Systematic Construction and Validation of an RNA-Binding Protein-Associated Model for Prognosis Prediction in Hepatocellular Carcinoma

Construction and validation of a prognostic model with RNA binding protein-related mRNAs for the HBV-related hepatocellular carcinoma patients

Hepatocellular carcinoma (HCC) is a common malignancy worldwide with poor clinical outcomes, and the infection of hepatitis B virus (HBV) is the leading cause of this disease. Mounting evidence shows that RNA binding proteins (RBPs) can modulate the progression of cancers. However, the functions and clinical implications of RBP-related mRNAs in HBV-related HCC remain largely unclear. Therefore, we aim to develop a prognostic model based on the RBP-related mRNAs for HBV-related HCC patients. Firstly, we identified 626 differentially expressed RBP-related mRNAs in the HBV-related HCC through the Pearson correlation analysis. Subsequently, the Kaplan-Meier survival, univariate, Least Absolute Shrinkage and Selection Operator (LASSO), and multivariate Cox regression analyses were used to construct a prognostic model comprised of five RBP-related mRNAs. Furthermore, the patients were categorized into the high- and low-risk groups by the prognostic model and the patients in the high-risk group had a poor prognosis. Additionally, the prognostic model was an independent predictor of prognosis, and the accuracy of the prognostic model was proved by the receiver operator characteristic (ROC) analysis. Furthermore, the functional enrichment analysis revealed that various cancer-promoting processes were enriched in the high-risk group. Taken together, our study may provide the HBV-related HCC biomarkers of prognosis to improve the clinical outcomes of patients.

A novel machine learning derived RNA-binding protein gene-based score system predicts prognosis of hepatocellular carcinoma patients

Background

Although the expression of RNA-binding protein (RBP) genes in hepatocellular carcinoma (HCC) varies and is associated with tumor progression, there has been no overview study with multiple cohorts and large samples. The HCC-associated RBP genes need to be more accurately identified, and their clinical application value needs to be further explored.

Methods

First, we used the robust rank aggregation (RRA) algorithm to extract HCC-associated RBP genes from nine HCC microarray datasets and verified them in The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) cohort and International Cancer Genome Consortium (ICGC) Japanese liver cancer (ICGC-LIRI-JP) cohort. In addition, the copy number variation (CNV), single-nucleotide variant (SNV), and promoter-region methylation data of HCC-associated RBP genes were analyzed. Using the random forest algorithm, we constructed an RBP gene–based prognostic score system (RBP-score). We then evaluated the ability of RBP-score to predict the prognosis of patients. The relationships between RBP-score and other clinical characteristics of patients were analyzed.

Results

The RRA algorithm identified 30 RBP mRNAs with consistent expression patterns across the nine HCC microarray datasets. These 30 RBP genes were defined as HCC-associated RBP genes. Their mRNA expression patterns were further verified in the TCGA-LIHC and ICGC-LIRI-JP cohorts. Among these 30 RBP genes, some showed significant copy number gain or loss, while others showed differences in the methylation levels of their promoter regions. Some RBP genes were risk factors or protective factors for the prognosis of patients. We extracted 10 key HCC-associated RBP genes using the random forest algorithm and constructed an RBP-score system. RBP-score effectively predicted the overall survival (OS) and disease-free survival (DFS) of HCC patients and was associated with the tumor, node, metastasis (TNM) stage, α-fetoprotein (AFP), and metastasis risk. The clinical value of RBP-score was validated in datasets from different platforms. Cox analysis suggested that a high RBP-score was an independent risk factor for poor prognosis in HCC patients. We also successfully established a combined RBP-score+TNM LASSO-Cox model that more accurately predicted the prognosis.

Conclusion

The RBP-score system constructed based on HCC-associated RBP genes is a simple and highly effective prognostic evaluation tool. It is suitable for different subgroups of HCC patients and has cross-platform characteristics. Combining RBP-score with the TNM staging system or other clinical parameters can lead to an even greater clinical benefit. In addition, the identified HCC-associated RBP genes may serve as novel targets for HCC treatment.

Impact of Alternative Splicing Variants on Liver Cancer Biology

Simple Summary

Among the top ten deadly solid tumors are the two most frequent liver cancers, hepatocellular carcinoma, and intrahepatic cholangiocarcinoma, whose development and malignancy are favored by multifactorial conditions, which include aberrant maturation of pre-mRNA due to abnormalities in either the machinery involved in the splicing, i.e., the spliceosome and associated factors, or the nucleotide sequences of essential sites for the exon recognition process. As a consequence of cancer-associated aberrant splicing in hepatocytes- and cholangiocytes-derived cancer cells, abnormal proteins are synthesized. They contribute to the dysregulated proliferation and eventually transformation of these cells to phenotypes with enhanced invasiveness, migration, and multidrug resistance, which contributes to the poor prognosis that characterizes these liver cancers.

Abstract

The two most frequent primary cancers affecting the liver, whose incidence is growing worldwide, are hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA), which are among the five most lethal solid tumors with meager 5-year survival rates. The common difficulty in most cases to reach an early diagnosis, the aggressive invasiveness of both tumors, and the lack of favorable response to pharmacotherapy, either classical chemotherapy or modern targeted therapy, account for the poor outcome of these patients. Alternative splicing (AS) during pre-mRNA maturation results in changes that might affect proteins involved in different aspects of cancer biology, such as cell cycle dysregulation, cytoskeleton disorganization, migration, and adhesion, which favors carcinogenesis, tumor promotion, and progression, allowing cancer cells to escape from pharmacological treatments. Reasons accounting for cancer-associated aberrant splicing include mutations that create or disrupt splicing sites or splicing enhancers or silencers, abnormal expression of splicing factors, and impaired signaling pathways affecting the activity of the splicing machinery. Here we have reviewed the available information regarding the impact of AS on liver carcinogenesis and the development of malignant characteristics of HCC and iCCA, whose understanding is required to develop novel therapeutical approaches aimed at manipulating the phenotype of cancer cells.

BOP1 Used as a Novel Prognostic Marker and Correlated with Tumor Microenvironment in Pan-Cancer

Previous studies have indicated the important role of block of proliferation 1 (BOP1) in the progression of several malignant tumors; no comprehensive pan-cancer analysis of BOP1 has been performed. Here, we aim to systematically identify the expression, prognostic value, and potential immunological functions of BOP1 in 33 malignancies. We obtained the gene expression data and clinical information from multiple public databases to assess the expression level and prognostic value of BOP1 in 33 cancers. We also analyzed the relationship between BOP1 expression and DNA methylation, tumor microenvironment (TME), microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints. Moreover, we conducted gene set enrichment analysis (GSEA) to investigate the biological function and signal transduction pathways of BOP1 in different types of tumors. Finally, we validated the expression of BOP1 in lung cancer cell line and detected the influence of BOP1 on lung cancer cell migration and the expression of epithelial-mesenchymal transition- (EMT-) related genes. Collectively, our findings elucidated that BOP1 has the potential to be a promising molecular prognostic biomarker for predicting poor survival in various malignant tumors, as well as a cancer-promoting gene involved in tumorigenesis and tumor immunity.