The human biological systems are a complex network of integrated functions to maintain a homeostatic body environment. In the event of pathology, these systems respond differently by producing signals and chemicals used in disease identification (diagnosis) and as a guide for therapy (Sobek et al., 2022). The genome is the basic unit of man, and the genetic interaction of the complex biological systems during treatment, in the form of molecular pathways, makes pathology a complex process requiring various forms of data. Studying these processes produces large data sets that rely on high throughput bioinformatic analytical tools like Microarrays to analyze and interpret the data (Sobek et al., 2022). This has been widely applied in the study of DNA biological basis of disease, as discussed in detail in the paper. This will also include its impact on systematic biological research, its application to answer biological problems, breakthroughs in the application, and challenges arising from its use.
Importance of microarray technology in human disease diagnosis and treatment
Microarrays in molecular biology have revolutionized the study of different biological and pathological processes in the body. This is because they have provided powerful analysis tools that can simultaneously detect and quantify the expression levels of thousands of genes (Cocharo et al., 2019). In the field of genomics, such a revolution has enabled researchers to study the molecular basis of human diseases and develop new diagnostic and therapeutic strategies, including therapy management and predictive ability (Yang et al., 2020).
Microarrays technology has been used to diagnose various complex diseases, such as cancer, viral and DNA bacterial infectious diseases, genetic neurological disorders, and cardiovascular diseases with genetic predisposition, such as heart failure (Sobek et al., 2022). In cancer diagnosis, for example, microarrays can identify genes that are differentially expressed between normal and tumor tissues, allowing for more accurate and personalized cancer treatments. Cocharo et al. (2019) studies have demonstrated the clinical utility of microarrays in cancer diagnosis and prognosis, such as in acute lymphoblastic leukemia, which is most familiar in childhood. The study involved genomic analysis and transcriptome microarray studies to identify leukemia genetic subtypes, which are used in prognosis and decisions on the choice of therapy. Such an analysis depends on DNA microarray analysis tools’ data computation and integration ability.
Computational data modeling and integration are essential in the analysis of microarray data. Several tools and algorithms have been developed to analyze microarray data, including clustering, classification, and pathway analysis (Sobek et al., 2022). Clustering algorithms group genes with similar expression patterns (Shekar et al., 2019). In contrast, classification algorithms can be used to predict the outcome of a particular disease based on gene expression profiles, which is crucial in prognosis. Pathway analysis can identify biological pathways enriched in differentially expressed genes and can be used to initiate targeted therapy, which is more effective with fewer side effects to the body. These models and technologies, as mentioned, are instrumental in the analysis of DNA microarray data to diagnose various diseases. Their application has significantly impacted the current approach to disease.
Impact of DNA – Microarray in research of, diagnosis, and treatment of biological problems
DNA microarrays have significantly impacted biotechnology research, particularly in diagnosing human disease and therapy. Over the past few years, several published research studies have evaluated the use of DNA microarrays in human disease diagnosis and treatment, including their impact on routine biotechnology research and their application to solving specific biological problems. These studies are used to develop and improve current disease approaches and protocols, including medications.
DNA Microarray can generate and interpret genetic data from patient samples. As such, it has been used to investigate various genetic predispositions or origin conditions, such as breast cancer and congenital hearing loss. For example, a study by Tang et al. (2021) evaluated using DNA microarrays to diagnose hereditary hearing loss. The researchers used DNA microarrays to analyze the genetic mutations in patients with congenital hearing loss, and the results identified several novel mutations that had not been previously reported. They also found that DNA microarrays improved the diagnostic yield, where they could process many samples concurrently, compared to traditional diagnostic methods, which run a sample at a time (Hambali et al., 2020). This study highlights the potential of DNA microarrays for improving the accuracy and efficiency of diagnosing hereditary diseases.
Similarly, a study by Yang et al. (2020) evaluated the use of DNA microarrays for predicting the response to chemotherapy in patients with breast cancer. The researchers used DNA microarrays to analyze the gene expression profiles of tumor tissue samples from patients with breast cancer and identified several genes that were predictive of chemotherapy response. They also developed a predictive model that could accurately predict the response to chemotherapy based on the gene expression profiles (Yang et al., 2020). The results from this study are valuable and critical in managing breast cancer, one of the most aggressive types of malignancy. Oncologists can slow disease progression and improve life expectancy through timely diagnosis and identification of the specific subtype and initiation of treatment (Shekar, 2020). Using microarray technology, they can run simulations on various targeted chemotherapeutic agents to determine the best course of treatment before and during the cycles.
Microarrays, as aforementioned, rely heavily on computational data modeling and integration for the analysis and interpretation of DNA microarray data. A study by Shahane et al. (2019) developed a novel computational model for analyzing DNA microarray data in the context of drug development. The researchers used a deep learning algorithm to identify potential drug targets based on the gene expression profiles of cancer cells. They also used pathway analysis to determine the biological pathways enriched in differentially expressed genes (Shehane et al., 2019). This study further demonstrates the potential of DNA microarrays, in the treatment of cancer cells, by allowing researchers and oncologists to have evidence-based data for the targeted delivery of therapy. Additionally, drug therapy plans can be customized for each patent depending on the DNA data therapeutic and prognostic analysis results.
Challenges in the application of DNA Microarray in biological disease research, diagnosis, and treatment
Various challenges have been linked to using DNA microarrays during research and disease diagnosis and treatment. Some of the significant challenges identified are Data Analysis. One of the major challenges in using DNA microarrays is the large amount of data generated. These require sophisticated analysis techniques which are either expensive, inconsistent, or unavailable. Zhang et al. (2019) noted that the need for standardized data analysis pipelines could lead to consistency in results.
Data Quality. The accuracy and reproducibility of microarray data can be affected by factors such as sample quality, variability in experimental conditions, and technical artifacts. Bolon-Conedo et al. (2019) emphasized the importance of quality control measures to ensure the accuracy and reliability of microarray data. Inaccurate data potentially harms patient diagnosis, treatment choice, and prognosis.
Cost. DNA microarrays can be expensive, limiting their accessibility and use in resource-limited settings. Zhang et al. (2019) noted that the cost of microarrays could be a barrier to their use in large-scale studies. This is incredibly challenging to young researchers and also contributes to limited available private, no-profit sponsored research.
Cross-hybridization: DNA microarrays are prone to cross-hybridization, which can lead to false positive or negative results. Hambali et al. (2020) highlighted the importance of using appropriate controls and normalization methods to minimize the effect of cross-hybridization. Additionally, the sensitivity of DNA microarrays can be affected by factors such as probe design, hybridization conditions, and sample quality. Therefore, it is paramount to ensure proper controls to avoid delivering inaccurate results.
Interpretation of Results: The performance of microarray results can be complex and requires specialized knowledge and expertise. This limits the use of microarray technology to those trained in their use.
DNA microarray technology is a high-throughput analytical tool that can simultaneously detect and quantify the expression levels of thousands of genes. It has revolutionized the study of different biological and pathological processes in the body, enabling researchers to study the molecular basis of human diseases and develop new diagnostic and therapeutic strategies, including therapy management and prognostic ability. Microarrays technology has been used to diagnose various complex diseases, such as cancer, viral and bacterial infectious diseases, genetic neurological disorders, and cardiovascular diseases with genetic predisposition, such as Alzheimer’s and heart failure, respectively. It can also identify genes differentially expressed between normal and cancer tissues, allowing for more accurate and personalized cancer treatments. Challenges associated with using DNA microarrays during biological disease research, diagnosis, and treatment include high cost, the need for skilled personnel, quality control, and interpretation of the large amounts of data generated. However, the potential benefits of DNA microarray technology in improving disease diagnosis and therapy outweigh the challenges.
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