NEDD9 Overexpression Causes Hyperproliferation of Luminal Cells and Cooperates with HER2 Oncogene in Tumor Initiation: A Novel Prognostic Marker in Breast Cancer

Purazo, Marc L.; Ice, Ryan J.; Shimpi, Rahul A.; Hoenerhoff, Mark J.; Pugacheva, Elena N.

doi:10.3390/cancers15041119

Cited by 3 publications

(2 citation statements)

References 58 publications

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“…For example, modules MYC, NRAS, PIK3CG, PLCE1 ( k = 4, index 407), and CBX8, PIK3R1, POLB ( k = 3, index 19) were significantly correlated with 112 and 111 pathways, respectively. The top 1 module ERBB4, FLT3, MAP3K1, NEDD9 has the highest IE value and furthermore NEDD9, which is not included NCG or other references, was proven to set permissive conditions for HER2-induced tumorigenesis by a recent study ( Purazo et al 2023 ).…”

Section: Resultsmentioning

confidence: 99%

MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules

Liu,

Ma,

Zhu

et al. 2023

Bioinformatics

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Motivation Cancer is caused by the accumulation of somatic mutations in multiple pathways, in which driver mutations are typically of the properties of high coverage and high exclusivity in patients. Identifying cancer driver genes has a pivotal role in understanding the mechanisms of oncogenesis and treatment. Results Here, we introduced MaxCLK, an algorithm for identifying cancer driver genes, which was developed by an integrated analysis of somatic mutation data and protein–protein interaction (PPI) networks and further improved by an information entropy index. Tested on pancancer and single cancers, MaxCLK outperformed other existing methods with higher accuracy. About pancancer, we predicted 154 driver genes and 787 driver modules. The analysis of co-occurrence and exclusivity between modules and pathways reveals the correlation of their combinations. Overall, our study has deepened the understanding of driver mechanism in PPI topology and found novel driver genes. Availability and implementation The source codes for MaxCLK are freely available at https://github.com/ShandongUniversityMasterMa/MaxCLK-main.

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Section: Resultsmentioning

confidence: 99%

MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules

Liu,

Ma,

Zhu

et al. 2023

Bioinformatics

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“…For NEDD9, Hu et al discovered histone deacetylase inhibitors promoted breast cancer metastasis by elevating NEDD9 expression [ 49 ]. Also, NEDD9 over-expression caused hyper-proliferation of luminal cells and cooperated with the HER2 oncogene in tumor initiation [ 50 ]. So, both CA9 and FOXM1 were novel markers of poor prognosis for breast cancer patients.…”

Section: Discussionmentioning

confidence: 99%

Construction and validation of a hypoxia-related gene signature to predict the prognosis of breast cancer

Qiu,

Wang,

et al. 2024

BMC Cancer

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Background Among the most common forms of cancer worldwide, breast cancer posed a serious threat to women. Recent research revealed a lack of oxygen, known as hypoxia, was crucial in forming breast cancer. This research aimed to create a robust signature with hypoxia-related genes to predict the prognosis of breast cancer patients. The function of hypoxia genes was further studied through cell line experiments. Materials and methods In the bioinformatic part, transcriptome and clinical information of breast cancer were obtained from The Cancer Genome Atlas(TCGA). Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. The TCGA filtered data was evenly split, ensuring a 1:1 distribution between the training and testing sets. Prognostic-related DEHRGs were identified through Cox regression. The signature was established through the training set. Then, it was validated using the test set and external validation set GSE131769 from Gene Expression Omnibus (GEO). The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristiccurve. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments. Results In the bioinformatic part, 141 up-regulated and 157 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis than higher-risk ones (P < 0.001). The signature was double-validated in the test set and GSE131769 (P = 0.006 and P = 0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancer cells, while the down-regulation of KCNJ11 did not have any functions. Conclusion Based on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast cancer patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast cancer by hypoxia-related genes.

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Construction and validation of a hypoxia-related gene signature to predict the prognosis of invasive ductal breast carcinoma

Qiu,

Wang,

et al. 2023

Preprint

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Background: Invasive ductal carcinoma (IDC), the most common type of breast cancer, was known for its aggressive nature. Recent research revealeda lack of oxygen, known as hypoxia, wascrucial in forming breast cancer. This research aimed to create a robustsignature with hypoxia-related genes to predict the prognosis of IDC patients. The function of hypoxia genes was further studied through cell line experiments. Materials and Methods: In the bioinformatic part, transcriptome and clinical information of breast IDC were obtained from The Cancer Genome Atlas. Hypoxia-related genes were downloaded from the Genecards Platform. Differentially expressed hypoxia-related genes (DEHRGs) were identified. Training and test sets were defined with a 1:1 ratio. Prognostic-related DEHRGs were identified through Cox regression; then the signature was established and validated. The nomogram was created by incorporating the signature and clinicopathological characteristics. The predictive value of the nomogram was evaluated by C-index and receiver operating characteristic cure. Immune microenvironment and mutation burden were also examined. In the experiment part, the function of the two most significant hypoxia-related genes were further explored by cell-line experiments. Results: In the bioinformatic part, 37 up-regulated and 55 down-regulated DEHRGs were screened out. A prognostic signature was constructed containing nine hypoxia genes (ALOX15B, CA9, CD24, CHEK1, FOXM1, HOTAIR, KCNJ11, NEDD9, PSME2) in the training set. Low-risk patients exhibited a much more favorable prognosis tthan higher-risk ones(P<0.001). The signature was double-validated in the test set and GSE131769 (P=0.006 and P=0.001). The nomogram showed excellent predictive value with 1-year OS AUC: 0.788, 3-year OS AUC: 0.783, and 5-year OS AUC: 0.817. Patients in the high-risk group had a higher tumor mutation burden when compared to the low-risk group. In the experiment part, the down-regulation of PSME2 inhibited cell growth ability and clone formation capability of breast cancercells, while the down-regulation of KCNJ11 did not have any functions. Conclusion: Based on 9 DEHRGs, a reliable signature was established through the bioinformatic method. It could accurately predict the prognosis of breast IDC patients. Cell line experiment indicated that PSME2 played a protective role. Summarily, we provided a new insight to predict the prognosis of breast IDC by hypoxia-related genes.

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NEDD9 Overexpression Causes Hyperproliferation of Luminal Cells and Cooperates with HER2 Oncogene in Tumor Initiation: A Novel Prognostic Marker in Breast Cancer

Cited by 3 publications

References 58 publications

MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules

MaxCLK: discovery of cancer driver genes via maximal clique and information entropy of modules

Construction and validation of a hypoxia-related gene signature to predict the prognosis of breast cancer

Construction and validation of a hypoxia-related gene signature to predict the prognosis of invasive ductal breast carcinoma

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