RNA N6-Methyladenosine-Related Gene Contribute to Clinical Prognostic Impact on Patients With Liver Cancer

Vesicle-mediated transport-related genes predict the prognosis and immune microenvironment in hepatocellular carcinoma

Background: Liver hepatocellular carcinoma (LIHC) is one of the leading causes of cancer-related death. The prognostic outcomes of advanced LIHC patients are poor. Hence, reliable prognostic biomarkers for LIHC are urgently needed. Methods: Data for vesicle-mediated transport-related genes (VMTRGs) were profiled from 338 LIHC and 50 normal tissue samples downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses were performed to construct and optimize the prognostic risk model. Five GEO datasets were used to validate the risk model. The roles of the differentially expressed genes (DEGs) were investigated via Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses. Differences in immune cell infiltration between the high- and low-risk groups were evaluated using five algorithms. The "pRRophetic" was used to calculate the anticancer drug sensitivity of the two groups. Transwell and wound healing assays were performed to assess the role of GDP dissociation inhibitor 2 (GDI2) on LIHC cells. Results: A total of 166 prognosis-associated VMTRGs were identified, and VMTRGs-based risk model was constructed for the prognosis of LIHC patients. Four VMTRGs (GDI2, DYNC1LI1, KIF2C, and RAB32) constitute the principal components of the risk model associated with the clinical outcomes of LIHC. Tumor stage and risk score were extracted as the main prognostic indicators for LIHC patients. The VMTRGs-based risk model was significantly associated with immune responses and high expression of immune checkpoint molecules. High-risk patients were less sensitive to most chemotherapeutic drugs but benefited from immunotherapies. In vitro cellular assays revealed that GDI2 significantly promoted the growth and migration of LIHC cells. Conclusions: A VMTRGs-based risk model was constructed to predict the prognosis of LIHC patients effectively. This risk model was closely associated with the immune infiltration microenvironment. The four key VMTRGs are powerful prognostic biomarkers and therapeutic targets for LIHC.

Ghost authors revealed: The structure and function of human N6 -methyladenosine RNA methyltransferases

Despite the discovery of modified nucleic acids nearly 75 years ago, their biological functions are still being elucidated. N6 -methyladenosine (m6 A) is the most abundant modification in eukaryotic messenger RNA (mRNA) and has also been detected in non-coding RNAs, including long non-coding RNA, ribosomal RNA, and small nuclear RNA. In general, m6 A marks can alter RNA secondary structure and initiate unique RNA-protein interactions that can alter splicing, mRNA turnover, and translation, just to name a few. Although m6 A marks in human RNAs have been known to exist since 1974, the structures and functions of methyltransferases responsible for writing m6 A marks have been established only recently. Thus far, there are four confirmed human methyltransferases that catalyze the transfer of a methyl group from S-adenosylmethionine (SAM) to the N6 position of adenosine, producing m6 A: methyltransferase-like protein (METTL) 3/METTL14 complex, METTL16, METTL5, and zinc-finger CCHC-domain-containing protein 4. Though the methyltransferases have unique RNA targets, all human m6 A RNA methyltransferases contain a Rossmann fold with a conserved SAM-binding pocket, suggesting that they utilize a similar catalytic mechanism for methyl transfer. For each of the human m6 A RNA methyltransferases, we present the biological functions and links to human disease, RNA targets, catalytic and kinetic mechanisms, and macromolecular structures. We also discuss m6 A marks in human viruses and parasites, assigning m6 A marks in the transcriptome to specific methyltransferases, small molecules targeting m6 A methyltransferases, and the enzymes responsible for hypermodified m6 A marks and their biological functions in humans. Understanding m6 A methyltransferases is a critical steppingstone toward establishing the m6 A epitranscriptome and more broadly the RNome. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications.

A novel pyroptosis risk model composed of NLRP6 effectively predicts the prognosis of hepatocellular carcinoma patients

BACKGROUND

Pyroptosis is a unique inflammatory-related cell death process, and inflammation is considered to be a key factor in hepatocellular carcinoma (HCC). However, the pyroptosis landscape in HCC has not been thoroughly studied.

METHODS

Clinical data and RNA sequencing data of HCC patients were collected from The Cancer Genome Atlas database, and differentially expressed genes (DEGs) associated with pyroptosis were discovered. The relationship between DEGs and prognosis was studied. Using The Cancer Genome Atlas cohort, a least absolute shrinkage and selection operator regression model was built on the basis of pyroptosis-related DEGs, which was then verified by the Gene Expression Omnibus (GEO) cohort. Functional enrichment analysis and immunological states were also studied between distinct risk subgroups. Finally, the potential tumor suppressive function of NLRP6 in HCC was analyzed.

RESULTS

In total, 26 pyroptosis-related DEGs were identified. Consensus clustering results showed patients with high levels of pyroptosis were associated with higher tumor stage, grade, and poor prognosis. The least absolute shrinkage and selection operator risk model was built using six genes linked with prognosis (GSDMC, GSDME, NOD2, NLRP6, CASP8, and SCAF11). Risk score was an independent risk factor that suggested shortened overall survival in both the training cohort (HR: 3.52, 95% CI: 1.351-9.193) and validation cohort (HR: 3.31, 95% CI: 1.435-7.617). Meanwhile, the low-risk population had higher immunological activity. We also found a novel potential tumor suppressor gene NLRP6, which may negatively regulate the E2F and MYC pathways and be associated with higher immune cell infiltration levels, which lead to better prognosis.

CONCLUSIONS

This study revealed the pyroptosis landscape of HCC and provided a promising clinical prognosis evaluation model. Additionally, some new targets related to prognosis such as NLRP6 are proposed for further study.

Identification and validation a costimulatory molecule gene signature to predict the prognosis and immunotherapy response for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. Costimulatory molecules have been proven to be the foundation of immunotherapy. However, the potential roles of costimulatory molecule genes (CMGs) in HCC remain unclear. Our study is aimed to develop a costimulatory molecule-related gene signature that could evaluate the prognosis of HCC patients.

METHODS

Based on The Cancer Gene Atlas (TCGA) database, univariate Cox regression analysis was applied in CMGs to identify prognosis-related CMGs. Consensus clustering analysis was performed to stratify HCC patients into different subtypes and compared them in OS. Subsequently, the LASSO Cox regression analysis was performed to construct the CMGs-related prognostic signature and Kaplan-Meier survival curves as well as ROC curve were used to validate the predictive capability. Then we explored the correlations of the risk signature with tumor-infiltrating immune cells, tumor mutation burden (TMB) and response to immunotherapy. The expression levels of prognosis-related CMGs were validated based on qRT-PCR and Human Protein Atlas (HPA) databases.

RESULTS

All HCC patients were classified into two clusters based on 11 CMGs with prognosis values and cluster 2 correlated with a poorer prognosis. Next, a prognostic signature of six CMGs was constructed, which was an independent risk factor for HCC patients. Patients with low-risk score were associated with better prognosis. The correlation analysis showed that the risk signature could predict the infiltration of immune cells and immune status of the immune microenvironment in HCC. The qRT-PCR and immunohistochemical results indicated six CMGs with differential expression in HCC tissues and normal tissues.

CONCLUSION

In conclusion, our CMGs-related risk signature could be used as a prediction tool in survival assessment and immunotherapy for HCC patients.

Identification and validation of a pyroptosis-related prognostic signature for thyroid cancer

Background

Pyroptosis is a form of programmed cell death triggered by inflammasomes. However, the roles of pyroptosis-related genes in thyroid cancer (THCA) remain still unclear.

Objective

This study aimed to construct a pyroptosis-related signature that could effectively predict THCA prognosis and survival.

Methods

A LASSO Cox regression analysis was performed to build a prognostic model based on the expression profile of each pyroptosis-related gene. The predictive value of the prognostic model was validated in the internal cohort.

Results

A pyroptosis-related signature consisting of four genes was constructed to predict THCA prognosis and all patients were classified into high- and low-risk groups. Patients with a high-risk score had a poorer overall survival (OS) than those in the low-risk group. The area under the curve (AUC) of the receiver operator characteristic (ROC) curves assessed and verified the predictive performance of this signature. Multivariate analysis showed the risk score was an independent prognostic factor. Tumor immune cell infiltration and immune status were significantly higher in low-risk groups, which indicated a better response to immune checkpoint inhibitors (ICIs). Of the four pyroptosis-related genes in the prognostic signature, qRT-PCR detected three of them with significantly differential expression in THCA tissues.

Conclusion

In summary, our pyroptosis-related risk signature may have an effective predictive and prognostic capability in THCA. Our results provide a potential foundation for future studies of the relationship between pyroptosis and the immunotherapy response.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02231-0.

N6-Methyladenosine, DNA Repair, and Genome Stability

N⁶-methyladenosine (m⁶A) modification in mRNAs and non-coding RNAs is a newly identified epitranscriptomic mark. It provides a fine-tuning of gene expression to serve as a cellular response to endogenous and exogenous stimuli. m⁶A is involved in regulating genes in multiple cellular pathways and functions, including circadian rhythm, cell renewal, differentiation, neurogenesis, immunity, among others. Disruption of m⁶A regulation is associated with cancer, obesity, and immune diseases. Recent studies have shown that m⁶A can be induced by oxidative stress and DNA damage to regulate DNA repair. Also, deficiency of the m⁶A eraser, fat mass obesity-associated protein (FTO) can increase cellular sensitivity to genotoxicants. These findings shed light on the novel roles of m⁶A in modulating DNA repair and genome integrity and stability through responding to DNA damage. In this mini-review, we discuss recent progress in the understanding of a unique role of m⁶As in mRNAs, lncRNAs, and microRNAs in DNA damage response and regulation of DNA repair and genome integrity and instability.

Clinical and Prognostic Pan-Cancer Analysis of N6-Methyladenosine Regulators in Two Types of Hematological Malignancies: A Retrospective Study Based on TCGA and GTEx Databases

N6-methyladenosine (m6A) is one of the most active modification factors of mRNA, which is closely related to cell proliferation, differentiation, and tumor development. Here, we explored the relationship between the pathogenesis of hematological malignancies and the clinicopathologic parameters. The datasets of hematological malignancies and controls were obtained from the TCGA [AML (n = 200), DLBCL (n = 48)] and GTEx [whole blood (n = 337), blood vascular artery (n = 606)]. We analyzed the m6A factor expression differences in normal tissue and tumor tissue and their correlations, clustered the express obvious clinical tumor subtypes, determined the tumor risk score, established Cox regression model, performed univariate and multivariate analysis on all datasets. We found that the AML patients with high expression of IGF2BP3, ALKBH5, and IGF2BP2 had poor survival, while the DLBCL patients with high expression of METTL14 had poor survival. In addition, "Total" datasets analysis revealed that IGF2BP1, ALKBH5, IGF2BP2, RBM15, METTL3, and ZNF217 were potential oncogenes for hematologic system tumors. Collectively, the expressions of some m6A regulators are closely related to the occurrence and development of hematologic system tumors, and the intervention of specific regulatory factors may lead to a breakthrough in the treatment in the future.

A Ten-N6-Methyladenosine (m6A)-Modified Gene Signature Based on a Risk Score System Predicts Patient Prognosis in Rectum Adenocarcinoma

OBJECTIVES

The study aims to analyze the expression of N6-methyladenosine (m6A)-modified genes in rectum adenocarcinoma (READ) and identify reliable prognostic biomarkers to predict the prognosis of READ.

MATERIALS AND METHODS

RNA sequence data of READ and corresponding clinical survival data were obtained from The Cancer Genome Atlas (TCGA) database. N6-methyladenosine (m6A)-modified genes in READ were downloaded from the "m6Avar" database. Differentially expressed m6A-modified genes in READ stratified by different clinicopathological characteristics were identified using the "limma" package in R. Protein-protein interaction (PPI) network and co-expression analysis of differentially expressed genes (DEGs) were performed using "STRING" and Cytoscape, respectively. Principal component analysis (PCA) was done using R. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were used to functionally annotate the differentially expressed genes in different subgroups. Univariate Cox regression analyses were conducted to identify the powerful independent prognostic factors in READ associated with overall survival (OS). A robust likelihood-based survival model was built using the "rbsurv" package to screen for survival-associated signature genes. The Support Vector Machine (SVM) was used to predict the prognosis of READ through the risk score of survival-associated signature genes. Correlation analysis were carried out using GraphPad prism 8.

RESULTS

We screened 974 differentially expressed m6A-modified genes among four types of READ samples. Two READ subgroups (group 1 and group 2) were identified by K means clustering according to the expression of DEGs. The two subgroups were significantly different in overall survival and pathological stages. Next, 118 differentially expressed genes between the two subgroups were screened and the expression of 112 genes was found to be related to the prognosis of READ. Next, a panel of 10 survival-associated signature genes including adamtsl1, csmd2, fam13c, fam184a, klhl4, olfml2b, pdzd4, sec14l5, setbp1, tmem132b was constructed. The signature performed very well for prognosis prediction, time-dependent receiver-operating characteristic (ROC) analysis displaying an area under the curve (AUC) of 0.863, 0.8721, and 0.8752 for 3-year survival rate, prognostic status, and pathological stage prediction, respectively. Correlation analysis showed that the expression levels of the 10 m6A-modified genes were positively correlated with that of m6A demethylase FTO and ALKBH5.

CONCLUSION

This study identified potential m6A-modified genes that may be involved in the pathophysiology of READ and constructed a novel gene expression panel for READ risk stratification and prognosis prediction.

Crosstalk between RNA m6A Modification and Non-coding RNA Contributes to Cancer Growth and Progression

N6-methyladenosine (m6A) is the most common RNA modification and has an important role in normal development and tumorigenesis. The abnormal expression of m6A regulators can lead to an imbalance in m6A levels in cancer cells, leading to the dysregulated expression of oncogenes and tumor suppressor genes that may contribute to cancer development, patient response to chemoradiotherapy, and clinical prognosis. Recent studies demonstrate that non-coding RNAs are involved in epigenetic modification of both DNA and RNA in tumor cells, and may also affect the development and progression of cancer by targeting m6A regulators. In this review, we describe the functional crosstalk between m6A and non-coding RNAs, particularly microRNA, long non-coding RNA, and circular RNA, and illustrate their roles in tumor regulation. Finally, we discuss the significance of non-coding RNA and m6A modification in the diagnosis, treatment, and prognosis of cancer patients, as well as potential future research directions.