In-depth proteomics analysis of sentinel lymph nodes from individuals with endometrial cancer

Aboulouard, Soulaimane; Wisztorski, Maxence; Duhamel, Marie; Saudemont, Philippe; Cardon, Tristan; Narducci, Fabrice; Lemaire, A.; Kobeissy, Firas; Leblanc, Éric; Fournier, Isabelle; Salzet, Michel

doi:10.1016/j.xcrm.2021.100318

Cited by 18 publications

(13 citation statements)

References 62 publications

(73 reference statements)

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“…nine were selected for validation. In addition, in vitro lossof-function assays using shC1GALT1 and SCRAMBLE ECC-1 cells confirmed the more tumorigenic and aggressive phenotype of ECC-1 cells due to C1GALT1 depletion, bladder, endometrial, colorectal, lung and pancreatic cancer, and melanoma [70][71][72][73][74]. Previous works described ANXA1 upregulated in the secretome and exosomes secreted by cancer cells [75], which has been associated with a major potential role of ANXA1 in promoting cancer angiogenesis, proliferation, invasion and metastatic properties of cancer cells [43,44].…”

Section: Relevance Of C1galt1 Dysregulation In Ec Patientsmentioning

confidence: 81%

In-depth quantitative proteomics analysis revealed C1GALT1 depletion in ECC-1 cells mimics an aggressive endometrial cancer phenotype observed in cancer patients with low C1GALT1 expression

et al. 2023

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Background Endometrial cancer (EC) is the most common cancer of the female reproductive organs. Despite the good overall prognosis of most low-grade ECs, FIGO I and FIGO II patients might experience tumor recurrence and worse prognosis. The study of alterations related to EC pathogenesis might help to get insights into underlying mechanisms involved in EC development and progression. Methods Core tumoral samples were used to investigate the role of C1GALT1 in EC by immunohistochemistry (IHC). ECC-1 cells were used as endometrioid EC model to investigate the effect of C1GALT1 depletion using C1GALT1 specific shRNAs. SILAC quantitative proteomics analyses and cell-based assays, PCR, qPCR, WB, dot-blot and IHC analyses were used to identify, quantify and validate dysregulation of proteins. Results Low C1GALT1 protein expression levels associate to a more aggressive phenotype of EC. Out of 5208 proteins identified and quantified by LC-MS/MS, 100 proteins showed dysregulation (log2fold-change ≥ 0.58 or ≤-0.58) in the cell protein extracts and 144 in the secretome of C1GALT1 depleted ECC-1 cells. Nine dysregulated proteins were validated. Bioinformatics analyses pointed out to an increase in pathways associated with an aggressive phenotype. This finding was corroborated by loss-of-function cell-based assays demonstrating higher proliferation, invasion, migration, colony formation and angiogenesis capacity in C1GALT1 depleted cells. These effects were associated to the overexpression of ANXA1, as demonstrated by ANXA1 transient silencing cell-based assays, and thus, correlating C1GALT and ANXA1 protein expression and biological effects. Finally, the negative protein expression correlation found by proteomics between C1GALT1 and LGALS3 was confirmed by IHC. Conclusion C1GALT1 stably depleted ECC-1 cells mimic an EC aggressive phenotype observed in patients and might be useful for the identification and validation of EC markers of progression.

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Section: Relevance Of C1galt1 Dysregulation In Ec Patientsmentioning

confidence: 81%

In-depth quantitative proteomics analysis revealed C1GALT1 depletion in ECC-1 cells mimics an aggressive endometrial cancer phenotype observed in cancer patients with low C1GALT1 expression

et al. 2023

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“…Fertility retention technology can effectively improve the quality of life of gynecological cancer patients, and has become the goal and hope for cancer survivors to live a better life [ 19 ]. Studies based on systems biology proteomics have highlighted the exact potential molecular mechanisms associated with SLN and EC grades [ 20 , 21 ]. The aim of these investigations is to examine the endometrial cells, determine the disease extent, and detect the presence/absence of metastasis.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Zhao

Zhang

Wang

et al. 2022

Genes

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Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.

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“…Aboulouard et al identified PTX-3 on a cohort of 41 human samples as a poor outcome gene marker in endometrial cancer, more specifically for grade I disease. Their results were obtained through in-depth proteomics analysis and are helpful in stratifying treatment decisions and guidance for professional healthcare [ 75 ].…”

Section: Gynecological Cancersmentioning

confidence: 99%

Insights into the Relationship between Pentraxin-3 and Cancer

Bogdan

Meca

Turcu-Știolică

et al. 2022

IJMS

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Although cancer can be cured if detected early and treated effectively, it is still a leading cause of death worldwide. Tumor development can be limited by an appropiate immune response, but it can be promoted by chronic extensive inflammation through metabolic dysregulation and angiogenesis. In the past decade, numerous efforts have been made in order to identify novel candidates with predictive values in cancer diagnostics. In line with this, researchers have investigated the involvement of pentraxin-3 (PTX-3) in cellular proliferation and immune escape in various types of cancers, although it has not been clearly elucidated. PTX-3 is a member of the long pentraxin subfamily which plays an important role in regulating inflammation, innate immunity response, angiogenesis, and tissue remodeling. Increased synthesis of inflammatory biomarkers and activation of different cellular mechanisms can induce PTX-3 expression in various types of cells (neutrophils, monocytes, lymphocytes, myeloid dendritic cells, fibroblasts, and epithelial cells). PTX-3 has both pro- and anti-tumor functions, thus dual functions in oncogenesis. This review elucidates the potential usefulness of PTX-3 as a serum biomarker in cancer. While future investigations are needed, PTX-3 is emerging as a promising tool for cancer’s diagnosis and prognosis, and also treatment monitoring.

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In-depth proteomics analysis of sentinel lymph nodes from individuals with endometrial cancer

Abstract: Highlights d Diagnosis of endometrial cancer does not include sentinel lymph node mapping d In depth-proteomics analysis of sentinel lymph nodes and individuals with endometrial cancer d We identify potential biomarkers for tumor grade and overall survival

Cited by 18 publications

References 62 publications

In-depth quantitative proteomics analysis revealed C1GALT1 depletion in ECC-1 cells mimics an aggressive endometrial cancer phenotype observed in cancer patients with low C1GALT1 expression

In-depth quantitative proteomics analysis revealed C1GALT1 depletion in ECC-1 cells mimics an aggressive endometrial cancer phenotype observed in cancer patients with low C1GALT1 expression

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Insights into the Relationship between Pentraxin-3 and Cancer

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