Bioinformatics profiling identifies seven immune-related risk signatures for hepatocellular carcinoma

Abstract

Background Density of tumor infiltrating lymphocytes (TIL) and expressions of certain immune-related genes have prognostic and predictive values in hepatocellular carcinoma (HCC); however, factors determining the immunophenotype of HCC patients are still unclear. In the current study, the transcript sequencing data of liver cancer were systematically analyzed to determine an immune gene marker for the prediction of clinical outcome of HCC. Methods RNASeq data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and the samples were assigned into high-stage and low-stage groups. Immune pathway-related genes were screened from the Molecular Signatures Database v4.0 (MsigDB) database. LASSO regression analysis was performed to identify robust immune-related biomarkers in predicting HCC clinical outcomes. Moreover, an immune gene-related prognostic model was established and validated by test sets and Gene Expression Omnibus (GEO) external validation sets. Results We obtained 319 immune genes from MsigDB, and the genes have different expression profiles in high-stage and low-stage of HCC. Univariate survival analysis found that 17 genes had a significant effect on HCC prognosis, among them, 13 (76.5%) genes were prognostically protective factors. Further lasso regression analysis identified seven potential prognostic markers (IL27, CD1D, NCOA6, CTSE, FCGRT, CFHR1, and APOA2) of robustness, most of which are related to tumor development. Cox regression analysis was further performed to establish a seven immune gene signature, which could stratify the risk of samples in training set, test set and external verification set (p < 0.01), and the AUC in both training set and test set was greater than 0.85, which also greater compared with previous studies. Conclusion This study constructed a 7-immunogenic marker as novel prognostic markers for predicting survival of HCC patients.

Identification of prognostic alternative splicing events related to the immune microenvironment of hepatocellular carcinoma

“…Some genes in our prognostic signature model have been clarified to be involved in the progression of various tumors, including FGL1 (fibrinogen-like protein 1), TUBB3 (tubulin beta 3), TNIP1 (TNF-α-induced protein 3-interacting protein 1), CALD1 (caldesmon 1), ARPP19 (cAMP-regulated phosphoproteins 19), FCGRT (Fc fragment of IgG receptor and transporter), ANKDD1A (ankyrin repeat and death domain containing 1A) and SMARCC2 (SWI/SNF related, matrix associated, actin dependent regulator of chromatin subfamily C member 2) (Wang et al 2019 ; Kanojia et al 2020 ; Yang et al 2019b ; Liu et al 2018 ; Ye et al 2020 ; Xue et al 2020 ; Feng et al 2018 ; Yuan et al 2018 ). Among these survival-related genes, FGL1 and ANKDD1A were intriguingly closely associated with the tumor microenvironment and immune infiltration.…”

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

“…Compared to previous research, our study used a different approach to identify novel gene signatures [25,26]. Our prognostic model building strategy include multivariate Cox regression with elastic net, lasso and adaptive lasso penalties can identify robust prognostic genes by reducing the multicollinearity problems in the genome [27], where multicollinearity is a situation in which two or more prognostic genes are pairwise correlated.…”

Bioinformatics analysis and identification of hub genes and immune-related molecular mechanisms in chronic myeloid leukemia

“…The intersection of the differentially expressed genes and the immune genes was used to find the IRGs. These results were displayed on a volcano map and a heat map ( Xue et al, 2020 ).…”