Seema Saraswathy, Krishnan Manu, Harith Arun K, Sahai Kavita, Iyer Satish R, Arora Vimal, Tripathi Rajendra P
Army Base Hospital, School of Medicine & Paramedical Health Sciences, Guru Gobind Singh Indraprastha University, Government of Delhi, Delhi, India.
J Oral Pathol Med. 2014 Aug;43(7):471-83. doi: 10.1111/jop.12117. Epub 2013 Sep 20.
Biomarker research in oral squamous cell carcinoma (OSCC) aims for screening/early diagnosis and in predicting its recurrence, metastasis and overall prognosis. This article reviews the current molecular perspectives and diagnosis of oral cancer with proteomics using matrix-assisted laser desorption ionization (MALDI) and surface-enhanced laser desorption ionization (SELDI) mass spectrometry (MS). This method shows higher sensitivity, accuracy, reproducibility and ability to handle complex tissues and biological fluid samples. However, the data interpretation tools of contemporary mass spectrometry still warrant further improvement. Based on the data available with laser-based mass spectrometry, biomarkers of OSCC are classified as (i) diagnosis and prognosis, (ii) secretory, (iii) recurrence and metastasis, and (iv) drug targets. Majority of these biomarkers are involved in cell homeostasis and are either physiologic responders or enzymes. Therefore, proteins directly related to tumorigenesis have more diagnostic value. Salivary secretory markers are another group that offers a favourable and easy strategy for non-invasive screening and early diagnosis in oral cancer. Key molecular inter-related pathways in oral carcinogenesis are also intensely researched with software analysis to facilitate targeted drug therapeutics. The review suggested the need for incorporating 'multiple MS or tandem approaches' and focusing on a 'group of biomarkers' instead of single protein entities, for making early diagnosis and treatment for oral cancer a reality.
口腔鳞状细胞癌（OSCC）的生物标志物研究旨在进行筛查/早期诊断，并预测其复发、转移和总体预后。本文综述了利用基质辅助激光解吸电离（MALDI）和表面增强激光解吸电离（SELDI）质谱（MS）的蛋白质组学对口腔癌的当前分子观点和诊断。该方法显示出更高的灵敏度、准确性、可重复性以及处理复杂组织和生物流体样本的能力。然而，当代质谱的数据解读工具仍需进一步改进。基于基于激光的质谱所获得的数据，OSCC的生物标志物可分为：（i）诊断和预后，（ii）分泌型，（iii）复发和转移，以及（iv）药物靶点。这些生物标志物大多数参与细胞稳态，要么是生理反应者，要么是酶。因此，与肿瘤发生直接相关的蛋白质具有更高的诊断价值。唾液分泌标志物是另一类为口腔癌的非侵入性筛查和早期诊断提供有利且简便策略的物质。口腔癌发生过程中关键的分子相互关联途径也通过软件分析进行了深入研究，以促进靶向药物治疗。该综述表明，为了实现口腔癌的早期诊断和治疗，需要采用“多重质谱或串联方法”，并关注“一组生物标志物”而非单个蛋白质实体。
