPC.

Methods: A total of 136 locally advanced NPC patients retrieved from a randomized controlled phase III trial, were included. For each patient, the expression levels of 33 clinicopathological biomarkers in tumor specimen, 3 Epstein-Barr virus related serological antibody titer and 5 clinicopathological variables, were detected and collected to construct the decision tree algorithm. The expression level of 33 clinicopathological biomarkers in tumor specimen was evaluated by immunohistochemistry staining.

Results: Three algorithm classifiers, augmented by the adaptive boosting algorithm for variable selection and classification, were designed to predict individual recurrence pattern. The classifiers were trained in the training subset and further tested using a 10-fold cross-validation scheme in the validation subset. In total, 13 molecules expression level in tumor specimen, including AKT1, Aurora-A, Bax, Bcl-2, N-Cadherin, CENP-H, HIF-1α, LMP-1, C-Met, MMP-2, MMP-9, Pontin and Stathmin, and N stage were selected to construct three 10-fold cross-validation decision tree classifiers. These classifiers showed high predictive sensitivity (87.2-93.3%), specificity (69.0-100.0%), and overall accuracy (84.5-95.2%) to predict recurrence pattern individually. Multivariate analyses confirmed the decision tree classifier was an independent prognostic factor to predict individual recurrence (algorithm 1: hazard ration (HR) 0.07, 95% confidence interval (CI) 0.03-0.16, P < 0.01; algorithm 2: HR 0.13, 95% CI 0.04-0.44, P < 0.01; algorithm 3: HR 0.13, 95% CI 0.03-0.68, P = 0.02).

Conclusion: Multiple molecular and clinicopathological variables integrated decision tree algorithms may individually predict the recurrence pattern for locally advanced NPC. This decision tree algorism provides a potential tool to select patients with high recurrence risk for intensive follow-up, and to diagnose recurrence at an earlier stage for salvage treatment in the NPC endemic region.

Baseline Low Prognostic Nutritional Index Predicts Poor Survival in Locally Advanced Nasopharyngeal Carcinomas Treated With Radical Concurrent Chemoradiotherapy

BACKGROUND

To retrospectively assess the impact of prognostic nutritional index (PNI) on survival outcomes of patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) treated with concurrent chemoradiotherapy (CCRT).

METHODS

This study incorporated 154 patients with LA-NPC who received exclusive cisplatinum-based CCRT. Receiver operating characteristic (ROC) curve analysis was utilized for accessibility of pretreatment PNI cutoffs influencing survival results. The primary end point was the interaction between the overall survival (OS) and PNI values, while cancer-specific survival (CSS) locoregional progression-free survival (LR-PFS), distant metastasis-free survival (DMFS), and PFS were the secondary end points.

RESULTS

A rounded PNI cutoff value of 51 was identified in ROC curve analyses to exhibit significant link with CSS, OS, DMFS, and PFS outcomes, but not LR-PFS. Patients grouping per PNI value (≥51 [N = 95] vs <51 [N = 49]) revealed that PNI < 51 group had significantly shorter median CSS (P < .001), OS (P < .001), DMFS (P < .001), and PFS (P < .001) times than the PNI ≥ 51 group, and the multivariate results confirmed the PNI < 51 as an independent predictor of poor outcomes for each end point (P < .05 for each). The unfavorable impact of the low PNI was also continued at 10-year time point with survival rates of 77.9% versus 42.4%, 73.6% versus 33.9%, 57.9% versus 27.1%, and 52.6% versus 23.7% for CSS, OS, DMFS, and PFS, respectively. Additionally, we found that PNI < 51 was significantly associated with higher rates of weight loss >5% over past 6 months (49.2% versus 11.6%; P = .002) compared to PNI < 51 group.

CONCLUSION

Low pre-CCRT PNI levels were independently associated with significantly reduced CSS, OS, DMFS, and PFS outcomes in patients with LA-NPC treated with definitive CCRT.

Machine learning and its potential applications to the genomic study of head and neck cancer-A systematic review

BACKGROUND

Machine learning (ML) is powerful tool that can identify and classify patterns from large quantities of cancer genomic data that may lead to the discovery of new biomarkers, new drug targets, and a better understanding of important cancer genes. The aim of this systematic review was to evaluate the existing literature and assess the application of machine learning of genomic data in head and neck cancer (HNC).

MATERIALS AND METHODS

The addressed focused question was "Does machine learning of genomic data play a role in prognostic prediction of HNC?" PubMed, EMBASE, Scopus, Web of Science, and gray literature from January 1990 up to and including May 2018 were searched. Two independent reviewers performed the study selection according to eligibility criteria.

RESULTS

A total of seven studies that met the eligibility criteria were included. The majority of studies were cohort studies, one a case-control study and one a randomized controlled trial. Two studies each evaluated oral cancer and laryngeal cancer, while other one study each evaluated nasopharyngeal cancer and oropharyngeal cancer. The majority of studies employed support vector machine (SVM) as a ML technique. Among the included studies, the accuracy rates for ML techniques ranged from 56.7% to 99.4%.

CONCLUSION

Our findings showed that ML techniques for the analysis of genomic data can play a role in the prognostic prediction of HNC.

Predicting Response to Neoadjuvant Chemotherapy in Patients With Breast Cancer: Combined Statistical Modeling Using Clinicopathological Factors and FDG PET/CT Texture Parameters

PURPOSE

The aim of this study was to develop a combined statistical model using both clinicopathological factors and texture parameters from F-FDG PET/CT to predict responses to neoadjuvant chemotherapy in patients with breast cancer.

MATERIALS AND METHODS

A total of 435 patients with breast cancer were retrospectively enrolled. Clinical and pathological data were obtained from electronic medical records. Texture parameters were extracted from pretreatment FDG PET/CT images. The end point was pathological complete response, defined as the absence of residual disease or the presence of residual ductal carcinoma in situ without residual lymph node metastasis. Multivariable logistic regression modeling was performed using clinicopathological factors and texture parameters as covariates.

RESULTS

In the multivariable logistic regression model, various factors and parameters, including HER2, histological grade or Ki-67, gradient skewness, gradient kurtosis, contrast, difference variance, angular second moment, and inverse difference moment, were selected as significant prognostic variables. The predictive power of the multivariable logistic regression model incorporating both clinicopathological factors and texture parameters was significantly higher than that of a model with only clinicopathological factors (P = 0.0067). In subgroup analysis, texture parameters, including gradient skewness and gradient kurtosis, were selected as independent prognostic factors in the HER2-negative group.

CONCLUSIONS

A combined statistical model was successfully generated using both clinicopathological factors and texture parameters to predict the response to neoadjuvant chemotherapy. Results suggest that addition of texture parameters from FDG PET/CT can provide more information regarding treatment response prediction compared with clinicopathological factors alone.

A new scheme for strain typing of methicillin-resistant Staphylococcus aureus on the basis of matrix-assisted laser desorption ionization time-of-flight mass spectrometry by using machine learning approach

Methicillin-resistant Staphylococcus aureus (MRSA), one of the most important clinical pathogens, conducts an increasing number of morbidity and mortality in the world. Rapid and accurate strain typing of bacteria would facilitate epidemiological investigation and infection control in near real time. Matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry is a rapid and cost-effective tool for presumptive strain typing. To develop robust method for strain typing based on MALDI-TOF spectrum, machine learning (ML) is a promising algorithm for the construction of predictive model. In this study, a strategy of building templates of specific types was used to facilitate generating predictive models of methicillin-resistant Staphylococcus aureus (MRSA) strain typing through various ML methods. The strain types of the isolates were determined through multilocus sequence typing (MLST). The area under the receiver operating characteristic curve (AUC) and the predictive accuracy of the models were compared. ST5, ST59, and ST239 were the major MLST types, and ST45 was the minor type. For binary classification, the AUC values of various ML methods ranged from 0.76 to 0.99 for ST5, ST59, and ST239 types. In multiclass classification, the predictive accuracy of all generated models was more than 0.83. This study has demonstrated that ML methods can serve as a cost-effective and promising tool that provides preliminary strain typing information about major MRSA lineages on the basis of MALDI-TOF spectra.

Biological function integrated prediction of severe radiographic progression in rheumatoid arthritis: a nested case control study

Background

Radiographic progression is reported to be highly heritable in rheumatoid arthritis (RA). However, previous study using genetic loci showed an insufficient accuracy of prediction for radiographic progression. The aim of this study is to identify a biologically relevant prediction model of radiographic progression in patients with RA using a genome-wide association study (GWAS) combined with bioinformatics analysis.

Methods

We obtained genome-wide single nucleotide polymorphism (SNP) data for 374 Korean patients with RA using Illumina HumanOmni2.5Exome-8 arrays. Radiographic progression was measured using the yearly Sharp/van der Heijde modified score rate, and categorized in no or severe progression. Significant SNPs for severe radiographic progression from GWAS were mapped on the functional genes and reprioritized by post-GWAS analysis. For robust prediction of radiographic progression, tenfold cross-validation using a support vector machine (SVM) classifier was conducted. Accuracy was used for selection of optimal SNPs set in the Hanyang Bae RA cohort. The performance of our final model was compared with that of other models based on GWAS results and SPOT (one of the post-GWAS analyses) using receiver operating characteristic (ROC) curves. The reliability of our model was confirmed using GWAS data of Caucasian patients with RA.

Results

A total of 36,091 significant SNPs with a p value <0.05 from GWAS were reprioritized using post-GWAS analysis and approximately 2700 were identified as SNPs related to RA biological features. The best average accuracy of ten groups was 0.6015 with 85 SNPs, and this increased to 0.7481 when combined with clinical information. In comparisons of the performance of the model, the 0.7872 area under the curve (AUC) in our model was superior to that obtained with GWAS (AUC 0.6586, p value 8.97 × 10^-5) or SPOT (AUC 0.7449, p value 0.0423). Our model strategy also showed superior prediction accuracy in Caucasian patients with RA compared with GWAS (p value 0.0049) and SPOT (p value 0.0151).

Conclusions

Using various biological functions of SNPs and repeated machine learning, our model could predict severe radiographic progression relevantly and robustly in patients with RA compared with models using only GWAS results or other post-GWAS tools.

Electronic supplementary material

The online version of this article (doi:10.1186/s13075-017-1414-x) contains supplementary material, which is available to authorized users.

Novel biomarkers of nasopharyngeal carcinoma metastasis risk identified by reverse phase protein array based tumor profiling with consideration of plasma Epstein-Barr virus DNA load

PURPOSE

In patients with Epstein-Barr virus (EBV) associated nasopharyngeal carcinoma (NPC), intertumor heterogeneity causes interpatient heterogeneity in the risk of distant metastasis. We aimed to identify novel biomarkers of metastasis risk using reverse phase protein array (RPPA) profiling of NPC patients at risk for metastasis and considering plasma EBV DNA load.

EXPERIMENTAL DESIGN

A total of 98 patients with NPC with and without metastasis after treatment, matched with respect to clinical parameters, are enrolled. Total protein expression is measured by RPPA, and protein functions are analyzed by pathway bioinformatics.

RESULTS

The RPPA analysis revealed a profile of 70 proteins that are differentially expressed in metastatic and nonmetastatic tumors. Plasma EBV DNA load after treatment correlated with protein expression level better than plasma EBV DNA load before treatment did. The biomarkers of NPC metastasis identified by proteomics regulate signaling pathways involved in cell cycle progression, apoptosis, and epithelial-mesenchymal transition. The authors identified 26 biomarkers associated with 5-year distant failure-free survival in univariate analysis; five biomarkers remained significant in multivariate analysis.

CONCLUSIONS AND CLINICAL RELEVANCE

A comprehensive RPPA profiling study is warranted to identify novel metastasis-related biomarkers and further examine the activation state of signaling proteins to improve estimation of metastasis risk for patients with EBV-associated NPC.

Low Prognostic Nutritional Index (PNI) Predicts Unfavorable Distant Metastasis-Free Survival in Nasopharyngeal Carcinoma: A Propensity Score-Matched Analysis

Background

Poor nutritional status is associated with progression and advanced disease in patients with cancer. The prognostic nutritional index (PNI) may represent a simple method of assessing host immunonutritional status. This study was designed to investigate the prognostic value of the PNI for distant metastasis-free survival (DMFS) in patients with nasopharyngeal carcinoma (NPC).

Methods

A training cohort of 1,168 patients with non-metastatic NPC from two institutions was retrospectively analyzed. The optimal PNI cutoff value for DMFS was identified using the online tool “Cutoff Finder”. DMFS was analyzed using stratified and adjusted analysis. Propensity score-matched analysis was performed to balance baseline characteristics between the high and low PNI groups. Subsequently, the prognostic value of the PNI for DMFS was validated in an external validation cohort of 756 patients with NPC. The area under the receiver operating characteristics curve (AUC) was calculated to compare the discriminatory ability of different prognostic scores.

Results

The optimal PNI cutoff value was determined to be 51. Low PNI was significantly associated with poorer DMFS than high PNI in univariate analysis (P<0.001) as well as multivariate analysis (P<0.001) before propensity score matching. In subgroup analyses, PNI could also stratify different risks of distant metastases. Propensity score-matched analyses confirmed the prognostic value of PNI, excluding other interpretations and selection bias. In the external validation cohort, patients with high PNI also had significantly lower risk of distant metastases than those with low PNI (Hazards Ratios, 0.487; P<0.001). The PNI consistently showed a higher AUC value at 1-year (0.780), 3-year (0.793) and 5-year (0.812) in comparison with other prognostic scores.

Conclusion

PNI, an inexpensive and easily assessable inflammatory index, could aid clinicians in developing individualized treatment and follow-up strategies for patients with non-metastatic NPC.

Cancers Screening in an Asymptomatic Population by Using Multiple Tumour Markers

BACKGROUND

Analytic measurement of serum tumour markers is one of commonly used methods for cancer risk management in certain areas of the world (e.g. Taiwan). Recently, cancer screening based on multiple serum tumour markers has been frequently discussed. However, the risk-benefit outcomes appear to be unfavourable for patients because of the low sensitivity and specificity. In this study, cancer screening models based on multiple serum tumour markers were designed using machine learning methods, namely support vector machine (SVM), k-nearest neighbour (KNN), and logistic regression, to improve the screening performance for multiple cancers in a large asymptomatic population.

METHODS

AFP, CEA, CA19-9, CYFRA21-1, and SCC were determined for 20 696 eligible individuals. PSA was measured in men and CA15-3 and CA125 in women. A variable selection process was applied to select robust variables from these serum tumour markers to design cancer detection models. The sensitivity, specificity, positive predictive value (PPV), negative predictive value, area under the curve, and Youden index of the models based on single tumour markers, combined test, and machine learning methods were compared. Moreover, relative risk reduction, absolute risk reduction (ARR), and absolute risk increase (ARI) were evaluated.

RESULTS

To design cancer detection models using machine learning methods, CYFRA21-1 and SCC were selected for women, and all tumour markers were selected for men. SVM and KNN models significantly outperformed the single tumour markers and the combined test for men. All 3 studied machine learning methods outperformed single tumour markers and the combined test for women. For either men or women, the ARRs were between 0.003-0.008; the ARIs were between 0.119-0.306.

CONCLUSION

Machine learning methods outperformed the combined test in analysing multiple tumour markers for cancer detection. However, cancer screening based solely on the application of multiple tumour markers remains unfavourable because of the inadequate PPV, ARR, and ARI, even when machine learning methods were incorporated into the analysis.

Overexpression of Wnt7α protein predicts poor survival in patients with colorectal carcinoma

Wnt7α (wingless-type MMTV integration site family, member 7A) is a secreted glycoprotein that plays a critical role in tumorigenesis and development by controlling cell proliferation and differentiation. Whether Wnt7α has the properties of an oncogene or not is an interesting issue because of its diverse expression in different tumors. In the present study, Wnt7α protein expression was evaluated through immunohistochemistry and Western blot analysis. Univariate and multivariate analyses were applied to explore the associations between Wnt7α staining score and various clinical parameters, including overall survival (OS) and disease-free survival (DFS), and a total of 212 patients with colorectal cancer (CRC) were surveyed. Wnt7α was strongly expressed in most CRC tissues but weakly expressed in adjacent normal mucosa, colorectal adenomas, and colonic polyps. High levels of Wnt7α expression were strongly associated with tumor size (P = 0.006), lymph node involvement (P < 0.001), and the international tumor-node-metastasis (TNM) stage (P = 0.005). Patients with strong Wnt7α expression showed significantly poorer OS and DFS than patients with weak Wnt7α expression (P < 0.0001, both). Multivariate Cox analysis confirmed that Wnt7α protein expression and TNM stage are independent factors of adverse OS and DFS in CRC patients. Taken together, our results present evidence that Wnt7α overexpression is associated with an unfavorable prognosis and that positive Wnt7α, in addition to TNM stage, may be an independent prognosis factor influencing OS and DFS prediction in CRC patients.

High expression of Beclin-1 predicts favorable prognosis for patients with colorectal cancer

PURPOSE

Beclin-1 is an autophagy gene. It promotes the formation of the autophagic vesicle as well as plays an essential role in guarding the cells against chromosomal instability. Overexpression of Beclin-1 has been reported to predict a favorable survival in various cancers. However, little is known about its prognostic significance in colorectal cancer.

METHODS AND MATERIALS

A total of three hundred and sixty-three (363) colorectal tissues from colorectal cancer (CRC) patients were collected. Tissue micro-arrays and immunohistochemistry were used to investigate the expression and prognostic significance of Beclin-1 in CRC. The associations among Beclin-1 expression, clinicopathological parameters and prognosis were evaluated.

RESULTS

Beclin-1 had a higher expression in CRC tissues than in normal tissues. A high expression of Beclin-1 was positively correlated with gender (P=0.027), histological grade (P=0.003), pM status (P=0.003) and clinical stage (P=0.024). Patients with a high Beclin-1 expression, when compared to those with a lower expression had both a better overall survival (OS, P=0.006) and disease-free survival (DFS, P=0.008). In the pT3 subgroup, Beclin-1 was also found to be a good prognostic indicator (P<0.05). Multivariate analysis showed a high expression of Beclin-1 was indeed a positive independent prognostic factor of OS and DFS for CRC patients (P<0.05).

CONCLUSION

Our results demonstrated that a high expression of Beclin-1 correlated with a better overall survival and disease-free survival, thus serving as a favorable independent prognostic marker in CRC.

Phosphorylated p38, a negative prognostic biomarker, complements TNM staging prognostication in colorectal cancer

Phosphorylated p38 (p-p38) played a pivotal role in the regulation of disease progression and correlated with tumor prognosis. Here, we characterized the prognostic effect of p-p38 in colorectal cancer (CRC). Three hundred and sixteen CRC patients in stages I-III were recruited in this study. P-p38 expression was semi-quantitatively evaluated using tissue microarrays and immunohistochemistry staining. Overall survival (OS), disease-free survival (DFS), local failure-free survival (LFFS), and distant metastasis-free survival (DMFS) of patient subgroups, segregated by p-p38 expression level and clinical stage, were compared using Kaplan-Meier analysis. We found that p-p38 was overexpressed in 48.1 % (152/316) CRC tissues, whereas low or deficiently expressed in normal adjacent epithelia. Overexpression of p-p38 predicted poor OS (P < 0.001), DFS (P = 0.002), LFFS (P = 0.016), and DMFS (P = 0.025) in CRC. Importantly, patient subgroups in the early stage (stages I + II) and with low p-p38 had similar OS, PFS, LFFS, and DMFS probabilities to that of stage I, whereas those with high p-p38 were similar to stage III disease. In addition, for stage III disease, the subgroup with low p-p38 had a similar survival probability to that of stage I, whereas the subgroup with high p-p38 had the worst survival. Multivariate Cox analysis confirmed that p-p38 was indeed a significantly independent factor for death, recurrence, and distant metastases in CRC. Our results demonstrated that p-p38 was a negative independent prognostic factor for CRC. Complementing TNM staging with p-p38 might refine the risk definition more accurately for a subset of patients.

Beclin 1 deficiency correlated with lymph node metastasis, predicts a distinct outcome in intrahepatic and extrahepatic cholangiocarcinoma

Autophagy can be tumor suppressive as well as promotive in regulation of tumorigenesis and disease progression. Accordingly, the prognostic significance of autophagy key regulator Beclin 1 was varied among different tumors. Here, we detected the clinicopathological and prognostic effect of Beclin 1 in the subtypes of intrahepatic cholangiocarcinoma (ICC) and extrahepatic cholangiocarcinoma (ECC). Beclin 1 expression level was detected by immunohistochemistry staining in 106 ICC and 74 ECC patients. We found that Beclin 1 was lowly expressed in 126 (70%) cholangiocarcinoma patients, consist of 72 ICC and 54 ECC. Moreover, the cholangiocarcinoma patients with lymph node metastasis (N1) had a lower Beclin 1 level than that of N0 subgroup (P=0.012). However, we did not detect any correlations between Beclin 1 and other clinicopathological features, including tumor subtypes, vascular invasion, HBV infection, liver cirrhosis, cholecystolithiasis and TNM stage. Survival analysis showed that, compared with the high expression subset, Beclin 1 low expression was correlated with a poorer 3-year progression-free survival (PFS, 69.1% VS 46.8%, P=041) for cholangiocarcinoma. Importantly, our stratified univariate and multivariate analysis confirmed that Beclin 1 lowly expressed ICC had an inferior PFS as well as overall survival than ECC, particularly than that of Beclin 1 highly expressed ECC patients. Thus, our study demonstrated that Beclin 1low expression, correlated with lymph node metastasis, and might be a negative prognostic biomarker for cholangiocarcinoma. Combined Beclin 1 level with the anatomical location might lead to refined prognosis for the subtypes of ICC and ECC.

High pretreatment serum lactate dehydrogenase level correlates with disease relapse and predicts an inferior outcome in locally advanced nasopharyngeal carcinoma

PURPOSE

Here, we evaluate the prognostic effect of pretreatment serum lactate dehydrogenase (LDH) in locally advanced nasopharyngeal carcinoma (NPC).

METHODS AND MATERIALS

Pretreatment serum samples from a randomized controlled trial, which contained 199 neoadjuvant chemoradiotherapy patients and 201 neoadjuvant-concurrent chemoradiotherapy cases with locally advanced NPC, were collected and examined for LDH. With 5-year follow-up, the prognostic effect of pretreatment serum LDH was analysed by Kaplan-Meier analysis and multivariate Cox regression model.

RESULTS

Three hundred and sixty-seven patients (91.75%) had a normal (109.0-245.0 U/L) pretreatment LDH level, compared to 33 cases (8.25%) that had a higher (≥245.0 U/L) LDH level. The mean and median pretreatment LDH levels of these 400 patients were 186.6 and 174.0 U/L (range, 83.0-751.0 U/L), respectively. Compared with the normal subset, elevated LDH level predicted an inferior 5-year overall survival (56.9% versus 76.8%, P=0.004), disease-free survival (DFS, 45.4% versus 64.7%, P=0.001), local relapse-free survival (76.1% versus 89.6%, P=0.019) and distant metastasis-free survival (DMFS, 54.3% versus 72.2%, P=0.001). Multivariate analysis confirmed that the LDH level was an independent prognostic factor to predict death, disease progression, local relapse and distant metastasis. For the subgroup with normal LDH (median point of 177.0 U/L), we detected an evident 5-year DFS (68.8% versus 59.5%, P=0.047) and DMFS advantage (77.3% versus 65.3%, P=0.016) in 109.0-177.0 U/L subset than that of 178.0-245.0 U/L subgroup.

CONCLUSIONS

Serological LDH level was an independent prognostic factor for locally advanced NPC. Combining pretreatment LDH with TNM staging might lead to more accurate risk definition.