ArticleCancer reports (Hoboken, N.J.)2024
An Inflammation-Related lncRNA Signature for Prognostic Prediction in Colorectal Cancer.
Article in Cancer reports (Hoboken, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundColorectal 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.
aimsThe current study aimed to develop an IRL signature for predicting prognosis in CRC and to examine the involved molecular mechanism. METHODS AND
resultsRNA-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
conclusionThe proposed 13-IRL signature is a promising biomarker and may help the clinical decision-making process and improve prognostic evaluation in CRC.
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