Since liquid biopsy is less invasive than tissue biopsy, studies on liquid biopsy biomarkers for the early detection of cancer and diagnosis are taken into consideration. Expression profiles of tumor-educated platelets (TEP) in liquid biopsy can be used as one of the biomarkers. The use of classification machine learning models, according to the features space derived from the expression data of TEPs, has given us the ability to predict cancer. In this study, we evaluate different types of classification models namely SVM, LDA, logistic regression, boosting, classification tree, and random forest, on the expression profile of TEPs in 230 patients with breast, liver, colorectal, brain, pancreatic, and lung cancers, as well as the expression profile of these genes in 55 healthy individuals. These models were examined on the expression profile of 2000 high variance selected genes. Also, pathway enrichment analysis was performed on these genes by the GSEA preranked method. The results showed that linear SVM and polynomial SVM models have lower error rates than two-class models and linear SVM models have lower error rates than multi-class models. In general, the results of TEP expression profile classification and pathway enrichment analysis indicate that the expression profile of TEPs can be considered as candidate biomarkers.

Platelets are multi-purpose cell parts circulating abundantly in the blood and can be educated by their environment. Platelets actively change their nucleic acid content in response to local and systemic pathological conditions and can play a role in cancer development and progression by supporting the growth, survival, and dissemination of tumor cells. In the current study, the importance of RNAs in platelets -as less invasive biomarkers- for early diagnosis of cancers and also for tracking tumor progression has been investigated.

Reliable scientific databases such as PubMed, Web of Science, Scopus, and Google Scholar were used to gather relevant articles for this review study.

The nucleic acid content of platelets is provided both by their generator (megakaryocyte) and mediators such as cancer cells. Different types of coding and non-coding RNA make up the nucleic acid content of platelets, and cancer cells affect platelet RNA content in tumor-educated platelets through direct and indirect pathways.

Advances in molecular technologies and subsequent deep insight into the profile of tumor-educated platelet RNAs, including the discovery of small and longer regulatory RNAs, have enabled the analysis of tumor-educated platelet RNA profiles as a new source for early cancer diagnosis.

Osteosarcoma is the most frequent malignant bone tumor with a peak incidence in the second and third decades of life. Diagnosis of patients with osteosarcoma is limited to clinico-pathological parameters whereas molecular markers of tumor initiation and promotion have not yet been identified. We aimed to study the expression of the anti-apoptotic survivin gene in osteosarcoma specimens. A total of 35 formalin-fixed, paraffin-embedded (FFPE) specimens including 25 tumoral and 20 non-tumoral paired-margins were collected. The expression of survivin was evaluated by employing real-time PCR and immunohistochemistry. Our data showed that survivin is differently expressed in osteosarcoma samples rather than non-tumoral margins. Real-time PCR showed that survivin is up-regulated in osteosarcoma specimens compared to margins (p<0.01). Furthermore, IHC confirmed that the survivin protein is significantly found in malignant parts of paraffin blocks. Our data show that survivin expression is modulated in osteosarcoma and may be considered as a diagnostic marker for further studies. Our data showed that survivin is differently expressed in osteosarcoma samples rather than non-tumoral margins. Real-time PCR showed that survivin is up-regulated in osteosarcoma specimens compared to margins (p<0.01). Furthermore, IHC confirmed that the survivin protein is significantly found in malignant parts of paraffin blocks. Our data show that survivin expression is modulated in osteosarcoma and could be considered as a diagnostic marker for further studies.