Diagnostic Value of Combining Tumor and Inflammatory Markers in Lung Cancer

Comparative Serum Proteome Profiling of Canine Benign Prostatic Hyperplasia before and after Castration

BPH is the most prevalent prostatic condition in aging dogs. Nevertheless, clinical diagnosis and management remain inconsistent. This study employed in-solution digestion coupled with nano-liquid chromatography tandem mass spectrometry to assess serum proteome profiling of dogs with BPH and those dogs after castration. Male dogs were divided into two groups; control and BPH groups. In the BPH group, each dog was evaluated at two time points: Day 0 (BF subgroup) and Day 30 after castration (AT subgroup). In the BF subgroup, three proteins were significantly upregulated and associated with dihydrotestosterone: solute carrier family 5 member 5, tyrosine-protein kinase, and FRAT regulator of WNT signaling pathway 1. Additionally, the overexpression of polymeric immunoglobulin receptors in the BF subgroup hints at its potential as a novel protein linked to the BPH development process. Conversely, alpha-1-B glycoprotein (A1BG) displayed significant downregulation in the BF subgroup, suggesting A1BG's potential as a predictive protein for canine BPH. Finasteride was associated with increased proteins in the AT subgroup, including apolipoprotein C-I, apolipoprotein E, apolipoprotein A-II, TAO kinase 1, DnaJ homolog subfamily C member 16, PH domain and leucine-rich repeat protein phosphatase 1, neuregulin 1, and pseudopodium enriched atypical kinase 1. In conclusion, this pilot study highlighted alterations in various serum proteins in canine BPH, reflecting different pathological changes occurring in this condition. These proteins could be a source of potential non-invasive biomarkers for diagnosing this disease.

Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer

BACKGROUND

For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme.

METHODS

TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set.

RESULTS

Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%.

CONCLUSIONS

Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.

Artificial intelligence in clinical applications for lung cancer: diagnosis, treatment and prognosis

Artificial Intelligence (AI) is a branch of computer science that includes research in robotics, language recognition, image recognition, natural language processing, and expert systems. AI is poised to change medical practice, and oncology is not an exception to this trend. As the matter of fact, lung cancer has the highest morbidity and mortality worldwide. The leading cause is the complexity of associating early pulmonary nodules with neoplastic changes and numerous factors leading to strenuous treatment choice and poor prognosis. AI can effectively enhance the diagnostic efficiency of lung cancer while providing optimal treatment and evaluating prognosis, thereby reducing mortality. This review seeks to provide an overview of AI relevant to all the fields of lung cancer. We define the core concepts of AI and cover the basics of the functioning of natural language processing, image recognition, human-computer interaction and machine learning. We also discuss the most recent breakthroughs in AI technologies and their clinical application regarding diagnosis, treatment, and prognosis in lung cancer. Finally, we highlight the future challenges of AI in lung cancer and its impact on medical practice.

Multiple biomarkers are more accurate than a combination of carbohydrate antigen 125 and human epididymis protein 4 for ovarian cancer screening

Objective

The objective of this study was to compare and evaluate the diagnostic value of serum carbohydrate antigen 125 (CA125) and/or human epididymis protein 4 (HE4) and a panel of novel multiple biomarkers in patients with ovarian tumors to identify more accurate and effective markers for screening ovarian cancer.

Methods

Candidate ovarian cancer biomarkers were selected based on a literature search. Dozens of candidate biomarkers were examined using 143 serum samples from patients with ovarian cancer and 157 healthy serum samples as non-cancer controls. To select the optimal marker panel for an ovarian cancer classification model, a set of biomarker panels was created with the number of possible combinations of 8 biomarkers. Using the set of biomarkers as an input variable, the optimal biomarker panel was selected by examining the performance of the biomarker panel set using the Random Forest algorithm as a non-linear classification method and a 10-fold cross-validation technique.

Results

The final selected optimal combination of five biomarkers (CA125, HE4, CA15.3, ApoA1, and ApoA2) exhibited a sensitivity of 93.71% and specificity of 93.63% for ovarian cancer detection during validation.

Conclusion

Combining multiple biomarkers is a valid strategy for ovarian cancer diagnosis and can be used as a minimally invasive screening method for early ovarian cancer. A panel of five optimal biomarkers, including CA125 and HE4, was verified in this study. These can potentially be used as clinical biomarkers for early detection of ovarian cancer.

iTRAQ-Based Proteomic Analysis Reveals Potential Serum Biomarkers for Pediatric Non-Hodgkin's Lymphoma

Non-Hodgkin’s lymphoma (NHL) is the third most common malignant tumor among children. However, at initial NHL diagnosis, most cases are at an advanced stage because of nonspecific clinical manifestations and currently limited diagnostic methods. This study aimed to screen and verify potential serum biomarkers of pediatric NHL using isobaric tags for relative and absolute quantification (iTRAQ)-based proteomic analysis. Serum protein expression profiles from children with B-NHL (n=20) and T-NHL (n=20) and healthy controls (n=20) were detected by utilizing iTRAQ in combination with two-dimensional liquid chromatography-tandem mass spectrometry (2D LC–MS/MS) and analyzed by applying Ingenuity Pathway Analysis (IPA). The candidate biomarkers S100A8 and LRG1 were further validated by using enzyme-linked immunosorbent assays (ELISAs). Receiver operating characteristic (ROC) analysis based on ELISA data was used to evaluate diagnostic efficacy. In total, 534 proteins were identified twice using iTRAQ combined with 2D LC–MS/MS. Further analysis identified 79 and 73 differentially expressed proteins in B-NHL and T-NHL serum, respectively, compared with control serum according to our defined criteria; 34 proteins were overexpressed and 45 proteins underexpressed in B-NHL, whereas 45 proteins were overexpressed and 28 proteins underexpressed in T-NHL (p < 0.05). IPA demonstrated a variety of signaling pathways, including acute phase response signaling and liver X receptor/retinoid X receptor (LXR/RXR) activation, to be strongly associated with pediatric NHL. S100A8 and LRG1 were elevated in NHL patients compared to normal controls according to ELISA (p < 0.05), which was consistent with iTRAQ results. The areas under the ROC curves of S100A8, LRG1, and the combination of S100A8 and LRG1 were 0.873, 0.898 and 0.970, respectively. Our findings indicate that analysis of the serum proteome using iTRAQ combined with 2D LC–MS/MS is a feasible approach for biomarker discovery. Serum S100A8 and LRG1 are promising candidate biomarkers for pediatric NHL, and these differential proteins illustrate a novel pathogenesis and may be clinically helpful for NHL diagnosis in the future.

Diagnostic value of combining tumor and inflammatory biomarkers in detecting common cancers in Korea

BACKGROUND

The ultimate goal of cancer screening is to diagnose invasive cancers early, while they are still curable. We aimed to validate the diagnostic value of blood-derived protein biomarkers that we developed for six common cancer in Korea.

METHODS

We have discovered 12 protein biomarkers that are useful in differentiating cancer patients from healthy controls using two-dimensional gel electrophoresis (2-DE), surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), and literature review. Cancer patients (stomach, colon, liver, lung, breast, and prostate) and control subjects were collected and tested data sets were used to generate predictive models that identify risk scores for each cancer. The validation study was done in serum samples of an independent patient cohort Receiver operating characteristic (ROC) analyses were conducted to evaluate the diagnostic performance of the biomarkercombinations.

RESULTS

The AUCs of the model in the test set were 0.971, 0.960, 0.969, 0.942, 0.834, and 0.985 for stomach, colon, liver, lung, breast, and prostate cancer, respectively.

CONCLUSIONS

Combining multiple tumor and systemic inflammatory biomarkers proved to be a valid strategy in the diagnosis of six common cancers in Korea. Further validation of appropriate screening populations through large-scale clinical trials are warranted.

Lack of Efficacy of Combined Carbohydrate Antigen Markers for Lung Cancer Diagnosis

Background

Lung cancer (LC) is top-ranked in cancer incidence and is the leading cause of cancer death globally. Combining serum biomarkers can improve the accuracy of LC diagnosis. The identification of the best potential combination of traditional tumor markers is essential for LC diagnosis. Patients and Methods. Blood samples were collected from 132 LC cases and 118 benign lung disease (BLD) controls. The expression levels of ten serum tumor markers (CYFR21, CEA, NSE, SCC, CA15-3, CA 19-9, CA 125, CA50, CA242, and CA724) were assayed, and that the expression in the levels of tumor markers were evaluated, isolated, and combined in different patients. The performance of the biomarkers was analyzed by receiver operating characteristic (ROC) analyses, and the difference between combinations of biomarkers was compared by Chi-square (χ²) tests.

Results

As single markers, CYFR21 and CEA showed good diagnostic efficacy for nonsmall cell lung cancer (NSCLC) patients, while NSE and CEA were the most sensitive in the diagnosis of small cell lung cancer (SCLC). The area under the curve (AUC) value was 0.854 for the panel of four biomarkers (CYFR21, CEA, NSE, and SCC), 0.875 for the panel of six biomarkers (CYFR21, CEA, NSE, SCC, CA125, and CA15-3), and 0.884 for the panel of ten markers (CYFR21, CEA, NSE, SCC, CA125, CA15-3, CA19-9, CA50, CA242, and CA724). With a higher sensitivity and negative predictive value (NPV), the diagnostic accuracy of the three panels was better than that of any single biomarker, but there were no statistically significant differences among them (all P values > 0.05). However, the panel of six carbohydrate antigen (CA) biomarkers (CA125, CA15-3, CA19-9, CA50, CA242, and CA724) showed a lower diagnostic value (AUC: 0.776, sensitivity: 59.8%, specificity: 73.0%, and NPV: 60.4%) than the three panels (P value < 0.05). The performance was similar even when analyzed individually by LC subtypes.

Conclusion

The biomarkers isolated are elevated for different types of lung cancer, and the panel of CYFR21, CEA, NSE, and SCC seems to be a promising serum biomarker for the diagnosis of lung cancer, while the combination with carbohydrate antigen markers does not improve the diagnostic efficacy.

Diagnostic Value of Combinatorial Markers in Colorectal Carcinoma

Objectives: Blood-based tests have been shown to be an effective strategy for colorectal cancer (CRC) detection in screening programs. This study was aimed to test the performance of 20 blood markers including tumor antigens, inflammatory markers, and apolipoproteins as well as their combinations.

Methods: In total 203 healthy volunteers and 102 patients with CRC were enrolled into the study. Differences between healthy and cancer subjects were evaluated using Wilcoxon rank-sum test. Several multivariate classification algorithms were employed using information about different combinations of biomarkers altered in CRC patients as well as age and gender of the subjects; random sub-sampling cross-validation was done to overcome overfitting problem. Diagnostic performance of single biomarkers and multivariate classification models was evaluated by receiver operating characteristic (ROC) analysis.

Results: Of 20 biomarkers, 16 were significantly different between the groups (p-value ≤ 0.001); ApoA1, ApoA2 and ApoA4 levels were decreased, whereas levels of tumor antigens (e.g. carcinoembriogenic antigen) and inflammatory markers (e.g., C-reactive protein) were increased in CRC patients vs. healthy subjects. Combinatorial markers including information about all 16 significant analytes, age and gender of patients, demonstrated better performance over single biomarkers with average accuracy on test datasets ≥95% and area under ROC curve (AUROC) ≥98%.

Conclusions: Combinatorial approach was shown to be a valid strategy to improve performance of blood-based CRC diagnostics. Further evaluation of the proposed models in screening programs will be performed to gain a better understanding of their diagnostic value.

Biomarker Panel for the Diagnosis of Pancreatic Ductal Adenocarcinoma

A single tumor marker has a low diagnostic value in pancreatic cancer. Combinations of multiple biomarkers and unique analysis algorithms can be applied to overcome these limitations. This study sought to develop diagnostic algorithms using multiple biomarker panels and to validate their performance in the diagnosis of pancreatic ductal adenocarcinoma (PDAC). We used blood samples from 180 PDAC patients and 573 healthy controls. Candidate markers consisted of 11 markers that are commonly expressed in various cancers and which have previously demonstrated increased expression in pancreatic cancer. Samples were divided into training and validation sets. Five linear or non-linear classification methods were used to determine the optimal model. Differences were identified in 10 out of the 11 markers tested. We identified 2047 combinations, all of which were applied to 5 separate algorithms. The new biomarker combination consisted of 6 markers (ApoA1, CA125, CA19-9, CEA, ApoA2, and TTR). The area under the curve, specificity, and sensitivity were 0.992, 95%, and 96%, respectively, in the training set. Meanwhile, the measures were 0.993, 96%, and 93% in the validation set. This study demonstrated the utility of multiple biomarker combinations in the early detection of PDAC. A diagnostic panel of 6 biomarkers was developed and validated. These algorithms will assist in the early diagnosis of PDAC.

Circulating cell-free DNA as a diagnostic and prognostic biomarker for non-small-cell lung cancer: a systematic review and meta-analysis

Aim: A meta-analysis was conducted to assess the application of circulating cell-free DNA (cfDNA) in non-small-cell lung carcinoma (NSCLC) screening, EGFR and KRAS mutation detection. Materials & methods: A comprehensive literature search was conducted. The summary sensitivity and specificity for cfDNA in NSCLC diagnosis, EGFR and KRAS mutation detection were calculated. Results: The sensitivity and specificity for NSCLC diagnosis, EGFR and KRAS mutation detection were 0.80 (95% CI: 0.72-0.87) and 0.81 (95% CI: 0.68-0.91), 0.780 (95% CI: 0.711-0.853) and 0.962 (95% CI: 0.942-0.984), 0.628 (95% CI: 0.244-0.919) and 0.959 (95% CI: 0.932-0.998), respectively. Conclusion: cfDNA was a minimally invasive approach for NSCLC diagnosis, but its clinical utility warranted more future investigations because of the suboptimal sensitivity.

Simple and robust polymer-based sensor for rapid cancer detection using serum

We report a polymer-based sensor that rapidly detects cancer based on changes in serum protein levels. Using three ratiometric fluorescence outputs, this simple system identifies early stage and metastatic lung cancer with a high level of accuracy exceeding many biomarker-based assays, making it an attractive strategy for point-of-care testing.

HE4 as a biomarker for diagnosis of lung cancer: A meta-analysis

BACKGROUND

The aim of our study was to assess the value of serum human epididymis protein 4 (HE4) to diagnose lung cancer and provide reliable scientific conclusions to guide clinical practice.

METHODS

A systematic search of the PubMed, EMBASE, Cochrane Library, Chinese National Knowledge Infrastructure, Chinese Biomedical Literature, and WANFANG databases was conducted to identify all studies examining serum HE4 in the diagnosis of lung cancer published up to June, 2017. The Quality Assessment of Diagnostic Accuracy Studies tool was used to evaluate the methodological quality of each trial. The meta-analysis was performed using STATA software and Review Manager 5.3.

RESULTS

There were 21 studies involving 1883 cases and 1696 controls included in our meta-analysis. The pooled sensitivity and specificity of HE4 for diagnosing lung cancer were 0.73 (95% confidence interval [CI] 0.68-0.78) and 0.86 (95% CI 0.81-0.91), respectively. The positive likelihood ratio and negative likelihood ratio were 5.4 (95% CI 3.8-7.5) and 0.31 (95% CI 0.26-0.37), respectively. The diagnostic odds ratio was 17 (95% CI 12-26). The area under the curve of the summary receiver-operating characteristic curve was 0.86 (95% CI 0.83-0.89). Race, assay method, type of cancer, sample size, and publication date might be sources of heterogeneity in our meta-analysis. Subgroup analyses showed that the sensitivity in Caucasians was higher than that in Asians (0.81, 95% CI 0.71-0.91; and 0.71, 95% CI 0.66-0.77, respectively), but the specificity in Asians was better than that in Caucasians (0.87, 95% CI 0.81-0.92; and 0.85, 95% CI 0.73-0.97, respectively). The chemiluminescent microparticle immunoassay had the highest sensitivity, with 0.79 (95% CI 0.73-0.97), and the enzyme-linked immunosorbent assay had the highest specificity, with 0.87 (95% CI 0.79-0.94). HE4 had high diagnostic efficacy when screening for small cell lung cancer with the highest specificity (0.90, 95% CI 0.77-1.00).

CONCLUSIONS

HE4 is a relatively promising and effective biomarker for the diagnosis of lung cancer. Furthermore, given the limitations of our study, additional large-scale and well-designed studies are needed in the future.

Diagnostic accuracy of human epididymis secretory protein 4 for lung cancer: a systematic review and meta-analysis

Background

Several studies have assessed the diagnostic accuracy of serum human epididymis secretory protein 4 (HE4) for lung cancer, but their results were heterogeneous. The aim of this study was to systematically review the available studies and pool their results using meta-analysis.

Methods

PubMed, EMBASE and Web of Science databases were searched up to January 1, 2019 to identify studies investigating the diagnostic accuracy of HE4 for lung cancer. We assessed the quality of eligible studies with the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The overall diagnostic sensitivity, specificity, positive and negative likelihood ratios were pooled using a bivariate model. Deeks's test was applied to detect the degree of publication bias.

Results

A total of 16 studies with 18 cohorts (1,756 lung cancers and 1,446 controls) were included. HE4 had a pooled sensitivity of 0.65 (95% CI: 0.54-0.75), specificity of 0.88 (95% CI: 0.82-0.92), positive likelihood ration of 5.3 (95% CI: 3.7-7.6) and negative likelihood ratio of 0.40 (95% CI: 0.30-0.52). Patient selection bias and partial verification bias were the major design weaknesses of available studies. No publication bias was observed.

Conclusions

HE4 has moderate diagnostic accuracy for lung cancer. Its result should be interpreted in parallel with clinical findings and the results of other conventional tests. Further studies are still needed to rigorously evaluate the diagnostic accuracy of HE4 for lung cancer.

Prognostic impact of serum transthyretin in patients with non-small cell lung cancer

The identification of novel biomarkers is of great importance for improving the outcome of patients with non-small cell lung cancer (NSCLC). Therefore, the aim of the present study was to determine whether the serum transthyretin (TTR) level could be used as a novel prognostic biomarker for patients with NSCLC. Serum TTR levels, and nutritional and inflammatory parameters were examined prior to treatment in 42 patients with NSCLC. Candidates for independent predictors of prognostic factors were subjected to univariate and multivariate analyses using a Cox proportional hazard model. IL-12-productivity, serum retinol binding protein, albumin and transferrin levels, and lymphocyte-to-monocyte ratio were significantly lower in the patients with TTR <22 mg/dl than those in the patients with TTR ≥22 mg/dl. Patients with serum TTR levels of <22 mg/dl exhibited a poorer overall (P=0.008) and recurrence-free survival (P=0.027) when compared with those with serum TTR levels of ≥22 mg/dl. The parameters, ≥T2 and age ≥75 years were independent prognostic factors for overall survival, and TTR <22 mg/dl and ≥T2 were independent prognostic factors for recurrence-free survival. In conclusion, anthropometric measurement of serum TTR, as well as T category, can be useful for predicting the 5-year recurrence-free survival of patients with NSCLC.

Beyond the Biomarker: Understanding the Diverse Roles of Human Epididymis Protein 4 in the Pathogenesis of Epithelial Ovarian Cancer

Human epididymis protein 4 (HE4) is an important clinical biomarker used for the detection of epithelial ovarian cancer (EOC). While much is known about the predictive power of HE4 clinically, less has been reported regarding its molecular role in the progression of EOC. A deeper understanding of HE4’s mechanistic functions may help contribute to the development of novel targeted therapies. Thus far, it has been difficult to recommend HE4 as a therapeutic target owing to the fact that its role in the progression of EOC has not been extensively evaluated. This review summarizes what is collectively known about HE4 signaling and how it functions to promote tumorigenesis, chemoresistance, and metastasis in EOC, with the goal of providing valuable insights that will have the potential to aide in the development of new HE4-targeted therapies.

Human epididymis protein 4 may not be a reliable screening biomarker for detecting lung carcinoma patients

Human epididymis protein 4 (HE4) acts as a protease inhibitor. It has been detected in the serum of patients with lung cancer patients and its utility for lung cancer screening was found in different studies. Nevertheless, little is known regarding the expression of HE4 in lung carcinoma subtypes. The aim of the present study was to investigate whether HE4 expression is a reliable marker for detecting any lung carcinoma subtypes, including small cell lung cancer (SCLC), non-small cell lung cancer (NSCLC) and adenocarcinoma (AC). In total, 141 lung carcinoma patients were enrolled in the study. Biopsy samples were obtained from bronchoscopic biopsy. The tumors were classified as SCLC (group 1, 54 cases) or NSCLC (group 2, 87 cases) based on histology and immunohistochemistry. The latter was sub-grouped as adenocarcinoma (group 2a, AC) and squamous cell carcinoma (group 2b, SCC). The immunohistochemical expression of HE4 was compared between the groups. The study revealed that the majority of the SCLC and SCC cases were devoid of HE4 (90.1 and 89.65%, respectively). Approximately 10% of cases had HE4 immune expression and the staining was focal and moderate throughout the tumor tissue. On the other hand, 78.8% of AC expressed HE4 and the staining was diffuse and strong. The overall HE4 expression in the lung cancer patients was 33.7%. In conclusion, the results of the present study have shown that HE4 is expressed in adenocarcinoma of the lung but it is not frequent in SCC and SCLC. The value of HE4 as a screening biomarker for lung cancer is limited but its presence exclusively in adenocarcinoma may provide new insight for targeted therapy.