Journal of Data Mining in Genomics & Proteomics

The Importance of Transcriptomics in Cancer Immunology

Mark Reverter^* Department of Clinical Neurosciences, The University of Queensland, Queensland, Australia
^*Correspondence: Mark Reverter, Department of Clinical Neurosciences, The University of Queensland, Queensland, Australia, Email:

Received: 03-Mar-2023, Manuscript No. JDMGP-23-20248; Editor assigned: 06-Mar-2023, Pre QC No. JDMGP-23-20248 (PQ); Reviewed: 20-Mar-2023, QC No. JDMGP-23-20248; Revised: 27-Mar-2023, Manuscript No. JDMGP-23-20248 (R); Published: 03-Apr-2023, DOI: 10.4172/2153-0602.23.14.286

Description

Cancer transcriptomics is the study of gene expression patterns and regulatory mechanisms in cancer cells and tissues. It aims to reveal the molecular mechanisms underlying cancer development, progression, and response to therapy. Transcriptomics can also identify novel biomarkers for cancer diagnosis, prognosis, and treatment selection. Transcriptomics is based on high-throughput technologies that measure the abundance of RNA molecules in a given sample. The most common methods are microarrays and RNA Sequencing (RNAseq), which can quantify thousands of genes simultaneously. Microarrays use hybridization probes to detect specific transcripts, while RNA-seq uses next-generation sequencing to sequence all transcripts in a sample. Both methods have advantages and limitations, such as sensitivity, specificity, cost, and data analysis.

Transcriptomics can be performed at different levels of resolution, such as bulk or single-cell. Bulk transcriptomics measures the average gene expression of a population of cells, which can mask the heterogeneity and complexity of cancer. Single-cell transcriptomics measures the gene expression of individual cells, which can reveal the diversity and dynamics of cancer cells and their interactions with the tumor microenvironment. Transcriptomics has been widely applied to cancer research using various technologies and platforms. The most common method is RNA Sequencing (RNA-seq), which uses high-throughput sequencing to capture and quantify the RNA molecules in a sample? RNA-seq can provide comprehensive and unbiased information about the transcriptome, including gene expression levels, transcript structures, isoform diversity, gene fusions, allele-specific expression, and RNA editing. RNA-seq can also detect different types of noncoding RNAs, such as MicroRNAs (miRNAs), Long Noncoding RNAs (lncRNAs), Circular RNAs (circRNAs), and Enhancer RNAs (eRNAs), which have important roles in regulating gene expression and modulating cellular functions.

Transcriptomics can also be performed at different stages of RNA processing, such as pre-mRNA or mature mRNA. PremRNA transcriptomics captures alternative splicing and polyadenylation events, which can generate different isoforms of the same gene with distinct functions. Mature mRNA transcriptomics captures the final products of transcription, which reflect the functional state of the cell. Transcriptomics can also capture different types of RNA molecules, such as coding or noncoding RNAs. Coding RNAs encode proteins that perform various cellular functions. Noncoding RNAs do not encode proteins but regulate gene expression at different levels, such as transcription, splicing, translation, and degradation. Noncoding RNAs include MicroRNAs (miRNAs), Long Noncoding RNAs (lncRNAs), Circular RNAs (circRNAs), and others.

Transcriptomics has many applications in cancer research

• Identifying molecular subtypes of cancer based on gene expression profiles.

• Discovering novel genes and pathways involved in cancer initiation and progression.

• Detecting fusion transcripts that result from chromosomal rearrangements and drive oncogenesis.

• Evaluating tumor immune infiltration and activation based on immune-related gene signatures.

• Predicting response and resistance to targeted therapies based on gene expression markers.

• Developing prognostic and predictive multigene assays for clinical decision making.

• Monitoring tumor evolution and clonal dynamics based on transcriptome changes over time.

• Transcriptomics faces many challenges in cancer research, etc.

• Dealing with the complexity and variability of transcriptome data across samples, platforms, and methods.

• Interpreting the biological meaning and functional impact of transcriptome changes in cancer

• Integrating transcriptome data with other types of omics data, such as genomics, proteomics, epigenetics, and metabolomics.

• Translating transcriptome findings into clinically actionable information for precision oncology.

Transcriptomics is a valuable tool for understanding cancer mechanisms and identifying biomarkers. The integration of transcriptomics with other omics approaches will improve biomarker discovery and our understanding of disease mechanisms and thereby accelerate the implementation of precision oncology.