Peroxisome proliferator-activated receptor γ (PPARγ) and PPARγ coactivator-1α (PGC-1α) expression levels are correlated with clinical outcome in breast cancer. However, the potential biological and clinical significance of PPARγ and PGC-1α in colorectal cancer remains unknown. Here we investigated PPARγ and PGC-1α expression in colorectal cancer, and the associations of these expression levels with clinicopathological features. We also evaluated the roles of PPARγ and PGC-1α as prognostic factors in colorectal cancer. We performed immunohistochemical analysis to investigate PPARγ and PGC-1α expression in human colorectal cancer tissues and adjacent normal tissues from 108 primary colorectal cancer patients. We then examined how these expression levels correlated with clinicopathological features. Using the Kaplan-Meier method, we evaluated 3-year disease-free survival (DFS) and overall survival (OS) in patients with tumors expressing different levels of PPARγ and PGC-1α. Our results revealed that PPARγ expression was not significantly correlated with age at surgery, gender, differentiation, depth of infiltration, relapse, or TNM stage. Additionally, PGC-1α expression was not significantly correlated with age at surgery, differentiation, depth of infiltration, relapse, or TNM stage. However, PGC-1α expression was significantly correlated with nodal metastasis (p=0.020). Survival analysis demonstrated reduced OS in the PGC-1α-positive group compared to the PGC-1α-negative group (p=0.03). Our present findings suggest that PGC-1α may be useful for predicting nodal metastasis, and may represent a biomarker for poor prognosis in colorectal cancer.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.anndiagpath.2017.11.007DOI Listing

Publication Analysis

Top Keywords

colorectal cancer
28
pgc-1α expression
20
pparγ pgc-1α
20
expression levels
12
expression correlated
12
pgc-1α
9
peroxisome proliferator-activated
8
proliferator-activated receptor
8
poor prognosis
8
human colorectal
8

Similar Publications

Background: Gastric cancer peritoneal metastasis lacks effective predictive indices. This article retrospectively explored predictive values of DNA ploidy, stroma, and nucleotyping in gastric cancer peritoneal metastasis.

Methods: A comprehensive analysis was conducted on specimens obtained from 80 gastric cancer patients who underwent gastric resection at the Department of Gastrointestinal Surgery of Wuhan University Renmin Hospital.

View Article and Find Full Text PDF

Encorafenib + cetuximab (EC) is approved for previously treated BRAF V600E-mutant metastatic colorectal cancer (mCRC) based on the BEACON phase 3 study. Historically, first-line treatment of BRAF V600E-mutant mCRC with chemotherapy regimens has had limited efficacy. The phase 3 BREAKWATER study investigated EC+mFOLFOX6 versus standard of care (SOC) in patients with previously untreated BRAF V600E mCRC.

View Article and Find Full Text PDF

Hereditary colorectal cancer syndromes and inflammatory bowel disease: results from a registry-based study.

Int J Colorectal Dis

January 2025

Hereditary Digestive Tract Tumors Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori, Via Giacomo Venezian 1, 20133, Milan, Italy.

Purpose: In this study, we investigated the progression of high-grade dysplasia (HGD)/CRC in patients with hereditary colorectal cancer syndromes (HCSS) and concomitant inflammatory bowel diseases (IBDs).

Methods: We described the natural history of a series of patients with confirmed diagnosis of hereditary colorectal cancer syndromes (HCCSs) and concomitant IBDs who were referred to the Hereditary Digestive Tumors Registry at the Fondazione IRCCS Istituto Nazionale dei Tumori of Milan.

Results: Between January 1989 and April 2024, among 450 patients with APC-associated polyposis and 1050 patients with Lynch syndrome (LS), we identified six patients with IBDs (five with UC, one with ileal penetrating CD) and concomitant HCCSs (five with LS, one with APC-associated polyposis).

View Article and Find Full Text PDF

The current work introduces the hybrid ensemble framework for the detection and segmentation of colorectal cancer. This framework will incorporate both supervised classification and unsupervised clustering methods to present more understandable and accurate diagnostic results. The method entails several steps with CNN models: ADa-22 and AD-22, transformer networks, and an SVM classifier, all inbuilt.

View Article and Find Full Text PDF

Want 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!