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Comprehensive single-cell atlas of colorectal neuroendocrine tumors with liver metastases: unraveling tumor microenvironment heterogeneity between primary lesions and metastases.
Mol Cancer
January 2025
Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
Background: Colorectal neuroendocrine tumors with liver metastases (CRNELM) are associated with a poorer prognosis compared to their nonmetastatic counterparts. A comprehensive understanding of the tumor microenvironment (TME) heterogeneity between primary lesions (PL) and liver metastases (LM) could provide crucial insights for enhancing clinical management strategies for these patients.
Methods: We utilized single-cell RNA sequencing to analyze fresh tissue samples from CRNELM patients, aiming to elucidate the variations in TME between PL and LM.
TGF-β signaling orchestrates cancer-associated fibroblasts in the tumor microenvironment of human hepatocellular carcinoma: unveiling insights and clinical significance.
BMC Cancer
January 2025
Department of Tumor Biological Treatment, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu, 213003, China.
Liver cancer, specifically hepatocellular carcinoma (HCC), stands out as one of the most formidable solid tumors, characterized by a dauntingly low survival rate. At the forefront of the tumor microenvironment (TME) orchestrating the initiation and advancement of HCC are cancer-associated fibroblasts (CAFs). TGF-β, widely recognized as a potent activator of CAFs, not only regulates their activity but also assumes a pivotal role in the metastatic journey of the tumor.
View Article and Find Full Text PDFEvaluation of MRI characterisation of histopathologically matched lymph nodes and other mesorectal nodal structures in rectal cancer.
Eur Radiol
January 2025
Department of Diagnostics and Intervention, Diagnostic Radiology, Umeå University, Umeå, Sweden.
Purpose: To evaluate current MRI-based criteria for malignancy in mesorectal nodal structures in rectal cancer.
Method: Mesorectal nodal structures identified on baseline MRI as lymph nodes were anatomically compared to their corresponding structures histopathologically, reported as lymph nodes, tumour deposits or extramural venous invasion. All anatomically matched nodal structures from patients with primary surgery and all malignant nodal structures from patients with neoadjuvant treatment were included.
Organoid modeling identifies USP3-AS1 as a novel promoter in colorectal cancer liver metastasis through increasing glucose-driven histone lactylation.
Acta Pharmacol Sin
January 2025
Department of Hepatic Surgery, Shanghai Cancer Center, Fudan University, Shanghai, 200032, China.
Dysregulation of long non-coding RNAs (lncRNAs) is common in colorectal cancer liver metastasis (CRLM). Emerging evidence links lncRNAs to multiple stages of metastasis from initial migration to colonization of distant organs. In this study we investigated the role of lncRNAs in metabolic reprogramming during CRLM using patient-derived organoid (PDO) models.
View Article and Find Full Text PDFConstruction and validation of nomograms to predict central lymph node metastasis in clinical node-negative unilateral papillary thyroid carcinoma.
Sci Rep
January 2025
Department of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan, 430022, China.
The scope of neck lymph node dissection remains controversial for unilateral papillary thyroid carcinoma (UPTC) patients with no clinical evidence of lymph node metastasis (cN0). This study aims to build and validate a model for predicting central lymph node metastasis (CLNM) in UPTC patients through preoperative basic information and intraoperative rapid frozen pathology results. Retrospective analysis covered 1928 patients with PTC from the Wuhan Union Hospital database (2010-2020), randomly split into training and validation sets in a 7:3 ratio.
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