m7G-lncRNAs are potential biomarkers for prognosis and tumor microenvironment in patients with colon cancer.
10.12122/j.issn.1673-4254.2022.05.08
- Author:
Shu Ran CHEN
1
;
Rui DONG
1
;
Yan LI
2
;
Hua Zhang WU
3
;
Mu Lin LIU
1
Author Information
1. Department of Gastroenterology, the First Affiliated Hospital of Bengbu Medical College, Bengbu Medical College, Bengbu 233000, China.
2. Department of gynecologic oncology, the First Affiliated Hospital of Bengbu Medical College Bengbu Medical College, Bengbu 233000, China.
3. School of Life Science, Anhui Province Key Laboratory of Translational Cancer Research, Bengbu Medical College, Bengbu 233000, China.
- Publication Type:Journal Article
- Keywords:
colon cancer;
long non-coding RNAs;
m7G;
prognostic model;
tumor microenvironment
- MeSH:
Biomarkers, Tumor/metabolism*;
Colonic Neoplasms;
DNA Helicases/metabolism*;
Gene Expression Regulation, Neoplastic;
Humans;
Microsatellite Instability;
Poly-ADP-Ribose Binding Proteins/metabolism*;
Prognosis;
RNA Helicases/metabolism*;
RNA Recognition Motif Proteins/metabolism*;
RNA, Long Noncoding/metabolism*;
Tumor Microenvironment
- From:
Journal of Southern Medical University
2022;42(5):681-689
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To assess the value of m7G-lncRNAs in predicting the prognosis and microenvironment of colorectal cancer (CRC).
METHODS:We screened m7G-lncRNAs from TCGA to construct an m7G-lncRNAs risk model using multivariate Cox analysis, which was validated using ROC and C-index curves. Calibration and nomogram were used to predict the prognosis of CRC patients. Point-bar charts and K-M survival curves were used to assess the correlation of risk scores with the patients' clinical staging and prognosis. CIBERSORT and ESTIMATE were used to explore the association between the tumor microenvironment and immune cell infiltration in patients in high and low risk groups and the correlation of risk scores with microsatellite instability, stem cell index and immune checkpoint expression. A protein-protein interaction network was constructed, and the key targets regulated by m7G-lncRNAs were identified and validated in paired samples of CRC and adjacent tissues by immunoblotting.
RESULTS:We identified a total of 1722 m7G-lncRNAs from TCGA database, from which 12 lncRNAs were screened to construct the risk model. The AUCs of the risk model for predicting survival outcomes at 1, 3 and 5 years were 0.727, 0.747 and 0.794, respectively. The AUC of the nomogram for predicting prognosis was 0.794, and the predicted results were consistent with actual survival outcomes of the patients. The patients in the high-risk group showed more advanced tumor stages and a greater likelihood of high microsatellite instability than those in the low-risk group (P < 0.05). The tumor stemness index was negatively correlated with the risk score (r=-0.19; P=7.3e-05). Patients in the high-risk group had higher stromal cell scores (P=0.0028) and higher total scores (P=0.007) with lowered expressions of activated mast cells (r=-0.11; P=0.045) and resting CD4+ T cells (r=-0.14; P=0.01) and increased expressions of most immune checkpoints (P < 0.05). ATXN2 (P= 0.006) and G3BP1 (P=0.007) were identified as the key targets regulated by m7G-lncRNAs, and their expressions were both higher in CRC than in adjacent tissues.
CONCLUSION:The risk model based on 12 m7G-lncRNAs has important prognostic value for CRC and can reflect the microenvironment and the efficacy of immunotherapy in the patients.
