An extensive body of literature describes anticancer property of dichloroacetate (DCA), but its effective clinical administration in cancer therapy is still limited to clinical trials. The occurrence of side effects such as neurotoxicity as well as the suspicion of DCA carcinogenicity still restricts the clinical use of DCA. However, in the last years, the number of reports supporting DCA employment against cancer increased also because of the great interest in targeting metabolism of tumour cells. Dissecting DCA mechanism of action helped to understand the bases of its selective efficacy against cancer cells. A successful coadministration of DCA with conventional chemotherapy, radiotherapy, other drugs, or natural compounds has been tested in several cancer models. New drug delivery systems and multiaction compounds containing DCA and other drugs seem to ameliorate bioavailability and appear more efficient thanks to a synergistic action of multiple agents. The spread of reports supporting the efficiency of DCA in cancer therapy has prompted additional studies that let to find other potential molecular targets of DCA. Interestingly, DCA could significantly affect cancer stem cell fraction and contribute to cancer eradication. Collectively, these findings provide a strong rationale towards novel clinical translational studies of DCA in cancer therapy.
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http://dx.doi.org/10.1155/2019/8201079 | DOI Listing |
Discov Oncol
January 2025
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, 230000, China.
Aim: To construct a predictive model based on the LODDS stage established for patients with late-onset colon adenocarcinoma to enhance survival stratification.
Methods: Late-onset colon adenocarcinoma data were obtained from the public database. After determining the optimal LODDS truncation value for the training set via X-tile software, we created a new staging system by integrating the T stage and M stage.
Front Immunol
January 2025
Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Aim: This study aims to create and validate a novel systematic immune-inflammation-nutrition (SIIN) score to provide a non-invasive and accurate prognostic tool for head and neck squamous cell carcinoma (HNSCC) patients.
Methods: 259 participants diagnosed with HNSCC from the First Affiliated Hospital of Xi'an Jiaotong University between 2008 and 2017 was included in this retrospective study. Patients were assigned to training (n=181) and validation (n=78) sets.
PLoS One
January 2025
Department of Geriatric Medicine, the Affiliated Hospital of Guilin Medical University, Guilin, Guangxi, China.
Objective: To develop a predictive model for microvascular invasion (MVI) in hepatocellular carcinoma (HCC) through radiomics analysis, integrating data from both enhanced computed tomography (CT) and magnetic resonance imaging (MRI).
Methods: A retrospective analysis was conducted on 93 HCC patients who underwent partial hepatectomy. The gold standard for MVI was based on the histopathological diagnosis of the tissue.
Langenbecks Arch Surg
January 2025
Department of General Surgery, Gansu Provincial Hospital, Lanzhou, 730000, China.
Background: In the last two decades, robotic-assisted gastrectomy has become a widely adopted surgical option for gastric cancer (GC) treatment. Despite its popularity, postoperative complications can significantly deteriorate patient quality of life and prognosis. Therefore, identifying risk factors for these complications is crucial for early detection and intervention.
View Article and Find Full Text PDFSci Rep
January 2025
Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
To retrospectively develop and validate an interpretable deep learning model and nomogram utilizing endoscopic ultrasound (EUS) images to predict pancreatic neuroendocrine tumors (PNETs). Following confirmation via pathological examination, a retrospective analysis was performed on a cohort of 266 patients, comprising 115 individuals diagnosed with PNETs and 151 with pancreatic cancer. These patients were randomly assigned to the training or test group in a 7:3 ratio.
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