Keywords

1 Introduction

The current challenge for effective treatment of gastrointestinal cancer is detection of cancer at an early stage. Detection of gastrointestinal cancer at an early stage requires biomarkers expressing at early stage, biosensors, promising technologies, and computational methods (Fig. 1.1). Biomarkers provide understanding on features of cancer or tumor and helps in determining the features of cancer or tumor. Biosensors are used to sense or determine the biomarkers (expressed features of cancer or tumor) both qualitatively and quantitatively. The promising technologies are used either to identify biomarkers or develop sensors or devices. Computational methods used mathematical methods or expressions to identify or model or quantify features of cancer or tumor. Thus, this chapter discusses in detail about the role of biomarkers, biosensors, promising technologies, and computational methods which can be used for detection of gastrointestinal cancer. This provides information and new insights which can be used for early detection of gastrointestinal cancer.

Fig. 1.1
figure 1

Potential role of biomarkers, biosensors, technologies, and computational methods in early detection of gastrointestinal cancer

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2 Role of Biomarkers in Early Detection of Gastrointestinal Cancer

Molecular signatures or profiles generated by tumor or cancer due to proteins, microsatellite instability, hypermethylation, single nucleotide polymorphism, volatile compounds, serum, etc. are known as biomarkers. These markers are known as protein markers (if protein profiles are used), microsatellite instability markers (if microsatellite instability signatures are defined), hypermethylation markers (if hypermethylation profiles are identified), single nucleotide polymorphism markers (when SNP profiles are characterized), volatile markers (when volatile compounds are expressed), and serum markers (if serum profiles are outlined). These markers can be used to diagnose gastrointestinal cancer. Biomarkers are generally classified based on disease state, types of biomolecules, and other criteria (Radhika et al. 2016). The disease state biomarkers available for early detection of cancer are risk assessment biomarkers, screening/detection biomarkers, diagnosis biomarkers, prognosis biomarkers, prediction biomarkers, and monitoring biomarkers. The biomolecule biomarkers are DNA biomarker, RNA biomarker, protein biomarker, glycol biomarkers, metabolite biomarkers, and serum biomarkers (Radhika et al. 2016). The biomarkers in other criteria are imaging biomarkers, pathological biomarker, next-generation biomarkers, and in silico biomarkers (Radhika et al. 2016). Molecular markers like CDH1 gene (Bussemakers et al. 1994), DNMT3A gene (De Carvalho et al. 2012), PTPRCAP gene (Hyoungseok et al. 2009), PSCA gene (Sakamoto et al. 2008), VEGF-A gene (Yancopoulos et al. 2000), XRCC1 gene (Caldecott et al. 1996), IL-1 gene (England et al. 2014), HER-2 gene (Baselga et al. 1996), and MUC1 gene (Bafna et al. 2010) are known as genetically susceptible markers. These genetically susceptible markers are inherited by individual or population leading to cancer, which can be used to diagnose gastrointestinal cancer. The new dimension of cancer diagnosis is use of serum biomarkers in the development of serum biomarker panels which made diagnosis of gastrointestinal cancer simple based on serum profiles. Thus, it can be established that biomarkers have a role in the early diagnosis of gastrointestinal cancer.

3 Role of Biosensors in Early Detection of Gastrointestinal Cancer

Biosensors are used in fields like drug discovery (Morris 2013), fermentation industry (Yan et al. 2014), defense (Pohanka 2019), food quality (Torun et al. 2012), environmental monitoring (Arora et al. 2011), metabolic studies, and plant studies (Berens and Suess 2015). Biosensors can now provide key information on cancer for effective and safe treatment. Cost effectiveness, reliability, accuracy, and less time consuming are the important aspects of biosensors. DNA, antibody, antigen, enzyme, whole cell, and cell organelle are used as a biological recognition element for biosensors (Malhotra et al. 2017). The biological sample interacts with the element of the biosensor and forms a product (Malhotra et al. 2017). The product then reaches the transducer, amplifies, records, and displays on the devices (Malhotra et al. 2017). The different types of biosensors are affinity biosensor, catalytic biosensor, metabolism biosensor, DNA biosensor, electrochemical biosensor, optical biosensor, mass change biosensor, graphene-based biosensor, amperometric biosensor, microbial biosensor, miRNA biosensor, and many more (Leech 1994; Freitas et al. 2018; Jainish and Prittesh 2017; Medley et al. 2008; Tothill 2009; Kavita 2017; Lei et al. 2006; D’Souza 2001; Steinberg et al. 1995; Kumar et al. 2006; Choi and Chae 2012; Correia et al. 2017; Morgan et al. 2016; Rogers et al. 2016; Liu et al. 2017; Cheng et al. 2015; Kwon et al. 2018; Zhang et al. 2014; Szunerits and Boukherroub 2018). Biosensors role in early detection and diagnosis of cancer is known. Biosensors improved the diagnostic capability by its sensitivity, specificity, reproducibility, linearity, and high-throughput screening (Bhalla et al. 2016). Thus, biosensors have an important role in early detection of gastrointestinal cancer.

The second and the new dimension in biosensors is the use of nanotechnology for the development of biosensors. This shows how nanotechnology is a powerful and promising technology for early detection of gastrointestinal cancer. Nanobiosensors are developed using nanomaterial’s like quantum dots, carbon nanotubes, nanopores, nanorods, nanowires, cantilevers, nanoparticles, and nanomembranes (Madani et al. 2013; de La Zerda and Gambhir 2007; Clarke et al. 2009; Hu et al. 2011; Israelsen et al. 2015; Zang et al. 2012; Daneshpour et al. 2016). Nanobiosensors role in early detection of cancer via carbon nanotube for CEA biomarker is reported (Länge et al. 2008). Nanobiosensors role in early detection of gastrointestinal cancer via electrochemical nanobiosensor for biomarkers miRNA 106A is also reported (Richardson et al. 2001). The different nanobiosensors are nanoparticles-based sensors (acoustic wave biosensors, magnetic biosensors, electrochemical biosensors), nanotube-based sensors, nanowire-based sensors, and ion channel-based sensors (Clark Jr and Lyons 1962; Desai et al. 1999; Cui et al. 2001; Cornell et al. 1997). The immobilization of biomolecules onto nanomaterials develops nanobiosensors for detection of analyte. The different strategies used for immobilization of biomolecules onto nanomaterials are covalent, noncovalent, and linker with covalent (Dubertret et al. 2002; Bruchez et al. 1998; Taton et al. 2001). The different parameters like selectivity, reproducibility, dynamic range, and negligible changes in concentrations of biomolecules indicate the performance of nanobiosensors. Thus, the potential role of nanobiosensors in early detection of gastrointestinal cancer can be materialized in the development of diagnostic devices or healthcare wearables in the near future.

4 Role of Technologies in Early Detection of Gastrointestinal Cancer

Expression profiling of microRNA, circulating microRNAs, serum microRNA, and plasma microRNA derive the signatures of the cancer. Expression profiling include RNA sequencing using next-generation sequencing technology or microarray of miRNAs using Affymetrix microarray or real-time reverse transcription PCR (qRT-PCR) to generate expression profile datasets. The analysis of these expression profile datasets between normal and gastric cancer cells would provide the differential expression profiles or patterns. The analysis indicates upregulated and downregulated miRNAs involved in signaling pathways related to environmental information processing and diseases. Therefore, these set of signature miRNAs may be promising biomarkers for the early diagnosis of gastrointesinal cancer.

In order to fulfill some of our knowledge gaps on cancer, it is essential to continually generate and explore omic’s data on cancer. There are five next-generation sequencing technologies available to generate NGS data: first-, second-, third-, fourth-, and fifth-generation sequencing technologies. The first-generation sequencing technologies include Sanger sequencing and Maxam Gilbert sequencing method (Neelapu and Surekha 2016). The second-generation sequencing method includes Roche/454 Sequencing, Ion torrent sequencing, Illumina/Solexa sequencing, and ABI/SOLiD sequencing (Neelapu and Surekha 2016). The third-generation sequencing method includes Single Molecule Real-Time (SMRT) sequencing approach and Oxford Nanopore Technology (ONT) sequencing approach (Neelapu and Surekha 2016). The fourth-generation sequencing method includes Nanopore-based sequencing by biological nanopores and solid-state nanopores (Neelapu and Surekha 2016). The fifth-generation sequencing method includes high-fidelity nanopore sequencing of ultra-short DNA targets and cyclomics: ultra-sensitive nanopore sequencing of cell-free tumor DNA (Neelapu and Surekha 2016). The technologies like Roche/454 Sequencing, Illumina/Solexa sequencing, Single Molecule Real-Time (SMRT) sequencing approach and Nanopore-based sequencing are used to generate whole-genome sequencing (WGS) data (Neelapu and Surekha 2016). WGS of tumor or cancer cell followed by the analysis of WGS data provides genetic information and heterogeneity of tumor or cancer cell when compared with the normal cell (Nakagawa and Fujita 2018).

Whole exome sequencing (WES), is a genomic technique for sequencing all of the protein-coding regions of genes in a genome (known as the exome) (Ng et al. 2009). This information provides insights on understanding nature of tumor or cancer cell. Epigenome sequencing of cancer or tumor cell helps in understanding the epigenetic features regulating cancer cells or tumor. The technologies such as methylation-sensitive restriction enzyme sequencing (MRE-seq), methylated DNA immunoprecipitation sequencing (MeDIP-seq), methyl-CpG-binding domain protein sequencing (MBD-seq), reduced representation bisulfite sequencing (RRBS), whole-genome bisulfite sequencing (WGBS), oxidative bisulfite sequencing (oxBS-seq) generate epigenome data-based methylation patterns (Sarda and Hannenhalli 2014). The other technologies like chromatin immunoprecipitation sequencing (ChIP-seq), and chromatin immunoprecipitation-exonuclease (ChIP-exo) generate epigenome data based on histone modifications (Sarda and Hannenhalli 2014). These technologies helped in understanding epigenetic features regulating cancer cells or tumor. The deep sequencing of mRNA-seq, long-read, direct RNA-seq, and short sequence reads (transcriptomes) by RNA sequencing technologies helps in generating transcriptome data (Stark et al. 2019). The short-read cDNA, long-read c DNA, and long-read RNA are generated using platforms Illumina and Ion Torrent; PacBio and ONT; and Nanopore technology, respectively. This helps in understanding single-cell gene expression, translation (the translatome), RNA structure (the structurome), and spatial transcriptomics (spatialomics) and also aids in understanding nature of tumor or cancer cell (Stark et al. 2019). De novo peptide sequencing via tandem mass spectrometry is used to generate proteomics data (Dancík et al. 1999). This proteomics approach can be used to understanding nature of tumor or cancer cell. Thus, genomics, epigenomics, transcriptomics, and proteomics approach can be used understanding the nature of tumor or cancer cell.

5 Role of Computational Methods in Early Detection of Gastrointestinal Cancer

The role of computational methods like genome-wide association studies (GWAS) (Challa and Neelapu 2018), big data analytics, and systems biology approach is known and can be used for early detection of gastrointestinal cancer. The genome sequencing projects of human led to genome-wide association studies (GWAS) to recognize genes and its respective variants related with any traits or diseases. GWAS was used for prediction of early onset of gastrointestinal cancer and can be utilized as biomarker in the detection and prevention of gastrointestinal cancer. Biomarkers, like carcinoembryonic antigen (Länge et al. 2008) and carbohydrate antigen 19-9 (Perkins et al. 2003), are in clinical use for detection of advanced stage of gastric cancer. Genome studies reported that expression level of CDH1 (Suriano et al. 2003; Bacani et al. 2006), CTNNB1 (Zhou et al. 2002), CDX-2 (Seno et al. 2002; Mizoshita et al. 2003; Fan et al. 2005), HER2 (Moelans et al. 2011), CD44v6 (Carvalho et al. 2006), 5p15 (Du et al. 2013), PRKAA1 (Jiang et al. 2018), and Reprimo (Bernal et al. 2008) predictive biomarker for the early onset of gastric cancer.

Imaging biomarkers, pathological biomarker, next-generation biomarkers generate large amounts of data. Big data analytics help in analyzing the big data to discover diagnostics and therapeutics for gastric cancer. Radiomics is a process of converting digital medical images into mineable high-dimensional data, and radiomics uses machine-learning approach to make clinical decision (Lambin et al. 2012). Radiomics include correlating and integrating omics data with radiomics features extracted from radiological images and integrate them to create a more efficient and robust prognostic model (Lambin et al. 2012). Thus, radiomics helps in early detection of gastrointestinal cancer. In the same way, big data generated by imaging technologies, pathological methods or technologies, and next-generation sequencing technologies can be analyzed by employing various methods in big data analytics for early detection of cancer.

Systems biology approach integrated high-throughput and “omics” data in understanding the disease (Kang et al. 2016). Systems biology is necessary to analyze the complexities of various pathways involving signaling, regulation of the gene, cell metabolism, and alterations in its system caused due to mutations leading to malignancy (Kang et al. 2016). These approaches seem to be complicated with several interlinks connecting pathways, and it is necessary to signify it in the form of a computational model (Kang et al. 2016). And, also in identifying the proteins and pathways of gastric cancer that can be useful sequentially in identifying major proteins and pathways (Kang et al. 2016). This helps in understanding the functional difference that takes place from a normal and disease cell. Thus, systems biology approach helps in early detection of gastrointestinal cancer. However, these findings require further wet-lab validation.

6 Significance

Detection of gastrointestinal cancer at an early stage requires biomarkers expressing at early stage, biosensors, promising technologies, and computational methods. Next-generation sequencing technologies especially the fifth-generation technology like high-fidelity nanopore sequencing of ultra-short DNA targets and cyclomics: ultra-sensitive nanopore sequencing of cell-free tumor DNA can be used to identify biomarkers which are expressed at an early stage of cancer. NGS technologies can also be used to develop gene panels, whole exome sequencing (WES), and whole-genome sequencing for cancer detection. NGS gene panels are already in use for diagnosis of hereditary cancers like breast, ovarian, colon, etc. Biomarkers identified can be integrated into biosensors or nanosensors to develop real-time measurement devices for early detection of gastrointestinal cancer. Healthcare monitoring devices or healthcare wearables are already in market for diagnosing diseases like diabetes. These healthcare wearables may help in early detection of cancer, as well as help in monitoring the cancer patient condition and treatment outcome from time to time. The NGS technologies and omic’s technologies may help in understanding the genes, epigenetic features, proteins, and other features responsible for transition of normal cell to cancer state. The computational methods help in developing new methods for analysis, and modeling of the data and also in mining the big data like radioimages. These computational methods may provide novel insights on cancer which can be used for detection of cancer. Thus, this chapter discusses about the potential role of biomarkers, biosensors, promising technologies, and computational methods for early detection of gastrointestinal cancer.