Kumari Pratima, Kumar Sugandh, Sethy Madhusmita, Bhue Shyamlal, Mohanta Bineet Kumar, Dixit Anshuman
Computational Biology and Bioinformatics Laboratory, Institute of Life Sciences, Bhubaneswar, India.
Regional Centre for Biotechnology (RCB), Faridabad, India.
Front Oncol. 2022 Sep 20;12:910494. doi: 10.3389/fonc.2022.910494. eCollection 2022.
Recent advancements in cancer biology have revealed molecular changes associated with carcinogenesis and chemotherapeutic exposure. The available information is being gainfully utilized to develop therapies targeting specific molecules involved in cancer cell growth, survival, and chemoresistance. Targeted therapies have dramatically increased overall survival (OS) in many cancers. Therefore, developing such targeted therapies against oral squamous cell carcinoma (OSCC) is anticipated to have significant clinical implications. In the current work, we have identified drug-specific sensitivity-related prognostic biomarkers (, , , , and ) using gene expression, Cox proportional hazards regression, and machine learning in OSCC. Dysregulation of these markers is significantly associated with OS in many cancers. Their elevated expression is related to cellular proliferation and aggressive malignancy in various cancers. Mechanistically, inhibition of these biomarkers should significantly reduce cellular proliferation and metastasis in OSCC and should result in better OS. It is pertinent to note that no effective small-molecule candidate has been identified against these biomarkers to date. Therefore, a comprehensive drug design strategy assimilating homology modeling, extensive molecular dynamics (MD) simulation, and ensemble molecular docking has been applied to identify potential compounds against identified targets, and potential molecules have been identified. We hope that this study will help in deciphering potential genes having roles in chemoresistance and a significant impact on OS. It will also result in the identification of new targeted therapeutics against OSCC.
癌症生物学的最新进展揭示了与致癌作用和化疗暴露相关的分子变化。现有的信息正被有效地用于开发针对参与癌细胞生长、存活和化疗耐药性的特定分子的疗法。靶向疗法显著提高了许多癌症的总生存期（OS）。因此，开发针对口腔鳞状细胞癌（OSCC）的此类靶向疗法预计具有重大的临床意义。在当前的工作中，我们利用基因表达、Cox比例风险回归和机器学习在OSCC中确定了与药物特异性敏感性相关的预后生物标志物（、、、和）。这些标志物的失调在许多癌症中与OS显著相关。它们的高表达与各种癌症中的细胞增殖和侵袭性恶性肿瘤有关。从机制上讲，抑制这些生物标志物应能显著降低OSCC中的细胞增殖和转移，并应能带来更好的OS。需要注意的是，迄今为止尚未发现针对这些生物标志物的有效小分子候选物。因此，一种综合的药物设计策略，融合了同源建模、广泛的分子动力学（MD）模拟和整体分子对接，已被应用于识别针对已确定靶点的潜在化合物，并已确定了潜在分子。我们希望这项研究将有助于解读在化疗耐药中起作用且对OS有重大影响的潜在基因。它还将导致识别针对OSCC的新的靶向治疗方法。
