Background:Cancer patients have a high risk of fungal infections, especially by Candida species.Non-C. albicans Candidainfections and less common yeast infections have been increasing in recent years. Identification by conventional methods can be difficult and sometimes inconclusive. This study aimed to detect the prevalence of oral yeast species isolated from cancer patients, from oral swab, sputum and urine, using Internal Transcribed Spacer (ITS) sequence analysis, since little is known about this problem in Sudan.Methods:The study involved 333 cancer patients (168 patients under treatment [study group] and 165 patients before treatment [control group]). Oral swabs were collected from all patients. Urine or sputum specimens were collected from patients under treatment showing clinical features of UTI or lower respiratory tract infection, respectively. ITS1 and ITS2 region of isolated yeast were amplified by PCR and sequenced. The obtained sequences were compared to reference sequence available in the GenBank database using BLAST.Results:Culture results showed oral yeastspecies were isolated from 69/168 (41.1%) and 74/165 (44.8%) of patients among study and control groups, respectively (P value > 0.05). 2/9 (22.2%) patients were urine growth positive and 8/14 (57.1%) patients were sputum culture positive. Sequence analysis showed,C. albicanswas the most prevalent organism (93; 52.5%) followed byC. tropicalis(29; 16.4%), andC. glabrata(24; 13.6%). Non-C. albicans Candidaand uncommon rare yeast were found to be associated with oral infections and colonization among the study and control groups, whereasC.albicans was the most common species (66.7%) associated with oral candidiasis among the treated patients. Conclusion:Cancer patients were highly colonized with different oral yeast species, which indicates that ITS sequence analysis is an accurate method for identification. This will aid effective management to prevent dissemination of disease especially among those who are under chemo and/or radiotherapy treatment.
Identification of Medically Important Yeast Species by Sequence Analysis of the Internal Transcribed Spacer Regions
