Exploring Prognostic Signatures of Hepatocellular Carcinoma and the Potential Implications in Tumor Immune...

The objective of this study is to construct a prognostic model using genetic markers of liver cancer and explore the signature genes associated with the tumor immune microenvironment.

Cox proportional hazards regression analysis was carried out to screen the significant HR using the dataset of TCGA Liver Cancer (LIHC) gene expression data. Then LASSO (least absolute shrinkage and selection operator) was performed to select the minimal variables with significant HR of genes. Thus, the prognostic model was constructed by the minimal variables with their HR. Time-dependent receiver-operating characteristic (ROC) curve and area under the ROC curve (AUC) value was used to assess the prognostic performance. Then the patients were divided into high and low-risk groups by the median of the model. Survival analysis was performed on the two groups with testing and an independent dataset. Furthermore, enrichment analysis of signature mRNAs and lncRNAs and their co-expression genes was performed. Then, Spearman rank correlation was used to calculate the correlation between immune cells and genes in the prognostic model, and abundance difference of the immune cells in high and low risks groups was tested.

A total of 5989 genes with significant HR were identified. 6 key genes (three mRNAs: DHX37, SMIM7, and MFSD1, three lncRNAs: PIWIL4, KCNE5, and LOC100128398) screened by LASSO were used to construct the model with their HR value respectively. The AUC values of 1 and 5-year overall survival were 0.78 and 0.76 in discovery data and 0.67 and 0.68 in testing data. Survival analysis performed significantly discriminated high and low groups with testing and independent data. Furthermore, many immune cells such as nTreg found a significant correlation with the genes in the prognostic model, and many immune cells showed significantly different abundance in high and low-risk groups.

In the study, we used Univariate Cox analyses and LASSO algorithm with TCGA gene expression data to construct the prognostic model in liver cancer patients. The prognostic model comprised of three mRNAs, including DHX37, SMIM7, MFSD1, and three lncRNAs, including PIWIL4, KCNE5, and LOC100128398. Furthermore, these gene expression levels were associated with the abundance of some immune cells, such as nTreg. Also, many immune cells have significantly different abundance in high and low-risk groups. All these results indicated that the combination with all these six genes could be the potential biomarker for the prognosis of liver cancer.