Hey, this is an English text. I want you to translate it into the Tunisian dialect. Please follow these steps:
1. Translate step by step and highlight differences between Tunisian dialect and Modern Standard Arabic.
2. Highlight sections you are confident translating into the Tunisian dialect.
Translate this text into the Tunisian dialect, focusing on capturing the essence of the language nuances:

Background {#Sec1} ========== Osteosarcoma is a bone malignancy primarily affecting adolescents and young adults, which is characterized by substantial clinical heterogeneity. Although patients with optimal neoadjuvant chemotherapy response (\>90% necrosis) have good prognosis, those with lower levels of tumor necrosis have more heterogeneous outcomes \[[@CR1], [@CR2]\]. Stratification of patients using pathologic necrosis as the only prognostic stratification factor has not led to improved outcomes in adjuvant clinical trials \[[@CR3]--[@CR5]\]. MicroRNAs (miRNAs) have emerged as novel candidate biomarkers as well as potential modulators of tumor behavior. We recently described miRNA expression models for recurrence and overall survival from formalin-fixed-paraffin-embedded (FFPE) biopsy specimens that confer strong prognostic discrimination independent of chemotherapy response \[[@CR6]\]. The majority of these prognostic miRNAs are located on the 14q32 locus, one of the few genomic regions that are imprinted in normal cells, which is thought to be critical in tissue development via a tightly controlled, allele-specific DNA methylation effect on gene expression. In addition, this locus contains a large cluster of non-coding elements, both miRNAs and other small nucleolar RNAs (snoRNAs) and long non-coding RNAs \[[@CR7], [@CR8]\]. In this study, we provide evidence for the clinical utility of 14q32 miRNAs as individualized prognostic biomarkers in osteosarcoma. Additionally, we show that there are substantial global transcriptional (miRNA and messenger RNA (mRNA)) changes across clinical risk groups, and we find in vitro and clinical evidence that differential methylation in the 14q32 non-coding cluster region may be underlying the miRNA expression changes and different tumor aggressiveness phenotypes. Our findings suggest that the non-coding 14q32 cluster contains a large number of useful clinically relevant biomarkers and is a locus of substantial genomic and epigenetic alterations that give rise to novel subtypes of osteosarcoma with distinct clinical, molecular, and biological context and therapeutic implications. Results {#Sec2} ======= 14q32 miRNAs accurately predict individual patient outcome {#Sec3} ---------------------------------------------------------- While miRNA profiles were prognostic of outcome in recent studies, the precise clinical utility of miRNAs located in the 14q32 region for individualized patient outcome prediction has not yet been determined. We used two previously published genomic datasets with outcome annotation (called "Boston" and "Utah" datasets) and studied the clinical prognostic utility of the 14q32 miRNAs via the time-dependent receiver operator characteristic (tdROC) curve method. A summary table for the two datasets, the details of which have been previously reported \[[@CR6], [@CR9]\], is provided in Additional file [1](#MOESM1){ref-type="media"}. In order to minimize overfitting that is frequently associated with the construction of complex multivariate models, we used the simple, yet robust "signed average" approach. First, we generated a tdROC curve based on the signed average of the top 5 prognostic miRNAs residing at the 14q32 locus, which were previously identified in the Boston dataset \[[@CR6]\] (and were able to be mapped on the Agilent platform used in the Utah dataset). These were miR-495, miR-329, miR-487b, miR-410, and miR-656, and the resulting tdROC curve was highly discriminatory for recurrence at 120 months (area under the curve (AUC) = 0.743; Fig. [1a](#Fig1){ref-type="fig"}). Then, we mapped this five-miRNA profile on