Atta-ur-Rahman School of Applied Biosciences (ASAB), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.
School of Biochemistry & Biotechnology, University of the Punjab, Lahore, Pakistan.
J Ovarian Res. 2023 Oct 13;16(1):202. doi: 10.1186/s13048-023-01292-1.
Gynecologic cancers comprise malignancies in the female reproductive organs. Ovarian cancer ranks sixth in terms of incidence rates while seventh in terms of mortality rates. The stage at which ovarian cancer is diagnosed mainly determines the survival outcomes of patients. Various screening approaches are presently employed for diagnosing ovarian cancer; however, these techniques have low accuracy and are non-specific, resulting in high mortality rates of patients due to this disease. Hence, it is crucial to identify improved screening and diagnostic markers to overcome this cancer. This study aimed to find new biomarkers to facilitate the prognosis and diagnosis of ovarian cancer.
Bioinformatics approaches were used to predict the tertiary structure and cellular localization along with phylogenetic analysis of TPD52. Its molecular interactions were determined through KEGG analysis, and real-time PCR-based expression analysis was performed to assess its co-expression with another oncogenic cellular pathway (miR-223, KLF9, and PKCε) proteins in ovarian cancer.
Bioinformatics analysis depicted the cytoplasmic localization of TPD52 and the high conservation of its coiled-coil domains. Further study revealed that TPD52 mRNA and miRNA-223 expression was elevated, while the expression of KLF 9 and PKCε was reduced in the blood of ovarian cancer patients. Furthermore, TPD52 and miR-223 expression were upregulated in the early stages of cancer and non-metastatic cancers.
TPD52, miR-223, PKCε, and KLF9, can be used as a blood based markers for disease prognosis, metastasis, and treatment response. The study outcomes hold great potential to be translated at the clinical level after further validation on larger cohorts.
妇科癌症包括女性生殖器官的恶性肿瘤。卵巢癌的发病率排名第六，死亡率排名第七。卵巢癌的诊断阶段主要决定了患者的生存结果。目前有多种筛查方法用于诊断卵巢癌；然而，这些技术的准确性低且特异性差，导致患者死亡率高。因此，识别改进的筛查和诊断标志物以克服这种癌症至关重要。本研究旨在寻找新的生物标志物以促进卵巢癌的预后和诊断。
使用生物信息学方法预测 TPD52 的三级结构和细胞定位以及系统发育分析。通过 KEGG 分析确定其分子相互作用，并通过实时 PCR 进行表达分析，以评估其与另一种致癌细胞途径（miR-223、KLF9 和 PKCε）蛋白在卵巢癌中的共表达。
生物信息学分析描绘了 TPD52 的细胞质定位及其卷曲螺旋结构域的高度保守性。进一步的研究表明，卵巢癌患者血液中 TPD52 mRNA 和 miR-223 的表达升高，而 KLF9 和 PKCε 的表达降低。此外，TPD52 和 miR-223 的表达在癌症的早期和非转移性癌症中上调。
TPD52、miR-223、PKCε 和 KLF9 可作为基于血液的疾病预后、转移和治疗反应的标志物。在更大的队列中进一步验证后，研究结果有望在临床水平上得到转化。
