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The Immortal Human Cell

Is it possible for a human cell to be immortal? The answer is yes. Unfortunately, the immortality of cells is intertwined with one of the leading causes of death: cancer. Cancer cells are functionally immortal via uncontrolled and uninhibited replication. Cancer is also the second leading cause of death, with approximately 600,000 deaths per year in the United States [1]. Chances are, you probably know or will know someone who will face cancer. The immortality of cancer cells equates to your own mortality.

While treatments are available, cancer remains a devastating disease with no single, systematically effective cure. To develop better therapeutic treatments, scientists, like myself, investigate how cancer cells function differently compared to healthy cells. We can then develop new drugs that can selectively kill off cancer cells while keeping the healthy cells alive. 

There are ten hallmarks of cancer (Fig. 1). Each of these characteristics within the pie is contributing in some way for cancer cells to overtake healthy cells. While we don’t know every piece of the puzzle required to  ‘solve’ (or ‘cure’) cancer – researchers are tackling different parts of the cancer pie in the laboratory to better understand the specific proteins or pathways hijacked or deregulated by cancer cells. For my Ph.D. in Biochemistry, I am studying how the proliferative signaling pathways (highlighted in green in Fig. 1) lead to cancer pathogenesis, specifically how signaling pathways reprogram the global gene expression profiles in cancer cells. Gene expression is a term we use to describe how genes coded in our DNA are made into messenger RNAs (mRNAs) and then into protein products to perform specific functions. The timing, location, and the amount of the mRNA production and degradation are tightly controlled. These individual aspects of gene expression can be altered in cancer cells. 

Fig 1. Hallmarks of cancer [1]. There are ten cellular and molecular characteristics that constitute cancer tumorigenesis (shown as the pie in the center). Scientists have developed therapeutic treatments targeting characteristics.
Fig 2. mTOR signaling is a master regulator of cell growth, metabolism, and protein translation. Figure provided by Jeongsik Yong, Ph.D.

In my Ph.D. project, I am trying to further understand how cancer cells dysregulate gene expression. The specific pathway that I study is called the Mammalian Target of Rapamycin (mTOR) pathway (Fig. 2). The mTOR pathway is vital for cell growth, protein synthesis, and cell proliferation – all functions critical to maintaining normal cell behaviors. However, when pushed into overdrive in cancer cells, this pathway facilitates the robust acquisition of energy and materials needed for cancer cells to replicate uncontrollably. Our lab discovered that certain mTOR activation by cancer cells changes the global gene expression, resulting from the modulation of RNA-binding proteins. 

RNA-binding proteins (RBPs for short) are the players that interact with mRNAs and coordinate the processing, stability, and export of these messengers to the appropriate location to be made into proteins. The expression of RBPs is important to control global gene expression of all of the mRNAs. Interestingly, mTOR signaling actually regulates some of these RBPs’ expression. My thesis work focuses on one important RBP that is involved in the processing of mRNAs, termed U2AF1. Why is this protein crucial in mRNA processing? Well, mRNAs are a small excerpt of the DNA with segments that need to be cut out and other segments that need to be stitched together to read a proper message; a little bit like a paper model where you have to push the pieces out and assemble them yourself (Fig. 3).

This processing involves thousands of different proteins working in unison, chopping, gluing, and interpreting an original string of RNAs together before the pre-mature mRNA is shipped off as the final, mature mRNA. Depending on these proteins’ binding and interaction, different parts of the mRNA will be kept and added. This process is called alternative splicing. It is like an editor adding and removing portions of a story to help bring the correct information across to the reader. U2AF1 is one of the regulatory proteins that help coordinate alternative-splicing. 

Fig 3. An example of a paper model. Picture taken from

In our lab, we seek to answer the questions: How does U2AF1 work in a healthy cell versus in a cancerous cell? How does the environment in cancerous cells make U2AF1 operate differently, and what consequences does that impose?

We begin by analyzing how mTOR affects U2AF1. This means looking at what happens to U2AF1 when we modulate the signals produced by the mTOR pathways using different chemotherapeutic drugs. Then, we observe how U2AF1 behaves amongst the ensemble of other RNA alternative splicing proteins as it is being modulated between cells with low or high levels of mTOR signaling. Does U2AF1 interact with other proteins differently in the presence of the drug? Does U2AF1 interact with RNAs differently? We use advanced biochemistry and molecular biology techniques to look at protein-RNA interactions and protein-protein interactions to answer these questions.

From my Ph.D. journey, I have learned to appreciate the basic research of human biology. This experience allowed me to appreciate and understand all of the hard work that generations of scientists have done behind the scenes in order to apply these knowledge for therapeutic development. My thesis work has also inspired me to pursue a more translationally relevant scientific career using RNA biology I have learned during my doctoral study. As a first-generation immigrant and college graduate, I hope to share a piece of writing that many others would be able to enjoy and learn from, including my family, who didn’t receive education beyond grade school. I hope my work in SPARK will connect with other first-generation scientists and other scholars in other fields. 


Siegel, R. L., Miller, K. D., & Jemal, A. (2020). Cancer statistics, 2020. CA: A Cancer Journal for Clinicians, 70(1), 7-30.

Figure 1. Siegel, R. L., Miller, K. D., & Jemal, A. (2020). Cancer statistics, 2020. CA: A Cancer Journal for Clinicians, 70(1), 7-30.

Figure 2. This figure was adapted by my advisor Jeongsik Yong, from the following source: Thompson, Herman, Wieman, Fuchs, Bode, Sabatini, . . . Shaw. (n.d.). PI3K/Akt/mTOR. – PPT download. Retrieved March 29, 2021.

Figure 3. Yousiri6. (n.d.). Paper model craft. Retrieved March 29, 2021.

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