Importance: Use of aspirin (which inhibits platelet function) after a colon cancer diagnosis is associated with improved overall survival. Identifying predictive biomarkers of this effect could individualize therapy and decrease toxic effects.
Objective: To demonstrate that survival benefit associated with low-dose aspirin use after a diagnosis of colorectal cancer might depend on HLA class I antigen expression.
Design, Setting, And Participants: A cohort study with tumor blocks from 999 patients with colon cancer (surgically resected between 2002 and 2008), analyzed for HLA class I antigen and prostaglandin endoperoxide synthase 2 (PTGS2) expression using a tissue microarray. Mutation analysis of PIK3CA was also performed. Data on aspirin use after diagnosis were obtained from a prescription database. Parametric survival models with exponential (Poisson) distribution were used to model the survival.
Main Outcomes And Measures: Overall survival.
Results: The overall survival benefit associated with aspirin use after a diagnosis of colon cancer had an adjusted rate ratio (RR) of 0.53 (95% CI, 0.38-0.74; P < .001) when tumors expressed HLA class I antigen compared with an RR of 1.03 (0.66-1.61; P = .91) when HLA antigen expression was lost. The benefit of aspirin was similar for tumors with strong PTGS2 expression (0.68; 0.48-0.97; P = .03), weak PTGS2 expression (0.59; 0.38-0.97; P = .02), and wild-type PIK3CA tumors (0.55; 0.40-0.75; P < .001). No association was observed with mutated PIK3CA tumors (0.73; 0.33-1.63; P = .44).
Conclusions And Relevance: Contrary to the original hypothesis, aspirin use after colon cancer diagnosis was associated with improved survival if tumors expressed HLA class I antigen. Increased PTGS2 expression or the presence of mutated PIK3CA did not predict benefit from aspirin. HLA class I antigen might serve as a predictive biomarker for adjuvant aspirin therapy in colon cancer.
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http://dx.doi.org/10.1001/jamainternmed.2014.511 | DOI Listing |
Ann Surg Oncol
January 2025
Department of Medical Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
Background: Colon and rectum cancer (CRC) is a major health burden in China, with notable gender disparities. This study was designed to analyze trends in CRC incidence, prevalence, and mortality from 1990 to 2021 and to project future trends.
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ACS Appl Bio Mater
January 2025
Department of Chemistry, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar, Gujarat 382355, India.
Golgi apparatus (GA) and endoplasmic reticulum (ER) are two of the interesting subcellular organelles that are critical for protein synthesis, folding, processing, post-translational modifications, and secretion. Consequently, dysregulation in GA and ER and cross-talk between them are implicated in numerous diseases including cancer. As a result, simultaneous visualization of the GA and ER in cancer cells is extremely crucial for developing cancer therapeutics.
View Article and Find Full Text PDFUnited European Gastroenterol J
January 2025
Department of General Surgery, Peking Union Medical College Hospital, Beijing, China.
RSC Adv
January 2025
Department of Pharmaceutical Sciences, Maharshi Dayanand University Rohtak 124001 India
Cancer is a major global concern. Despite considerable advancements in cancer therapy and control, there are still large gaps and requirements for development. In recent years, various naturally occurring anticancer drugs have been derived from natural resources, such as alkaloids, glycosides, terpenes, terpenoids, flavones, and polyphenols.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of General Surgery, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuxi, China.
Background: Gastroparesis following complete mesocolic excision (CME) can precipitate a cascade of severe complications, which may significantly hinder postoperative recovery and diminish the patient's quality of life. In the present study, four advanced machine learning algorithms-Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and -nearest neighbor (KNN)-were employed to develop predictive models. The clinical data of critically ill patients transferred to the intensive care unit (ICU) post-CME were meticulously analyzed to identify key risk factors associated with the development of gastroparesis.
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