Cytoskeleton-Associated Protein 4 Is a Novel Serodiagnostic Marker for Lung Cancer

ClearF++: Improved Supervised Feature Scoring Using Feature Clustering in Class-Wise Embedding and Reconstruction

Feature selection methods are essential for accurate disease classification and identifying informative biomarkers. While information-theoretic methods have been widely used, they often exhibit limitations such as high computational costs. Our previously proposed method, ClearF, addresses these issues by using reconstruction error from low-dimensional embeddings as a proxy for the entropy term in the mutual information. However, ClearF still has limitations, including a nontransparent bottleneck layer selection process, which can result in unstable feature selection. To address these limitations, we propose ClearF++, which simplifies the bottleneck layer selection and incorporates feature-wise clustering to enhance biomarker detection. We compare its performance with other commonly used methods such as MultiSURF and IFS, as well as ClearF, across multiple benchmark datasets. Our results demonstrate that ClearF++ consistently outperforms these methods in terms of prediction accuracy and stability, even with limited samples. We also observe that employing the Deep Embedded Clustering (DEC) algorithm for feature-wise clustering improves performance, indicating its suitability for handling complex data structures with limited samples. ClearF++ offers an improved biomarker prioritization approach with enhanced prediction performance and faster execution. Its stability and effectiveness with limited samples make it particularly valuable for biomedical data analysis.

Advancements in Oncoproteomics Technologies: Treading toward Translation into Clinical Practice

Proteomics continues to forge significant strides in the discovery of essential biological processes, uncovering valuable information on the identity, global protein abundance, protein modifications, proteoform levels, and signal transduction pathways. Cancer is a complicated and heterogeneous disease, and the onset and progression involve multiple dysregulated proteoforms and their downstream signaling pathways. These are modulated by various factors such as molecular, genetic, tissue, cellular, ethnic/racial, socioeconomic status, environmental, and demographic differences that vary with time. The knowledge of cancer has improved the treatment and clinical management; however, the survival rates have not increased significantly, and cancer remains a major cause of mortality. Oncoproteomics studies help to develop and validate proteomics technologies for routine application in clinical laboratories for (1) diagnostic and prognostic categorization of cancer, (2) real-time monitoring of treatment, (3) assessing drug efficacy and toxicity, (4) therapeutic modulations based on the changes with prognosis and drug resistance, and (5) personalized medication. Investigation of tumor-specific proteomic profiles in conjunction with healthy controls provides crucial information in mechanistic studies on tumorigenesis, metastasis, and drug resistance. This review provides an overview of proteomics technologies that assist the discovery of novel drug targets, biomarkers for early detection, surveillance, prognosis, drug monitoring, and tailoring therapy to the cancer patient. The information gained from such technologies has drastically improved cancer research. We further provide exemplars from recent oncoproteomics applications in the discovery of biomarkers in various cancers, drug discovery, and clinical treatment. Overall, the future of oncoproteomics holds enormous potential for translating technologies from the bench to the bedside.

Research progress in protein microarrays: Focussing on cancer research

Although several effective treatment modalities have been developed for cancers, the morbidity and mortality associated with cancer continues to increase every year. As one of the most exciting emerging technologies, protein microarrays represent a powerful tool in the field of cancer research because of their advantages such as high throughput, small sample usage, more flexibility, high sensitivity and direct readout of results. In this review, we focus on the research progress in four types of protein microarrays (proteome microarray, antibody microarray, lectin microarray and reversed protein array) with emphasis on their application in cancer research. Finally, we discuss the current challenges faced by protein microarrays and directions for future developments. We firmly believe that this novel systems biology research tool holds immense potential in cancer research and will become an irreplaceable tool in this field.

Blood protein biomarkers in lung cancer

Lung cancer has consistently ranked first as the cause of cancer-associated mortality. The 5-year survival rate has risen slowly, and the main obstacle to improving the prognosis of patients has been that lung cancer is usually diagnosed at an advanced or incurable stage. Thus, early detection and timely intervention are the most effective ways to reduce lung cancer mortality. Tumor-specific molecules and cellular elements are abundant in circulation, providing real-time information in a noninvasive and cost-effective manner during lung cancer development. These circulating biomarkers are emerging as promising tools for early detection of lung cancer and can be used to supplement computed tomography screening, as well as for prognosis prediction and treatment response monitoring. Serum and plasma are the main sources of circulating biomarkers, and protein biomarkers have been most extensively studied. In this review, we summarize the research progress on three most common types of blood protein biomarkers (tumor-associated antigens, autoantibodies, and exosomal proteins) in lung cancer. This review will potentially guide researchers toward a more comprehensive understanding of candidate lung cancer protein biomarkers in the blood to facilitate their translation to the clinic.

DJ-1 Expression Might Serve as a Biologic Marker in Patients with Bladder Cancer

The overexpression of DJ-1 protein and its secretion into the bloodstream has been reported in various neoplasms. However, serum levels and the subcellular localization of DJ-1 have not been analyzed in detail in bladder cancer (BC). Our comprehensive analysis of these variables started with the measurement of DJ-1 in serum from 172 patients with BC, 20 patients with urolithiasis and 100 healthy participants. Next, an immunohistochemical study of DJ-1 expression and localization was conducted in 92 patients with BC, and associations with clinicopathologic factors and patient outcomes were evaluated. Serum DJ-1 was significantly higher in patients with BC than in those with urolithiasis or in healthy participants. Immunohistochemically, a cytoplasm-positive (Cy+) and nucleus-negative (N-) DJ-1 pattern was associated with age and pathologic stage. Log-rank tests indicated that the Cy+, N- pattern was significantly associated with overall survival (OS), recurrence-free survival (RFS), and cancer specific survival (CSS). In addition, the Cy+, N- pattern was an independent prognostic factor in the multivariate analysis adjusted for the effects of the clinicopathologic outcomes. The investigation of DJ-1 expression might help physicians to make decisions regarding further follow-up and additional treatments.

Proteomics technologies for cancer liquid biopsies

Alterations in DNAs could not reveal what happened in proteins. The accumulated alterations of DNAs would change the manifestation of proteins. Therefore, as is the case in cancer liquid biopsies, deep proteome profiling will likely provide invaluable and clinically relevant information in real-time throughout all stages of cancer progression. However, due to the great complexity of proteomes in liquid biopsy samples and the limitations of proteomic technologies compared to high-plex sequencing technologies, proteomic discoveries have yet lagged behind their counterpart, genomic technologies. Therefore, novel protein technologies are in urgent demand to fulfill the goals set out for biomarker discovery in cancer liquid biopsies.Notably, conventional and innovative technologies are being rapidly developed for proteomic analysis in cancer liquid biopsies. These advances have greatly facilitated early detection, diagnosis, prognosis, and monitoring of cancer evolution, adapted or adopted in response to therapeutic interventions. In this paper, we review the high-plex proteomics technologies that are capable of measuring at least hundreds of proteins simultaneously from liquid biopsy samples, ranging from traditional technologies based on mass spectrometry (MS) and antibody/antigen arrays to innovative technologies based on aptamer, proximity extension assay (PEA), and reverse phase protein arrays (RPPA).

Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

Dickkopf signaling, beyond Wnt-mediated biology

Dickkopf1 (DKK1) was originally identified as a secreted protein that antagonizes Wnt signaling. Although DKK1 is essential for the developmental process, its functions in postnatal and adult life are unclear. However, evidence is accumulating that DKK1 is involved in tumorigenesis in a manner unrelated to Wnt signaling. In addition, recent studies have revealed that DKK1 may control immune reactions, although the relationship of this to Wnt signaling is unknown. Other DKK family members, DKK2-4, are likely to have their own functions. Here, we review the possible novel functions of DKKs. We summarize the characteristics of receptors of DKKs and the signaling mechanisms through DKKs and their receptors, provide evidence showing that DKKs are involved in tumor aggressiveness independently of Wnt signaling, and emphasize promising cancer therapies targeting DKKs and receptors. Lastly, we discuss various physiological and pathological processes controlled by DKKs.

Serum Epiplakin Might Be a Potential Serodiagnostic Biomarker for Bladder Cancer

Tumor markers that can be detected at an early stage are needed. Here, we evaluated the epiplakin expression levels in sera from patients with bladder cancer (BC). Using a micro-dot blot array, we evaluated epiplakin expression levels in 60 patients with BC, 20 patients with stone disease, and 28 healthy volunteers. The area under the curve (AUC) and best cut-off point were calculated using receiver-operating characteristic (ROC) analysis. Serum epiplakin levels were significantly higher in patients with BC than in those with stone disease (p = 0.0013) and in healthy volunteers (p < 0.0001). The AUC-ROC level for BC was 0.78 (95% confidence interval (CI) = 0.69-0.87). Using a cut-off point of 873, epiplakin expression levels exhibited 68.3% sensitivity and 79.2% specificity for BC. However, the serum epiplakin levels did not significantly differ by sex, age, pathological stage and grade, or urine cytology. We performed immunohistochemical staining using the same antibody on another cohort of 127 patients who underwent radical cystectomy. Univariate and multivariate analysis results showed no significant differences between epiplakin expression, clinicopathological findings, and patient prognoses. Our results showed that serum epiplakin might be a potential serodiagnostic biomarker in patients with BC.

CKAP4 Antibody-Conjugated Si Quantum Dot Micelles for Targeted Imaging of Lung Cancer

At present, various fluorescent nanomaterials have been designed and synthesized as optical contrast agents for surgical navigation. However, there have been no reports on the preparation of fluorescent contrast agents for lung cancer surgery navigation using silicon quantum dots (Si QDs). This study improved and modified the water-dispersible Si QD micelles reported by Pi et al. to prepare Si QD micelles-CKAP4. The data showed that the Si QD micelles-CKAP4 were spherical particles with a mean hydrodiameter of approximately 78.8 nm. UV-visible absorption of the Si QD micelles-CKAP4 ranged from 200 to 500 nm. With an excitation wavelength of 330 nm, strong fluorescence at 640 nm was observed in the fluorescence emission spectra. Laser confocal microscopy and fluorescence microscopy assay showed that the Si QD micelles-CKAP4 exhibited good targeting ability to lung cancer cells and lung cancer tissues in vitro. The in vivo fluorescence-imaging assay showed that the Si QD micelles-CKAP4 was metabolized by the liver and excreted by the kidney. In addition, Si QD micelles-CKAP4 specifically targeted lung cancer tissue in vivo compared with healthy lung tissue. Cytotoxicity and hematoxylin and eosin staining assays showed that the Si QD micelles-CKAP4 exhibited high biosafety in vitro and in vivo. Si QD micelles-CKAP4 is a specifically targeted imaging agent for lung cancer and is expected to be a fluorescent contrast agent for lung cancer surgical navigation in the future.

Exploring Volatile Organic Compounds in Breath for High-Accuracy Prediction of Lung Cancer

(1) Background: Lung cancer is silent in its early stages and fatal in its advanced stages. The current examinations for lung cancer are usually based on imaging. Conventional chest X-rays lack accuracy, and chest computed tomography (CT) is associated with radiation exposure and cost, limiting screening effectiveness. Breathomics, a noninvasive strategy, has recently been studied extensively. Volatile organic compounds (VOCs) derived from human breath can reflect metabolic changes caused by diseases and possibly serve as biomarkers of lung cancer. (2) Methods: The selected ion flow tube mass spectrometry (SIFT-MS) technique was used to quantitatively analyze 116 VOCs in breath samples from 148 patients with histologically confirmed lung cancers and 168 healthy volunteers. We used eXtreme Gradient Boosting (XGBoost), a machine learning method, to build a model for predicting lung cancer occurrence based on quantitative VOC measurements. (3) Results: The proposed prediction model achieved better performance than other previous approaches, with an accuracy, sensitivity, specificity, and area under the curve (AUC) of 0.89, 0.82, 0.94, and 0.95, respectively. When we further adjusted the confounding effect of environmental VOCs on the relationship between participants' exhaled VOCs and lung cancer occurrence, our model was improved to reach 0.92 accuracy, 0.96 sensitivity, 0.88 specificity, and 0.98 AUC. (4) Conclusion: A quantitative VOCs databank integrated with the application of an XGBoost classifier provides a persuasive platform for lung cancer prediction.

Proteomic blood profiling in mild, severe and critical COVID-19 patients

The recent SARS-CoV-2 pandemic manifests itself as a mild respiratory tract infection in most individuals, leading to COVID-19 disease. However, in some infected individuals, this can progress to severe pneumonia and acute respiratory distress syndrome (ARDS), leading to multi-organ failure and death. This study explores the proteomic differences between mild, severe, and critical COVID-19 positive patients to further understand the disease progression, identify proteins associated with disease severity, and identify potential therapeutic targets. Blood protein profiling was performed on 59 COVID-19 mild (n = 26), severe (n = 9) or critical (n = 24) cases and 28 controls using the OLINK inflammation, autoimmune, cardiovascular and neurology panels. Differential expression analysis was performed within and between disease groups to generate nine different analyses. From the 368 proteins measured per individual, more than 75% were observed to be significantly perturbed in COVID-19 cases. Six proteins (IL6, CKAP4, Gal-9, IL-1ra, LILRB4 and PD-L1) were identified to be associated with disease severity. The results have been made readily available through an interactive web-based application for instant data exploration and visualization, and can be accessed at https://phidatalab-shiny.rosalind.kcl.ac.uk/COVID19/ . Our results demonstrate that dynamic changes in blood proteins associated with disease severity can potentially be used as early biomarkers to monitor disease severity in COVID-19 and serve as potential therapeutic targets.

Prediction System for the Lung Cancer Patients and Classification Accuracy Enhancement Using Ensemble Method

Lung cancer is the biggest challenge in the research world. Blood samples for lung cancer patients are significant for the prediction method in the research environment. Accurate prediction is essential to increase the survival period and may help to take proper medication. The feature selection is an essential part of this prediction system. In this method, the essential features are analyzed from lung cancer patient’s blood. Biomarkers can be identified from blood and other tests. Biomarkers are used in many scientific fields. The Serum CKAP4 levels were analyzed from lung cancer patient’s blood at the TNM stages. White blood counts and Hemoglobin are viewed as an analysis of immunity, and CKAP4 is located at an immunity level that has been recognized in the body fluid of patients with lung cancer. The system is analyzed immunity from collected real health information by a proposed Gaussian Kb Ensemble Weighting Classifier method. The novel attributes are composed of a cancer research center and cancer sanatoriums. The measurement of weighting value {low = 0, high = 1} of blood in Serum CKAP4, Hemoglobin, and the WBC are classified by immunity range. High immunity people’s survival rate is high at TNM stage 1. If not, high immunity was survival rate is low at TNM stage 1. Lung cancer patient’s survivability classification was performed by various supervised machine learning models such as Ada boost, neural networks, and SVM model. The Gaussian Kb Ensemble Weighting Classifier method helped to improve the classification accuracy by feature selection and the algorithm was implemented in the MATLAB environment. Kaplan–Meier curve method used to analyzes results. The classified labels are compared with existing results. The area proved the Gaussian Kb Ensemble weighting classifier algorithm under the Curve through the ROC method.

Neurobiochemical Cross-talk Between COVID-19 and Alzheimer's Disease

COVID-19, the global threat to humanity, shares etiological cofactors with multiple diseases including Alzheimer's disease (AD). Understanding the common links between COVID-19 and AD would harness strategizing therapeutic approaches against both. Considering the urgency of formulating COVID-19 medication, its AD association and manifestations have been reviewed here, putting emphasis on memory and learning disruption. COVID-19 and AD share common links with respect to angiotensin-converting enzyme 2 (ACE2) receptors and pro-inflammatory markers such as interleukin-1 (IL-1), IL-6, cytoskeleton-associated protein 4 (CKAP4), galectin-9 (GAL-9 or Gal-9), and APOE4 allele. Common etiological factors and common manifestations described in this review would aid in developing therapeutic strategies for both COVID-19 and AD and thus impact on eradicating the ongoing global threat. Thus, people suffering from COVID-19 or who have come round of it as well as people at risk of developing AD or already suffering from AD, would be benefitted.

Cytoskeleton-Associated Protein 4, a Promising Biomarker for Tumor Diagnosis and Therapy

Cytoskeleton-associated protein 4 (CKAP4) is located in the rough endoplasmic reticulum (ER) and plays an important role in stabilizing the structure of ER. Meanwhile, CKAP4 is also found to act as an activated receptor at the cell surface. The multifunction of CKAP4 was gradually discovered with growing research evidence. In addition to the involvement in various physiological events including cell proliferation, cell migration, and stabilizing the structure of ER, CKAP4 has been implicated in tumorigenesis. However, the role of CKAP4 is still controversial in tumor biology, which may be related to different signal transduction pathways mediated by binding to different ligands in various microenvironments. Interestingly, CKAP4 has been recently recognized as a serological marker of several tumors and CKAP4 is expected to be a tumor therapeutic target. Therefore, deciphering the gene status, expression regulation, functions of CKAP4 in different diseases may shed new light on CKAP4-based cancer diagnosis and therapeutic strategy. This review discusses the publications that describe CKAP4 in various diseases, especially on tumor promotion and suppression, and provides a detailed discussion on the discrepancy.

Clinical Application of Mass Spectrometry-Based Proteomics in Lung Cancer Early Diagnosis

The current knowledge on proteomic biomarker analysis for the early diagnosis of lung cancer is summarized, underlining the diversity among the results and the current interest in translating research results into clinical practice. A MEDLINE/PubMed literature search to retrieve all the papers published in the last 10 years is performed. Proteomics studies on lung cancer have gathered evidence on the potential role of biomarkers in early diagnosis. Although promising, none of them have proved to be sufficiently reliable to achieve validation. Future research should evolve toward a multipanel analysis of proteins, considering the possibility that individual biomarkers might not be specific enough to diagnose lung cancer, but could be related to oncological conditions.

Emerging non-invasive detection methodologies for lung cancer

The potential for non-invasive lung cancer (LC) diagnosis based on molecular, cellular and volatile biomarkers has been attracting increasing attention, with the development of advanced techniques and methodologies. It is standard practice to tailor the treatments of LC for certain specific genetic alterations, including the epidermal growth factor receptor, anaplastic lymphoma kinase and BRAF genes. Despite these advances, little is known about the internal mechanisms of different types of biomarkers and the involvement of their related biochemical pathways during the development of LC. The development of faster and more effective techniques is essential for the identification of different biomarkers. The present review summarizes some of the latest methods used for detecting molecular, cellular and volatile biomarkers in LC and their potential use in clinical diagnosis and targeted therapy.

Biomarkers for Early Cancer Diagnosis: Prospects for Success through the Lens of Tumor Genetics

Thousands of candidate cancer biomarkers have been proposed, but so far, few are used in cancer screening. Failure to implement these biomarkers is attributed to technical and design flaws in the discovery and validation phases, but a major obstacle stems from cancer biology itself. Oncogenomics has revealed broad genetic heterogeneity among tumors of the same histology and same tissue (or organ) from different patients, while tumors of different tissue origins also share common genetic mutations. Moreover, there is wide intratumor genetic heterogeneity among cells within any single neoplasm. These findings seriously limit the prospects of finding a single biomarker with high specificity for early cancer detection. Current research focuses on developing biomarker panels, with data assessment by machine-learning algorithms. Whether such approaches will overcome the inherent limitations posed by tumor biology and lead to tests with true clinical value remains to be seen.

Prognostic significance of IMMT expression in surgically-resected lung adenocarcinoma

BACKGROUND

Mitochondrial dysfunction contributes to many types of human disorders and cancer progression. Inner membrane mitochondrial protein (IMMT) plays an important role in the maintenance of mitochondrial structure and function. The aims of this study were to examine IMMT expression in lung adenocarcinoma and evaluate its correlation with clinicopathological parameters and patient prognosis.

METHODS

IMMT expression was immunohistochemically studied in 176 consecutive lung adenocarcinoma resection tissues, and its correlations with clinicopathological parameters were evaluated. Kaplan-Meier survival analysis and Cox-proportional hazards models were used to estimate the effect of IMMT expression on survival.

RESULTS

High-IMMT expression was detected in 84 of 176 (47.7%) lung adenocarcinomas. Levels were significantly correlated with advanced disease stage (stage II and III; P = 0.024), larger tumor size (>3 cm; P = 0.002), intratumoral vascular invasion (P < 0.001), and poorer adenocarcinoma patient prognosis (P = 0.002). Based on 176 patients with adenocarcinoma, multivariate analysis revealed that IMMT expression was an independent predictor of poorer survival (HR, 1.99; 95% confidence interval [CI], 1.06-3.74; P = 0.031). Further, treating A549 cells derived from lung adenocarcinoma, with IMMT siRNA resulted in significantly decreased proliferation.

CONCLUSION

Here, we first demonstrated that high-IMMT expression is related to some clinicopathological parameters, and that its expression is an independent prognostic predictor of poorer survival in patients with lung adenocarcinoma; further studies are required to clarify the biological function of IMMT in lung adenocarcinoma. However, results suggest that this protein could be a novel prognostic indicator and therapeutic target.

Epigenome-Wide Association Analysis of Differentially Methylated Signals in Blood Samples of Patients with Non-Small-Cell Lung Cancer

Lung cancer is a common form of cancer and the leading cause of cancer-related deaths worldwide. Early diagnosis using noninvasive biomarkers may play an important role in increasing the survival rate of patients with lung cancer. Biomarkers of DNA methylation in blood samples may improve the early diagnosis of lung cancer. Here, we used peripheral blood samples obtained from 150 patients diagnosed with non-small-cell lung cancer (NSCLC) and 150 healthy controls. The latter were selected by frequency matching with the 150 patients with NSCLC, based on age, sex, and smoking status. Genome-wide methylation profiles were obtained using a MethylationEPIC BeadChip Kit, which covers the 850k bp cytosine–phosphate–guanine site. This analysis showed two significant differentially methylated changes (cg12169243 [DPH6] and cg25429010 [IMP3]) associated with NSCLC in current smokers, six changes (cg09245319, cg17183999 [USP7], cg06366994 [CPE], cg24992236 [MEG9], cg22144719, and cg22448179 [epidermal growth factor receptor]) associated with epidermal growth factor receptor mutation in patients with adenocarcinoma, and four changes (cg25021476 [RSL24D1], cg04989085 [FAM113B], cg20905681 [CKAP4], and cg26379694) associated with advanced-stage NSCLC compared with stage I NSCLC. The validation of these DNA methylation changes and further research on the related genes may help develop easily accessible biomarkers for the early diagnosis or prognosis of NSCLC.

CKAP4 Regulates Cell Migration via the Interaction with and Recycling of Integrin

Cytoskeleton-associated protein 4 (CKAP4) is an endoplasmic reticulum protein that is also present in the cell surface membrane, where it acts as a receptor for Dickkopf1 (DKK1). In this study, we found that CKAP4 interacts with β1 integrin and controls the recycling of α5β1 integrin independently of DKK1. In S2-CP8 cells, knockdown of CKAP4 but not DKK1 enlarged the size of cell adhesion sites and enhanced cell adhesion to fibronectin, resulting in decreased cell migration. When CKAP4 was depleted, the levels of α5 but not β1 integrin were increased in the cell surface membrane. A similar phenotype was observed in other cells expressing low levels of DKK1. In S2-CP8 cells, α5 integrin was trafficked with β1 integrin and CKAP4 to the lysosome or recycled with β1 integrin. In CKAP4-depleted cells, the internalization of α5β1 integrin was unchanged, but its recycling was upregulated. Knockdown of sorting nexin 17 (SNX17), a mediator of integrin recycling, abrogated the increased α5 integrin levels caused by CKAP4 knockdown. CKAP4 bound to SNX17, and its knockdown enhanced the recruitment of α5β1 integrin to SNX17. These results suggest that CKAP4 suppresses the recycling of α5β1 integrin and coordinates cell adhesion sites and migration independently of DKK1.

Clinicopathological and prognostic significance of nuclear UGDH localization in lung adenocarcinoma

This study aimed to clarify relationships among UDP-glucose-6 dehydrogenase (UGDH) expression, clinicopathological factors, and the prognosis of patients, and to determine the role of UGDH in lung adenocarcinoma (AC). Firstly, UGDH expression and localization in 126 lung AC tissues were immunohistochemically studied, and associations with clinicopathological parameters and patients' prognosis were evaluated. Secondly, serum UGDH levels were measured in 267 lung cancer patients and 100 healthy controls. Finally, the effects of UGDH knockdown by siRNA on migration and invasion abilities were analyzed. As a result, nuclear UGDH staining was significantly correlated with poorer differentiation, a larger tumor size, higher p-TNM stage, positive nodal metastasis, positive lymphatic invasion, and positive vascular invasion in lung AC patients. Nuclear UGDH-positive patients showed significantly poorer survival than nuclear UGDH-negative patients. Serum UGDH levels were especially higher in lung AC patients even in stage I than those in healthy controls. In lung AC cell lines, nuclear expression levels of UGDH were higher in LC-2/ad cells than in A549 cells. UGDH siRNA-treated LC-2/ad cells showed significantly decreased migration and invasion abilities, but no significant differences were observed in UGDH siRNA-treated A549 cells. These data indicate that UGDH expression and localization are an early sero-diagnostic marker in addition to a poor prognostic indicator in lung AC patients.

Serum cytoskeleton-associated protein 4 as a biomarker for the diagnosis of hepatocellular carcinoma

Background

Alpha-fetoprotein (AFP) is the most commonly applied biomarker for diagnosis of hepatocellular carcinoma (HCC), but the low sensitivity and specificity limit its clinical application. Cytoskeleton-associated protein 4 (CKAP4) is a novel oncogenic protein involved in the development and progression of HCC. This study aimed to evaluate whether measurement of circulating CKAP4 could improve diagnostic accuracy for HCC.

Methods

We analyzed data for patients with HCC, chronic hepatitis B infection, and cirrhosis and healthy controls (n=100 in each group), recruited from two centers between July 2013 and December 2015. Circulating levels of CKAP4 were measured with commercial enzyme-linked immunosorbent assay kits. Receiver operating characteristics were used to evaluate diagnostic accuracy.

Results

Serum concentrations of CKAP4 were significantly elevated in the HCC group, in comparison with the three control groups (all P<0.001). The combined biomarker panel (AFP and CKAP4), created by binary logistic regression, presented better performance (area under the curve [AUC] 0.936, 95% CI [0.908–0.965], sensitivity 0.800, specificity 0.963) than AFP (AUC 0.875 [0.835–0.914], sensitivity 0.930, specificity 0.430, P=0.001) or CKAP4 (AUC 0.821 [0.776–0.866], sensitivity 0.790, specificity 0.670, P<0.001) alone to identify HCC, even though CKAP4 alone was not better than AFP (P=0.093). Furthermore, the combined panel also presented a better performance even in identifying early HCC (AUC 0.922 [0.833–0.961]).

Conclusion

Serum CKAP4 is a novel biomarker for HCC, and it could complement AFP in improving diagnostic accuracy.

Cytoskeleton-associated protein 4 is a novel serodiagnostic marker for esophageal squamous-cell carcinoma

Background

Recent years have witnessed significant progress in the treatment of esophageal squamous-cell carcinoma (ESCC); however, the prognosis of ESCC is still unsatisfactory. Bio-markers are required to improve identification of high-risk populations and help management of ESCC. This study was to evaluate the role of serum CKAP4 in ESCC.

Methods

This longitudinal study recruited 207 ESCC patients and age-/sex-matched healthy controls. Circulating levels of CKAP4 were measured using ELISA kits, while the expression of CKAP4 in esophageal tissue was evaluated using Western blotting.

Results

Serum CKAP4 levels were higher in ESCC patients (380.2±171.3 pg/mL) than healthy controls (271.8±97.4 pg/mL; P<0.001). The area under the receiver-operating characteristic curve of serum CKAP4 levels to identify the presence of ESCC was 0.675 (95% CI 0.622–0.728; P<0.001). According to Youden’s index, the best cutoff value was 429.1 pg/mL (sensitivity 0.415 and specificity 0.995). Furthermore, after follow-up, multivariate analyses identified that pathological lymph node metastases were the poorest prognostic factor (HR 1.862, 95% CI 1.093–3.173; P=0.022), followed by serum CKAP4 (HR 1.437, 95% CI 1.025–2.014; P=0.035). When stratified by tertiles of serum CKAP4, subjects in the first tertile presented a mean survival time of 75.4 months (95% CI 68.0–81.9), which decreased significantly in the second tertile (73.8 months, 95% CI 61.4–86.3) and the third tertile (59.9 months, 95% CI 49.8–70.0, log-rank χ²=8.235; P=0.016).

Conclusion

These results suggested that serum CKAP4 could be a potential biomarker for clinical management of ESCC.