HCMDB: the human cancer metastasis database

Zheng, Guantao; Ma, Yijie; Zou, Yang; Yin, An; Li, Wushuang; Dong, Dong

doi:10.1093/nar/gkx1008

Cited by 115 publications

(97 citation statements)

References 50 publications

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“…Three lists of cancer-related proteins were scanned with the final ML prediction model: 1232 CIPs were taken from Patel et al [37], 1903 MDPs were taken from the Human Cancer Metastasis Database (HCMDB) (http://hcmdb.i-sanger.com/index) [68], and 1369 RBPs were taken from Hentze et al , [41, 68] (Tables S3-S5).…”

Section: Methodsmentioning

confidence: 99%

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

López‐Cortés

Cabrera-Andrade

Vázquez-Naya

et al. 2019

Preprint

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BackgroundBreast cancer (BC) is a heterogeneous disease characterized by an intricate interplay between different biological aspects such as ethnicity, genomic alterations, gene expression deregulation, hormone disruption, signaling pathway alterations and environmental determinants. Due to the complexity of BC, the prediction of proteins involved in this disease is a trending topic in drug design.MethodsThis work is proposing accurate prediction classifier for BC proteins using six sets of protein sequence descriptors and 13 machine learning methods. After using a univariate feature selection for the mix of five descriptor families, the best classifier was obtained using multilayer perceptron method (artificial neural network) and 300 features.ResultsThe performance of the model is demonstrated by the area under the receiver operating characteristics (AUROC) of 0.980 ± 0.0037 and accuracy of 0.936 ± 0.0056 (3-fold cross-validation). Regarding the prediction of 4504 cancer-associated proteins using this model, the best ranked cancer immunotherapy proteins related to BC were RPS27, SUPT4H1, CLPSL2, POLR2K, RPL38, AKT3, CDK3, RPS20, RASL11A and UBTD1; the best ranked metastasis driver proteins related to BC were S100A9, DDA1, TXN, PRNP, RPS27, S100A14, S100A7, MAPK1, AGR3 and NDUFA13; and the best ranked RNA-binding proteins related to BC were S100A9, TXN, RPS27L, RPS27, RPS27A, RPL38, MRPL54, PPAN, RPS20 and CSRP1.ConclusionsThis powerful model predicts several BC-related proteins which should be deeply studied to find new biomarkers and better therapeutic targets. The script and the results are available as a free repository at https://github.com/muntisa/neural-networks-for-breast-cancer-proteins.

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Section: Methodsmentioning

confidence: 99%

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

López‐Cortés

Cabrera-Andrade

Vázquez-Naya

et al. 2019

Preprint

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“…The PPi network was performed to better understand the behavior of hypoxia-related proteins in cancer and metastasis using the String Database and Cytoscape (Figure 7 (Table S25). Additionally, top ten proteins with the highest degree centrality in the HIF-1 signaling pathway were AKT1, PIK3CA, HIF1A, VEGFA, EGFR, EP300, CREBBP, STAT3, On the other hand, the Human Cancer Metastasis Database (HCMDB) is an integrated database designed to analyze large scale expression data of cancer metastasis 59 . Our PPi network was conformed by 179 (62%) metastatic driver proteins ( Figure 7) (Table S26).…”

Section: Protein-protein Interactome Networkmentioning

confidence: 99%

“…Metastasis driver proteins that interact with the hypoxia-related proteins were taken from the Human Cancer Metastasis Database (https://hcmdb.i-sanger.com/). HCMDB is an integrated database designed to analyze large scale expression data of cancer metastasis 59 . The network visualization was analyzed through the Cytoscape software 58 .…”

Section: Protein-protein Interactome Networkmentioning

confidence: 99%

Metastatic signaling of hypoxia-related genes across TCGA Pan-Cancer types

López‐Cortés

Guevara-Ramírez

Guerrero

et al. 2020

Preprint

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Many primary-tumor subregions have low levels of molecular oxygen, termed hypoxia.Hypoxic tumors are at elevated risk for local failure and distant metastasis. Metastatic disease is the leading cause of cancer-related deaths and involves critical interactions between tumor cells and the microenvironment. Here we focused on elucidating the molecular hallmarks of tumor hypoxia that remains poorly defined. To fill this gap, we analyzed the genomic alterations and hypoxia score of 233 hypoxia-related genes of 6,343 individuals across 17 TCGA Pan-Cancer types. In addition, we analyzed a protein-protein interactome (PPi) network and the shortest paths from hypoxic proteins to metastasis. As results, mRNA high alteration was prevalent in all cancer types. Genomic alterations and hypoxia score presented a highest frequency in tumor stage 4 and positive metastasis status in all cancer types. The most significant signaling pathways were HIF-1, ErbB, PI3K-Akt, FoxO, mTOR, Ras and VEGF. The PPi network revealed a strong association among hypoxic proteins, cancer driver proteins and metastasis driver proteins. The analysis of shortest paths revealed 99 ways to spread metastasis signaling from hypoxic proteins. Additionally, we proposed 62 hypoxic genes strongly associated with metastasis and 27 of them with high amount of genomic alterations. Overall, tumor hypoxia may drive aggressive molecular features across cancer types. Hence, we identified potential biomarkers and therapeutic targets regulated by hypoxia that could be incorporated into strategies aimed at improving novel drug development and treating metastasis.

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“…Besides functional annotations, public literature retrieval further provided associated evidence for the specific functions of these target genes in tumor metastasis. Through the human cancer metastasis database, 10 which contained systematically collected transcriptome data and manually curated publications related to human cancer metastasis, we found that 107 target genes had been demonstrated to be related to the metastasis of multiple types of cancer ( Figure 6C).…”

Section: Functional Enrichment Analysis Of Downstream Target Genesmentioning

confidence: 99%

Integrative analysis of significant RNA‐binding proteins in colorectal cancer metastasis

Zhou

Guo

2018

J of Cellular Biochemistry

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The aberrant expression of RNA-binding proteins (RBPs) plays a crucial role in the occurrence and progression of human cancer. However, the key functions of RBPs in the metastasis of colorectal cancer have not yet been fully elucidated. Here, we integrated multi-omics data and identified four differentially expressed RBPs (APOBEC3G, EEF1A2, EIF5AL1 and CELF3) in patients with colorectal cancer metastasis. To clarify the underlying molecular mechanisms, we systematically analyzed the genomic features and downstream regulatory relationships of the four RBPs. In a genomic level, the copy number variations of APOBEC3G, EEF1A2, and CELF3 demonstrated significantly differential distributions between metastatic and nonmetastatic patients. Besides that, combining sequence and expression information, we identified 436 putative RNA targets regulated by the four RBPs through strict multistep bioinformatics screening. For the downstream analysis, the evidence from functional enrichment analysis and public literature indicated the roles of these target genes in the carcinogenesis and progression of colorectal cancer. Furthermore, through the machine learning algorithm and statistical analysis, we obtained two gene candidates that had obvious effects on the metastasis and overall survival status of patients with colorectal cancer. In summary, our study comprehensively explored the influence of APOBEC3G, EEF1A2, EIF5AL1, and CELF3 in colorectal cancer metastasis, which may offer favorable perspectives for clinical diagnosis and therapy.

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HCMDB: the human cancer metastasis database

Cited by 115 publications

References 50 publications

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

Prediction of breast cancer proteins using molecular descriptors and artificial neural networks: a focus on cancer immunotherapy proteins, metastasis driver proteins, and RNA-binding proteins

Metastatic signaling of hypoxia-related genes across TCGA Pan-Cancer types

Integrative analysis of significant RNA‐binding proteins in colorectal cancer metastasis

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