Integrating high-throughput technologies in the quest for effective biomarkers for ovarian cancer

Recent Progress in Prompt Molecular Detection of Exosomes Using CRISPR/Cas and Microfluidic-Assisted Approaches Toward Smart Cancer Diagnosis and Analysis

Exosomes are essential indicators of molecular mechanisms involved in interacting with cancer cells and the tumor environment. As nanostructures based on lipids and nucleic acids, exosomes provide a communication pathway for information transfer by transporting biomolecules from the target cell to other cells. Importantly, these extracellular vesicles are released into the bloodstream by the most invasive cells, i. e., cancer cells; in this way, they could be considered a promising specific biomarker for cancer diagnosis. In this matter, CRISPR-Cas systems and microfluidic approaches could be considered practical tools for cancer diagnosis and understanding cancer biology. CRISPR-Cas systems, as a genome editing approach, provide a way to inactivate or even remove a target gene from the cell without affecting intracellular mechanisms. These practical systems provide vital information about the factors involved in cancer development that could lead to more effective cancer treatment. Meanwhile, microfluidic approaches can also significantly benefit cancer research due to their proper sensitivity, high throughput, low material consumption, low cost, and advanced spatial and temporal control. Thereby, employing CRISPR-Cas- and microfluidics-based approaches toward exosome monitoring could be considered a valuable source of information for cancer therapy and diagnosis. This review assesses the recent progress in these promising diagnosis approaches toward accurate cancer therapy and in-depth study of cancer cell behavior.

Artificial intelligence for neurodegenerative experimental models

INTRODUCTION

Experimental models are essential tools in neurodegenerative disease research. However, the translation of insights and drugs discovered in model systems has proven immensely challenging, marred by high failure rates in human clinical trials.

METHODS

Here we review the application of artificial intelligence (AI) and machine learning (ML) in experimental medicine for dementia research.

RESULTS

Considering the specific challenges of reproducibility and translation between other species or model systems and human biology in preclinical dementia research, we highlight best practices and resources that can be leveraged to quantify and evaluate translatability. We then evaluate how AI and ML approaches could be applied to enhance both cross-model reproducibility and translation to human biology, while sustaining biological interpretability.

DISCUSSION

AI and ML approaches in experimental medicine remain in their infancy. However, they have great potential to strengthen preclinical research and translation if based upon adequate, robust, and reproducible experimental data.

HIGHLIGHTS

There are increasing applications of AI in experimental medicine. We identified issues in reproducibility, cross-species translation, and data curation in the field. Our review highlights data resources and AI approaches as solutions. Multi-omics analysis with AI offers exciting future possibilities in drug discovery.

CellGO: a novel deep learning-based framework and webserver for cell-type-specific gene function interpretation

Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to consider the critical biological context, such as tissue or cell-type specificity. To address this limitation, we introduced CellGO. CellGO tackles this challenge by leveraging the visible neural network (VNN) and single-cell gene expressions to mimic cell-type-specific signaling propagation along the Gene Ontology tree within a cell. This design enables a novel scoring system to calculate the cell-type-specific gene-pathway paired active scores, based on which, CellGO is able to identify cell-type-specific active pathways associated with single genes. In addition, by aggregating the activities of single genes, CellGO extends its capability to identify cell-type-specific active pathways for a given gene set. To enhance biological interpretation, CellGO offers additional features, including the identification of significantly active cell types and driver genes and community analysis of pathways. To validate its performance, CellGO was assessed using a gene set comprising mixed cell-type markers, confirming its ability to discern active pathways across distinct cell types. Subsequent benchmarking analyses demonstrated CellGO's superiority in effectively identifying cell types and their corresponding cell-type-specific pathways affected by gene knockouts, using either single genes or sets of genes differentially expressed between knockout and control samples. Moreover, CellGO demonstrated its ability to infer cell-type-specific pathogenesis for disease risk genes. Accessible as a Python package, CellGO also provides a user-friendly web interface, making it a versatile and accessible tool for researchers in the field.

Mutations in circulating tumor DNA detected in the postoperative period predict poor survival in patients with ovarian cancer

BACKGROUND

We investigated whether mutations in plasma circulating tumor DNA (ctDNA) can provide prognostic insight in patients with different histological types of ovarian carcinoma. We also examined the concordance of mutations detected in ctDNA samples with those identified in the corresponding formalin-fixed paraffin-embedded (FFPE) tumor specimens.

METHODS

Between July 2016 and December 2017, 29 patients with ovarian carcinoma were prospectively enrolled. FFPE tumor specimens were obtained from all participants. A total of 187 blood samples for ctDNA analysis were collected before surgery (C0), immediate after surgery before adjuvant chemotherapy (C1), and at six-month intervals. Progression-free survival (PFS) and overall survival (OS) served as the main outcome measures.

RESULTS

The study cohort consisted of 13 (44.8%) patients with high-grade serous carcinomas (HGSC), 9 (31.0%) with clear cell carcinoma, 2 (6.9%) with mucinous carcinomas, 4 (13.8%) with low-grade serous carcinomas, and 1 (3.4%) with endometrioid carcinoma. Twenty-four (82.8%) patients had at least one detectable ctDNA variant. The concordance rate between mutations identified in pretreatment ctDNA and corresponding FFPE tumor specimens was 92.3% for patients with HGSC and 58.6% for the entire cohort. The median follow-up time was 33.15 months (range: 0.79-46.13 months). Patients with an advanced stage disease more likely had detectable ctDNA mutations before surgery (C0) and after surgery at C1, while those with HGSC more likely had ctDNA mutations detected before surgery. The presence of ctDNA mutations at C1 was an independent predictor of worse OS with a hazard ratio of 6.56 (95% confidence interval, (1.07-40.17) for detectable versus undetectable C1 ctDNA variants, p = 0.042).

CONCLUSIONS

ctDNA mutations are common in patients with ovarian carcinoma. The presence of ctDNA mutations after surgery was an independent predictor of less favorable PFS and OS.

Mass Spectrometry-based Proteomics of Epithelial Ovarian Cancers: a Clinical Perspective

Increasing proteomic studies focused on epithelial ovarian cancer (EOC) have attempted to identify early disease biomarkers, establish molecular stratification, and discover novel druggable targets. Here we review these recent studies from a clinical perspective. Multiple blood proteins have been used clinically as diagnostic markers. The ROMA test integrates CA125 and HE4, while the OVA1 and OVA2 tests analyze multiple proteins identified by proteomics. Targeted proteomics has been widely used to identify and validate potential diagnostic biomarkers in EOCs, but none has yet been approved for clinical adoption. Discovery proteomic characterization of bulk EOC tissue specimens has uncovered a large number of dysregulated proteins, proposed new stratification schemes, and revealed novel targets of therapeutic potential. A major hurdle facing clinical translation of these stratification schemes based on bulk proteomic profiling is intra-tumor heterogeneity, namely that single tumor specimens may harbor molecular features of multiple subtypes. We reviewed over 2500 interventional clinical trials of ovarian cancers since 1990, and cataloged 22 types of interventions adopted in these trials. Among 1418 clinical trials which have been completed or are not recruiting new patients, about 50% investigated chemotherapies. Thirty-seven clinical trials are at phase 3 or 4, of which 12 focus on PARP, 10 on VEGFR, 9 on conventional anti-cancer agents, and the remaining on sex hormones, MEK1/2, PD-L1, ERBB, and FRα. Although none of the foregoing therapeutic targets were discovered by proteomics, newer targets discovered by proteomics, including HSP90 and cancer/testis antigens, are being tested also in clinical trials. To accelerate the translation of proteomic findings to clinical practice, future studies need to be designed and executed to the stringent standards of practice-changing clinical trials. We anticipate that the rapidly evolving technology of spatial and single-cell proteomics will deconvolute the intra-tumor heterogeneity of EOCs, further facilitating their precise stratification and superior treatment outcomes.

Mucin 13 (MUC13) as a candidate biomarker for ovarian cancer detection: potential to complement CA125 in detecting non-serous subtypes

OBJECTIVES

Ovarian cancer is the most lethal gynecological malignancy in developed countries. One of the key associations with the high mortality rate is diagnosis at late stages. This clinical limitation is primarily due to a lack of distinct symptoms and detection at the early stages. The ovarian cancer biomarker, CA125, is mainly effective for identifying serous ovarian carcinomas, leaving a gap in non-serous ovarian cancer detection. Mucin 13 (MUC13) is a transmembrane, glycosylated protein with aberrant expression in malignancies, including ovarian cancer. We explored the potential of MUC13 to complement CA125 as an ovarian cancer biomarker, by evaluating its ability to discriminate serous and non-serous subtypes of ovarian cancer at FIGO stages I-IV from benign conditions.

METHODS

We used our newly developed, high sensitivity ELISA to measure MUC13 protein in a large, well-defined cohort of 389 serum samples from patients with ovarian cancer and benign conditions.

RESULTS

MUC13 and CA125 serum levels were elevated in malignant compared to benign cases (p<0.0001). Receiver-operating characteristic (ROC) curve analysis showed similar area under the curve (AUC) of 0.74 (MUC13) and 0.76 (CA125). MUC13 concentrations were significantly higher in mucinous adenocarcinomas compared to benign controls (p=0.0005), with AUC of 0.80. MUC13 and CA125 showed significant elevation in early-stage cases (stage I-II) in relation to benign controls (p=0.0012 and p=0.014, respectively).

CONCLUSIONS

We report the novel role of MUC13 as a serum ovarian cancer biomarker, where it could complement CA125 for detecting some subtypes of non-serous ovarian carcinoma and early-stage disease.

Artificial intelligence aids in development of nanomedicines for cancer management

Over the last decade, the nanomedicine has experienced unprecedented development in diagnosis and management of diseases. A number of nanomedicines have been approved in clinical use, which has demonstrated the potential value of clinical transition of nanotechnology-modified medicines from bench to bedside. The application of artificial intelligence (AI) in development of nanotechnology-based products could transform the healthcare sector by realizing acquisition and analysis of large datasets, and tailoring precision nanomedicines for cancer management. AI-enabled nanotechnology could improve the accuracy of molecular profiling and early diagnosis of patients, and optimize the design pipeline of nanomedicines by tuning the properties of nanomedicines, achieving effective drug synergy, and decreasing the nanotoxicity, thereby, enhancing the targetability, personalized dosing and treatment potency of nanomedicines. Herein, the advances in AI-enabled nanomedicines in cancer management are elaborated and their application in diagnosis, monitoring and therapy as well in precision medicine development is discussed.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

Identification and analysis of C17orf53 as a prognostic signature for hepatocellular carcinoma

C17orf53 is a novel gene for DNA synthesis and homologous recombination. However, the exact role of C17orf53 in hepatocellular carcinoma (HCC) remains unclear. In this study, we analyzed it using a set of public datasets. UALCAN, Human Protein Atlas (HPA), Kaplan‒Meier Plotter, Tumor Immune Estimation Resource (TIMER), cBioPortal, GEPIA, GeneMANIA, and LinkedOmics were used. Functional analysis was conducted in SK-Hep-1 cells by using small interfering RNA (siRNA). C17orf53 was highly expressed and predicted unfavorable survival in HCC patients. Moreover, it showed positive correlations with the abundance of B cells, macrophages and dendritic cells. In addition, we identified 126 genes that were positively correlated with C17orf53 and its coeffector minichromosome maintenance 8 (MCM8). These genes were mainly enriched in the cell cycle, DNA replication and Fanconi anemia pathways. Knockdown of C17orf53 significantly inhibited the proliferation of SK-Hep-1 cells and decreased the expression of MCM8, cyclin D1 and proliferating cell nuclear antigen (PCNA). Overall, C17orf53 is a novel prognostic signature for HCC.

A review of deep learning applications in human genomics using next-generation sequencing data

Genomics is advancing towards data-driven science. Through the advent of high-throughput data generating technologies in human genomics, we are overwhelmed with the heap of genomic data. To extract knowledge and pattern out of this genomic data, artificial intelligence especially deep learning methods has been instrumental. In the current review, we address development and application of deep learning methods/models in different subarea of human genomics. We assessed over- and under-charted area of genomics by deep learning techniques. Deep learning algorithms underlying the genomic tools have been discussed briefly in later part of this review. Finally, we discussed briefly about the late application of deep learning tools in genomic. Conclusively, this review is timely for biotechnology or genomic scientists in order to guide them why, when and how to use deep learning methods to analyse human genomic data.

FER-mediated phosphorylation and PIK3R2 recruitment on IRS4 promotes AKT activation and tumorigenesis in ovarian cancer cells

Tyrosine phosphorylation, orchestrated by tyrosine kinases and phosphatases, modulates a multi-layered signaling network in a time- and space-dependent manner. Dysregulation of this post-translational modification is inevitably associated with pathological diseases. Our previous work has demonstrated that non-receptor tyrosine kinase FER is upregulated in ovarian cancer, knocking down which attenuates metastatic phenotypes. However, due to the limited number of known substrates in the ovarian cancer context, the molecular basis for its pro-proliferation activity remains enigmatic. Here, we employed mass spectrometry and biochemical approaches to identify insulin receptor substrate 4 (IRS4) as a novel substrate of FER. FER engaged its kinase domain to associate with the PH and PTB domains of IRS4. Using a proximity-based tagging system in ovarian carcinoma-derived OVCAR-5 cells, we determined that FER-mediated phosphorylation of Tyr779 enables IRS4 to recruit PIK3R2/p85β, the regulatory subunit of PI3K, and activate the PI3K-AKT pathway. Rescuing IRS4-null ovarian tumor cells with phosphorylation-defective mutant, but not WT IRS4 delayed ovarian tumor cell proliferation both in vitro and in vivo. Overall, we revealed a kinase-substrate mode between FER and IRS4, and the pharmacological inhibition of FER kinase may be beneficial for ovarian cancer patients with PI3K-AKT hyperactivation.

Ceramide Composition in Exosomes for Characterization of Glioblastoma Stem-Like Cell Phenotypes

Glioblastoma (GBM) is one of the most malignant central nervous system tumor types. Comparative analysis of GBM tissues has rendered four major molecular subtypes. From them, two molecular subtypes are mainly found in their glioblastoma cancer stem-like cells (GSCs) derived in vitro: proneural (PN) and mesenchymal (MES) with nodular (MES-N) and semi-nodular (MES-SN) disseminations, which exhibit different metabolic, growth, and malignancy properties. Many studies suggest that cancer cells communicate between them, and the surrounding microenvironment, via exosomes. Identifying molecular markers that allow the specific isolation of GSC-derived exosomes is key in the development of new therapies. However, the differential exosome composition produced by main GSCs remains unknown. The aim of this study was to determine ceramide (Cer) composition, one of the critical lipids in both cells and their cell-derived exosomes, from the main three GSC phenotypes using mass spectrometry-based lipidomics. GSCs from human tissue samples and their cell-derived exosomes were measured using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC/Q-TOF-MS) in an untargeted analysis. Complete characterization of the ceramide profile, in both cells and cell-derived exosomes from GSC phenotypes, showed differential distributions among them. Results indicate that such differences of ceramide are chain-length dependent. Significant changes for the C16 Cer and C24:1 Cer and their ratio were observed among GSC phenotypes, being different for cells and their cell-derived exosomes.

ELK3: A New Molecular Marker for the Diagnosis and Prognosis of Glioma

ETS transcription factor ELK3 (ELK3), a novel oncogene, affects pathological processes and progression of many cancers in human tissues. However, it remains unclear whether ELK3, as a key gene, affects the pathological process of gliomas and the prognosis of patients with gliomas. This study aimed to comprehensively and systematically reveal the correlation between ELK3 and the malignant progression of gliomas by analyzing clinical sample information stored in multiple databases. We revealed the putative mechanism of ELK3 involvement in malignant gliomas progression and identified a new and efficient biomarker for glioma diagnosis and targeted therapy. Based on the sample data from multiple databases and real-time quantitative polymerase chain reaction (RT-qPCR), the abnormally high expression of ELK3 in gliomas was confirmed. Kaplan-Meier and Cox regression analyses demonstrated that a high ELK3 expression was markedly associated with low patient survival and served as an independent biomarker of gliomas. Wilcox and Kruskal-Wallis tests revealed that expression of ELK3 was positively correlated with several clinical characteristics of patients with gliomas, such as age, WHO classification, and recurrence. Moreover, Cell Counting Kit‐8 (CCK-8), immunofluorescence, and wound healing assays confirmed that ELK3 overexpression markedly promoted the proliferation and migration of glioma cells. Finally, gene set enrichment analysis (GSEA) and western blotting confirmed that overexpression of ELK3 regulated the JAK–STAT signaling pathway and upregulate the expression of signal transducer and activator of transcription 3 (STAT3) and phosphorylated STAT3 (P-STAT3) to promote the malignant transition of gliomas. Therefore, ELK3 may serve as an efficient biomarker for the diagnosis and prognosis of gliomas and it can also be used as a therapeutic target to improve the poor prognosis of patients with gliomas.

miR-1306 Promotes Lung Squamous Cell Carcinoma Progression and Predicts Clinical Prognosis of Patients

Purpose

Lung squamous cell carcinoma (LUSC) is one of the major subtypes of non-small cell lung cancer (NSCLC) with high mortality. Identification of novel biomarkers of the development of LUSC could provide basis for clinical treatment and improve patients’ prognosis. The function of miR-1306 in the development of LUSC was investigated.

Patients and Methods

A total of 103 paired LUSC and normal tissues were collected from LUSC patients. The expression of miR-1306 in collected tissues and cultured cells was evaluated by PCR. The clinical significance of miR-1306 was assessed by a series of statistical analyses, and the biological effect of miR-1306 was also estimated with the CCK8 and Transwell assay.

Results

The significant upregulation of miR-1306 was observed in LUSC, which was associated with positive lymph node metastasis and advanced TNM stage of patients. miR-1306 was also identified as an independent prognostic factor negatively associated with the prognosis of patients. Additionally, the upregulation of miR-1306 was found to promote the proliferation, migration, and invasion of LUSC cells, indicating its tumor enhancer role in the development of LUSC. While miR-1306 was also found to regulate RBM3, which was speculated to be the mechanism underlying the function of miR-1306.

Conclusion

miR-1306 functions as a prognostic indicator and tumor promoter of LUSC through targeting RBM3, which provides a potential therapeutic target of LUSC.

Extracellular Vesicles and Pancreatic Cancer: Insights on the Roles of miRNA, lncRNA, and Protein Cargos in Cancer Progression

Pancreatic cancer (PC) is among the most devastating digestive tract cancers worldwide. This cancer is characterized by poor diagnostic detection, lack of therapy, and difficulty in predicting tumorigenesis progression. Although mutations of key oncogenes and oncosuppressor involved in tumor growth and in immunosurveillance escape are known, the underlying mechanisms that orchestrate PC initiation and progression are poorly understood or still under debate. In recent years, the attention of many researchers has been concentrated on the role of extracellular vesicles and of a particular subset of extracellular vesicles, known as exosomes. Literature data report that these nanovesicles are able to deliver their cargos to recipient cells playing key roles in the pathogenesis and progression of many pancreatic precancerous conditions. In this review, we have summarized and discussed principal cargos of extracellular vesicles characterized in PC, such as miRNAs, lncRNAs, and several proteins, to offer a systematic overview of their function in PC progression. The study of extracellular vesicles is allowing to understand that investigation of their secretion and analysis of their content might represent a new and potential diagnostic and prognostic tools for PC.

Comprehending the Proteomic Landscape of Ovarian Cancer: A Road to the Discovery of Disease Biomarkers

Despite recent technological advancements allowing the characterization of cancers at a molecular level along with biomarkers for cancer diagnosis, the management of ovarian cancers (OC) remains challenging. Proteins assume functions encoded by the genome and the complete set of proteins, termed the proteome, reflects the health state. Comprehending the circulatory proteomic profiles for OC subtypes, therefore, has the potential to reveal biomarkers with clinical utility concerning early diagnosis or to predict response to specific therapies. Furthermore, characterization of the proteomic landscape of tumor-derived tissue, cell lines, and PDX models has led to the molecular stratification of patient groups, with implications for personalized therapy and management of drug resistance. Here, we review single and multiple marker panels that have been identified through proteomic investigations of patient sera, effusions, and other biospecimens. We discuss their clinical utility and implementation into clinical practice.

Exosomal Long Noncoding RNAs: Insights into Emerging Diagnostic and Therapeutic Applications in Lung Cancer

Lung cancer is the most common cause of cancer-related deaths worldwide. Annually, millions of people die from lung cancer because of late detection and ineffective therapies. Recently, exosomes have been introduced as new therapeutic players with the potential to improve upon current diagnostic and treatment options. Exosomes are small membranous vesicles produced during endosomal merging. This allows for cell packaging of nucleic acids, proteins, and lipids and transfer to adjacent or distant cells. While exosomes are a part of normal intercellular signaling, they also allow malignant cells to transfer oncogenic material leading to tumor spread and metastasis. Exosomes are an interesting field of discovery for biomarkers and therapeutic targets. Among exosomal materials, lncRNAs have priority; lncRNAs are a class of noncoding RNAs longer than 200 base pairs. In the case of cancer, primary interest regards their oncogene and tumor suppressor functions. In this review, the advantages of exosomal lncRNAs as biomarkers and therapeutic targets will be discussed in addition to reviewing studies of their application in lung cancer.

A Quest for New Cancer Diagnosis, Prognosis and Prediction Biomarkers and Their Use in Biosensors Development

Traditional techniques for cancer diagnosis, such as nuclear magnetic resonance, ultrasound and tissue analysis, require sophisticated devices and highly trained personnel, which are characterized by elevated operation costs. The use of biomarkers has emerged as an alternative for cancer diagnosis, prognosis and prediction because their measurement in tissues or fluids, such as blood, urine or saliva, is characterized by shorter processing times. However, the biomarkers used currently, and the techniques used for their measurement, including ELISA, western-blot, polymerase chain reaction (PCR) or immunohistochemistry, possess low sensitivity and specificity. Therefore, the search for new proteomic, genomic or immunological biomarkers and the development of new noninvasive, easier and cheaper techniques that meet the sensitivity and specificity criteria for the diagnosis, prognosis and prediction of this disease has become a relevant topic. The purpose of this review is to provide an overview about the search for new cancer biomarkers, including the strategies that must be followed to identify them, as well as presenting the latest advances in the development of biosensors that possess a high potential for cancer diagnosis, prognosis and prediction, mainly focusing on their relevance in lung, prostate and breast cancers.

MCM10 Acts as a Potential Prognostic Biomarker and Promotes Cell Proliferation in Hepatocellular Carcinoma: Integrated Bioinformatics Analysis and Experimental Validation

Background

Hepatocellular carcinoma (HCC) is one of the most common human malignant tumors. The prognosis of HCC patients is still unsatisfying. Thus, it is of great importance to identify novel molecules and functional pathways associated with the pathophysiology of HCC. In this study, we performed the integrated bioinformatics analysis and experiment validation to identify novel biomarkers in the prognosis and progression of HCC.

Materials and Methods

Gene expression profiles were obtained from Gene Expression Omnibus database (GSE33294) for the screening of the differentially expressed genes (DEGs) between HCC tissues and matched non-tumor tissues. The DEGs were subjected to Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Set Enrichment Analysis (GSEA). The key genes in HCC were further subjected to overall survival analysis of HCC patients. The in vitro functional studies were performed to validate the biological functions of the key gene in HCC cell progression.

Results

A total of 2,334 DEGs were screened from GSE33294 dataset, including 1,120 up-regulated and 1,214 down-regulated genes. GO, KEGG and GSEA results showed that DEGs are significantly associated with the biological process of cell cycle, cell division and DNA replication. The Kaplan–Meier survival analysis results showed that the key genes from the minichromosome maintenance protein complex (MCM) family including MCM8, MCM10, MCM2, MCM3, MCM4, MCM6 and MCM7 were significantly correlated with the overall survival of the HCC patients. Further validation studies showed that MCM10 was significantly up-regulated in the HCC cell lines, and knockdown of MCM10 significantly suppressed cell proliferation as determined by the cell counting kit-8 and BrdU incorporation assays and increased the caspase-3 activity of HCC cells.

Conclusion

The comprehensive bioinformatics analysis identified several key genes that were associated with the prognosis of HCC patients. The validation study results indicated that MCM10 may be an important predictor for poorer prognosis of HCC patients and may act as an oncogene to promote HCC cell progression.

Identification of MMP1 as a potential gene conferring erlotinib resistance in non-small cell lung cancer based on bioinformatics analyses

BACKGROUND

Non-small cell lung cancer (NSCLC) is the major type of lung cancer with high morbidity and poor prognosis. Erlotinib, an inhibitor of epidermal growth factor receptor (EGFR), has been clinically applied for NSCLC treatment. Nevertheless, the erlotinib acquired resistance of NSCLC occurs inevitably in recent years.

METHODS

Through analyzing two microarray datasets, erlotinib resistant NSCLC cells microarray (GSE80344) and NSCLC tissue microarray (GSE19188), the differentially expressed genes (DEGs) were screened via R language. DEGs were then functionally annotated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, which up-regulated more than 2-folds in both datasets were further functionally analyzed by Oncomine, GeneMANIA, R2, Coremine, and FunRich.

RESULTS

We found that matrix metalloproteinase 1 (MMP1) may confer the erlotinib therapeutic resistance in NSCLC. MMP1 highly expressed in erlotinib-resistant cells and NSCLC tissues, and it associated with poor overall survival. In addition, MMP1 may be associated with COPS5 and be involve in an increasing transcription factors HOXA9 and PBX1 in erlotinib resistance.

CONCLUSIONS

Generally, these results demonstrated that MMP1 may play a crucial role in erlotinib resistance in NSCLC, and MMP1 could be a prognostic biomarker for erlotinib treatment.

Pitfalls in Cancer Biomarker Discovery and Validation with Emphasis on Circulating Tumor DNA

Despite significant investment of funds and resources, few new cancer biomarkers have been introduced to the clinic in the last few decades. Although many candidates produce promising results in the laboratory, deficiencies in sensitivity, specificity, and predictive value make them less than desirable in a patient setting. This review will analyze these challenges in detail as well as discuss false discovery, problems with reproducibility, and tumor heterogeneity. Circulating tumor DNA (ctDNA), an emerging cancer biomarker, is also analyzed, particularly in the contexts of assay specificity, sensitivity, fragmentation, lead time, mutant allele fraction, and clinical relevance. Emerging artificial intelligence technologies will likely be valuable tools in maximizing the clinical utility of ctDNA which is often found in very small quantities in patients with early-stage tumors. Finally, the implications of challenging false discoveries are examined and some insights about improving cancer biomarker discovery are provided.See all articles in this CEBP Focus section, "NCI Early Detection Research Network: Making Cancer Detection Possible."

Circulating lncRNA SNHG11 as a novel biomarker for early diagnosis and prognosis of colorectal cancer

Colorectal cancer (CRC) is the third most common cancer and the second leading cause of cancer mortality worldwide. Emerging evidence indicates that tumour cells release substantial amounts of RNA into the bloodstream, in which RNA strongly resists RNases and is present at sufficient levels for quantitative analyses. Our study aimed to discover blood-based markers for the early detection of CRC and to ascertain their efficiency in discriminating healthy controls, patients with polyps and adenomas and cancer patients. We first analysed and screened ZFAS1, SNHG11, LINC00909 and LINC00654 in a bioinformatics database and then collected clinical plasma samples for preliminary small-scale analysis and further large-scale verification. We then explored the mechanism of dominant lncRNA SNHG11 expression in CRC by in vitro and in vivo assays. The combination of ZFAS1, SNHG11, LINC00909 and LINC00654 showed high diagnostic performance for CRC (AUC: 0.937), especially early-stage disease (AUC: 0.935). Plasma levels of the four candidate lncRNAs were significantly reduced in postoperative samples compared to preoperative samples. A panel including these four lncRNAs performed well in distinguishing patient groups with different stages of colon disease, and SNHG11 exhibited the greatest diagnostic ability to identify precancerous lesions and early-stage tumour formation. Mechanistically, high SNHG11 expression promotes proliferation and metastasis by targeting the Hippo pathway. Taken together, the data indicate that SNHG11 may be a novel therapeutic target for the treatment of CRC and a potential biomarker for the early detection of CRC.

Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions

Like physics in the 19th century, biology and molecular biology in particular, has been fertilized and enhanced like few other scientific fields, by the incorporation of mathematical methods. In the last decades, a whole new scientific field, bioinformatics, has developed with an output of over 30,000 papers a year (Pubmed search using the keyword "bioinformatics"). Huge databases of mass throughput data have been established, with ArrayExpress alone containing more than 2.7 million assays (October 2019). Computational methods have become indispensable tools in molecular biology, particularly in one of the most challenging areas of cancer research, multidrug resistance (MDR). However, confronted with a plethora of different algorithms, approaches, and methods, the average researcher faces key questions: Which methods do exist? Which methods can be used to tackle the aims of a given study? Or, more generally, how do I use computational biology/bioinformatics to bolster my research? The current review is aimed at providing guidance to existing methods with relevance to MDR research. In particular, we provide an overview on: a) the identification of potential biomarkers using expression data; b) the prediction of treatment response by machine learning methods; c) the employment of network approaches to identify gene/protein regulatory networks and potential key players; d) the identification of drug-target interactions; e) the use of bipartite networks to identify multidrug targets; f) the identification of cellular subpopulations with the MDR phenotype; and, finally, g) the use of molecular modeling methods to guide and enhance drug discovery. This review shall serve as a guide through some of the basic concepts useful in MDR research. It shall give the reader some ideas about the possibilities in MDR research by using computational tools, and, finally, it shall provide a short overview of relevant literature.

The Role of Exo-miRNAs in Cancer: A Focus on Therapeutic and Diagnostic Applications

Exosomes are extracellular vesicles released into biological fluids where they act as carriers of various molecules, including proteins, lipids, and RNAs, between cells, modulating or perturbing specific physiological processes. Recently, it has been suggested that tumoral cells release excessive amounts of exosomes that, through their cargo, promote tumor progression, stimulating growth, angiogenesis, metastasis, insensitivity to chemotherapy, and immune evasion. Increasing evidence highlights exosomal microRNAs (exo-miRNAs) as important players in tumorigenesis. MicroRNA (miRNA) are a class of small non-coding RNA able to regulate gene expression, targeting multiple mRNAs and inducing translational repression and/or mRNA degradation. Exo-miRNAs are highly stable and easily detectable in biological fluids, and for these reasons, miRNAs are potential cancer biomarkers useful diagnostically and prognostically. Furthermore, since exosomes are natural delivery systems between cells, they can be appropriately modified to carry therapeutic miRNAs to specific recipient cells. Here we summarize the main functions of exo-miRNAs and their possible role for diagnostic and therapeutic applications.

Integrated multiomic predictors for ovarian cancer survival

Increasingly affordable high-throughput molecular profiling technologies have made feasible the measurement of omics-wide interindividual variations for the purposes of predicting cancer prognosis. While multiple types of genetic, epigenetic and expression changes have been implicated in ovarian cancer, existing prognostic biomarker strategies are constrained to analyzing a single class of molecular variations. The extra predictive power afforded by the integration of multiple omics types remains largely unexplored. In this study, we performed integrative analysis on tumor-based exome-, transcriptome- and methylome-wide molecular profiles from The Cancer Genome Atlas (TCGA) for variations in cancer-relevant genes to construct robust, cross-validated multiomic predictors for ovarian cancer survival. These integrated polygenic survival scores (PSSs) were able to predict 5-year overall (OS) and progression-free survival in the Caucasian subsample with high accuracy (AUROC = 0.87 and 0.81, respectively). These findings suggest that the PSSs are able to predict long-term OS in TCGA patients with accuracy beyond that of previously proposed protein-based biomarker strategies. Our findings reveal the promise of an integrated omics-based approach in enhancing existing prognostic strategies. Future investigations should be aimed toward prospective external validation, strategies for standardizing application and the integration of germline variants.

Current Progresses of Exosomes as Cancer Diagnostic and Prognostic Biomarkers

Cancer related exosomes are nano-size membrane vesicles that play important roles in tumor microenvironment. Emerging evidence indicates that exosomes can load unique cargoes, including proteins and nucleic acids that reflect the condition of tumor. Therefore, exosomes are being used as diagnostic and prognostic biomarkers for various cancers. In this review, we describe the current progresses of cancer related exosomes, including their biogenesis, molecular contents, biological functions, sources where they are derived from, and methods for their detection. We will also discuss the current exosomal biomarkers and the utilization of them for early diagnosis and prognostics in cancer.

Designing an In Silico Strategy to Select Tissue-Leakage Biomarkers Using the Galaxy Framework

Knowledge-based approaches using large-scale biological ("omics") data are a powerful way to identify mechanistic biomarkers, provided that scientists have access to computational solutions even when they have little programming experience or bioinformatics support. To achieve this goal, we designed a set of tools under the Galaxy framework to allow biologists to define their own strategy for reproducible biomarker selection. These tools rely on retrieving experimental data from public databases, and applying successive filters derived from information relating to disease pathophysiology. A step-by-step protocol linking these tools was implemented to select tissue-leakage biomarker candidates of myocardial infarction. A list of 24 candidates suitable for experimental assessment by MS-based proteomics is proposed. These tools have been made publicly available at http://www.proteore.org , allowing researchers to reuse them in their quest for biomarker discovery.

Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples

BACKGROUND

Towards discovering robust cancer biomarkers, it is imperative to unravel the cellular heterogeneity of patient samples and comprehend the interactions between cancer cells and the various cell types in the tumor microenvironment. The first generation of 'partial' computational deconvolution methods required prior information either on the cell/tissue type proportions or the cell/tissue type-specific expression signatures and the number of involved cell/tissue types. The second generation of 'complete' approaches allowed estimating both of the cell/tissue type proportions and cell/tissue type-specific expression profiles directly from the mixed gene expression data, based on known (or automatically identified) cell/tissue type-specific marker genes.

RESULTS

We present Deblender, a flexible complete deconvolution tool operating in semi-/unsupervised mode based on the user's access to known marker gene lists and information about cell/tissue composition. In case of no prior knowledge, global gene expression variability is used in clustering the mixed data to substitute marker sets with cluster sets. In addition, we integrate a model selection criterion to predict the number of constituent cell/tissue types. Moreover, we provide a tailored algorithmic scheme to estimate mixture proportions for realistic experimental cases where the number of involved cell/tissue types exceeds the number of mixed samples. We assess the performance of Deblender and a set of state-of-the-art existing tools on a comprehensive set of benchmark and patient cancer mixture expression datasets (including TCGA).

CONCLUSION

Our results corroborate that Deblender can be a valuable tool to improve understanding of gene expression datasets with implications for prediction and clinical utilization. Deblender is implemented in MATLAB and is available from ( https://github.com/kondim1983/Deblender/ ).

Identification of NMU as a potential gene conferring alectinib resistance in non-small cell lung cancer based on bioinformatics analyses

Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, and adjuvant targeted therapy has shown great benefits for the NSCLC patients with specific genomic mutations. Alectinib, a selective anaplastic lymphoma kinase (ALK) inhibitor, has been clinically used for the NSCLC patients with ALK-rearrangement, however, irreversible therapeutic resistance for the patients receiving alectinib treatment frequently occurs. Here we show that neuromedin U (NMU) may confer the alectinib resistance in NSCLC via multiple mechanisms based on the integrative bioinformatics analyses. Through employing the bioinformatics analyses of three microarray datasets, NMU, overexpressed in both NSCLC tissues and alectinib-resistant NSCLC cells, was initially identified as potential candidate for causing alectinib resistance in NSCLC. The resistance function of NMU in NSCLC was validated by performing protein/gene interactions and biological process annotation analyses, and further validated by analyzing the transcription factors targeting NMU mRNA. Collectively, these results indicated that NMU may confer alectinib resistance in NSCLC.

Integration of magnetic resonance imaging and protein and metabolite CSF measurements to enable early diagnosis of secondary progressive multiple sclerosis

Molecular networks in neurological diseases are complex. Despite this fact, contemporary biomarkers are in most cases interpreted in isolation, leading to a significant loss of information and power. We present an analytical approach to scrutinize and combine information from biomarkers originating from multiple sources with the aim of discovering a condensed set of biomarkers that in combination could distinguish the progressive degenerative phenotype of multiple sclerosis (SPMS) from the relapsing-remitting phenotype (RRMS). Methods: Clinical and magnetic resonance imaging (MRI) data were integrated with data from protein and metabolite measurements of cerebrospinal fluid, and a method was developed to sift through all the variables to establish a small set of highly informative measurements. This prospective study included 16 SPMS patients, 30 RRMS patients and 10 controls. Protein concentrations were quantitated with multiplexed fluorescent bead-based immunoassays and ELISA. The metabolome was recorded using liquid chromatography-mass spectrometry. Clinical follow-up data of the SPMS patients were used to assess disease progression and development of disability. Results: Eleven variables were in combination able to distinguish SPMS from RRMS patients with high confidence superior to any single measurement. The identified variables consisted of three MRI variables: the size of the spinal cord and the third ventricle and the total number of T1 hypointense lesions; six proteins: galectin-9, monocyte chemoattractant protein-1 (MCP-1), transforming growth factor alpha (TGF-α), tumor necrosis factor alpha (TNF-α), soluble CD40L (sCD40L) and platelet-derived growth factor AA (PDGF-AA); and two metabolites: 20β-dihydrocortisol (20β-DHF) and indolepyruvate. The proteins myelin basic protein (MBP) and macrophage-derived chemokine (MDC), as well as the metabolites 20β-DHF and 5,6-dihydroxyprostaglandin F1a (5,6-DH-PGF1), were identified as potential biomarkers of disability progression. Conclusion: Our study demonstrates, in a limited but well-defined and data-rich cohort, the importance and value of combining multiple biomarkers to aid diagnostics and track disease progression.

Integrated Chemometrics and Statistics to Drive Successful Proteomics Biomarker Discovery

Protein biomarkers are of great benefit for clinical research and applications, as they are powerful means for diagnosing, monitoring and treatment prediction of different diseases. Even though numerous biomarkers have been reported, the translation to clinical practice is still limited. This mainly due to: (i) incorrect biomarker selection, (ii) insufficient validation of potential biomarkers, and (iii) insufficient clinical use. In this review, we focus on the biomarker selection process and critically discuss the chemometrical and statistical decisions made in proteomics biomarker discovery to increase to selection of high value biomarkers. The characteristics of the data, the computational resources, the type of biomarker that is searched for and the validation strategy influence the decision making of the chemometrical and statistical methods and a decision made for one component directly influences the choice for another. Incorrect decisions could increase the false positive and negative rate of biomarkers which requires independent confirmation of outcome by other techniques and for comparison between different related studies. There are few guidelines for authors regarding data analysis documentation in peer reviewed journals, making it hard to reproduce successful data analysis strategies. Here we review multiple chemometrical and statistical methods for their value in proteomics-based biomarker discovery and propose to include key components in scientific documentation.

Transmembrane protein 170B is a novel breast tumorigenesis suppressor gene that inhibits the Wnt/β-catenin pathway

The identification of specific drug targets guides the development of precise cancer treatments. Compared with oncogenes, tumor suppressor genes have been poorly studied in the treatment of breast cancer. We integrate the microRNA expression array from GEO (Gene Expression Omnibus) and TCGA (The Cancer Genome Atlas) databases in clinical breast cancer tissues, and find that miR-27a is significantly upregulated and correlated with poor survival outcome and tumor progression. Transmembrane protein 170B (TMEM170B), a new functional target of miR-27a, is identified via target prediction and experimental validation, suppressing breast cancer proliferation, metastasis, and tumorigenesis. Furthermore, TMEM170B overexpression promotes cytoplasmic β-catenin phosphorylation, resulting in the inhibition of β-catenin stabilization, reduction of nuclear β-catenin levels and downstream targets expression. Clinically, TMEM170B or β-catenin expression is significantly correlated with overall survival ratio in breast cancer patients. Thus, these results highlight TMEM170B as a novel tumor suppressor target in association with the β-catenin pathway, which may provide a new therapeutic approach for human breast cancer therapy.

Exploitation of Gene Expression and Cancer Biomarkers in Paving the Path to Era of Personalized Medicine

Cancer therapy agents have been used extensively as cytotoxic drugs against tissue or organ of a specific type of cancer. With the better understanding of molecular mechanisms underlying carcinogenesis and cellular events during cancer progression and metastasis, it is now possible to use targeted therapy for these molecular events. Targeted therapy is able to identify cancer patients with dissimilar genetic defects at cellular level for the same cancer type and consequently requires individualized approach for treatment. Cancer therapy begins to shift steadily from the traditional approach of “one regimen for all patients” to a more individualized approach, through which each patient will be treated specifically according to their specific genetic defects. Personalized medicine accordingly requires identification of indicators or markers that guide in the decision making of such therapy to the chosen patients for more effective therapy. Cancer biomarkers are frequently used in clinical practice for diagnosis and prognosis, as well as identification of responsive patients and prediction of treatment response of cancer patient. The rapid breakthrough and development of microarray and sequencing technologies is probably the main tool for paving the way toward “individualized biomarker-driven cancer therapy” or “personalized medicine”. In this review, we aim to provide an updated knowledge and overview of the current landscape of cancer biomarkers and their role in personalized medicine, emphasizing the impact of genomics on the implementation of new potential targeted therapies and development of novel cancer biomarkers in improving the outcome of cancer therapy.

Identification of microRNA differentially expressed in three subtypes of non-small cell lung cancer and in silico functional analysis

Emerging studies demonstrated that miRNAs played fundamental roles in lung cancer. In this study, we attempted to explore the clinical significance of the miRNA signature in different histological subtypes of non-small cell lung cancer (NSCLC). Three miRNome profiling datasets (GSE19945, GSE25508 and GSE51853) containing lung squamous cell carcinoma (SCC), lung adenocarcinoma (ADC) and large cell lung cancer (LCLC) samples were obtained for bioinformatics and survival analysis. Moreover, pathway enrichment and coexpression network were performed to explore underlying molecular mechanism. MicroRNA-375 (miR-375), miR-203 and miR-205 were identified as differentially expressed miRNAs (DEmiRNAs) which distinguished SCC from other NSCLC subtypes. Pathway enrichment analysis suggested that Hippo signaling pathway was combinatorically affected by above mentioned three miRNAs. Coexpression analysis of three miRNAs and the Hippo signaling pathway related genes were conducted based on another dataset, GSE51852. Four hub genes (TP63, RERE, TJP1 and YWHAE) were identified as the candidate targets of three miRNAs, and three of them (TP63, TJP1 and YWHAE) were validated to be downregulated by miR-203 and miR-375, respectively. Finally, survival analysis further suggested the prognostic value of three-miRNA signature in SCC patients. Taken together, our study compared the miRNA profiles among three histological subtypes of NSCLC, and suggested that a three-miRNA signature might be potential diagnostic and prognostic biomarkers for SCC patients.

The Potential of Targeting Ribosome Biogenesis in High-Grade Serous Ovarian Cancer

Overall survival for patients with ovarian cancer (OC) has shown little improvement for decades meaning new therapeutic options are critical. OC comprises multiple histological subtypes, of which the most common and aggressive subtype is high-grade serous ovarian cancer (HGSOC). HGSOC is characterized by genomic structural variations with relatively few recurrent somatic mutations or dominantly acting oncogenes that can be targeted for the development of novel therapies. However, deregulation of pathways controlling homologous recombination (HR) and ribosome biogenesis has been observed in a high proportion of HGSOC, raising the possibility that targeting these basic cellular processes may provide improved patient outcomes. The poly (ADP-ribose) polymerase (PARP) inhibitor olaparib has been approved to treat women with defects in HR due to germline BRCA mutations. Recent evidence demonstrated the efficacy of targeting ribosome biogenesis with the specific inhibitor of ribosomal RNA synthesis, CX-5461 in v-myc avian myelocytomatosis viral oncogene homolog (MYC)-driven haematological and prostate cancers. CX-5461 has now progressed to a phase I clinical trial in patients with haematological malignancies and phase I/II trial in breast cancer. Here we review the currently available targeted therapies for HGSOC and discuss the potential of targeting ribosome biogenesis as a novel therapeutic approach against HGSOC.

Targeted Proteomics for Multiplexed Verification of Markers of Colorectal Tumorigenesis

Targeted proteomic methods can accelerate the verification of multiple tumor marker candidates in large series of patient samples. We utilized the targeted approach known as selected/multiple reaction monitoring (S/MRM) to verify potential protein markers of colorectal adenoma identified by our group in previous transcriptomic and quantitative shotgun proteomic studies of a large cohort of precancerous colorectal lesions. We developed SRM assays to reproducibly detect and quantify 25 (62.5%) of the 40 selected proteins in an independent series of precancerous and cancerous tissue samples (19 adenoma/normal mucosa pairs; 17 adenocarcinoma/normal mucosa pairs). Twenty-three proteins were significantly up-regulated (n = 17) or downregulated (n = 6) in adenomas and/or adenocarcinomas, as compared with normal mucosa (linear fold changes ≥ ±1.3, adjusted p value <0.05). Most changes were observed in both tumor types (up-regulation of ANP32A, ANXA3, SORD, LDHA, LCN2, NCL, S100A11, SERPINB5, CDV3, OLFM4, and REG4; downregulation of ARF6 and PGM5), and a five-protein biomarker signature distinguished neoplastic tissue from normal mucosa with a maximum area under the receiver operating curve greater than 0.83. Other changes were specific for adenomas (PPA1 and PPA2 up-regulation; KCTD12 downregulation) or adenocarcinoma (ANP32B, G6PD, RCN1, and SET up-regulation; downregulated AKR1B1, APEX1, and PPA1). Some changes significantly correlated with a few patient- or tumor-related phenotypes. Twenty-two (96%) of the 23 proteins have a potential to be released from the tumors into the bloodstream, and their detectability in plasma has been previously reported. The proteins identified in this study expand the pool of biomarker candidates that can be used to develop a standardized precolonoscopy blood test for the early detection of colorectal tumors.

MALDI imaging mass spectrometry - From bench to bedside

Today, pathologists face many challenges in defining the precise morphomolecular diagnosis and in guiding clinicians to the optimal patients' treatment. To achieve this goal, increasingly, classical histomorphological methods have to be supplemented by high throughput molecular assays. Since MALDI imaging mass spectrometry (IMS) enables the assessment of spatial molecular arrangements in tissue sections, it goes far beyond microscopy in providing hundreds of different molecular images from a single scan without the need of target-specific reagents. Thus, this technology has the potential to uncover new markers for diagnostic purposes or markers that correlate with disease severity as well as prognosis and therapeutic response. Additionally, in the future MALDI IMS based classifiers measured with this technology in real time in the diagnostic setting might be applicable in the routine diagnostic setting. In this review, recently published studies that show the usefulness, advantages, and applicability of MALDI IMS in different fields of pathology (diagnosis, prognosis and treatment response) are highlighted. This article is part of a Special Issue entitled: MALDI Imaging, edited by Dr. Corinna Henkel and Prof. Peter Hoffmann.

Evolution-informed modeling improves outcome prediction for cancers

Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution-informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene-specific, position-specific, or allele-specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous "omics" data to accelerate biomarker discoveries.

Biological Networks for Cancer Candidate Biomarkers Discovery

Due to its extraordinary heterogeneity and complexity, cancer is often proposed as a model case of a systems biology disease or network disease. There is a critical need of effective biomarkers for cancer diagnosis and/or outcome prediction from system level analyses. Methods based on integrating omics data into networks have the potential to revolutionize the identification of cancer biomarkers. Deciphering the biological networks underlying cancer is undoubtedly important for understanding the molecular mechanisms of the disease and identifying effective biomarkers. In this review, the networks constructed for cancer biomarker discovery based on different omics level data are described and illustrated from recent advances in the field.

Validation of a Novel Biomarker Panel for the Detection of Ovarian Cancer

BACKGROUND

Ovarian cancer is the most lethal gynecological malignancy. Our integrated -omics approach to ovarian cancer biomarker discovery has identified kallikrein 6 (KLK6) and folate-receptor 1 (FOLR1) as promising candidates but these markers require further validation.

METHODS

KLK6, FOLR1, CA125, and HE4 were investigated in three independent serum cohorts with a total of 20 healthy controls, 150 benign controls, and 216 ovarian cancer patients. The serum biomarker levels were determined by ELISA or automated immunoassay.

RESULTS

All biomarkers demonstrated elevations in the sera of ovarian cancer patients compared with controls (P < 0.01). Overall, CA125 and HE4 displayed the strongest ability (AUC 0.80 and 0.82, respectively) to identify ovarian cancer patients and the addition of HE4 to CA125 improved the sensitivity from 36% to 67% at a set specificity of 95%. In addition, the combination of HE4 and FOLR1 was a strong predictor of ovarian cancer diagnosis, displaying comparable sensitivity (65%) to the best-performing CA125-based models (67%) at a set specificity of 95%.

CONCLUSIONS

The markers identified through our integrated -omics approach performed similarly to the clinically approved markers CA125 and HE4. Furthermore, HE4 represents a powerful diagnostic marker for ovarian cancer and should be used more routinely in a clinical setting.

IMPACT

The implications of our study are 2-fold: (i) we have demonstrated the strengths of HE4 alone and in combination with CA125, lending credence to increasing its usage in the clinic; and (ii) we have demonstrated the clinical utility of our integrated -omics approach to identifying novel serum markers with comparable performance to clinical markers. Cancer Epidemiol Biomarkers Prev; 25(9); 1333-40. ©2016 AACR.

The Prognostic Value of Circulating Cell-Free DNA in Colorectal Cancer: A Meta-Analysis

BACKGROUND

Circulating cell-free DNA (cfDNA) is a promising candidate biomarker for detection, monitoring and survival prediction of colorectal cancer (CRC). However, its prognostic significance for patients with CRC remains controversial. To derive a precise estimation of the prognostic significance of cfDNA, a meta-analysis was performed.

METHODS

We made a systematic search in data base of the Science Citation Index Embase and Pubmed for studies reporting prognostic data of cfDNA in CRC patients. The data of cfDNA on recurrences-free survival (RFS) and overall survival (OS) were extracted and measured in hazard rates (HRs) and 95% confident intervals (CIs). Subgroup analyses were carried out as well. Finally, the meta-analysis is accompanied with nine studies including 19 subunits.

RESULTS

The pooled HRs with 95% CIs revealed strong associations between cfDNA and RFS (HR [95%CI]=2.78[2.08-3.72], I(2)=32.23%, n=7) along with OS (HR [95%CI]=3.03[2.51-3.66], I(2)=29.24%, n=12) in patients with CRC. Entire subgroup analyses indicated strong prognostic value of cfDNA irrespective tumor stage, study size, tumor markers, detection methods and marker origin.

CONCLUSIONS

All the results exhibits that appearance of cfDNA in blood is an indicator for adverse RFS and OS in CRC patients.

Systems biology study of mucopolysaccharidosis using a human metabolic reconstruction network

Mucopolysaccharidosis (MPS) is a group of lysosomal storage diseases (LSD), characterized by the deficiency of a lysosomal enzyme responsible for the degradation of glycosaminoglycans (GAG). This deficiency leads to the lysosomal accumulation of partially degraded GAG. Nevertheless, deficiency of a single lysosomal enzyme has been associated with impairment in other cell mechanism, such as apoptosis and redox balance. Although GAG analysis represents the main biomarker for MPS diagnosis, it has several limitations that can lead to a misdiagnosis, whereby the identification of new biomarkers represents an important issue for MPS. In this study, we used a system biology approach, through the use of a genome-scale human metabolic reconstruction to understand the effect of metabolism alterations in cell homeostasis and to identify potential new biomarkers in MPS. In-silico MPS models were generated by silencing of MPS-related enzymes, and were analyzed through a flux balance and variability analysis. We found that MPS models used approximately 2286 reactions to satisfy the objective function. Impaired reactions were mainly involved in cellular respiration, mitochondrial process, amino acid and lipid metabolism, and ion exchange. Metabolic changes were similar for MPS I and II, and MPS III A to C; while the remaining MPS showed unique metabolic profiles. Eight and thirteen potential high-confidence biomarkers were identified for MPS IVB and VII, respectively, which were associated with the secondary pathologic process of LSD. In vivo evaluation of predicted intermediate confidence biomarkers (β-hexosaminidase and β-glucoronidase) for MPS IVA and VI correlated with the in-silico prediction. These results show the potential of a computational human metabolic reconstruction to understand the molecular mechanisms this group of diseases, which can be used to identify new biomarkers for MPS.

A microfluidic platform with digital readout and ultra-low detection limit for quantitative point-of-care diagnostics

Quantitative assays are of great importance for point-of-care (POC) diagnostics because they can offer accurate information on the analytes. However, current POC devices often require an accessory instrument to give quantitative readouts for protein biomarkers, especially for those at very low concentration levels. Here, we report a microfluidic platform, the digital volumetric bar-chart chip (DV-chip), for quantitative POC diagnostics with ultra-low detection limits that are readable with the naked eye. Requiring no calibration, the DV-chip presents a digital ink bar chart (representing multiple bits composed of 0 and 1) for the target biomarker based on direct competition between O2 generated by the experimental and control samples. The bar chart clearly and accurately defines target concentration, allowing identification of disease status. For the standard PtNP solutions, the detection limit of the platform is approximately 0.1 pM and the dynamic range covers four orders of magnitude from 0.1 to 1000 pM. CEA samples with concentrations of 1 ng mL(-1) and 1.5 ng mL(-1) could be differentiated by the device. We also performed the ELISA assay for B-type natriuretic peptide (BNP) in 20 plasma samples from heart failure patients and the obtained on-chip data were in agreement with the clinical results. In addition, BNP was detectable at concentrations of less than 5 pM, which is three orders of magnitude lower than the detection limit of the previously reported readerless digital methods. By the integration of gas competition, volumetric bar chart, and digital readout, the DV-chip possesses merits of portability, visible readout, and ultra-low detection limit, which should offer a powerful platform for quantitative POC diagnostics in clinical settings and personalized detection.

Artesunate sensitizes ovarian cancer cells to cisplatin by downregulating RAD51

Artesunate, a semi-synthetic derivative of arteminisin originally developed for the treatment of malaria, has recently been shown to possess antitumor properties. One of the cytotoxic effects of artesunate on cancer cells is mediated by induction of oxidative stress and DNA double-strand breaks (DSBs). We report here that in addition to inducing oxidative stress and DSBs, artesunate can also downregulate RAD51 and impair DSB repair in ovarian cancer cells. We observed that the formation of RAD51 foci and homologous recombination repair (HRR) were significantly reduced in artesunate-treated cells. As a consequence, artesunate and cisplatin synergistically induced DSBs and inhibited the clonogenic formation of ovarian cancer cells. Ectopic expression of RAD51 was able to rescue the increased chemosensitivity conferred by artesunate, confirming that the chemosensitizing effect of artesuante is at least partially mediated by the downregulation of RAD51. Our results indicated that artesunatecan compromise the repair of DSBs in ovarian cancer cells, and thus could be employed as a sensitizing agent in chemotherapy.

Tumor Interstitial Fluid Formation, Characterization, and Clinical Implications

The interstitium, situated between the blood and lymph vessels and the cells, consists of a solid or matrix phase and a fluid phase representing the tissue microenvironment. In the present review, we focus on the interstitial fluid phase of solid tumors, the tumor interstitial fluid (TIF), i.e., the fluid bathing the tumor and stroma cells, also including immune cells. This is a component of the internal milieu of a solid tumor that has attracted regained attention. Access to this space may provide important insight into tumor development and therapy response. TIF is formed by transcapillary filtration, and since this fluid is not readily available we discuss available techniques for TIF isolation, results from subsequent characterization and implications of recent findings with respect to fluid filtration and uptake of macromolecular therapeutic agents. There appear to be local gradients in signaling substances from neoplastic tissue to plasma that may provide new understanding of tumor biology. The development of sensitive proteomic technologies has made TIF a valuable source for tumor specific proteins and biomarker candidates. Potential biomarkers will appear locally in high concentrations in tumors and may eventually be found diluted in the plasma. Access to TIF that reliably reflects the local tumor microenvironment enables identification of substances that can be used in early detection and monitoring of disease.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Towards identification of true cancer biomarkers

BACKGROUND

Most of newly discovered cancer biomarkers fail in the clinic because they lack sensitivity and/or specificity. The current explosion in knowledge of the mutational spectrum of many cancer types, as a result of whole exome and whole genome sequencing, has revealed a wide spectrum of mutations that appear to be highly specific for various cancer types.

DISCUSSION

Mass spectrometry (MS) has the ability to monitor tryptic peptides in complex biological mixtures with high sensitivity and specificity. It may be possible in the near future to combine the known spectrum of gene mutations revealed by genomics with the power of MS, in order to quantify mutant peptides that are highly specific for cancer, in a multiplex fashion. Such mutant peptides, quantified in the circulation and other fluids, may represent tumor markers that are suitable for detection and monitoring of cancer.

SUMMARY

The power of genomic and proteomic technologies can be combined to identify highly specific analytes for biomarker applications.