<p>Compared to traditional circRNA detection approaches, the LDN-based method presents several advantages:</p>
<ulclass="bullets">
<li>It does not require advanced laboratory equipment and expensive reagents. After LDN is formed, it can perform enzyme-free detection. It is an isothermal process and, unlike PCR, does not require a thermal cycler.</li>
<li>It does not require expensive reagents. After LDN is formed, it can perform enzyme-free detection. It is an isothermal process and, unlike PCR, does not require a thermal cycler.</li>
<li>It can accurately recognize circRNA without interference from its linear isoforms. Therefore, no RNAse treatment is required before the reaction.</li>
<li>Biomarker detection happens in only 70 min, using a total RNA extract from a potential patient’s blood sample.</li>
</ul>
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<ulclass="bullets">
<li>We also propose applying our methodology to visualize intracellular circRNA in situ. Certain circRNAs with prognostic value are not secreted into the circulation. Therefore, their analysis can be a crucial point in obtaining a clearer picture of the state of the tumor. With electroporation, LDN can be inserted in cells derived from a tissue biopsy. With confocal fluorescence microscopy, images can be obtained, analyzing whether or not the circRNA target is present. Testing that aspect of our project was not possible due to time limitations, but trying to implement the same technique for both the prognosis and diagnosis of lung cancer undoubtedly will improve the efficiency of lung cancer treatment.</li>
<li>In order to validate both our method and our biomarker selection, testing our diagnostic tool in early-stage lung cancer patients' blood samples is essential. Complementary experiments such as RNA-seq to analyze the expression profile of hsa_circ_0070354, hsa_circ_0102533, and hsa_circ_0005962 will determine if these novel biomarkers can be readily used for lung detection or if the various lung cancer subtypes differentiate their expression. Cross-validating these results with the ones obtained from patients tested with the LDN system will prove the sensitivity and specificity of our method. However, a large number of patients is necessary to obtain high statistical power data, indicating that clinical trials are in order.</li>
<li>Implementing the LDN system for diagnosing various other diseases and cancer types through our Modeling work proves the versatility of our design. Just by inputting the mature sequence of a new circRNA target, all the possible LDN structures can be obtained using our simple ldn_generator.py Python script. Furthermore, the same protocol can be applied using isothermal target detection. For more information about the creation and usage of the ldn_generator.py as well as the detection protocol, please refer to the <ahref="software"class="link-ref">Software</a> and <ahref="experiments"class="link-ref">Experiments</a> pages, respectively.</li>
<li>In order to validate both our method and our biomarker selection, testing our diagnostic tool in early-stage lung cancer patients' blood samples is essential. Complementary experiments such as RNA-seq to analyze the expression profile of hsa_circ_0070354, hsa_circ_0102533, and hsa_circ_0005962 and other circular RNAs will determine if these novel biomarkers can be readily used for lung detection or if the various lung cancer subtypes differentiate their expression. Cross-validating these results with the ones obtained from patients tested with the LDN system will prove the sensitivity and specificity of our method. However, a large number of patients is necessary to obtain high statistical power data, indicating that clinical trials are in order.</li>
<li>Implementing the LDN system for diagnosing various other diseases and cancer types through our Modeling work proves the versatility of our design. Just by inputting the mature sequence of a new circRNA target, all the possible LDN structures can be obtained using our simple ldn_generator.py Python script. Furthermore, the same protocol can be applied using isothermal target detection. For more information about the creation and usage of the ldn_generator.py as well as the detection protocol, please refer to the <ahref="contribution"class="link-ref">Contribution</a> and <ahref="experiments"class="link-ref">Experiments</a> pages, respectively.</li>
<li>Our team aspires to create a start-up company with the brand name syn-PNOIA. Distribution of our kit to health clinics and microbiological laboratories and analysis of patient samples in-house will provide a multifaceted approach to lung cancer diagnosis. Please refer to the <ahref="entrepreneurship"class="link-ref">Entrepreneurship</a> page for more information about our next financial steps.</li>
