ABSTRACT
INTRODUCTION: Oral cancer is a significant health problem in India. Diagnosis is often delayed. The effectiveness of conventional oral screening has been shown in the Trivandrum oral cancer screening study. The present study will be a step forward to test a mobile phone-based (the mHealth approach) comparing it with the conventional approach. The purpose of this paper is to report the protocol for this study. The primary objective will be to compare both methods in diagnosing oral potentially malignant disorders and cancers. The secondary objective would be to study the cost-effectiveness. METHODS AND ANALYSIS: This will be a cluster-randomized clinical trial of the population in Ernakulam district of Kerala state in India. They will undergo oral cancer screening by community health workers, who will be pre-assigned to the randomly allotted intervention (mHealth) or control (conventional method) clusters. We will enrol a minimum of 9696 subjects from all 6 clusters over 18 months. The cost-effectiveness of the two strategies for oral screening will be determined using data from this randomized controlled trial. The incremental cost per oral cancer/high-risk dysplasia detected, and the incremental cost per life saved will be reported. We will conduct sensitivity and scenario analysis to evaluate the robustness of the findings. ETHICS AND DISSEMINATION: When completed, this will be the first cluster randomized population-based study to test the technology-based approach in India. The knowledge from this study will indicate whether specialists can make a remote diagnosis of oral lesions accurately based on the information gathered using a mobile phone health application and whether the mHealth strategy will be cost-effective in Oral cancer screening. The study will follow the ethical guidelines and will be published in an indexed journal.
ABSTRACT
Salivary proteins are an important source for developing marker-based assays for oral cancers. To get an insight into the proteins present in human saliva, we applied multiple strategies involving affinity-based depletion of abundant proteins, fractionation of the resulting proteins or their tryptic peptides followed by LC-MS/MS analysis, using high resolution mass spectrometry. By integrating the protein identifications observed by us with those from similar workflows employed in earlier investigations, we compiled an updated salivary proteome. We have mapped the salivary proteome to the published data on differentially expressed proteins from oral cancer tissues and also for their secretory features using prediction tools, SignalP 4.1, TMHMM 2c and Exocarta. Proteotypic peptides for the subset of proteins implicated in oral cancer and mapped to any two of the prediction tools for secretory potential have been listed. The data here are related to the research article "Human saliva proteome - a resource of potential biomarkers for oral cancer" in the Journal of Proteomics [1].
