Open Access

Construction and validation of a novel algorithm based on oncosis‑related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis

  • Authors:
    • Haoyi Xiang
    • Xuning Shen
    • Engeng Chen
    • Wei Chen
    • Zhangfa Song
  • View Affiliations

  • Published online on: December 23, 2022     https://doi.org/10.3892/ol.2022.13650
  • Article Number: 63
  • Copyright: © Xiang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

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.
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February-2023
Volume 25 Issue 2

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Copy and paste a formatted citation
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Spandidos Publications style
Xiang H, Shen X, Chen E, Chen W and Song Z: Construction and validation of a novel algorithm based on oncosis‑related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis. Oncol Lett 25: 63, 2023
APA
Xiang, H., Shen, X., Chen, E., Chen, W., & Song, Z. (2023). Construction and validation of a novel algorithm based on oncosis‑related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis. Oncology Letters, 25, 63. https://doi.org/10.3892/ol.2022.13650
MLA
Xiang, H., Shen, X., Chen, E., Chen, W., Song, Z."Construction and validation of a novel algorithm based on oncosis‑related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis". Oncology Letters 25.2 (2023): 63.
Chicago
Xiang, H., Shen, X., Chen, E., Chen, W., Song, Z."Construction and validation of a novel algorithm based on oncosis‑related lncRNAs comprising the immune landscape and prediction of colorectal cancer prognosis". Oncology Letters 25, no. 2 (2023): 63. https://doi.org/10.3892/ol.2022.13650