JC JMIR Cancer JMIR Cancer 2369-1999 JMIR Publications Toronto, Canada v8i2e35020 35430561 10.2196/35020 Original Paper Original Paper The Cancer Research Database (CRDB): Integrated Platform to Gain Statistical Insight Into the Correlation Between Cancer and COVID-19 Mavragani Amaryllis Liu Dr. Ze Chrisman Brianna Sierra Ullah Shahid PhD 1 https://orcid.org/0000-0001-6694-5590 Ullah Farhan PhD 1 https://orcid.org/0000-0001-8929-9229 Rahman Wajeeha PhD 1 https://orcid.org/0000-0001-6638-7760 Karras Dimitrios A PhD 2 https://orcid.org/0000-0002-2759-8482 Ullah Anees MBBS 3 https://orcid.org/0000-0002-3184-2791 Ahmad Gulzar MPhil 1 https://orcid.org/0000-0001-6832-1685 Ijaz Muhammad MS 1 https://orcid.org/0000-0002-2272-6430 Gao Tianshun PhD 4
Research Center The Seventh Affiliated Hospital of Sun Yat-sen University No 628, Zhenyuan Rd Guangming (New) Dist Shenzhen, 511464 China 86 18126408738 gts.hust@gmail.com
https://orcid.org/0000-0002-0466-9081 S-Khan Lab Mardan Khyber Pakhtunkhwa Pakistan Department General Faculty of Science National and Kapodistrian University of Athens Athens Greece Kyrgyz State Medical University Bishkek Kyrgyzstan Research Center The Seventh Affiliated Hospital of Sun Yat-sen University Shenzhen China Corresponding Author: Tianshun Gao gts.hust@gmail.com Apr-Jun 2022 10 6 2022 8 2 e35020 17 11 2021 23 1 2022 7 2 2022 10 4 2022 ©Shahid Ullah, Farhan Ullah, Wajeeha Rahman, Dimitrios A Karras, Anees Ullah, Gulzar Ahmad, Muhammad Ijaz, Tianshun Gao. Originally published in JMIR Cancer (https://cancer.jmir.org), 10.06.2022. 2022

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cancer, is properly cited. The complete bibliographic information, a link to the original publication on https://cancer.jmir.org/, as well as this copyright and license information must be included.

 Background

The advancement of cancer research has been facilitated through freely available cancer literature, databases, and tools. The age of genomics and big data has given rise to the need for cooperation and data sharing in order to make efficient use of this new information in the COVID-19 pandemic. Although there are many databases for cancer research, their access is not easy owing to different ways of processing and managing the data. There is an absence of a unified platform to manage all of them in a transparent and more comprehensible way.

 Objective

In this study, an improved integrated cancer research database and platform is provided to facilitate a deeper statistical insight into the correlation between cancer and the COVID-19 pandemic, unifying the collection of almost all previous published cancer databases and defining a model web database for cancer research, and scoring databases on the basis of the variety types of cancer, sample size, completeness of omics results, and user interface.

 Methods

Databases examined and integrated include the Data Portal database, Genomic database, Proteomic database, Expression database, Gene database, and Mutation database; and it is expected that this launch will sort, save, advance the understanding and encourage the use of these resources in the cancer research environment.

 Results

To make it easy to search valuable information, 85 cancer databases are provided in the form of a table, and a database of databases named the Cancer Research Database (CRDB) has been built and presented herein. Furthermore, the CRDB has been herein equipped with unique navigation tools in order to be explored by three methods; that is, any single database can be browsed by typing the name in the given search bar, while all categories can be browsed by clicking on the name of the category or image expression icon, thus serving as a facility that could provide all the category databases on a single click.

 Conclusions

The computational platform (PHP, HTML, CSS, and MySQL) used to build CRDB for the cancer scientific community can be freely investigated and browsed on the internet and is planned to be updated in a timely manner. In addition, based on the proposed platform, the status and diagnoses statistics of cancer during the COVID-19 pandemic have been thoroughly investigated herein using CRDB, thus providing an easy-to-manage, understandable framework that mines knowledge for future researchers.

 cancer database COVID-19 CRDB genomics PHP CRDB cancer and COVID-19 cancer statistics cancer research health database research platform Introduction

Cancer is a category of diseases causing irregular cell growth with the ability to infiltrate or spread to other areas of the body. As of 2019, approximately 18 million new cases are reported per year [1], among which 22% of cancer deaths are caused by tobacco use and 10% are caused by obesity, an unhealthy diet, a lack of physical activities, or excessive alcohol use [2]. In the past 2 or 3 decades, recent data have shown that approximately 5%-10% of cancers are caused by genetic disorders [3]. Patients with cancer seem to exhibit exacerbated conditions and a higher mortality rate when exposed to the virus [4]. The COVID-19 pandemic has spread over the world. As in 18 October 2021, there have been 219 million confirmed cases and 4.55 million deaths worldwide, with the number of cases continuing to rise in 216 countries [5]. Patients with cancer are thought to be particularly prone to SARS-CoV-2 infection and the development of more severe COVID-19 symptoms, which could be related to a systemic immunosuppressive condition caused directly by tumor growth and indirectly by anticancer therapy’s side effects [6]. Infection can affect people of various ages, but in most situations, disease severity is linked to age limit and pre-existing disorders that decrease immunity, such as cancer. COVID-19 has been linked to an increased risk of severe sickness and death among patients with cancer, according to several studies [7]. Brunello et al [8] suggested that people with cancer are faced with two challenges that potentially lead to death: one is from getting cancer and the other is COVID-19—the latter resulting from undertreatment or overtreatment conditions. Initial investigations revealed that patients with cancer were more likely to contract the virus and become infected with COVID-19.

Because of the effects of antineoplastic therapy, supportive drugs including steroids, and the immunosuppressive qualities of cancer, people with cancer could be immunocompromised [5]. The advancement of modern genomic technology—such as microarrays, proteomics, transcriptomics, and gene sequencing—and the serious situation resulting from the COVID-19 pandemic have resulted in the generation of a huge amount of data [9,10]; therefore, the first challenge of these multi-omics cancer and COVID-19 data was the design and usage of electronic databases to store and manage the large amount of knowledge [11]. A number of databases have been published in this research area, which have explained the wide-ranging information about cancer research and COVID-19 [10,12-16], such as The Cancer Genome Atlas (TCGA), RespCanDB, cBioPortal, Co-19PDB, and ICGC, these databases have been exploring, analyzing, and visualizing multidisciplinary genomics data. Further, we have presented a comparison table with previously published work, in which we have noticed a considerable growth in the number of databases, and we have provided the list of all the cancer databases and have built a database of the databases named the Cancer Research Database (CRDB), with the improvement highlighted in Table 1. To make it easier for researchers and the scientific community, a well-organized and easily accessible platform is required, where all cancer research data can be accessed with a single click. To that end, we have gathered almost all cancer databases and classified them into six categories based on data types: Data Portal database, Genomic database, Proteomic database, Expression database, Gene database, and Mutation database; this would provide an easy way to search data, and users can directly type the name of the needed databases in the search bar or can click the required category, which will lead them to all the databases with a single click. In addition, we have obtained deep insight into the link between cancer and the COVID-19 pandemic, having explained up and down of cancer, new cases, death ratios, etc, before and during the COVID-19 pandemic.

Comparison of the Cancer Research Database with other published work.

Database Databases, n Type Year Component Journal Reference
Cancer research database 98 Database+list 2022 Cancer N/Aa N/A
Munich Information Center for Protein Sequences 22 List 2011 Different categories Nucleic Acids Research [17]
No name 6 Database 2018 Aging Nucleic Acids Research [18]
COVID-19 pandemic database 59 Database+list 2021 COVID-19 Computer Methods and Programs in Biomedicine Update [10]
Swiss Institute of Bioinformatics 12 Database 2016 Different categories Nucleic Acids Research [19]
Human cancer databases 58 List 2015 Cancer Oncology Reports [20]
No name 38 List 2014 Hepatology Journal of Hepatology [21]
LiverAtlas 53 Databases 2013 Liver Liver International [22]
No name 16 List 2015 Cancer Genomics, Proteomics & Bioinformatics [3]

aN/A: not applicable.

Previously, we have published several articles in well-known journals, such as the database of Phospho-sites in Animals and Fungi [23] in Scientific Reports, the Circadian Gene Database [24] in Nucleic Acids Research, Co-19PDB [10] in Computer Methods and Programs in Biomedicine Update, DataBases relevant to Human Research [25] in Future Science and DataBase of Plant Research [26]; we have provided 15 databases to the scientific community during the COVID-19 pandemic, which can be accessed on the internet [27].

 Methods Construction of the CRDB and Content

We integrated the data from multiple different sources including PubMed, Google, Google Scholar, etc. We used various keywords such as “Cancer database,” “cancer database list,” and “database of cancer” as search terms to retrieve published cancer-related databases with the help of PubMed. To circumvent missing data, we have manually collected the latest cancer databases from Nucleic Acids Research, and Genomics, Proteomics & Bioinformatics, which are the leading journals on the database issue. We only collected all cancer databases and have removed all nonfunctional links and programming platforms such as PHP, MySQL, HTML, CSS, and JavaScript have been used to construct the CRDB. Figure 1 shows all the procedures of our database. Finally, we provided a compressive cancer research database to the scientific community, which is easy to operate and will be updated over time.

Flowchart and procedure for the collection and integration of cancer databases and the construction of the Cancer Research Database (CRDB).

 Database Classification

Several articles have been published in this research area [28-30], each has its their own classification of databases based on its function, application, technological feature, and organism, such as human and mouse [29], Plant [26], Drosophila [31], fungi, COVID-19, etc. According to such published works, we have also classified the cancer databases into six categories: Data Portal database, Genomic database, Proteomic database, Expression database, Gene database, and Mutation database—their details are given bellow.

Expression Databases

In cancer expression databases, the expression levels of thousands of genes can be continuously measured under particular experimental environments and conditions resulting from marked advancements in DNA microarray technology. This technology made it possible to understand life at the molecular level, and enables us to generate large-scale gene expression data. It has also been applied in a wide range of applications such as cancer prediction, diagnosis, and drug discovery, which are very important issues for cancer treatment [32]. Some well-known expression databases including BioXpress [33], miRCancer [34], and Gene Expression Database [35] are shown in Figure 2A.

Main pages of some commonly using cancer databases. (A) A screenshot of the Expression Database named GXD, (B) a screenshot of the Data Portal category named CNVs (copy number variations), (C) “IARCTP53”: a database of the Gene category, (D, E, and F) main pages of Proteomic database, Mutation database, and Genomic database respectively.

 Data Portal Databases

Data Portal is a type of database that provides comprehensive genomic, epigenomic, transcriptomic, and proteomic data. a large number of data are publicly available for anyone in the research community and are used to diagnose, treat, and prevent cancer [12]. There are different published databases such as The European Genome-phenome Archive (EGA), which is a data center for all types of sequencing and genotyping experiments. Almost 58% of all studies in the EGA are related to cancer [36]. The “CanEvolve” database fulfills the need for data integration and interpretation. It contains data from 90 studies involving more than 10,000 patients. Data analysis can be performed at different levels: primary analysis including mRNA, microRNA (miRNA), and protein expression, genome variations, and protein–protein interactions; integrative analysis of gene and miRNA expression, gene expression, and copy number variations, and gene set enrichment analysis; network analysis; and survival analysis [37]; the main page of this database is shown in Figure 2B.

 Gene Databases

Gene databases collect various types of gene data and information related to cancer [38] and also provide help to researchers in understanding the genetic architecture of complex diseases and improve the accuracy of diagnosis and the effectiveness of therapy [39]. Various databases have been published such as the “IARCTP53” shown in Figure 2C; this database compiles various types of data and information on human TP53 variations related to cancer [38]. The “TGDBs” gene database includes mechanisms of oncogenic activation, regulation, frequency of involvement in various tumor types, and chromosomal location. Data about the encoded proteins includes the cell type in which they are found, subcellular location, DNA-, protein-, and ligand-binding, role in development, and normal biochemical function [40].

 Proteomic Databases

Cancer proteome databases encompass tumor tissues, cells, and biological fluids to interpret signaling pathways, identify signatures related to tumor initiation, invasion, and metastasis, and determine analytical, predictive, and prognostic markers [41], and also help determine the molecular details of proteome differentiation in various human tissues and organs, thus greatly improving our understanding of disease and human biology [23,42]. A number of databases have been published in this research area such as those shown in Figure 2D: “CanProVar” is designed to store and display single amino acid alterations including both germline and somatic variations in the human proteome, particularly those related to the genesis or development of human cancer based on the published studies and sources [43].

 Mutation Databases

Mutation databases play an important role in science, diagnostics, and genetic health care and can play a vital role in life and death decisions. These databases are extensively used, but only gene- or locus-specific databases have been previously reviewed for their utility, accuracy, completeness, and currency [44]. Mutations in the tumor suppressor gene TP53 are associated with a variety of cancers [45]. More than 50% of the human tumors harbor TP53 mutations, resulting in a collection of over 45,000 somatic and germline mutations in the UMDTP53 database, as shown in Figure 2E. Analyses of these mutations have been helpful for improving our knowledge on the structure-function relations within the TP53 protein [46]. A number of databases have been published in this research area, which are of marked utility in the scientific community.

 Genomic Databases

The Cancer Genome Database represents one of numerous international groups dedicated to performing wide-ranging genomic and epigenomic studies of selected cancer types to develop our understanding of disease and provide an open-access resource for international cancer research [47]. This database is aimed at improving the understanding of the molecular basis of cancer development [48]. Several databases have been published in this research area; for example, the MethCNA comprehensive database for genomic data in human cancer (Figure 2F). Per a most recent publication, this database contains approximately 10,000 tumor samples covering 37 cancer types. All the data were collected from the TCGA and the National Center for Biotechnology Information and were evaluated using a pipeline that combined multiple computational resources and tools [49]. BioMuta is a single-nucleotide variation and disease association database where variations are mapped to genomes and RefSeq nucleotide entries and are incorporated through UniProtKB/Swiss-Prot positional coordinates. The recent version of BioMuta contains only nonsynonymous single-nucleotide variations associated with cancer [50].

 Results and Discussion Database Statistics

In this work, we have provided almost all cancer databases (Table S1 in Multimedia Appendix 1) and shown the year-wise growth of the CRDB. Figure 3 shows the magnitude of category-wise growth of the databases. Table 2 shows the year-wise growth distribution of cancer databases, which marks tremendous growth and is an achievement for the cancer scientific community. Further, we have modified or deleted all the nonfunctional and inaccessible database links and have provided a new, updated cancer database in the form of a database named CRDB and a table (Table S1 in Multimedia Appendix 1).

The statistics data of the Cancer Research Database (DB)—distribution of the database category.

Year-wise growth of the Cancer Research Database.

Year Database growth, %
1999 1
2004 1
2005 1
2006 2
2007 2
2008 2
2009 2
2010 5
2011 6
2012 2
2013 7
2014 5
2015 6
2016 6
2017 11
2018 7
2019 7
2020 14
2021 12
Usage of the CRDB

The CRDB has been developed to provide an easy and user-friendly search experience; for easier and faster search, three options are provided for finding cancer databases. First, browsing can be carried out by typing the name of the database in the search bar, which is highlighted in Figure 4A, or by clicking on the name of the category or image expression, which is shown in Figure 4B. with CRDB statistics as well, which will lead to the category list page (Figure 4C), and a brief overview with the original link of the required search will be accessed by clicking the needed database. Further, for a specific database search, the “CT Database” is used as an example from the expression Databases to make it more user-friendly.

Browse options of the Cancer Research Database (CRDB). (A) Can be browsed by typing the name. (B) Can be browsed by category name or image expression. (C) An example and the final result.

 Example of Cancer Diagnosis During the COVID-19 Pandemic

As of the previously reported reductions in cancer screening and other preventive care visits during the COVID-19 pandemic, the number of new cancer cases in 2020 is likely to be smaller than anticipated. According to one survey of diagnostic results, there was a 46% decrease in diagnosis of six different cancers (colorectal, pancreatic breast, lung, esophageal, and stomach cancer) from March 1 to April 18, 2020, relative to the period between January 6, 2019, and February 29, 2020, varying from a 25% decrease in the detection rate of pancreatic cancer to a 52% decrease in that of breast cancer [51,52]. Another study found that new CRC diagnoses were 30% lower from January to mid-April 2020 relative to the same timeframe in 2019 [53]. Across the world, similar losses have been noted, including those in the United Kingdom [54], the United States [54], and the Netherlands [55]. While these preliminary observations may provide insight into the pandemic’s effect on cancer diagnosis, population-based cancer registry evidence and the degree to which these delays may lead to more advanced-stage disease will not be available for some time.

 COVID-19 and Cancer

People with active cancer are more vulnerable to infectious pathogens as a result of a compromised immune system due to the malignancy and its treatment (eg, surgery and chemotherapy). This has raised fears that COVID-19–related problems and mortality may be more common among patients with cancer [56]. According to a 2020 study, patients with cancer may be at a higher risk of COVID-19 than those without cancer [57]. COVID-19 infection can impact persons with a wide range of hematologic diseases; however, the risk of infection is lower in patients with chronic myeloid proliferative neoplasms such as chronic myeloid leukemia and greater in persons on immunosuppressive medication [57,58]. A study at a tertiary care hospital in Wuhan, China, reported that patients with lung cancer above the age 60 years are at a high risk of COVID-19 locally [14,58,59] and worldwide [59,60]. It was also revealed that of the many cancer types, people with lung cancer who are over 60 years old are especially susceptible to COVID-19. Although it may seem intuitive that people with a defective respiratory epithelium are more susceptible to rapid virus entry into the lungs [61]. Another study shows a decrease in 6 types of cancer, with the total number of detected cancers being 4310 before and 2310 during the COVID-19 pandemic, with breast cancer ranking the highest with 2208 cases before and 2310 cases during the COVID-19 pandemic, followed by colorectal cancer at 946 cases before and 840 cases during the pandemic, and Figure 5 shows details regarding all 6 cancer databases before and during the COVID-19 pandemic [52].

The number of cancers detected before and during the COVID-19 pandemic.

 Global Cancer Rate

According to American institute for cancer research [62], the rates of all cancers have increased in almost in all countries, the privation of which is the major task for public health in 2021. Decreasing the cancer rate involves coordinated and comprehensive intervention from all facets of society, particularly the public sphere, civil society, and health and other occupations. Figure 6 shows the top 10 country-wise cancer rate from Oceania, Europe, and North America, in which the highest rate of cancer was reported in Australia (468.0 people per 100,000 population). For these 10 countries, the age-standardized average was at least 320 people per 100,000 population. With 579.9 men per 100,000 population, the age-standardized average was at least 360 people per 100,000 population, and with 363.0 women per 100,000 population, the age-standardized average was at least 300 people per 100,000 population.

Country-wise cancer rate with an age-standardized average (per 100,000 population).

 New Cases and Deaths

The American Cancer Society Cancer Action NetworkSM works worldwide to increase the quality of care for patients with and survivors of cancer. With time and the emergence of new cases worldwide, we have compiled a list of the top 10 cancers diagnosed in the United States in 2021. Table 3 shows the number of new cancer cases, with breast cancer in women and prostate cancer in men ranking first (30% cases) and second (26%), respectively. Although mortality estimates are shown in Table 3, lung and bronchial cancer showed the same rates in both male and female patients and ranked the highest, followed by breast and prostate cancer.

Rates of new cancers and mortality between male and female patients.

Cancer type New cancers, % Mortality, %
Male Female Male Female
Prostate 26 N/Aa 11 N/A
Breast N/A 30 N/A 15
Lung and bronchial 12 13 22 22
Colorectal 8 8 9 8
Urinary bladder 7 —b 4 —
Skin melanoma 6 5 — —
Kidney and renal pelvis 5 3 — —
Non-Hodgkin lymphoma 5 4 4 3
Oral cavity and pharynx 4 — — —
Leukemia 4 3 4 3
Pancreatic 3 3 8 8
Uterine corpus N/A 7 N/A 4
Brain and other nervous system regions — — 3 3
Liver and intrahepatic bile duct — — 6 3
Esophageal — — 4 —
Ovarian N/A — N/A 5

aN/A: not applicable.

b—: not determined.

 Conclusions

A biological database provides facilities for storing, organizing, and retrieving biological data such as DNA, RNA, carbohydrates, proteins, and cancers. It can be easily viewed, managed, and modified. A number of papers have been published in this research field, which have their own classification of cancer databases based on their function, use, certain technical aspects, and on species such as human, mouse, plant, and fungi. According to such published studies, we have classified the cancer databases into six categories: Data Portal database, Genomic database, Proteomic database, Expression database, Gene database, and Mutation database. Further, we have collected almost all cancer databases with a short introduction and have updated or removed all nonfunctional links. Furthermore, we have understood the current situation of cancer and its correlation with COVID-19; for example, the up-down, mortality, and new case count based on continent and countries, etc. Our database can be searched through an easy-to-use, user-friendly method, can be searched by clicking on category name of image expression, or users can type the name of needed databases in the given search bar and they will be updated with time. In addition, we have examined the status and diagnoses of cancer during the COVID-19 pandemic and have provided easy and understandable information for future researchers.

Cancer databases.

 Abbreviations CNV

copy number variation

 CRDB

cancer research database

 EGA

European Genome-phenome Archive

 miRNA

microRNA

 TCGA

The Cancer Genome Atlas

This project is supported by Shenzhen's introduction of talents and research start-up (392020) and this project is supported by National Natural Science Foundation of China (32100434).

 Availability of Data and Material

These data will be available under the journal rule and regulation. To avoid future conflict and plagiarism issue, CRDB database is uploaded on the internet [63] so that we have provided some content in this article.

SU supervised the study with TG, DAK, WR, GA, FU, MI, AU and collected and verified the data carefully. SU drafted the manuscript. All authors reviewed the manuscript and agreed to submit it. Both TG and SU are the corresponding authors for this manuscript.

None declared.

 Sciacovelli M Schmidt C Maher ER Frezza C Metabolic Drivers in Hereditary Cancer Syndromes Annu Rev Cancer Biol 2020 03 09 4 1 77 97 10.1146/annurev-cancerbio-030419-033612 Jayasekara H MacInnis RJ Room R English DR Long-Term Alcohol Consumption and Breast, Upper Aero-Digestive Tract and Colorectal Cancer Risk: A Systematic Review and Meta-Analysis Alcohol Alcohol 2016 05 51 3 315 330 10.1093/alcalc/agv110 26400678 agv110 Yang Y Dong X Xie B Ding N Chen J Li Y Zhang Q Qu H Fang X Databases and web tools for cancer genomics study Genomics Proteomics Bioinformatics 2015 02 13 1 46 50 10.1016/j.gpb.2015.01.005 25707591 S1672-0229(15)00006-6 PMC4411507 Han HJ Nwagwu C Anyim O Ekweremadu C Kim S COVID-19 and cancer: From basic mechanisms to vaccine development using nanotechnology Int Immunopharmacol 2021 01 90 107247 10.1016/j.intimp.2020.107247 33307513 S1567-5769(20)33714-0 PMC7709613 Tian J Miao X Challenges and recommendations for cancer care in the COVID-19 pandemic Cancer Biol Med 2020 08 15 17 3 515 518 10.20892/j.issn.2095-3941.2020.0300 32944386 j.issn.2095-3941.2020.0300 PMC7476082 Liu C Zhao Y Okwan-Duodu D Basho R Cui X COVID-19 in cancer patients: risk, clinical features, and management Cancer Biol Med 2020 08 15 17 3 519 527 10.20892/j.issn.2095-3941.2020.0289 32944387 j.issn.2095-3941.2020.0289 PMC7476081 Pathania AS Prathipati P Abdul BA Chava S Katta SS Gupta SC Gangula PR Pandey MK Durden DL Byrareddy SN Challagundla KB COVID-19 and Cancer Comorbidity: Therapeutic Opportunities and Challenges Theranostics 2021 11 2 731 753 10.7150/thno.51471 33391502 thnov11p0731 PMC7738845 Brunello A Galiano A Finotto S Monfardini S Colloca G Balducci L Zagonel V Older cancer patients and COVID-19 outbreak: Practical considerations and recommendations Cancer Med 2020 12 9 24 9193 9204 10.1002/cam4.3517 33219746 PMC7774711 Sarode GS Sarode SC Maniyar N Anand R Patil S Oral cancer databases: A comprehensive review J Oral Pathol Med 2018 07 47 6 547 556 10.1111/jop.12667 29193424 Ullah S Ullah A Rahman W Ullah F Khan SB Ahmad G Ijaz M Gao T An innovative user-friendly platform for Covid-19 pandemic databases and resources Comput Methods Programs Biomed Update 2021 1 100031 10.1016/j.cmpbup.2021.100031 34604832 S2666-9900(21)00030-6 PMC8465267 Percudani R Carnevali D Puggioni V Ureidoglycolate hydrolase, amidohydrolase, lyase: how errors in biological databases are incorporated in scientific papers and vice versa Database (Oxford) 2013 2013 bat071 10.1093/database/bat071 24107613 bat071 PMC3793230 Cancer Genome Atlas Research Network Weinstein JN Collisson EA Mills GB Shaw KRM Ozenberger BA Ellrott K Shmulevich I Sander C Stuart JM The Cancer Genome Atlas Pan-Cancer analysis project Nat Genet 2013 10 45 10 1113 1120 10.1038/ng.2764 24071849 ng.2764 PMC3919969 Zhang J Baran J Cros A Guberman JM Haider S Hsu J Liang Y Rivkin E Wang J Whitty B Wong-Erasmus M Yao L Kasprzyk A International Cancer Genome Consortium Data Portal--a one-stop shop for cancer genomics data Database (Oxford) 2011 2011 bar026 10.1093/database/bar026 21930502 bar026 PMC3263593 Ullah S EDBCO-19: Emergency Data Base of COVID-19 J Clin Med Res 2020 8 1 10.37191/mapsci-2582-4333-2(5)-047 Bakouny Z Hawley J Choueiri T Peters S Rini B Warner J Painter C COVID-19 and Cancer: Current Challenges and Perspectives Cancer Cell 2020 11 09 38 5 629 646 10.1016/j.ccell.2020.09.018 33049215 S1535-6108(20)30492-X PMC7528740 International Cancer Genome Consortium T Hudson TJ Anderson W Artez A Barker AD Bell C Bernabé RR Bhan MK Calvo F Eerola I Gerhard DS Guttmacher A Guyer M Hemsley FM Jennings JL Kerr D Klatt P Kolar P Kusada J Lane DP Laplace F Youyong L Nettekoven G Ozenberger B Peterson J Rao TS Remacle J Schafer AJ Shibata T Stratton MR Vockley JG Watanabe K Yang H Yuen MMF Knoppers BM Bobrow M Cambon-Thomsen A Dressler LG Dyke SOM Joly Y Kato K Kennedy KL Nicolás P Parker MJ Rial-Sebbag E Romeo-Casabona CM Shaw KM Wallace S Wiesner GL Zeps N Lichter P Biankin AV Chabannon C Chin L Clément B de Alava E Degos F Ferguson ML Geary P Hayes DN Hudson TJ Johns AL Kasprzyk A Nakagawa H Penny R Piris MA Sarin R Scarpa A Shibata T van de Vijver M Futreal PA Aburatani H Bayés M Botwell DDL Campbell PJ Estivill X Gerhard DS Grimmond SM Gut I Hirst M López-Otín C Majumder P Marra M McPherson JD Nakagawa H Ning Z Puente XS Ruan Y Shibata T Stratton MR Stunnenberg HG Swerdlow H Velculescu VE Wilson RK Xue HH Yang L Spellman PT Bader GD Boutros PC Campbell PJ Flicek P Getz G Guigó R Guo G Haussler D Heath S Hubbard TJ Jiang T Jones SM Li Q López-Bigas N Luo R Muthuswamy L Ouellette BFF Pearson JV Puente XS Quesada V Raphael BJ Sander C Shibata T Speed TP Stein LD Stuart JM Teague JW Totoki Y Tsunoda T Valencia A Wheeler DA Wu H Zhao S Zhou G Stein LD Guigó R Hubbard TJ Joly Y Jones SM Kasprzyk A Lathrop M López-Bigas N Ouellette BFF Spellman PT Teague JW Thomas G Valencia A Yoshida T Kennedy KL Axton M Dyke SOM Futreal PA Gerhard DS Gunter C Guyer M Hudson TJ McPherson JD Miller LJ Ozenberger B Shaw KM Kasprzyk A Stein LD Zhang J Haider SA Wang J Yung CK Cros A Cross A Liang Y Gnaneshan S Guberman J Hsu J Bobrow M Chalmers DRC Hasel KW Joly Y Kaan TSH Kennedy KL Knoppers BM Lowrance WW Masui T Nicolás P Rial-Sebbag E Rodriguez LL Vergely C Yoshida T Grimmond SM Biankin AV Bowtell DDL Cloonan N deFazio A Eshleman JR Etemadmoghadam D Gardiner BB Gardiner BA Kench JG Scarpa A Sutherland RL Tempero MA Waddell NJ Wilson PJ McPherson JD Gallinger S Tsao M-S Shaw PA Petersen GM Mukhopadhyay D Chin L DePinho RA Thayer S Muthuswamy L Shazand K Beck T Sam M Timms L Ballin V Lu Y Ji J Zhang X Chen F Hu X Zhou G Yang Q Tian G Zhang L Xing X Li X Zhu Z Yu Y Yu J Yang H Lathrop M Tost J Brennan P Holcatova I Zaridze D Brazma A Egevard L Prokhortchouk E Banks RE Uhlén M Cambon-Thomsen A Viksna J Ponten F Skryabin K Stratton MR Futreal PA Birney E Borg A Børresen-Dale A-L Caldas C Foekens JA Martin S Reis-Filho JS Richardson AL Sotiriou C Stunnenberg HG Thoms G van de Vijver M van't Veer L Calvo F Birnbaum D Blanche H Boucher P Boyault S Chabannon C Gut I Masson-Jacquemier JD Lathrop M Pauporté I Pivot X Vincent-Salomon A Tabone E Theillet C Thomas G Tost J Treilleux I Calvo F Bioulac-Sage P Clément B Decaens T Degos F Franco D Gut I Gut M Heath S Lathrop M Samuel D Thomas G Zucman-Rossi J Lichter P Eils R Brors B Korbel JO Korshunov A Landgraf P Lehrach H Pfister S Radlwimmer B Reifenberger G Taylor MD von Kalle C Majumder PP Sarin R Rao TS Bhan MK Scarpa A Pederzoli P Lawlor RA Delledonne M Bardelli A Biankin AV Grimmond SM Gress T Klimstra D Zamboni G Shibata T Nakamura Y Nakagawa H Kusada J Tsunoda T Miyano S Aburatani H Kato K Fujimoto A Yoshida T Campo E López-Otín C Estivill X Guigó R de Sanjosé S Piris MA Montserrat E González-Díaz M Puente XS Jares P Valencia A Himmelbauer H Himmelbaue H Quesada V Bea S Stratton MR Futreal PA Campbell PJ Vincent-Salomon A Richardson AL Reis-Filho JS van de Vijver M Thomas G Masson-Jacquemier JD Aparicio S Borg A Børresen-Dale A-L Caldas C Foekens JA Stunnenberg HG van't Veer L Easton DF Spellman PT Martin S Barker AD Chin L Collins FS Compton CC Ferguson ML Gerhard DS Getz G Gunter C Guttmacher A Guyer M Hayes DN Lander ES Ozenberger B Penny R Peterson J Sander C Shaw KM Speed TP Spellman PT Vockley JG Wheeler DA Wilson RK Hudson TJ Chin L Knoppers BM Lander ES Lichter P Stein LD Stratton MR Anderson W Barker AD Bell C Bobrow M Burke W Collins FS Compton CC DePinho RA Easton DF Futreal PA Gerhard DS Green AR Guyer M Hamilton SR Hubbard TJ Kallioniemi OP Kennedy KL Ley TJ Liu ET Lu Y Majumder P Marra M Ozenberger B Peterson J Schafer AJ Spellman PT Stunnenberg HG Wainwright BJ Wilson RK Yang H International network of cancer genome projects Nature 2010 04 15 464 7291 993 998 10.1038/nature08987 20393554 nature08987 PMC2902243 Mewes HW Ruepp A Theis F Rattei T Walter M Frishman D Suhre K Spannagl M Mayer K Stümpflen V Antonov A MIPS: curated databases and comprehensive secondary data resources in 2010 Nucleic Acids Res 2011 01 39 Database issue D220 D224 10.1093/nar/gkq1157 21109531 gkq1157 PMC3013725 Tacutu R Thornton D Johnson E Budovsky A Barardo D Craig T Diana E Lehmann G Toren D Wang J Fraifeld VE de Magalhães JP Human Ageing Genomic Resources: new and updated databases Nucleic Acids Res 2018 01 04 46 D1 D1083 D1090 10.1093/nar/gkx1042 29121237 4599180 PMC5753192 SIB Swiss Institute of Bioinformatics Members The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases Nucleic Acids Res 2016 01 04 44 D1 D27 D37 10.1093/nar/gkv1310 26615188 gkv1310 PMC4702916 Pavlopoulou A Spandidos D Michalopoulos I Human cancer databases (review) Oncol Rep 2015 01 33 1 3 18 10.3892/or.2014.3579 25369839 PMC4254674 Teufel A Bioinformatics and database resources in hepatology J Hepatol 2015 03 62 3 712 719 10.1016/j.jhep.2014.10.036 25450718 S0168-8278(14)00802-2 Zhang Y Yang C Wang S Chen T Li M Wang X Li D Wang K Ma J Wu S Zhang X Zhu Y Wu J He F LiverAtlas: a unique integrated knowledge database for systems-level research of liver and hepatic disease Liver Int 2013 09 33 8 1239 1248 10.1111/liv.12173 23601370 Ullah S Lin S Xu Y Deng W Ma L Zhang Y Liu Z Xue Y dbPAF: an integrative database of protein phosphorylation in animals and fungi Sci Rep 2016 03 24 6 23534 10.1038/srep23534 27010073 srep23534 PMC4806352 Li S Shui K Zhang Y Lv Y Deng W Ullah S Zhang L Xue Y CGDB: a database of circadian genes in eukaryotes Nucleic Acids Res 2017 01 04 45 D1 D397 D403 10.1093/nar/gkw1028 27789706 gkw1028 PMC5210527 Ullah S Rahman W Ullah F Ahmad G Ijaz M Gao T DBHR: a collection of databases relevant to human research Future Sci OA 2022 03 8 3 FSO780 10.2144/fsoa-2021-0101 35251694 PMC8890137 Ullah S Rahman W Ullah F Ahmad G Ijaz M Gao T DBPR: DataBase of Plant Research Research Square 2022-04-22 https://www.researchsquare.com/article/rs-153167/v1 Khan SA Tremendous Contribution of Dr. Shahid Ullah to Scientific Community during COVID-19 Pandemic in the Form of Scientific Research J Clin Med Res 2020 8 1 10.37191/mapsci-2582-4333-2(5)-049 Xu D Protein databases on the internet Curr Protoc Protein Sci 2012 11 Chapter 2 Unit2.6 10.1002/0471140864.ps0206s70 23151744 Celis J Ostergaard M Jensen N Gromova I Rasmussen H Gromov P Human and mouse proteomic databases: novel resources in the protein universe FEBS Lett 1998 06 23 430 1-2 64 72 10.1016/s0014-5793(98)00527-4 9678596 S0014-5793(98)00527-4 Harper R Access to DNA and protein databases on the Internet Curr Opin Biotechnol 1994 02 5 1 4 18 10.1016/s0958-1669(05)80063-1 7764641 S0958-1669(05)80063-1 Sanchez C Lachaize C Janody F Bellon B Röder L Euzenat J Rechenmann F Jacq B Grasping at molecular interactions and genetic networks in Drosophila melanogaster using FlyNets, an Internet database Nucleic Acids Res 1999 01 01 27 1 89 94 10.1093/nar/27.1.89 9847149 gkc093 PMC148104 Tarek S Abd Elwahab R Shoman M Gene expression based cancer classification Egypt Inform J 2017 11 18 3 151 159 10.1016/j.eij.2016.12.001 Wan Q Dingerdissen H Fan Y Gulzar N Pan Y Wu T Yan C Zhang H Mazumder R BioXpress: an integrated RNA-seq-derived gene expression database for pan-cancer analysis Database (Oxford) 2015 2015 10.1093/database/bav019 25819073 bav019 PMC4377087 Xie B Ding Q Han H Wu D miRCancer: a microRNA-cancer association database constructed by text mining on literature Bioinformatics 2013 03 01 29 5 638 644 10.1093/bioinformatics/btt014 23325619 btt014 Smith C Hayamizu T Finger J Bello S McCright IJ Xu J Baldarelli R Beal J Campbell J Corbani L Frost PJ Lewis JR Giannatto SC Miers D Shaw DR Kadin JA Richardson JE Smith CL Ringwald M The mouse Gene Expression Database (GXD): 2019 update Nucleic Acids Res 2019 01 08 47 D1 D774 D779 10.1093/nar/gky922 30335138 5133672 PMC6324054 Lappalainen I Almeida-King J Kumanduri V Senf A Spalding JD Ur-Rehman S Saunders G Kandasamy J Caccamo M Leinonen R Vaughan B Laurent T Rowland F Marin-Garcia P Barker J Jokinen P Torres AC de Argila JR Llobet OM Medina I Puy MS Alberich M de la Torre S Navarro A Paschall J Flicek P The European Genome-phenome Archive of human data consented for biomedical research Nat Genet 2015 07 47 7 692 695 10.1038/ng.3312 26111507 ng.3312 PMC5426533 Samur MK Yan Z Wang X Cao Q Munshi NC Li C Shah PK canEvolve: a web portal for integrative oncogenomics PLoS One 2013 8 2 e56228 10.1371/journal.pone.0056228 23418540 PONE-D-12-30212 PMC3572035 Olivier M Eeles R Hollstein M Khan MA Harris CC Hainaut P The IARC TP53 database: new online mutation analysis and recommendations to users Hum Mutat 2002 06 19 6 607 614 10.1002/humu.10081 12007217 Su L Liu G Bai T Meng X Ma Q MGOGP: a gene module-based heuristic algorithm for cancer-related gene prioritization BMC Bioinformatics 2018 06 05 19 1 215 10.1186/s12859-018-2216-0 29871590 10.1186/s12859-018-2216-0 PMC5989416 Gong X Wu R Zhang Y Zhao W Cheng L Gu Y Zhang L Wang J Zhu J Guo Z Extracting consistent knowledge from highly inconsistent cancer gene data sources BMC Bioinformatics 2010 02 05 11 76 10.1186/1471-2105-11-76 20137077 1471-2105-11-76 PMC2832783 Arntzen MØ Boddie P Frick R Koehler CJ Thiede B Consolidation of proteomics data in the Cancer Proteomics database Proteomics 2015 11 15 22 3765 3771 10.1002/pmic.201500144 26316313 Uhlén M Fagerberg L Hallström BM Lindskog C Oksvold P Mardinoglu A Sivertsson Å Kampf C Sjöstedt E Asplund A Olsson I Edlund K Lundberg E Navani S Szigyarto CA Odeberg J Djureinovic D Takanen JO Hober S Alm T Edqvist P Berling H Tegel H Mulder J Rockberg J Nilsson P Schwenk JM Hamsten M von Feilitzen K Forsberg M Persson L Johansson F Zwahlen M von Heijne G Nielsen J Pontén F Proteomics. Tissue-based map of the human proteome Science 2015 01 23 347 6220 1260419 10.1126/science.1260419 25613900 347/6220/1260419 Li J Duncan DT Zhang B CanProVar: a human cancer proteome variation database Hum Mutat 2010 03 31 3 219 228 10.1002/humu.21176 20052754 PMC2829365 George RA Smith TD Callaghan S Hardman L Pierides C Horaitis O Wouters MA Cotton RGH General mutation databases: analysis and review J Med Genet 2008 02 45 2 65 70 10.1136/jmg.2007.052639 17893115 jmg.2007.052639 Li VD Li KH Li JT TP53 mutations as potential prognostic markers for specific cancers: analysis of data from The Cancer Genome Atlas and the International Agency for Research on Cancer TP53 Database J Cancer Res Clin Oncol 2019 03 145 3 625 636 10.1007/s00432-018-2817-z 30542790 10.1007/s00432-018-2817-z Leroy B Anderson M Soussi T TP53 mutations in human cancer: database reassessment and prospects for the next decade Hum Mutat 2014 06 35 6 672 688 10.1002/humu.22552 24665023 Cooper LA Demicco EG Saltz JH Powell RT Rao A Lazar AJ PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective J Pathol 2018 04 244 5 512 524 10.1002/path.5028 29288495 PMC6240356 Lee J Exploring cancer genomic data from the cancer genome atlas project BMB Rep 2016 11 49 11 607 611 10.5483/bmbrep.2016.49.11.145 27530686 3648 PMC5346320 Deng G Yang J Zhang Q Xiao Z Cai H MethCNA: a database for integrating genomic and epigenomic data in human cancer BMC Genomics 2018 02 13 19 1 138 10.1186/s12864-018-4525-0 29433427 10.1186/s12864-018-4525-0 PMC5810021 Dingerdissen H Torcivia-Rodriguez J Hu Y Chang T Mazumder R Kahsay R BioMuta and BioXpress: mutation and expression knowledgebases for cancer biomarker discovery Nucleic Acids Res 2018 01 04 46 D1 D1128 D1136 10.1093/nar/gkx907 30053270 4372542 PMC5753215 Biondi‐Zoccai G Sciarretta S Bullen C Nocella C Violi F Loffredo L Pignatelli P Perri L Peruzzi M Marullo AG De Falco E Chimenti I Cammisotto V Valenti V Coluzzi F Cavarretta E Carrizzo A Prati F Carnevale R Frati G Acute Effects of Heat‐Not‐Burn, Electronic Vaping, and Traditional Tobacco Combustion Cigarettes: The Sapienza University of Rome‐Vascular Assessment of Proatherosclerotic Effects of Smoking (SUR‐VAPES) 2 Randomized Trial JAHA 2019 03 19 8 6 10.1161/jaha.118.010455 Kaufman HW Chen Z Niles J Fesko Y Changes in the Number of US Patients With Newly Identified Cancer Before and During the Coronavirus Disease 2019 (COVID-19) Pandemic JAMA Netw Open 2020 08 03 3 8 e2017267 10.1001/jamanetworkopen.2020.17267 32749465 2768946 PMC7403918 Antoniewicz L Brynedal A Hedman L Lundbäck M Bosson JA Acute Effects of Electronic Cigarette Inhalation on the Vasculature and the Conducting Airways Cardiovasc Toxicol 2019 10 19 5 441 450 10.1007/s12012-019-09516-x 30963443 10.1007/s12012-019-09516-x PMC6746878 Waziry R Jawad M Ballout RA Al Akel M Akl EA The effects of waterpipe tobacco smoking on health outcomes: an updated systematic review and meta-analysis Int J Epidemiol 2017 02 01 46 1 32 43 10.1093/ije/dyw021 27075769 dyw021 Leventhal AM Strong DR Kirkpatrick MG Unger JB Sussman S Riggs NR Stone MD Khoddam R Samet JM Audrain-McGovern J Association of Electronic Cigarette Use With Initiation of Combustible Tobacco Product Smoking in Early Adolescence JAMA 2015 08 18 314 7 700 707 10.1001/jama.2015.8950 26284721 2428954 PMC4771179 Siegel R Miller K Fuchs H Jemal A Cancer Statistics, 2021 CA Cancer J Clin 2021 01 71 1 7 33 10.3322/caac.21654 33433946 Cattaneo C Daffini R Pagani C Salvetti M Mancini V Borlenghi E D'Adda M Oberti M Paini A De Ciuceis C Barbullushi K Cancelli V Belotti A Re A Motta M Peli A Bianchetti N Anastasia A Dalceggio D Roccaro AM Tucci A Cairoli R Muiesan ML Rossi G Clinical characteristics and risk factors for mortality in hematologic patients affected by COVID-19 Cancer 2020 12 01 126 23 5069 5076 10.1002/cncr.33160 32910456 Yu J Ouyang W Chua MLK Xie C SARS-CoV-2 Transmission in Patients With Cancer at a Tertiary Care Hospital in Wuhan, China JAMA Oncol 2020 07 01 6 7 1108 1110 10.1001/jamaoncol.2020.0980 32211820 2763673 PMC7097836 UK Coronavirus Cancer Monitoring Project team The UK Coronavirus Cancer Monitoring Project: protecting patients with cancer in the era of COVID-19 Lancet Oncol 2020 05 21 5 622 624 10.1016/S1470-2045(20)30230-8 32304634 S1470-2045(20)30230-8 PMC7159870 Li H Liu S-M Yu X-H Tang S-L Tang C-K Coronavirus disease 2019 (COVID-19): current status and future perspectives Int J Antimicrob Agents 2020 05 55 5 105951 10.1016/j.ijantimicag.2020.105951 32234466 S0924-8579(20)30101-1 PMC7139247 Zhang H Wang L Chen Y Wu Q Chen G Shen X Wang Q Yan Y Yu Y Zhong Y Wang X Chua MLK Xie C Outcomes of novel coronavirus disease 2019 (COVID-19) infection in 107 patients with cancer from Wuhan, China Cancer 2020 09 01 126 17 4023 4031 10.1002/cncr.33042 32573776 PMC7361610 Global cancer data by country World Cancer Research Fund International 2022-04-25 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