Abstract—Ovarian cancer detection remains elusive due to a lack of screening tests and non-specific symptoms. A crucial factor in cancer development is DNA sequence mutations, particularly missense mutations that can alter protein structure, thereby potentially initiating carcinogenesis. Advances in sequencing technology have paved the way for detailed analysis of individual genetic profiles, spotlighting genes with missense mutations as prospective biomarkers. Such biomarkers are pivotal for personalizing cancer therapies, as they can guide medication choices, ensuring efficacy and minimizing detrimental effects. Despite tools like AlphaFold predicting 3D protein structures and Phyre2 assessing mutated amino acid impacts, no model concurrently predicts wild-type and mutated protein structures. Also, integrating structure changes with drug target identification remains under-explored. Analyzing the TCGA Ovarian Cancer transcriptome data, this research postulated that missense mutations in highly expressed genes significantly influence protein structure, earmarking these genes as potential therapeutic targets. Twelve genes were discerned to affect ovarian cancer patient survival rates. An original platform, MiSeVis, was introduced, offering insights into potential drug targets for specific genes, survival analysis, and 3D protein structure alterations. This comprehensive methodology, unifying transcriptome analysis, pinpointing genes with impactful missense mutations, and presenting a user-centric visualization tool, marks considerable progress in ovarian cancer treatment and precision medicine. 
 

Keywords—ovarian cancer, missense mutations, sequencing technology, biomarkers, transcriptome analysis, protein structure, survival analysis, personalized therapy

Cite: Ian Hou and Yongsheng Bai, "Computational Identification of Ovarian Cancer Candidate Driver Genes with Mutated Protein Structures Caused by Missense Variants," International Journal of Pharma Medicine and Biological Sciences, Vol. 13, No. 2, pp. 61-66, 2024.

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