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Decoding and targeting the molecular basis of MACC1-driven metastatic spread: Lessons from big data mining and
Jan Budczies1, Klaus Kluck2, Wolfgang Walther3
1Institute of Pathology, University Hospital Heidelberg, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany; German Cancer Consortium, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany.
Abstract:
Metastasis remains the key issue impacting cancer patient survival and failure or success of cancer therapies. Metastatic spread is a complex process including dissemination of single cells or collective cell migration, penetration of the blood or lymphatic vessels and seeding at a distant organ site. Hundreds of genes involved in metastasis have been identified in studies across numerous cancer types. Here, we analyzed how the metastasis-associated gene MACC1 cooperates with other genes in metastatic spread and how these coactions could be exploited by combination therapies: We performed (i) a MACC1 correlation analysis across 33 cancer types in the mRNA expression data of TCGA and (ii) a comprehensive literature search on reported MACC1 combinations and regulation mechanisms. The key genes MET, HGF and MMP7 reported together with MACC1 showed significant positive correlations with MACC1 in more than half of the cancer types included in the big data analysis. However, ten other genes also reported together with MACC1 in the literature showed significant positive correlations with MACC1 in only a minority of 5 to 15 cancer types. To uncover transcriptional regulation mechanisms that are activated simultaneously with MACC1, we isolated pan-cancer consensus lists of 1306 positively and 590 negatively MACC1-correlating genes from the TCGA data and analyzed each of these lists for sharing transcription factor binding motifs in the promotor region. In these lists, binding sites for the transcription factors TELF1, ETS2, ETV4, TEAD1, FOXO4, NFE2L1, ELK1, SP1 and NFE2L2 were significantly enriched, but none of them except SP1 was reported in combination with MACC1 in the literature. Thus, while some of the results of the big data analysis were in line with the reported experimental results, hypotheses on new genes involved in MACC1-driven metastasis formation could be generated and warrant experimental validation. Furthermore, the results of the big data analysis can help to prioritize cancer types for experimental studies and testing of combination therapies.
Insights
Metastasis-associated gene MACC1 interacts with key genes like MET, HGF, and MMP7 across many cancers. Big data analysis reveals new potential gene targets and transcription factors for combination cancer therapies.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- Metastasis is a critical challenge in cancer patient survival and treatment efficacy.
- Metastatic spread involves complex cellular processes and numerous associated genes.
- The metastasis-associated gene MACC1 plays a role in this process.
Purpose of the Study:
- To investigate the cooperative interactions of MACC1 with other genes in metastatic spread.
- To identify potential combination therapies by understanding these gene coactions.
- To uncover transcriptional regulation mechanisms activated with MACC1.
Main Methods:
- Correlation analysis of MACC1 mRNA expression across 33 cancer types using TCGA data.
- Comprehensive literature search for MACC1 combinations and regulatory mechanisms.
- Identification of shared transcription factor binding motifs in promoter regions of MACC1-correlating genes.
Main Results:
- Significant positive correlations between MACC1 and MET, HGF, and MMP7 in over half of analyzed cancer types.
- Identification of 1306 positively and 590 negatively MACC1-correlating genes.
- Enrichment of binding sites for transcription factors TELF1, ETS2, ETV4, TEAD1, FOXO4, NFE2L1, ELK1, SP1, and NFE2L2, with SP1 being previously reported in combination with MACC1.
Conclusions:
- Big data analysis partially aligns with existing experimental findings on MACC1.
- New hypotheses on genes involved in MACC1-driven metastasis can be generated for experimental validation.
- Results can guide prioritization of cancer types for further research and combination therapy development.
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