Cancer-cell-derived exosomes confer oncogenic properties in their tumor microenvironment and to other cells; however, the exact mechanism underlying this process is unclear. Here, we investigated the roles of cancer-cell-derived exosomes in colon cancer. Exosomes were isolated from colon cancer cell lines, HT-29, SW480, and LoVo, using an ExoQuick-TC kit, identified using Western blotting for exosome markers, and characterized using transmission electron microscopy and nanosight tracking analysis. The isolated exosomes were used to treat HT-29 to evaluate their effect on cancer progression, specifically cell viability and migration. Cancer-associated fibroblasts (CAFs) were obtained from patients with colorectal cancer to analyze the effect of the exosomes on the tumor microenvironment. RNA sequencing was performed to evaluate the effect of the exosomes on the mRNA component of CAFs. The results showed that exosome treatment significantly increased cancer cell proliferation, upregulated N-cadherin, and downregulated E-cadherin. Exosome-treated cells exhibited higher motility than control cells. Compared with control CAFs, exosome-treated CAFs showed more downregulated genes. The exosomes also altered the regulation of different genes involved in CAFs. In conclusion, colon cancer-cell-derived exosomes affect cancer cell proliferation and the epithelial–mesenchymal transition. They promote tumor progression and metastasis and affect the tumor microenvironment.

Purpose: The integration of artificial intelligence (AI) and magnetic resonance imaging in rectal cancer has the potential to enhance diagnostic accuracy by identifying subtle patterns and aiding tumor delineation and lymph node assessment. According to our systematic review focusing on convolutional neural networks, AI-driven tumor staging and the prediction of treatment response facilitate tailored treat­ment strategies for patients with rectal cancer. Methods: This paper summarizes the current landscape of AI in the imaging field of rectal cancer, emphasizing the performance reporting design based on the quality of the dataset, model performance, and external validation. Results: AI-driven tumor segmentation has demonstrated promising results using various convolutional neural network models. AI-based predictions of staging and treatment response have exhibited potential as auxiliary tools for personalized treatment strategies. Some studies have indicated superior performance than conventional models in predicting microsatellite instability and KRAS status, offer­ing noninvasive and cost-effective alternatives for identifying genetic mutations. Conclusion: Image-based AI studies for rectal can­cer have shown acceptable diagnostic performance but face several challenges, including limited dataset sizes with standardized data, the need for multicenter studies, and the absence of oncologic relevance and external validation for clinical implantation. Overcoming these pitfalls and hurdles is essential for the feasible integration of AI models in clinical settings for rectal cancer, warranting further research.

Kim M, et al. Korean J Radiol. 2023 Sep;24(9):849-859. https://doi.org/10.3348/kjr.2023.0109

Kim HR, et al. Ann Surg Treat Res. 2021 Jan;100(1):33-39. https://doi.org/10.4174/astr.2021.100.1.33

Purpose: Carcinoembryonic antigen (CEA) is a useful marker for rectal cancer. The aim of this study was to investigate the prognostic impact of CEA level according to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer patients who underwent radical surgery.Methods: A total of 245 patients with rectal cancer who underwent radical surgery were retrospectively evaluated. Serum CEA level was measured preoperatively and postoperatively. We compared survival outcomes based on CEA level before and after surgery according to nCRT.Results: Of the 245 patients, elevation of CEA level was observed preoperatively in 79 and postoperatively in 30, respectively. Eighty-seven (35.5%) patients received nCRT, and elevated CEA level was a significant prognostic factor both before and after surgery. In patients who had not received nCRT, an elevated CEA level was a significant prognostic factor before surgery but was not significant after surgery. In a multivariate analysis for prognostic factors, elevation of preoperative CEA level was an independent prognostic factor of disease-free survival (DFS) regardless of nCRT. Postoperative CEA level was an independent prognostic factor of DFS in patients who had received nCRT but was not a factor in patients who had not received nCRT.Conclusion: Serum CEA level was an independent prognostic factor both preoperatively and postoperatively in rectal cancer patients who had received nCRT.

Colorectal cancer (CRC) is one of the most commonly diagnosed malignancies and a leading cause of cancer-related death worldwide [1,2]. Advances in screening and treatment modalities have improved the survival of patients with CRC; however, the mortality rate remains high in cases of metastasis or recurrence [1,3]. The complexity of tumor progression—including, for instance, tumor heterogeneity, resistance mechanisms, genetic alterations, and micromolecular biology—contributes to the difficulty of achieving improvements in prognosis; therefore, more sophisticated and tailored treatment strategies are needed [4-7]. It is thus important to identify factors associated with a poor prognosis in patients with CRC. The conventional prognostic model for CRC is the TNM staging system, which was proposed by the American Joint Committee on Cancer. Clinical characteristics and additional pathologic features are also used to predict the patient’s prognosis [5-6,8,9]. However, patients with a similar clinicopathologic status and staging may have different prognoses.