Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of cancer mortality. Although advances have been made in understanding the pathogenesis of PDAC, the outcome still remains poor. The aim of this study is to conduct a meta-analysis to evaluate the precise association between SMAD4 loss and clinicopathological significance in PDAC. A literature search was made in PubMed, Web of Science, Google scholar, and EMBASE for related publications. The data were extracted and assessed by two reviewers independently. Analysis of pooled data was performed, Odds Ratio or Hazard Ratio with corresponding confidence intervals was calculated and summarized. 12 relevant articles were included for full review in detail and meta-analysis. The frequency of SMAD4 protein loss was significantly increased in PDAC than in nonmalignant pancreatic tissue, Odd Ratio was 0.05 with 95% confidence interval 0.01-0.23, p<0.0001. SMAD4 loss was significantly associated with poor overall survival in patients with PDAC, Hazard Ratio was 0.61 with 95% confidence interval 0.38-0.99, p=0.05. SMAD4 loss was not correlated with the size, grades, and lymph node metastasis of PDAC. In conclusion, SMAD4 is a biomarker for the diagnosis of PDAC. SMAD4 loss is significantly related to poor prognosis in patients with PDAC.
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http://dx.doi.org/10.18632/oncotarget.14335 | DOI Listing |
Sci Rep
December 2024
Department of Gastroenterological Surgery, Graduate School of Medicine, Osaka University, 2-2-E2, Yamada-Oka, Suita, Osaka, 565-0871, Japan.
Esophageal cancer is a highly aggressive disease, and acquired resistance to chemotherapy remains a significant hurdle in its treatment. mtDNA, crucial for cellular energy production, is prone to mutations at a higher rate than nuclear DNA. These mutations can accumulate and disrupt cellular function; however, mtDNA mutations induced by chemotherapy in esophageal cancer remain unexplored.
View Article and Find Full Text PDFActa Otolaryngol
December 2024
Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
Background: There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC).
Objectives: We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined.
Front Immunol
December 2024
Department of Otolaryngology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, Jiangsu, China.
Background: B-cell receptor-associated protein 31 (BCAP31) is a widely expressed transmembrane protein primarily located in the endoplasmic reticulum (ER), including the ER-mitochondria associated membranes. Emerging evidence suggests that BCAP31 may play a role in cancer development and progression, although its specific effects across different cancer types remain incompletely understood.
Methods: The raw data on BCAP31 expression in tumor and adjacent non-tumor (paracancerous) samples were obtained from the Broad Institute Cancer Cell Line Encyclopedia (CCLE) and UCSC databases.
BMC Womens Health
December 2024
Department of Blood Transfusion, Shengjing Hospital of China Medical University, Shenyang, 110004, China.
Objective: This study aimed to analyse the correlation between the expression of cell proliferation-associated antigen (Ki-67), cell cycle protein-dependent kinase 4 (CDK4), epidermal growth factor receptor (EGFR), tumour-infiltrating lymphocytes (TILs) and circulating tumour DNA (ctDNA) with the outcome and prognosis of patients with breast cancer (BC) undergoing neoadjuvant chemotherapy (NACT).
Methods: We retrospectively analysed the clinicopathological data of 231 patients with BC who underwent preoperative NACT at XX Hospital between 1 January 2018 and 31 December 2021. Logistic regression models were used to analyse factors influencing NACT efficacy.
Front Oncol
December 2024
Department of Radiology, Jinshan Hospital, Fudan University, Shanghai, China.
Objectives: The accurate assessment of lymph node metastasis (LNM) can facilitate clinical decision-making on radiotherapy or radical hysterectomy (RH) in cervical adenocarcinoma (AC)/adenosquamous carcinoma (ASC). This study aims to develop a deep learning radiomics nomogram (DLRN) to preoperatively evaluate LNM in cervical AC/ASC.
Materials And Methods: A total of 652 patients from a multicenter were enrolled and randomly allocated into primary, internal, and external validation cohorts.
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