Objective: To identify tumor-associated macrophages (TAMs) related molecular subtypes and develop a TAMs related prognostic model for prostate cancer (PCa).
Methods: Consensus clustering analysis was used to identify TAMs related molecular clusters. A TAMs related prognostic model was developed using univariate and multivariate Cox analysis.
Results: Three TAMs related molecular clusters were identified and were confirmed to be associated with prognosis, clinicopathological characteristics, PD-L1 expression levels and tumor microenvironment. A TAMs related prognostic model was constructed. Patients in low-risk group all showed a more appreciable biochemical recurrence-free survival (BCRFS) than patients in high-risk group in train cohort, test cohort, entire TCGA cohort and validation cohort. SLC26A3 attenuated progression of PCa and prevented macrophage polarizing to TAMs phenotype, which was initially verified.
Conclusions: We successfully identified molecular clusters related to TAMs. Additionally, we developed a prognostic model involving TAMs that exhibits excellent predictive performance for biochemical recurrence-free survival in PCa.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.ygeno.2023.110691 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Pathological features of the differentiation landscape in esophageal squamous cell cancer and their correlations with prognosis.
Front Oncol
December 2024
Department of Radiation Oncology, Fudan University Shanghai Cancer Center, Shanghai, China.
Background: For esophageal squamous cell carcinoma (ESCC), universally accepted pathological criteria for classification by differentiation degree are lacking. Tumor budding, single-cell invasion, and nuclear grade, recognized as prognostic factors in other carcinomas, have rarely been investigated for their correlation with differentiation and prognosis in ESCC. This study aims to determine if pathological findings can predict differentiation degree and prognosis in ESCC.
View Article and Find Full Text PDFClinical predictive models for recurrence and survival in treated laryngeal and hypopharyngeal cancer: a systematic review and meta-analysis.
Front Oncol
December 2024
Institute for Head and Neck Studies and Education, University of Birmingham, Birmingham, United Kingdom.
Background: The limitations of the traditional TNM system have spurred interest in multivariable models for personalized prognostication in laryngeal and hypopharyngeal cancers (LSCC/HPSCC). However, the performance of these models depends on the quality of data and modelling methodology, affecting their potential for clinical adoption. This systematic review and meta-analysis (SR-MA) evaluated clinical predictive models (CPMs) for recurrence and survival in treated LSCC/HPSCC.
View Article and Find Full Text PDFNomograms Predicting Survival, Recurrence and Beneficiary Identification of Adjuvant Chemotherapy in Treatment-naïve Patients with Rectal Cancer who Underwent Upfront Curative Resection: A multi-institutional study.
Ann Surg Open
December 2024
Tokyo Medical and Dental University, The University of Tokyo, Tokyo, Japan.
Objective: To create and validate nomograms predicting overall survival and recurrence in treatment-naïve rectal cancer (RC) patients who underwent upfront surgery.
Background: Although multidisciplinary treatment is standard for locally advanced RC, understanding surgical efficacy is important for determining indications for perioperative adjuvant therapy.
Methods: RC patients who underwent upfront surgery at the Japanese Society for Cancer of the Colon and Rectum institutions were analyzed.
Acute Myeloid Leukemia (AML) is an aggressive cancer with dismal outcomes, vast subtype heterogeneity, and suboptimal risk stratification. In this study, we harmonized DNA methylation data from 3,314 patients across 11 cohorts to develop the Acute Leukemia Methylome Atlas (ALMA) of diagnostic relevance that predicted 27 WHO 2022 acute leukemia subtypes with an overall accuracy of 96.3% in discovery and 90.
View Article and Find Full Text PDFWhile deep brain stimulation (DBS) remains an effective therapy for Parkinson's disease (PD), sources of variance in patient outcomes are still not fully understood, underscoring a need for better prognostic criteria. Here we leveraged routinely collected T1-weighted (T1-w) magnetic resonance imaging (MRI) data to derive patient-specific measures of brain structure and evaluate their usefulness in predicting changes in PD medications in response to DBS. Preoperative T1-w MRI data from 231 patients with PD were used to extract regional measures of fractal dimension (FD), sensitive to the structural complexities of cortical and subcortical areas.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!