CBD: a biomarker database for colorectal cancer

CBD2: A functional biomarker database for colorectal cancer

The rapidly evolving landscape of biomarkers for colorectal cancer (CRC) necessitates an integrative, updated repository. In response, we constructed the Colorectal Cancer Biomarker Database (CBD), which collected and displayed the curated biomedicine information for 870 CRC biomarkers in the previous study. Building on CBD, we have now developed CBD2, which includes information on 1569 newly reported biomarkers derived from different biological sources (DNA, RNA, protein, and others) and clinical applications (diagnosis, treatment, and prognosis). CBD2 also incorporates information on nonbiomarkers that have been identified as unsuitable for use as biomarkers in CRC. A key new feature of CBD2 is its network analysis function, by which users can investigate the visible and topological network between biomarkers and identify their relevant pathways. CBD2 also allows users to query a series of chemicals, drug combinations, or multiple targets, to enable multidrug, multitarget, multipathway analyses, toward facilitating the design of polypharmacological treatments for CRC. CBD2 is freely available at http://www.eyeseeworld.com/cbd.

BioKA: a curated and integrated biomarker knowledgebase for animals

Biomarkers play an important role in various area such as personalized medicine, drug development, clinical care, and molecule breeding. However, existing animals' biomarker resources predominantly focus on human diseases, leaving a significant gap in non-human animal disease understanding and breeding research. To address this limitation, we present BioKA (Biomarker Knowledgebase for Animals, https://ngdc.cncb.ac.cn/bioka), a curated and integrated knowledgebase encompassing multiple animal species, diseases/traits, and annotated resources. Currently, BioKA houses 16 296 biomarkers associated with 951 mapped diseases/traits across 31 species from 4747 references, including 11 925 gene/protein biomarkers, 1784 miRNA biomarkers, 1043 mutation biomarkers, 773 metabolic biomarkers, 357 circRNA biomarkers and 127 lncRNA biomarkers. Furthermore, BioKA integrates various annotations such as GOs, protein structures, protein-protein interaction networks, miRNA targets and so on, and constructs an interactive knowledge network of biomarkers including circRNA-miRNA-mRNA associations, lncRNA-miRNA associations and protein-protein associations, which is convenient for efficient data exploration. Moreover, BioKA provides detailed information on 308 breeds/strains of 13 species, and homologous annotations for 8784 biomarkers across 16 species, and offers three online application tools. The comprehensive knowledge provided by BioKA not only advances human disease research but also contributes to a deeper understanding of animal diseases and supports livestock breeding.

A comprehensive survey of intestine histopathological image analysis using machine vision approaches

Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.

Cell cycle protein BORA is associated with colorectal cancer progression by AURORA-PLK1 cascades: a bioinformatics analysis

Colorectal cancer (CRC) is the third most diagnosed cancer in the world. A better understanding of the molecular mechanism of CRC is essential for making novel strategies for the CRC management and its prevention. The present study aims to explore the molecular mechanism through integrated bioinformatics analysis by analyzing genes and their co-expression pattern in normal and CRC states. GSE110223, GSE110224 and GSE113513 gene expression profiles were analyzed in this study. The co-expression networks for normal and tumor samples were constructed separately and analyzed to identify the modules, sub-networks and key genes. Gene regulatory network analysis was done to understand the regulatory mechanism of selected genes. Survival analysis was performed for the identified sub-networks and key genes to understand their role in CRC progression. A total of seven modules were detected and the KEGG pathway analysis revealed these modules were mainly enriched with cell cycle, metabolism and signaling-related pathways. E2F6 and ETV4 transcription factors regulating the activity of multiple genes of identified modules were found to be up-regulated in CRC. Six Sub-networks and seven key genes, BORA, CCT7, DTL, RUVBL1, RUVBL2, THEM6 and TMEM97 associated with the CRC progression were identified. Disease-gene association analysis identified a novel association of the BORA gene with CRC that activates and regulates the AURORA-PLK1 cascades in the cell cycle. Survival analysis indicates that the overexpressed BORA is associated with unfavourable overall survival in CRC. The mechanistic role of BORA in the regulation of cell cycle progression suggests that BORA might act as a potential therapeutic target for CRC.

MicroRNA-455-3p accelerate malignant progression of tumor by targeting H2AFZ in colorectal cancer

Colorectal cancer (CRC) becomes the second leading cause of cancer-related deaths in 2020. Emerging studies have indicated that microRNAs (miRNAs) play a key role in tumorigenesis and progression. The dysfunctions of miR-455-3p are observed in many cancers. However, its biological function in CRC remains to be confirmed. By sequencing serum sample, miR-455-3p was found to be up-regulated in CRC patients. RT-qPCR demonstrated that the miR-455-3p expression was both higher in the serum and tumor tissues of CRC patients. Furthermore, it indicated that miR-455-3p had the ability in promoting cell proliferation, suppressing cell apoptosis, and stimulating cell migration. In vivo experiments also showed that miR-455-3p promoted tumor growth. Additionally, H2AFZ was proved as the direct gene target of miR-455-3p by dual-luciferase assay. Taken together, miR-455-3p functioned as a tumor promoter in CRC development by regulating H2AFZ directly. Thus, it has enormous potential as a biomarker in the diagnosis of CRC.

Organ-On-A-Chip Database Revealed-Achieving the Human Avatar in Silicon

Organ-on-a-chip (OOC) provides microphysiological conditions on a microfluidic chip, which makes up for the shortcomings of traditional in vitro cellular culture models and animal models. It has broad application prospects in drug development and screening, toxicological mechanism research, and precision medicine. A large amount of data could be generated through its applications, including image data, measurement data from sensors, ~omics data, etc. A database with proper architecture is required to help scholars in this field design experiments, organize inputted data, perform analysis, and promote the future development of novel OOC systems. In this review, we overview existing OOC databases that have been developed, including the BioSystics Analytics Platform (BAP) developed by the University of Pittsburgh, which supports study design as well as data uploading, storage, visualization, analysis, etc., and the organ-on-a-chip database (Ocdb) developed by Southeast University, which has collected a large amount of literature and patents as well as relevant toxicological and pharmaceutical data and provides other major functions. We used examples to overview how the BAP database has contributed to the development and applications of OOC technology in the United States for the MPS consortium and how the Ocdb has supported researchers in the Chinese Organoid and Organs-On-A-Chip society. Lastly, the characteristics, advantages, and limitations of these two databases were discussed.

A critical review of datasets and computational suites for improving cancer theranostics and biomarker discovery

Cancer has been constantly evolving and so is the research pertaining to cancer diagnosis and therapeutic regimens. Early detection and specific therapeutics are the key features of modern cancer therapy. These requirements can only be fulfilled with the integration of diverse high-throughput technologies. Integration of advanced omics methodology involving genomics, epigenomics, proteomics, and transcriptomics provide a clear understanding of multi-faceted cancer. In the past few years, tremendous high-throughput data have been generated from cancer genomics and epigenomic analyses, which on further methodological analyses can yield better biological insights. The major epigenetic alterations reported in cancer are DNA methylation levels, histone post-translational modifications, and epi-miRNA regulating the oncogenes and tumor suppressor genes. While the genomic analyses like gene expression profiling, cancer gene prediction, and genome annotation divulge the genetic alterations in oncogenes or tumor suppressor genes. Also, systems biology approach using biological networks is being extensively used to identify novel cancer biomarkers. Therefore, integration of these multi-dimensional approaches will help to identify potential diagnostic and therapeutic biomarkers. Here, we reviewed the critical databases and tools dedicated to various epigenomic and genomic alterations in cancer. The review further focuses on the multi-omics resources available for further validating the identified cancer biomarkers. We also highlighted the tools for cancer biomarker discovery using a systems biology approach utilizing genomic and epigenomic data. Biomarkers predicted using such integrative approaches are shown to be more clinically relevant.

LiqBioer: a manually curated database of cancer biomarkers in body fluid

Cancer biomarkers are measurable indicators that play vital roles in clinical applications. Biomarkers in body fluids have gained considerable attention since the development of liquid biopsy, and their data volume is rapidly increasing. Nevertheless, current research lacks the compilation of published cancer body fluid biomarkers into a centralized and sustainable repository for researchers and clinicians, despite a handful of small-scale and specific data resources. To fulfill this purpose, we developed liquid biomarker (LiqBioer) containing 6231 manually curated records from 3447 studies, covering 3056 biomarkers and 74 types of cancer in 22 tissues. LiqBioer allows users to browse and download comprehensive information on body liquid biomarkers, including cancer types, source studies and clinical usage. As a comprehensive resource for body fluid biomarkers of cancer, LiqBioer is a powerful tool for researchers and clinicians to query and retrieve biomarkers in liquid biopsy.

GGDB: A Grameneae genome alignment database of homologous genes hierarchically related to evolutionary events

The genomes of Gramineae plants have been preferentially sequenced owing to their economic value. These genomes are often quite complex, for example harboring many duplicated genes, and are the main source of genetic innovation and often the result of recurrent polyploidization. Deciphering these complex genome structures and linking duplicated genes to specific polyploidization events are important for understanding the biology and evolution of plants. However, efforts have been hampered by the complexity of analyzing these genomes. Here, we analyzed 29 well-assembled and up-to-date Gramineae genome sequences by hierarchically relating duplicated genes in collinear regions to specific polyploidization or speciation events. We separated duplicated genes produced by each event, established lists of paralogous and orthologous genes, and ultimately constructed an online database, GGDB (http://www.grassgenome.com/). Homologous gene lists from each plant and between plants can be displayed, searched, and downloaded from the database. Interactive comparison tools are deployed to demonstrate homology among user-selected plants and to draw genome-scale or local alignment figures and gene-based phylogenetic trees corrected by exploiting gene collinearity. Using these tools and figures, users can easily detect structural changes in genomes and explore the effects of paleo-polyploidy on crop genome structure and function. The GGDB will provide a useful platform for improving our understanding of genome changes and functional innovation in Gramineae plants.

GlioMarker: An integrated database for knowledge exploration of diagnostic biomarkers in gliomas

Gliomas are the most frequent malignant and aggressive tumors in the central nervous system. Early and effective diagnosis of glioma using diagnostic biomarkers can prolong patients' lives and aid in the development of new personalized treatments. Therefore, a thorough and comprehensive understanding of the diagnostic biomarkers in gliomas is of great significance. To this end, we developed the integrated and web-based database GlioMarker (http://gliomarker.prophetdb.org/), the first comprehensive database for knowledge exploration of glioma diagnostic biomarkers. In GlioMarker, accurate information on 406 glioma diagnostic biomarkers from 1559 publications was manually extracted, including biomarker descriptions, clinical information, associated literature, experimental records, associated diseases, statistical indicators, etc. Importantly, we integrated many external resources to provide clinicians and researchers with the capability to further explore knowledge on these diagnostic biomarkers based on three aspects. (1) Obtain more ontology annotations of the biomarker. (2) Identify the relationship between any two or more components of diseases, drugs, genes, and variants to explore the knowledge related to precision medicine. (3) Explore the clinical application value of a specific diagnostic biomarker through online analysis of genomic and expression data from glioma cohort studies. GlioMarker provides a powerful, practical, and user-friendly web-based tool that may serve as a specialized platform for clinicians and researchers by providing rapid and comprehensive knowledge of glioma diagnostic biomarkers to subsequently facilitates high-quality research and applications.

Loss of CHGA Protein as a Potential Biomarker for Colon Cancer Diagnosis: A Study on Biomarker Discovery by Machine Learning and Confirmation by Immunohistochemistry in Colorectal Cancer Tissue Microarrays

Simple Summary

The identification of effective novel biomarkers is emergently needed in colon cancer patients. In the present study, firstly we predicted that CHGA could be a biomarker for colon cancer based on the protein–protein interaction network of all the reported biomarkers that were collected from our colorectal cancer biomarker database (CBD). Then we verified our results using a diagnostic test in gene expression data and an immunohistochemistry test. The results of this study suggest that a loss of CHGA expression from the normal colon and adjacent mucosa to colon cancer may be used as a valuable biomarker for early diagnosis of colon cancer patients.

Abstract

Background. The incidence of colorectal cancers has been constantly increasing. Although the mortality has slightly decreased, it is far from satisfaction. Precise early diagnosis for colorectal cancer has been a great challenge in order to improve patient survival. Patients and Methods. We started with searching for protein biomarkers based on our colorectal cancer biomarker database (CBD), finding differential expressed genes (GEGs) and non-DEGs from RNA sequencing (RNA-seq) data, and further predicted new biomarkers of protein–protein interaction (PPI) networks by machine learning (ML) methods. The best-selected biomarker was further verified by a receiver operating characteristic (ROC) test from microarray and RNA-seq data, biological network, and functional analysis, and immunohistochemistry in the tissue arrays from 198 specimens. Results. There were twelve proteins (MYO5A, CHGA, MAPK13, VDAC1, CCNA2, YWHAZ, CDK5, GNB3, CAMK2G, MAPK10, SDC2, and ADCY5) which were predicted by ML as colon cancer candidate diagnosis biomarkers. These predicted biomarkers showed close relationships with reported biomarkers of the PPI network and shared some pathways. An ROC test showed the CHGA protein with the best diagnostic accuracy (AUC = 0.9 in microarray data and 0.995 in RNA-seq data) among these candidate protein biomarkers. Furthermore, immunohistochemistry examination on our colon cancer tissue microarray samples further confirmed our bioinformatical prediction, indicating that CHGA may be used as a potential biomarker for early diagnosis of colon cancer patients. Conclusions. CHGA could be a potential candidate biomarker for diagnosing earlier colon cancer in the patients.

Yusof NF, Mohd Yunos RI, Ishak M, Saidin S, Rose IM, Sagap I, Mazlan L, Mohd Azman ZA, Mazlan M, Ab Rahim S, Wan Ngah WZ, Nathan S, Hashim NAA, Mohamed-Hussein ZA, Jamal R. TCGA-My: A Systematic Repository for Systems Biology of Malaysian Colorectal Cancer

Colorectal cancer (CRC) ranks second among the most commonly occurring cancers in Malaysia, and unfortunately, its pathobiology remains unknown. CRC pathobiology can be understood in detail with the implementation of omics technology that is able to generate vast amounts of molecular data. The generation of omics data has introduced a new challenge for data organization. Therefore, a knowledge-based repository, namely TCGA-My, was developed to systematically store and organize CRC omics data for Malaysian patients. TCGA-My stores the genome and metabolome of Malaysian CRC patients. The genome and metabolome datasets were organized using a Python module, pandas. The variants and metabolites were first annotated with their biological information using gene ontologies (GOs) vocabulary. The TCGA-My relational database was then built using HeidiSQL PorTable 9.4.0.512, and Laravel was used to design the web interface. Currently, TCGA-My stores 1,517,841 variants, 23,695 genes, and 167,451 metabolites from the samples of 50 CRC patients. Data entries can be accessed via search and browse menus. TCGA-My aims to offer effective and systematic omics data management, allowing it to become the main resource for Malaysian CRC research, particularly in the context of biomarker identification for precision medicine.

MarkerGenie: an NLP-enabled text-mining system for biomedical entity relation extraction

Motivation

Natural language processing (NLP) tasks aim to convert unstructured text data (e.g. articles or dialogues) to structured information. In recent years, we have witnessed fundamental advances of NLP technique, which has been widely used in many applications such as financial text mining, news recommendation and machine translation. However, its application in the biomedical space remains challenging due to a lack of labeled data, ambiguities and inconsistencies of biological terminology. In biomedical marker discovery studies, tools that rely on NLP models to automatically and accurately extract relations of biomedical entities are valuable as they can provide a more thorough survey of all available literature, hence providing a less biased result compared to manual curation. In addition, the fast speed of machine reader helps quickly orient research and development.

Results

To address the aforementioned needs, we developed automatic training data labeling, rule-based biological terminology cleaning and a more accurate NLP model for binary associative and multi-relation prediction into the MarkerGenie program. We demonstrated the effectiveness of the proposed methods in identifying relations between biomedical entities on various benchmark datasets and case studies.

Availability and implementation

MarkerGenie is available at https://www.genegeniedx.com/markergenie/. Data for model training and evaluation, term lists of biomedical entities, details of the case studies and all trained models are provided at https://drive.google.com/drive/folders/14RypiIfIr3W_K-mNIAx9BNtObHSZoAyn?usp=sharing.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

In silico tools and databases for designing cancer immunotherapy

Immunotherapy is a rapidly growing therapy for cancer which have numerous benefits over conventional treatments like surgery, chemotherapy, and radiation. Overall survival of cancer patients has improved significantly due to the use of immunotherapy. It acts as a novel pillar for treating different malignancies from their primary to the metastatic stage. Recent preferments in high-throughput sequencing and computational immunology leads to the development of targeted immunotherapy for precision oncology. In the last few decades, several computational methods and resources have been developed for designing immunotherapy against cancer. In this review, we have summarized cancer-associated genomic, transcriptomic, and mutation profile repositories. We have also enlisted in silico methods for the prediction of vaccine candidates, HLA binders, cytokines inducing peptides, and potential neoepitopes. Of note, we have incorporated the most important bioinformatics pipelines and resources for the designing of cancer immunotherapy. Moreover, to facilitate the scientific community, we have developed a web portal entitled ImmCancer (https://webs.iiitd.edu.in/raghava/immcancer/), comprises cancer immunotherapy tools and repositories.

ProCanBio: A Database of Manually Curated Biomarkers for Prostate Cancer

Prostate cancer (PCa) is the second lethal malignancy in men worldwide. In the past, numerous research groups investigated the omics profiles of patients and scrutinized biomarkers for the diagnosis and prognosis of PCa. However, information related to the biomarkers is widely scattered across numerous resources in complex textual format, which poses hindrance to understand the tumorigenesis of this malignancy and scrutinization of robust signature. To create a comprehensive resource, we collected all the relevant literature on PCa biomarkers from the PubMed. We scrutinize the extensive information about each biomarker from a total of 412 unique research articles. Each entry of the database incorporates PubMed ID, biomarker name, biomarker type, biomolecule, source, subjects, validation status, and performance measures such as sensitivity, specificity, and hazard ratio (HR). In this study, we present ProCanBio, a manually curated database that maintains detailed data on 2053 entries of potential PCa biomarkers obtained from 412 publications in user-friendly tabular format. Among them are 766 protein-based, 507 RNA-based, 157 genomic mutations, 260 miRNA-based, and 122 metabolites-based biomarkers. To explore the information in the resource, a web-based interactive platform was developed with searching and browsing facilities. To the best of the authors' knowledge, there is no resource that can consolidate the information contained in all the published literature. Besides this, ProCanBio is freely available and is compatible with most web browsers and devices. Eventually, we anticipate this resource will be highly useful for the research community involved in the area of prostate malignancy.

miR‑451 suppresses the malignant characteristics of colorectal cancer via targeting SAMD4B

Cancer metastasis and recurrence are major causes of poor survival in patients with colorectal cancer (CRC). Therefore, the biological behavior of microRNA (miR)‑451 in CRC deserves further investigation. Reverse transcription‑quantitative PCR was applied to measure the relative expression of miR‑451 in blood serum specimens from patients with CRC and CRC cells. In vitro, HCT116 cells were transfected with miR‑451 mimics, a miR‑451 inhibitor, or SAMD4B plasmids. Proliferation, migration and apoptosis were measured using CCK‑8, Transwell assays and flow cytometry, respectively. Luciferase reporter assay was used to identify targets of miR‑451 and western blotting performed to explore the internal mechanisms of miR‑451 regulation. In vivo, the effect of miR‑451 and SAMD4B plasmids on tumor growth was analyzed using a nude mouse xenograft model. Results indicated that serum miR‑451 expression was lower in patients with CRC compared with healthy controls. Patients with elevated expression of miR‑451 had longer survival times compared with those with low expression. Overexpression of miR‑451 inhibited proliferation and migration, promoted apoptosis and enhanced the sensitivity of CRC cells to chemotherapy. SAMD4B was identified as a direct target of miR‑451 using miRNA target prediction programs and dual luciferase reporter assay validated the binding site of miR‑451 in the 3‑'UTR region of SAMD4B. Further studies confirmed that miR‑451 inhibited CRC progression via targeting SAMD4B. Results indicated that miR‑451 is essential for blocking tumor growth via targeting SAMD4B in vivo and in vitro. The miR‑451/SAMD4B axis may serve as a novel therapeutic target in patients with CRC.

NDDRF: a risk factor knowledgebase for personalized prevention of neurodegenerative diseases

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A risk factor knowledgebase (NDDRF) is built for neurodegenerative diseases (NDDs).

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NDDRF collects the risk factors associated with diagnosis and prevention of NDDs.

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NDDRF is helpful to the systematic understanding of the heterogeneous NDDs

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NDDRF provides knowledge for personalized diagnosis and prevention of NDDs.

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NDDRF can be used to the future explainable artificial intelligent modeling.

Introduction

Neurodegenerative diseases (NDDs) are a series of chronic diseases, which are associated with progressive loss of neuronal structure or function. The complex etiologies of the NDDs remain unclear, thus the prevention and early diagnosis of NDDs are critical to reducing the mortality and morbidity of these diseases.

Objectives

To provide a systematic understanding of the heterogeneity of the risk factors associated with different NDDs (pan-neurodegenerative diseases or pan-NDDs), the knowledgebase is established to facilitate the personalized and knowledge-guided diagnosis, prevention and prediction of NDDs.

Methods

Before data collection, the medical, life science and informatics experts as well as the potential users of the database were consulted and discussed for the scope of data and the classification of risk factors. The PubMed database was used as the resource of the data and knowledge extraction. Risk factors of NDDs were manually collected from literature published between 1975 and 2020.

Results

The comprehensive risk factors database for NDDs (NDDRF) was established including 998 single or combined risk factors, 2293 records and 1071 articles relevant to the 14 most common NDDs. The single risk factors are classified into 3 categories, i.e. epidemiological factors (469), genetic factors (324) and biochemical factors (153). Among all the factors, 179 factors are positive and protective, while 880 factors have negative influence for NDDs. The knowledgebase is available at http://sysbio.org.cn/NDDRF/.

Conclusion

NDDRF provides the structured information and knowledge resource on risk factors of NDDs. It could benefit the future systematic and personalized investigation of pan-NDDs genesis and progression. Meanwhile it may be used for the future explainable artificial intelligence modeling for smart diagnosis and prevention of NDDs.

Computational resources for identification of cancer biomarkers from omics data

Cancer is one of the most prevailing, deadly and challenging diseases worldwide. The advancement in technology led to the generation of different types of omics data at each genome level that may potentially improve the current status of cancer patients. These data have tremendous applications in managing cancer effectively with improved outcome in patients. This review summarizes the various computational resources and tools housing several types of omics data related to cancer. Major categorization of resources includes-cancer-associated multiomics data repositories, visualization/analysis tools for omics data, machine learning-based diagnostic, prognostic, and predictive biomarker tools, and data analysis algorithms employing the multiomics data. The review primarily focuses on providing comprehensive information on the open-source multiomics tools and data repositories, owing to their broader applicability, economic-benefit and usability. Sections including the comparative analysis, tools applicability and possible future directions have also been discussed in detail. We hope that this information will significantly benefit the researchers and clinicians, especially those with no sound background in bioinformatics and who lack sufficient data analysis skills to interpret something from the plethora of cancer-specific data generated nowadays.

MarkerDB: an online database of molecular biomarkers

MarkerDB is a freely available electronic database that attempts to consolidate information on all known clinical and a selected set of pre-clinical molecular biomarkers into a single resource. The database includes four major types of molecular biomarkers (chemical, protein, DNA [genetic] and karyotypic) and four biomarker categories (diagnostic, predictive, prognostic and exposure). MarkerDB provides information such as: biomarker names and synonyms, associated conditions or pathologies, detailed disease descriptions, detailed biomarker descriptions, biomarker specificity, sensitivity and ROC curves, standard reference values (for protein and chemical markers), variants (for SNP or genetic markers), sequence information (for genetic and protein markers), molecular structures (for protein and chemical markers), tissue or biofluid sources (for protein and chemical markers), chromosomal location and structure (for genetic and karyotype markers), clinical approval status and relevant literature references. Users can browse the data by conditions, condition categories, biomarker types, biomarker categories or search by sequence similarity through the advanced search function. Currently, the database contains 142 protein biomarkers, 1089 chemical biomarkers, 154 karyotype biomarkers and 26 374 genetic markers. These are categorized into 25 560 diagnostic biomarkers, 102 prognostic biomarkers, 265 exposure biomarkers and 6746 predictive biomarkers or biomarker panels. Collectively, these markers can be used to detect, monitor or predict 670 specific human conditions which are grouped into 27 broad condition categories. MarkerDB is available at https://markerdb.ca.

Identification of novel prognostic biomarkers in renal cell carcinoma

Objective: To identify novel prognostic biomarkers in renal cell carcinoma (RCC).

Results: 12 coding genes and one miRNA were finally identified as prognostic biomarkers. All of them were related to a poor prognosis. Lower expression levels of the coding genes were observed in higher clinical stages. Prognostic signatures including 7 biomarkers were identified. Patients in the high-risk group had worse survival than those in the low-risk group. The areas under the curves in different years indicated that it was a valuable signature in prognosis. It was found that elevated WDR72 inhibited the survival and invasion of 786-O and 769P cells in vitro.

Conclusions: Thirteen prognostic biomarkers of RCC were identified. Among them, 7 biomarkers comprised a signature to evaluate the RCC prognosis. WDR72 was a cancer suppressor and a potential therapeutic target in RCC.

Methods: Differentially expressed genes/miRNAs (DEGs/DEMs) and prognosis-related genes/miRNAs were acquired from public database. Prognostic biomarkers were identified by overlapping the significant DEGs/DEMs and prognosis-related genes/miRNAs. The associations between these biomarkers and the clinical stages were analyzed. All of these prognostic biomarkers were further investigated with multi-variable Cox regression. Finally, the inhibitory effect of WDR72 on the growth and invasion of RCC cells was studied.

Plant miRNAs Reduce Cancer Cell Proliferation by Targeting MALAT1 and NEAT1: A Beneficial Cross-Kingdom Interaction

MicroRNAs (miRNAs) are ubiquitous regulators of gene expression, evolutionarily conserved in plants and mammals. In recent years, although a growing number of papers debate the role of plant miRNAs on human gene expression, the molecular mechanisms through which this effect is achieved are still not completely elucidated. Some evidence suggest that this interaction might be sequence specific, and in this work, we investigated this possibility by transcriptomic and bioinformatics approaches. Plant and human miRNA sequences from primary databases were collected and compared for their similarities (global or local alignments). Out of 2,588 human miRNAs, 1,606 showed a perfect match of their seed sequence with the 5′ end of 3,172 plant miRNAs. Further selections were applied based on the role of the human target genes or of the miRNA in cell cycle regulation (as an oncogene, tumor suppressor, or a biomarker for prognosis, or diagnosis in cancer). Based on these criteria, 20 human miRNAs were selected as potential functional analogous of 7 plant miRNAs, which were in turn transfected in different cell lines to evaluate their effect on cell proliferation. A significant decrease was observed in colorectal carcinoma HCT116 cell line. RNA-Seq demonstrated that 446 genes were differentially expressed 72 h after transfection. Noteworthy, we demonstrated that the plant mtr-miR-5754 and gma-miR4995 directly target the tumor-associated long non-coding RNA metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and nuclear paraspeckle assembly transcript 1 (NEAT1) in a sequence-specific manner. In conclusion, according to other recent discoveries, our study strengthens and expands the hypothesis that plant miRNAs can have a regulatory effect in mammals by targeting both protein-coding and non-coding RNA, thus suggesting new biotechnological applications.

OncoMX: A Knowledgebase for Exploring Cancer Biomarkers in the Context of Related Cancer and Healthy Data

PURPOSE

The purpose of OncoMX¹ knowledgebase development was to integrate cancer biomarker and relevant data types into a meta-portal, enabling the research of cancer biomarkers side by side with other pertinent multidimensional data types.

METHODS

Cancer mutation, cancer differential expression, cancer expression specificity, healthy gene expression from human and mouse, literature mining for cancer mutation and cancer expression, and biomarker data were integrated, unified by relevant biomedical ontologies, and subjected to rule-based automated quality control before ingestion into the database.

RESULTS

OncoMX provides integrated data encompassing more than 1,000 unique biomarker entries (939 from the Early Detection Research Network [EDRN] and 96 from the US Food and Drug Administration) mapped to 20,576 genes that have either mutation or differential expression in cancer. Sentences reporting mutation or differential expression in cancer were extracted from more than 40,000 publications, and healthy gene expression data with samples mapped to organs are available for both human genes and their mouse orthologs.

CONCLUSION

OncoMX has prioritized user feedback as a means of guiding development priorities. By mapping to and integrating data from several cancer genomics resources, it is hoped that OncoMX will foster a dynamic engagement between bioinformaticians and cancer biomarker researchers. This engagement should culminate in a community resource that substantially improves the ability and efficiency of exploring cancer biomarker data and related multidimensional data.

MOB1 Inhibits Malignant Progression of Colorectal Cancer by Targeting PAK2

Objective

We aimed at studying the mechanism of MOB1 inhibiting the proliferation and metastasis of colorectal cancer (CRC), to provide a new guidance for the early diagnosis and treatment of CRC.

Methods

MOB1 expression level in 68 pairs of CRC tissues and adjacent ones was detected by quantitative real-time polymerase chain reaction (qRT-PCR) analysis, and the associations between the expression level of MOB1 and the clinicopathological indicators as well as the prognosis of CRC patients were analyzed. After constructing CRC cell lines that stably overexpressing or silencing MOB1, the changes of cell proliferation and metastasis ability were examined by Cell Counting Kit (CCK-8) and Transwell assay. In addition, the interaction between MOB1 and PAK2 and how the these two genes affect the biological functions of CRC cell lines were investigated by luciferase assay, qRT-PCR and Western Blot experiments.

Results

Our data showed that MOB1 expression level in CRC tissues was remarkably lower than that in adjacent ones. In comparison to patients of the group of high MOB1 expression, these patients of low MOB1 expression group showed higher incidence of distant or lymph node metastasis and lower survival rate. Cell functional experiments revealed that overexpression of MOB1 markedly attenuated the proliferation and migration ability of CRC cell lines compared to the NC group; In contrast, knockdown of MOB1 enhanced the above-mentioned cell abilities compared to anti-NC group. Luciferase assay verified an interaction between MOB1 and PAK2; and Western blot analysis showed a negative correlation between the expression of the MOB1 and PAK2 protein levels in CRC tissues. Subsequently, we demonstrated that MOB1 interacted with PAK2 to regulate its expression and affected the proliferation and migration capacity of CRC cell lines in vitro.

Conclusion

In summary, the lowly expressed MOB1 in CRC tissues and cell lines may accelerate the proliferation and migration through modulating PAK2 expression.

Identification of microRNA-451a as a Novel Circulating Biomarker for Colorectal Cancer Diagnosis

Background

Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Successful treatment of CRC relies on accurate early diagnosis, which is currently a challenge due to its complexity and personalized pathologies. Thus, novel molecular biomarkers are needed for early CRC detection.

Methods

Gene and microRNA microarray profiling of CRC tissues and miRNA-seq data were analyzed. Candidate microRNA biomarkers were predicted using both CRC-specific network and miRNA-BD tool. Validation analyses were carried out to interrogate the identified candidate CRC biomarkers.

Results

We identified miR-451a as a potential early CRC biomarker circulating in patient's serum. The dysregulation of miR-451a was revealed both in primary tumors and in patients' sera. Downstream analysis validated the tumor suppressor role of miR-451a and high sensitivity of miR-451a in CRC patients, further confirming its potential role as CRC circulation biomarker.

Conclusion

The miR-451a is a potential circulating biomarker for early CRC diagnosis.

Novel MicroRNA Biomarkers for Colorectal Cancer Early Diagnosis and 5-Fluorouracil Chemotherapy Resistance but Not Prognosis: A Study from Databases to AI-Assisted Verifications

Colorectal cancer (CRC) is one of the major causes of cancer death worldwide. In general, early diagnosis for CRC and individual therapy have led to better survival for the cancer patients. Accumulating studies concerning biomarkers have provided positive evidence to improve cancer early diagnosis and better therapy. It is, however, still necessary to further investigate the precise biomarkers for cancer early diagnosis and precision therapy and predicting prognosis. In this study, AI-assisted systems with bioinformatics algorithm integrated with microarray and RNA sequencing (RNA-seq) gene expression (GE) data has been approached to predict microRNA (miRNA) biomarkers for early diagnosis of CRC based on the miRNA-messenger RNA (mRNA) interaction network. The relationships between the predicted miRNA biomarkers and other biological components were further analyzed on biological networks. Bayesian meta-analysis of diagnostic test was utilized to verify the diagnostic value of the miRNA candidate biomarkers and the combined multiple biomarkers. Biological function analysis was performed to detect the relationship of candidate miRNA biomarkers and identified biomarkers in pathways. Text mining was used to analyze the relationships of predicted miRNAs and their target genes with 5-fluorouracil (5-FU). Survival analyses were conducted to evaluate the prognostic values of these miRNAs in CRC. According to the number of miRNAs single regulated mRNAs (NSR) and the number of their regulated transcription factor gene percentage (TFP) on the miRNA-mRNA network, there were 12 promising miRNA biomarkers were selected. There were five potential candidate miRNAs (miRNA-186-5p, miRNA-10b-5, miRNA-30e-5p, miRNA-21 and miRNA-30e) were confirmed as CRC diagnostic biomarkers, and two of them (miRNA-21 and miRNA-30e) were previously reported. Furthermore, the combinations of the five candidate miRNAs biomarkers showed better prediction accuracy for CRC early diagnosis than the single miRNA biomarkers. miRNA-10b-5p and miRNA-30e-5p were associated with the 5-FU therapy resistance by targeting the related genes. These miRNAs biomarkers were not statistically associated with CRC prognosis.

An algorithm-based meta-analysis of genome- and proteome-wide data identifies a combination of potential plasma biomarkers for colorectal cancer

Screening programs for colorectal cancer (CRC) often rely on detection of blood in stools, which is unspecific and leads to a large number of colonoscopies of healthy subjects. Painstaking research has led to the identification of a large number of different types of biomarkers, few of which are in general clinical use. Here, we searched for highly accurate combinations of biomarkers by meta-analyses of genome- and proteome-wide data from CRC tumors. We focused on secreted proteins identified by the Human Protein Atlas and used our recently described algorithms to find optimal combinations of proteins. We identified nine proteins, three of which had been previously identified as potential biomarkers for CRC, namely CEACAM5, LCN2 and TRIM28. The remaining proteins were PLOD1, MAD1L1, P4HA1, GNS, C12orf10 and P3H1. We analyzed these proteins in plasma from 80 patients with newly diagnosed CRC and 80 healthy controls. A combination of four of these proteins, TRIM28, PLOD1, CEACAM5 and P4HA1, separated a training set consisting of 90% patients and 90% of the controls with high accuracy, which was verified in a test set consisting of the remaining 10%. Further studies are warranted to test our algorithms and proteins for early CRC diagnosis.

Chromogranin-A Expression as a Novel Biomarker for Early Diagnosis of Colon Cancer Patients

Colon cancer is one of the major causes of cancer death worldwide. The five-year survival rate for the early-stage patients is more than 90%, and only around 10% for the later stages. Moreover, half of the colon cancer patients have been clinically diagnosed at the later stages. It is; therefore, of importance to enhance the ability for the early diagnosis of colon cancer. Taking advantages from our previous studies, there are several potential biomarkers which have been associated with the early diagnosis of the colon cancer. In order to investigate these early diagnostic biomarkers for colon cancer, human chromogranin-A (CHGA) was further analyzed among the most powerful diagnostic biomarkers. In this study, we used a logistic regression-based meta-analysis to clarify associations of CHGA expression with colon cancer diagnosis. Both healthy populations and the normal mucosa from the colon cancer patients were selected as the double normal controls. The results showed decreased expression of CHGA in the early stages of colon cancer as compared to the normal controls. The decline of CHGA expression in the early stages of colon cancer is probably a new diagnostic biomarker for colon cancer diagnosis with high predicting possibility and verification performance. We have also compared the diagnostic powers of CHGA expression with the typical oncogene KRAS, classic tumor suppressor TP53, and well-known cellular proliferation index MKI67, and the CHGA showed stronger ability to predict early diagnosis for colon cancer than these other cancer biomarkers. In the protein-protein interaction (PPI) network, CHGA was revealed to share some common pathways with KRAS and TP53. CHGA might be considered as a novel, promising, and powerful biomarker for early diagnosis of colon cancer.

Potential Applications of DNA, RNA and Protein Biomarkers in Diagnosis, Therapy and Prognosis for Colorectal Cancer: A Study from Databases to AI-Assisted Verification

In order to find out the most valuable biomarkers and pathways for diagnosis, therapy and prognosis in colorectal cancer (CRC) we have collected the published CRC biomarkers and established a CRC biomarker database (CBD: http://sysbio.suda.edu.cn/CBD/index.html). In this study, we analysed the single and multiple DNA, RNA and protein biomarkers as well as their positions in cancer related pathways and protein-protein interaction (PPI) networks to describe their potential applications in diagnosis, therapy and prognosis. CRC biomarkers were collected from the CBD. The RNA and protein biomarkers were matched to their corresponding DNAs by the miRDB database and the PubMed Gene database, respectively. The PPI networks were used to investigate the relationships between protein biomarkers and further detect the multiple biomarkers. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene Ontology (GO) annotation were used to analyse biological functions of the biomarkers. AI classification techniques were utilized to further verify the significances of the multiple biomarkers in diagnosis and prognosis for CRC. We showed that a large number of the DNA, RNA and protein biomarkers were associated with the diagnosis, therapy and prognosis in various degrees in the CRC biomarker networks. The CRC biomarkers were closely related to the CRC initiation and progression. Moreover, the biomarkers played critical roles in cellular proliferation, apoptosis and angiogenesis and they were involved in Ras, p53 and PI3K pathways. There were overlaps among the DNA, RNA and protein biomarkers. AI classification verifications showed that the combined multiple protein biomarkers played important roles to accurate early diagnosis and predict outcome for CRC. There were several single and multiple CRC protein biomarkers which were associated with diagnosis, therapy and prognosis in CRC. Further, AI-assisted analysis revealed that multiple biomarkers had potential applications for diagnosis and prognosis in CRC.

MIRKB: a myocardial infarction risk knowledge base

Myocardial infarction (MI) is a common cardiovascular disease and a leading cause of death worldwide. The etiology of MI is complicated and not completely understood. Many risk factors are reported important for the development of MI, including lifestyle factors, environmental factors, psychosocial factors, genetic factors, etc. Identifying individuals with an increased risk of MI is urgent and a major challenge for improving prevention. The MI risk knowledge base (MIRKB) is developed for facilitating MI research and prevention. The goal of MIRKB is to collect risk factors and models related to MI to increase the efficiency of systems biological level understanding of the disease. MIRKB contains 8436 entries collected from 4366 articles in PubMed before 5 July 2019 with 7902 entries for 1847 single factors, 195 entries for 157 combined factors and 339 entries for 174 risk models. The single factors are classified into the following five categories based on their characteristics: molecular factor (2356 entries, 649 factors), imaging (821 entries, 252 factors), physiological factor (1566 entries, 219 factors), clinical factor (2523 entries, 561 factors), environmental factor (46 entries, 26 factors), lifestyle factor (306 entries, 65 factors) and psychosocial factor (284 entries, 75 factors). MIRKB will be helpful to the future systems level unraveling of the complex mechanism of MI genesis and progression.

Endometriosis Knowledgebase: a gene-based resource on endometriosis

Endometriosis is a complex, benign, estrogen-dependent gynecological disorder with an incidence of ~10% women in reproductive age. The implantation and growth of endometrial cells outside the uterus leads to the development of endometriosis. Endometriosis is also associated with comorbid conditions like cardiovascular and autoimmune diseases. The absence of non-invasive diagnostic markers, delayed diagnosis, high risk of recurrence of the disease on surgical removal of the tissue and absence of a definitive cure for endometriosis makes it imperative to gain insights into the complex etiology of endometriosis. A plethora of genes identified from blood and endometrial biopsies, involved in different pathways like steroid metabolism, angiogenesis, inflammation, etc. have been associated with endometriosis. However, the exact mechanism and genetic etiology of endometriosis still remain unclear. The polygenic nature of the disease, incongruent phenotypic manifestations in different ethnic populations and information scattered in literature makes it difficult to delineate the sub-network of genes that will aid in disease diagnosis and effective treatment. Endometriosis Knowledgebase is a manually curated database with information on genes associated with endometriosis. It holds information on 831 genes, their associated polymorphisms, gene ontologys, pathways and diseases. Genes in the database are enriched in pathways important for cell signaling, immune regulation and reproduction. A genetic overlap is seen between endometriosis and cancers, endocrine/reproductive, nervous system, immune and metabolic diseases. Network analysis of genes in the Endometriosis Knowledgebase helped predict 13 new candidate genes for endometriosis. These genes were found to be enriched in biological processes associated with endometriosis. The Endometriosis Knowledgebase and incorporated tools for gene and sequence-based analysis will benefit both researchers and clinicians working in the realm of reproductive biology.

PTSD Biomarker Database: deep dive metadatabase for PTSD biomarkers, visualizations and analysis tools

The PTSD Biomarker Database (PTSDDB) is a database that provides a landscape view of physiological markers being studied as putative biomarkers in the current post-traumatic stress disorder (PTSD) literature to enable researchers to explore and compare findings quickly. The PTSDDB currently contains over 900 biomarkers and their relevant information from 109 original articles published from 1997 to 2017. Further, the curated content stored in this database is complemented by a web application consisting of multiple interactive visualizations that enable the investigation of biomarker knowledge in PTSD (e.g. clinical study metadata, biomarker findings, experimental methods, etc.) by compiling results from biomarker studies to visualize the level of evidence for single biomarkers and across functional categories. This resource is the first attempt, to the best of our knowledge, to capture and organize biomarker and metadata in the area of PTSD for storage in a comprehensive database that may, in turn, facilitate future analysis and research in the field.