A bioinformatics analysis: ZFHX4 is associated with metastasis and poor survival in ovarian cancer

Zong, Shuai; Xu, Ping-ping; Xu, Yinhai; Guo, Yi

doi:10.1186/s13048-022-01024-x

Cited by 16 publications

(8 citation statements)

References 51 publications

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“…Studies demonstrate its involvement in p53-mediated stress response, promoting an antiapoptotic role in breast cancer and contributing to transcriptional repression of pro-apoptotic factors [Koeppel et al, 2009], characteristic attributes of the proliferative subtype. Another hub gene for both mesenchymal and proliferative subtypes, the two subtypes with worse prognostic, is ZFHX4, which has been previously described as a prognostic biomarker for HGSOC [Millstein et al, 2020; Zong et al, 2022].…”

Section: Resultsmentioning

confidence: 99%

MORE interpretable multi-omic regulatory networks to characterize phenotypes

Aguerralde-Martin,

Clemente-Císcar,

Lopez-Cárcel

et al. 2024

Preprint

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The emergence of omics has revolutionized how we study and understand biological systems, enabling breakthrough discoveries with profound implications for medicine, agriculture, biotechnology, and more. However, with the help of advanced computational tools and artificial intelligence, meaningful patterns and relationships can now be uncovered in omics data, offering a unique opportunity to gain a deeper understanding and contribute to new insights into the complex regulatory mechanisms of biological systems. In this context, we have developed MORE (Multi-Omics REgulation), a tool designed to identify relevant regulations of the gene expression for the biological system under study and subsequently construct the regulatory networks for the considered experimental conditions. The presented method not only allows the incorporation of prior biological information into network construction but also can infer relationships de novo in the absence of such information. Moreover, we effectively addressed multicollinearity issues inherent in such data types, ensuring precise and reliable inference of regulatory networks when performing GLM models. In our comparison to KiMONo, our tool exhibited superior evaluation metrics, including F1-score, R2, and computational efficiency. Finally, applying our tool to a real ovarian cancer dataset yielded intriguing and biologically meaningful results. Our developed methodology represents a versatile and powerful multi-omic regulatory network inference approach, demonstrating good performance and applicability to real-world biological datasets. It is freely available at https://github.com/ConesaLab/MORE.git.

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Section: Resultsmentioning

confidence: 99%

MORE interpretable multi-omic regulatory networks to characterize phenotypes

Aguerralde-Martin,

Clemente-Císcar,

Lopez-Cárcel

et al. 2024

Preprint

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“…Concerning oncogenic drivers, the TERT promoter mutation was present in all cases except case 2, and KRAS and NRAS amplifications were detected in two cases. In the TWM cases, except case 1, ARID3A [ 35 ], ASXL1 [ 36 ], ASXL2 [ 36 ], FMN2 [ 37 ], FAM83B [ 38 ] and ZFHX4 [ 39 ] mutations were also identified as potential oncogenic drivers, known from other tumor types. Furthermore, PARP4 and PARP14 mutations were revealed in some cases.…”

Section: Resultsmentioning

confidence: 99%

The Driverless Triple-Wild-Type (BRAF, RAS, KIT) Cutaneous Melanoma: Whole Genome Sequencing Discoveries

Pipek

Vízkeleti

Doma

et al. 2023

Cancers

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The genetic makeup of the triple-wild-type melanoma (BRAF, NRAS and NF1) has been known for some time, but those studies grouped together rare histopathological versions with common ones, as well as mucosal and even uveal ones. Here we used whole genome sequencing to genetically characterize the triple-wild-type melanoma (TWM), termed here as BRAF, RAS and KIT wild type (the most frequent oncogenic drivers of skin melanoma), using the most common histological forms and excluding rare ones. All these tumors except one were clearly induced by UV based on the mutational signature. The tumor mutational burden was low in TWM, except in the NF1 mutant forms, and a relatively high frequency of elevated LOH scores suggested frequent homologue recombination deficiency, but this was only confirmed by the mutation signature in one case. Furthermore, all these TWMs were microsatellite-stabile. In this driverless setting, we revealed rare oncogenic drivers known from melanoma or other cancer types and identified rare actionable tyrosine kinase mutations in NTRK1, RET and VEGFR1. Mutations of TWM identified genes involved in antitumor immunity (negative and positive predictors of immunotherapy), Ca++ and BMP signaling. The two regressed melanomas of this cohort shared a 17-gene mutation signature, containing genes involved in antitumor immunity and several cell surface receptors. Even with this comprehensive genomic approach, a few cases remained driverless, suggesting that unrecognized drivers are hiding among passenger mutations.

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“…ZFHX4 was linked to metastasis and poor survival in OC. [18] KIF26B was involved in cell migration and proliferation in OC. [19] EMP1 accelerated tumor progression of OC by activating the MAPK pathway.…”

Section: Discussionmentioning

confidence: 99%

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer

Fu,

Feng,

Tao

2024

Medicine

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Ovarian cancer (OC) is the leading cause of gynecological cancer death. Cancer-associated fibroblasts (CAF) is involved in wound healing and inflammatory processes, tumor occurrence and progression, and chemotherapy resistance in OC. GSE184880 dataset was used to identify CAF-related genes in OC. CAF-related signature (CRS) was constructed using integrative 10 machine learning methods with the datasets from the Cancer Genome Atlas, GSE14764, GSE26193, GSE26712, GSE63885, and GSE140082. The performance of CRS in predicting immunotherapy benefits was verified using 3 immunotherapy datasets (GSE91061, GSE78220, and IMvigor210) and several immune calculating scores. The Lasso + StepCox[forward] method-based predicting model having a highest average C index of 0.69 was referred as the optimal CRS and it had a stable and powerful performance in predicting clinical outcome of OC patients, with the 1-, 3-, and 5-year area under curves were 0.699, 0.708, and 0.767 in the Cancer Genome Atlas cohort. The C index of CRS was higher than that of tumor grade, clinical stage, and many developed signatures. Low CRS score demonstrated lower tumor immune dysfunction and exclusion score, lower immune escape score, higher PD1&CTLA4 immunophenoscore, higher tumor mutation burden score, higher response rate and better prognosis in OC, suggesting a better immunotherapy response. OC patients with low CRS score had a lower half maximal inhibitory concentration value of some drugs (Gemcitabine, Tamoxifen, and Nilotinib, etc) and lower score of some cancer-related hallmarks (Notch signaling, hypoxia, and glycolysis, etc). The current study developed an optimal CRS in OC, which acted as an indicator for the prognosis, stratifying risk and guiding treatment for OC patients.

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A bioinformatics analysis: ZFHX4 is associated with metastasis and poor survival in ovarian cancer

Cited by 16 publications

References 51 publications

MORE interpretable multi-omic regulatory networks to characterize phenotypes

MORE interpretable multi-omic regulatory networks to characterize phenotypes

The Driverless Triple-Wild-Type (BRAF, RAS, KIT) Cutaneous Melanoma: Whole Genome Sequencing Discoveries

Machine learning developed a fibroblast-related signature for predicting clinical outcome and drug sensitivity in ovarian cancer

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