Cancer biomarker discovery: current status and future perspectives

Lung Adenocarcinoma Systems Biomarker and Drug Candidates Identified by Machine Learning, Gene Expression Data, and Integrative Bioinformatics Pipeline

Lung adenocarcinoma (LUAD) is a significant planetary health challenge with its high morbidity and mortality rate, not to mention the marked interindividual variability in treatment outcomes and side effects. There is an urgent need for robust systems biomarkers that can help with early cancer diagnosis, prediction of treatment outcomes, and design of precision/personalized medicines for LUAD. The present study aimed at systems biomarkers of LUAD and deployed integrative bioinformatics and machine learning tools to harness gene expression data. Predictive models were developed to stratify patients based on prognostic outcomes. Importantly, we report here several potential key genes, for example, PMEL and BRIP1, and pathways implicated in the progression and prognosis of LUAD that could potentially be targeted for precision/personalized medicine in the future. Our drug repurposing analysis and molecular docking simulations suggested eight drug candidates for LUAD such as heat shock protein 90 inhibitors, cardiac glycosides, an antipsychotic agent (trifluoperazine), and a calcium ionophore (ionomycin). In summary, this study identifies several promising leads on systems biomarkers and drug candidates for LUAD. The findings also attest to the importance of integrative bioinformatics, structural biology and machine learning techniques in biomarker discovery, and precision oncology research and development.

Exploring Importance and Regulation of Autophagy in Cancer Stem Cells and Stem Cell-Based Therapies

Autophagy is a globally conserved cellular activity that plays a critical role in maintaining cellular homeostasis through the breakdown and recycling of cellular constituents. In recent years, there has been much emphasis given to its complex role in cancer stem cells (CSCs) and stem cell treatment. This study examines the molecular processes that support autophagy and how it is regulated in the context of CSCs and stem cell treatment. Although autophagy plays a dual role in the management of CSCs, affecting their removal as well as their maintenance, the intricate interaction between the several signaling channels that control cellular survival and death as part of the molecular mechanism of autophagy has not been well elucidated. Given that CSCs have a role in the development, progression, and resistance to treatment of tumors, it is imperative to comprehend their biological activities. CSCs are important for cancer biology because they also show a tissue regeneration model that helps with organoid regeneration. In other words, the manipulation of autophagy is a viable therapeutic approach in the treatment of cancer and stem cell therapy. Both synthetic and natural substances that target autophagy pathways have demonstrated promise in improving stem cell-based therapies and eliminating CSCs. Nevertheless, there are difficulties associated with the limitations of autophagy in CSC regulation, including resistance mechanisms and off-target effects. Thus, the regulation of autophagy offers a versatile strategy for focusing on CSCs and enhancing the results of stem cell therapy. Therefore, understanding the complex interactions between autophagy and CSC biology would be essential for creating therapeutic treatments that work in both regenerative medicine and cancer treatment.

Mucins as Potential Biomarkers for Early Detection of Cancer

Early detection significantly correlates with improved survival in cancer patients. So far, a limited number of biomarkers have been validated to diagnose cancers at an early stage. Considering the leading cancer types that contribute to more than 50% of deaths in the USA, we discuss the ongoing endeavors toward early detection of lung, breast, ovarian, colon, prostate, liver, and pancreatic cancers to highlight the significance of mucin glycoproteins in cancer diagnosis. As mucin deregulation is one of the earliest events in most epithelial malignancies following oncogenic transformation, these high-molecular-weight glycoproteins are considered potential candidates for biomarker development. The diagnostic potential of mucins is mainly attributed to their deregulated expression, altered glycosylation, splicing, and ability to induce autoantibodies. Secretory and shed mucins are commonly detected in patients' sera, body fluids, and tumor biopsies. For instance, CA125, also called MUC16, is one of the biomarkers implemented for the diagnosis of ovarian cancer and is currently being investigated for other malignancies. Similarly, MUC5AC, a secretory mucin, is a potential biomarker for pancreatic cancer. Moreover, anti-mucin autoantibodies and mucin-packaged exosomes have opened new avenues of biomarker development for early cancer diagnosis. In this review, we discuss the diagnostic potential of mucins in epithelial cancers and provide evidence and a rationale for developing a mucin-based biomarker panel for early cancer detection.

The Clinical Utility of Droplet Digital PCR for Profiling Circulating Tumor DNA in Breast Cancer Patients

Breast cancer is the most common cancer affecting women worldwide. It is a malignant and heterogeneous disease with distinct molecular subtypes, which has prognostic and predictive implications. Circulating tumor DNA (ctDNA), cell-free fragmented tumor-derived DNA in blood plasma, is an invaluable source of specific cancer-associated mutations and holds great promise for the development of minimally invasive diagnostic tests. Furthermore, serial monitoring of ctDNA over the course of systemic and targeted therapies not only allows unparalleled efficacy assessments but also enables the identification of patients who are at risk of progression or recurrence. Droplet digital PCR (ddPCR) is a powerful technique for the detection and monitoring of ctDNA. Due to its relatively high accuracy, sensitivity, reproducibility, and capacity for absolute quantification, it is increasingly used as a tool for managing cancer patients through liquid biopsies. In this review paper, we gauge the clinical utility of ddPCR as a technique for mutational profiling in breast cancer patients and focus on HER2, PIK3CA, ESR1, and TP53, which represent the most frequently mutated genes in breast cancers.

The Predictive Value of CA-125 and Hb for Ocular Metastasis in Hepatocellular Carcinoma Patients

Objective

To explore the risk factors of ocular metastasis (OM) in patients with hepatocellular carcinoma (HCC) by analyzing the demographic characteristics and serum markers.

Methods

From July 2002 to December 2012, 1064 HCC patients were included in our study. The chi-squared test and Student's t-test were used to assess the difference between OM and any other metastasis (NOM). Receiver operating curve (ROC) was used to analyze the diagnostic value of serum biomarkers in HCC patients with OM.

Results

The incidence of OM in HCC patients was 1.88% in our research. There are no significant differences in age, gender, or histopathology in the OM group and the group without any metastasis. Binary logistic regression analysis presented that compared with the patients without cancer metastasis, carbohydrate antigen 125 (CA-125) and hemoglobin (Hb) were risk factors in hepatocellular carcinoma patients with OM (P < 0.05). The ROC curve analysis showed that the areas under the CA-125, Hb, and CA125+Hb curves were 0.877, 0.554, and 0.431, and the cutoff values of CA-125 and Hb each were 115.78 u/mL and 120.50 g/L.

Conclusion

Our data suggest that CA-125 and Hb are risk indicators in hepatocellular carcinoma patients with OM, and that CA-125+Hb has potentially greater utility in diagnosing hepatocellular carcinoma.

Electrochemical Immunosensor for Detection of H. pylori Secretory Protein VacA on g-C3N4/ZnO Nanocomposite-Modified Au Electrode

A g-C₃N₄/ZnO (graphitic carbon nitride/zinc oxide) nanocomposite-decorated gold electrode was employed to design an antigen-antibody-based electrochemical biosensor to detect Helicobacter pylori specific toxin, vacuolating cytotoxin A (VacA). The thermal condensation method was used to synthesize the g-C₃N₄/ZnO nanocomposite, and the nanocomposite was deposited electrochemically on a gold electrode. The morphology as well as the structure of the synthesized nanocomposite were confirmed by scanning electron microscopy, energy-dispersive X-ray analysis, X-ray diffraction, and Fourier transform infrared techniques. The nanocomposite efficiently increased the sensor performance by amplifying the signals. EDC-NHS chemistry was exploited for attachment of VacA antibodies covalently with the g-C₃N₄/ZnO-modified gold electrode. This modified electrode was exploited for immunosensing of H. pylori-specific VacA antigen. The immunosensor was stable for up to 30 days and exhibited good sensitivity of 0.3 μA^-1 ng mL^-1 in a linear detection range of 0.1 to 12.8 ng mL^-1. Apart from this, the fabricated sensor showed unprecedented reproducibility and remarkable selectivity toward the H. pylori toxin VacA. Thus, the highly sensitive immunosensor is a desirable platform for H. pylori detection in practical applications and clinical diagnosis.

Immune-related gene signature associates with immune landscape and predicts prognosis accurately in patients with Wilms tumour

Wilms tumour (WT) is the most common kidney malignancy in children. Chemoresistance is the leading cause of tumour recurrence and poses a substantial therapeutic challenge. Increasing evidence has underscored the role of the tumour immune microenvironment (TIM) in cancers and the potential for immunotherapy to improve prognosis. There remain no reliable molecular markers for reflecting the immune landscape and predicting patient survival in WT. Here, we examine differences in gene expression by high-throughput RNA sequencing, focused on differentially expressed immune-related genes (IRGs) based on the ImmPort database. Via univariate Cox regression analysis and Lasso-penalized Cox regression analysis, IRGs were screened out to establish an immune signature. Kaplan-Meier curves, time-related ROC analysis, univariate and multivariate Cox regression studies, and nomograms were used to evaluate the accuracy and prognostic significance of this signature. Furthermore, we found that the immune signature could reflect the immune status and the immune cell infiltration character played in the tumour microenvironment (TME) and showed significant association with immune checkpoint molecules, suggesting that the poor outcome may be partially explained by its immunosuppressive TME. Remarkably, TIDE, a computational method to model tumour immune evasion mechanisms, showed that this signature holds great potential for predicting immunotherapy responses in the TARGET-wt cohort. To decipher the underlying mechanism, GSEA was applied to explore enriched pathways and biological processes associated with immunophenotyping and Connectivity map (CMap) along with DeSigN analysis for drug exploration. Finally, four candidate immune genes were selected, and their expression levels in WT cell lines were monitored via qRT-PCR. Meanwhile, we validated the function of a critical gene, NRP2. Taken together, we established a novel immune signature that may serve as an effective prognostic signature and predictive biomarker for immunotherapy response in WT patients. This study may give light on therapeutic strategies for WT patients from an immunological viewpoint.

Zeolitic imidazolate framework-based nanoparticles for the cascade enhancement of cancer chemodynamic therapy by targeting glutamine metabolism

The reprogrammed amino acid metabolism maintains the powerful antioxidant defense and DNA damage repair capacity of cancer cells, which could promote their escape from reactive oxygen species (ROS)-induced damage and inevitably diminish the efficacy of ROS-based therapies. Herein, we propose a strategy to enhance the effect of chemodynamic therapy (CDT) via glutaminolysis-targeted inhibition for cancer cells dependent on abnormal glutamine metabolism. To screen optimum drugs targeting glutamine metabolism, transcriptomic analysis is performed to identify predictive biomarkers. Eventually, telaglenastat (CB-839) is used to block mitochondrial glutaminase 1 (GLS 1) in basal-like breast cancer and loaded into the developed iron-doped zeolitic imidazolate frameworks (ZIF(Fe) NPs) to form ZIF(Fe)&CB nanoparticles, which are able to co-deliver Fe²⁺ and CB-839 into the tumor. CB-839 induced-glutaminolysis inhibition not only reduces intracellular antioxidants (glutathione, taurine) to amplify Fe²⁺-induced oxidative stress, but also decreases nucleotide pools (e.g., adenosine, dihydroorotate) to incur the deficiency of building blocks for DNA damage repair, thereby promoting the cell-killing effect of CDT. In vivo assessments further confirm the enhanced anticancer performance and good biocompatibility of ZIF(Fe)&CB nanoparticles. This study provides a promising strategy for the development and improvement of ROS-based anticancer nanosystems.

Applications and Achievements of Single-Cell Sequencing in Gastrointestinal Cancer

Gastrointestinal cancer represents a public health concern that seriously endangers human health. The emerging single-cell sequencing (SCS) technologies are different from the large-scale sequencing technologies which provide inaccurate data. SCS is a powerful tool for deciphering the single-cell resolutions of cellular and molecular landscapes, revealing the features of single-cell genomes, transcriptomes, and epigenomes. Recently, SCS has been applied in the field of gastrointestinal cancer research for clarifying the origin and heterogeneity of gastrointestinal cancer, acquiring micro-environmental information, and improving diagnostic and treatment methods. This review outlines the applications of SCS in gastrointestinal cancer research and summarizes the most recent advances in the field.

Selection of Cancer Stem Cell-Targeting Agents Using Bacteriophage Display

There is a growing need to develop tumor targeting agents for aggressive cancers. Aggressive cancers frequently relapse and are resistant to various therapies. Cancer stem cells (CSCs) are believed to be the cause of relapse and the aggressive nature of many cancers. Targeting CSCs could lead to novel diagnostic and treatment options. Bacteriophage (phage) display is a powerful tool developed by George Smith in 1985 to aid in the discovery of CSC targeting agents. Phage display selections are typically performed in vitro against an immobilized target. There are inherent disadvantages with this technique that can be circumvented by performing phage display selections in vivo. However, in vivo phage display selections present new challenges. A combination of both in vitro and in vivo selections, however, can take advantage of both selection methods. In this chapter, we discuss in detail how to isolate a CSC like population of cells from an aggressive cancer cell line, perform in vivo and in vitro phage display selections against the CSCs, and then characterize the resulting phage/peptides for further use as a diagnostic and therapeutic tool.

Overview on Molecular Biomarkers for Laryngeal Cancer: Looking for New Answers to an Old Problem

Laryngeal squamous cell cancer (LSCC) accounts for almost 25-30% of all head and neck squamous cell cancers and is clustered according to the affected districts, as this determines distinct tendency to recur and metastasize. A major role for numerous genetic alterations in driving the onset and progression of this neoplasm is emerging. However, major efforts are still required for the identification of molecular markers useful for both early diagnosis and prognostic definition of LSCC that is still characterized by significant morbidity and mortality. Non-coding RNAs appear the most promising as they circulate in all the biological fluids allowing liquid biopsy determination, as well as due to their quick and characteristic modulation useful for non-invasive detection and monitoring of cancer. Other critical aspects are related to recent progress in circulating tumor cells and DNA detection, in metastatic status and chemo-refractoriness prediction, and in the functional interaction of LSCC with chronic inflammation and innate immunity. We review all these aspects taking into account the progress of the technologies in the field of next generation sequencing.

Ascites and Serial Plasma Circulating Tumor DNA for Predicting the Effectiveness of Hyperthermic Intraperitoneal Chemotherapy in Patients With Peritoneal Carcinomatosis

Purpose

We investigated the value of ascites and serial plasma circulating tumor DNA (ctDNA) for predicting response to hyperthermic intraperitoneal chemotherapy (HIPEC), monitoring tumor burden, and predicting prognosis in patients with peritoneal carcinomatosis (PC).

Experimental Design

In this observational study, 19 patients with PC were enrolled. Serial plasma ctDNA was analyzed using next-generation sequencing. The molecular tumor burden index (mTBI) was used to detect ctDNA, and concurrent changes in the dominant clone variant allele frequency (VAF) and common tumor markers were used as controls. The correlation between ascites and plasma ctDNA comutated genes was expressed by VAF. The overall response rate (complete response + partial response) after HIPEC was determined. Ascites progression-free survival (PFS) and overall survival (OS) were determined, and potential correlations between these outcomes and change in mTBI (△mTBI), change in sum-VAF (△sum-VAF), dominant close VAF, and tumor markers were assessed.

Results

The overall response rate at 1 month after HIPEC was 100%. The △mTBI (r = 0.673; P = 0.023) and △sum-VAF (r = 0.945; P &lt;0.001) were significantly positively correlated with ascites PFS; these correlations were stronger than those of the dominant clone VAF (r = 0.588; P = 0.057) and tumor markers in the same period (r =0.091; P = 0.790). Patients with a low baseline mTBI (&lt;0.67) demonstrated significantly longer ascites PFS (P = 0.003; HR = 0.157; 95% CI: 0.046–0.540) and OS (P = 0.017; HR = 0.296; 95% CI: 0.109–0.804) than those with a high baseline mTBI (≥0.67). Consistent mutations were detected in plasma and ascites (r = 0.794; P = 0.001).

Conclusion

A real-time serial plasma ctDNA assay accurately reflected tumor burden. The △mTBI and △sum-VAF can be used as predictors of HIPEC efficacy in patients with PC. A high baseline mTBI may be a negative risk factor for prognosis.

Thymoquinone: A Review of Pharmacological Importance, Oxidative Stress, COVID-19, and Radiotherapy

Widely consumed worldwide, Nigella sativa (NS) is a medicinal herb commonly used in various alternative medicine systems such as Unani and Tibb, Ayurveda, and Siddha. Recommended for regular use in Tibb-e-Nabwi (Prophetic Medicine), NS is considered one of the most notable forms of healing medicine in Islamic literature. Thymoquinone (TQ), the main component of the essential oil of NS, has been reported to have many properties such as antioxidant, anti-inflammatory, antiviral, and antineoplastic. Its chemical structure indicates antiviral potential against many viruses, including the hepatitis C virus, human immunodeficiency virus, and other coronavirus diseases. Interestingly, molecular docking studies have demonstrated that TQ can potentially inhibit the development of the coronavirus disease 2019 (COVID-19) by binding to the receptor site on the transmembrane serine proteinase 2 (the activator enzyme that attaches the virus to the cell). In addition, TQ has been shown to be effective against cancer cells due to its inhibitory effect by binding to the different regions of MDM2, according to the proposed molecular docking study. Detailed in this review is the origin of TQ, its significance in alternative medicine, pharmacological value, potential as a cancer anti-proliferative agent, use against the coronavirus disease 2019 (COVID-19), and treatment of other diseases.

Cancer driver gene and non-coding RNA alterations as biomarkers of brain metastasis in lung cancer: A review of the literature

Brain metastasis (BM) is the most common event in patients with lung cancer. Despite multimodal treatments and advances in systemic therapies, development of BM remains one of the main factors associated with poor prognosis and mortality in patients with lung cancer. Therefore, better understanding of mechanisms involved in lung cancer brain metastasis (LCBM) is of great importance to suppress cancer cells and to improve the overall survival of patients. Several cancer-related genes such as EGFR and KRAS have been proposed as potential predictors of LCBM. In addition, there is ample evidence supporting crucial roles of non-coding RNAs (ncRNAs) in mediating LCBM. In this review, we provide comprehensive information on risk assessment, predictive, and prognostic panels for early detection of BM in patients with lung cancer. Moreover, we present an overview of LCBM molecular mechanisms, cancer driver genes, and ncRNAs which may predict the risk of BM in lung cancer patients. Recent clinical studies have focused on determining mechanisms involved in LCBM and their association with diagnosis, prognosis, and treatment outcomes. These studies have shown that alterations in EGFR, KRAS, BRAF, and ALK, as the most frequent coding gene alterations, and dysregulation of ncRNAs such as miR-423, miR-330-3p, miR-145, piR-651, and MALAT1 can be considered as potential biomarkers of LCBM.

Circulating MicroRNAs in Small-bowel Neuroendocrine Tumors: A Potential Tool for Diagnosis and Assessment of Effectiveness of Surgical Resection

OBJECTIVE

To discover serum-based microRNA (miRNA) biomarkers for small-bowel neuroendocrine tumors (SBNET) to help guide clinical decisions.

BACKGROUND

MiRNAs are small noncoding RNA molecules implicated in the initiation and progression of many cancers. MiRNAs are remarkably stable in bodily fluids, and can potentially be translated into clinically useful biomarkers. Novel biomarkers are needed in SBNET to determine disease aggressiveness, select patients for treatment, detect early recurrence, and monitor response.

METHODS

This study was performed in 3 stages (discovery, validation, and a prospective, longitudinal assessment). Discovery comprised of global profiling of 376 miRNA in sera from SBNET patients (n = 11) versus healthy controls (HCs; n = 3). Up-regulated miRNAs were subsequently validated in additional SBNET (n = 33) and HC sera (n = 14); and then longitudinally after SBNET resection (n = 12), with serial serum sampling (preoperatively day 0; postoperatively at 1 week, 1 month, and 12 months).

RESULTS

Four serum miRNAs (miR-125b-5p, -362-5p, -425-5p and -500a-5p) were significantly up-regulated in SBNET (P < 0.05; fold-change >2) based on multiple normalization strategies, and were validated by RT-qPCR. This combination was able to differentiate SBNET from HC with an area under the curve of 0.951. Longitudinal assessment revealed that miR-125b-5p returned towards HC levels at 1 month postoperatively in patients without disease, whereas remaining up-regulated in those with residual disease (RSD). This was also true at 12 months postoperatively. In addition, miR-362-5p appeared up-regulated at 12 months in RSD and recurrent disease (RCD).

CONCLUSIONS

Our study represents the largest global profiling of serum miRNAs in SBNET patients, and the first to evaluate ongoing serum miRNA expression changes after surgical resection. Serum miR-125b-5p and miR-362-5p have potential to be used to detect RSD/RCD.

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.

Identification of Differential N-Glycan Compositions in the Serum and Tissue of Colon Cancer Patients by Mass Spectrometry

Simple Summary

Incidence of colorectal cancer (CRC) has been rising in Brazil. To date, no reliable biomarker has been described in CRC for diagnosis and prognosis. Modifications in the N-glycosylation profile are usually associated with many cancers, as CRC. In turn, mass spectrometry (MS)-based methods are the most accurate technology in quantification of N-glycans. Therefore, we described a unique pattern of compositions altered in serum and tissues of stages II and III colon cancer patients, identified by MALDI-TOF/MS and LC-MS technology. N-glycans were mostly found decreased in serum whilst oligomannosidic, hypogalactosylated, and tetra-antennary forms were overexpressed in tumor tissues. Total N-glycome in serum of cancer patients was different from the profile found in serum of healthy individuals. Strikingly, no correlation between tissue N-glycosylation profile and serum profile was observed in cancer patients, posing the question where these compositions are originated from.

Abstract

Colorectal cancer (CRC) ranks second as the leading cause of cancer-related deaths worldwide. N-glycosylation is one of the most common posttranslational protein modifications. Therefore, we studied the total serum N-glycome (TSNG) of 13 colon cancer patients compared to healthy controls using MALDI-TOF/MS and LC-MS. N-glycosylation of cancer tumor samples from the same cohort were further quantified using a similar methodology. In total, 23 N-glycan compositions were down-regulated in the serum of colon cancer patients, mostly galactosylated forms whilst the mannose-rich HexNAc2Hex7, the fucosylated bi-antennary glycan HexNAc4Hex5Fuc1NeuAc2, and the tetra-antennary HexNAc6Hex7NeuAc3 were up-regulated in serum. Hierarchical clustering analysis of TSNG correctly singled out 85% of the patients from controls. Albeit heterogenous, N-glycosylation of tumor samples showed overrepresented oligomannosidic, bi-antennary hypogalactosylated, and branched compositions related to normal colonic tissue, in both MALDI-TOF/MS and LC-MS analysis. Moreover, compositions found upregulated in tumor tissue were mostly uncorrelated to compositions in serum of cancer patients. Mass spectrometry-based N-glycan profiling in serum shows potential in the discrimination of patients from healthy controls. However, the compositions profile in serum showed no parallel with N-glycans in tumor microenvironment, which suggests a different origin of compositions found in serum of cancer patients.

Carbohydrate antigen-125, calcium, and hemoglobin as predictive clinical indicator for ocular metastasis in male liver cancer patients

Background Primary liver cancer (PLC) is a common type of cancer among men worldwide. Little is known regarding the relationship of liver cancer with ocular metastasis (OM). Drinking has been also reported to be related not only to the occurrence of liver cancer but also to the causes of some ocular lesions. Purpose A diagnostic standard for the levels of serum biomarkers associated with OM derived from liver cancer in men is urgently needed. Material and methods We examined the association between OM in liver cancer and its serum biomarkers. A total of 1254 male patients with liver cancer were recruited in this retrospective study between July 2002 and December 2012. We assessed the relationship between drinking preference and OM in male patients with liver cancer, and aimed to identify an independent prognostic factor or establish a quantitative indicator for OM. Results By assessing the potential indicators, carbohydrate antigen-125 (CA-125), calcium, and hemoglobin (Hb) were found to be most valuable in the diagnosis of OM in male patients with liver cancer. Conclusion CA-125, calcium, and Hb are independent risk factors of OM in patients with liver cancer who consume alcohol.

Recent advances in rolling circle amplification-based biosensing strategies-A review

Rolling circle amplification (RCA) is an efficient enzymatic isothermal reaction that using circular probe as a template to generate long tandem single-stranded DNA or RNA products under the initiation of short DNA or RNA primers. As a simplified derivative of natural rolling circle replication which synthesizes copies of circular nucleic acids molecules such as plasmids, RCA amplifies the circular template rapidly without thermal cycling and finds various applications in molecular biology. Compared with other amplification strategies, RCA has many obvious advantages. Firstly, because of the strict complementarity required in ligation of a padlock probe, it endows the RCA reaction with high specificity and can even be utilized to distinguish single base mismatches. Secondly, through the introduction of multiple primers, exponential amplification can be achieved easily and leads to a good sensitivity. Thirdly, RCA products can be customized by manipulating circular templates to generate functional nucleic acids such as aptamer, DNAzymes and restriction enzyme sites. Moreover, the RCA has good biocompatibility and is especially suitable for in situ detection. Therefore, RCA has attracted considerable attention as an efficient and potential tool for highly sensitive detection of biomarkers. Herein, we comprehensively introduce the fundamental principles of RCA technology, summarize it from three aspects including initiation mode, amplification mode and signal output mode, and discuss the recent application of RCA-based biosensor in this review.

Proteomic profiles and cytokeratin 13 as a potential biomarker of Ovis aries papillomavirus 3-positive and negative cutaneous squamous cell carcinomas

Ovis aries papillomavirus 3 (OaPV3) is an epidermotropic PV reported in sheep cutaneous squamous cell carcinoma (SCC). The presence of OaPV3 DNA and its transcriptional activity in cutaneous SCC, as well as its in vitro transforming properties, suggest a viral etiology for this neoplasm. Nevertheless, the reactome associated with viral-host interaction is still unexplored. Here, we investigated and compared the proteomic profiles of OaPV3-positive SCCs, OaPV3-negative SCCs, and non-SCC samples by liquid chromatography tandem-mass spectrometry (LC-MS/MS) analysis, bioinformatics tools, and immunohistochemistry (IHC). OaPV3-positive SCCs (n = 3), OaPV3-negative SCCs (n = 3), and non-SCCs samples (n = 3) were subjected to a shotgun proteomic analysis workflow to assess protein abundance differences among the three sample classes. Proteins involved in epithelial cell differentiation, extracellular matrix organization, and apoptotic signaling showed different abundances in OaPV3-positive SCCs tissues (P ≤ 0.05) when compared to the other tissues. Cytokeratin 13 (CK 13) was among the most increased proteins in OaPV3-positive SCC and was validated by immunohistochemistry on 10 samples per class, confirming its potential as a biomarker of OaPV3 infection in SCC. Collectively, results provide a preliminary insight into the reactome associated with viral-host interaction and pave the way to the development of specific biomarkers for viral-induced sheep SCC.

Analytical Challenges in Development of Chemoresistance Predictors for Precision Oncology

Chemoresistance, i.e., tumor insensitivity to chemotherapy, shortens life expectancy of cancer patients. Despite the availability of new treatment options, initial systemic regimens for solid tumors are dominated by a set of standard chemotherapy drugs, and alternative therapies are used only when a patient has demonstrated chemoresistance clinically. Chemoresistance predictors use laboratory parameters measured on tissue samples to predict the patient's response to chemotherapy and help to avoid application of chemotherapy to chemoresistant patients. Despite thousands of publications on putative chemoresistance predictors, there are only about a dozen predictors that are sufficiently accurate for precision oncology. One of the major reasons for inaccuracy of predictors is inaccuracy of analytical methods utilized to measure their laboratory parameters: an inaccurate method leads to an inaccurate predictor. The goal of this study was to identify analytical challenges in chemoresistance-predictor development and suggest ways to overcome them. Here we describe principles of chemoresistance predictor development via correlating a clinical parameter, which manifests disease state, with a laboratory parameter. We further classify predictors based on the nature of laboratory parameters and analyze advantages and limitations of different predictors using the reliability of analytical methods utilized for measuring laboratory parameters as a criterion. Our eventual focus is on predictors with known mechanisms of reactions involved in drug resistance (drug extrusion, drug degradation, and DNA damage repair) and using rate constants of these reactions to establish accurate and robust laboratory parameters. Many aspects and conclusions of our analysis are applicable to all types of disease biomarkers built upon the correlation of clinical and laboratory parameters.

Spheroid-Based Approach to Assess the Tissue Relevance of Analysis of Dispersed-Settled Tissue Cells by Cytometry of the Reaction Rate Constant

Cytometry of Reaction Rate Constant (CRRC) uses time-lapse fluorescence microscopy to measure a rate constant of a catalytic reaction in individual cells and, thus, facilitate accurate size determination for cell subpopulations with distinct efficiencies of this reaction. Reliable CRRC requires uniform exposure of cells to the reaction substrate followed by their uniform imaging, which in turn, requires that a tissue sample be disintegrated into a suspension of dispersed cells, and these cells settle on the support surface before being analyzed by CRRC. We call such cells "dispersed-settled" to distinguish them from cells cultured as a monolayer. Studies of the dispersed-settled cells can be tissue-relevant only if the cells maintain their 3D tissue state during the multi-hour CRRC procedure. Here, we propose an approach for assessing tissue relevance of the CRRC-based analysis of the dispersed-settled cells. Our approach utilizes cultured multicellular spheroids as a 3D cell model and cultured cell monolayers as a 2D cell model. The CRRC results of the dispersed-settled cells derived from spheroids are compared to those of spheroids and monolayers in order to find if the dispersed-settled cells are representative of the spheroids. To demonstrate its practical use, we applied this approach to a cellular reaction of multidrug resistance (MDR) transport, which was followed by extrusion of a fluorescent substrate from the cells. The approach proved to be reliable and revealed long-term maintenance of MDR transport in the dispersed-settled cells obtained from cultured ovarian cancer spheroids. Accordingly, CRRC can be used to determine accurately the size of a cell subpopulation with an elevated level of MDR transport in tumor samples, which makes CRRC a suitable method for the development of MDR-based predictors of chemoresistance. The proposed spheroid-based approach for validation of CRRC is applicable to other types of cellular reactions and, thus, will be an indispensable tool for transforming CRRC from an experimental technique into a practical analytical tool.

Cell free DNA: revolution in molecular diagnostics - the journey so far

Cell free DNA (cf-DNA) refers to all non -ncapsulated DNA present in the blood stream which may originate from apoptotic cells as a part of the physiological cell turnover, or from cancer cells or fetal cells. Recent studies have highlighted the utility of cfDNA analysis for genetic profiling of cancer, non-invasive prenatal testing besides many other clinical applications. In our review we discuss the sources of cfDNA in the body, the techniques most commonly being used for its isolation and analysis, the applications of cfDNA testing and the associated pros-cons. We conclude that for prenatal testing, cfDNA analysis provides an effective, non-invasive and safer alternative to traditional amniocentesis and chorionic villus sampling tests. Also, in cancer patients, cfDNA analysis is useful for genetic profiling and follow-up during treatment. However, standardization of methods of isolation and analysis has become crucial for the success of widespread use of cfDNA analysis.

Precision medicine for gastrointestinal cancer: Recent progress and future perspective

Gastrointestinal (GI) cancer has a high tumor incidence and mortality rate worldwide. Despite significant improvements in radiotherapy, chemotherapy, and targeted therapy for GI cancer over the last decade, GI cancer is characterized by high recurrence rates and a dismal prognosis. There is an urgent need for new diagnostic and therapeutic approaches. Recent technological advances and the accumulation of clinical data are moving toward the use of precision medicine in GI cancer. Here we review the application and status of precision medicine in GI cancer. Analyses of liquid biopsy specimens provide comprehensive real-time data of the tumor-associated changes in an individual GI cancer patient with malignancy. With the introduction of gene panels including next-generation sequencing, it has become possible to identify a variety of mutations and genetic biomarkers in GI cancer. Although the genomic aberration of GI cancer is apparently less actionable compared to other solid tumors, novel informative analyses derived from comprehensive gene profiling may lead to the discovery of precise molecular targeted drugs. These progressions will make it feasible to incorporate clinical, genome-based, and phenotype-based diagnostic and therapeutic approaches and apply them to individual GI cancer patients for precision medicine.

Ligand Engineering via Yeast Surface Display and Adherent Cell Panning

High-throughput ligand discovery and evolution-via genotype-phenotype linkage strategies-empower molecularly targeted therapy, diagnostics, and fundamental science. Maintaining high-quality target antigen in these selections, particularly for membrane targets, is often a technical challenge. Panning yeast-displayed ligand libraries on intact mammalian cells expressing the molecular target has emerged as an effective strategy. Herein we describe the techniques used to select target-binding ligands via this approach including the use of target-negative cells to deplete non-specific binders and avidity reduction to preferentially select high-affinity ligands.

The value of miR-155 as a biomarker for the diagnosis and prognosis of lung cancer: a systematic review with meta-analysis

BACKGROUND

Recently, a growing number of studies have reported the coorelation between miR-155 and the diagnosis and prognosis of lung cancer, but results of these researches were still controversial due to insufficient sample size. Thus, we carried out the systematic review and meta-analysis to figure out whether miR-155 could be a screening tool in the detection and prognosis of lung cancer.

METHODS

A meta-analysis of 13 articles with 19 studies was performed by retrieving the PubMed, Embase and Web of Science. We screened all correlated literaters until December 1st, 2018. For the diagnosis analysis of miR-155 in lung cancer, sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR) and area under the ROC curve (AUC) were pooled to evaluate the accuracy of miRNA-155 in the diagnosis of lung cancer. For the prognosis analysis of miR-155 in lung cancer, the pooled HRs and 95% CIs of miR-155 for overall survival/disease free survival/progression-free survival (OS/DFS/PFS) were calculated. In addition, Subgroup and meta-regression analyses were performed to distinguish the potential sources of heterogeneity between studies.

RESULTS

For the diagnostic analysis of miR-155 in lung cancer, the pooled SEN and SPE were 0.82 (95% CI: 0.72-0.88) and 0.78 (95% CI: 0.71-0.84), respectively. Besides, the pooled PLR was 3.75 (95% CI: 2.76-5.10), NLR was 0.23 (95% CI: 0.15-0.37), DOR was 15.99 (95% CI: 8.11-31.52) and AUC was 0.87 (95% CI: 0.84-0.90), indicating a significant value of miR-155 in the lung cancer detection. For the prognostic analysis of miR-155 in lung cancer, up-regulated miRNA-155 expression was not significantly associated with a poor OS (pooled HR = 1.26, 95% CI: 0.66-2.40) or DFS/PFS (pooled HR = 1.28, 95% CI: 0.82-1.97).

CONCLUSIONS

The present meta-analysis demonstrated that miR-155 could be a potential biomarker for the detection of lung cancer but not an effective biomarker for predicting the outcomes of lung cancer. Furthermore, more well-designed researches with larger cohorts were warranted to confirm the value of miR-155 for the diagnosis and prognosis of lung cancer.

Cancer stem cells: A review from origin to therapeutic implications

Cancer stem cells (CSCs), also known as tumor-initiating cells (TICs), are elucidated as cells that can perpetuate themselves via autorestoration. These cells are highly resistant to current therapeutic approaches and are the main reason for cancer recurrence. Radiotherapy has made a lot of contributions to cancer treatment. However, despite continuous achievements, therapy resistance and tumor recurrence are still prevalent in most patients. This resistance might be partly related to the existence of CSCs. In the present study, recent advances in the investigation of different biological properties of CSCs, such as their origin, markers, characteristics, and targeting have been reviewed. We have also focused our discussion on radioresistance and adaptive responses of CSCs and their related extrinsic and intrinsic influential factors. In summary, we suggest CSCs as the prime therapeutic target for cancer treatment.

Prognostic value of aspartate transaminase to alanine transaminase (De Ritis) ratio in solid tumors: a pooled analysis of 9,400 patients

BACKGROUND

Numerous studies have reported the association between pretreatment serum aspartate transaminase to alanine transaminase (AST/ALT) ratio and prognosis in multiple cancers. However, the results remain controversial and no consensus has been reached. Thus, we conducted this meta-analysis to quantitatively assess the prognostic value of pretreatment AST/ALT ratio in solid tumors.

METHODS

A systematic literature search was conducted by using PubMed, EMBASE, Web of Science, Cochrane Library, and Wanfang databases, as well as several trial registry platforms, including ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, and Chinese Clinical Trial Registry, up to April 5, 2019. HR and 95% CI for overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS) were calculated to estimate the effect size.

RESULTS

A total of 18 studies with 9,400 patients were included. Overall, a high level of pretreatment AST/ALT ratio was significantly associated with worse OS (pooled HR=1.70, 95% CI=1.38-2.09). The statistical significance was observed in all cancer types, including renal cell carcinoma (pooled HR=1.64, 95% CI=1.30-2.05), liver cancer (pooled HR=1.16, 95% CI=1.04-1.29), urinary tract urothelial carcinoma (pooled HR=1.96, 95% CI=1.53-2.51), bladder cancer (pooled HR =2.66, 95% CI=1.69-4.20), and other cancers (pooled HR=1.44, 95% CI=1.18-1.76). Moreover, an increased level of serum AST/ALT ratio predicted unfavorable CSS (pooled HR=2.07, 95% CI=1.74-2.46) and RFS (pooled HR=1.51, 95% CI=1.15-1.99).

CONCLUSION

Elevated level of serum AST/ALT ratio before treatment is significantly associated with poor clinical outcomes of OS, CSS, and RFS in patients with solid tumors. Pretreatment AST/ALT ratio can serve as a useful prognostic predictor for malignant patients.

Design of a liver cancer-specific selector for the analysis of circulating tumor DNA

Circulating tumor DNA (ctDNA) has been frequently investigated to monitor tumor dynamics and measure tumor burden. This non-invasive method concerning ctDNA has been recognized as a promising biomarker. Recently, next generation sequencing has been used in ctDNA detection by researchers. However, those reports have been limited by modest sensitivity, and only a minority of patients with cancer were applicable. Additionally, a limited number of cases of liver cancer have been analyzed. A more precise method is required to be established to evaluate ctDNA noninvasively. In the present study, a novel method to design a liver cancer-associated chip region (spanning 211 kb, containing 159 genes) was performed with high specificity using International Cancer Genome Consortium datasets. Following evaluation with datasets from The Cancer Genome Atlas and data from 3 patients with liver cancer, the selected regions were demonstrated to be beneficial to locate specific somatic mutations associated with liver cancer therapy and to monitor cancer dynamics in the plasma samples of the patients. In addition to establishing performance benchmarks supporting direct clinical use, the chip designed and the high-resolution sequencing analyses pipeline would allow the development a set of patient specific markers that could monitor the process of cancer with high accuracy and low cost. Furthermore, the present study is essential to understanding the dynamics and providing insight into the basic mechanisms of liver cancer.

Prognostic and Predictive Immunohistochemistry-Based Biomarkers in Cancer and Immunotherapy

Immunotherapy has drastically improved the prognosis of many patients with cancer, but it can also lead to severe immune-related adverse events. Biomarkers, which are molecular markers that indicate a patient's disease outcome or a patient's response to treatment, are therefore crucial to helping clinicians weigh the potential benefits of immunotherapy against its potential toxicities. Immunohistochemistry (IHC) has thus far been a powerful technique for discovery and use of biomarkers such as CD8+ tumor-infiltrating lymphocytes. However, IHC has limited reproducibility. Thus, if more IHC-based biomarkers are to reach the clinic, refinement of the technique using multiplexing or automation is key.

Cancer protein biomarker discovery based on nucleic acid aptamers

Identification of biomarkers is essential for diagnosis, targeted therapy and prognosis evaluation of diseases, especially cancers. Currently, the number of ideal clinical biomarkers is still limited partially because of lacking efficient methods in biomarker discovery. Nucleic acid aptamers are artificial single-stranded DNA or RNA sequences that can selectively bind to various targets with high specificity and affinity. Moreover, aptamers possess desirable advantages, including easy synthesis, convenient modification, relative chemical stability and low immunogenicity. Recently, different aptamer-based strategies have been developed to facilitate the discovery of biomarkers. Based on cell-SELEX technology, the selected aptamers can be used to identify cell-surface protein biomarkers of different cancer cells. SOMAscan can analyze thousands of proteins of different biological samples, which becomes a multiplexed protein biomarker discovery platform. Additionally, secreted protein biomarkers can be discovered by aptamers screened through secretome SELEX. In order to facilitate the identification of target proteins, several covalent cross-linking strategies have been developed, such as aptamer-based affinity labeling (ABAL), DNA-templated aptamer and protein-aptamer template (PAT). In this review, we mainly highlight the emerging nucleic acid aptamer-based biomarker discovery strategies and demonstrate their unique technological advantages in discovering cancer biomarkers. The challenges and perspectives of aptamer-based methods are also discussed.

Cellular-Based Selections Aid Yeast-Display Discovery of Genuine Cell-Binding Ligands: Targeting Oncology Vascular Biomarker CD276

Yeast surface display is a proven tool for the selection and evolution of ligands with novel binding activity. Selections from yeast surface display libraries against transmembrane targets are generally carried out using recombinant soluble extracellular domains. Unfortunately, these molecules may not be good models of their true, membrane-bound form for a variety of reasons. Such selection campaigns often yield ligands that bind a recombinant target but not target-expressing cells or tissues. Advances in cell-based selections with yeast surface display may aid the frequency of evolving ligands that do bind true, membrane-bound antigens. This study aims to evaluate ligand selection strategies using both soluble target-driven and cellular selection techniques to determine which methods yield translatable ligands most efficiently and generate novel binders against CD276 (B7-H3) and Thy1, two promising tumor vasculature targets. Out of four ligand selection campaigns carried out using only soluble extracellular domains, only an affibody library sorted against CD276 yielded translatable binders. In contrast, fibronectin domains against CD276 and affibodies against CD276 were discovered in campaigns that either combined soluble target and cellular selection methods or used cellular selection methods alone. A high frequency of non target-specific ligands discovered from the use of cellular selection methods alone motivated the development of a depletion scheme using disadhered, antigen-negative mammalian cells as a blocking agent. Affinity maturation of CD276-binding affibodies by error-prone PCR and helix walking resulted in strong, specific cellular CD276 affinity ( Kd = 0.9 ± 0.6 nM). Collectively, these results motivate the use of cellular selections in tandem with recombinant selections and introduce promising affibody molecules specific to CD276 for further applications.

Metabolomic diagnostics and human digital image

The existing clinical laboratory practice has limitations in terms of specificity and sensitivity of diagnosis, making the introduction of new methods in medicine more topical. Application of 'omics' technologies, especially metabolomics, allows overcoming these limitations. The composition of blood metabolites reflects the physical state of an organism at the molecular level. The analysis of blood metabolome can serve as effective means of diagnosis, implementation of which in healthcare is timely and relevant. This paper demonstrates the versatility of metabolomic diagnostics, its applicability to various diseases. We discussed the standard of human digital image, which includes the metabolomic data sufficient to make an accurate assessment of general health and carry out precision diagnostics of a wide range of diseases.

Transcriptomic analysis reveals key lncRNAs associated with ribosomal biogenesis and epidermis differentiation in head and neck squamous cell carcinoma

OBJECTIVE

In this study, we aimed to expand current knowledge of head and neck squamous cell carcinoma (HNSCC)-associated long noncoding RNAs (lncRNAs), and to discover potential lncRNA prognostic biomarkers for HNSCC based on next-generation RNA-seq.

METHODS

RNA-seq data of 546 samples from patients with HNSCC were downloaded from The Cancer Genome Atlas (TCGA), including 43 paired samples of tumor tissue and adjacent normal tissue. An integrated analysis incorporating differential expression, weighted gene co-expression networks, functional enrichment, clinical parameters, and survival analysis was conducted to discover HNSCC-associated lncRNAs. The function of CYTOR was verified by cell-based experiments. To further identify lncRNAs with prognostic significance, a multivariate Cox proportional hazard regression analysis was performed. The identified lncRNAs were validated with an independent cohort using clinical feature relevance analysis and multivariate Cox regression analysis.

RESULTS

We identified nine HNSCC-relevant lncRNAs likely to play pivotal roles in HNSCC onset and development. By functional enrichment analysis, we revealed that CYTOR might participate in the multistep pathological processes of cancer, such as ribosome biogenesis and maintenance of genomic stability. CYTOR was identified to be positively correlated with lymph node metastasis, and significantly negatively correlated with overall survival (OS) and disease free survival (DFS) of HNSCC patients. Moreover, CYTOR inhibited cell apoptosis following treatment with the chemotherapeutic drug diamminedichloroplatinum (DDP). HCG22, the most dramatically down-regulated lncRNA in tumor tissue, may function in epidermis differentiation. It was also significantly associated with several clinical features of patients with HNSCC, and positively correlated with patient survival. CYTOR and HCG22 maintained their prognostic values independent of several clinical features in multivariate Cox hazards analysis. Notably, validation either based on an independent HNSCC cohort or by laboratory experiments confirmed these findings.

CONCLUSIONS

Our transcriptomic analysis suggested that dysregulation of these HNSCC-associated lncRNAs might be involved in HNSCC oncogenesis and progression. Moreover, CYTOR and HCG22 were confirmed as two independent prognostic factors for HNSCC patient survival, providing new insights into the roles of these lncRNAs in HNSCC as well as clinical applications.

Use case driven evaluation of open databases for pediatric cancer research

BACKGROUND

A plethora of Web resources are available offering information on clinical, pre-clinical, genomic and theoretical aspects of cancer, including not only the comprehensive cancer projects as ICGC and TCGA, but also less-known and more specialized projects on pediatric diseases such as PCGP. However, in case of data on childhood cancer there is very little information openly available. Several web-based resources and tools offer general biomedical data which are not purpose-built, for neither pediatric nor cancer analysis. Additionally, many Web resources on cancer focus on incidence data and statistical social characteristics as well as self-regulating communities.

METHODS

We summarize those resources which are open and are considered to support scientific fundamental research, while we address our comparison to 11 identified pediatric cancer-specific resources (5 tools, 6 databases). The evaluation consists of 5 use cases on the example of brain tumor research and covers user-defined search scenarios as well as data mining tasks, also examining interactive visual analysis features.

RESULTS

Web resources differ in terms of information quantity and presentation. Pedican lists an abundance of entries with few selection features. PeCan and PedcBioPortal include visual analysis tools while the latter integrates published and new consortia-based data. UCSC Xena Browser offers an in-depth analysis of genomic data. ICGC data portal provides various features for data analysis and an option to submit own data. Its focus lies on adult Pan-Cancer projects. Pediatric Pan-Cancer datasets are being integrated into PeCan and PedcBioPortal. Comparing information on prominent mutations within glioma discloses well-known, unknown, possible, as well as inapplicable biomarkers. This summary further emphasizes the varying data allocation. Tested tools show advantages and disadvantages, depending on the respective use case scenario, providing inhomogeneous data quantity and information specifics.

CONCLUSIONS

Web resources on specific pediatric cancers are less abundant and less-known compared to those offering adult cancer research data. Meanwhile, current efforts of ongoing pediatric data collection and Pan-Cancer projects indicate future opportunities for childhood cancer research, that is greatly needed for both fundamental as well as clinical research.

Systematic Glycolytic Enzyme Activity Analysis from Human Serum with PEP Technology

A functional proteomics technology was used to systematically monitor metabolic enzyme activities from resolved serum proteins produced by a modified 2-D gel separation and subsequent Protein Elution Plate, a method collectively called PEP. Both qualitative and quantitative differences in the metabolic enzyme activity could be detected between cancer patient and control group, providing excellent biomarker candidates for cancer diagnosis and drug development. This technology has a wide range of applications; it can be used for rapid functional protein purification and characterization as well as drug target identification and validation. The ability for the PEP technology to efficiently separate and recover functional proteins makes it useful for the analysis of any proteins and its variants; this is especially advantageous for enzyme families with large number of enzymes such as protein kinases, phosphatases, proteases, and metabolic enzymes.

Machine learning algorithms enhance the specificity of cancer biomarker detection using SERS-based immunoassays in microfluidic chips

Specificity is a challenge in liquid biopsy and early diagnosis of various diseases. There are only a few biomarkers that have been approved for use in cancer diagnostics; however, these biomarkers suffer from a lack of high specificity. Moreover, determining the exact type of disorder for patients with positive liquid biopsy tests is difficult, especially when the aberrant expression of one single biomarker can be found in various other disorders. In this study, a SERS-based protein biomarker detection platform in a microfluidic chip and two machine learning algorithms (K-nearest neighbor and classification tree) are used to improve the reproducibility and specificity of the SERS-based liquid biopsy assay. Applying machine learning algorithms to the analysis of the expression level data of 5 protein biomarkers (CA19-9, HE4, MUC4, MMP7, and mesothelin) in pancreatic cancer patients, ovarian cancer patients, pancreatitis patients, and healthy individuals improves the chance of recognition for one specific disorder among the aforementioned diseases with overlapping protein biomarker changes. Our results demonstrate a convenient but highly specific approach for cancer diagnostics using serum samples.

Machine learning algorithms enhance the specificity of cancer biomarkers detection using SERS-based immunoassays.

Blood-Based Cancer Biomarkers in Liquid Biopsy: A Promising Non-Invasive Alternative to Tissue Biopsy

Cancer is one of the greatest threats facing our society, being the second leading cause of death globally. Currents strategies for cancer diagnosis consist of the extraction of a solid tissue from the affected area. This sample enables the study of specific biomarkers and the genetic nature of the tumor. However, the tissue extraction is risky and painful for the patient and in some cases is unavailable in inaccessible tumors. Moreover, a solid biopsy is expensive and time consuming and cannot be applied repeatedly. New alternatives that overcome these drawbacks are rising up nowadays, such as liquid biopsy. A liquid biopsy is the analysis of biomarkers in a non-solid biological tissue, mainly blood, which has remarkable advantages over the traditional method; it has no risk, it is non-invasive and painless, it does not require surgery and reduces cost and diagnosis time. The most studied cancer non-invasive biomarkers are circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and exosomes. These circulating biomarkers play a key role in the understanding of metastasis and tumorigenesis, which could provide a better insight into the evolution of the tumor dynamics during treatment and disease progression. Improvements in isolation technologies, based on a higher grade of purification of CTCs, exosomes, and ctDNA, will provide a better characterization of biomarkers and give rise to a wide range of clinical applications, such as early detection of diseases, and the prediction of treatment responses due to the discovery of personalized tumor-related biomarkers.

Nanosecond Electric Pulses Induce Early and Late Phases of DNA Damage and Cell Death in Cisplatin-Resistant Human Ovarian Cancer Cells

Chemoresistance is a challenge for management of ovarian cancer, and therefore the response of resistant cells to nanosecond electric pulses (nsEP) was explored. Human ovarian cancer cell line COC1 and the cisplatin-resistant subline COC1/DDP were subjected to nsEP (32 ns, 10 kV/cm, 10 Hz pulse repletion frequency, and 10 min exposure duration), and then the cellular responses were followed. The percentages of dead cells and of comet-formed cells in the alkaline assay displayed two peak levels (i.e., 2 and 8 h after nsEP exposure), with the highest value noted at 8 h; the percentage of comet-formed cells in the neutral assay was increased at 8 h; the apoptotic percentage was increased at 8 h, with collapse of the mitochondrial membrane potential and the activation of caspase-3 and caspase-9. The comet assay demonstrated DNA single-strand break at 2 h and double-strand break at 8 h. nsEP resulted in lower cytotoxicity in COC1/DDP cells compared with COC1 cells. These findings indicated that nsEP induced early and late phases of DNA damage and cell death, and these two types of cell death may have distinct applications to treatments of chemoresistant ovarian cancers.

Big Data Approaches for Modeling Response and Resistance to Cancer Drugs

Despite significant progress in cancer research, current standard-of-care drugs fail to cure many types of cancers. Hence, there is an urgent need to identify better predictive biomarkers and treatment regimes. Conventionally, insights from hypothesis-driven studies are the primary force for cancer biology and therapeutic discoveries. Recently, the rapid growth of big data resources, catalyzed by breakthroughs in high-throughput technologies, has resulted in a paradigm shift in cancer therapeutic research. The combination of computational methods and genomics data has led to several successful clinical applications. In this review, we focus on recent advances in data-driven methods to model anticancer drug efficacy, and we present the challenges and opportunities for data science in cancer therapeutic research.

Serum sialylation changes in cancer

Cancer is a major cause of death in both developing and developed countries. Early detection and efficient therapy can greatly enhance survival. Aberrant glycosylation has been recognized to be one of the hallmarks of cancer as glycans participate in many cancer-associated events. Cancer-associated glycosylation changes often involve sialic acids which play important roles in cell-cell interaction, recognition and immunological response. This review aims at giving a comprehensive overview of the literature on changes of sialylation in serum of cancer patients. Furthermore, the methods available to measure serum and plasma sialic acids as well as possible underlying biochemical mechanisms involved in the serum sialylation changes are surveyed. In general, total serum sialylation levels appear to be increased with various malignancies and show a potential for clinical applications, especially for disease monitoring and prognosis. In addition to overall sialic acid levels and the amount of sialic acid per total protein, glycoprofiling of specific cancer-associated glycoproteins, acute phase proteins and immunoglobulins in serum as well as the measurements of sialylation-related enzymes such as sialidases and sialyltransferases have been reported for early detection of cancer, assessing cancer progression and improving prognosis of cancer patients. Moreover, sialic-acid containing glycan antigens such as CA19-9, sialyl Lewis X and sialyl Tn on serum proteins have also displayed their value in cancer diagnosis and management whereby increased levels of these factors positively correlated with metastasis or poor prognosis.

The clinical role of microRNA-21 as a promising biomarker in the diagnosis and prognosis of colorectal cancer: a systematic review and meta-analysis

This systematic analysis aimed to investigate the value of microRNA-21 (miR-21) in colorectal cancer for multiple purposes, including diagnosis and prognosis, as well as its predictive power in combination biomarkers. Fifty-seven eligible studies were included in our meta-analysis, including 25 studies for diagnostic meta-analysis and 32 for prognostic meta-analysis. For the diagnostic meta-analysis of miR-21 alone, the overall pooled results for sensitivity, specificity, and area under the curve (AUC) were 0.64 (95% CI: 0.53-0.74), 0.85 (0.79-0.90), and 0.85 (0.81-0.87), respectively. Circulating samples presented corresponding values of 0.72 (0.63-0.79), 0.84 (0.78-0.89), and 0.86 (0.83-0.89), respectively. For the diagnostic meta-analysis of miR-21-related combination biomarkers, the above three parameters were 0.79 (0.69-0.86), 0.79 (0.68-0.87), and 0.86 (0.83-0.89), respectively. Notably, subgroup analysis suggested that miRNA combination markers in circulation exhibited high predictive power, with sensitivity of 0.85 (0.70-0.93), specificity of 0.86 (0.77-0.92), and AUC of 0.92 (0.89-0.94). For the prognostic meta-analysis, patients with higher expression of miR-21 had significant shorter disease-free survival [DFS; pooled hazard ratio (HR): 1.60; 95% CI: 1.20-2.15] and overall survival (OS; 1.54; 1.27-1.86). The combined HR in tissues for DFS and OS were 1.76 (1.31-2.36) and 1.58 (1.30-1.93), respectively. Our comprehensive systematic review revealed that circulating miR-21 may be suitable as a diagnostic biomarker, while tissue miR-21 could be a prognostic marker for colorectal cancer. In addition, miRNA combination biomarkers may provide a new approach for clinical application.

Genome-Scale Signatures of Gene Interaction from Compound Screens Predict Clinical Efficacy of Targeted Cancer Therapies

Identifying reliable drug response biomarkers is a significant challenge in cancer research. We present computational analysis of resistance (CARE), a computational method focused on targeted therapies, to infer genome-wide transcriptomic signatures of drug efficacy from cell line compound screens. CARE outputs genome-scale scores to measure how the drug target gene interacts with other genes to affect the inhibitor efficacy in the compound screens. Such statistical interactions between drug targets and other genes were not considered in previous studies but are critical in identifying predictive biomarkers. When evaluated using transcriptome data from clinical studies, CARE can predict the therapy outcome better than signatures from other computational methods and genomics experiments. Moreover, the CARE signatures for the PLX4720 BRAF inhibitor are associated with an anti-programmed death 1 clinical response, suggesting a common efficacy signature between a targeted therapy and immunotherapy. When searching for genes related to lapatinib resistance, CARE identified PRKD3 as the top candidate. PRKD3 inhibition, by both small interfering RNA and compounds, significantly sensitized breast cancer cells to lapatinib. Thus, CARE should enable large-scale inference of response biomarkers and drug combinations for targeted therapies using compound screen data.

Circulating tumor DNA: a promising biomarker in the liquid biopsy of cancer

Tissue biopsy is the standard diagnostic procedure for cancers and also provides a material for genotyping, which can assist in the targeted therapies of cancers. However, tissue biopsy-based cancer diagnostic procedures have limitations in their assessment of cancer development, prognosis and genotyping, due to tumor heterogeneity and evolution. Circulating tumor DNA (ctDNA) is single- or double-stranded DNA released by the tumor cells into the blood and it thus harbors the mutations of the original tumor. In recent years, liquid biopsy based on ctDNA analysis has shed a new light on the molecular diagnosis and monitoring of cancer. Studies found that the screening of genetic mutations using ctDNA is highly sensitive and specific, suggesting that ctDNA analysis may significantly improve current systems of tumor diagnosis, even facilitating early-stage detection. Moreover, ctDNA analysis is capable of accurately determining the tumor progression, prognosis and assisting in targeted therapy. Therefore, using ctDNA as a liquid biopsy may herald a revolution for tumor management. Herein, we review the biology of ctDNA, its detection methods and potential applications in tumor diagnosis, treatment and prognosis.

Epigenetic biomarkers: Current strategies and future challenges for their use in the clinical laboratory

Epigenetic modifications and regulators represent potential molecular elements which control relevant physiological and pathological features, thereby contributing to the natural history of human disease. These epigenetic modulators can be employed as disease biomarkers, since they show several advantages and provide information about gene function, thus explaining differences among patient endophenotypes. In addition, epigenetic biomarkers can incorporate information regarding the effects of the environment and lifestyle on health and disease, and monitor the effect of applied therapies. Technologies used to analyze these epigenetic biomarkers are constantly improving, becoming much easier to use. Laboratory professionals can easily acquire experience and techniques are becoming more affordable. A high number of epigenetic biomarker candidates are being continuously proposed, making now the moment to adopt epigenetics in the clinical laboratory and convert epigenetic marks into reliable biomarkers. In this review, we describe some current promising epigenetic biomarkers and technologies being applied in clinical practice. Furthermore, we will discuss some laboratory strategies and kits to accelerate the adoption of epigenetic biomarkers into clinical routine. The likelihood is that over time, better markers will be identified and will likely be incorporated into future multi-target assays that might help to optimize its application in a clinical laboratory. This will improve cost-effectiveness, and consequently encourage the development of theragnosis and the application of precision medicine.

Mass spectrometry-assisted gel-based proteomics in cancer biomarker discovery: approaches and application

There is a critical need for the discovery of novel biomarkers for early detection and targeted therapy of cancer, a major cause of deaths worldwide. In this respect, proteomic technologies, such as mass spectrometry (MS), enable the identification of pathologically significant proteins in various types of samples. MS is capable of high-throughput profiling of complex biological samples including blood, tissues, urine, milk, and cells. MS-assisted proteomics has contributed to the development of cancer biomarkers that may form the foundation for new clinical tests. It can also aid in elucidating the molecular mechanisms underlying cancer. In this review, we discuss MS principles and instrumentation as well as approaches in MS-based proteomics, which have been employed in the development of potential biomarkers. Furthermore, the challenges in validation of MS biomarkers for their use in clinical practice are also reviewed.