Increased expression of PSME2 is associated with clear cell renal cell carcinoma invasion by regulating BNIP3‑mediated autophagy

Wang, Xiaoyun; Wu, Fengbo; Deng, Yutong; Chai, Jinlong; Zhang, Yuehua; He, Gu; Li, Xiang

doi:10.3892/ijo.2021.5286

Cited by 14 publications

(8 citation statements)

References 80 publications

(85 reference statements)

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“…According to previous studies, PSME2 plays the role of promoting tumor progression in clear cell renal cell carcinoma, which is positively correlated with the ability of invasion regulating BNIP3-mediated autophagy ( Wang et al, 2021 ). In addition, increasing numbers of studies showed that PSME2 may be used as a tumor biomarker.…”

Section: Discussionmentioning

confidence: 96%

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PSME2 identifies immune-hot tumors in breast cancer and associates with well therapeutic response to immunotherapy

Wu¹,

Ren²,

Sun³

et al. 2022

Front. Genet.

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Breast cancer (BrCa) is a heterogeneous disease, which leads to unsatisfactory prognosis in females worldwide. Previous studies have proved that tumor immune microenvironment (TIME) plays crucial roles in oncogenesis, progression, and therapeutic resistance in Breast cancer. However, biomarkers related to TIME features have not been fully discovered. Proteasome activator complex subunit 2 (PSME2) is a member of proteasome activator subunit gene family, which is critical to protein degradation mediated by the proteasome. In the current research, we comprehensively analyzed the expression and immuno-correlations of Proteasome activator complex subunit 2 in Breast cancer. Proteasome activator complex subunit 2 was significantly upregulated in tumor tissues but associated with well prognosis. In addition, Proteasome activator complex subunit 2 was overexpressed in HER2-positive Breast cancer but not related to other clinicopathological features. Interestingly, Proteasome activator complex subunit 2 was positively related to immune-related processes and identified immuno-hot TIME in Breast cancer. Specifically, Proteasome activator complex subunit 2 was positively correlated with immunomodulators, tumor-infiltrating immune cells (TIICs), immune checkpoints, and tumor mutation burden (TMB) levels. Moreover, the positive correlation between Proteasome activator complex subunit 2 and PD-L1 expression was confirmed in a tissue microarray (TMA) cohort. Furthermore, in an immunotherapy cohort of Breast cancer, patients with pathological complete response (pCR) expressed higher Proteasome activator complex subunit 2 compared with those with non-pathological complete response. In conclusion, Proteasome activator complex subunit 2 is upregulated in tumor tissues and correlated with the immuno-hot tumor immune microenvironment, which can be a novel biomarker for the recognition of tumor immune microenvironment features and immunotherapeutic response in Breast cancer.

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Section: Discussionmentioning

confidence: 96%

“…Proteasome activator complex subunit 2 (PSME2), a member of proteasome activator subunit gene family, is located on 14q12, and expresses mainly intracellular ( Wang et al, 2021 ). The protein encoded by PSME2 gene is critical to protein degradation mediated by the proteasome ( Wang et al, 2004 ).…”

Section: Introductionmentioning

confidence: 99%

PSME2 identifies immune-hot tumors in breast cancer and associates with well therapeutic response to immunotherapy

Wu¹,

Ren²,

Sun³

et al. 2022

Front. Genet.

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“…Its expression is increased in gastric and renal malignancies while decreasing in lung cancer ( 56 – 58 ). PSME2 overexpression has been linked to clear cell renal cell carcinoma invasion by blocking BNIP3-mediated autophagy ( 59 ). Previous studies on the role of PSME2 in tumorigenesis showed contradictory results.…”

Section: Discussionmentioning

confidence: 99%

Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer

Zheng

Wang

et al. 2022

Front. Surg.

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BackgroundBreast cancer (BC) is the most common cancer among women worldwide, with enormous heterogeneity. Pyroptosis has a significant impact on the development and progression of tumors. Nonetheless, the possible correlation between pyroptosis-related genes (PRGs) and the BC immune microenvironment has yet to be investigated.Materials and methodsIn The Cancer Genome Atlas Breast Cancer cohort, 38 PRGs were shown to be significantly different between malignant and non-malignant breast tissues. The 38 PRGs’ consensus clustering grouped 1,089 individuals into two pyroptosis-related (PR) patterns. Using univariate and LASSO-Cox analyses, a PR five-gene predictive signature was constructed based on the differentially expressed genes between two clusters. The tools estimation of stromal and immune cells in malignant tumours using expression data (ESTIMATE), cell type identification by estimating relative subsets Of RNA transcripts (CIBERSORT), and single-sample gene set enrichment analysis (ssGSEA) were used to investigate the BC tumor microenvironment (TME).ResultsIn TME, the two PR clusters displayed distinct clinicopathological characteristics, survival outcomes, and immunocyte infiltration features. The developed five-signature model (SEMA3B, IGKC, KLRB1, BIRC3, and PSME2) classified BC patients into two risk groups based on the estimated median risk score. Patients in the low-scoring category had a higher chance of survival and more extensive immunocyte infiltration. An external validation set can yield similar results.ConclusionOur data suggest that PRGs have a significant impact on the BC immunological microenvironment. The PR clusters and associated predictive signature stimulate additional research into pyroptosis in order to optimize therapeutic strategies for BC patients and their responses to immune therapy.

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“…CFB is stably upregulated in various cancer tissues, and in studies of adenocarcinoma, this gene has been shown to alleviate cancer progression by activating cellular immune responses, consistent with the trend of this study in predicting progression of breast cancer [ 35 ]. PSME2 has been less studied in cancer, and reports indicate that this gene is a typical poor prognostic marker in renal cell carcinoma and promotes malignant tumor progression by inhibiting autophagy [ 36 ]. High expression of EVA1B is bound up closely with high infiltration levels of T cells, macrophages, and neutrophils in cancer tissues, and high expression of this gene implies poor prognosis in glioma patients [ 37 ].…”

Section: Discussionmentioning

confidence: 99%

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

Zhao

Lin

2022

Computational and Mathematical Methods in Medicine

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Background. Breast cancer is a big threat to the women across the world with substantial morbidity and mortality. The pressing matter of our study is to establish a prognostic gene model for breast cancer based on mRNAsi for predicting patient’s prognostic survival. Methods. From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded the expression profiles of genes in breast cancer. On the basis of one-class logistic regression (OCLR) machine learning algorithm, mRNAsi of samples was calculated. Kaplan-Meier (K-M) and Kruskal-Wallis (K-W) tests were utilized for the assessment of the connection between mRNAsi and clinicopathological variables of the samples. As for the analysis on the correlation between mRNAsi and immune infiltration, ESTIMATE combined with Spearman test was employed. The weighted gene coexpression network analysis (WGCNA) network was established by utilizing the differentially expressed genes in breast cancer, and the target module with the most significant correlation with mRNAsi was screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to figure out the biological functions of the target module. As for the construction of the prognostic model, univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on genes in the module. The single sample gene set enrichment analysis (ssGSEA) and tumor mutational burden were employed for the analysis on immune infiltration and gene mutations in the high- and low-risk groups. As for the analysis on whether this model had the prognostic value, the nomogram and calibration curves of risk scores and clinical characteristics were drawn. Results. Nine mRNAsi-related genes (CFB, MAL2, PSME2, MRPL13, HMGB3, DCTPP1, SHCBP1, SLC35A2, and EVA1B) comprised the prognostic model. According to the results of ssGSEA and gene mutation analysis, differences were shown in immune cell infiltration and gene mutation frequency between the high- and low-risk groups. Conclusion. Nine mRNAsi-related genes screened in our research can be considered as the biomarkers to predict breast cancer patients’ prognoses, and this model has a potential relationship with individual somatic gene mutations and immune regulation. This study can offer new insight into the development of diagnostic and clinical treatment strategies for breast cancer.

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Increased expression of PSME2 is associated with clear cell renal cell carcinoma invasion by regulating BNIP3‑mediated autophagy

Cited by 14 publications

References 80 publications

PSME2 identifies immune-hot tumors in breast cancer and associates with well therapeutic response to immunotherapy

PSME2 identifies immune-hot tumors in breast cancer and associates with well therapeutic response to immunotherapy

Prognostic and Immune Implications of a Novel Pyroptosis-Related Five-Gene Signature in Breast Cancer

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

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