MRPS31 loss is a key driver of mitochondrial deregulation and hepatocellular carcinoma aggressiveness

Min, Seungwook; Lee, Young-Kyoung; Hong, Jiwon; Park, Tae Jun; Woo, Hyun Goo; Kwon, So Mee; Yoon, Gyesoon

doi:10.1038/s41419-021-04370-8

Cited by 16 publications

(9 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This may regulate the expression of corresponding genes through their negatively related promoter methylation to adapt to the wildness training process. NME7 gene knockdown causes primary ciliary dyskinesia ( Šedová et al, 2021 ), MRPS31 gene deletion causes mitochondrial deregulation and the aggression of hepatocellular carcinoma ( Min et al, 2021 ), and TPM1 gene knockdown causes early embryonic death ( Ma et al, 2021 ). The deletion or low expression of these genes above can cause disease development.…”

Section: Discussionmentioning

confidence: 99%

Whole genome bisulfite sequencing reveals DNA methylation roles in the adaptive response of wildness training giant pandas to wild environment

Jie

Wu²,

Yang³

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

DNA methylation modification can regulate gene expression without changing the genome sequence, which helps organisms to rapidly adapt to new environments. However, few studies have been reported in non-model mammals. Giant panda (Ailuropoda melanoleuca) is a flagship species for global biodiversity conservation. Wildness and reintroduction of giant pandas are the important content of giant pandas’ protection. However, it is unclear how wildness training affects the epigenetics of giant pandas, and we lack the means to assess the adaptive capacity of wildness training giant pandas. We comparatively analyzed genome-level methylation differences in captive giant pandas with and without wildness training to determine whether methylation modification played a role in the adaptive response of wildness training pandas. The whole genome DNA methylation sequencing results showed that genomic cytosine methylation ratio of all samples was 5.35%–5.49%, and the methylation ratio of the CpG site was the highest. Differential methylation analysis identified 544 differentially methylated genes (DMGs). The results of KEGG pathway enrichment of DMGs showed that VAV3, PLCG2, TEC and PTPRC participated in multiple immune-related pathways, and may participate in the immune response of wildness training giant pandas by regulating adaptive immune cells. A large number of DMGs enriched in GO terms may also be related to the regulation of immune activation during wildness training of giant pandas. Promoter differentially methylation analysis identified 1,199 genes with differential methylation at promoter regions. Genes with low methylation level at promoter regions and high expression such as, CCL5, P2Y13, GZMA, ANP32A, VWF, MYOZ1, NME7, MRPS31 and TPM1 were important in environmental adaptation for wildness training giant pandas. The methylation and expression patterns of these genes indicated that wildness training giant pandas have strong immunity, blood coagulation, athletic abilities and disease resistance. The adaptive response of giant pandas undergoing wildness training may be regulated by their negatively related promoter methylation. We are the first to describe the DNA methylation profile of giant panda blood tissue and our results indicated methylation modification is involved in the adaptation of captive giant pandas when undergoing wildness training. Our study also provided potential monitoring indicators for the successful reintroduction of valuable and threatened animals to the wild.

show abstract

Section: Discussionmentioning

confidence: 99%

Whole genome bisulfite sequencing reveals DNA methylation roles in the adaptive response of wildness training giant pandas to wild environment

Jie

Wu²,

Yang³

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

show abstract

“…In the broader literature, Min's research proposed MRPS31 as an emergent biomarker for HCC, and Foo's work underscored the pivotal role of CYCS in driving apoptotic processes within HCC cells. 33,34 To validate the prognostic robustness of these identified genes, we segregated patients from the TCGA cohort into high-risk and low-risk categories based on their median risk scores. Kaplan-Meier survival curves painted a clear dichotomy in the prognoses between these groups.…”

Section: Discussionmentioning

confidence: 99%

“…Through the COX survival analysis of DEGs using the tinyarray package, we identified five genes—MRPL15, CYCS, MRPS35, NDFIP1, and ATP6V1G1—as having significant prognostic implications. In the broader literature, Min's research proposed MRPS31 as an emergent biomarker for HCC, and Foo's work underscored the pivotal role of CYCS in driving apoptotic processes within HCC cells 33,34 . To validate the prognostic robustness of these identified genes, we segregated patients from the TCGA cohort into high‐risk and low‐risk categories based on their median risk scores.…”

Section: Discussionmentioning

confidence: 99%

Genomic and immune landscape in hepatocellular carcinoma: Implications for personalized therapeutics

Zheng,

Chen,

Wang

et al. 2023

Environmental Toxicology

View full text Add to dashboard Cite

Hepatocellular carcinoma (HCC) is a globally prevalent malignancy, marked by genetic heterogeneity and intricate tumor microenvironment interactions. In this study, we undertook a detailed single‐cell analysis of six active HCC patients, highlighting strong correlations between gene expression levels and cellular characteristics. UMAP clustering revealed seven distinct cell categories with associated gene expressions. A divergence was observed in tumor cells into high and low cuproptosis groups, each associated with distinct pathways: oxidative stress for the high cuproptosis group and inflammatory and angiogenesis pathways for the low group. CellChat analysis on the TCGA‐LIHC cohort displayed unique intercellular interactions among hepatocytes, T cells, and other cells, with pathways like COLLAGEN and VEGF being pivotal. Functional enrichment analyses exposed pathways enriched between cuproptosis groups, with KEGG emphasizing diseases like Parkinson's. COX survival analysis identified key prognostic genes, revealing distinct survival rates between risk groups in TCGA and GSE14520 cohorts. Mutation data highlighted missense mutations, with TTN, TP53, and CTNNB1 being the most mutated in HCC. Immune infiltration analysis via CIBERSORTx indicated differences between risk groups in NK cells, neutrophils, and other cells. Our drug sensitivity investigation showed significant correlations between model genes and drug responsiveness, emphasizing the importance of patient risk stratification for therapeutic approaches. Further, ATP6V1G1 was recognized in its role in apoptosis and migration in HCC cells. In conclusion, our findings illuminate the complexities of HCC progression, potential predictive genetic markers for drug response, and the pivotal role of ATP6V1G1, suggesting avenues for targeted therapeutic strategies in HCC.

show abstract

“…Results of the functional enrichment analysis of the m7G-related genes with GO revealed that some of them were associated with the pathogenesis of HCC. Mitochondrial dysregulation is associated with the aggressiveness of HCC ( Min et al, 2021 ). Actin−mediated cellular activity is associated with cell migration and invasion, which may play important role in the development of cancer ( Guan et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

N7-Methylguanosine Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Regmi

Lia

et al. 2022

Front. Genet.

View full text Add to dashboard Cite

Background: About 90% of liver cancer-related deaths are caused by hepatocellular carcinoma (HCC). N7-methylguanosine (m7G) modification is associated with the biological process and regulation of various diseases. To the best of our knowledge, its role in the pathogenesis and prognosis of HCC has not been thoroughly investigated.Aim: To identify N7-methylguanosine (m7G) related prognostic biomarkers in HCC. Furthermore, we also studied the association of m7G–related prognostic gene signature with immune infiltration in HCC.Methods: The TCGA datasets were used as a training and GEO dataset “GSE76427” for validation of the results. Statistical analyses were performed using the R statistical software version 4.1.2.Results: Functional enrichment analysis identified some pathogenesis related to HCC. We identified 3 m7G-related genes (CDK1, ANO1, and PDGFRA) as prognostic biomarkers for HCC. A risk score was calculated from these 3 prognostic m7G-related genes which showed the high-risk group had a significantly poorer prognosis than the low-risk group in both training and validation datasets. The 3- and 5-years overall survival was predicted better with the risk score than the ideal model in the entire cohort in the predictive nomogram. Furthermore, immune checkpoint genes like CTLA4, HAVCR2, LAG3, and TIGT were expressed significantly higher in the high-risk group and the chemotherapy sensitivity analysis showed that the high-risk groups were responsive to sorafenib treatment.Conclusion: These 3 m7G genes related signature model can be used as prognostic biomarkers in HCC and a guide for immunotherapy and chemotherapy response. Future clinical study on this biomarker model is required to verify its clinical implications.

show abstract

MRPS31 loss is a key driver of mitochondrial deregulation and hepatocellular carcinoma aggressiveness

Cited by 16 publications

References 55 publications

Whole genome bisulfite sequencing reveals DNA methylation roles in the adaptive response of wildness training giant pandas to wild environment

Whole genome bisulfite sequencing reveals DNA methylation roles in the adaptive response of wildness training giant pandas to wild environment

Genomic and immune landscape in hepatocellular carcinoma: Implications for personalized therapeutics

N7-Methylguanosine Genes Related Prognostic Biomarker in Hepatocellular Carcinoma

Contact Info

Product

Resources

About