Abstract

A relatively new area of biology called bioinformatics deals with the processing and interpretation of biological data using computational, analytical, and algebraic methods. The broad term "bioinformatics" describes the application of digital technology to the study of biological processes through the collection of high-dimensional data from several sources. The main focus of bioinformatics research, which is mostly done in silico and usually entails synthesizing new knowledge from existing data, is the design and testing of the software tools needed to assess the data. A cancer patient's prognosis is enhanced by early detection; nonetheless, early diagnosis might be challenging to accept. Proteomics research and DNA microarrays have been utilized for extensive gene expression studies. In order to facilitate researchers' rapid acquisition and comprehension of these tools' functions, this review explores the use, features, and web servers, including KM plotter, GEPIA2, TCGA, PrognoScan, UALCAN, UCSC Xena, HPA, GENT2, cBioportal, GDC portal, TIMER, others multi-omics statistical tools.

Keywords

Introduction

Table 1 List of the database and server used in Cancer Bioinformatics

Bioinformatics Approach and steps in cancer detection and prognosis

The servers above are generally used for cancer research via computational approach. The steps that are taken into consideration during the data collection, data analysis, data interpretation are described below:

1. The Gene expression in different kinds of cancer:

The first and foremost important things needed for biomarker identification is to analyze gene expression and comparison their expression between normal and tumor cells. Oncomine, GEPIA, GENT2 are normally used for this. In this case during statistical analysis, p value considered <0>

2. The pattern of target gene expression in various cancer:

More than 30 cancers are till now have been documented. Gene expression patterns are most of the cases different but sometimes they have some sort similarities regarding gene expression patterns. For example, gene expression pattern of TAP1 in various cancer shows difference. Using the ONCOMINE database to observe the gene expression and fold changes, itt was proceeded by considering four cancer types: breast, lung, liver, and ovarian cancers. The expression was seen to be upregulated in breast, liver, ovary, and lung cancers. The GEPIA2 tool was used to look into the expression of the TAP1 gene, where the expression levels for LIHC and OV cancers were significantly higher than the normal tissues. The expression in the primary tumor and the normal was compared using the TCGA database in the UALCAN tool. A significant overexpression for the TAP1 expression was seen in the primary tumor in comparison with the normal in BRCA, LIHC, and LUAD [41].

3. Expression analysis of Target gene with clinical characteristics:

UALCAN online database is used to analyze gene expression with clinical characteristics. As example, the expression of the TAP1 gene in normal tissue was compared with tissues in patients with different clinical outcomes for breast, liver, lung, and ovarian cancers. Overexpression of TAP1 gene in cancer patient compared to normal tissue was most in stage 2 for LUAD (p = 3.37e-09) and LIHC (p = 2.97e-08) cancer, stage 2 (p = 1.62e-12) and stage 4 (p = 1.08e-03) were among the highest for BRCA. Stage 3 (p < 1e xss=removed xss=removed xss=removed>

4. Promoter methylation of gene with clinical characteristics:

The level of target gene promoter methylation with different clinical characteristics can be done using the UALCAN online database. In case of TAP1 gene, CpG probes were used to identify a correlation between the expression levels and promoter methylation. The promoter methylation of the TAP1 gene in normal tissue was compared with tissues in patients with different clinical outcomes for breast, liver, lung, and ovarian cancer. Promoter methylation was increased significantly in tumor samples than normal tissues for BRCA (p= 4.00e-03), LIHC (p = 2.36e-02), and LUAD (p = 5.99e-03). Compared to normal tissue, patients having stage 1 (p = 3.25e-02) and stage 2 (p = 4.17e-04) BRCA showed a significant upregulation in beta value, patients with LIHC had the highest promoter methylation in stage 1 (p = 2.32e-02), patients having stage 1 and 3 LUAD had a significant rise in promoter methylation than normal tissue (Compared to normal tissue, Asian and African American patients had increased promoter methylation in BRCA, and Caucasian patients had increased methylation for LIHC and LUAD. In breast, liver, and lung cancer, TAP1 promoter methylation was significantly unchanged for females compared to normal tissue. However, the analysis suggests no specific relation of TAP1 DNA methylation and clinico-pathological subtypes [42].

5. Mutant mRNA, gene mutations, and copy number alterations of Gene:

By utilizing the cBioPortal database, genetic alteration of gene in different cancers can be studied. For example, generated database queried to observe the genetic mutation of TAP1 in 7710 specimens from 12 cases of BRCA, LIHC, LUAD and OV cancers. Of the total queried samples, there was a 2% alteration in the gene set or pathways with the somatic mutation frequency of 0.3%. Considering multiple sample studies, in total 21 mutations, including 9 duplications, were reported for the TAP1 gene area. We observed between 1 and 808 amino acids in the TAP1 pro-peptide and TAP1 domain for the query. Amidst those mutations, 19 were missense, and 2 truncating mutations were identified thoroughly. Breast adenocarcinoma and lung cancer had the highest level of mutations found in them, and the mutations laid among a hotspot in R547C/H. Two mutations were identified in the R547C/H. The site contained mutations, such as missense mutations, that were discovered in 8 breast adenocarcinoma specimens, 3 lung cancer expressed missense mutations. For ovarian cancer datasets, alteration frequency was found highest (>6%) among four cancer types. Consequently, it generated the expression of TAP1 mRNA (RNA Seq V2) among 12 cases of cancers by utilizing the cBioPortal. Breast cancer, 8 (7 mis-sense and 1 truncating) cases, had the highest level of mutation in mRNA expression, and subsequently, the liver cancer was next in mutation with 4 affected [42].

6. Prognostic investigation for the TAP1 expression among cancer patients:

Different categories of cancer need to be considered for the prognosis of gene mRNA expression and summarization of the data using the prognostic databases with Cox p-value of a significance of (p <0 xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed xss=removed n =161 xss=removed>

7. Analyses of pathway and gene ontologies of co-expressed genes of gene:

Lastly, it needed to figure out the genes that positively correlated with the target. As example TAP1 gene has some co-expressed genes in BRCA, LIHC, LUAD, and OV cancer that was detected by using the R2 genomics analysis and visualization platform. The correlated genes were used in Venn Draw to draw a Venn diagram giving us the common correlated genes in BRCA (6058 genes), LIHC (3773 genes), LUAD (4012 genes), and OV cancer (2901 genes). Then it was extracted the positively correlated common genes to conduct an ontology investigation. We used the Enrichr software in order to understand which signaling pathways were influenced by the positively co-expressed genes and the TAP1 gene in BRCA, LIHC, LUAD, and OV. In the pathway analysis of the KEGG database, We saw that the most correlated 10 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway of TAP1 and the gene which are in positive correlation with TAP1, were primarily associated with cytokine-cytokine receptor pathway, chemokine signaling pathway, hematopoietic cell lineage, primary immunodeficiency, rheumatoid arthritis, cell adhesion molecules, Chagas disease, toll-like receptor signaling pathway, Salmonella infection, and human immunodeficiency virus-1 infection. Cytokine-cytokine receptor pathway and chemokine signaling pathway being the most significant pathways to be influenced [42].

DISCUSSION:

1. Bioinformatics in Lung Cancer:

At present, the diagnosis and treatment of NSCLC is still far from satisfactory, and the number of this case is still rising year by year. It is necessary to investigate the pathogenesis and biomarker of NSCLC to provide effective treatment. Great progress has been made on the mechanism of initiation and development of NSCLC. Many experiments including vitro tumor cell lines, animal tumor models, and patients’ tumor model have been done, however, NSCLC demands more comprehensive analysis because the progress of lung cancer is a multi-stage and multi-cause process. Fortunately, with the development of human genome sequencing, the high throughput and associated tumor database developed and were more available to get. The integration of data by bioinformatics analyses from multiple datasets has become a vital source of data for studies of lung cancer [43].

2. Bioinformatics in Breast Cancer:

Using serial analysis of gene [removed]SAGE), cell-type-specific cell surface indicators, and magnetic resonance imaging, Allinen et al. characterized the full transcriptome of every cell type making up healthy breast tissue as well as in situ and metastatic breast carcinomas. Beads were utilized for the quick isolation of each step. Their findings imply that all cell groups undergo modifications, but only cancer epithelial cells have genetic changes identified [44].

3. Bioinformatics in Liver Cancer:

After lung cancer, liver cancer accounts for the majority of cancer-related fatalities. Using a mouse hepatoblast model and RNAi, Sawey et al. carried out a forward genetic screening under the direction of human hepatocellular cancer amplification data. They discovered that the selected susceptibility to FGF19 inhibition was caused by overexpression. Since CCND1 and FGF19 are both equally significant driver genes of the 11q13.3 amplicons in liver cancer, 11q13.3 amplifications may serve as a useful biomarker for individuals who are expected to respond favorably to anti-FGF19 treatment [45].

4. Bioinformatics in Brain Tumors:

By employing this categorization to separate medulloblastomas from other histologically comparable brain tumors, Pomeroy et al. were able to anticipate the treatment activity of medulloblastomas [46]. Moreover, the molecular profile demonstrated that medulloblastomas and primitive neuroectodermal tumors (PNETS), two forms of brain cancers frequently regarded as single entities, are physiologically separate from one another. The medulloblastoma gene expression pattern revealed unanticipated participation of the sonic hedgehog signaling pathway and suggested cerebellar granule cells as their cell of origin. Bredel et al. also applied genomic network knowledge to the investigation of critical activities and addressing glioma genesis to employ gene transcription profiling in the biology view of human gliomas [47]. Several cancer investigators have used microarray technology to study multiple myeloma. The morphological uniformity of multiple myeloma was verified by Claudio et al. [48]. Locati et al. used self-organizing map techniques to organize publicly accessible HPV+ cancer information and derived gene signatures related to three different subgroups of the illness as a substitute strategy and to truly comprehend how gene groups may connect with prognosis [49]. A 10-gene proposed methodology of relapse duration and prognosis in epithelial ovarian cancer was discovered by Lu et al. using a support vector machine learning algorithm to examine the information from the cancer cell line encyclopedia. This model was verified on two different pieces of information [50].

5. Bioinformatics in Oral Cancer Detection:

Oral cancer is one of the most commonly seen neoplasms in the head and neck region and has a poor prognosis, and among oral cancer, oral squamous cell carcinoma (OSCC) is the most common [51,52]. Kumar et al. compared the top-ranked genes with the genes corresponding to strongly enriched GO keywords relevant to oral cancer. A total of 39 prospective oral cancer target genes were identified. Initial analysis of research and experimental data revealed 29 genes to be associated with OSCC. They proposed a function for the chosen candidate genes in the invasion and metastasis in OSCC following a thorough pathway analysis. Using immunohistochemistry (IHC), they further verified their hypotheses and discovered that in the OSCC specimens, FLNA was elevated, whereas ARRB1 and HTT were downregulated [52]. Nakashima et al. found that the miR-1290 expression level in the plasma of oral squamous cell carcinoma patients is lower than in healthy individuals. However, circulating miR-1290 status has been proposed as a promising biomarker for evaluating both overall survival and clinical response to chemo radiotherapy in patients with oral squamous cell carcinoma [53]. Differentially expressed genes, hub proteins, and pathways demonstrated a strong correlation with OSCC development. Thorough investigation using bioinformatics is necessary for understanding the underlying process of OSCC advancement. Important genes and pathways may serve as OSCC therapy management goals [54].

6. Bioinformatics in Ovarian Cancer:

Ovarian cancer is the most prevalent and main cause of female death globally among numerous gynecological cancers. The pathophysiology and underlying causes of illness development remain unexplained despite substantial investigation. Different non-coding RNAs have been recognized as key regulators in the development of ovarian cancer. Beg et al. highlighted the significance of several ncRNAs, which have a strong promise as a therapeutic strategy for the treatment of ovarian cancer [55].

7. Bioinformatics in Leukemia:

This study screened DEGs through the transcriptome sequencing results of AML samples. The GO enrichment analysis illustrated that the DEGs of AML were significantly enriched in functional items such as positive regulation of cell promotion, protein, and immune response. These items are closely related to the occurrence and function of tumors. The key process of malignant tumor proliferation is positive regulation of cell proliferation, and proteolysis and immune response inhibition are involved in tumor proliferation and invasion. The KEGG pathway enrichment analysis indicated that AML DEGs were significantly enriched in p53 signal pathway, TNF signal pathway, HIF-1 signal pathway, and other signal pathways. These pathways are common pathways for the progression of malignant tumors and are involved in regulating gastric cancer, bladder cancer, lung cancer, liver cancer, leukemia, and other malignant tumors. The results of GO enrichment analysis and KEGG pathway enrichment analysis showed that the DEGs of AML screened of this study were representative, which may be the key pathogenic genes of AML, participating in the regulation of tumor cell proliferation and invasion as well as in the occurrence and progression of AML [56].

8. Bioinformatics in Prostate Cancer:

Prostate cancer (PCa) is one of the pervasive carcinoma occurring in men and a large health burden worldwide [57]. The lethality of PCa is due to the lack of treatment options that can produce a lasting response at the genetic and cellular biological levels. About 15% of patients with PCa are diagnosed with high-risk disease [58]. Therefore, through utilizing univariate Cox and iterative lasso Cox regression analyses, a 3-gene (KCNK3, AK5, and ARHGEF38) risk signature model in PCas was constructed. The ROC curves further approved the accuracy of our risk model. It is reported that KCNK3 influenced physiological processes, ranging from vascular tone to metabolic diet through inflammation [59]. Also, KCNK3 was correlated with prolonged survival after surgery in colorectal cancer [60]. AK5 was reported as a new prognosis marker that promotes autophagy and proliferation in human gastric cancer [61]. Interestingly, ARHGEF3 was proved to be an oncogene and may be a novel biomarker for predicting invasive PCa [61, 62].

CONCLUSION: 

Although these tools provide great convenience for prognostic biomarker development, several key aspects of these tools remain elusive. Differences in datasets collected and split points may result in significantly different results. So, it is essential to collect dataset and their source of these web servers and find excluding TCGA data and there are significant differences in other data sources. This may be one of the reasons why the analysis results of different tools are not completely consistent. In the future, efforts should be made in data optimization, prognostic tools should be improved to be able to predict multi-gene markers, select optimal cut-off computation, use hierarchical clustering and consider complex multi-omics networks of interactions. In addition, more molecular subtypes and clinical information including tumor tissue image and treatment data should be collected and mined to identify more meaningful prognostic markers through more detailed subtype analysis.

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