EVI1 as a Prognostic and Predictive Biomarker of Clear Cell Renal Cell Carcinoma

Palomero, Luís; Bodnar, Lubomir; Mateo, Francesca; Herranz, Carmen; Espín, Roderic; García-Varelo, Mar; Jesiotr, M; Garibay, Gorka Ruíz de; Casanovas, Oriol; López, José I.; Pujana, Miquel Àngel

doi:10.3390/cancers12020300

Cited by 10 publications

(10 citation statements)

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“…The expression of ATP1A1-AS1 decreased in the more advanced T-stages, while the expression levels of IL10RB-DT and MELTF-AS1 were enhanced more novel options and prolonged survival of plenty of patients with ccRCC, the curative effects of another part of patients remain unsatisfactory [2,6,19]. Multiplied studies revealed the individual variation at the genetic level should be responsible for the phenomenon [20,21]. Therefore, more explorations and understanding of ccRCC characterized by variant molecular heterogeneous, and further identifying more sensitive and effective immune prognostic indicators are above rubies.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

et al. 2020

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Background: Renal cell carcinoma (RCC) is one of the most common aggressive malignant tumors in urogenital system, and the clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal carcinoma. Immune related long non-coding RNAs (IRlncRs) plentiful in immune cells and immune microenvironment (IME) are potential in evaluating prognosis and assessing the effects of immunotherapy. A completed and meaningful IRlncRs analysis based on abundant ccRCC gene samples from The Cancer Genome Atlas (TCGA) will provide insight in this field. Methods: Based on the TCGA dataset, we integrated the expression profiles of IRlncRs and overall survival (OS) in the 611 ccRCC patients. The immune score of each sample was calculated based on the expression level of immunerelated genes and used to identify the most meaningful IRlncRs. Survival-related IRlncRs (sIRlncRs) was estimated by calculating the algorithm of difference and COX regression analysis in ccRCC patients. Based on the median immune-related risk score (IRRS) developed from the screened sIRlncRs, the high-risk and low-risk components were distinguished. Functional annotation was detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA), and the immune composition and purity of the tumor was evaluated by microenvironment cell population records. The expression levels of three sIRlncRs were verified in various tissues and cell lines. Results: A total of 39 IRlncRs were collected by Pearson correlation analyses among immune score and the lncRNA expression. A total of 7 sIRlncRs were significantly associated with the clinical outcomes of ccRCC patients. Three sIRl-ncRs (ATP1A1-AS1, IL10RB-DT and MELTF-AS1) with the most significant prognostic values were enrolled to build the IRRS model in which the OS of in the high-risk group was shorter than that in the low-risk group. The IRRS was identified as an independent prognosis factor and correlated with the OS. The high-risk group and low-risk group illustrated different distributions in PCA and different immune status in GSEA. Besides, we found the more significant expression in certain ccRCC cell lines and tumor tissues of ccRCC patients compared with the HK-2 and adjacent tissues respectively. Additionally, the expression levels of lncR-MELTF-AS1 and IL10RB-DT were remarkably enhanced along the more advanced T-stages, but the lncR-ATP1A1-AS1 showed the inverse gradient.

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Section: Discussionmentioning

confidence: 99%

Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

et al. 2020

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“…2,6,19 Multiplied studies revealed the individual variation at the genetic level should be responsible for the phenomenon. 20,21 Therefore, more explorations and understanding of ccRCC characterized by variant molecular heterogeneous, and further identifying more sensitive and effective immune prognostic indicators are above rubies.…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics profiling intergrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

Zhou

Gou

Wei

et al. 2020

Preprint

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Background: Immune related long non-coding RNAs (IRlncRs) plentiful in immune cells and immune microenvironment (IME) are potential in evaluating prognosis and assessing the effects of immunotherapy. A complete and meaningful IRlncRs analysis based on abundant clear cell renal cell carcinoma (ccRCC) gene samples from The Cancer Genome Atlas (TCGA) will provide insight in this field. Methods: Based on the TCGA dataset, we integrated the expression profiles of IRlncRs and overall survival (OS) in the 611 ccRCC patients. The immune score of each sample was calculated based on the expression level of immune-related genes and used to identify the most meaningful IRlncRs. Survival-related IRlncRs (sIRlncRs) was estimated by calculating the algorithm of difference and COX regression analysis in ccRCC patients. Based on the median immune-related risk score (IRRS) developed from the screened sIRlncRs, the high-risk and low-risk components were distinguished. Functional annotation was detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA), and the immune composition and purity of the tumor was evaluated by microenvironment cell population records. The expression levels of three sIRlncRs were verified in various tissues and cell lines. Results: A total of 39 IRlncRs were collected by Pearson correlation analyses among immune score and the lncRNA expression. A total of 7 sIRlncRs were significantly associated with the clinical outcomes of ccRCC patients. Three sIRlncRs (ATP1A1-AS1, IL10RB-DT and MELTF-AS1) with the most significant prognostic values were enrolled to build the IRRS model in which the OS of in the high-risk group was shorter than that in the low-risk group. The IRRS was identified as an independent prognosis factor and correlated with the OS. The high-risk group and low-risk group illustrated different distributions in PCA and different immune status in GSEA. Besides, we found the more significant expression in certain ccRCC cell lines and tumor tissues of ccRCC patients compared with the HK-2 and adjacent tissues respectively. Additionally, the expression levels of lncR-MELTF-AS1 and IL10RB-DT were remarkably enhanced along the more advanced T-stages, but the lncR-ATP1A1-AS1 showed the inverse gradient. Conclusion: Our results demonstrated some sIRlncRs with remark clinical relevance shown the latent monitoring and prognosis value of ccRCC patients and may provide new insight for immunological research and treatment strategies in ccRCC patients.

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“…Previous studies have shown that these genes are all related to prognosis in different cancer type. [35][36][37] No significant kinase was found to be enriched. In essence, these findings suggested that MMP28 is tightly connected to prognosis and survival of PAAD patients.…”

Section: Underlying Regulators Of Mmp28-correlated Genes In Paadmentioning

confidence: 96%

High Matrix Metalloproteinase 28 Expression is Associated with Poor Prognosis in Pancreatic Adenocarcinoma

Liu¹,

Zhong²,

Ni³

et al. 2021

OTT

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Purpose Pancreatic adenocarcinoma (PAAD) is a devastating disease with high mortality and morbidity. Matrix metalloproteinase 28 (MMP28) has been associated with carcinogenesis of many human cancers. However, little is known about the potential prognostic value and underlying regulatory mechanisms of MMP28 in PAAD. Methods The relationship between MMP28 expression level and various clinicopathological parameters was analyzed in TCGA-PAAD cohorts. MMP28-correlated genes in the TCGA-PAAD cohort were identified and enrichment analysis according to the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes was conducted using LinkedOmics. Protein–protein interaction and transcription factors-miRNA co-regulatory networks were constructed with the use of NetworkAnalyst. Then, the distribution of immune cells related to MMP28 expression in blood was analyzed using the Human Protein Atlas, and the tumor microenvironment of PAAD was analyzed by the TIMER 2.0 database. To investigate the biological function of MMP28 in PAAD, siRNA was constructed to knock down the MMP28 gene in vitro. Results High MMP28 expression is associated with poor overall survival and disease-free survival in PAAD patients. The expression of MMP28 in PAAD is most significantly correlated with KRT19, IL1RN, and ANXA2 genes. Network analysis revealed that MIR-181 family, TAFs, and CDC6 are potential regulators of MMP28. Furthermore, naive CD4 + T cell, naive CD8 + T cell, and mucosal-associated invariant T cell enrichment in blood were correlated with MMP28 expression. Furthermore, high MMP28 expression was correlated with a decrease in B cell, naive CD4 + T cell, naive CD8 + T cell, and endothelial cell presence in the tumor microenvironment in PAAD. Finally, genetic knockdown of MMP28 could restrain the proliferation, migration, and invasion of PAAD cells. Conclusion Our findings indicate that high MMP28 expression in PAAD is associated with cancer progression, invasion, and metastasis. Hence, MMP28 might serve as an independent prognostic biomarker and a prospective therapeutic target for PAAD.

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EVI1 as a Prognostic and Predictive Biomarker of Clear Cell Renal Cell Carcinoma

Cited by 10 publications

References 50 publications

Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

Bioinformatics profiling integrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

Bioinformatics profiling intergrating a three immune-related long non-coding RNA signature as a prognostic model for clear cell renal cell carcinoma

High Matrix Metalloproteinase 28 Expression is Associated with Poor Prognosis in Pancreatic Adenocarcinoma

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