Construction and evaluation of a prognosis lncRNA model for hepatocellular carcinoma

Downregulation of circLIFR exerts cancer-promoting effects on hepatocellular carcinoma in vitro

Hepatocellular carcinoma (HCC) is one of the most fatal malignant tumors worldwide. Circular RNAs (circRNAs) are a special type of RNA that lacks the 5′ and 3’ ends. The functional roles of circRNAs in HCC remain largely unknown. Using high-throughput sequencing, we found several differentially expressed circRNAs in HCC tissues compared with nearby normal tissues. Among them, circRNA derived from the LIFR gene, named circLIFR, was significantly downregulated in HCC. Intriguingly, circLIFR overexpression in SK-Hep-1 cells promoted cell growth and invasion. RNA pull-down and mass spectrometry detection revealed circLIFR interacting with TANK binding kinase 1 (TBK1). Anti-TBK1 RIP confirmed the interaction between circLIFR and TBK1. TBK1 is a serine/threonine kinase that regulates several signaling pathways, including the NF-κB pathway. TBK1 inhibitors inhibit NF-κB activation. Overexpression of circLIFR overcame the in-hibitory function of TBK1, resulting in the upregulation of several genes, including MMP13, MMP3, VEGF, and MAPK. This study shows that the downregulation of circLIFR in HCC has a can-cer-promoting effect by interacting with TBK1 to promote the activation of downstream NF-κB pathway genes related to cell proliferation, migration, and invasion. This novel finding reveals the diversity of circRNA functions in HCC and provides novel insights into the role of circRNAs.

Novel Role of Long Non-Coding RNA ASAP1-IT1 in Progression of Hepatocellular Carcinoma

The long non-coding RNA (lncRNA) ASAP1-IT1 has been recently shown to aberrantly increase in ovarian and bladder cancer, while its role in other malignancies remains unexplored. This study was to characterize the expression and assess the potential role of ASAP1-IT1 in hepatocellular carcinoma (HCC). Fifty-four paired HCC and histologically normal tissues were obtained from HCC patients. Human HCC cell lines (HepG2, Huh7, SMMC-7721, and BEL-7402) and a normal liver cell line (LO2) were used for in vitro studies. ASAP1-IT1-specific siRNAs were used to silence ASAP1-IT1 expression, while the pcDNA-ASAP1-IT1 vector was constructed to up-regulate its expression. In situ hybridization and qRT-PCR were performed to characterize subcellular localization and expression of ASAP1-IT1. Cell proliferation and migration assays were conducted to examine the role of ASAP1-IT1 in the progression of HCC. In silico analysis was conducted to predict putative miRNA binding sites, which were validated by luciferase reporter assays. ASAP1-IT1 levels were significantly increased in HCC tissues and cells compared with controls. Notably, higher ASAP1-IT1 levels were significantly associated with poorer prognosis of HCC patients. In situ hybridization analysis revealed that ASAP1-IT1 was mainly localized in the nucleus of hepatoma cells and differentially expressed in trabecular, compact, and pseudoglandular forms of liver cancer. Furthermore, knockdown of ASAP1-IT1 significantly suppressed cell proliferation and migration, while its overexpression significantly promoted cell proliferation and migration of HCC cells. Mechanistically, ASAP1-IT1 might exert its role in HCC progression, at least in part, by directly interacting with miR-221-3p. In conclusion, ASAP1-IT1 is abnormally elevated in HCC, and higher levels are correlated with poorer prognosis. An underlying mechanism has been proposed for ASAP1-IT1-associated promotion of proliferation and migration in HCC cells. These findings have provided evidence supporting the oncogenic role of ASAP1-IT1 in HCC.

Construction of a Novel LncRNA Signature Related to Genomic Instability to Predict the Prognosis and Immune Activity of Patients With Hepatocellular Carcinoma

Background

Genomic instability (GI) plays a crucial role in the development of various cancers including hepatocellular carcinoma. Hence, it is meaningful for us to use long non-coding RNAs related to genomic instability to construct a prognostic signature for patients with HCC.

Methods

Combining the lncRNA expression profiles and somatic mutation profiles in The Cancer Genome Atlas database, we identified GI-related lncRNAs (GILncRNAs) and obtained the prognosis-related GILncRNAs through univariate regression analysis. These lncRNAs obtained risk coefficients through multivariate regression analysis for constructing GI-associated lncRNA signature (GILncSig). ROC curves were used to evaluate signature performance. The International Cancer Genomics Consortium (ICGC) cohort, and in vitro experiments were used for signature external validation. Immunotherapy efficacy, tumor microenvironments, the half-maximal inhibitory concentration (IC50), and immune infiltration were compared between the high- and low-risk groups with TIDE, ESTIMATE, pRRophetic, and ssGSEA program.

Results

Five GILncRNAs were used to construct a GILncSig. It was confirmed that the GILncSig has good prognostic evaluation performance for patients with HCC by drawing a time-dependent ROC curve. Patients were divided into high- and low-risk groups according to the GILncSig risk score. The prognosis of the low-risk group was significantly better than that of the high-risk group. Independent prognostic analysis showed that the GILncSig could independently predict the prognosis of patients with HCC. In addition, the GILncSig was correlated with the mutation rate of the HCC genome, indicating that it has the potential to measure the degree of genome instability. In GILncSig, LUCAT1 with the highest risk factor was further validated as a risk factor for HCC in vitro. The ESTIMATE analysis showed a significant difference in stromal scores and ESTIMATE scores between the two groups. Multiple immune checkpoints had higher expression levels in the high-risk group. The ssGSEA results showed higher levels of tumor-antagonizing immune cells in the low-risk group compared with the high-risk group. Finally, the GILncSig score was associated with chemotherapeutic drug sensitivity and immunotherapy efficacy of patients with HCC.

Conclusion

Our research indicates that GILncSig can be used for prognostic evaluation of patients with HCC and provide new insights for clinical decision-making and potential therapeutic strategies.

Construction of a genome instability-derived lncRNA-based risk scoring system for the prognosis of hepatocellular carcinoma

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, genome instability-associated lncRNAs (GILncRNAs) and their performance in clinical prognostic significance in hepatocellular carcinoma (HCC) are rarely reported. Our study constructed a computational framework integrating somatic mutation information and lncRNA expression profiles of HCC genome and we identified 88 GILncRNAs of HCC. Function enrichment analysis revealed that GILncRNAs were involved in various metabolism processes and genome instability of cancer. A genome instability-derived lncRNA-based gene signature (GILncSig) was constructed using training set data. The performance of GILncSig for outcome prediction was validated in testing set and The Cancer Genome Atlas (TCGA) set. The multivariate cox regression analysis and stratification analysis demonstrated GILncSig could serve as an independent prognostic factor for the overall survival of HCC patients. The time-dependent Receiver Operating Characteristic (ROC) curve illustrated GILncSig outperformed two recently published lncRNA signatures for overall survival prediction. The combination of GILncSig and tumor protein p53 (TP53) mutation status exhibited better prognostic performance in survival evaluation compared to TP53 mutation status alone. AC145343.1 was further validated to be a risk factor for HCC in vitro among GILncSig. Overall, our study provided a novel approach for identification of genome instability-associated lncRNAs and established an independent risk score system for outcome prediction of HCC patients, which provided a new insight for exploring in-depth mechanism and potential therapy strategy.

Construction of a genome instability-derived lncRNA-based risk scoring system for the prognosis of hepatocellular carcinoma

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, genome instability-associated lncRNAs (GILncRNAs) and their performance in clinical prognostic significance in hepatocellular carcinoma (HCC) are rarely reported. Our study constructed a computational framework integrating somatic mutation information and lncRNA expression profiles of HCC genome and we identified 88 GILncRNAs of HCC. Function enrichment analysis revealed that GILncRNAs were involved in various metabolism processes and genome instability of cancer. A genome instability-derived lncRNA-based gene signature (GILncSig) was constructed using training set data. The performance of GILncSig for outcome prediction was validated in testing set and The Cancer Genome Atlas (TCGA) set. The multivariate cox regression analysis and stratification analysis demonstrated GILncSig could serve as an independent prognostic factor for the overall survival of HCC patients. The time-dependent Receiver Operating Characteristic (ROC) curve illustrated GILncSig outperformed two recently published lncRNA signatures for overall survival prediction. The combination of GILncSig and tumor protein p53 (TP53) mutation status exhibited better prognostic performance in survival evaluation compared to TP53 mutation status alone. AC145343.1 was further validated to be a risk factor for HCC in vitro among GILncSig. Overall, our study provided a novel approach for identification of genome instability-associated lncRNAs and established an independent risk score system for outcome prediction of HCC patients, which provided a new insight for exploring in-depth mechanism and potential therapy strategy.

High expression of TOP2A in hepatocellular carcinoma is associated with disease progression and poor prognosis

Hepatocellular carcinoma (HCC) is a common malignant tumor in the clinic. Although there are increasing numbers of available treatment methods, their therapeutic effects are not satisfactory. The clinical indicators commonly used to predict the prognosis of HCC include tumor size, degree of cirrhosis, degree of tumor differentiation and tumor microvascular invasion; however, there are currently no molecular indicators that can predict the prognosis of HCC. Due to the differences in the progression of liver cancer among individuals, there is a growing need for prognostic biomarkers to accurately stratify patients for appropriate risk-adaptive treatment. The DNA topoisomerase 2-α (TOP2A) gene, which is located on human chromosome 17, encodes DNA topoisomerase IIα. Previous studies have demonstrated that TOP2A indicates a poor prognosis in patients with various types of tumors, but no such studies are currently available on HCC. By analyzing the differential expression of TOP2A in 50 pairs of tumor and paracancerous tissue samples in The Cancer Genome Atlas (TCGA) database, the present study revealed that the expression of TOP2A was significantly higher in tumor tissue compared with that in paracancerous tissue (P=6.319×10-16). In the collected clinical samples, the mRNA expression levels of TOP2A were significantly upregulated in HCC tumor tissues compared with those in the paracancerous tissues (P=6.40×10-3), suggesting that TOP2A was associated with the occurrence and development of liver cancer. In addition, the associations between TOP2A expression, clinicopathological features and prognosis were analyzed using a multi-center large sample dataset from TCGA database, and the results demonstrated that high expression of TOP2A was associated with a higher T stage, poorer clinical stage and higher histological grade compared with those in patients with low TOP2A expression. High expression of TOP2A was also identified to be associated with a poor prognosis of HCC, particularly in Asian populations. These results suggested that high expression of TOP2A in HCC tissues may be closely associated with tumor progression and metastasis, which may be used as a biological indicator to predict tumor prognosis in clinical practice.