Fascin1 suppresses RIG-I–like receptor signaling and interferon-β production by associating with IκB kinase Ε (IKKΕ) in colon cancer

Matsumura, Tomohiro; Hida, Shigeaki; Kitazawa, Masato; Fujii, Chifumi; Kobayashi, Adam; Takeoka, Michiko; Taniguchi, Shun’ichiro; Miyagawa, Shinichi

doi:10.1074/jbc.m117.819201

Cited by 15 publications

(16 citation statements)

References 44 publications

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“…However, considering that identification of protein interaction partners by co‐immunoprecipitation coupling MS may produce false positive results, further study is required to validate whether these proteins are real FSCN1‐binding partners. Moreover, we did not find that these proteins were reported in other studies but naturally these findings may be relevant to other cancer cell‐types and even normal cell physiology . This might be due to of different cell and tissue context.…”

Section: Discussioncontrasting

confidence: 70%

Mass Spectrometric Analysis Identifies AIMP1 and LTA4H as FSCN1‐Binding Proteins in Laryngeal Squamous Cell Carcinoma

Gao

Xue

et al. 2019

Proteomics

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Dysregulation of fascin actin‐bundling protein 1 (FSCN1) enhances cell proliferation, invasion, and motility in laryngeal squamous cell carcinoma (LSCC), while the mechanism remains unclear. Here, co‐immunoprecipitation and mass spectrometry is utilized to identify potential FSCN1‐binding proteins. Functional annotation of FSCN1‐binding proteins are performed by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. Furthermore, the protein–protein interaction network of FSNC1‐binding proteins is constructed and the interactions between FSCN1 and novel identified interacting proteins AIMP1 and LTA4H are validated. Moreover, the expression and functional role of AIMP1 and LTA4H in LSCC are investigated. A total of 123 proteins are identified as potential FSCN1‐binding proteins, and functional annotation shows that FSCN1‐binding proteins are significantly enriched in carcinogenic processes, such as filopodium assembly‐regulation and GTPase activity. Co‐IP/western blotting and immunofluorescence confirm that AIMP1 and LTA4H bind and colocalize with FSCN1. Furthermore, both AIMP1 and LTA4H are upregulated in LSCC tissues, and knockdown of AIMP1 or LTA4H inhibits LSCC cell proliferation, migration, and invasion. Collectively, the identification of FSCN1‐binding partners enhances understanding of the mechanism of FSCN1‐mediated malignant phenotypes, and these findings indicate that FSCN1 binds to AIMP1 and LTA4H might promote the progression of LSCC.

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Section: Discussioncontrasting

confidence: 70%

Mass Spectrometric Analysis Identifies AIMP1 and LTA4H as FSCN1‐Binding Proteins in Laryngeal Squamous Cell Carcinoma

Gao

Xue

et al. 2019

Proteomics

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“…RIG-I-LIKE receptor signaling pathway has a high impact on cell growth, migration, and invasion of tumor. 13 VEGF signaling pathway is involved in angiogenesis, migration and invasion and of OC. 30 At last, several limitations to this study should be discussed.…”

Section: Discussionmentioning

confidence: 99%

Identification of pathological grade and prognosis‐associated lncRNA for ovarian cancer

Chen

et al. 2019

J of Cellular Biochemistry

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Ovarian carcinoma (OC) is one of the most common malignant tumors in female genitals. In recent years, the therapeutic effect of OC has been significantly improved through the application of effective chemotherapy regimen. However, the 5‐year survival rate is also lower than 30% due to high rate of relapse. So, it is needed to screen reliable predictive and prognostic markers of OC. Ovarian cancer gene expression data and corresponding clinical data used were downloaded from Gene Expression Omnibus database. Weighted gene expression network analysis (WGCNA) and Cox proportional hazards regression (PHR) were used to screen Pathological Grade and Prognosis‐associated long noncoding RNA (lncRNA). Kaplan‐Meier analysis and receiver operating characteristic curves analysis were performed to evaluate the predictive ability of the selected lncRNA. Gene Ontology (GO) enrichment and Gene Set Enrichment Analysis (GSEA) enrichment analysis methods were used to explore the possible mechanisms of the selected lncRNA affecting the development of OC. Five reliably lncRNAs (LINC00664, LINC00667, LINC01139, LINC01419, and LOC286437) was identified through a series of bioinformatics methods. In testing cohorts, we found that the five lncRNAs in predicting the risk of OC recurrence is robustness, and multivariate Cox PHR analysis indicate that the five lncRNAs is an independent risk factor for OC recurrence. Moreover, GO and GSEA enrichment analysis showed that the five lncRNAs are involved in multiple ovarian cancer occurrence mechanism. In summary, all these findings indicated that the five lncRNAs can effectively predict the risk of recurrence of ovarian cancer.

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“…For subtype c5, 181 DEGs (51 up-regulated and 130 down-regulated) were detected and were enriched in Rig-I-like receptor signaling pathway, FoxO signaling pathway, autophagy-animal, insulin signaling pathway, toxoplasmosis, and focal adhesion, and so on. Among the enriched pathways, RIG-I-like receptor signaling plays important roles in colon cancer [ 52 ]; FoxO signaling pathway has been reported as therapeutic targets in cancer [ 53 ]; autophagy was reported as a promising target for CRC [ 54 ]; insulin signaling pathway could be a potential CRC therapy [ 55 ]. In consequence, these pathways could be the targets for subtype c5.…”

Section: Discussionmentioning

confidence: 99%

Network-based identification of biomarkers for colon adenocarcinoma

Wang

Yang

et al. 2020

BMC Cancer

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Background: As one of the most common cancers with high mortality in the world, we are still facing a huge challenge in the prevention and treatment of colon cancer. With the rapid development of high throughput technologies, new biomarkers identification for colon cancer has been confronted with the new opportunities and challenges. Methods: We firstly constructed functional networks for each sample of colon adenocarcinoma (COAD) by using a sample-specific network (SSN) method which can construct individual-specific networks based on gene expression profiles of a single sample. The functional genes and interactions were identified from the functional networks, respectively. Results: Classification and subtyping were used to test the function of the functional genes and interactions. The results of classification showed that the functional genes could be used as diagnostic biomarkers. The subtypes displayed different mechanisms, which were shown by the functional and pathway enrichment analysis for the representative genes of each subtype. Besides, subtype-specific molecular patterns were also detected, such as subtype-specific clinical and mutation features. Finally, 12 functional genes and 13 functional edges could serve as prognosis biomarkers since they were associated with the survival rate of COAD. Conclusions: In conclusion, the functional genes and interactions in the constructed functional network could be used as new biomarkers for COAD.

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Fascin1 suppresses RIG-I–like receptor signaling and interferon-β production by associating with IκB kinase Ε (IKKΕ) in colon cancer

Cited by 15 publications

References 44 publications

Mass Spectrometric Analysis Identifies AIMP1 and LTA4H as FSCN1‐Binding Proteins in Laryngeal Squamous Cell Carcinoma

Mass Spectrometric Analysis Identifies AIMP1 and LTA4H as FSCN1‐Binding Proteins in Laryngeal Squamous Cell Carcinoma

Identification of pathological grade and prognosis‐associated lncRNA for ovarian cancer

Network-based identification of biomarkers for colon adenocarcinoma

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