Molecular biomarkers for cancer detection in blood and bodily fluids

Lymphocyte percentage as a valuable predictor of prognosis in lung cancer

Lymphocytes and neutrophils are involved in the immune response against cancer. This study aimed to investigate the relationship between lymphocyte percentage/neutrophil percentage and the clinical characteristics of lung cancer patients, and to explore whether they could act as valuable predictors to ameliorate lung cancer prognosis. A total of 1312 patients were eligible to be recruited. Lymphocyte percentage and neutrophil percentage were classified based on their reference ranges. Survival curves were determined using Kaplan–Meier method, and univariate and multivariate cox regression analyses were performed to identify the significant predictors. Decision curve analysis was used to evaluate the clinical benefit. The results of both training and validation cohorts indicated that lymphocyte percentage exhibited high correlation with clinical characteristics and metastasis of lung cancer patients. Both lymphocyte percentage and neutrophil percentage were closely associated with survival status (all p < 0.0001). Low lymphocyte percentage could act as an indicator of poor prognosis; it offered a higher clinical benefit when combined with the clinical characteristic model. Our findings suggested that pretreatment lymphocyte percentage served as a reliable predictor of lung cancer prognosis, and it was also an accurate response indicator in lung adenocarcinoma and advanced lung cancer. Measurement of lymphocyte percentage improved the clinical utility of patient characteristics in predicting mortality of lung cancer patients.

A Radiomics Nomogram for Preoperative Prediction of Clinical Occult Lymph Node Metastasis in cT1-2N0M0 Solid Lung Adenocarcinoma

Background

Clinical occult lymph node metastasis (cOLNM) means that the lymph node is negatively diagnosed by preoperative computed tomography (CT), but has been proven to be positive by postoperative pathology. The aim of this study was to establish and validate a nomogram based on radiomics features for the preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Methods

A total of 244 patients with clinical T1-2N0M0 solid lung adenocarcinoma who underwent preoperative contrast-enhanced chest CT were divided into a primary group (n = 160) and an independent validation group from another hospital (n = 84). The records of 851 radiomics features of each primary tumor were extracted. LASSO analysis was used to reduce the data dimensionality and select features. Multivariable logistic regression was utilized to identify independent predictors of cOLNM and develop a predictive nomogram. The performance of the predictive model was assessed by its calibration and discrimination. Decision curve analysis (DCA) was performed to estimate the clinical usefulness of the nomogram.

Results

The predictive model consisted of a clinical factor (CT-reported tumor size) and a radiomics feature (Rad-score). The nomogram presented good discrimination, with a C-index of 0.782 (95% CI, 0.768–0.796) in the primary cohort and 0.813 (95% CI, 0.787–0.839) in the validation cohort, and good calibration. DCA showed that the radiomics nomogram was clinically useful.

Conclusion

This study develops and validates a nomogram that incorporates clinical and radiomics factors. It can be tailored for the individualized preoperative prediction of cOLNM in early-stage solid lung adenocarcinoma patients.

Evaluation of the Diagnostic and Predicative Values of 8-Iso-Prostaglandin F2α as a Biomarker of Breast Cancer

BACKGROUND

Breast cancer (BC) is a commonly reported cancer that is widely prevalent among women. Its early detection improves patient survival and results in better outcomes. For diagnosis and follow-up care, tumor markers are one of the feasible investigations to be ordered. 8-Iso-prostaglandin F2α (8-iso-PGF2α) serves as a serum marker reflecting oxidative stress and subsequent damaging of DNA. In the present study, we aimed to evaluate both diagnostic and predictive values of 8-iso-PGF2α in BC patients.

MATERIALS AND METHODS

Serum levels of 8-iso-PGF2α were assessed for 66 women with benign breast tumors and 65 women who had malignant BC. To compare the patients who had breast tumors with healthy individuals, 63 women free of breast diseases were selected as controls.

RESULTS

The serum level of 8-iso-PGF2α in the BC patients (57.92 pg/mL) was significantly higher compared to those with benign tumors (18.89 pg/mL) (p < 0.001). In addition, individuals with no breast diseases had less 8-iso-PGF2α (4.02 pg/mL) compared to those who had developed a tumor (p < 0.001). Serum 8-iso-PGF2α was found to be positively correlated with both carcinoembryonic antigen (r = 0.74, p < 0.001) and cancer antigen 15-3 (r = 0.80, p < 0.001). Furthermore, serum 8-iso-PGF2α showed high diagnostic performance in BC (AUC = 0.999, sensitivity = 100%, specificity = 99.2% at a cutoff value of 36.18 pg/mL).

CONCLUSIONS

Our study found that the high level of serum 8-iso-PGF2α helps to provide a non-invasive indicator to detect BC. Future work with a larger sample size and various phases of BC can confirm the current results which provide insights into the early detection of cancer.

A method of inferring the relationship between Biomedical entities through correlation analysis on text

Background

One of the most important processes in a machine learning-based natural language processing is to represent words. The one-hot representation that has been commonly used has a large size of vector and assumes that the features that make up the vector are independent of each other. On the other hand, it is known that word embedding has a great effect in estimating the similarity between words because it expresses the meaning of the word well. In this study, we try to clarify the correlation between various terms in the biomedical texts based on the excellent ability of estimating similarity between words shown by word embedding. Therefore, we used word embedding to find new biomarkers and microorganisms related to a specific diseases.

Methods

In this study, we try to analyze the correlation between diseases-markers and diseases-microorganisms. First, we need to construct a corpus that seems to be related to them. To do this, we extract the titles and abstracts from the biomedical texts on the PubMed site. Second, we express diseases, markers, and microorganisms’ terms in word embedding using Canonical Correlation Analysis (CCA). CCA is a statistical based methodology that has a very good performance on vector dimension reduction. Finally, we tried to estimate the relationship between diseases-markers pairs and diseases-microorganisms pairs by measuring their similarity.

Results

In the experiment, we tried to confirm the correlation derived through word embedding using Google Scholar search results. Of the top 20 highly correlated disease-marker pairs, about 85% of the pairs have actually undergone a lot of research as a result of Google Scholars search. Conversely, for 85% of the 20 pairs with the lowest correlation, we could not actually find any other study to determine the relationship between the disease and the marker. This trend was similar for disease-microbe pairs.

Conclusions

The correlation between diseases and markers and diseases and microorganisms calculated through word embedding reflects actual research trends. If the word-embedding correlation is high, but there are not many published actual studies, additional research can be proposed for the pair.

The important role of circulating CYFRA21-1 in metastasis diagnosis and prognostic value compared with carcinoembryonic antigen and neuron-specific enolase in lung cancer patients

Background

The roles of carcinoembryonic antigen (CEA), cytokeratin 19 fragments (CYFRA21-1) and neuron-specific enolase (NSE) in metastases occurrence and poor diagnosis in specific histological classifications of lung cancer need further exploring. In this study, we investigated relationship between elevated levels of three biomarkers of CEA, CYFRA21-1 and NSE (individually and in combination) and metastasis, survival status and prognosis in lung cancer patients.

Methods

Eight hundred and sixty eight lung cancer patients including adenocarcinoma (ADC, N = 445), squamous cell carcinoma (SCC, N = 215), small cell lung cancer (SCLC, N = 159) and other types (N = 49) were categorized into negative, moderate and high groups according to serum levels of biomarkers, and were then categorized into negative, single, double and triple groups according to any positive combination of three biomarkers. The cutoff values of three biomarkers for groupings were developed on the training group (N = 432) and verified in a validation group (N = 436). Clinical and laboratory characteristics were then assessed for correlation with occurrence of metastasis, survival status and prognosis between the two groups. Further correlation analyses were also conducted by different subtypes (ADC, SCC and SCLC) and tumor stages (I + II, III and IV) of lung cancers.

Results

The consistent results between training and validation group confirmed the rationality of grouping methods. CYFRA21-1 levels had stronger association with metastases and survival status than CEA and NSE in all lung cancer patients. When stratified by subtypes, these significances only existed in ADC patients for CYFRA21-1. Cox regression analyses showed that CYFRA21-1 and NSE were independent prognostic factors for lung cancer patients. However, only CYFRA21-1 was an independent prognostic factor in ADC and SCLC patients subtypes. Cox-regression results also indicated that CYFRA21-1 could act as independent prognostic factor in different stages (I + II, III and IV) of lung cancer.

Conclusion

CYFRA21-1 was more important in metastasis occurrence and in predicting poor prognosis in lung cancer patients than CEA, NSE and positive numbers of biomarkers.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3070-6) contains supplementary material, which is available to authorized users.

Identification of urine protein biomarkers with the potential for early detection of lung cancer

Lung cancer is the leading cause of cancer-related deaths and has an overall 5-year survival rate lower than 15%. Large-scale clinical trials have demonstrated a significant relative reduction in mortality in high-risk individuals with low-dose computed tomography screening. However, biomarkers capable of identifying the most at-risk population and detecting lung cancer before it becomes clinically apparent are urgently needed in the clinic. Here, we report the identification of urine biomarkers capable of detecting lung cancer. Using the well-characterized inducible Kras (G12D) mouse model of lung cancer, we identified alterations in the urine proteome in tumor-bearing mice compared with sibling controls. Marked differences at the proteomic level were also detected between the urine of patients and that of healthy population controls. Importantly, we identified 7 proteins commonly found to be significantly up-regulated in both tumor-bearing mice and patients. In an independent cohort, we showed that 2 of the 7 proteins were up-regulated in urine samples from lung cancer patients but not in those from controls. The kinetics of these proteins correlated with the disease state in the mouse model. These tumor biomarkers could potentially aid in the early detection of lung cancer.

Biomarkers in systemic sclerosis

Systemic sclerosis is an autoimmune inflammatory disorder of unknown etiologycharacterized b y pronounced fibroproliferative alterations in the microvasculature, and frequent cellular and humoral immunity abnormalities, culminating in a severe and often progressive fibrotic process. Numerous biomarkers reflecting the three main pathogenetic mechanisms in systemic sclerosis have been described; however, aside from several disease-specific autoantibodies, other biomarkers have not been thoroughly validated and require further study. Thus, there is an unmet need for validated biomarkers for diagnosis, disease classification, and evaluation of organ involvement and therapeutic response in systemic sclerosis.