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Colorectal cancer (CRC) is the second most common cause of cancer-related death among both men and women worldwide and the third most common cancer overall. About 20% of patients diagnosed with CRC were discovered to have distant metastatic lesions, the majority of which were located in the liver. For the optimum treatment of CRC patients with hepatic metastases, interventional radiologists, medical oncologists, and surgeons must all collaborate. The surgical excision of the primary tumor is an important part of CRC treatment since it has been found to be curative in cases of CRC with minimal metastases. However, given the evidence to date was gathered from retrospective data, there is still controversy over the effectiveness of primary tumor resection (PTR) in improving the median overall survival (OS) and quality of life. Patients who have hepatic metastases make up a very tiny fraction of those who are candidates for resection. With a focus on the PTR, this minireview attempted to review the current advancements in the treatment options for hepatic colorectal metastatic illness. This evaluation also included information on PTR’s risks when performed on individuals with stage IV CRC.

Due to advances in understanding the immune microenvironment of colorectal cancer (CRC), microsatellite classification (dMMR/MSI‐H and pMMR/MSS) has become a key biomarker for the diagnosis and treatment of CRC patients and therefore has important clinical value. Microsatellite status is associated with a variety of clinicopathological features and affects drug resistance and the prognosis of patients. CRC patients with different microsatellite statuses have different compositions and distributions of immune cells and cytokines within their tumor microenvironments (TMEs). Therefore, there is great interest in reversing or reshaping CRC TMEs to transform immune tolerant "cold" tumors into immune sensitive "hot" tumors. This requires a thorough understanding of differences in the immune microenvironments of MSI‐H and MSS type tumors. This review focuses on the relationship between CRC microsatellite status and the immune microenvironment. It focuses on how this relationship has value for clinical application in diagnosis and treatment, as well as exploring the limitations of its current application., The main differences of TME among different MSI types.

Cancer stemness, chemoresistance, and metastasis are related biological events. However, whether they have common molecular mechanisms remains to be determined. Here, we report that imiquimod (IMQ) facilitates the acquisition of stem-cell-like properties and chemoresistance via the upregulation of matrix metalloproteinase 1 (MMP1) and downregulation of microRNA-145 (miR-145). MiR-145-5p was found to suppress MMP1 expression through direct binding, and miR-145-mediated downregulation of MMP1 reversed the effects of IMQ. In addition, IMQ downregulated miR-145 by promoting DNA methylation at its promoter. DNA methyltransferase inhibitors limited IMQ-induced MMP1 expression, stemness, and chemoresistance. Collectively, our results highlight the miR-145-MMP1 axis as a potential coordinator of cancer stemness and chemoresistance. Given the role of MMP1 in the initiation of metastasis, the miR-145-MMP1 axis serves as a promising therapeutic target for improved cancer treatment.

Under the emergency accidents, implementation of the rescue measures is directly related to the traffic state identification. However, lacking of information collecting caused by road damage would lead to the results of identification by conventional fixed detector had a larger deviation. Although the existing way of probe vehicle with a large coverage area but also from an environmental impact, the algorithm of the traffic state identification based on probe vehicle are limited to fixed sections. For this reason, in this paper, an algorithm by calculating the average speed of the probe vehicle in a fixed time period is introduced to provide effective traffic information for the emergency rescue. Simulation results show that the algorithm has high Identification Rate (91.5%), low False Identification Rate (1.6%) and shorter time of Mean Time to Identification (1.7min).