Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2023
A nomogram based on 4-lncRNAs signature for improving prognostic prediction of hepatocellular carcinoma.
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Mo Qingguo, Li Wenjing, Liu Lin, Hao Zhidong, Jia Shengjun, Duo Yongsheng
Аннотация
PURPOSE: Long noncoding RNAs (lncRNAs) with abnormal expression are frequently seen in hepatocellular cancer patients (HCC). Previous studies have reported the correlation between lncRNA and prognosis processes of HCC patients. In this research, a graphical nomogram with lncRNAs signatures, T, M phases was developed using the rms R package to estimate the survival rates of HCC patients in year 1, 3, and 5.
METHODS: To find the prognostic lncRNA and create the lncRNA signatures, univariate Cox survival analysis and multivariate Cox regression analysis were chosen. The rms R software package was used to build a graphical nomogram based on lncRNAs signatures to predict the survival rates in of HCC patients in 1, 3, and 5 years. Using "edgeR", "DEseq" R packages to find the differentially expressed genes (DEGs).
RESULTS: Firstly, a total of 5581 DEGs including 1526 lncRNAs and 3109 mRNAs were identified through bioinformatic analysis, of which 4 lncRNAs (LINC00578, RP11-298O21.2, RP11-383H13.1, RP11-440G9.1) were identified to be strongly related to the prognosis of liver cancer (P < 0.05). Moreover, we constructed a 4-lncRNAs signature by using the calculated regression coefficient. 4-lncRNAs signature is identified to significantly correlated with clinical and pathological characteristics (such as T stage, and death status of HCC patients).
CONCLUSIONS: A prognostic nomogram on the base of 4-lncRNAs markers was built, which is capable to accurately predict the 1-year, 3-year, and 5-year survival of HCC patients after the construction of the 4-lncRNAs signature linked with prognosis of HCC.
METHODS: To find the prognostic lncRNA and create the lncRNA signatures, univariate Cox survival analysis and multivariate Cox regression analysis were chosen. The rms R software package was used to build a graphical nomogram based on lncRNAs signatures to predict the survival rates in of HCC patients in 1, 3, and 5 years. Using "edgeR", "DEseq" R packages to find the differentially expressed genes (DEGs).
RESULTS: Firstly, a total of 5581 DEGs including 1526 lncRNAs and 3109 mRNAs were identified through bioinformatic analysis, of which 4 lncRNAs (LINC00578, RP11-298O21.2, RP11-383H13.1, RP11-440G9.1) were identified to be strongly related to the prognosis of liver cancer (P < 0.05). Moreover, we constructed a 4-lncRNAs signature by using the calculated regression coefficient. 4-lncRNAs signature is identified to significantly correlated with clinical and pathological characteristics (such as T stage, and death status of HCC patients).
CONCLUSIONS: A prognostic nomogram on the base of 4-lncRNAs markers was built, which is capable to accurately predict the 1-year, 3-year, and 5-year survival of HCC patients after the construction of the 4-lncRNAs signature linked with prognosis of HCC.