Whole mitochondrial genome sequencing highlights mitochondrial impact in gastric cancer

Cavalcante, Giovanna C.; Marinho, Andrey Moacir do Rosário; Anaissi, Ana Karyssa Mendes; Vinasco-Sandoval, Tatiana; Ribeiro-dos-Santos, Ândrea; Vidal, Amanda Ferreira; Araújo, Gilderlanio Santana de; Demachki, Sâmia

doi:10.1038/s41598-019-51951-x

Cited by 21 publications

(24 citation statements)

References 54 publications

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“…Mitochondria are organelles that perform major roles in cellular operation. Therefore, alterations in mitochondrial genome may lead to mitochondrial dysfunction and cellular deregulation, influencing carcinogenesis [ 45 ]. A recent study indicated that ZCCHC4 is a new human N(6)-methyladenosine methyltransferase, and knockout of ZCCHC4 eliminates the N(6)-methyladenosine modification on 28S rRNA, thereby reducing overall translation activity, which contributes to inhibiting liver cancer cells proliferation and reducing liver tumor size [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

RETRACTED ARTICLE: Downregulation of lncRNA ZNF582-AS1 due to DNA hypermethylation promotes clear cell renal cell carcinoma growth and metastasis by regulating the N(6)-methyladenosine modification of MT-RNR1

Yang

Zhang

et al. 2021

J Exp Clin Cancer Res

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Background Emerging evidence confirms that lncRNAs (long non-coding RNAs) are potential biomarkers that play vital roles in tumors. ZNF582-AS1 is a novel lncRNA that serves as a potential prognostic marker of cancers. However, the specific clinical significance and molecular mechanism of ZNF582-AS1 in ccRCC (clear cell renal cell carcinoma) are unclear. Methods Expression level and clinical significance of ZNF582-AS1 were determined by TCGA-KIRC data and qRT-PCR results of 62 ccRCCs. DNA methylation status of ZNF582-AS1 promoter was examined by MSP, MassARRAY methylation and demethylation analysis. Gain-of-function experiments were conducted to investigate the biological roles of ZNF582-AS1 in the phenotype of ccRCC. The subcellular localization of ZNF582-AS1 was detected by RNA FISH. iTRAQ, RNA pull-down and RIP-qRT-PCR were used to identify the downstream targets of ZNF582-AS1. rRNA MeRIP-seq and MeRIP-qRT-PCR were utilized to examine the N(6)-methyladenosine modification status. Western blot and immunohistochemistry assays were used to determine the protein expression level. Results ZNF582-AS1 was downregulated in ccRCC, and decreased ZNF582-AS1 expression was significantly correlated with advanced tumor stage, higher pathological stage, distant metastasis and poor prognosis. Decreased ZNF582-AS1 expression was caused by DNA methylation at the CpG islands within its promoter. ZNF582-AS1 overexpression inhibited cell proliferative, migratory and invasive ability, and increased cell apoptotic rate in vitro and in vivo. Mechanistically, we found that ZNF582-AS1 overexpression suppressed the N(6)-methyladenosine modification of MT-RNR1 by reducing rRNA adenine N(6)-methyltransferase A8K0B9 protein level, resulting in the decrease of MT-RNR1 expression, followed by the inhibition of MT-CO2 protein expression. Furthermore, MT-RNR1 overexpression reversed the decreased MT-CO2 expression and phenotype inhibition of ccRCC induced by increased ZNF582-AS1 expression. Conclusions This study demonstrates for the first time that ZNF582-AS1 functions as a tumor suppressor gene in ccRCC and ZNF582-AS1 may serve as a potential biomarker and therapeutic target of ccRCC.

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Section: Discussionmentioning

confidence: 99%

RETRACTED ARTICLE: Downregulation of lncRNA ZNF582-AS1 due to DNA hypermethylation promotes clear cell renal cell carcinoma growth and metastasis by regulating the N(6)-methyladenosine modification of MT-RNR1

Yang

Zhang

et al. 2021

J Exp Clin Cancer Res

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“…All found variants were distributed in 11 mitochondrial regions: MT-RNR1 (1438), MT-RNR2 (2706), T-ND1 (3594 and 3666), MT-CO1 (7028), MT-ATP6 (8584), MT-CO3 (9545), MT-ND4 (10810, 10873, 11719 and 11914), MT-ND5 (12705, 13650 and 13789), MT-ND6 (14178 and 14560), MT-CYB (15043) and D-loop (73, 489, 16093, 16111, 16129, 16189, 16223, 16278, 16311, 16327, 16360, 16519 and 16527). It is noteworthy that 11 of these variants were exclusive to tumours (somatic) in the study by [ 6 ] and distributed in four regions: D-loop (73, 489, 16093, 16189 and 16360), MT-ND1 (3594 and 3666), MT-ND4 (10810 and 10873), and MT-ND5 (13650 and 13789). Regardless, from the total of 30 variants, the most frequent variants in GC were 11719 and 16223 (present in 12 individuals each), while the most frequent in TCMA was 11914 (15 individuals).…”

Section: Resultsmentioning

confidence: 96%

“…After dataset merging, we observed 90 individuals and 22 cancer types from TCMA that presented shared variants with GC. This was probably a reflection of different genetic ancestries in both datasets, considering that the GC dataset consisted of individuals from the Brazilian Amazon region, in which Native American mtDNA haplogroups are frequently found [ 6 ], while the TCMA dataset does not seem to include the Brazilian population. This emphasizes the importance of considering the genetic background of the studied populations and stresses the potential of the shared mutations observed in this analysis.…”

Section: Resultsmentioning

confidence: 99%

“…In this context, we recently published a study performing whole-genome sequencing for the first time in regard to gastric cancer (GC) in a Brazilian population [ 6 ]. Even more recently, a study by [ 7 ] was published with the whole mtgenome of multiple types of cancer from different populations – which do not seem to include Brazil – as part of the International Cancer Genome Consortium/The Cancer Genome Atlas Pan-Cancer Analysis of Whole Genomes Consortium (ICGC/TCGA PAWG Consortium), and the database was named The Cancer Mitochondria Atlas (TCMA).…”

Section: Introductionmentioning

confidence: 99%

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Mitochondria in tumour progression: a network of mtDNA variants in different types of cancer

2022

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Background Mitochondrial participation in tumorigenesis and metastasis has been studied for many years, but several aspects of this mechanism remain unclear, such as the association of mitochondrial DNA (mtDNA) with different cancers. Here, based on two independent datasets, we modelled an mtDNA mutation-cancer network by systematic integrative analysis including 37 cancer types to identify the mitochondrial variants found in common among them. Results Our network showed mtDNA associations between gastric cancer and other cancer types, particularly kidney, liver, and prostate cancers, which is suggestive of a potential role of such variants in the metastatic processes among these cancer types. A graph-based interactive web tool was made available at www2.lghm.ufpa.br/mtdna. We also highlighted that most shared variants were in the MT-ND4, MT-ND5 and D-loop, and that some of these variants were nonsynonymous, indicating a special importance of these variants and regions regarding cancer progression, involving genomic and epigenomic alterations. Conclusions This study reinforces the importance of studying mtDNA in cancer and offers new perspectives on the potential involvement of different mitochondrial variants in cancer development and metastasis.

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“…Mutserve is a local version of the scalable web server “mtDNA-server,” which was released in 2016 ( Weissensteiner et al, 2016 ). It has been used in variant identification studies for diseases like congenital lactic acidosis and gastric cancer ( Bravo-Alonso et al, 2019 ; Cavalcante et al, 2019 ). Mutserve performs internal quality control by excluding mitochondrial hotspots and sites with <10 reads and estimates a strand bias, as it handles forward and reverse reads separately.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking the Effectiveness and Accuracy of Multiple Mitochondrial DNA Variant Callers: Practical Implications for Clinical Application

Troup

Xu³

et al. 2022

Front. Genet.

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Mitochondrial DNA (mtDNA) mutations contribute to human disease across a range of severity, from rare, highly penetrant mutations causal for monogenic disorders to mutations with milder contributions to phenotypes. mtDNA variation can exist in all copies of mtDNA or in a percentage of mtDNA copies and can be detected with levels as low as 1%. The large number of copies of mtDNA and the possibility of multiple alternative alleles at the same DNA nucleotide position make the task of identifying allelic variation in mtDNA very challenging. In recent years, specialized variant calling algorithms have been developed that are tailored to identify mtDNA variation from whole-genome sequencing (WGS) data. However, very few studies have systematically evaluated and compared these methods for the detection of both homoplasmy and heteroplasmy. A publicly available synthetic gold standard dataset was used to assess four mtDNA variant callers (Mutserve, mitoCaller, MitoSeek, and MToolBox), and the commonly used Genome Analysis Toolkit “best practices” pipeline, which is included in most current WGS pipelines. We also used WGS data from 126 trios and calculated the percentage of maternally inherited variants as a metric of calling accuracy, especially for homoplasmic variants. We additionally compared multiple pathogenicity prediction resources for mtDNA variants. Although the accuracy of homoplasmic variant detection was high for the majority of the callers with high concordance across callers, we found a very low concordance rate between mtDNA variant callers for heteroplasmic variants ranging from 2.8% to 3.6%, for heteroplasmy thresholds of 5% and 1%. Overall, Mutserve showed the best performance using the synthetic benchmark dataset. The analysis of mtDNA pathogenicity resources also showed low concordance in prediction results. We have shown that while homoplasmic variant calling is consistent between callers, there remains a significant discrepancy in heteroplasmic variant calling. We found that resources like population frequency databases and pathogenicity predictors are now available for variant annotation but still need refinement and improvement. With its peculiarities, the mitochondria require special considerations, and we advocate that caution needs to be taken when analyzing mtDNA data from WGS data.

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Whole mitochondrial genome sequencing highlights mitochondrial impact in gastric cancer

Cited by 21 publications

References 54 publications

RETRACTED ARTICLE: Downregulation of lncRNA ZNF582-AS1 due to DNA hypermethylation promotes clear cell renal cell carcinoma growth and metastasis by regulating the N(6)-methyladenosine modification of MT-RNR1

RETRACTED ARTICLE: Downregulation of lncRNA ZNF582-AS1 due to DNA hypermethylation promotes clear cell renal cell carcinoma growth and metastasis by regulating the N(6)-methyladenosine modification of MT-RNR1

Mitochondria in tumour progression: a network of mtDNA variants in different types of cancer

Benchmarking the Effectiveness and Accuracy of Multiple Mitochondrial DNA Variant Callers: Practical Implications for Clinical Application

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