Introduction: Colorectal cancer (CRC) is one of the most common gastrointestinal malignancies worldwide and represents a relevant public health problem. Patients are usually diagnosed at advanced stages due to the absence of symptoms in the early stages of the disease. Thus, tumor-educated platelets (TEPs) have emerged as a minimally invasive liquid biopsy tool for the detection of various types of tumors, including CRC. Objectives: Therefore, we aimed to identify a molecular signature in TEPs for the diagnosis of CRC, as well as to determine the differentially expressed genes and compare two different machine learning decision tree-based algorithms for CRC detection. Methods: We downloaded platelet gene expression data from the Gene Expression Omnibus (GEO; GSE183635), containing 326 samples from CRC patients and 67 samples from control subjects. Low-quality reads were filtered using FastP, and the remaining high-quality reads were aligned to the human genome (GRCh38.p13 v43) using the Salmon tool. Aligned reads were then imported into RStudio via the Tximport package for further analysis. We performed differential expression analysis using DESeq2 (|log2FoldChange| > 1, adjusted p-value < 0.05). Gene ontology (GO) enrichment analysis (p-value < 0.05) was conducted on differentially expressed genes (DEGs). DEGs were used as input for two algorithms: randomForest and XGBoost. The model with the highest AUC was selected as the best performer, allowing the identification of relevant genes. Results: Differential analysis showed that 7,177 DEGs were found, in which 55 (0.77%) were upregulated and 7,122 (99.23%) were downregulated in CRC patients compared to controls. Upregulated genes are involved in oxygen transport and oxidative stress processes, while downregulated genes are related to immune system regulation and cell differentiation. XGBoost outperformed randomForest (AUC 0.848 vs. 0.788). The upregulated genes considered relevant were MYL9 (AUC = 0.806 and CI = 0.748-0.864), IGFBP2 (AUC = 0.661 and CI = 0.585-0.737) and ALAS2 (AUC = 0.674 and CI = 0.599-0.749), with MYL9 standing out with the highest AUC value. Conclusion: Our results reveal the potential of MYL9 expression in TEP cells as a diagnostic biomarker for CRC, reinforcing the potential of TEP cells as a minimally invasive source of biomarkers.
It is with great enthusiasm that we present the Annals of the Oncology International Symposium 2025, an event that continues to solidify its significance in the oncology landscape of northern Brazil. Held in Belém, Pará, Oncology 2025 centered around the theme "The cancer control challenge: better knowing it to best facing it," dedicating itself to exploring the latest frontiers in cancer treatment and prevention.
This year, the symposium provided a deep dive into the essential role of knowledge in the fight against cancer, presenting new perspectives and scientific advancements across various areas of oncology. Renowned global experts gathered to share their most recent research and innovative approaches, offering participants a comprehensive view of the challenges faced by healthcare professionals and patients worldwide.
Presentations and discussions during the event focused on critical topics such as the use of new technologies, advancements in personalized therapies, and more effective prevention strategies. Additionally, particular attention was given to the unique challenges faced by the Amazon region, with efforts aimed at developing region-specific solutions to meet local needs.
Beyond being a high-caliber academic event, Oncology 2025 stood out as a moment for integration and professional networking, with the warm hospitality of the city of Belém offering participants a unique experience. This event became a platform for exchanging ideas, where science, culture, and humanity came together in pursuit of a common goal: to improve cancer control both in Brazil and globally.
This collection of abstracts and articles presented during the event reflects the ongoing dedication to research and the development of innovative solutions, highlighting the importance of collaboration and shared knowledge in the fight against cancer.
General Submission Guidelines:
The presenting author, who does not have to be the first author, must be registered for Oncology 2025.
Each abstract may have up to 10 authors, including the main author and co-authors.
Only original, unpublished work will be accepted.
Submissions must be related to oncology. However, project descriptions, work proposals, experience reports, and literature reviews will not be considered.
Clinical case reports are allowed, provided the abstract addresses scientific questions, details clinical observations, and includes primary scientific data.
The abstract must be written in English, but presentations may be given in Portuguese.
Abstracts must be between 300 and 500 words.
Comissão Organizadora
Comissão Científica
See Annals of Oncology 2023 at:
https://www.even3.com.br/anais/oncology-2023-international-symposium/
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