Decoding and targeting the molecular basis of MACC1-driven metastatic spread: Lessons from big data mining and clinical-experimental approaches

Budczies, Jan; Kluck, Klaus; Walther, Wolfgang; Stein, Ulrike

doi:10.1016/j.semcancer.2019.08.010

Cited by 19 publications

(12 citation statements)

References 121 publications

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“…However, it is unconvincing to use a single lncRNA as a target due to the significant heterogeneity of GBM (26). More recently, the molecular research and gene exploration of the tumor, especially the statistical analysis research based on big data, has made rapid progress (27,28). Polygenic research has become the focus of this field.…”

Section: Discussionmentioning

confidence: 99%

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Niu¹,

Sun

Meng

et al. 2020

Front. Oncol.

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Accumulating studies have confirmed the crucial role of long non-coding RNAs (ncRNAs) as favorable biomarkers for cancer diagnosis, therapy, and prognosis prediction. In our recent study, we established a robust model which is based on multi-gene signature to predict the therapeutic efficacy and prognosis in glioblastoma (GBM), based on Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. lncRNA-seq data of GBM from TCGA and CGGA datasets were used to identify differentially expressed genes (DEGs) compared to normal brain tissues. The DEGs were then used for survival analysis by univariate and multivariate COX regression. Then we established a risk score model, depending on the gene signature of multiple survival-associated DEGs. Subsequently, Kaplan-Meier analysis was used for estimating the prognostic and predictive role of the model. Gene set enrichment analysis (GSEA) was applied to investigate the potential pathways associated to high-risk score by the R package “cluster profile” and Wiki-pathway. And five survival associated lncRNAs of GBM were identified: LNC01545, WDR11-AS1, NDUFA6-DT, FRY-AS1, TBX5-AS1. Then the risk score model was established and shows a desirable function for predicting overall survival (OS) in the GBM patients, which means the high-risk score significantly correlated with lower OS both in TCGA and CGGA cohort. GSEA showed that the high-risk score was enriched with PI3K-Akt, VEGFA-VEGFR2, TGF-beta, Notch, T-Cell pathways. Collectively, the five-lncRNAs signature-derived risk score presented satisfactory efficacies in predicting the therapeutic efficacy and prognosis in GBM and will be significant for guiding therapeutic strategies and research direction for GBM.

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Section: Discussionmentioning

confidence: 99%

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Niu¹,

Sun

Meng

et al. 2020

Front. Oncol.

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“…The gene Metastasis-associated in colon cancer 1 (MACC1) was newly discovered in our group and is a key regulator of metastasis as well as a prognostic and predictive marker in many tumor entities [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ]. However, its role in cancer metabolism is underinvestigated.…”

Section: Introductionmentioning

confidence: 99%

Systematic Identification of MACC1-Driven Metabolic Networks in Colorectal Cancer

Lisec

Kobelt

Walther

et al. 2021

Cancers

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MACC1 is a prognostic and predictive metastasis biomarker for more than 20 solid cancer entities. However, its role in cancer metabolism is not sufficiently explored. Here, we report on how MACC1 impacts the use of glucose, glutamine, lactate, pyruvate and fatty acids and show the comprehensive analysis of MACC1-driven metabolic networks. We analyzed concentration-dependent changes in nutrient use, nutrient depletion, metabolic tracing employing 13C-labeled substrates, and in vivo studies. We found that MACC1 permits numerous effects on cancer metabolism. Most of those effects increased nutrient uptake. Furthermore, MACC1 alters metabolic pathways by affecting metabolite production or turnover from metabolic substrates. MACC1 supports use of glucose, glutamine and pyruvate via their increased depletion or altered distribution within metabolic pathways. In summary, we demonstrate that MACC1 is an important regulator of metabolism in cancer cells.

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“…Only a few samples showed MACC1 gene gain, high mRNA expression but low protein levels. It could be that for these few tumors, the regulation occurred at another level in addition to the described ones: A large body of reports describe post-transcriptional regulations of MACC1 expression by miRNA, lnc-RNA and circRNA [ 5 , 16 ]. The identification of the MACC1 gene promoter enabled the identification of transcription factors and respective transcription factor binding sites regulating the transcription of the metastasis inducer MACC1 [ 14 , 15 ].…”

Section: Discussionmentioning

confidence: 99%

“…Identification of the MACC1 gene promoter has unveiled transcription factors and respective transcription factor binding sites regulating the transcription of the metastasis inducer MACC1 [ 14 , 15 ]. Post-transcriptional regulations of MACC1 expression by miRNA, lnc-RNA and circRNA are also reported [ 5 , 16 ].…”

Section: Introductionmentioning

confidence: 99%

Elevated MACC1 Expression in Colorectal Cancer Is Driven by Chromosomal Instability and Is Associated with Molecular Subtype and Worse Patient Survival

Vuaroqueaux¹,

Musch²,

Kobelt

et al. 2022

Cancers

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Metastasis-Associated in Colon Cancer 1 (MACC1) is a strong prognostic biomarker inducing proliferation, migration, invasiveness, and metastasis of cancer cells. The context of MACC1 dysregulation in cancers is, however, still poorly understood. Here, we investigated whether chromosomal instability and somatic copy number alterations (SCNA) frequently occurring in CRC contribute to MACC1 dysregulation, with prognostic and predictive impacts. Using the Oncotrack and Charité CRC cohorts of CRC patients, we showed that elevated MACC1 mRNA expression was tightly dependent on increased MACC1 gene SCNA and was associated with metastasis and shorter metastasis free survival. Deep analysis of the COAD-READ TCGA cohort revealed elevated MACC1 expression due to SCNA for advanced tumors exhibiting high chromosomal instability (CIN), and predominantly classified as CMS2 and CMS4 transcriptomic subtypes. For that cohort, we validated that elevated MACC1 mRNA expression correlated with reduced disease-free and overall survival. In conclusion, this study gives insights into the context of MACC1 expression in CRC. Increased MACC1 expression is largely driven by CIN, SCNA gains, and molecular subtypes, potentially determining the molecular risk for metastasis that might serve as a basis for patient-tailored treatment decisions.

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Decoding and targeting the molecular basis of MACC1-driven metastatic spread: Lessons from big data mining and clinical-experimental approaches

Cited by 19 publications

References 121 publications

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

A Five-lncRNAs Signature-Derived Risk Score Based on TCGA and CGGA for Glioblastoma: Potential Prospects for Treatment Evaluation and Prognostic Prediction

Systematic Identification of MACC1-Driven Metabolic Networks in Colorectal Cancer

Elevated MACC1 Expression in Colorectal Cancer Is Driven by Chromosomal Instability and Is Associated with Molecular Subtype and Worse Patient Survival

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