Abstract 91: Unsupervised Machine Learning Predicts Invasive and Undruggable Long Coding Rna Linc00662, Linc01234, and Spanxa1, Rabphilin 3a, Svil Antisense Rna 1 Like From Oral Cancer Transcriptomics
Authors: Yadalam Pradeep Kumar , Anegundi Raghavendra Vamsi , Ramadoss Ramya 
Published: June 2023

Abstract
Purpose: Numerous cancer disease-modifying targets look “undruggable” or “difficult to target” because they lack accessible deep hydrophobic pockets where small molecules can bind, or they lack enzymatic activity (have no active site), RAS, and transcription factors (STAT3, TP53, MYC) are archetypal cancer undruggable. Designing a small molecule to bind to a PPI interface has proven difficult for several reasons. First, the unique interface structure is a challenge for drug design. Compared with the binding pockets of conventional protein targets, the interface of PPIs tends to be flat and contains few pockets, making it difficult for small-molecule compounds to bind. Designing a tiny chemical to bind to a PPI interface is tricky. First, the unusual interface structure presents a hurdle for drug design: the PPI interface is flat compared to traditional protein targets. It includes few pockets, making it challenging for small-molecule drugs to attach. Although initiatives to regulate RNA and protein expression with small molecules, biological compounds, such as anti-sense technology, have remained the most often deployed approach to target disease-associated RNAs. This study aims at predicting undruggable long coding RNA and transcription factors using unsupervised analysis from oral cancer transcriptomics.
Methods: Using the NCBI geo database, GSE160395 data was retrieved for unsupervised analysis. PCA(Principal Component Analysis), T SNE (t-Distributed Stochastic Neighbor Embedding and MDS analysis of multi-dimensional data were analyzed for multi-dimensional data. Using the GEO2R online analysis tool (http://www.ncbi.nlm.nih.gov/geo/geo2r), the DEGs between HSC-3 and HSC-3-M3 cell lines samples were analyzed. DEGs with a threshold criterion of 1:0 log fold change and a P value of 0.05 were considered significantly differentially expressed.
Results: Top differential genes include LINC00662, MYEOV, LGALS7, MAGED1, NT5M, AKR1C1, RPH3AL, LINC01234, KRT31, SPANXA1, SVIL ANTI-SENSE RNA 1.Clustering is an unsupervised machine learning technique that is effective at finding hidden groups in data. Targeting non-coding RNA and less hydrophobic proteins is crucial for cancer prevention and metastasis. By controlling the Wnt/-catenin pathway, LINC00662 and SVIL ANTISENSE RNA 1 can facilitate and hasten the formation of OSCC.
Conclusion: Anti-sense technology and proteolysis-targeting chimeras will help solve complex biological undruggable proteins.
Citation Format: Pradeep Kumar Yadalam, Raghavendra Vamsi Anegundi, Ramya Ramadoss. Unsupervised Machine Learning Predicts Invasive and Undruggable Long Coding Rna Linc00662, Linc01234, and Spanxa1, Rabphilin 3a, Svil Antisense Rna 1 Like From Oral Cancer Transcriptomics [abstract]. In: Proceedings of the 11th Annual Symposium on Global Cancer Research; Closing the Research-to-Implementation Gap; 2023 Apr 4-6. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(6_Suppl):Abstract nr 91.