Year: 2024
Type: article
Abstract: ABSTRACT Background Colorectal cancer (CRC) represents a commonly diagnosed malignancy affecting the digestive system. Mounting evidence shows long noncoding RNAs (lncRNAs) contribute to carcinogenesis. However, inflammation‐related lncRNAs (IRLs) regulating CRC are poorly defined. Aims The current study aimed to develop an IRL signature for predicting prognosis in CRC and to examine the involved molecular mechanism. Methods and Results RNA‐seq findings and patient data were retrieved from The Cancer Genome Atlas (TCGA), and inflammation‐associated genes were obtained from the GeneCards database. IRLs with differential expression were determined with “limma” in R. Using correlation and univariable Cox analyses, prognostic IRLs were identified. The least absolute shrinkage and selection operator (LASSO) algorithm was employed to construct a prognostic model including 13 IRLs. The model's prognostic value was examined by Kaplan–Meier (K‐M) survival curve and receiver operating characteristic (ROC) curve analyses. Furthermore, the association of the signature with the immune profile was assessed. Finally, RT‐qPCR was carried out for verifying the expression of inflammation‐related lncRNAs in nonmalignant and malignant tissue samples. A model containing 13 inflammation‐related lncRNAs was built and utilized to classify cases into two risk groups based on risk score. The signature‐derived risk score had a higher value in predicting survival compared with traditionally used clinicopathological properties in CRC cases. In addition, marked differences were detected in immune cells between the two groups, including CD4+ T cells and M2 macrophages. Furthermore, RT‐qPCR confirmed the expression patterns of these 13 lncRNAs were comparable to those of the TCGA‐CRC cohort. Conclusion The proposed 13‐IRL signature is a promising biomarker and may help the clinical decision‐making process and improve prognostic evaluation in CRC.
Source: Cancer Reports
DOI: 10.1002/cnr2.70043
Authors: Zhenling Zhang, Yingshu Luo, Yuan Liu, Jiangnan Ren, Zhaoxiong Fang, Yanzhi Han
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,970
Award: G969424624
OpenAlex ID: W4405090579