AUTHOR=Hou Dong , Tan Jia-nan , Zhou Sheng-ning , Yang Xu , Zhang Zhi-hong , Zhong Guang-yu , Zhong Lin , Yang Bin , Han Fang-hai 

TITLE=A novel prognostic signature based on cuproptosis-related lncRNA mining in colorectal cancer

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.969845

DOI=10.3389/fgene.2022.969845

ISSN=1664-8021

ABSTRACT=Colorectal cancer (CRC) is a common malignant tumor. Cuproptosis is a developing programmed cell death process. Long non-coding RNAs (lncRNAs) can alter the proliferation of colorectal cancer cells through the control and activation of gene expression. However, cuproptosis-related lncRNAs have not been explored as prospective predictive biomarkers in colorectal cancer. 
Methods: The mRNA and lncRNA expression data of colorectal cancer were gathered from the public database of The Tumor Genome Atlas (TCGA), and Pearson correlation analysis and univariate Cox regression analysis were used to identify the lncRNAs with differential prognosis. Consistent clustering was utilized to classify colorectal cancer and to explore the clinical importance of distinct types, as well as tumor heterogeneity and immune microenvironment differences. Through LASSO regression, the characteristic lncRN was further screened to develop a risk scoring model, paired with clinicopathological variables to draw a nomogram, and to analyze the difference in copy number variation between high- and low-risk groups. 
RESULTS: According to the 28 cuproptosis-related lncRNAs with prognostic differences, 2 clusters were divided, and the prognosis of the 2 clusters was different. The prognosis of cluster 2 was lower than that of cluster 1. In cluster 1, the amount of immune cell infiltration and immunological score increased, and immune checkpoint genes also exhibited strong expression. LASSO regression selected out 11 distinctive lncRNAs. A risk score model was created in the training set to distinguish between high and low risk groups. The survival rate of patients in the high risk group was lower than that in the low risk group, and the test set and the total set exhibited consistent results. The AUC value of the ROC curve revealed the efficacy of the scoring model for predicting long-term OS in patients. Combined with multivariate analysis of clinicopathological features, the model can be utilized as an independent predictor, and then a nomogram can be produced to intuitively predict prognosis. 
Conclusions: Finally, we built a risk model characterized by 11 lncRNAs and proved that the model has predictive value and clinical and therapeutic implications for predicting CRC.