Human cancer databases (review)

Optimization of machine learning classification models for tumor cells based on cell elements heterogeneity with laser-induced breakdown spectroscopy

The rapid and accurate diagnosis of cancer is an important topic in clinical medicine. In the present work, an innovative method based on laser-induced breakdown spectroscopy (LIBS) combined with machine learning was developed to distinguish and classify different tumor cell lines. The LIBS spectra of cells were first acquired. Then the spectral pre-processing was performed as well as detailed optimization to improve the classification accuracy. After that, the convolutional neural network (CNN), support vector machine (SVM), and K-nearest neighbors were further compared for the optimized classification ability of tumor cells. Both the CNN algorithm and SVM algorithm have achieved impressive discrimination performances for tumor cells distinguishing, with an accuracy of 97.72%. The results show that the heterogeneity of elements in tumor cells plays an important role in distinguishing the cells. It also means that the LIBS technique can be used as a fast classification method for classifying tumor cells.

An Updated Overview of Existing Cancer Databases and Identified Needs

Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lipidomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.

Translating Data Science Results into Precision Oncology Decisions: A Mini Review

While reviewing and discussing the potential of data science in oncology, we emphasize medical imaging and radiomics as the leading contextual frameworks to measure the impacts of Artificial Intelligence (AI) and Machine Learning (ML) developments. We envision some domains and research directions in which radiomics should become more significant in view of current barriers and limitations.

Survival Analysis of Oncological Patients Using Machine Learning Method

Currently, a considerable volume of information is collected and stored by large health institutions. These data come from medical records and hospital records, and the Hospital Cancer Registry is a database for integrating data from hospitals throughout Iraq. The data mining (DM) technique provides knowledge previously not visible in the database and can be used to predict trends or describe characteristics of the past. DM methods can include classification, generalisation, characterisation, clustering, association, evolution, pattern discovery, data visualisation, and rule-guided mining techniques to perform survival analyses that take into account all the patient's medical record variables. For four of the eleven groups examined, this accuracy was relatively high. The database of patients treated by the Baghdad Teaching Hospital between 2018 and 2021 was examined using a classification of the most crucial variables for event prediction, and a distinctive pattern was found. Machine learning techniques allow a global assessment of the data that is available and produce results that can be interpreted as significant information for epidemiological studies, even in cases where the sample is small and there is a lack of information on several variables.

Artificial Intelligence in Cancer Research: Trends, Challenges and Future Directions

The World Health Organization (WHO), in their 2022 report, identified cancer as one of the leading causes of death, accounting for about 16% of deaths worldwide. The Cancer-Moonshot community aims to reduce the cancer death rate by half in the next 25 years and wants to improve the lives of cancer-affected people. Cancer mortality can be reduced if detected early and treated appropriately. Cancers like breast cancer and cervical cancer have high cure probabilities when treated early in accordance with best practices. Integration of artificial intelligence (AI) into cancer research is currently addressing many of the challenges where medical experts fail to bring cancer to control and cure, and the outcomes are quite encouraging. AI offers many tools and platforms to facilitate more understanding and tackling of this life-threatening disease. AI-based systems can help pathologists in diagnosing cancer more accurately and consistently, reducing the case error rates. Predictive-AI models can estimate the likelihood for a person to get cancer by identifying the risk factors. Big data, together with AI, can enable medical experts to develop customized treatments for cancer patients. The side effects from this kind of customized therapy will be less severe in comparison with the generalized therapies. However, many of these AI tools will remain ineffective in fighting against cancer and saving the lives of millions of patients unless they are accessible and understandable to biologists, oncologists, and other medical cancer researchers. This paper presents the trends, challenges, and future directions of AI in cancer research. We hope that this paper will be of help to both medical experts and technical experts in getting a better understanding of the challenges and research opportunities in cancer diagnosis and treatment.

Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis

The recent development of imaging and sequencing technologies enables systematic advances in the clinical study of lung cancer. Meanwhile, the human mind is limited in effectively handling and fully utilizing the accumulation of such enormous amounts of data. Machine learning-based approaches play a critical role in integrating and analyzing these large and complex datasets, which have extensively characterized lung cancer through the use of different perspectives from these accrued data. In this review, we provide an overview of machine learning-based approaches that strengthen the varying aspects of lung cancer diagnosis and therapy, including early detection, auxiliary diagnosis, prognosis prediction, and immunotherapy practice. Moreover, we highlight the challenges and opportunities for future applications of machine learning in lung cancer.

Recent updates on anticancer mechanisms of polyphenols

In today’s scenario, when cancer cases are increasing rapidly, anticancer herbal compounds become imperative. Studies on the molecular mechanisms of action of polyphenols published in specialized databases such as Web of Science, Pubmed/Medline, Google Scholar, and Science Direct were used as sources of information for this review. Natural polyphenols provide established efficacy against chemically induced tumor growth with fewer side effects. They can sensitize cells to various therapies and increase the effectiveness of biotherapy. Further pharmacological translational research and clinical trials are needed to evaluate theirs in vivo efficacy, possible side effects and toxicity. Polyphenols can be used to design a potential treatment in conjunction with existing cancer drug regimens such as chemotherapy and radiotherapy.

Synthesis and Anticancer Potential of New Hydroxamic Acid Derivatives as Chemotherapeutic Agents

Histone deacetylase (HDAC) inhibitors have been shown to induce differentiation, cell cycle arrest, and apoptosis due to their low toxicity, inhibiting migration, invasion, and angiogenesis in many cancer cells. Studies show that hydroxamic acids are generally used as anticancers. For this reason, it is aimed to synthesize new derivatives of hydroxamic acids, to examine the anticancer properties of these candidate inhibitors, and to investigate the inhibition effects on some enzymes that cause multidrug resistance in cancer cells. For this reason, new (4-amino-2-methoxy benzohydroxamic acid (a), 4-amino-3-methyl benzohydroxamic acid (b), 3-amino-5-methyl benzohydroxamic acid (c)) amino benzohydroxamic acid derivatives were synthesized in this study. The effects on healthy fibroblast, lung (A549), and cervical (HeLa) cancer cells were investigated. In addition, their effects on TRXR1, GST, and GR activities, which are important for the development of chemotherapeutic strategies, were also examined. It was determined that molecule b was the most effective molecule in HeLa cancer cells with the lowest IC₅₀ value of 0.54. It was determined that molecule c was the most effective molecules for A549 and HeLa cancer cells, with the lowest IC₅₀ values of 0.78 mM and 0.25 mM, respectively. It was determined that b and c molecules directed cancer cells to necrosis rather than apoptosis. c molecule showed anticancer effect in A549 and HeLa cancer cells. It was found that molecule c significantly suppressed both GR and TRXR1 activities. In GST activities, however, inhibitors did not have a significant effect on cancer cells.

Synthesis and biological evaluation of thiosemicarbazone derivatives

In this study, firstly, 22 thiosemicarbazone derivatives (3a-y) were synthesized. Then, ADME parameters, pharmacokinetic properties, drug-like structures, and suitability for medicinal chemistry of these molecules were studied theoretically by using SwissADME and admetSAR programs. According to the results of these theoretical studies, it can be said that the bioavailability and bioactivity of these compounds may be high. In silico molecular docking between ligands (thiosemicarbazone derivatives) and targeted proteins (protein-78 (GRP78) for C6 and quinone reductase-2 (4ZVM for MCF 7) was analyzed using Hex 8.0.0 docking software. According to the docking data, almost all molecules had higher negative E values than Imatinib (already used as a drug). For this, in vitro anticancer studies of these molecules were done. The cytotoxic activities of thiosemicarbazone derivatives (3a-y) were evaluated on C6 glioma and MCF7 breast cancer cell lines at 24 h, and Imatinib was used as the positive control. According to the results of the cytotoxicity assay, it can be said that the five compounds (3b, c, f, g, and m with IC₅₀ = 10.59-9.08 μg/mL; Imatinib IC₅₀ = 11.68 μg/mL) showed more potent cytotoxic activity than Imatinib on C6 cell line. Together with to these results ten compounds (3b, d, f, g, I, k, l, m, n, and r with IC₅₀ = 7.02-9.08 μg/mL; Imatinib IC₅₀ = 9.24 μg/mL) had a more effective cytotoxic activity against MCF7 cell line than Imatinib. Compound 3 m showed the highest antiproliferative effect against C6 and MCF7 cell lines.

Progressing Towards a Human-Centric Approach in Cancer Research

Despite the advancement in research methodologies and technologies for cancer research, there is a high rate of anti-cancer drug attrition. In this review, we discuss different conventional and modern approaches in cancer research and how human-centric models can improve on the voids conferred by more traditional animal-centric models, thereby offering a more reliable platform for drug discovery. Advanced three-dimensional cell culture methodologies, along with in silico computational analysis form the core of human-centric cancer research. This can provide a holistic understanding of the research problems and help design specific and accurate experiments that could lead to the development of better cancer therapeutics. Here, we propose a new human-centric research roadmap that promises to provide a better platform for cancer research and drug discovery.

MutaXome: A Novel Database for Identified Somatic Variations of In silico Analyzed Cancer Exome Datasets

Advancements in the field of cancer research have enabled researchers and clinicians to access a massive amount of data to aid cancer patients and to add to the existing knowledge of research. However, despite the existence of reliable sources for extricating this data, it remains a challenge to accurately comprehend and draw conclusions based on the entirety of available information. Therefore, the current study aimed to design and develop a database for the identified variants of 5 different cancer types using 20 different cancer exomes. The exome data were retrieved from NCBI SRA and an NGS data clean-up protocol was implemented to obtain the best quality reads. The reads which passed the quality checks were then used for calling the variants which were then processed and filtered. This data was used to normalize and the normalized data generated was used for developing the database. MutaXome, which stands for mutations in cancer exome was designed in SQL, with the front end in bootstrap and HTML, and backend in PHP. The normalized data containing the variants inclusive of Single Nucleotide Polymorphisms (SNPs), were added into MutaXome, which contains detailed information regarding each type of identified variant. This database, available online via http://www.vidyalab.rf.gd/, serves as a knowledge base for cancer exome variations and holds much potential for enriching it by linking it to a decision support system as prospective studies.

Life Entrapped in a Network of Atavistic Attractors: How to Find a Rescue

In view of unified cell bioenergetics, cell bioenergetic problems related to cell overenergization can cause excessive disturbances in current cell fate and, as a result, lead to a change of cell-fate. At the onset of the problem, cell overenergization of multicellular organisms (especially overenergization of mitochondria) is solved inter alia by activation and then stimulation of the reversible Crabtree effect by cells. Unfortunately, this apparently good solution can also lead to a much bigger problem when, despite the activation of the Crabtree effect, cell overenergization persists for a long time. In such a case, cancer transformation, along with the Warburg effect, may occur to further reduce or stop the charging of mitochondria by high-energy molecules. Understanding the phenomena of cancer transformation and cancer development has become a real challenge for humanity. To date, many models have been developed to understand cancer-related mechanisms. Nowadays, combining all these models into one coherent universal model of cancer transformation and development can be considered a new challenge. In this light, the aim of this article is to present such a potentially universal model supported by a proposed new model of cellular functionality evolution. The methods of fighting cancer resulting from unified cell bioenergetics and the two presented models are also considered.

Identification of a DNA Methylation-Driven Genes-Based Prognostic Model and Drug Targets in Breast Cancer: In silico Screening of Therapeutic Compounds and in vitro Characterization

DNA methylation is a vital epigenetic change that regulates gene transcription and helps to keep the genome stable. The deregulation hallmark of human cancer is often defined by aberrant DNA methylation which is critical for tumor formation and controls the expression of several tumor-associated genes. In various cancers, methylation changes such as tumor suppressor gene hypermethylation and oncogene hypomethylation are critical in tumor occurrences, especially in breast cancer. Detecting DNA methylation-driven genes and understanding the molecular features of such genes could thus help to enhance our understanding of pathogenesis and molecular mechanisms of breast cancer, facilitating the development of precision medicine and drug discovery. In the present study, we retrospectively analyzed over one thousand breast cancer patients and established a robust prognostic signature based on DNA methylation-driven genes. Then, we calculated immune cells abundance in each patient and lower immune activity existed in high-risk patients. The expression of leukocyte antigen (HLA) family genes and immune checkpoints genes were consistent with the above results. In addition, more mutated genes were observed in the high-risk group. Furthermore, a in silico screening of druggable targets and compounds from CTRP and PRISM databases was performed, resulting in the identification of five target genes (HMMR, CCNB1, CDC25C, AURKA, and CENPE) and five agents (oligomycin A, panobinostat, (+)-JQ1, voxtalisib, and arcyriaflavin A), which might have therapeutic potential in treating high-risk breast cancer patients. Further in vitro evaluation confirmed that (+)-JQ1 had the best cancer cell selectivity and exerted its anti-breast cancer activity through CENPE. In conclusion, our study provided new insights into personalized prognostication and may inspire the integration of risk stratification and precision therapy.

Insights into the Evolving Roles of Circular RNAs in Cancer

The majority of RNAs transcribed from the human genome have no coding capacity and are termed non-coding RNAs (ncRNAs). It is now widely accepted that ncRNAs play key roles in cell regulation and disease. Circular RNAs (circRNAs) are a form of ncRNA, characterised by a closed loop structure with roles as competing endogenous RNAs (ceRNAs), protein interactors and transcriptional regulators. Functioning as key cellular regulators, dysregulated circRNAs have a significant impact on disease progression, particularly in cancer. Evidence is emerging of specific circRNAs having oncogenic or tumour suppressive properties. The multifaceted nature of circRNA function may additionally have merit as a novel therapeutic target, either in treatment or as a novel biomarker, due to their cell-and disease-state specific expression and long-term stability. This review aims to summarise current findings on how circRNAs are dysregulated in cancer, the effects this has on disease progression, and how circRNAs may be targeted or utilised as future potential therapeutic options.

Exploring polyps to colon carcinoma voyage: can blocking the crossroad halt the sequence?

Colorectal cancer is an important public health concern leading to significant cancer associate mortality. A vast majority of colon cancer arises from polyp which later follows adenoma, adenocarcinoma, and carcinoma sequence. This whole process takes several years to complete and recent genomic and proteomic technologies are identifying several targets involved in each step of polyp to carcinoma transformation in a large number of studies. Current text presents interaction network of targets involved in polyp to carcinoma transformation. In addition, important targets involved in each step according to network biological parameters are also presented. The functional overrepresentation analysis of each step targets and common top biological processes and pathways involved in carcinoma indicate several insights about this whole mechanism. Interaction networks indicate TP53, AKT1, GAPDH, INS, EGFR, and ALB as the most important targets commonly involved in polyp to carcinoma sequence. Though several important pathways are known to be involved in CRC, the central common involvement of PI3K-AKT indicates its potential for devising CRC management strategies. The common and central targets and pathways involved in polyp to carcinoma progression can shed light on its mechanism and potential management strategies. The data-driven approach aims to add valuable inputs to the mechanism of the years-long polyp-carcinoma sequence.

Genomic characteristics and drug screening among organoids derived from non-small cell lung cancer patients

BACKGROUND

Patient-derived organoid (PDO) models are highly valuable and have potentially widespread clinical applications. However, limited information is available regarding organoid models of non-small cell lung cancer (NSCLC). This study aimed to characterize the consistency between primary tumors in NSCLC and PDOs and to explore the applications of PDOs as preclinical models to understand and predict treatment response during lung cancer.

METHODS

Fresh tumor samples were harvested for organoid culture. Primary tumor samples and PDOs were analyzed via whole-exome sequencing. Paired samples were subjected to immunohistochemical analysis. There were 26 antineoplastic drugs tested in the PDOs. Cell viability was assessed using the Cell Titer Glo assay 7-10 days after drug treatment. A heatmap of log-transformed values of the half-maximal inhibitory concentrations was generated on the basis of drug responses of PDOs through nonlinear regression (curve fit). A total of 12 patients (stages I-III) were enrolled, and 7 paired surgical tumors and PDOs were analyzed.

RESULTS

PDOs retained the histological and genetic characteristics of the primary tumors. The concordance between tumors and PDOs in mutations in the top 20 NSCLC-related genes was >80% in five patients. Sample purity was significantly and positively associated with variant allele frequency (Pearson r = 0.82, P = 0.0005) and chromosome stability. The in vitro response to drug screening with PDOs revealed high correlation with the mutation profiles in the primary tumors.

CONCLUSIONS

PDOs are highly credible models for detecting NSCLC and for prospective prediction of the treatment response for personalized precision medicine.

KEY POINTS

Lung cancer organoid models could save precious time of drug testing on patients, and accurately select anticancer drugs according to the drug sensitivity results, so as to provide a powerful supplement and verification for the gene sequencing.

Coumarins and Coumarin-Related Compounds in Pharmacotherapy of Cancer

Cancer is one of the most common causes of disease-related deaths worldwide. Despite the discovery of many chemotherapeutic drugs that inhibit uncontrolled cell division processes for the treatment of various cancers, serious side effects of these drugs are a crucial disadvantage. In addition, multi-drug resistance is another important problem in anticancer treatment. Due to problems such as cytotoxicity and drug resistance, many investigations are being conducted to discover and develop effective anticancer drugs. In recent years, researchers have focused on the anticancer activity coumarins, due to their high biological activity and low toxicity. Coumarins are commonly used in the treatment of prostate cancer, renal cell carcinoma and leukemia, and they also have the ability to counteract the side effects caused by radiotherapy. Both natural and synthetic coumarin derivatives draw attention due to their photochemotherapy and therapeutic applications in cancer. In this review, a compilation of various research reports on coumarins with anticancer activity and investigation and a review of structure-activity relationship studies on coumarin core are presented. Determination of important structural features around the coumarin core may help researchers to design and develop new analogues with a strong anticancer effect and reduce the potential side effects of existing therapeutics.

methCancer-gen: a DNA methylome dataset generator for user-specified cancer type based on conditional variational autoencoder

Background

Recently, DNA methylation has drawn great attention due to its strong correlation with abnormal gene activities and informative representation of the cancer status. As a number of studies focus on DNA methylation signatures in cancer, demand for utilizing publicly available methylome dataset has been increased. To satisfy this, large-scale projects were launched to discover biological insights into cancer, providing a collection of the dataset. However, public cancer data, especially for certain cancer types, is still limited to be used in research. Several simulation tools for producing epigenetic dataset have been introduced in order to alleviate the issue, still, to date, generation for user-specified cancer type dataset has not been proposed.

Results

In this paper, we present methCancer-gen, a tool for generating DNA methylome dataset considering type for cancer. Employing conditional variational autoencoder, a neural network-based generative model, it estimates the conditional distribution with latent variables and data, and generates samples for specified cancer type.

Conclusions

To evaluate the simulation performance of methCancer-gen for the user-specified cancer type, our proposed model was compared to a benchmark method and it could successfully reproduce cancer type-wise data with high accuracy helping to alleviate the lack of condition-specific data issue. methCancer-gen is publicly available at https://github.com/cbi-bioinfo/methCancer-gen.

Oxamusplatin: a cytotoxic Pt(ii) complex of a nitrogen mustard with resistance to thiol based sequestration displays enhanced selectivity towards cancer

Pt(ii) drugs and nitrogen mustards show severe side effects, poor tumour selectivity and face growing resistance by cancer cells due to sequestration by thiol-containing molecules (viz. glutathione (GSH) and copper ATPases like ATP7A/7B). ATP7A and ATP7B-sequestered Pt(ii) complexes show dose inefficacy and resistance. The incorporation of bulky ligands and chelating leaving groups may prevent deactivation by thiols. In this work, we have synthesised four new Pt(ii) complexes (3-6) of two carrier ligands, bis(2-hydroxyethyl)pyridylmethylamine (L1) and bis(2-chloroethyl)pyridylmethylamine (L2) with oxalato and cyclobutanedicarboxylato leaving groups. Among these four new complexes, the Pt(ii) complex of L2 with the oxalato leaving group (5, termed "oxamusplatin") is cytotoxic. Oxamusplatin is more resistant than cisplatin or oxaliplatin towards hydrolysis, thiol binding and sequestration by ATP7B. It targets cellular DNA and is capable of disrupting the microtubule network in the cytoskeleton. Oxamusplatin demonstrates better selectivity than oxaliplatin towards cancerous cells. It is ca. 4-10 times more cytotoxic towards metastatic prostate carcinoma (DU-145, IC50 = 21 ± 1 μM) and ca. 10-24 times more cytotoxic towards breast adenocarcinoma (MCF-7, IC50 = 8.1 ± 0.8 μM) compared to the three noncancerous cells investigated.

Transcriptomics and Epigenomics in head and neck cancer: available repositories and molecular signatures

For many years, head and neck squamous cell carcinoma (HNSCC) has been considered as a single entity. However, in the last decades HNSCC complexity and heterogeneity have been recognized. In parallel, high-throughput omics techniques had allowed picturing a larger spectrum of the behavior and characteristics of molecules in cancer and a large set of omics web-based tools and informative repository databases have been developed. The objective of the present review is to provide an overview on biological, prognostic and predictive molecular signatures in HNSCC. To contextualize the selected data, our literature survey includes a short summary of the main characteristics of omics data repositories and web-tools for data analyses. The timeframe of our analysis was fixed, encompassing papers published between January 2015 and January 2019. From more than 1000 papers evaluated, 61 omics studies were selected: 33 investigating mRNA signatures, 11 and 13 related to miRNA and other non-coding-RNA signatures and 4 analyzing DNA methylation signatures. More than half of identified signatures (36) had a prognostic value but only in 10 studies selection of a specific anatomical sub-site (8 oral cavity, 1 oropharynx and 1 both oral cavity and oropharynx) was performed. Noteworthy, although the sample size included in many studies was limited, about one-half of the retrieved studies reported an external validation on independent dataset(s), strengthening the relevance of the obtained data. Finally, we highlighted the development and exploitation of three gene-expression signatures, whose clinical impact on prognosis/prediction of treatment response could be high. Based on this overview on omics-related literature in HNSCC, we identified some limits and strengths. The major limits are represented by the low number of signatures associated to DNA methylation and to non-coding RNA (miRNA, lncRNA and piRNAs) and the availability of a single dataset with multiple omics on more than 500 HNSCC (i.e. TCGA). The major strengths rely on the integration of multiple datasets through meta-analysis approaches and on the growing integration among omics data obtained on the same cohort of patients. Moreover, new approaches based on artificial intelligence and informatic analyses are expected to be available in the next future.

Investigation of Precise Molecular Mechanistic Action of Tobacco-Associated Carcinogen `NNK´ Induced Carcinogenesis: A System Biology Approach

Cancer is the second deadliest disease listed by the WHO. One of the major causes of cancer disease is tobacco and consumption possibly due to its main component, 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). A plethora of studies have been conducted in the past aiming to decipher the association of NNK with other diseases. However, it is strongly linked with cancer development. Despite these studies, a clear molecular mechanism and the impact of NNK on various system-level networks is not known. In the present study, system biology tools were employed to understand the key regulatory mechanisms and the perturbations that will happen in the cellular processes due to NNK. To investigate the system level influence of the carcinogen, NNK rewired protein–protein interaction network (PPIN) was generated from 544 reported proteins drawn out from 1317 articles retrieved from PubMed. The noise was removed from PPIN by the method of modulation. Gene ontology (GO) enrichment was performed on the seed proteins extracted from various modules to find the most affected pathways by the genes/proteins. For the modulation, Molecular COmplex DEtection (MCODE) was used to generate 19 modules containing 115 seed proteins. Further, scrutiny of the targeted biomolecules was done by the graph theory and molecular docking. GO enrichment analysis revealed that mostly cell cycle regulatory proteins were affected by NNK.

Genomic Common Data Model for Seamless Interoperation of Biomedical Data in Clinical Practice: Retrospective Study

Background

Clinical sequencing data should be shared in order to achieve the sufficient scale and diversity required to provide strong evidence for improving patient care. A distributed research network allows researchers to share this evidence rather than the patient-level data across centers, thereby avoiding privacy issues. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) used in distributed research networks has low coverage of sequencing data and does not reflect the latest trends of precision medicine.

Objective

The aim of this study was to develop and evaluate the feasibility of a genomic CDM (G-CDM), as an extension of the OMOP-CDM, for application of genomic data in clinical practice.

Methods

Existing genomic data models and sequencing reports were reviewed to extend the OMOP-CDM to cover genomic data. The Human Genome Organisation Gene Nomenclature Committee and Human Genome Variation Society nomenclature were adopted to standardize the terminology in the model. Sequencing data of 114 and 1060 patients with lung cancer were obtained from the Ajou University School of Medicine database of Ajou University Hospital and The Cancer Genome Atlas, respectively, which were transformed to a format appropriate for the G-CDM. The data were compared with respect to gene name, variant type, and actionable mutations.

Results

The G-CDM was extended into four tables linked to tables of the OMOP-CDM. Upon comparison with The Cancer Genome Atlas data, a clinically actionable mutation, p.Leu858Arg, in the EGFR gene was 6.64 times more frequent in the Ajou University School of Medicine database, while the p.Gly12Xaa mutation in the KRAS gene was 2.02 times more frequent in The Cancer Genome Atlas dataset. The data-exploring tool GeneProfiler was further developed to conduct descriptive analyses automatically using the G-CDM, which provides the proportions of genes, variant types, and actionable mutations. GeneProfiler also allows for querying the specific gene name and Human Genome Variation Society nomenclature to calculate the proportion of patients with a given mutation.

Conclusions

We developed the G-CDM for effective integration of genomic data with standardized clinical data, allowing for data sharing across institutes. The feasibility of the G-CDM was validated by assessing the differences in data characteristics between two different genomic databases through the proposed data-exploring tool GeneProfiler. The G-CDM may facilitate analyses of interoperating clinical and genomic datasets across multiple institutions, minimizing privacy issues and enabling researchers to better understand the characteristics of patients and promote personalized medicine in clinical practice.

Use case driven evaluation of open databases for pediatric cancer research

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Inhibition of constructed SEC3-ES lentiviral vector to proliferation, migration of Hela cells

AIM

To construct a lentiviral vector with endostatin (ES) and staphylococcal enterotoxin C₃(SEC3) gene, and investigate its capacities of inhibition on proliferation and migration of Hela cells.

METHODS

By inserting ES and SEC3 gene into the plasmid and then transfect 293 T cell, the co-expressed (SEC3-ES) vector were constructed. A series of experiments in vitro were carried out to detect its anti-tumor capacity.

RESULTS

SEC3 expression of the vector is about 3 times of GV365-SEC3 vector, and ES expression is over 22.5-fold compared with GV365-ES vector. Moreover, OD490 value of CO group (1.212 ± 0.003) was notably lower than NC (negative control) group (1.124 ± 0.01) (P < 0.05) in MTT assay. Cell cycle analysis showed it could block Hela cells in S phase. Meanwhile, in wound healing assay, cells of CO group migrated at a slower rate (0.59 ± 0.02) compared with NC group (0.65 ± 0.02)(P < 0.01).

CONCLUSION

The successful construction of co-expressed vector lays the foundation for further studies in vivo. These promising results suggest a new strategy to treating cervical cancer.

The prognostic roles of mRNAs of the exosomes derived from bone marrow stromal cells in common malignancies: a bioinformatic study

Background

The inclusion of exosomes enters the recipient cells by means of endocytosis or direct fusion for information exchange between cells and cells. The inclusion of BMSCs-exo helps to guide the diagnosis and prognosis of cancer, especially cancer.

Purpose

This research was to systematically elucidate the prognostic value of mRNAs of the exosomes derived from bone marrow stromal cells (BMSCs) in common malignant neoplasms, such as breast cancer, ovarian cancer, lung cancer, and gastric cancer.

Methods

Gene expression data (GSE78235) for the exosomes derived from BMSCs were extracted from the Gene Expression Omnibus database. Firstly, the differentially expressed genes were detected by comparing the RNA expression from exosomes derived from BMSCs between four tumor patients and two healthy controls using the limma package. Subsequently, functional enrichment analysis, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes of differentially expressed genes, was performed using the Functional Enrichment analysis tool (FunRich v3.1.3) software followed by the construction of a protein-protein interaction (PPI) network via STRING v3.6.0. Molecular Complex Detection was used to screen the hub proteins by setting up the following threshold score ≥4 and nodes ≥10. Cytoscape v3.6.1 was used to for visualizing PPI network. Finally, Kaplan-Meier analysis for hub proteins was performed by Kaplan-Meier plotter online platform.

Results

A total of 386 genes originating from the exosomes derived from BMSCs were identifed as statistically signifcant (P < 0.05, FDR <0.05), which consisted of 150 upregulated genes and 236 downregulated genes. Also, 32 pathways were identifed as signifcant (P < 0.05, FDR < 0.05). The PPI network of exosomes derived from BMSC proteins included 100 protein nodes with 579 interaction edges. The hub proteins, including PODN, ZNF521, and CFI, which interacted with ten or more other proteins, were indicated as the hub proteins of PPN of exosomes derived from BMSCs.

Conclusion

Taken together, our findings revealed the prognostic roles of mRNAs of exosomes derived from BMSCs and provided implications for targeted therapy for common malignant neoplasms. However, further studies require large samples and experimental verification.

The growing role of precision and personalized medicine for cancer treatment

Cancer is a devastating disease that takes the lives of hundreds of thousands of people every year. Due to disease heterogeneity, standard treatments, such as chemotherapy or radiation, are effective in only a subset of the patient population. Tumors can have different underlying genetic causes and may express different proteins in one patient versus another. This inherent variability of cancer lends itself to the growing field of precision and personalized medicine (PPM). There are many ongoing efforts to acquire PPM data in order to characterize molecular differences between tumors. Some PPM products are already available to link these differences to an effective drug. It is clear that PPM cancer treatments can result in immense patient benefits, and companies and regulatory agencies have begun to recognize this. However, broader changes to the healthcare and insurance systems must be addressed if PPM is to become part of standard cancer care.

Bipartite graphs in systems biology and medicine: a survey of methods and applications

The latest advances in high-throughput techniques during the past decade allowed the systems biology field to expand significantly. Today, the focus of biologists has shifted from the study of individual biological components to the study of complex biological systems and their dynamics at a larger scale. Through the discovery of novel bioentity relationships, researchers reveal new information about biological functions and processes. Graphs are widely used to represent bioentities such as proteins, genes, small molecules, ligands, and others such as nodes and their connections as edges within a network. In this review, special focus is given to the usability of bipartite graphs and their impact on the field of network biology and medicine. Furthermore, their topological properties and how these can be applied to certain biological case studies are discussed. Finally, available methodologies and software are presented, and useful insights on how bipartite graphs can shape the path toward the solution of challenging biological problems are provided.

In silico prediction of cancer immunogens: current state of the art

Cancer kills 8 million annually worldwide. Although survival rates in prevalent cancers continue to increase, many cancers have no effective treatment, prompting the search for new and improved protocols. Immunotherapy is a new and exciting addition to the anti-cancer arsenal. The successful and accurate identification of aberrant host proteins acting as antigens for vaccination and immunotherapy is a key aspiration for both experimental and computational research. Here we describe key elements of in silico prediction, including databases of cancer antigens and bleeding-edge methodology for their prediction. We also highlight the role dendritic cell vaccines can play and how they can act as delivery mechanisms for epitope ensemble vaccines. Immunoinformatics can help streamline the discovery and utility of Cancer Immunogens.

Big Data in Drug Discovery

Interpretation of Big Data in the drug discovery community should enhance project timelines and reduce clinical attrition through improved early decision making. The issues we encounter start with the sheer volume of data and how we first ingest it before building an infrastructure to house it to make use of the data in an efficient and productive way. There are many problems associated with the data itself including general reproducibility, but often, it is the context surrounding an experiment that is critical to success. Help, in the form of artificial intelligence (AI), is required to understand and translate the context. On the back of natural language processing pipelines, AI is also used to prospectively generate new hypotheses by linking data together. We explain Big Data from the context of biology, chemistry and clinical trials, showcasing some of the impressive public domain sources and initiatives now available for interrogation.

Oral cancer databases: A comprehensive review

Cancer database is a systematic collection and analysis of information on various human cancers at genomic and molecular level that can be utilized to understand various steps in carcinogenesis and for therapeutic advancement in cancer field. Oral cancer is one of the leading causes of morbidity and mortality all over the world. The current research efforts in this field are aimed at cancer etiology and therapy. Advanced genomic technologies including microarrays, proteomics, transcrpitomics, and gene sequencing development have culminated in generation of extensive data and subjection of several genes and microRNAs that are distinctively expressed and this information is stored in the form of various databases. Extensive data from various resources have brought the need for collaboration and data sharing to make effective use of this new knowledge. The current review provides comprehensive information of various publicly accessible databases that contain information pertinent to oral squamous cell carcinoma (OSCC) and databases designed exclusively for OSCC. The databases discussed in this paper are Protein-Coding Gene Databases and microRNA Databases. This paper also describes gene overlap in various databases, which will help researchers to reduce redundancy and focus on only those genes, which are common to more than one databases. We hope such introduction will promote awareness and facilitate the usage of these resources in the cancer research community, and researchers can explore the molecular mechanisms involved in the development of cancer, which can help in subsequent crafting of therapeutic strategies.

Tumor molecular profiling of NSCLC patients using next generation sequencing

Non‑small cell lung cancer (NSCLC) is the most common type of lung cancer and a tumor with a broad spectrum of targeted therapies already available or in clinical trials. Thus, molecular characterization of the tumor using next generation sequencing (NGS) technology, has become a key tool for facilitating treatment decisions and the clinical management of NSCLC patients. The performance of a custom 23 gene multiplex amplification hot spot panel, based on Ion AmpliSeq™ technology, was evaluated for the analysis of tumor DNA extracted from formalin-fixed and paraffin-embedded (FFPE) tissues. Furthermore, the Ion AmpliSeq™ RNA Fusion Lung Cancer Research Panel was used for fusion RNA transcript analysis. The mutation spectrum of the tumors was determined in a cohort of 502 patients with NSCLC using the aforementioned targeted gene panels. The panel used for tumor DNA analysis in this study exhibited high rates (100%) of sensitivity, specificity and reproducibility at a mutation allelic frequency of 3%. At least one DNA mutation was detected in 374 patients (74.5%) and an RNA fusion was identified in 16 patients, (3.2%). In total, alterations in a cancer-driver gene were identified (including point mutations, gene rearrangements and MET amplifications) in 77.6% of the tumors tested. Among the NSCLC patients, 23% presented a mutation in a gene associated with approved or emerging targeted therapy. More specifically, 13.5% (68/502) presented a mutation in a gene with approved targeted therapy (EGFR, ALK, ROS1) and 9.4% (47/502) had an alteration in a gene related to emerging targeted therapies (ERBB2, BRAF, MET and RET). Furthermore, 51.6% of the patients had a mutation in a gene that could be related to an off label therapy or indicative for access to a clinical trial. Thus, the targeted NGS panel used in this study is a reliable approach for tumor molecular profiling and can be applied in personalized treatment decision making for NSCLC patients.

Molecular study of patients with auditory neuropathy

Auditory neuropathy is a type of hearing loss that constitutes a change in the conduct of the auditory stimulus by the involvement of inner hair cells or auditory nerve synapses. It is characterized by the absence or alteration of waves in the examination of brainstem auditory evoked potentials, with otoacoustic and/or cochlear microphonic issues. At present, four loci associated with non‑syndromic auditory neuropathy have been mapped: Autosomal recessive deafness‑9 [DFNB9; the otoferlin (OTOF) gene] and autosomal recessive deafness‑59 [DFNB59; the pejvakin (PJVK) gene], associated with autosomal recessive inheritance; the autosomal dominant auditory neuropathy gene [AUNA1; the diaphanous‑3 (DIAPH3) gene]; and AUNX1, linked to chromosome X. Furthermore, mutations of connexin 26 [the gap junction β2 (GJB2) gene] have also been associated with the disease. OTOF gene mutations exert a significant role in auditory neuropathy. In excess of 80 pathogenic mutations have been identified in individuals with non‑syndromic deafness in populations of different origins, with an emphasis on the p.Q829X mutation, which was found in ~3% of cases of deafness in the Spanish population. The identification of genetic alterations responsible for auditory neuropathy is one of the challenges contributing to understand the molecular bases of the different phenotypes of hearing loss. Thus, the present study aimed to investigate molecular changes in the OTOF gene in patients with auditory neuropathy, and to develop a DNA chip for the molecular diagnosis of auditory neuropathy using mass spectrometry for genotyping. Genetic alterations were investigated in 47 patients with hearing loss and clinical diagnosis of auditory neuropathy, and the c.35delG mutation in the GJB2 gene was identified in three homozygous patients, and the heterozygous parents of one of these cases. Additionally, OTOF gene mutations were tracked by complete sequencing of 48 exons, although these results are still preliminary. Studying the genetic basis of auditory neuropathy is of utmost importance for obtaining a differential diagnosis, developing more specific treatments and more accurate genetic counseling.

Sydnone 1: A Mesoionic Compound with Antitumoral and Haematological Effects In Vivo

This study evaluated the antitumour activity of the mesoionic compound sydnone 1 (Syd-1) against Walker-256 carcinosarcoma. Tumour cells were subcutaneously inoculated in the hind limb in male Wistar rats. The animals were orally treated for 12 days with Syd-1 (75 mg/kg) or vehicle. At the end of treatment, considerable decreases in tumour volume and tumour weight were observed in treated animals. Samples of these tumours presented increases in apoptotic bodies and pro-apoptotic protein expression (Bax and p53), while the expression of the anti-apoptotic protein Bcl-2 was reduced. A decrease in reduced glutathione levels and an increase in glutathione peroxidase activity were observed in tumour after Syd-1 treatment. However, significant splenomegaly was evident in animals that received Syd-1, most likely attributable to the induction of haemolysis. This study demonstrated the antitumour activity of Syd-1 against Walker-256 carcinosarcoma. Its mechanism of action is linked to the activation of apoptotic pathways that lead to tumour cell death.