Absence of AIF1L contributes to cell migration and a poor prognosis of breast cancer

Liu, Peipei; Li, Wenhui; Hu, Yuanyuan; Jiang, Youyu

doi:10.2147/ott.s165874

Cited by 12 publications

(5 citation statements)

References 51 publications

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“…Of the three proteins related to cellular structure, AIF1L is involved in the cytoskeletal apparatus as an actin-bundling protein [ 24 ]. Al-though lower levels of AIF1L have been linked to poor prognosis during breast cancer, AIF1L overexpression in a cell line, similar to what is observed for our ME/CFS cohort ( Table 2 and Figure 1 ), suppressed cell spreading and altered cell shape [ 25 ]. The same study predicted a potential role of AIF1L in tight junctions, cell junctions, and focal adhesion, while showing that its overexpression was correlated with decreased RhoA expression, something we also observe in our ME/CFS patient cohort ( Figure S2 ).…”

Section: Discussionsupporting

confidence: 62%

In-Depth Analysis of the Plasma Proteome in ME/CFS Exposes Disrupted Ephrin-Eph and Immune System Signaling

2021

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Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disabling disease with worldwide prevalence and limited therapies exclusively aimed at treating symptoms. To gain insights into the molecular disruptions in ME/CFS, we utilized an aptamer-based technology that quantified 4790 unique human proteins, allowing us to obtain the largest proteomics dataset yet available for this disease, detecting highly abundant proteins as well as rare proteins over a nine-log dynamic range. We report a pilot study of 20 ME/CFS patients and 20 controls, all females. Significant differences in the levels of 19 proteins between cohorts implicate pathways related to the extracellular matrix, the immune system and cell–cell communication. Outputs of pathway and cluster analyses robustly highlight the ephrin pathway, which is involved in cell–cell signaling and regulation of an expansive variety of biological processes, including axon guidance, angiogenesis, epithelial cell migration, and immune response. Receiver Operating Characteristic (ROC) curve analyses distinguish the plasma proteomes of ME/CFS patients from controls with a high degree of accuracy (Area Under the Curve (AUC) > 0.85), and even higher when using protein ratios (AUC up to 0.95), that include some protein pairs with established biological relevance. Our results illustrate the promise of plasma proteomics for diagnosing and deciphering the molecular basis of ME/CFS.

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Section: Discussionsupporting

confidence: 62%

In-Depth Analysis of the Plasma Proteome in ME/CFS Exposes Disrupted Ephrin-Eph and Immune System Signaling

2021

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“…Allograft Inflammatory Factor-1 (AIF1) is expressed in myeloid cells and functions to enhance immune responses and regulation ( da Silva et al, 2021 ). There is evidence that AIF1 can regulate monocyte/macrophage, microglia, and lymphocyte immune activity, and it may stimulate these cells to produce cytokines, chemokines, and nitric oxide synthase ( Liu et al, 2018 ). Also, AIF1 plays a critical role in migration, phagocytosis, proliferation, survival, and pro-inflammatory activity in macrophages ( da Silva et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

Mo,

Wu,

Luo

et al. 2024

Front. Genet.

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Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP).Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed.Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937–0.990) and 0.862 (95% CI 0.742–0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways.Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.

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“…Other studies have revealed extensive accumulation of AIF1L within discrete filopodial protrusions [33]. It is well known that filopodia are associated with migration from the primary tumor, degradation of the basal layer, and intravascular infiltration [34]. AIF1L has been reported to inhibit the migration and invasion of breast cancer cells by regulating actin remodeling, with low expression of AIF1L being associated with poor prognosis [35].…”

Section: Discussionmentioning

confidence: 99%

A prognostic index model for assessing the prognosis of ccRCC patients by using the mRNA expression profiles of AIF1L, SERPINC1 and CES1

Zheng¹,

Chen²,

Chen³

et al. 2022

JOMH

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Background: Kidney carcinoma is a major cause of carcinoma-related death, with the prognosis for advanced or metastatic renal cell carcinoma still very poor. The aim of this study was to investigate feasible prognostic biomarkers that can be used to construct a prognostic index model for clear cell renal cell carcinoma (ccRCC) patients. Methods: The mRNA expression profiles of ccRCC samples were downloaded from the The Cancer Genome Atlas (TCGA) dataset and the correlation of AIF1L with malignancy, tumor stage and prognosis were evaluated. Differentially expressed genes (DEGs) between AIF1L-low and AIF1L-high expression groups were selected. Those with prognostic value as determined by univariate and multivariate Cox regression analysis were then used to construct a prognostic index model capable of predicting the outcome of ccRCC patients. Results: The expression level of AIF1L was lower in ccRCC samples than in normal kidney samples. AIF1L expression showed an inverse correlation with tumor stage and a positive association with better prognosis. ccRCC samples were divided into high-and low-expression groups according to the median value of AIF1L expression. In the AIF1L-high expression group, 165 up-regulated DEGs and 601 down-regulated DEGs were identified. Three genes (AIF1L, SERPINC1 and CES1) were selected following univariate and multivariate Cox regression analysis. The hazard ratio (HR) and 95% confidence intervals (CI) for these genes were: AIF1L (HR = 0.83, 95% CI: 0.76-0.91), SERPINC1 (HR = 1.33, 95% CI: 1.12-1.58), and CES1 (HR = 0.87, 95% CI: 0.78-0.97). A prognostic index model based on the expression level of the three genes showed good performance in predicting ccRCC patient outcome, with an area under the ROC curve (AUC) of 0.671. Conclusion: This research provides a better understanding of the correlation between AIF1L expression and ccRCC. We propose a novel prognostic index model comprising AIF1L, SERPINC1 and CES1 expression that may assist physicians in determining the prognosis of ccRCC patients.

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Absence of AIF1L contributes to cell migration and a poor prognosis of breast cancer

Cited by 12 publications

References 51 publications

In-Depth Analysis of the Plasma Proteome in ME/CFS Exposes Disrupted Ephrin-Eph and Immune System Signaling

In-Depth Analysis of the Plasma Proteome in ME/CFS Exposes Disrupted Ephrin-Eph and Immune System Signaling

Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model

A prognostic index model for assessing the prognosis of ccRCC patients by using the mRNA expression profiles of AIF1L, SERPINC1 and CES1

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