RNA Sequencing as a Tool in the Search for Cancer Biomarkers

Shrushti Joshi
May 21, 2023

Cancer biomarkers indicate the presence or progression of cancer in an individual. They can be used to diagnose cancer early, monitor the efficacy of cancer treatments, and predict patient outcomes. RNA sequencing (RNA-seq) has proven to be a powerful tool that has revolutionized cancer biology by enhancing the study of gene expression and transcriptome analysis.

The ability to rapidly and accurately analyze RNA expression levels in cancer cells has led to a deeper understanding of the genetic and molecular mechanisms underlying cancer development and progression.  Biomarkers can be detected by analyzing gene expression patterns in cancer cells.

In this blog, we discuss the potential of RNA-seq technology in cancer biomarker identification, the bottlenecks involved, and possible workarounds. Read on!

Decoding Cancer's Clues with RNA Sequencing

RNA-seq can identify gene expression signatures associated with a cancer diagnosis, prognosis, or treatment response, providing potential biomarkers for early detection or personalized therapy. It can quantify gene expression levels in different cancer subtypes or reactions to treatment, allowing for the identification of differentially expressed genes that can work as biomarkers.

Critical applications of RNA- seq in cancer biomarker research include:

  1. Diagnosis: RNA-seq can be used to identify gene expression patterns specific to different types of cancer, allowing for accurate diagnosis and classification of tumors. For example, RNA-seq has been used to distinguish between different breast cancer subtypes based on their gene expression profiles.
  2. Prognosis: RNA-seq can identify biomarkers associated with disease progression and patient outcomes. For instance, RNA-seq has been used to identify genes predicting pancreatic cancer survival.
  3. Treatment Selection: RNA-seq can identify biomarkers predictive of treatment response, allowing for personalized treatment selection. It has been used to identify genes associated with response to immunotherapy in melanoma.
  4. Drug Discovery: RNA-seq can be used to identify new targets for cancer therapy and to identify potential drug resistance mechanisms. It has been used successfully to identify genes involved in drug resistance in ovarian cancer.
  5. Mechanistic Insights: RNA-seq can also identify alterations in signaling pathways, cellular processes, and molecular interactions that contribute to cancer development and progression, providing insights into the underlying mechanisms of cancer.

Navigating the RNA Sequencing Workflow

The power of RNA sequencing as a game-changing tool in the quest for cancer biomarkers lies within its remarkable capacity to detect changes in gene expression. Changes in gene expression can result in altered protein production, which can have significant consequences for cellular function. In cancer cells, changes in gene expression can lead to uncontrolled growth and division, as well as resistance to chemotherapy and other treatments.

One of the critical advantages of RNA sequencing is its ability to provide a comprehensive view of gene expression. Unlike traditional microarray-based methods, RNA sequencing is not limited to pre-selected genes and can simultaneously detect expression changes in thousands of genes. This allows for identifying novel biomarkers that may have been missed using other methods. RNA seq technology involves some key steps, as depicted in the figure.

RNA Sequencing Workflow

Overcoming Challenges in Using RNA-seq for Cancer Biomarker Identification

With continued improvements in sequencing technology and data analysis methods, RNA seq has become increasingly helpful. However, despite its many advantages, RNA sequencing faces several challenges, including data complexity, standardization, etc. Addressing these challenges will be critical for successfully translating RNA sequencing-based biomarkers into clinical practice. Let’s dive deep into some major challenges and how they can be rectified.

  1. Variability in RNA Quality: The quality of RNA samples can vary widely, and low-quality samples can lead to inaccurate gene expression measurements. A careful evaluation of the quality of RNA samples before performing RNA-seq experiments is required to minimize sample variability.
  2. Standardization of Data Analysis: The lack of standardized analysis protocols can lead to inconsistencies and difficulties in comparing results across studies. Hence, an optimized protocol for RNA-seq data analysis is critical to ensure the reliability and reproducibility of the results.
  3. Variability in Tumor Heterogeneity: Tumor heterogeneity can complicate the identification of cancer biomarkers, as different cell populations within a tumor may have distinct gene expression profiles. Researchers must consider tumor heterogeneity when analyzing RNA-seq data and may need to use single-cell sequencing techniques to capture the tumor's heterogeneity accurately.
  4. Interpretation of Non-Coding RNAs: Non-coding RNAs, such as microRNAs and long non-coding RNAs, play essential roles in cancer development and progression but can be difficult to interpret using RNA-seq data. Accurate analysis of non-coding RNA data is required to identify potential biomarkers and understand their functional roles in cancer.
  5. Integration with Other Data Types: RNA-seq data must be integrated with different data types, such as genomic, epigenomic, and proteomic data. The development of integrated analysis approaches to fully exploit the potential of RNA-seq data in cancer biomarker identification is needed.

RNA-seq data can be complex and noisy. Data curation is essential to ensure the reliability and accuracy of RNA-seq data in cancer biomarker research.

Processes like Quality control, Normalization, Filtering, and Annotation help streamline the translational journey from sequencing data to actionable insights.

Elucidata has transformed biological discovery by providing high-quality bulk RNA-seq and single-cell data, among other data types. In the data warehouse (aka OmixAtlas), the metadata is harmonized, data is standardized and normalized through consistent pipelines, cell types are accurately expert-annotated, and standard ontologies are followed to ensure reliable results and empower scientists in achieving their research goals.

Elucidata’s data platform, Polly, is a biomedical data platform for life sciences R&D, primarily delivering bulk RNA-seq and single-cell data. It handles data ingestion, transformation, and storage. It has enabled the detection of multiple validated drug targets across immunology, oncology, and metabolic disorders using ML-ready and a scalable data infrastructure for downstream analysis provided by Polly. Therefore, researchers can focus on insight derivation via data analysis and visualization instead of data wrangling and engineering. Incorporating Polly into existing data infrastructure and analysis/visualization is easy, and computational tools can be utilized.

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