Revolutionary Cancer Research Breakthroughs

The impact of RNA-seq and Proteomics technologies in advancing cancer treatments, improving therapies and driving cancer research breakthroughs.

Published on June 6th, 2023 by BigOmics
⏱ 5 min read

Lens looking into DNA

Introduction

Cancer remains one of the leading causes of death worldwide, which is why there is a constant search for innovative and effective treatments. There have been some groundbreaking advances in cancer research in recent years. However, we are still quite helpless in the face of much of it. RNA sequencing and proteomics offer a ray of hope: providing insights into molecular mechanisms and identification of potential therapeutic targets and biomarkers. In this blog post, we will delve into how RNA-Seq and proteomics are driving cancer research breakthroughs.

RNA Seq: A Game-Changer for Cancer diagnosis, classification, and tracking treatment effectiveness

RNA sequencing is a powerful tool for identifying differentially expressed genes (DEGs) in cancer cells. This technique enables researchers to pinpoint genes that are over- or underexpressed in cancer cells compared to normal cells, providing insights into the molecular mechanisms of cancer.

A major strength of RNA-Seq is that it can be used to identify novel biomarkers and potential therapeutic targets and provide insights into drug sensitivity, making it an indispensable tool for precision medicine. In fact, drug sensitivity discovery is one of the strengths of the BigOmics platform, Omics Playground.

For example, a recent study (2023) aimed to identify gene biomarkers for the diagnosis and prognosis of non-small cell lung cancer (NSCLC) using RNA-Seq data through bioinformatics techniques [Sultana et al.]. NSCLC is the most common form of lung cancer and the leading cause of cancer-related deaths worldwide. Therefore, the identification of gene biomarkers and their regulatory factors and signaling pathways is enormously important to uncover the molecular mechanisms of NSCLC initiation and progression.

Examining drug screening data from extensive gene expression databases provides valuable information for optimizing the use of cancer drugs in clinical settings. Additionally, researchers can study the different responses that subgroups of cancer cells show to drugs by looking into single-cell RNA sequencing data. This approach can enable more effective treatment approaches. [Chen et al.]

There are several more key benefits of RNA-Seq in cancer research: One is the ability to understand the heterogeneity of tumors. Because tumors often contain a mix of cells with different genetic backgrounds, analyzing the transcriptome of cancer cells makes it possible to identify these different cell populations and understand how they contribute to tumor growth and metastasis.

Another advantage of RNA-Seq: It allows us to identify alternative splicing events that occur in cancer cells. The production of different mRNA transcripts of the same gene could contribute to tumorigenesis, so understanding the alternative splicing patterns in cancer cells can give us better understanding of the molecular mechanisms. This may offer insights into new therapeutic targets.

RNA sequencing has become a truly versatile tool in cancer research. For example, it aids in cancer diagnosis, classification, and tracking treatment effectiveness. In one study, RNA-Seq was used to create predictive models for clinical markers, subtypes, and recurrence risk. [Staaf et al.] And another study found that a simple nine genes blood RNA-based signature predicts sensitivity to gemcitabine, one of the main regimens to treat pancreatic adenocarcinoma patients. [Piquemal et al.]

Revealing Cancer's Molecular Complexity with Proteomics

The development of next-generation sequencing has made it possible to achieve the simultaneous study of multiple genes in a rapid and cost-effective manner. Proteomics technologies, such as mass spectrometry (MS) and protein array analysis, have further advanced the deciphering of underlying molecular signaling events and proteomic characterization of cancers. Proteomic analysis of cancers, as well as their adaptive responses to therapy, can reveal new therapeutic opportunities that reduce the incidence of drug resistance and improve patient outcomes. [Ghose et al.]

Proteomics involves identifying and quantifying differentially expressed proteins in cancer cells. The technique provides valuable information in several areas, including protein profiles, protein levels, sites of modification, protein interactions in pathophysiological conditions and post-translational modifications. It can be used accordingly to identify clinically applicable novel biomarkers and therapeutic targets. [Kwon et al.]

Proteomics has contributed to the identification of key protein targets and signaling pathways related to the growth and metastasis of cancer cells. Cancer proteomics databases have been established and are being shared worldwide, providing extensive data related to molecular mechanisms and target modulators. These data can be analyzed and processed through bioinformatic pipelines to obtain useful information.

For example, large-scale proteomics studies of clinical prostate cancer and prostate cancer models help us understand how prostate cancer evolves and evades current drug treatments, as summarized in a review of recent studies. [Sadeesh et al.] In another study, proteomic analysis characterizes the heterogeneity of breast cancer in a clinically applicable manner, identifies potential biomarkers and therapeutic targets, and provides a resource for clinical breast cancer classification. [Asleh et al.]

The Power of Integration: How RNA-Seq and Proteomics Together Provide a Systems-Level Understanding of Cancer

Integrating RNA-Seq and proteomics data can provide a more comprehensive understanding of cancer biology and a more complete picture of the underlying molecular mechanisms. One advantage of integrating RNA-Seq and proteomics data is the ability to identify differentially expressed genes (DEGs) and proteins that are critical to cancer growth and metastasis. For example, a recent study [Yang et al.] used integrated analysis of RNA-Seq and proteomics data to identify potential therapeutic targets for colorectal cancer.

Sometimes researchers focus on the differences between transcriptome and proteome to identify post-translational modifications or environmental factors that cannot be detected at the RNA level. Tumor classification based on RNA expression is also often performed at the proteomic level. Therefore, the relationship between the two data sets should not only serve validation purposes, but provide a complementary view that would be overlooked if considered alone.

In addition to identifying potential therapeutic targets, integrating RNA-Seq and proteomics data can also provide insights into the underlying molecular mechanisms of cancer. However, integrating RNA-Seq and proteomics data can be challenging due to the sheer volume of data generated by these techniques and the specialized skills required for data analysis.

Bioinformaticians play a critical role in this process by developing and applying computational tools to integrate, visualize, and interpret large-scale omics data.

Conclusion: RNA-Seq and Proteomics Are Leading the Way in Cancer Research

doctor looking at samples

RNA-Seq and proteomics are essential for understanding the molecular mechanisms underlying cancer growth and metastasis. These technologies have led to the identification of potential biomarkers and therapeutic targets, providing hope for the development of more effective cancer treatments. Integration of RNA-Seq and proteomics data can provide a systemic understanding of cancer biology and reveal potential therapeutic targets and molecular mechanisms.

While the process can be challenging, the benefits are clear and can lead to significant advances in cancer research and treatment. However, analyzing the vast amounts of data generated by these technologies can be challenging. Innovative and powerful bioinformatics platforms therefore have a critical role to play in enabling scientists to analyze and interpret the data.

The BigOmics platform tackles these challenges head-on by offering a comprehensive workflow tailored specifically for analyzing RNA-Seq and proteomics data. Our goal is to create smart tools that empower individuals to delve into advanced omics analysis. We streamline the integration of datasets, unveiling crucial genes and proteins involved in cancer biology. By supporting drug repurposing and unraveling the intricate mechanisms of novel compounds, we contribute to a deeper comprehension of disease mechanisms.

Interested in discovering how RNA-Seq and proteomics provide invaluable insights to drive cancer research breakthroughs? Curious about how bioinformatics data discovery software can simplify the exploration of your RNA-Seq and proteomics data? 

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References

Staaf, J., Häkkinen, J., Hegardt, C. et al. RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer. npj Breast Cancer 8, 94 (2022). https://doi.org/10.1038/s41523-022-00465-3

Piquemal, D., Noguier, F., Pierrat, F., Bruno, R., & Cros, J. (2020). Predictive Values of Blood-Based RNA Signatures for the Gemcitabine Response in Advanced Pancreatic Cancer. Cancers, 12(11), 3204. https://doi.org/10.3390/cancers12113204

Kwon, Y. W., Jo, H. S., Bae, S., Seo, Y., Song, P., Song, M., & Yoon, J. H. (2021). Application of Proteomics in Cancer: Recent Trends and Approaches for Biomarkers Discovery. Frontiers in medicine, 8, 747333. https://doi.org/10.3389/fmed.2021.747333

Sultana, A., Alam, M. S., Liu, X., Sharma, R., Singla, R. K., Gundamaraju, R., & Shen, B. (2023). Single-cell RNA-seq analysis to identify potential biomarkers for diagnosis, and prognosis of non-small cell lung cancer by using comprehensive bioinformatics approaches. Translational oncology, 27, 101571. https://doi.org/10.1016/j.tranon.2022.101571

Chen, J., Wang, X., Ma, A., Wang, Q. E., Liu, B., Li, L., Xu, D., & Ma, Q. (2022). Deep transfer learning of cancer drug responses by integrating bulk and single-cell RNA-seq data. Nature communications, 13(1), 6494. https://doi.org/10.1038/s41467-022-34277-7

Ghose, A., Gullapalli, S. V. N., Chohan, N., Bolina, A., Moschetta, M., Rassy, E., & Boussios, S. (2022). Applications of Proteomics in Ovarian Cancer: Dawn of a New Era. Proteomes, 10(2), 16. https://doi.org/10.3390/proteomes10020016

Sadeesh, N., Scaravilli, M., & Latonen, L. (2021). Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes. Cancers, 13(19), 4829. https://doi.org/10.3390/cancers13194829

Asleh, K., Negri, G.L., Spencer Miko, S.E. et al. Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes. Nat Commun 13, 896 (2022). https://doi.org/10.1038/s41467-022-28524-0

Yang, W., Shi, J., Zhou, Y., Liu, T., Zhan, F., Zhang, K., & Liu, N. (2019). Integrating proteomics and transcriptomics for the identification of potential targets in early colorectal cancer. International journal of oncology, 55(2), 439–450. https://doi.org/10.3892/ijo.2019.4833