Genomic Instability of Mutation-Derived Gene Prognostic Signatures for Hepatocellular Carcinoma

Urgent need for prognostic markers for hepatocellular carcinoma in the light of genomic instability and non-coding RNA signatures

In this editorial, we comment on an original article by Duan et al. Despite advancements in the diagnosis and treatment of hepatocellular carcinoma (HCC), the identification of suitable prognostic factors remains challenging. In their paper, Duan et al identified long non-coding RNAs (LncRNAs) to quantify genomic instability (GI) by combining LncRNA expression and somatic mutation profiles. They confirmed that the GI-derived LncRNA signature (GI-LncSig) could be an independent prognostic factor with the area under the curve of 0.773. Furthermore, the authors stated that GI-LncSig may have a better predictive performance than TP53 mutation status alone. However, studies exploring genetic markers for predicting the prognosis of HCC are crucial for identifying therapeutic targets and enhancing diagnostic and treatment strategies to mitigate the global burden of liver cancer.

Advancing prognostic understanding in hepatocellular carcinoma through the integration of genomic instability and lncRNA signatures: GILncSig model

The recently published study by Duan et al introduces a promising method that combines genomic instability and long non-coding RNAs to improve the prognostic evaluation of hepatocellular carcinoma (HCC), a deadly cancer associated with considerable morbidity and mortality. This editorial aims to analyze the methodology, key findings, and broader implications of the study within the fields of gastroenterology and oncological surgery, highlighting the shift towards precision medicine in the management of HCC.

Pan-cancer analysis of SLC2A family genes as prognostic biomarkers and therapeutic targets

Background

The major facilitator superfamily glucose transporters (GLUTs), encoded by solute carrier 2A (SLC2A) genes, mediate the transmembrane movement and uptake of glucose. To satisfy the improved energy demands, glycolysis flux is increased in cancers compared with healthy tissues. Multiple diseases, including cancer, have been associated with GLUTs. Nevertheless, not much research has been done on the functions of SLC2As in pan-cancer prognosis or their clinical treatment potential.

Methods

The SLC2A family genes' level of expression and prognostic values were analyzed in relation to pan-cancer. We then examined the association among SLC2As expression and TME, Stemness score, clinical characteristics, immune subtypes, and drug sensitivity. We merged bioinformatics analysis techniques with up-to-date public databases. Additionally, SLC2As from the KOBAS database were subjected to enrichment analysis.

Results

We discovered that SLC2As' gene expression differed significantly between normal tissues and many malignancies. A number of tumors from various databases demonstrate a relationship between prognosis and SLC2A family gene expression. For instance, SLC2A2 and SLC2A5 were associated with the overall survival (OS) of hepatocellular carcinoma. SLC2A1 was associated with the OS of lung adenocarcinoma and pancreatic adenocarcinoma. Moreover, the SLC2A family gene expression is significantly correlated with the pan-cancer stromal and immune scores, and the RNA and DNA stemness scores. Furthermore, we found that the majority of SLC2As had a strong correlation with the tumor stages in KIRC. The immunological subtypes and all members of the SLC2A gene family exhibited a substantial correlation. Moreover, pathways containing insulin resistance and adipocytokine signaling pathway may influence the progression of some cancers. Finally, there is a significant positive or negative connection between drug sensitivity and SLC2A1 expression.

Conclusion

Our research highlights the significant promise of SLC2As as prognostic indicators and offers insightful approaches for upcoming exploration of SLC2As as putative therapeutic targets in malignancies.

Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies

Objective

Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.

Methods

We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.

Results

We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.

Conclusion

The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.

Construction and validation of somatic mutation-derived long non-coding RNAs signatures of genomic instability to predict prognosis of hepatocellular carcinoma

BACKGROUND

Long non-coding RNAs (LncRNAs) have been found to be a potential prognostic factor for cancers, including hepatocellular carcinoma (HCC). Some LncRNAs have been confirmed as potential indicators to quantify genomic instability (GI). Nevertheless, GI-LncRNAs remain largely unexplored. This study established a GI-derived LncRNA signature (GILncSig) that can predict the prognosis of HCC patients.

AIM

To establish a GILncSig that can predict the prognosis of HCC patients.

METHODS

Identification of GI-LncRNAs was conducted by combining LncRNA expression and somatic mutation profiles. The GI-LncRNAs were then analyzed for functional enrichment. The GILncSig was established in the training set by Cox regression analysis, and its predictive ability was verified in the testing set and TCGA set. In addition, we explored the effects of the GILncSig and TP53 on prognosis.

RESULTS

A total of 88 GI-LncRNAs were found, and functional enrichment analysis showed that their functions were mainly involved in small molecule metabolism and GI. The GILncSig was constructed by 5 LncRNAs (miR210HG, AC016735.1, AC116351.1, AC010643.1, LUCAT1). In the training set, the prognosis of high-risk patients was significantly worse than that of low-risk patients, and similar results were verified in the testing set and TCGA set. Multivariate Cox regression analysis and stratified analysis confirmed that the GILncSig could be used as an independent prognostic factor. Receiver operating characteristic curve analysis of the GILncSig showed that the area under the curve (0.773) was higher than the two LncRNA signatures published recently. Furthermore, the GILncSig may have a better predictive performance than TP53 mutation status alone.

CONCLUSION

We established a GILncSig that can predict the prognosis of HCC patients, which will help to guide prognostic evaluation and treatment decisions.

Identified RP2 as a prognostic biomarker for glioma, facilitating glioma pathogenesis mainly via regulating tumor immunity

Glioma is the most common primary intracranial tumor in the central nervous system, with a high degree of malignancy and poor prognosis, easy to recur, difficult to cure. The mutation of Retinitis Pigmentosa 2 (RP2) can cause retinitis pigmentosa, it is a prognostic factor of osteosarcoma, however, its role in glioma remains unclear. Based on the data from TCGA and GTEx, we identified RP2 as the most related gene for glioma by WGCNA, and used a series of bioinformatics analyses including LinkedOmics, GSCA, CTD, and so on, to explore the expression of RP2 in glioma and the biological functions it is involved in. The results showed that RP2 was highly expressed in glioma, and its overexpression could lead to poor prognosis. In addition, the results of enrichment analysis showed that RP2 was highly correlated with cell proliferation and immune response. And then, we found significant enrichment of Macrophages among immune cells. Furthermore, our experiments have confirmed that Macrophages can promote the development of glioma by secreting or influencing the secretion of some cytokines. Moreover, we investigated the influence of RP2 on the immunotherapy of glioma and the role of m6A modification in the influence of RP2 on glioma. Ultimately, we determined that RP2 is an independent prognostic factor that is mainly closely related to immune for glioma.

Genome instability-derived genes as a novel prognostic signature for lung adenocarcinoma

Background: An increasing number of patients are being diagnosed with lung adenocarcinoma, but there remains limited progress in enhancing prognostic outcomes and improving survival rates for these patients. Genome instability is considered a contributing factor, as it enables other hallmarks of cancer to acquire functional capabilities, thus allowing cancer cells to survive, proliferate, and disseminate. Despite the importance of genome instability in cancer development, few studies have explored the prognostic signature associated with genome instability for lung adenocarcinoma. Methods: In the study, we randomly divided 397 lung adenocarcinoma patients from The Cancer Genome Atlas database into a training group (n = 199) and a testing group (n = 198). By calculating the cumulative counts of genomic alterations for each patient in the training group, we distinguished the top 25% and bottom 25% of patients. We then compared their gene expressions to identify genome instability-related genes. Next, we used univariate and multivariate Cox regression analyses to identify the prognostic signature. We also performed the Kaplan-Meier survival analysis and the log-rank test to evaluate the performance of the identified prognostic signature. The performance of the signature was further validated in the testing group, in The Cancer Genome Atlas dataset, and in external datasets. We also conducted a time-dependent receiver operating characteristic analysis to compare our signature with established prognostic signatures to demonstrate its potential clinical value. Results: We identified GULPsig, which includes IGF2BP1, IGF2BP3, SMC1B, CLDN6, and LY6K, as a prognostic signature for lung adenocarcinoma patients from 42 genome instability-related genes. Based on the risk score of the risk model with GULPsig, we successfully stratified the patients into high- and low-risk groups according to the results of the Kaplan-Meier survival analysis and the log-rank test. We further validated the performance of GULPsig as an independent prognostic signature and observed that it outperformed established prognostic signatures. Conclusion: We provided new insights to explore the clinical application of genome instability and identified GULPsig as a potential prognostic signature for lung adenocarcinoma patients.

FARSB serves as a novel hypomethylated and immune cell infiltration related prognostic biomarker in hepatocellular carcinoma

PURPOSE

Hepatocellular carcinoma (HCC) is a prevalent tumor with high morbidity, and an unfavourable prognosis. FARSB is an aminoacyl tRNA synthase, and plays a key role in protein synthesis in cells. Furthermore, previous reports have indicated that FARSB is overexpressed in gastric tumor tissues and is associated with a poor prognosis and tumorigenesis. However, the function of FARSB in HCC has not been studied.

RESULTS

The results showed that FARSB mRNA and protein levels were upregulated in HCC and were closely related to many clinicopathological characteristics. Besides, according to multivariate Cox analysis, high FARSB expression was linked with a shorter survival time in HCC and may be an independent prognostic factor. In addition, the FARSB promoter methylation level was negatively associated with the expression of FARSB. Furthermore, enrichment analysis showed that FARSB was related to the cell cycle. And TIMER analysis revealed that the FARSB expression was closely linked to tumor purity and immune cell infiltration. The TCGA and ICGC data analysis suggested that FARSB expression is greatly related to m6A modifier related genes. Potential FARSB-related ceRNA regulatory networks were also constructed. What's more, based on the FARSB-protein interaction network, molecular docking models of FARSB and RPLP1 were constructed. Finally, drug susceptibility testing revealed that FARSB was susceptible to 38 different drugs or small molecules.

CONCLUSIONS

FARSB can serve as a prognostic biomarker for HCC and provide clues about immune infiltration, and m6A modification.

The regulation loop of MARVELD1 interacting with PARP1 in DNA damage response maintains genome stability and promotes therapy resistance of cancer cells

The DNA damage response (DDR) plays crucial roles in cancer prevention and therapy. Poly(ADP-ribose) polymerase 1 (PARP1) mediates multiple signal transduction in the DDR as a master regulator. Uncovering the regulatory factors of PARP1 contributes to a more comprehensive view of tumorigenesis and treatment strategies. Here, we reveal that MARVELD1 acts as a mediator of DDR to perform early events and maintain genome stability. Mechanistically, PARP1 PARylates MARVELD1 at D102, D118 and D130, and in turn, MARVELD1 stabilizes PARP1 by enhancing NAA50-mediated acetylation, thus forming a positive feedback loop. MARVELD1 knockout mice and their embryo fibroblasts exhibit genomic instability and shorter half-life of PARP1. Moreover, MARVELD1 partnering with PARP1 facilitates resistance to genotoxic drugs and disrupts PARP inhibitor (PARPi) effect in PDX model of colorectal cancer (CRC). Overall, our results underline the link between MARVELD1 and PARP1 in therapeutic resistance based on DDR and provide new insights for clinical tumor therapy of PARPi.

Epigenetic aberrations of gene expression in a rat model of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is mostly triggered by environmental and life-style factors and may involve epigenetic aberrations. However, a comprehensive documentation of the link between the dysregulated epigenome, transcriptome, and liver carcinogenesis is lacking. In the present study, Fischer-344 rats were fed a choline-deficient (CDAA, cancer group) or choline-sufficient (CSAA, healthy group) L-amino acid-defined diet. At the end of 52 weeks, transcriptomic alterations in livers of rats with HCC tumours and healthy livers were investigated by RNA sequencing. DNA methylation and gene expression were assessed by pyrosequencing and quantitative reverse-transcription PCR (qRT-PCR), respectively. We discovered 1,848 genes that were significantly differentially expressed in livers of rats with HCC tumours (CDAA) as compared with healthy livers (CSAA). Upregulated genes in the CDAA group were associated with cancer-related functions, whereas macronutrient metabolic processes were enriched by downregulated genes. Changes of highest magnitude were detected in numerous upregulated genes that govern key oncogenic signalling pathways, including Notch, Wnt, Hedgehog, and extracellular matrix degradation. We further detected perturbations in DNA methylating and demethylating enzymes, which was reflected in decreased global DNA methylation and increased global DNA hydroxymethylation. Four selected upregulated candidates, Mmp12, Jag1, Wnt4, and Smo, demonstrated promoter hypomethylation with the most profound decrease in Mmp12. MMP12 was also strongly overexpressed and hypomethylated in human HCC HepG2 cells as compared with primary hepatocytes, which coincided with binding of Ten-eleven translocation 1 (TET1). Our findings provide comprehensive evidence for gene expression changes and dysregulated epigenome in HCC pathogenesis, potentially revealing novel targets for HCC prevention/treatment.

ZNF320 is a hypomethylated prognostic biomarker involved in immune infiltration of hepatocellular carcinoma and associated with cell cycle

Hepatocellular carcinoma (HCC) is one of the most deadly and common malignant cancers around the world, and the prognosis of HCC patients is not optimistic. ZNF320 belongs to Krüppel like zinc finger gene family. However, no studies have focused on the influence of ZNF320 in HCC. We first analyzed ZNF320 expression in HCC by using data from TCGA and ICGC, then conducted a joint analysis with TIMER and UALCAN, and validated by immunohistochemistry in clinical HCC samples. Then we applied UALCAN to explore the correlation between ZNF320 expression and clinicopathological characteristics. Consequently, using Kaplan-Meier Plotter analysis and the Cox regression, we can predict the prognostic value of ZNF320 for HCC patients. Next, the analysis by GO, KEGG, and GSEA revealed that ZNF320 was significantly correlated to cell cycle and immunity. Finally, TIMER and GEPIA analysis verified that ZNF320 expression is closely related to tumor infiltrating immune cells (TIIC), including B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. The analysis of the TCGA and ICGC data sets revealed that ZNF320 expression was significantly correlated with m6A related genes (RBMX, YTHDF1, and METTL3). In conclusion, ZNF320 may be a prognostic biomarker related to immunity as a candidate for liver cancer.

Contribution of DNA methylation to the risk of hepatitis C virus-associated hepatocellular carcinoma: A meta-analysis

DNA methylation is a crucial epigenetic modification in hepatocellular carcinoma (HCC), and hepatitis C virus (HCV) can induce hepatocarcinogenesis. Nevertheless, the interaction mechanism between DNA methylation and HCV infection in HCC is still ambiguous. In this study, we performed a comprehensive meta-analysis to assess the contribution of DNA methylation in HCV-associated HCC. After four steps of literature screening, we finally obtained 33 qualified case-control studies for this meta-analysis. These studies consisted of 587 HCV-positive cancer tissues and 326 HCV-negative cancer tissues. Our results revealed that four genes (p16, GSTP1, APC, and RUNX3) were more hypermethylated in the HCV-positive liver cancer tissues than in the HCV-negative liver cancer tissues. In addition, the p16 gene was more hypermethylated in the HCV-positive paracancerous tissues than in the HCV-negative paracancerous tissues. Subgroup meta-analysis by geographical populations showed that p16 methylation was significantly higher in HCV-positive cancerous tissues from Japanese and Chinese. Besides, p16 methylation was significantly higher among patients (> 60 years) but not among the others (≤ 60 years). However, there was no obvious association between DNA methylation and other clinicopathological characteristics, including gender, tumor size, differentiation, and clinical stage. Our study suggested that DNA methylation could become potential biomarkers for HCV-associated HCC. DNA methylation contributed to the risk of HCV-associated HCC.

Characterization of cancer-related fibroblasts (CAF) in hepatocellular carcinoma and construction of CAF-based risk signature based on single-cell RNA-seq and bulk RNA-seq data

Background

Cancer-associated fibroblasts (CAFs) are involved in tumor growth, angiogenesis, metastasis, and resistance to therapy. We sought to explore the CAFs characteristics in hepatocellular carcinoma (HCC) and establish a CAF-based risk signature for predicting the prognosis of HCC patients.

Methods

The signal-cell RNA sequencing (scRNA-seq) data was obtained from the GEO database. Bulk RNA-seq data and microarray data of HCC were obtained from the TCGA and GEO databases respectively. Seurat R package was applied to process scRNA-seq data and identify CAF clusters according to the CAF markers. Differential expression analysis was performed to screen differentially expressed genes (DEGs) between normal and tumor samples in TCGA dataset. Then Pearson correlation analysis was used to determine the DEGs associated with CAF clusters, followed by the univariate Cox regression analysis to identify CAF-related prognostic genes. Lasso regression was implemented to construct a risk signature based on CAF-related prognostic genes. Finally, a nomogram model based on the risk signature and clinicopathological characteristics was developed.

Results

Based on scRNA-seq data, we identified 4 CAF clusters in HCC, 3 of which were associated with prognosis in HCC. A total of 423 genes were identified from 2811 DEGs to be significantly correlated with CAF clusters, and were narrowed down to generate a risk signature with 6 genes. These six genes were primarily connected with 39 pathways, such as angiogenesis, apoptosis, and hypoxia. Meanwhile, the risk signature was significantly associated with stromal and immune scores, as well as some immune cells. Multivariate analysis revealed that risk signature was an independent prognostic factor for HCC, and its value in predicting immunotherapeutic outcomes was confirmed. A novel nomogram integrating the stage and CAF-based risk signature was constructed, which exhibited favorable predictability and reliability in the prognosis prediction of HCC.

Conclusion

CAF-based risk signatures can effectively predict the prognosis of HCC, and comprehensive characterization of the CAF signature of HCC may help to interpret the response of HCC to immunotherapy and provide new strategies for cancer treatment.