Dataset Information

Diagnostic Value of Combining Tumor and Inflammatory Markers in Lung Cancer.

ABSTRACT:

Background

Despite major advances in lung cancer treatment, early detection remains the most promising way of improving outcomes. To detect lung cancer in earlier stages, many serum biomarkers have been tested. Unfortunately, no single biomarker can reliably detect lung cancer. We combined a set of 2 tumor markers and 4 inflammatory or metabolic markers and tried to validate the diagnostic performance in lung cancer.

Methods

We collected serum samples from 355 lung cancer patients and 590 control subjects and divided them into training and validation datasets. After measuring serum levels of 6 biomarkers (human epididymis secretory protein 4 [HE4], carcinoembryonic antigen [CEA], regulated on activation, normal T cell expressed and secreted [RANTES], apolipoprotein A2 [ApoA2], transthyretin [TTR], and secretory vascular cell adhesion molecule-1 [sVCAM-1]), we tested various sets of biomarkers for their diagnostic performance in lung cancer.

Results

In a training dataset, the area under the curve (AUC) values were 0.821 for HE4, 0.753 for CEA, 0.858 for RANTES, 0.867 for ApoA2, 0.830 for TTR, and 0.552 for sVCAM-1. A model using all 6 biomarkers and age yielded an AUC value of 0.986 and sensitivity of 93.2% (cutoff at specificity 94%). Applying this model to the validation dataset showed similar results. The AUC value of the model was 0.988, with sensitivity of 93.33% and specificity of 92.00% at the same cutoff point used in the validation dataset. Analyses by stages and histologic subtypes all yielded similar results.

Conclusions

Combining multiple tumor and systemic inflammatory markers proved to be a valid strategy in the diagnosis of lung cancer.

SUBMITTER: Yoon HI

PROVIDER: S-EPMC5051593 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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Publications

Diagnostic Value of Combining Tumor and Inflammatory Markers in Lung Cancer.

Yoon Ho Il HI Kwon Oh-Ran OR Kang Kyung Nam KN Shin Yong Sung YS Shin Ho Sang HS Yeon Eun Hee EH Kwon Keon Young KY Hwang Ilseon I Jeon Yoon Kyung YK Kim Yongdai Y Kim Chul Woo CW

Journal of cancer prevention 20160930 3

<h4>Background</h4>Despite major advances in lung cancer treatment, early detection remains the most promising way of improving outcomes. To detect lung cancer in earlier stages, many serum biomarkers have been tested. Unfortunately, no single biomarker can reliably detect lung cancer. We combined a set of 2 tumor markers and 4 inflammatory or metabolic markers and tried to validate the diagnostic performance in lung cancer.<h4>Methods</h4>We collected serum samples from 355 lung cancer patients ...[more]

PMID: 27722145

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Project description:BackgroundAn early diagnosis of pancreatic cancer (PC) is extremely difficult because of the lack of sensitive liquid biopsy methods and effective biomarkers. We attempted to evaluate whether circulating inflammatory marker could complement CA199 for the detection of early-stage PC.MethodsWe enrolled 430 patients with early-stage PC, 287 patients with other pancreatic tumors (OPT), and 401 healthy controls (HC). The patients and HC were randomly divided into a training set (n = 872) and two testing sets (n1 = 218, n2 = 28). The receiver operating characteristic (ROC) curves were investigated to evaluate the diagnostic performance of circulating inflammatory markers ratios, CA199, and combinations of the markers ratios in the training set, which would then be validated in the two testing sets.ResultsCirculating fibrinogen, neutrophils, and monocytes in patients with PC were significantly higher while circulating albumin, prealbumin, lymphocytes, and platelets of patients with PC were significantly lower compared to those of HC and OPT (all P < 0.05). The fibrinogen-to-albumin (FAR), fibrinogen-to-prealbumin (FPR), neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), monocyte-to-lymphocyte (MLR), and fibrinogen-to-lymphocyte (FLR) ratios were significantly higher while the prognostic nutrition index values (PNI) were lower in patients with PC than in HC and OPT (all P < 0.05). Combining the FAR, FPR, and FLR with CA199 exhibited the best diagnostic value for distinguishing patients with early-stage PC from HC with an area under the curve (AUC) of 0.964, and for distinguishing patients with early-stage PC from OPT with an AUC of 0.924 in the training sets. In the testing set, compared with HC, the combination markers had powerful efficiency for PC with an AUC 0.947 and AUC 0.942 when comparing PC with OPT. The AUC was 0.915 for the combination of CA199, FAR, FPR, and FLR for differentiating between patients with pancreatic head cancer (PHC) and other pancreatic head tumors (OPHT), and 0.894 for differentiating between patients with pancreatic body and tail cancer (PBTC) and other pancreatic body and tail tumors (OPBTT).ConclusionA combination of FAR, FPR, FLR, and CA199 may serve as a potential non-invasive biomarker for differentiating early-stage PC from HC and OPT, especially early-stage PHC.

Project description:BACKGROUND:Leptomeningeal metastasis (LM) is one of the most common causes of death in patients with advanced non-small cell lung cancer (NSCLC), which is defined as malignant cells spreading to meninges and cerebrospinal ?uid (CSF). Therefore, early diagnosis and timely treatment are essential. CSF cytology is the gold standard for LM diagnosis, however, it has a low sensitivity for diagnosis and can't be used to evaluate the treatment effect. The aim of this study was to assess the clinical value of serum and CSF tumor markers (TM) in the diagnosis and treatment of NSCLC patients with LM. METHODS:Nineteen patients with NSCLC-LM and 27 patients with nonmalignant neurological diseases (NMNDs) were included. We tested the levels and positive rates of carbohydrate antigen (CEA), carbohydrate antigen-125 (CA125), cytokeratin 19 fragments (CYFRA21-1) and neurone specific enolase (NSE) in CSF and serum, compared the sensitivity and specificity in the diagnosis of LM between different groups, and analyzed the correlation of detection between serum and CSF. Finally, we measured serum and CSF TM dynamically in 2 patients with NSCLC developing LM in an attempt to correlate these with the treatment response of extracranial and intracranial, respectively. RESULTS:The levels and positive rates of TM in CSF and serum in LM group were higher than those in NMNDs (P<0.05). In LM group, the levels of CEA, CYFRA21-1 and CEA were significantly higher in CSF than that in the serum (P<0.05), whereas, there was no statistical significance in positive rates of TM between CSF and serum (P>0.05). In CSF, CYFRA21-1 has the highest sensitivity (88.2%) and CEA has the best specificity (92.3%) to distinguish patients between LM and NMNDs. For combined detection of CEA, CA125, CYFRA 21-1 and NSE in CSF, when at least CEA or NSE was positive in patients with LM, the sensitivity and negative predictive value were 100.0%, and the specificity was 74.1%. When both CYFRA21-1 and NSE were positive, the specificity and positive predictive value were 100.0%, and the sensitivity was 78.9%. Furthermore, subgroup analysis showed that the detection rates of TM in CSF cytology positive population was higher than that in typical abnormalities magnetic resonance imaging population, but there was no statistical difference (P>0.05). The detection of TM between serum and CSF in LM patients had no significant correlation. Moreover, biochemical properties of CSF from ventricle and lumbar puncture are similar, therefore evaluating the levels of TM in serum and CSF dynamically can be used to assess the extracranial and intracranial treatment effect, respectively. CONCLUSIONS:Our study demonstrates that Serum and CSF TM can work as an auxiliary clinical diagnostic tool, which has a potential value in early diagnosis of NSCLC patients with LM. Serial measurement of TM may play an important role in the clinical management of NSCLC patients with LM, which is worthy of further promotion and clinical application.

Dataset Information

Diagnostic Value of Combining Tumor and Inflammatory Markers in Lung Cancer.

Background

Methods

Results

Conclusions

Publications

Diagnostic Value of Combining Tumor and Inflammatory Markers in Lung Cancer.

Similar Datasets

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