Analysis of tumor markers in pleural effusion and serum to verify the correlations between serum tumor markers and tumor size, TNM stage of lung adenocarcinoma

Prognostic value of the ratio of pretreatment carcinoembryonic antigen to tumor volume in rectal cancer

Background

As a commonly used biomarker in rectal cancer (RC), the prognostic value of carcinoembryonic antigen (CEA) remains underexplored. This study aims to evaluate the prognostic value of pretreatment CEA/tumor volume in RC.

Methods

This retrospective study included patients who underwent pretreatment magnetic resonance imaging (MRI) with histologically confirmed primary rectal adenocarcinoma from November 2012 to April 2018. Patients were divided into high-risk and low-risk groups according to the median values of CEA/Diapath (CEA to pathological diameter), CEA/DiaMRI (CEA to MRI tumor diameter), and CEA/VolMRI (CEA to MRI tumor volume). Cox regression analysis was utilized to determine the prognostic value of CEA, CEA/Diapath, CEA/DiaMRI, and CEA/VolMRI. Stepwise regression was used to establish nomograms for predicting disease-free survival (DFS) and overall survival (OS). Predictive performance was estimated by using the concordance index (C-index) and area under curve receiver operating characteristic (AUC).

Results

A total of 343 patients [median age 58.99 years, 206 (60.06%) males] were included. After adjusting for patient-related and tumor-related factors, CEA/VolMRI was superior to CEA, CEA/Diapath, and CEA/DiaMRI in distinguishing high-risk from low-risk patients in terms of DFS [hazard ratio (HR) =1.83; P=0.010] and OS (HR =1.67; P=0.048). Subanalysis revealed that CEA/VolMRI stratified high death risk in CEA-negative individuals (HR =2.50; P=0.038), and also stratified low recurrence risk in CEA-positive individuals (HR =2.06; P=0.024). In the subanalysis of stage II or III cases, the highest HRs and the smallest P values were observed in distinguishing high-risk from low-risk patients according to CEA/VolMRI in terms of DFS (HR =2.44; P=0.046 or HR =2.41; P=0.001) and OS (HR =1.96; P=0.130 or HR =2.22; P=0.008). The nomograms incorporating CEA/VolMRI showed good performance, with a C-index of 0.72 [95% confidence interval (CI): 0.68-0.79] for DFS and 0.73 (95% CI: 0.68-0.80) for OS.

Conclusions

Higher CEA/VolMRI was associated with worse DFS and OS. CEA/VolMRI was superior to CEA, CEA/Diapath, and CEA/DiaMRI in predicting DFS and OS. Pretreatment CEA/VolMRI may facilitate risk stratification and treatment decision-making.

High-precision bladder cancer diagnosis method: 2D Raman spectrum figures based on maintenance technology combined with automatic weighted feature fusion network

BACKGROUND

Raman spectroscopy has been extensively utilized as a marker-free detection method in the complementary diagnosis of cancer. Multivariate statistical classification analysis is frequently employed for Raman spectral data classification. Nevertheless, traditional multivariate statistical classification analysis performs poorly when analyzing large samples and multicategory spectral data. In addition, with the advancement of computer vision, convolutional neural networks (CNNs) have demonstrated extraordinarily precise analysis of two-dimensional image processing.

RESULT

Combining 2D Raman spectrograms with automatic weighted feature fusion network (AWFFN) for bladder cancer detection is presented in this paper. Initially, the s-transform (ST) is implemented for the first time to convert 1D Raman data into 2D spectrograms, achieving 99.2% detection accuracy. Second, four upscaling techniques, including short time fourier transform (STFT), recurrence map (RP), markov transform field (MTF), and grammy angle field (GAF), were used to transform the 1D Raman spectral data into a variety of 2D Raman spectrograms. In addition, a particle swarm optimization (PSO) algorithm is combined with VGG19, ResNet50, and ResNet101 to construct a weighted feature fusion network, and this parallel network is employed for evaluating multiple spectrograms. Class activation mapping (CAM) is additionally employed to illustrate and evaluate the process of feature extraction via the three parallel network branches. The results demonstrate that the combination of a 2D Raman spectrogram along with a CNN for the diagnosis of bladder cancer obtains a 99.2% accuracy rate，which indicates that it is an extremely promising auxiliary technology for cancer diagnosis.

SIGNIFICANCE

The proposed two-dimensional Raman spectroscopy method has an improved precision than one-dimensional spectroscopic data, which presents a potential methodology for assisted cancer detection and providing crucial technical support for assisted diagnosis.

Update value and clinical application of MUC16 (cancer antigen 125)

INTRODUCTION

The largest transmembrane mucin, mucin 16 (MUC16), contains abundant glycosylation sites on the molecular surface, allowing it to participate in various molecular pathways. When cells lose polarity and become cancerous, MUC16 is overexpressed, and more of the extracellular region (cancer antigen [CA]125) is released into serum and possibly, promote the development of diseases. Thus, MUC16 plays an indispensable role in clinical research and application.

AREAS COVERED

This review summarizes the update proposed role of MUC16 in carcinogenesis and metastasis. Most importantly, we prospect its potential value in targeted therapy after screening 1226 articles published within the last 10 years from PubMed. Two reviewers screened each record and each report retrieved independently. We have summarized the progress of MUC16/CA125 in basic research and clinical application, and predicted its possible future development directions.

EXPERT OPINION

As an important noninvasive co-factor in the diagnosis of gynecological diseases, MUC16 has been used for a long time, especially in the diagnosis and treatment of ovarian cancer. The overexpression of MUC16 plays a very obvious role in regulating inflammatory response, supporting immune suppression, and promoting the proliferation, division, and metastasis of cancer cells. In the next 20 years, there will be a luxuriant clinical application of MUC16 as a target for immune monitoring and immunotherapy.

The rational application of liquid biopsy based on next-generation sequencing in advanced non-small cell lung cancer

BACKGROUND

Plasma and tissue biopsy have both used for targeting actionable driver gene mutations in lung cancer, whose concordance is imperfect. A reliable method to predict the concordance is urgently needed to ease clinical application.

METHODS

A total of 1012 plasma samples, including 519 with paired-tissue biopsy samples, derived from lung adenocarcinoma patients were retrospectively enrolled. We assessed the associations of several clinicopathological characteristics and serum tumor markers with the concordance between plasma and tissue biopsies.

RESULTS

When carcinoembryonic antigen (CEA) levels were higher than thresholds of 15.01 ng/ml and 51.15 ng/ml, the positive predictive value of concordance reached 90% and 95%, respectively. When CEA levels were lower than thresholds of 5.19 ng/ml and 3.26 ng/mL, the negative predictive value of concordance reached 45% and 50%. The performance of CYFRA21-1 in predicting concordance was similar but inferior to CEA (AUC: 0.727 vs. 0.741, p = 0.633). The performance of CEA combined with CYFRA21-1 in predicting the concordance was similar to that of the combination of independent factors derived from the LASSO regression model (AUC: 0.796 vs. 0.818, p = 0.067). CEA (r = 0.47, p < 0.01) and CYFRA21-1 levels (r = 0.45, p < 0.05) were significantly correlated with the maximum variant allele frequency, respectively.

CONCLUSIONS

CEA combined with CYFRA21-1 could effectively predict the concordance between plasma and tissue biopsies, which could be used for evaluating the priority of plasma and tissue biopsies for gene testing to timely guide clinical applications in advanced lung adenocarcinoma patients.

Diagnostic value of combination of exfoliative cytology with CA125, CEA, NSE, CYFRA21-1 and CA15-3 for lung cancer

Background: To explore the diagnostic value of combination of exfoliative cytology with detection of tumor markers carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), neuron specific enolase (NSE), cytokeratin 19 fragment antigen 21-1 (CYFRA21-1) and CA15-3 for lung cancer.

Methods: A total of 256 patients were enrolled, including 164 males and 92 females aged (64.51±22.68) years old. Among them, 189 patients (100 males and 89 females) were randomly selected as Tumor group, and the remaining 67 patients were used for validation. Another 514 healthy people receiving physical examination in our hospital during the same period were selected, from which 397 cases (266 males and 131 females) were randomly selected as No Tumor group, and the remaining 117 cases were used for validation. The biochemical criteria were detected in all subjects. The diagnostic value of each index for lung cancer was analyzed using receiver operating characteristic (ROC) curves.

Results: The results of ROC curve analysis revealed that in Tumor group, the area under curve (AUC) of exfoliative cytology, CA125, CYFRA21-1, CA15-3, CEA and NSE was ≥0.7, while that of CA72-4, CA19-9, TSGF, AFP, CA242, SCCAg and CA50 was <0.7. The indices in each factor were comprehensively assessed, and then exfoliative cytology, CA125, CA15-3, CYFRA21-1, CEA and NSE were screened to establish the lung cancer prediction model. The diagnostic value was comparable between the prediction model and the combined detection of 9 indices (Z=1.682, P=0.079).

Conclusions: The lung cancer prediction model balances sensitivity and specificity without reducing the diagnostic efficiency.

Diagnostic Value of Six Tumor Markers for Malignant Pleural Effusion in 1,230 Patients: A Single-Center Retrospective Study

Background: The diagnostic value of tumor markers in pleural effusion (PE) and serum for malignant pleural effusion (MPE) is still in debate. This study aimed to evaluate the diagnostic value of six tumor markers in PE, serum, and the corresponding PE/serum (PE/S) ratio in distinguishing MPE from benign pleural effusion (BPE).

Methods: A total of 1,230 patients with PE (452 MPEs and 778 BPEs) were retrospectively included in the study. PE and serum levels of carcinoembryonic antigen (CEA), carbohydrate antigen 15-3 (CA15-3), carbohydrate antigen 125 (CA125), carbohydrate antigen 19-9 (CA19-9), cytokeratin 19 fragment (CYFRA 21-1), and neuron-specific enolase (NSE) were measured. The area under the curve (AUC) was used to assess the single and combined diagnostic values of the six tumor markers for MPE.

Results: The levels of the six tumor markers in PE, serum, and PE/S were significantly higher in MPE than that in BPE, except for serum CA125. PE CEA showed the highest AUC [0.890 (0.871–0.907)] at a cut-off value of 3.7 ng/ml compared to any single tumor marker using receiver operating characteristic (ROC) analysis. The specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), and negative likelihood ratio (NLR) of PE CEA were 74.1%, 95.5%, 90.5%, 86.4%, 16.47, and 0.27, respectively. The combination of PE CEA and serum CYFRA21-1 showed the best diagnostic performance with an AUC of 0.934 (sensitivity, 79.9%; specificity, 95.7%, PPV, 90.5; PLR, 17.35) among all two or three combinations. Besides, serum CYFRA21-1 was the best diagnostic tumor marker in distinguishing cytology-negative MPE from BPE at a cut-off value of 3.0 ng/ml.

Conclusion: PE CEA was the best diagnostic tumor marker in distinguishing MPE from BPE. Serum CYFRA21-1 was the best diagnostic tumor marker in distinguishing cytology-negative MPE from BPE. The combination of PE CEA and serum CYFRA21-1 could increase the diagnostic performance in distinguishing MPE from BPE and cytology-negative MPE from BPE.

Development and Validation of a Scoring System for Early Diagnosis of Malignant Pleural Effusion Based on a Nomogram

Background

The diagnostic value of clinical and laboratory features to differentiate between malignant pleural effusion (MPE) and benign pleural effusion (BPE) has not yet been established.

Objectives

The present study aimed to develop and validate the diagnostic accuracy of a scoring system based on a nomogram to distinguish MPE from BPE.

Methods

A total of 1,239 eligible patients with PE were recruited in this study and randomly divided into a training set and an internal validation set at a ratio of 7:3. Logistic regression analysis was performed in the training set, and a nomogram was developed using selected predictors. The diagnostic accuracy of an innovative scoring system based on the nomogram was established and validated in the training, internal validation, and external validation sets (n = 217). The discriminatory power and the calibration and clinical values of the prediction model were evaluated.

Results

Seven variables [effusion carcinoembryonic antigen (CEA), effusion adenosine deaminase (ADA), erythrocyte sedimentation rate (ESR), PE/serum CEA ratio (CEA ratio), effusion carbohydrate antigen 19-9 (CA19-9), effusion cytokeratin 19 fragment (CYFRA 21-1), and serum lactate dehydrogenase (LDH)/effusion ADA ratio (cancer ratio, CR)] were validated and used to develop a nomogram. The prediction model showed both good discrimination and calibration capabilities for all sets. A scoring system was established based on the nomogram scores to distinguish MPE from BPE. The scoring system showed favorable diagnostic performance in the training set [area under the curve (AUC) = 0.955, 95% confidence interval (CI) = 0.942-0.968], the internal validation set (AUC = 0.952, 95% CI = 0.932-0.973), and the external validation set (AUC = 0.973, 95% CI = 0.956-0.990). In addition, the scoring system achieved satisfactory discriminative abilities at separating lung cancer-associated MPE from tuberculous pleurisy effusion (TPE) in the combined training and validation sets.

Conclusions

The present study developed and validated a scoring system based on seven parameters. The scoring system exhibited a reliable diagnostic performance in distinguishing MPE from BPE and might guide clinical decision-making.

Accurate diagnosis of lung tissues for 2D Raman spectrogram by deep learning based on short-time Fourier transform

Multivariate statistical analysis methods have an important role in spectrochemical analyses to rapidly identify and diagnose cancer and the subtype. However, utilizing these methods to analyze lager amount spectral data is challenging, and poses a major bottleneck toward achieving high accuracy. Here, a new convolutional neural networks (CNN) method based on short-time Fourier transform (STFT) to diagnose lung tissues via Raman spectra readily is proposed. The models yield that the accuracies of the new method are higher than the conventional methods (principal components analysis -linear discriminant analysis and support vector machine) for validation group (95.2% vs 85.5%, 94.4%) and test group (96.5% vs 90.4%, 93.9%) after cross-validation. The results illustrate that the new method which converts one-dimensional Raman data into two-dimensional Raman spectrograms improve the discriminatory ability of lung tissues and can achieve automatically accurate diagnosis of lung tissues.

Exploring the utility of Au@PVP-polyamide-Triton X-114 for SERS tracking of extracellular senescence associated-beta-galactosidase activity

A compound with enrichment and SERS enhancement was successfully developed, which could rapidly adsorb X-gal hydrolysates from a liquid matrix in 5 minutes and further be used for SERS analysis with a detection limit of less than 1 × 10-9 mol L-1. This novel strategy will facilitate the development of an analytical approach for cellular senescence.

Discovery and validation of PZP as a novel serum biomarker for screening lung adenocarcinoma in type 2 diabetes mellitus patients

Background

Patients with type 2 diabetes mellitus (T2DM) have an increased risk of suffering from various malignancies. This study aimed to identify specific biomarkers that can detect lung adenocarcinoma (LAC) in T2DM patients for the early diagnosis of LAC.

Methods

The clinical information of hospitalized T2DM patients diagnosed with various cancers was collected by reviewing medical records in Wuxi People’s Hospital Affiliated to Nanjing Medical University from January 1, 2015, to June 30, 2020. To discover diagnostic biomarkers for early-stage LAC in the T2DM population, 20 samples obtained from 5 healthy controls, 5 T2DM patients, 5 LAC patients and 5 T2DM patients with LAC (T2DM + LAC) were subjected to sequential windowed acquisition of all theoretical fragment ion mass spectrum (SWATH-MS) analysis to identify specific differentially-expressed proteins (DEPs) for LAC in patients with T2DM. Then, these results were validated by parallel reaction monitoring MS (PRM-MS) and ELISA analyses.

Results

Lung cancer was the most common malignant tumor in patients with T2DM, and LAC accounted for the majority of cases. Using SWATH-MS analysis, we found 13 proteins to be unique in T2DM patients with early LAC. Two serum proteins were further validated by PRM-MS analysis, namely, pregnancy-zone protein (PZP) and insulin-like growth factor binding protein 3 (IGFBP3). Furthermore, the diagnostic values of these proteins were validated by ELISA, and PZP was validated as a novel serum biomarker for screening LAC in T2DM patients.

Conclusions

Our findings indicated that PZP could be used as a novel serum biomarker for the identification of LAC in T2DM patients, which will enhance auxiliary diagnosis and assist in the selection of surgical treatment at an early stage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01861-8.

Auxiliary diagnostic value of tumor biomarkers in pleural fluid for lung cancer-associated malignant pleural effusion

Background

Pleural effusion (PE) can be divided into benign pleural effusion (BPE) and malignant pleural effusion (MPE). There is no consensus on the identification of lung cancer-associated MPE using the optimal cut-off levels from five common tumor biomarkers (CEA, CYFRA 21-1, CA125, SCC-Ag, and NSE). Therefore, we aimed to find indicators for the auxiliary diagnosis of lung cancer-associated MPE by analyzing and then validating the optimal threshold levels of these biomarkers in pleural fluid (PF) and serum, as well as the PF/serum ratio.

Patients and method

The study has two sets of patients, i.e. the training set and the test set. In the training set, 348 patients with PE, between January 1, 2016 and December 31, 2017, were divided into BPE and MPE based on the cytological diagnosis. Subsequently, the optimal cut-off levels of tumor biomarkers were analyzed. In the test set, the diagnostic compliance rate was verified with 271 patients with PE from January 1, 2018 to July 31, 2019 to evaluate the auxiliary diagnostic value of the aforementioned indicators.

Result

In the training set, PF CEA at the cut-off value of 5.23 ng/ml was the most effective indicator for MPE compared with other tumor biomarkers (all p < 0.001). In the test set, PF CEA at the cut-off value of 5.23 ng/ml showed the highest sensitivity, specificity and accuracy, positive and negative predictive value among other tumor biomarkers, which were 99.0%, 69.1%, 91.6%, 90.7%, and 95.9%, respectively.

Conclusion

PF CEA at the cut-off level of 5.23 ng/ml was the most effective indicator for identifying lung cancer-associated MPE among the five common tumor biomarkers.

Analysis of tumor markers in pleural effusion and serum to verify the correlations between serum tumor markers and tumor size, TNM stage of lung adenocarcinoma

BACKGROUND

The study of tumor markers (TM) in pleural effusion (PE) was not extensive.

METHODS

TM in PE and serum were analyzed to determine whether TM was expressed in intrathoracic and extrathoracic tissues. To further verify the correlations between serum TM and tumor size, TNM stage of lung adenocarcinoma.

RESULTS

Serum AFP was not correlated with tumor size, T stage, N stage, and M stage (P > .05). Serum CEA, serum CA125, serum CA15-3 were positively correlated with tumor size, T stage, N stage, M stage (P < .05). Serum CA19-9 was not significantly correlated with tumor size and T stage (P > .05), but was positively correlated with N stage and M stage (P < .05). The levels of PE CEA, PE CA125, PE CA15-3 were higher than those of serum CEA, serum CA125, serum CA15-3 (all P < .05). The level of PE AFP was lower than that of serum AFP (P < .05). The level of PE CA19-9 was not significantly different from that of serum CA19-9 (P > .05). The positive rates of PE CEA and PE CA125 were higher than those of serum CEA and serum CA125 (P < .05). The positive rates of PE AFP, PE CA15-3, PE CA19-9 were not significantly different from those of serum AFP, serum CA15-3, serum CA19-9 (P > .05).PE CEA, PE CA125, PE CA15-3 were moderately positively correlated with serum CEA, serum CA125, serum CA15-3, respectively (r = 0.597; r = 0.46; r = 0.583, all P < .05). However, PE AFP and PE CA19-9 were very strongly positively correlated with serum AFP and serum CA19-9, respectively (r = 0.888; r = 0.874, all P < .05).

CONCLUSION

The expression characteristics of TM in PE and serum supported the correlations between serum TM and tumor size, TNM stage of lung adenocarcinoma.