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The apoptotic machinery as a biological complex system: analysis of its omics and evolution, identification of candidate genes for fourteen major types of cancer, and experimental validation in CML and neuroblastoma.

Di Pietro C, Ragusa M, Barbagallo D, Duro LR, Guglielmino MR, Majorana A, Angelica R, Scalia M, Statello L, Salito L, Tomasello L, Pernagallo S, Valenti S, D'Agostino V, Triberio P, Tandurella I, Palumbo GA, La Cava P, Cafiso V, Bertuccio T, Santagati M, Li Destri G, Lanzafame S, Di Raimondo F, Stefani S, Mishra B, Purrello M - BMC Med Genomics (2009)

Bottom Line: This project exploited the methodology commonly used in Computational Biology (i.e., mining of many omics databases of the web) as well as the High Throughput biomolecular analytical techniques.The comparison of the fourteen mutated AM networks (both protein- as MIR-based) has allowed us to pinpoint the hubs with a general and critical role in tumour development and, conversely, in cell physiology: in particular, we found that some of these had already been used as targets for pharmacological anticancer therapy.This approach could pave the way for future studies and applications in molecular and clinical Medicine with important perspectives both for Oncology as for Regenerative Medicine.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dipartimento di Scienze BioMediche, Sezione di Biologia Generale, Biologia Cellulare, Genetica Molecolare G Sichel, Unità di Biologia Genomica e dei Sistemi Complessi, Genetica, Bioinformatica, Università di Catania, 95123 Catania, Italy. dipietro@unict.it

ABSTRACT

Background: Apoptosis is a critical biological phenomenon, executed under the guidance of the Apoptotic Machinery (AM), which allows the physiologic elimination of terminally differentiated, senescent or diseased cells. Because of its relevance to BioMedicine, we have sought to obtain a detailed characterization of AM Omics in Homo sapiens, namely its Genomics and Evolution, Transcriptomics, Proteomics, Interactomics, Oncogenomics, and Pharmacogenomics.

Methods: This project exploited the methodology commonly used in Computational Biology (i.e., mining of many omics databases of the web) as well as the High Throughput biomolecular analytical techniques.

Results: In Homo sapiens AM is comprised of 342 protein-encoding genes (possessing either anti- or pro-apoptotic activity, or a regulatory function) and 110 MIR-encoding genes targeting them: some have a critical role within the system (core AM nodes), others perform tissue-, pathway-, or disease-specific functions (peripheral AM nodes). By overlapping the cancer type-specific AM mutation map in the fourteen most frequent cancers in western societies (breast, colon, kidney, leukaemia, liver, lung, neuroblastoma, ovary, pancreas, prostate, skin, stomach, thyroid, and uterus) to their transcriptome, proteome and interactome in the same tumour type, we have identified the most prominent AM molecular alterations within each class. The comparison of the fourteen mutated AM networks (both protein- as MIR-based) has allowed us to pinpoint the hubs with a general and critical role in tumour development and, conversely, in cell physiology: in particular, we found that some of these had already been used as targets for pharmacological anticancer therapy. For a better understanding of the relationship between AM molecular alterations and pharmacological induction of apoptosis in cancer, we examined the expression of AM genes in K562 and SH-SY5Y after anticancer treatment.

Conclusion: We believe that our data on the Apoptotic Machinery will lead to the identification of new cancer genes and to the discovery of new biomarkers, which could then be used to profile cancers for diagnostic purposes and to pinpoint new targets for pharmacological therapy. This approach could pave the way for future studies and applications in molecular and clinical Medicine with important perspectives both for Oncology as for Regenerative Medicine.

No MeSH data available.


Related in: MedlinePlus

Correlation between mutated links and transcriptome alterations. The X-axis represents the distribution classes of AM network genes (without mutations), based on the number of the cancer mutated genes linked to a specific gene. The Y-axis represents the weighted average of the altered transcriptome AM genes for each class.
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Figure 14: Correlation between mutated links and transcriptome alterations. The X-axis represents the distribution classes of AM network genes (without mutations), based on the number of the cancer mutated genes linked to a specific gene. The Y-axis represents the weighted average of the altered transcriptome AM genes for each class.

Mentions: Analysis of the relationship between the link number of a gene to genome mutations and transcriptome alterations allowed us to discover that the genes with more links to mutated genes are more likely to be dysregulated in tumours (Figure 14). Our analysis demonstrates that the hubs of the AM network typically represent the nodes with the highest number of genome, transcriptome or proteome alterations in all cancer models analyzed, even though the oncogenic relevance of each hub seems to be tumour (or tumour group) – specific (Additional file 12). Moreover, we found that the average degree of the mutated nodes is significantly higher than the average degree of not mutated ones (p < 0.0001, Wilcoxon signed-rank test). Intriguingly, approximately 70% of NUPs are nodes with a higher degree of connectivity than the average AM proteins and some of them are hubs (i.e., AKT1, BCL2, BCL2L1, CDKN2A, and TP53): indeed, the NUPs showed a higher degree (degree > 17) than the other non-NUP proteins (p < 0.01, Fisher's exact test).


The apoptotic machinery as a biological complex system: analysis of its omics and evolution, identification of candidate genes for fourteen major types of cancer, and experimental validation in CML and neuroblastoma.

Di Pietro C, Ragusa M, Barbagallo D, Duro LR, Guglielmino MR, Majorana A, Angelica R, Scalia M, Statello L, Salito L, Tomasello L, Pernagallo S, Valenti S, D'Agostino V, Triberio P, Tandurella I, Palumbo GA, La Cava P, Cafiso V, Bertuccio T, Santagati M, Li Destri G, Lanzafame S, Di Raimondo F, Stefani S, Mishra B, Purrello M - BMC Med Genomics (2009)

Correlation between mutated links and transcriptome alterations. The X-axis represents the distribution classes of AM network genes (without mutations), based on the number of the cancer mutated genes linked to a specific gene. The Y-axis represents the weighted average of the altered transcriptome AM genes for each class.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2683874&req=5

Figure 14: Correlation between mutated links and transcriptome alterations. The X-axis represents the distribution classes of AM network genes (without mutations), based on the number of the cancer mutated genes linked to a specific gene. The Y-axis represents the weighted average of the altered transcriptome AM genes for each class.
Mentions: Analysis of the relationship between the link number of a gene to genome mutations and transcriptome alterations allowed us to discover that the genes with more links to mutated genes are more likely to be dysregulated in tumours (Figure 14). Our analysis demonstrates that the hubs of the AM network typically represent the nodes with the highest number of genome, transcriptome or proteome alterations in all cancer models analyzed, even though the oncogenic relevance of each hub seems to be tumour (or tumour group) – specific (Additional file 12). Moreover, we found that the average degree of the mutated nodes is significantly higher than the average degree of not mutated ones (p < 0.0001, Wilcoxon signed-rank test). Intriguingly, approximately 70% of NUPs are nodes with a higher degree of connectivity than the average AM proteins and some of them are hubs (i.e., AKT1, BCL2, BCL2L1, CDKN2A, and TP53): indeed, the NUPs showed a higher degree (degree > 17) than the other non-NUP proteins (p < 0.01, Fisher's exact test).

Bottom Line: This project exploited the methodology commonly used in Computational Biology (i.e., mining of many omics databases of the web) as well as the High Throughput biomolecular analytical techniques.The comparison of the fourteen mutated AM networks (both protein- as MIR-based) has allowed us to pinpoint the hubs with a general and critical role in tumour development and, conversely, in cell physiology: in particular, we found that some of these had already been used as targets for pharmacological anticancer therapy.This approach could pave the way for future studies and applications in molecular and clinical Medicine with important perspectives both for Oncology as for Regenerative Medicine.

View Article: PubMed Central - HTML - PubMed

Affiliation: Dipartimento di Scienze BioMediche, Sezione di Biologia Generale, Biologia Cellulare, Genetica Molecolare G Sichel, Unità di Biologia Genomica e dei Sistemi Complessi, Genetica, Bioinformatica, Università di Catania, 95123 Catania, Italy. dipietro@unict.it

ABSTRACT

Background: Apoptosis is a critical biological phenomenon, executed under the guidance of the Apoptotic Machinery (AM), which allows the physiologic elimination of terminally differentiated, senescent or diseased cells. Because of its relevance to BioMedicine, we have sought to obtain a detailed characterization of AM Omics in Homo sapiens, namely its Genomics and Evolution, Transcriptomics, Proteomics, Interactomics, Oncogenomics, and Pharmacogenomics.

Methods: This project exploited the methodology commonly used in Computational Biology (i.e., mining of many omics databases of the web) as well as the High Throughput biomolecular analytical techniques.

Results: In Homo sapiens AM is comprised of 342 protein-encoding genes (possessing either anti- or pro-apoptotic activity, or a regulatory function) and 110 MIR-encoding genes targeting them: some have a critical role within the system (core AM nodes), others perform tissue-, pathway-, or disease-specific functions (peripheral AM nodes). By overlapping the cancer type-specific AM mutation map in the fourteen most frequent cancers in western societies (breast, colon, kidney, leukaemia, liver, lung, neuroblastoma, ovary, pancreas, prostate, skin, stomach, thyroid, and uterus) to their transcriptome, proteome and interactome in the same tumour type, we have identified the most prominent AM molecular alterations within each class. The comparison of the fourteen mutated AM networks (both protein- as MIR-based) has allowed us to pinpoint the hubs with a general and critical role in tumour development and, conversely, in cell physiology: in particular, we found that some of these had already been used as targets for pharmacological anticancer therapy. For a better understanding of the relationship between AM molecular alterations and pharmacological induction of apoptosis in cancer, we examined the expression of AM genes in K562 and SH-SY5Y after anticancer treatment.

Conclusion: We believe that our data on the Apoptotic Machinery will lead to the identification of new cancer genes and to the discovery of new biomarkers, which could then be used to profile cancers for diagnostic purposes and to pinpoint new targets for pharmacological therapy. This approach could pave the way for future studies and applications in molecular and clinical Medicine with important perspectives both for Oncology as for Regenerative Medicine.

No MeSH data available.


Related in: MedlinePlus