WASF2 Serves as a Potential Biomarker and Therapeutic Target in Ovarian Cancer: A Pan-Cancer Analysis

Yang, Xiaofeng; Ding, Yuzhen; Sun, Lu; Shi, Meiting; Zhang, Ping; He, Andong; Zhang, Xiaotan; Huang, Zhengrui; Li, Ruiman

doi:10.3389/fonc.2022.840038

Cited by 8 publications

(5 citation statements)

References 62 publications

(74 reference statements)

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“…The genes WASF2 and LRIG2 have been linked with many forms of cancer detection as well ( Wang et al 2014 ; Kitagawa et al 2019 ). WASF2 expression levels have been studied as a biomarker in detection of pancreatic ( Kitagawa et al 2019 ) and ovarian cancers ( Yang et al 2022 ), whereas LRIG2 has been identified as a biomarker for detection of nonsmall cell cancer ( Wang et al 2014 ). On the other hand, SDAD1 has been identified as a gene responsible for suppressing colon cancer metastasis ( Zeng et al 2017 ).…”

Section: Resultsmentioning

confidence: 99%

Uncovering Footprints of Natural Selection Through Spectral Analysis of Genomic Summary Statistics

Arnab

Amin

DeGiorgio

2023

Molecular Biology and Evolution

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Natural selection leaves a spatial pattern along the genome, with a haplotype distribution distortion near the selected locus that fades with distance. Evaluating the spatial signal of a population-genetic summary statistic across the genome allows for patterns of natural selection to be distinguished from neutrality. Considering the genomic spatial distribution of multiple summary statistics is expected to aid in uncovering subtle signatures of selection. In recent years, numerous methods have been devised that consider genomic spatial distributions across summary statistics, utilizing both classical machine learning and deep learning architectures. However, better predictions may be attainable by improving the way in which features are extracted from these summary statistics. We apply wavelet transform, multitaper spectral analysis, and S-transform to summary statistic arrays to achieve this goal. Each analysis method converts one-dimensional summary statistic arrays to two-dimensional images of spectral analysis, allowing simultaneous temporal and spectral assessment. We feed these images into convolutional neural networks and consider combining models using ensemble stacking. Our modeling framework achieves high accuracy and power across a diverse set of evolutionary settings, including population size changes and test sets of varying sweep strength, softness, and timing. A scan of central European whole-genome sequences recapitulated well-established sweep candidates and predicted novel cancer-associated genes as sweeps with high support. Given that this modeling framework is also robust to missing genomic segments, we believe that it will represent a welcome addition to the population-genomic toolkit for learning about adaptive processes from genomic data.

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Section: Resultsmentioning

confidence: 99%

Uncovering Footprints of Natural Selection Through Spectral Analysis of Genomic Summary Statistics

Arnab

Amin

DeGiorgio

2023

Molecular Biology and Evolution

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“…These top significant 6 genes, including ATP1A1, KMT2E, RAD23A, TRR, WASF2 and DNAJC9, were screened from the 34 negatively-related stemness genes, using the random survival forest algorithm. ATP1A1, KMT2E and WASF2 were known as the hallmarker of cancers and overexpression of these genes promote the survival of cancer cells ( 20 – 22 ), while RAD23A acts as a negative regulator of anti-virus response and might correlate directly with the HPV infection ( 23 ). TPR is a nucleoporin which prevents DNA damage ( 24 ) and DNAJC9 is a dual histone chaperone and heat shock cochaperone, which recruits HSP70 enzymes to guard histone structural integrity ( 25 ).…”

Section: Discussionmentioning

confidence: 99%

Human papillomavirus infection can alter the level of tumour stemness and T cell infiltration in patients with head and neck squamous cell carcinoma

Meng

et al. 2022

Front. Immunol.

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Head and neck squamous cell carcinoma (HNSCC) usually has a poor prognosis and is associated with a high mortality rate. Its etiology is mainly the result from long-term exposure to either alcohol, tobacco or human papillomavirus (HPV) infection or a combination of these insults. However, HNSCC patients with HPV have been found to show a survival advantage over those without the virus, but the mechanism that confers this advantage is unclear. Due to the large number of HPV-independent HNSCC cases, there is a possibility that the difference in prognosis between HPV-positive (HPV+) and negative (HPV-) patients is due to different carcinogens. To clarify this, we used scRNA data and viral tracking methods in order to identify HPV+ and HPV- cells in the tumour tissues of patients infected with HPV. By comparing HPV+ and HPV- malignant cells, we found a higher level of tumour stemness in HPV- tumour cells. Using tumour stemness-related genes, we established a six-gene prognostic signature that was used to divide the patients into low- and high-risk groups. It was found that HPV patients who were at low-risk of contracting HNSCC had a higher number of CD8+ T-cells as well as a higher expression of immune checkpoint molecules. Correspondingly, we found that HPV+ tumour cells expressed higher levels of CCL4, and these were highly correlated with CD8+ T cells infiltration and immune checkpoint molecules. These data suggest that the stemness features of tumour cells are not only associated with the prognostic risk, but that it could also affect the immune cell interactions and associated signalling pathways.

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“…ABI3BP is a potential partner of ABI3, and previous studies have shown that their expression levels are synchronized during cancer progression ( Latini et al, 2011 ). Moreover, there is existing literature demonstrating the important roles of ABI3BP, WASF2, RUNX1T1, and TNFAIP8L2 in cancer processes through pan-cancer analysis ( Bai et al, 2022 ; Lin, 2022 ; Yang et al, 2022 ; Feng et al, 2023 ). This analysis suggests that ABI3 may influence cancer progression by interacting with the aforementioned genes.…”

Section: Resultsmentioning

confidence: 99%

Comprehensive pan-cancer investigation: unraveling the oncogenic, prognostic, and immunological significance of Abelson interactor family member 3 gene in human malignancies

Sun,

Cai,

Xiong

et al. 2023

Front. Mol. Biosci.

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Background: Abelson interactor Family Member 3 (ABI3) encodes protein that not only suppresses the ectopic metastasis of tumor cells but also hinders their migration. Although ABI3 had been found to modulate the advancement of diverse neoplasms, there is no comprehensive pan-cancer analysis of its effects.Methods: The transcriptomics data of neoplasm and normal tissues were retrieved from the Genomic Data Commons (GDC) data portal, and UCSC XENA database. To gather protein information for ABI3, Human Protein Atlas (HPA) and GeneMANIA websites were utilized. Additionally, Tumor Immune Single-cell Hub (TISCH) database was consulted to determine the primary cell types expressing ABI3 in cancer microenvironments. Univariate Cox regression approach was leveraged to evaluate ABI3’s prognostic role across cancers. The Cbioportal and Gene Set Cancer Analysis (GSCA) website were leveraged to scrutinize the genomic landscape information across cancers. TIMER2.0 was leveraged to probe the immune cell infiltrations associated with ABI3 across cancers. The associations of ABI3 with immune-related genes were analyzed through Spearman correlation method. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were utilized to search associated biological pathways. The CellMiner database and molecular docking were implemented to identify potential interactions between the ABI3 protein and specific anticarcinogen.Findings: ABI3 expression and its ability to predict prognosis varied distinct tumor, with particularly high expression observed in Tprolif cells and monocytes/macrophages. Copy number variation (CNV) and methylation negatively correlated with ABI3 expression in the majority of malignancies. Corresponding mutation survival analysis indicated that the mutation status of ABI3 was strongly connected to the prognosis of LGG patients. ABI3 expression was linked to immunotherapeutic biomarkers and response in cancers. ESTIMATE and immune infiltrations analyses presented ABI3 association with immunosuppression. ABI3 was significantly correlated with immunoregulators and immune-related pathways. Lastly, prospective ABI3-targeted drugs were filtered and docked to ABI3 protein.Interpretation: Our study reveals that ABI3 acts as a robust tumor biomarker. Its functions are vital that could inhibit ectopic metastasis of tumor cells and modulate cellular adhesion and migration. The discoveries presented here may have noteworthy consequences for the creation of fresh anticancer suppressors, especially those targeting BRCA.

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WASF2 Serves as a Potential Biomarker and Therapeutic Target in Ovarian Cancer: A Pan-Cancer Analysis

Cited by 8 publications

References 62 publications

Uncovering Footprints of Natural Selection Through Spectral Analysis of Genomic Summary Statistics

Uncovering Footprints of Natural Selection Through Spectral Analysis of Genomic Summary Statistics

Human papillomavirus infection can alter the level of tumour stemness and T cell infiltration in patients with head and neck squamous cell carcinoma

Comprehensive pan-cancer investigation: unraveling the oncogenic, prognostic, and immunological significance of Abelson interactor family member 3 gene in human malignancies

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