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

(E-pub Ahead of Print)

Author(s): Hongxu Chen, Zhijing Jiang*, Bingshi Yang, Guiling Yan, Xiaochen Wang, Shuning Zang

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research


Become EABM
Become Reviewer
Call for Editor

Abstract:

Objective: 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 microenviroment.

Methods: Cox proportional hazards regression analysis was carried out to screen the significant HR using 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 and time-dependent receiver-operating characteristic (ROC) curve and area under the ROC curve (AUC) value used to assess the prognostic performance. Then dividing the patients into high and low risk groups by the median of the model, survival analysis was performed by two groups with testing and independent dataset. Furthermore, enrichment analysis of signature mRNAs and lncRNAs and their co-expression genes were performed, then, spearman rank correlation used to calculate the correlation between immune cells and genes in the prognostic model, and testing abundance difference of the immune cells in high and low risks groups.

Results: A total of 5989 genes with significant HR were identified, then 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 in discriminating 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 show significantly different abundance in high and low risk groups.

Conclusion: 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. And the prognostic model comprising three mRNAs including DHX37, SMIM7, MFSD1, and three lncRNAs including PIWIL4, KCNE5 and LOC100128398. Furthermore, these genes 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 combination with all these six genes could be the potential biomarker for the prognosis of liver cancer.

Keywords: Prognostic Model Construction, signature, lncRNA, Survival analysis, ROC curve, tumor immune microenvironment.

Rights & PermissionsPrintExport Cite as

Article Details

(E-pub Ahead of Print)
DOI: 10.2174/1386207324666210309100923
Price: $95

Article Metrics

PDF: 9