TY - JOUR AB - Colorectal cancer (CRC) has high morbidity and mortality, particularly if diagnosed at an advanced stage. Although there have been several studies on CRC, few have investigated the relationship between oncosis and CRC. Thus, the purpose of the present study was to identify oncosis‑related long noncoding RNAs (lncRNAs) and to establish a clinical prognostic model. Original data were acquired from The Cancer Genome Atlas database and PubMed. Differentially expressed oncosis‑related lncRNAs (DEorlncRNAs) were identified and were subsequently formed into pairs. Next, a series of tests and analyses, including both univariate and multivariate analyses, as well as Lasso and Cox regression analyses, were performed to establish a receiver operating characteristic curve. A cut‑off point was subsequently used to divide the samples into groups labelled as high‑ or low‑risk. Thus, a model was established and evaluated in several dimensions. Six pairs of DEorlncRNAs associated with prognosis according to the algorithm were screened out and the CRC cases were divided into high‑ and low‑risk groups. Significant differences between patients in the different risk groups were observed for several traits, including survival outcomes, clinical pathology characteristics, immune cell infiltration status and drug sensitivity. In addition, PCR and flow cytometry were performed to further verify the model. In summary, a new risk model algorithm based on six pairs of DEorlncRNAs in CRC, which does not require specific data regarding the level of gene expression, was established and validated. This algorithm may be used to predict patient prognosis, immune cell infiltration and drug sensitivity. AD - Department of Colorectal Surgery, Sir Run Run Shaw Hospital of Zhejiang University, Hangzhou, Zhejiang 310016, P.R. China Cancer Institute of Integrated Traditional Chinese and Western Medicine, Zhejiang Academy of Traditional Chinese Medicine, Tongde Hospital of Zhejiang Province, Hangzhou, Zhejiang 310012, P.R. China AU - Xiang,Haoyi AU - Shen,Xuning AU - Chen,Engeng AU - Chen,Wei AU - Song,Zhangfa DA - 2023/02/01 DO - 10.3892/ol.2022.13650 IS - 2 JO - Oncol Lett KW - colorectal cancer oncosis‑related lncRNAs prognostic signature The Cancer Genome Atlas immune drug sensitivity PY - 2023 SN - 1792-1074 1792-1082 SP - 63 ST - Construction and validation of a novel algorithm based on oncosis‑related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis T2 - Oncology Letters TI - Construction and validation of a novel algorithm based on oncosis‑related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis UR - https://doi.org/10.3892/ol.2022.13650 VL - 25 ER -