1. Introduction
Globally, colorectal cancer (CRC) is the second leading cause of cancer death in both males and females. In 2020, there were an estimated 935,000 mortalities from CRC, which accounted for over 1.9 million cases [1]. CRC has become a major public health concern in the Asia-Pacific region, including Malaysia [2]. Malaysia is a developing country located in Southeast Asia with an estimated population of 32.7 million in 2021 and an annual population growth rate of 0.2%. The Malaysian National Cancer Registry Report 2012–2016 found that CRC is the most common cancer in males (16.9% of all cancers diagnosed) and the second most common cancer in females (10.7% of all cancers diagnosed) [3]. A study of the National Cancer Registry for CRC from 2008 to 2013 revealed that the overall age-standardized incidence rate for CRC was 21.32 per 100,000 population. Those of Chinese ethnicity had the highest CRC incidence rate (27.35), followed by Malay (18.95) and Indian (17.55) [4]. Despite the severity caused by CRC, local studies pertaining to this field are scarce. The first study on the local CRC omics has been conducted on 50 CRC patients from Hospital Canselor Tunku Muhriz, Cheras, Kuala Lumpur. Data from the 50 samples was used as a model for CRC database development, and it will also guide the next experiments on what type of data/samples will be needed to ensure the development of a comprehensive repository. In addition, TCGA-My was developed with the aim of providing a repository platform for a Malaysian cancer consortium. Cancer databases have been developed and curated using bioinformatics with the support of highly advanced experimental evidence to assist the discovery of novel and unknown information about cancer.
Even though there is a growing trend in CRC incidence and mortality, the pathobiology of CRC remains unknown [1,3], hence the need for a CRC omics database that will provide pools of genes, proteins or metabolites that are associated with this cancer, which can be identified through integrative analysis. To this end, genomic and metabolomic data was collected from 50 Malaysian CRC patients, and a database was developed to provide a systematic data storage and retrieval platform, which is known as Malaysian TCGA (TCGA-My). TCGA-My stores CRC data for the Malaysian ethnic population, and this feature distinguishes TCGA-My from other colorectal cancer databases, such as Colorectal Cancer Atlas (
In TCGA-My, most of the genes and metabolites are fully annotated to help users explore functional information for the data. Biological information for chromosomes, DNA region, tissue occurrence and type of mutation for each variant are also included. Circos plots are generated and provided to help users visualize the variants on human chromosomes. Additionally, details for the samples such as gender, age and ethnicity are also provided as they are useful for the interpretation of various analyses of the data. Eventually, TCGA-My will provide CRC raw data and results from the integrative analysis that can be used to predict phenotypic changes in cancer cells upon chemo-immunotherapy treatment toward precision and personalized medicine initiatives. With these features, TCGA-My should serve as a comprehensive database that will be an excellent aid in providing and integrating accurate molecular information to understand the relationship between CRC and ethnicity.
We envision TCGA-My will expand and serve as a Malaysian cancer multi-omics data repository and provide a unique opportunity for a systems biology approach to tackle the complexity of cancer cells and CRC pathobiology through the unification of experimental data and computational/mathematical models. Currently, TCGA-My stores 1,517,841 variants, 23,695 genes and 167,451 metabolites obtained from 13 genomic and 50 metabolomic samples. Genomic experiments with another 37 samples are in progress and the data will be gradually deposited into this database. We used the available data to design the TCGA-My architecture. The development of TCGA-My is ongoing and accessible at
2. Materials and Methods
2.1. Data Collection and Organization
The CRC genome data for 13 patients were obtained from Universiti Kebangsaan Malaysia Medical Molecular Biology Institute (UMBI). The Gene Analysis Tool Kit (GATK) was used to perform variant calling. ANNOVAR [8] was then used to annotate the variant with the associated gene. The samples from 13 patients were used as a test set to guide the CRC database development. As new data are generated, the database will be extended accordingly. The new data will also guide the choice of data/samples needed to ensure comprehensive repository coverage.
The metabolome data were obtained from the serum of 50 healthy controls and 50 CRC patients collected at UKM Medical Centre. Data analysis was conducted as described by Amir Hashim et al. [9].
Additional information on the individual molecules was obtained from external databases; for example, variants were linked to a dbSNP identifier (ID) [10] and COSMIC v70 ID [11]; genes were compared against GeneCards [12], Protein Databank (PDB) [13], RefSeq ncRNA [14] and UniProt [15]; metabolites were identified using an ID browser from Mass Profiler Professional software for the Metlin database [9]. Twenty-three CRC driver genes were identified by Abdullah and Muhammad [16]. The metabolites were also linked to Kyoto Encyclopedia Genes and Genomes (KEGG) ID [17]. CRC staging of the samples were compared against the Cancer Research UK website (
The data were organized using pandas, which is a Python module [18]. All variants for each sample were mapped onto a Circos plot. This plot was constructed using a protocol from Strawberry Perl [19].
2.2. Functional Annotation
Annotation was conducted to better understand the function of CRC sequences and metabolites. This was conducted using a standard bioinformatic procedure where extensive information on the sequences such as chromosomal location, gene ontology (GO) and pathway were retrieved from online databases such as NCBI Gene [20], Gene Ontology Consortium [21], KEGG [17], BioCarta [22], WikiPathways [23], InterPro [24], Human Protein Atlas [25], DisGeNET [26] and PANTHER [27], or were obtained from our bioinformatics analysis (where necessary).
2.3. Data Normalization
The repetition of the variant data was minimized by deploying four levels of data normalization [28] on the annotated variant dataset (Figure 1) as follows:
∘. 1NF: First Normal Form—Removal of redundant variants/genes data. The ANNOVAR output has the same variants for multiple genes and patients.
∘. 2NF: Second Normal Form—Insertion of variants/genes primary keys. Each unique variant and gene obtained its own primary key.
∘. 3NF: Third Normal Form—Insertion of foreign keys. The information and links between the variants and genes were converted into foreign keys.
∘. 4NF: Forth Normal Form—Separation of variants and genes data into separate tables. The primary keys for variants and genes were transferred into a pivot table.
2.4. Database Organization and Architecture
All collected data, including relevant information on the variants, genes and metabolites with their functional information were organized in 19 tables. Two types of tables were designed, which were (1) main tables that contained the collected data and (2) pivot tables that were linked with the main tables. TCGA-My was built as a relational database using HeidiSQL PorTable 9.4.0.512. The web interfaces were designed using a PHP web framework, Laravel.
3. Results
3.1. Database Summary
Figure 2 illustrates the organization of four datasets, i.e., sample; variants with an ID obtained from COSMIC and dbSNP databases; genes with an ID retrieved from PDB, RefSeq ncRNA and UniProt; and metabolites. Table 1 summarizes the number of entries for each dataset.
3.2. Main Datasets
3.2.1. Samples
Each sample of CRC represents data from different patients. Each sample was provided with the patients’ details, such as gender, age, ethnicity, diagnosis information, anatomical location and cancer stage (Table 2). Two cancer staging systems were included in this database, i.e., TNM [29] and Dukes staging [30] (Table 3).
3.2.2. Variants
TCGA-My provides a list of variants with comprehensive mutation details that includes chromosomal location, variant start and end point, reference and alternate nucleotide, variant position in DNA region, affected gene, tissue occurrence and type of mutation caused by the variant. Figure 3 shows the correlation between the number of variants, gender and age of the CRC patients. The number of variants in 12 samples showed slight differences with a range of 95,000± to 116,000± variants. Only one sample (C474), a female with the oldest age of 79, showed the highest number of variants (751,000± variants). Age might influence the number of variants [31].
Table 4 and Table 5 describe the position of variants in DNA regions and mutations in genome data, respectively, for 13 CRC patients. Circos plots display the position of the variants on 24 human chromosomes and the mutation types in a sample. A Circos plot was constructed for every patient and Figure 4 shows an example of the plot constructed for patient C474.
3.2.3. Genes
The variant genes were provided with gene symbol descriptions and linked with three different IDs from ncRNA, PDB and UniProt IDs. Forty-four genes were recently annotated as CRC driver genes by Abdullah and Muhammad [16], where a computational approach was applied to identify CRC driver genes using a bioinformatics pipeline that consisted of the Cancer Genome Interpreter (CGI) [32] and Integrated Cancer Genome Score (iCAGES) [33] analysis platforms. Driver genes refer to genes whose mutations promote tumor growth [34], and further investigation of these genes is critical in precision oncology [35]. Table 6 lists the number of genes and driver genes for each patient.
3.2.4. Metabolites
The metabolites in TCGA-My were described with the name, class and mass. From 89,256 identified metabolites, only eleven metabolites were significantly altered, suggesting their potential as biomarkers for CRC (Figure 5).
3.3. Database Interface and Access
The TCGA-My interface contains four main menus, i.e., About, Browse, Search and Help, that were designed to help users easily navigate the respective pages. Each primary page has a header with a logo, search box and tabs that can be used to navigate to the primary pages and the dropdown menu. The logo on the header can be clicked to return to the homepage and the search box serves as the homepage search box.
-
Homepage displays statistics for the main datasets, i.e., sample, variant, gene and metabolites. The number of entries can be clicked, directing users to the respective dataset. Menus for about, browse and search functions were also included on this page. The search box on the homepage can be used to search any ID or terms in the datasets.
-
About page provides general information for TCGA-My and CRC.
-
Browse page lists all TCGA-My datasets, four of which are the main datasets and two datasets that contain functional annotation information. These datasets can also be retrieved from the dropdown tab, named Menu, which can be found at the header of each primary page. The datasets on this page are described as follows:
Sample dataset: contains genome and metabolome data for CRC. The sample datasets are categorized into gender and ethnicity.
Variant dataset: contains variants that were obtained from the 13 samples of genome data for CRC. This dataset is also categorized into chromosomes, DNA regions, tissue occurrence and type of mutations.
Gene dataset: contains genes affected by the variants. A list of driver genes can also be obtained.
Metabolite dataset: contains metabolites that are profiled in the metabolome data for CRC. This dataset is categorized into class and regulation.
Gene ontology dataset: contains GOs information (biological process, molecular function and cellular component) for variant genes in CRC.
Pathway dataset: contains pathways information for genes and metabolites of CRC.
-
Search page allows two search options, i.e., simple search and variant advanced search. Simple search serves a similar function to the search box on the homepage and the header of the primary pages. Advanced search allows the users to find variant(s) with a combination of different keywords. Users can conduct a quick search for the variants from a certain sample that are linked to a specific driver gene.
-
Help page provides an entity-relationship diagram and the table information deposited in this database. The entity-relationship diagram shows the relationship between datasets stored in this database. Table information defines all terms used in TCGA-My. Additionally, this page also provides the contact details for questions or invitations to collaborate.
Each entry in TCGA-My provides a brief description. For instance, if a user searches for one of the patient ID, for example, C187, it will redirect the user to the description page for C187. Four tabs for the mutations/variants, Circos, genes and metabolites identified from C187 data will appear. If a user clicks on one of the entries in the gene tab, for example, gene of ACACA, a description of ACACA will appear and display five tabs for sample, GOs, variants, KEGG and PANTHER pathways. All datasets in TCGA-My are freely downloadable in comma-separated values (CSV) format by clicking the “Download CSV” button located at the top-right of each table.
4. Discussion
4.1. Strength of TCGA-My
TCGA-My is the first database that houses genome and metabolome data for Malaysian CRC patients. Given the high number of patients and deaths caused by CRC in Malaysia [1], this effort is vital for collecting and collating omics data for in-depth studies that will lead to the development of precision medicine in Malaysia. Details for each patient, such as gender, age, ethnicity, diagnosis information, anatomical location and cancer stage are provided along with other important information to facilitate a reliable prognosis, accurate diagnosis and effective treatment of cancer [36,37].
TCGA-My also contains lists of variants, related genes and metabolites, which are useful for researchers investigating and characterizing the molecules responsible for CRC in specific conditions, such as gender, age, ethnicity, anatomical location or cancer stage. In addition, the decision to treat CRC and other cancers relies on many factors, and each patient has a different condition, so one treatment is ineffective for all conditions. Precision medicine or personalized medicine benefits from omics data and it can provide customized treatment based on the patient’s molecular signatures that will be most effective. Such molecular datasets are crucial for finding potential biomarkers and signatures for CRC that can be used to design the most effective therapy and toward precision medicine [38].
TCGA-My is an integrated functional database that contains relevant biological information for genomics and metabolomics data for CRC with links to external databases to ease users into exploring the genes and metabolites. For example, users can find further information about a particular gene by clicking on the GeneCards or UniProt symbol. Further information about GOs and pathways can also be found by clicking on the identifier for the Gene Ontology Consortium [21], KEGG [17] and PANTHER [27] databases, and a window will automatically appear in your browser. In this way, the user is redirected from TCGA-My to a particular database and avoids manual browsing of the original database when searching for a gene of interest.
This database is systematically organized and provides a dynamic web interface. The users can find all information for each entry on a single page in a separate Table For example, the sample entry provides available information about the patients. All variants, related genes and metabolites can be found on a similar page in different tabs. The genomic data is displayed using a Circos plot to help visualization of the mutation that occurred in a particular genome. Related information on a gene entry can also be found on a similar page with different tabs.
TCGA-My was developed with the aim of providing a platform for a Malaysian cancer consortium, where a CRC dataset was used to provide a model for CRC database development that will be extended gradually. It will also guide the choice of data/samples that will be needed to ensure a comprehensive repository and provide accessible datasets for the epidemiological study of CRC.
4.2. Weaknesses of TCGA-My and Future Perspectives
One of the weaknesses of TCGA-My is the absence of another two omics datasets, which are transcriptomics and proteomics. Integrating all omics data for CRC, i.e., genomics, transcriptomics, proteomics and metabolomics, will provide clearer insights into the molecular mechanisms of CRC [39]. Currently, TCGA-My only provides datasets for two major ethnicities in Malaysia, Malay and Chinese, and none for Indians and other minor ethnicities in Malaysia. Nevertheless, the Chinese have been shown to have the highest CRC incidence rate in Malaysia, followed by the Malays [4]. Additionally, there are no datasets available for a younger generation with CRC. TCGA-My provides datasets for ages 41–82 years, but according to the descriptive statistics from the Malaysia Cancer Patient Registry-Colorectal Cancer, CRC incidence rises with age [4]. In addition, early onset of CRC occurs at an age of more than 50 years old in many countries, such as the United States, Canada and Australia [1]. Finally, the Circos plots in TCGA-My are static, making it difficult for users trying to identify the location of particular mutations that occurred in a sample.
Hence, it will be very important to periodically update the datasets in TCGA-My and further improve the interface in order to provide a comprehensive and convenient resource for other researchers to utilize.
4.3. Example of Applications
The first example concerns a molecular cancer researcher who wants to search for a specific gene of interest. Starting with the browse menu, a list of genes with known variants can be obtained, and the relevant information, such as the functional annotation of the molecule and patient statistics, can be retrieved. The users can download the datasets for more advanced analysis as per their intention.
The second example concerns bioinformaticians or biostatisticians interested in conducting big data analysis. With 1.5 million variants that can be downloaded from TCGA-My, there are several analyses that can be conducted, such as advanced data analytics in the search for a correlation between the DNA region and nucleotide changes in the variants. One may also deploy machine learning algorithms, building a model of the relationship between the variants and other columns in the table. Due to clear and unique labelling, this can be deployed easily.
The last example concerns the data owner. The TCGA-My database provides easy retrieval of the variants and pathways for a patient for future reference or research. The robust architecture of the TCGA-My database also ensures future data can be uploaded smoothly and requires minimal storage and computational resources due to the normalization steps that were deployed during data processing.
5. Conclusions
With rapid advancements in sequencing technologies, new sets of omics data will expand the size of current datasets in TCGA-My. Thus, this database will be periodically updated to ensure it is always up to date. It is essential to provide a specific Malaysian CRC database to ensure the information is accessible by local and global CRC researchers and clinicians for quick and easy reference for further investigation. This database will serve as a systematic and comprehensive omics resource that can be used to search for potential CRC biomarkers for developing improved prognostics, diagnostics, and treatment for CRC.
Conceptualization, Z.-A.M.-H.; formal analysis, M.A.A. and N.A.N.M.; resources, N.-S.A.M., N.F.M.Y., R.I.M.Y., M.I., S.S., I.M.R., I.S., L.M., Z.A.M.A., M.M., S.A.R., W.Z.W.N., S.N., N.A.A.H. and R.J.; writing—original draft preparation, M.A.A. and N.A.-A.; writing—review and editing, Z.-A.M.-H.; supervision, Z.-A.M.-H.; project administration, Z.-A.M.-H.; funding acquisition, R.J., M.M. and Z.-A.M.-H. All authors have read and agreed to the published version of the manuscript.
Not applicable.
Not applicable.
Data is accessible in TCGA-My database.
The authors thank Mohd Rusman Arief A-Rahman for his guidance in database development training, and the Centre for Bioinformatics Research (CBR), Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia for the computational facilities.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. The variants and genes data normalization algorithm that was deployed for generating an SQL file for TCGA-My.
Enlarge this image.Figure 2. The relational tables for the TCGA-My schema. The yellow tables are the main tables, and the blue tables are the pivot tables. The relationship between main and pivot tables is indicated with a straight line (―●). The dotted line represents the relationship between main tables (◊---●).
Enlarge this image.Figure 3. The correlation between the number of variants with the age and gender of CRC patients.
Enlarge this image.Figure 4. Circos plot for sample C474. The plot was constructed using Strawberry Perl to visualize the location of variants in the chromosomes.
Enlarge this image.Figure 5. Significantly altered metabolites. The CRC metabolome samples reveal nine upregulated (red) and two downregulated (blue) metabolites.
Number of entries in the datasets of TCGA-My.
| Dataset | Number of Entries |
|---|---|
| Sample | 50 |
| Variant | 1,517,841 |
| COSMIC | 1113 |
| dbSNP | 291,397 |
| Gene | 23,695 |
| PDB | 4420 |
| RefSeq ncRNA | 2637 |
| UniProt | 17,910 |
| Metabolite | 89,256 |
| Pathway | 344 |
| KEGG | 186 |
| PANTHER | 158 |
| Gene ontology | 17,459 |
Patients details.
| Patient | Gender | Age | Ethnicity | Diagnosis | Anatomical Location | Stage | |
|---|---|---|---|---|---|---|---|
| TNM | Dukes | ||||||
| C187 | Male | 63 | Malay | Well differentiated adenocarcinoma | Rectosigmoid | pT3 N2 MX | C2 |
| C330 | Male | 71 | Chinese | Well differentiated adenocarcinoma | Sigmoid colon | T3 N0 MX | B2 |
| C404 | Male | 68 | Chinese | Well differentiated adenocarcinoma | Rectum | pT3 pN1a MX | - |
| Sessile polyp in ascending colon | pT1 | A | |||||
| C414 | Male | 76 | Malay | Well differentiated adenocarcinoma (WHO Grade 1) | Sigmoid colon | pT3 pN1b pMX | C |
| C449 | Male | 65 | Malay | Moderately differentiated adenocarcinoma | Rectosigmoid colon | pT3 N2 MX | C |
| C476 | Male | 72 | Chinese | Well differentiated adenocarcinoma. | Recto-sigmoidectomy | pT4a N1 MX | |
| C194 | Female | 70 | Malay | Well differentiated adenocarcinoma | Sigmoid colon | - | B |
| C273 | Female | 73 | Malay | Moderately differentiated adenocarcinoma | Rectosigmoid colon | pT1 N0 MX | A |
| C373 | Female | 74 | Chinese | Moderately differentiated adenocarcinoma | Anterior resection specimen | T2 N0 MX | B |
| C388 | Female | 65 | Chinese | Moderately differentiated adenocarcinoma | Anterior resection specimen | pT2 pN1 pMx | C |
| C398 | Female | 71 | Chinese | Moderately differentiated adenocarcinoma. | Sigmoid colon with bladder | pT4 N1 MX | C |
| C467 | Female | 65 | Malay | Well differentiated adenocarcinoma | Rectum | T4b N1b pMX | C |
| C474 | Female | 79 | Malay | Well-differentiated adenocarcinoma | Left hemicolectomy | pT3 N0 MX | B1 |
Note: p indicates the pathological state has been examined for the respective component of the TNM staging system.
TNM and Dukes staging systems.
| Staging System | Component | Explanation | |
|---|---|---|---|
| TNM | Primary Tumor (T) | T1 | Tumor invades submucosa. |
| T2 | Tumor invades muscularis propria. | ||
| T3 | Tumor invades into the subserosa or perirectal tissues via muscularis propria. | ||
| T4 | Tumor has spread to other organs or structures directly and/or the visceral peritoneum. | ||
| T4a | The tumor has expanded into the surface of the visceral peritoneum, where it has penetrated all layers of the colon. | ||
| T4b | The tumor has spread to other organs or structures or has attached itself to them. | ||
| Regional lymph node (N) | N0 | Negative regional lymph node metastases. | |
| N1 | Metastases in one to three regional lymph nodes. | ||
| N1a | Tumor cells have been detected in one regional lymph node. | ||
| N1b | Tumor cells have been detected in two or three regional lymph nodes. | ||
| N2 | Metastases in four or more regional lymph nodes. | ||
| Distant metastases (M) | MX | Distant metastases could not be assessed. | |
| Dukes | A | Tumor limited to the submucosa. | |
| B | Tumor grows through the colon wall into muscular layers, no lymph nodes involved | ||
| B1 | Into but not through the muscularis propria, nodes not involved. | ||
| B2 | Through the muscularis propria, nodes not involved. | ||
| C | Lymph node involved. | ||
| C2 | Through the muscularis propria with nodes involved. | ||
List of DNA regions for the variants listed in TCGA-My.
| DNA Region | Number of Variants | Description |
|---|---|---|
| Intergenic | 926,482 | Variant overlaps in intergenic region. |
| Intronic | 409,632 | Variant overlaps in intronic region. |
| Non-coding RNA, intronic | 84,913 | Non-coding transcript variant overlaps with one of the transcripts in the intronic region. |
| Exonic | 8381 | Variant overlaps in exonic region. |
| Upstream | 8855 | Variant overlaps a 1-kb region upstream of the transcription start site. |
| Downstream | 9116 | Variant overlaps a 1-kb region downstream of the transcription termination site. |
| UTR3 | 8603 | Variant overlap in 3′ untranslated region. |
| Upstream, downstream | 922 | Variant overlaps in both upstream and downstream regions. |
| UTR5 | 1176 | Variant overlaps in 5′ untranslated region. |
| Splicing | 108 | Variant overlaps in splice region. |
| Non-coding RNA, splicing | 34 | Non-coding transcript variant overlaps with one of the transcripts in the splice region. |
| Exonic, splicing | 2 | Variant overlaps in both exonic and splice regions. |
Type of mutations identified for the variants listed in TCGA-My.
| Type of Mutations | Number of Variants | Description |
|---|---|---|
| Nonsynonymous SNV | 3922 | A single nucleotide change that alters an amino acid of a protein. |
| Frameshift insertion | 510 | Insertion of one or more nucleotides that shifts the codon reading frame. |
| Frameshift deletion | 917 | Deletion of one or more nucleotides that shifts the codon reading frame. |
| Stop-gain | 271 | Mutations caused by nonsynonymous SNV, frameshift insertion and frameshift deletion that leads to the gain of a stop codon. |
| Stop-loss | 9 | Mutations caused by nonsynonymous SNV, frameshift insertion and frameshift deletion that leads to the loss of a stop codon. |
| Non-frameshift deletion | 587 | Deletion of a set of nucleotides divisible by three that may not shift a reading frame. |
| Synonymous SNV | 2226 | A change of a single nucleotide that retains an amino acid of a protein. |
| Non-frameshift insertion | 153 | Insertion of a set of nucleotides divisible by three that may not shift a reading frame. |
| Unknown | 223 | Unknown mutation. |
Number of variant genes in genome sample.
| Patient | Number of Genes | Number of Driver Genes |
|---|---|---|
| C187 | 11,988 | 6 |
| C194 | 11,644 | 7 |
| C273 | 11,837 | 5 |
| C373 | 11,951 | 11 |
| C404 | 13,188 | 6 |
| C414 | 12,446 | 9 |
| C449 | 13,888 | 5 |
| C474 | 23,213 | 12 |
| C330 | 11,989 | 2 |
| C388 | 11,515 | 8 |
| C398 | 11,763 | 2 |
| C467 | 12,489 | 7 |
| C476 | 11,666 | 3 |
References
1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J. Clin.; 2021; 71, pp. 209-249. [DOI: https://dx.doi.org/10.3322/caac.21660] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33538338]
2. Center, M.M.; Jemal, A.; Ward, E. International trends in colorectal cancer incidence rates. Cancer Epidemiol. Prev. Biomark.; 2009; 18, pp. 1688-1694. [DOI: https://dx.doi.org/10.1158/1055-9965.EPI-09-0090] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19505900]
3. Azizah, A.M.; Hashimah, B.; Nirmal, K.; Siti Zubaidah, A.R.; Puteri, N.A.; Nabihah, A.; Sukumaran, R.; Balqis, B.; Nadia, S.M.R.; Sharifah, S.S.S. et al. Malaysia National Cancer Registry Report (MNCRR) 2012–2016; National Cancer Registry: Kuala Lumpur, Malaysia, 2019; ISBN 9789671614228
4. Abu Hassan, M.R.; Ismail, I.; Mohd Suan, M.A.; Ahmad, F.; Wan Khazim, W.K.; Othman, Z.; Mat Said, R.; Tan, W.L.; Mohammed, N.S.; Soelar, S.A. et al. Incidence and mortality rates of colorectal cancer in Malaysia. Epidemiol. Health; 2016; 38, e2016007. [DOI: https://dx.doi.org/10.4178/epih.e2016007] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26971697]
5. Chisanga, D.; Keerthikumar, S.; Pathan, M.; Ariyaratne, D.; Kalra, H.; Boukouris, S.; Mathew, N.A.; Al Saffar, H.; Gangoda, L.; Ang, C.S. et al. Colorectal cancer atlas: An integrative resource for genomic and proteomic annotations from colorectal cancer cell lines and tissues. Nucleic Acids Res.; 2016; 44, pp. D969-D974. [DOI: https://dx.doi.org/10.1093/nar/gkv1097] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26496946]
6. Zhang, X.; Sun, X.F.; Cao, Y.; Ye, B.; Peng, Q.; Liu, X.; Shen, B.; Zhang, H. CBD: A biomarker database for colorectal cancer. Database; 2018; 2018, bay046. [DOI: https://dx.doi.org/10.1093/database/bay046]
7. Agarwal, R.; Kumar, B.; Jayadev, M.; Raghav, D.; Singh, A. CoReCG: A comprehensive database of genes associated with colon-rectal cancer. Database; 2016; 2016, baw059. [DOI: https://dx.doi.org/10.1093/database/baw059]
8. Wang, K.; Li, M.; Hakonarson, H. ANNOVAR: Functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res.; 2010; 38, e164. [DOI: https://dx.doi.org/10.1093/nar/gkq603]
9. Amir Hashim, N.A.; Ab-Rahim, S.; Ngah, W.Z.W.; Nathan, S.; Mutalib, N.S.A.; Sagap, I.; Rahman, A.J.; Mazlan, M. Global metabolomics profiling of colorectal cancer in Malaysian patients. BioImpacts; 2021; 11, pp. 33-43. [DOI: https://dx.doi.org/10.34172/bi.2021.05]
10. Sherry, S.T.; Ward, M.H.; Kholodov, M.; Baker, J.; Phan, L.; Smigielski, E.M.; Sirotkin, K. DbSNP: The NCBI database of genetic variation. Nucleic Acids Res.; 2001; 29, pp. 308-311. [DOI: https://dx.doi.org/10.1093/nar/29.1.308]
11. Forbes, S.A.; Beare, D.; Gunasekaran, P.; Leung, K.; Bindal, N.; Boutselakis, H.; Ding, M.; Bamford, S.; Cole, C.; Ward, S. et al. COSMIC: Exploring the world’s knowledge of somatic mutations in human cancer. Nucleic Acids Res.; 2015; 43, pp. D805-D811. [DOI: https://dx.doi.org/10.1093/nar/gku1075]
12. Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y. et al. The GeneCards suite: From gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinform.; 2016; 54, pp. 1-30. [DOI: https://dx.doi.org/10.1002/cpbi.5] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/27322403]
13. Berman, H.M.; Westbrook, J.D.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Wissig, H.; Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Res.; 2000; 28, pp. 235-242. [DOI: https://dx.doi.org/10.1093/nar/28.1.235] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10592235]
14. O’Leary, N.A.; Wright, M.W.; Brister, J.R.; Ciufo, S.; Haddad, D.; McVeigh, R.; Rajput, B.; Robbertse, B.; Smith-White, B.; Ako-Adjei, D. et al. Reference Sequence (RefSeq) database at NCBI: Current status, taxonomic expansion, and functional annotation. Nucleic Acids Res.; 2016; 44, pp. D733-D745. [DOI: https://dx.doi.org/10.1093/nar/gkv1189]
15. The UniProt Consortium. UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Res.; 2021; 49, pp. D480-D489. [DOI: https://dx.doi.org/10.1093/nar/gkaa1100]
16. Abdullah, M.I.; Muhammad, N.A.N. Prediction of colorectal cancer driver genes from patients’ genome data. Sains Malaysiana; 2018; 47, pp. 3095-3105. [DOI: https://dx.doi.org/10.17576/jsm-2018-4712-20]
17. Kanehisha, M.; Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res.; 2000; 28, pp. 27-30. [DOI: https://dx.doi.org/10.1093/nar/28.1.27] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10592173]
18. Reback, J.; jbrockmendel,; McKinney, W.; Van den Bossche, J.; Augspurger, T.; Cloud, P.; Hawkins, S.; gfyoung,; Sinhrks,; Roeschke, M. et al. Pandas-Dev/Pandas: Pandas 1.3.0rc1. 2021; Available online: https://doi.org/10.5281/zenodo.4940217 (accessed on 20 June 2021).
19. Krzywinski, M.; Schein, J.; Birol, I.; Connors, J.; Gascoyne, R.; Horsman, D.; Jones, S.J.; Marra, M.A. Circos: An information aesthetic for comparative genomics. Genome Res.; 2009; 19, pp. 1639-1645. [DOI: https://dx.doi.org/10.1101/gr.092759.109]
20. Brown, G.R.; Hem, V.; Katz, K.S.; Ovetsky, M.; Wallin, C.; Ermolaeva, O.; Tolstoy, I.; Tatusova, T.; Pruitt, K.D.; Maglott, D.R. et al. Gene: A gene-centered information resource at NCBI. Nucleic Acids Res.; 2015; 43, pp. D36-D42. [DOI: https://dx.doi.org/10.1093/nar/gku1055] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25355515]
21. The Gene Ontology Consortium. The Gene Ontology Resource: 20 years and still GOing strong. Nucleic Acids Res.; 2019; 47, pp. D330-D338. [DOI: https://dx.doi.org/10.1093/nar/gky1055]
22. Nishimura, D. BioCarta. Biotech Softw. Internet Rep. Comput. Softw. J. Sci.; 2001; 2, pp. 117-120. [DOI: https://dx.doi.org/10.1089/152791601750294344]
23. Martens, M.; Ammar, A.; Riutta, A.; Waagmeester, A.; Slenter, D.N.; Hanspers, K.; Miller, R.A.; Digles, D.; Lopes, E.N.; Ehrhart, F. et al. WikiPathways: Connecting communities. Nucleic Acids Res.; 2021; 49, pp. D613-D621. [DOI: https://dx.doi.org/10.1093/nar/gkaa1024] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33211851]
24. Blum, M.; Chang, H.-Y.; Chuguransky, S.; Grego, T.; Kandasaamy, S.; Mitchell, A.; Nuka, G.; Paysan-Lafosse, T.; Qureshi, M.; Raj, S. et al. The InterPro protein families and domains database: 20 years on. Nucleic Acids Res.; 2021; 49, pp. D344-D354. [DOI: https://dx.doi.org/10.1093/nar/gkaa977] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33156333]
25. Thul, P.J.; Lindskog, C. The human protein atlas: A spatial map of the human proteome. Protein Sci.; 2018; 27, pp. 233-244. [DOI: https://dx.doi.org/10.1002/pro.3307] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28940711]
26. Piñero, J.; Ramírez-Anguita, J.M.; Saüch-Pitarch, J.; Ronzano, F.; Centeno, E.; Sanz, F.; Furlong, L.I. he DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res.; 2020; 48, pp. D845-D855. [DOI: https://dx.doi.org/10.1093/nar/gkz1021] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31680165]
27. Mi, H.; Muruganujan, A.; Ebert, D.; Huang, X.; Thomas, P.D. PANTHER version 14: More genomes, a new PANTHER GO-slim and improvements in enrichment analysis tools. Nucleic Acids Res.; 2019; 47, pp. D419-D426. [DOI: https://dx.doi.org/10.1093/nar/gky1038]
28. Kaula, R. Normalizing with Entity Relationship Diagramming. Available online: https://tdan.com/normalizing-with-entity-relationship-diagramming/4583 (accessed on 20 June 2021).
29. Edge, S.B.; Compton, C.C. The American Joint Committee on Cancer: The 7th edition of the AJCC cancer staging manual and the future of TNM. Ann. Surg. Oncol.; 2010; 17, pp. 1471-1474. [DOI: https://dx.doi.org/10.1245/s10434-010-0985-4]
30. Haq, A.I.; Schneeweiss, J.; Kalsi, V.; Arya, M. The Dukes staging system: A cornerstone in the clinical management of colorectal cancer. Lancet Oncol.; 2009; 10, 1128. [DOI: https://dx.doi.org/10.1016/S1470-2045(09)70157-3]
31. DePinho, R.A. The age of cancer. Nature; 2000; 408, pp. 248-254. [DOI: https://dx.doi.org/10.1038/35041694]
32. Tamborero, D.; Rubio-Perez, C.; Deu-Pons, J.; Schroeder, M.P.; Vivancos, A.; Rovira, A.; Tusquets, I.; Albanell, J.; Rodon, J.; Tabernero, J. et al. Cancer genome interpreter annotates the biological and clinical relevance of tumor alterations. Genome Med.; 2018; 10, 25. [DOI: https://dx.doi.org/10.1186/s13073-018-0531-8]
33. Dong, C.; Guo, Y.; Yang, H.; He, Z.; Liu, X.; Wang, K. ICAGES: Integrated CAncer GEnome Score for comprehensively prioritizing driver genes in personal cancer genomes. Genome Med.; 2016; 8, 135. [DOI: https://dx.doi.org/10.1186/s13073-016-0390-0]
34. Rahimi, M.; Teimourpour, B.; Marashi, S.A. Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach. Biol. Med.; 2019; 114, 103362. [DOI: https://dx.doi.org/10.1016/j.compbiomed.2019.103362] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31561101]
35. Bailey, M.H.; Tokheim, C.; Porta-Pardo, E.; Sengupta, S.; Bertrand, D.; Weerasinghe, A.; Colaprico, A.; Wendl, M.C.; Kim, J.; Reardon, B. et al. Comprehensive characterization of cancer driver genes and mutations. Cell; 2018; 173, pp. 371-385.e18. [DOI: https://dx.doi.org/10.1016/j.cell.2018.02.060]
36. White, A.; Ironmonger, L.; Steele, R.J.C.; Ormiston-Smith, N.; Crawford, C.; Seims, A. A Review of sex-related differences in colorectal cancer incidence, screening uptake, routes to diagnosis, cancer stage and survival in the UK. BMC Cancer; 2018; 18, 906. [DOI: https://dx.doi.org/10.1186/s12885-018-4786-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30236083]
37. Chlebowski, R.T.; Chen, Z.; Anderson, G.L.; Rohan, T.; Aragaki, A.; Lane, D.; Dolan, N.C.; Paskett, E.D.; McTiernan, A.; Hubbell, F.A. et al. Ethnicity and breast cancer: Factors influencing differences in incidence and outcome. J. Natl. Cancer Inst.; 2005; 97, pp. 439-447. [DOI: https://dx.doi.org/10.1093/jnci/dji064] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15770008]
38. Yau, T.O. Precision treatment in colorectal cancer: Now and the future. JGH Open; 2019; 3, pp. 361-369. [DOI: https://dx.doi.org/10.1002/jgh3.12153]
39. Manzoni, C.; Kia, D.A.; Vandrovcova, J.; Hardy, J.; Wood, N.W.; Lewis, P.A.; Ferrari, R. Genome, transcriptome and proteome: The rise of omics data and their integration in biomedical sciences. Brief. Bioinform.; 2018; 19, pp. 286-302. [DOI: https://dx.doi.org/10.1093/bib/bbw114]
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Nor Afiqah-Aleng 2 ; Nurul-Syakima Ab Mutalib 3
; Najwa Farhah Md Yusof 3 ; Ryia Illani Mohd Yunos 3 ; Ishak, Muhiddin 3
; Saidin, Sazuita 3 ; Isa Mohamed Rose 4 ; Ismail Sagap 5 ; Luqman Mazlan 5 ; Zairul Azwan Mohd Azman 5 ; Musalmah Mazlan 6 ; Sharaniza Ab Rahim 6
; Wan Zurinah Wan Ngah 7 ; Nathan, Sheila 8
; Nurul Azmir Amir Hashim 6
; Mohamed-Hussein, Zeti-Azura 9
; Rahman, Jamal 3 1 Center for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia;
2 Institute of Marine Biotechnology, Universiti Malaysia Terengganu, Kuala Nerus 21030, Malaysia;
3 UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur 56000, Malaysia;
4 Department of Pathology, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Cheras, Kuala Lumpur 56000, Malaysia;
5 Department of Surgery, Faculty of Medicine, Universiti Kebangsaan Malaysia, Jalan Yaacob Latif, Cheras, Kuala Lumpur 56000, Malaysia;
6 Department of Biochemistry and Molecular Medicine, Faculty of Medicine, Universiti Teknologi MARA, Campus Sungai Buloh, Sungai Buloh 47000, Malaysia;
7 Department of Biochemistry, Faculty of Medicine, Universiti Kebangsaan Malaysia Medical Centre, Kuala Lumpur 56000, Malaysia;
8 Department of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia;
9 Center for Bioinformatics Research, Institute of Systems Biology (INBIOSIS), Universiti Kebangsaan Malaysia, UKM, Bangi 43600, Malaysia;