Prominin-1 (PROM1) Significantly Correlated With Bone Metastasis and Influenced Prognosis in Breast Cancer

Abstract BackgroundThis study aimed to explore the important biomarker associated with bone metastasis (BM) in breast cancer (BRCA).MethodsThe GSE175692 dataset was used to detect significant differential expressed genes (DEGs) between BRCA samples with or without BM, and important pathways were then explored. Further, we constructed protein-protein interaction (PPI) network on GEGs and filtered 5 vital nodes. Through performing cox regression, Kaplan-Meier analysis, nomogram, ROC curve, and risk score model, significant prognostic factor was gradually identified. Finally, gene set enrichment analysis (GSEA) analysis was performed to reveal the potential mechanism.ResultsTotally, 74 DEGs were detected, which mainly correlated with infectious disease, signaling molecules and interaction. The 5 important DEGs were filtered, and cox regression further showed that prominin-1 (PROM1) and C-C Motif Chemokine Ligand 2 (CCL2) were prognosis-related factors. A negative correlation was observed between the expression of these 2 genes and the overall survival of metastatic BRCA patients. Especially, PROM1 presented a better prognostic performance on the survival probability of patients. Prognosis verification analysis also confirmed the significant influence of PROM1 on patient’s survival. In addition, we found that PROM1 expression was related to the distant metastasis-free survival in BRCA. Finally, GSEA analysis revealed that PROM1 was negatively related to IGF1 and mTOR pathways involved in BRCA metastasis. ConclusionPROM1 was identified as an important DEGs associated with BRCA bone metastasis. It acted as a vital prognostic biomarker involved in BRCA metastasis, which may be due to the negative regulation of IGF1 and mTOR pathways.

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Overexpression of CENPF correlates with poor prognosis and tumor bone metastasis in breast cancer

Cancer Cell International ◽

10.1186/s12935-019-0986-8 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 17

Author(s):

Jingbo Sun ◽

Jingzhan Huang ◽

Jin Lan ◽

Kun Zhou ◽

Yuan Gao ◽

...

Keyword(s):

Breast Cancer ◽

Bone Metastasis ◽

Western Blot ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Free Survival ◽

Gene Set ◽

Oncomine Database ◽

Kaplan Meier

Abstract Background Centromere Protein F (CENPF) associates with the centromere–kinetochore complex and influences cell proliferation and metastasis in several cancers. The role of CENPF in breast cancer (BC) bone metastasis remains unclear. Methods Using the ONCOMINE database, we compared the expression of CENPF in breast cancer and normal tissues. Findings were confirmed in 60 BC patients through immunohistochemical (IHC) staining. Microarray data from GEO and Kaplan–Meier plots were used analyze the overall survival (OS) and relapse free survival (RFS). Using the GEO databases, we compared the expression of CENPF in primary lesions, lung metastasis lesions and bone metastasis lesions, and validated our findings in BALB/C mouse 4T1 BC models. Based on gene set enrichment analysis (GSEA) and western blot, we predicted the mechanisms by which CENPF regulates BC bone metastasis. Results The ONCOMINE database and immunohistochemical (IHC) showed higher CENPF expression in BC tissue compared to normal tissue. Kaplan–Meier plots also revealed that high CENPF mRNA expression correlated to poor survival and shorter progression-free survival (RFS). From BALB/C mice 4T1 BC models and the GEO database, CENPF was overexpressed in primary lesions, other target organs, and in bone metastasis. Based on gene set enrichment analysis (GSEA) and western blot, we predicted that CENPF regulates the secretion of parathyroid hormone-related peptide (PTHrP) through its ability to activate PI3K–AKT–mTORC1. Conclusion CENPF promotes BC bone metastasis by activating PI3K–AKT–mTORC1 signaling and represents a novel therapeutic target for BC treatment.

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Evaluation of the TRIP13 level in breast cancer and insights into potential molecular pathways

10.21203/rs.3.rs-19358/v1 ◽

2020 ◽

Author(s):

Jin Lan ◽

Jingzhan Huang ◽

Yuan Gao ◽

Jingbo Sun ◽

Longshan Zhang ◽

...

Keyword(s):

Breast Cancer ◽

Therapeutic Target ◽

Cancer Metastasis ◽

Interaction Network ◽

Enrichment Analysis ◽

Bioinformatic Analysis ◽

Breast Cancer Metastasis ◽

Gene Set Enrichment Analysis ◽

Protein Protein Interaction ◽

Kaplan Meier

Abstract Background: TRIP13 is a member belonging to a large AAA+ ATPases protein super family. Emerging evidences had shown that TRIP13 may serve as an oncogene However, the function of TRIP13 in BC has not yet been elucidated. Methods: By utilizing the multiple database across BC, we presented the expression of TRIP13 in BC tissue and normal control. We then verified the expression of TRIP13 in patients with BC by immunohistochemical (IHC) staining. Kaplan-Meier plots were used to perform the survival analysis. Further, gene ontology (GO) analysis, gene set enrichment analysis (GSEA), and PPI (protein-protein interaction) network were performed to explore the biological function and potential regular pathway of TRIP13 in BC. Results: The multiple database and immunohistochemical (IHC) showed that higher TRIP13 expression in BC tissue compared to normal tissue. TRIP13 was highly exprssed in lung metastasis lesion compared with primary tumor in our BALB/C mice 4T1 BC models. Kaplan-Meier plots also revealed that high TRIP13 expression correlated to poor survival in patients with BC. Moreover, GSEA analysis revealed that TRIP13 was primarily enriched in the processes of cell division and proliferation. Finally 10 hub genes with a high score of connectivity were filtered from the PPI network, including MAD2L1, CDC20, CDC5L, CDK1, CCNA2, BUB1B, RAD51, SPO11, KIF11 and AURKB. Conclusion: High TRIP13 expression predicted poor prognosis and contributed tumor growth and metastasis in the BC. Thus, ARL3 may be a prognostic marker and therapeutic target for glioma. TRIP13 may be a favorable biomarker and effective therapeutic target for BC. Keywords: TRIP13; breast cancer; metastasis; bioinformatic analysis, prognosis

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Development of a Novel Prognostic Signature Based on Antigen Processing and Presentation in Patients with Breast Cancer

Pathology & Oncology Research ◽

10.3389/pore.2021.600727 ◽

2021 ◽

Vol 27 ◽

Author(s):

Aoshuang Qi ◽

Mingyi Ju ◽

Yinfeng Liu ◽

Jia Bi ◽

Qian Wei ◽

...

Keyword(s):

Breast Cancer ◽

Antigen Processing ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Prognostic Indicators ◽

Gene Set ◽

Antigen Processing And Presentation

Background: Complex antigen processing and presentation processes are involved in the development and progression of breast cancer (BC). A single biomarker is unlikely to adequately reflect the complex interplay between immune cells and cancer; however, there have been few attempts to find a robust antigen processing and presentation-related signature to predict the survival outcome of BC patients with respect to tumor immunology. Therefore, we aimed to develop an accurate gene signature based on immune-related genes for prognosis prediction of BC.Methods: Information on BC patients was obtained from The Cancer Genome Atlas. Gene set enrichment analysis was used to confirm the gene set related to antigen processing and presentation that contributed to BC. Cox proportional regression, multivariate Cox regression, and stratified analysis were used to identify the prognostic power of the gene signature. Differentially expressed mRNAs between high- and low-risk groups were determined by KEGG analysis.Results: A three-gene signature comprising HSPA5 (heat shock protein family A member 5), PSME2 (proteasome activator subunit 2), and HLA-F (major histocompatibility complex, class I, F) was significantly associated with OS. HSPA5 and PSME2 were protective (hazard ratio (HR) < 1), and HLA-F was risky (HR > 1). Risk score, estrogen receptor (ER), progesterone receptor (PR) and PD-L1 were independent prognostic indicators. KIT and ACACB may have important roles in the mechanism by which the gene signature regulates prognosis of BC.Conclusion: The proposed three-gene signature is a promising biomarker for estimating survival outcomes in BC patients.

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A Prognostic Autophagy-Related Long Non-coding RNA (ARlncRNA) Signature in Acute Myeloid Leukemia (AML)

Frontiers in Genetics ◽

10.3389/fgene.2021.681867 ◽

2021 ◽

Vol 12 ◽

Author(s):

Chunxia Zhao ◽

Yulu Wang ◽

Famei Tu ◽

Shuai Zhao ◽

Xiaoying Ye ◽

...

Keyword(s):

Cox Regression ◽

Survival Curve ◽

Pearson Correlation ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Kaplan Meier ◽

Non Coding Rna ◽

Novel Biomarkers ◽

Meier Survival Curve

BackgroundSome studies have proven that autophagy and lncRNA play important roles in AML. Several autophagy related lncRNA signatures have been shown to affect the survival of patients in some other cancers. However, the role of autophagy related lncRNA in AML has not been explored yet. Hence, this study aims to find an autophagy related lncRNA signature that can affect survival for AML patients.MethodA Pearson correlation analysis, a Kaplan–Meier survival curve, a univariate cox regression, and a multivariate cox regression were performed to establish an autophagy related lncRNA signature. A univariate cox regression, a multivariate cox regression, a Kaplan–Meier survival curve, and a ROC curve were applied to confirm if the signature is an independent prognosis for AML patients. The relationship between the signature and the clinical features was explored by using a T test. Gene Set Enrichment Analysis (GSEA) was used to investigate the potential tumor related pathways.ResultsA four-autophagy related lncRNA (MIR133A1HG, AL359715.1, MIRLET7BHG, and AL356752.1) signature was established. The high risk score based on signature was related to the short survival time of AML patients. The signature was an independent factor for the prognosis for AML patients (HR = 1.684, 95% CI = 1.324–2.142, P < 0.001). The signature was correlated with age, leukocyte numbers, and FAB (M3 or non-M3). The P53, IL6/JAK/STAT3, TNF-α, INF-γ, and IL2/STAT5 pathways might contribute to the differences between the risk groups based on signature in AML.ConclusionThe four autophagy related lncRNAs and their signature might be novel biomarkers for predicting the survival of AML patients. Some biological pathways might be the potential mechanisms of the signature for the survival of AML patients.

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Identification of Epithelial–Mesenchymal Transition-Related Prognostic lncRNAs Biomarkers Associated With Melanoma Microenvironment

Frontiers in Cell and Developmental Biology ◽

10.3389/fcell.2021.679133 ◽

2021 ◽

Vol 9 ◽

Author(s):

Bo Xiao ◽

Liyan Liu ◽

Zhuoyuan Chen ◽

Aoyu Li ◽

Pingxiao Wang ◽

...

Keyword(s):

Cox Regression ◽

Epithelial Mesenchymal Transition ◽

Enrichment Analysis ◽

Roc Curves ◽

Risk Groups ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Mesenchymal Transition ◽

Cox Regression Analysis ◽

Kaplan Meier

Melanoma is the most common cancer of the skin, associated with a worse prognosis and distant metastasis. Epithelial–mesenchymal transition (EMT) is a reversible cellular biological process that plays significant roles in diverse tumor functions, and it is modulated by specific genes and transcription factors. The relevance of EMT-related lncRNAs in melanoma has not been determined. Therefore, RNA expression data and clinical features were collected from the TCGA database (N = 447). Melanoma samples were randomly assigned into the training (315) and testing sets (132). An EMT-related lncRNA signature was constructed via comprehensive analyses of lncRNA expression level and corresponding clinical data. The Kaplan-Meier analysis showed significant differences in overall survival in patients with melanoma in the low and high-risk groups in two sets. Receiver operating characteristic (ROC) curves were used to measure the performance of the model. Cox regression analysis indicated that the risk score was an independent prognostic factor in two sets. Besides, a nomogram was constructed based on the independent variables. Gene Set Enrichment Analysis (GSEA) was applied to evaluate the potential biological functions in the two risk groups. Furthermore, the melanoma microenvironment was evaluated using ESTIMATE and CIBERSORT algorithms in the risk groups. This study indicates that EMT-related lncRNAs can function as potential independent prognostic biomarkers for melanoma survival.

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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Current Bioinformatics ◽

10.2174/1574893616666211005110732 ◽

2021 ◽

Vol 16 ◽

Author(s):

Dongqing Su ◽

Qianzi Lu ◽

Yi Pan ◽

Yao Yu ◽

Shiyuan Wang ◽

...

Keyword(s):

Breast Cancer ◽

Regression Analysis ◽

Cancer Patients ◽

Risk Score ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Patients ◽

Cox Regression Analysis ◽

Score Model

Background: Breast cancer has plagued women for many years and caused many deaths around the world. Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study. Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts. Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

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Decreased DHRS1 expression is a novel predictor of poor survival in patients with hepatocellular carcinoma

Biomarkers in Medicine ◽

10.2217/bmm-2021-0041 ◽

2021 ◽

Vol 15 (15) ◽

pp. 1319-1331

Author(s):

Li Li ◽

Hui-Jing Situ ◽

Wen-Cheng Ma ◽

Xuan Liu ◽

Lu-Lu Wang

Keyword(s):

Hepatocellular Carcinoma ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Aberrant Expression ◽

Normal Tissues ◽

Multiple Data ◽

Kaplan Meier ◽

Survival Gene ◽

Mrna And Protein Expression

Aim: To investigate the effect of aberrant expression of DHRS1 on hepatocellular carcinoma (HCC). Materials & methods: Kaplan–Meier and Cox regression analyses were performed to evaluate the correlation between DHRS1 and overall survival. Gene set enrichment analysis was performed to explore the potential function of DHRS1 in HCC. Results: Multiple data analysis revealed that DHRS1 mRNA and protein expression level were remarkably lower in HCC than that in normal tissues. In survival analysis, patients with low DHRS1 expression presented a poorer prognosis, and was an independent risk factor for HCC. Conclusion: Decreased DHRS1 expression may be a potential predictor of poor prognosis in HCC.

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Identification of Seven Novel Ferroptosis-Related Long Non - Coding RNA Signatures as a Diagnostic Biomarker for Acute Myeloid Leukemia

10.21203/rs.3.rs-502041/v1 ◽

2021 ◽

Author(s):

Zhiyuan Zheng ◽

Wei Wu ◽

Zehang Lin ◽

Shuhan Liu ◽

Qiaoqian Chen ◽

...

Keyword(s):

Acute Myeloid Leukemia ◽

Risk Score ◽

Myeloid Leukemia ◽

Cox Regression ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Gene Set Enrichment ◽

Gene Set ◽

Kaplan Meier ◽

Acute Myeloid

Abstract Background: Ferroptosis is a newly discovered type of programmed cell death that participates in the biological processes of various cancers. However, the mechanism by which ferroptosis modulates acute myeloid leukemia (AML) remains unclear. This study aimed to investigate the role of ferroptosis-related long non-coding RNAs (lncRNAs) in AML and establish a corresponding prognostic model.Methods: RNA-sequencing data and clinicopathological characteristics were obtained from The Cancer Genome Atlas database, and ferroptosis-related genes were obtained from the FerrDb database. The “limma” R package, Cox regression, and the least absolute shrinkage and selection operator were used to determine the ferroptosis-related lncRNA signature with the lowest Akaike information criteria (AIC). The risk score of ferroptosis-related lncRNAs was calculated and patients with AML were divided into high- and low-risk groups based on the median risk score. The Kaplan-Meier curve and Cox regression were used to evaluate the prognostic value of the risk score. Finally, gene set enrichment analysis (GSEA) and single-sample gene set enrichment analysis (ssGSEA) were performed to explore the biological functions of the ferroptosis-related lncRNAs.Results: Seven ferroptosis-related lncRNA signatures were identified in the training group, and Kaplan-Meier and Cox regression analyses confirmed that risk scores were independent prognostic predictors of AML in both the training and validation groups (All P < 0.05). In addition, the area under the curve (AUC) analysis confirmed that the signatures had a good predictive ability for the prognosis of AML. GSEA and ssGSEA showed that the seven ferroptosis-related lncRNAs were related to glutathione metabolism and tumor immunity.Conclusions: In this study, seven novel ferroptosis-related lncRNA signatures (AP001266.2, AC133961.1, AF064858.3, AC007383.2, AC008906.1, AC026771.1, and KIF26B-AS1) were established. These signatures were shown to accurately predict the prognosis of AML, which would provide new insights into strategies for the development of new AML therapies.

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Establishment and Validation of a Novel Metabolism-related Gene Signature for Predicting Overall Survival of Patients with Breast Cancer

10.21203/rs.3.rs-222322/v1 ◽

2021 ◽

Author(s):

Jian Li ◽

Yang Liu ◽

Fei Liu ◽

Qiang Tian ◽

Baojiang Li ◽

...

Keyword(s):

Breast Cancer ◽

Cox Regression ◽

Treatment Strategies ◽

Enrichment Analysis ◽

Risk Groups ◽

Gene Signature ◽

Gene Set Enrichment Analysis ◽

Current Status ◽

Lasso Regression ◽

Related Gene

Abstract It is well known that Breast cancer is a heterogeneous disease.Although the current recurrence and mortality rate have been greatly improved, many people still suffer relapse and metastasis.Metabolic reprograming is currently considered to be a new hallmark of cancer.Therefore,in this study, we comprehensively analyzed the prognostic effect of metabolic-related gene signatures in breast cancer and its relationship with the immune microenvironment.We constructed a novel metabolic-related gene signature containing 6 genes to distinguish between high and low risk groups by univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression, and validated its robustness and accuracy through multiple databases.The metabolic gene signature may be an independent risk factor for BC both in the training and the testing set,the nomogram has a moderately accurate performance,and the C index was 0.757 and 0.728 respectively.The signature can reveal metabolic characteristics based on gene set enrichment analysis and at the same time monitor the status of TME.This gene signature can be used as a promising independent prognostic marker for BC patients, and can indicate the current status of TME, providing more clues for exploring new diagnostic and treatment strategies.

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Overexpression of SKA3 correlates with poor prognosis in female early breast cancer

PeerJ ◽

10.7717/peerj.12506 ◽

2021 ◽

Vol 9 ◽

pp. e12506

Author(s):

Yue Zhong ◽

Zhenjie Zhuang ◽

Peiju Mo ◽

Mandi Lin ◽

Jiaqian Gong ◽

...

Keyword(s):

Breast Cancer ◽

Early Breast Cancer ◽

Poor Prognosis ◽

Cox Regression ◽

Molecular Subtype ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Chi Square ◽

Exact Test ◽

Chi Square Test

Background Spindle and kinetochore associated complex subunit 3 (SKA3) plays an important role in tumorigenesis and the progression of various tumors. But the relationship between SKA3 and early breast cancer remains unclear. The study aimed to explore the prognostic significance of SKA3 in breast cancer. Methods In the study, SKA3 expression was initially assessed using the Oncomine database and The Cancer Genome Atlas database (TCGA). Then, we presented validation results for RT-qPCR (quantitative reverse transcription PCR) and ELISA (enzyme-linked immunosorbent assay). The relationship between clinical characteristics and SKA3 expression was assessed by Chi-square test and Fisher’s exact test. Kaplan–Meier method and Cox regression analysis were conducted to evaluate the prognostic value of SKA3. Gene set enrichment analysis (GSEA) was performed to screen biological pathways using the TCGA dataset. Besides, single sample gene set enrichment analysis (ssGSEA) was utilized to identify immune infiltration cells about SKA3. Results SKA3 mRNA was expressed at high levels in breast cancer tissues compared with normal tissues. Chi-square test and Fisher’s exact test showed SKA3 expression was related to age, tumor (T) classification, node (N) classification, tumor-node-metastasis (TNM) stage, estrogen receptor (ER), progesterone receptor (PR), molecular subtype, and race. RT-qPCR results showed that SKA3 expression was overexpressed in ER, PR status, and molecular subtype in Chinese people. Kaplan–Meier curves implicated that high SKA3 expression was related to a poor prognosis in female early breast cancer patients. Cox regression models showed that high SKA3 expression could be used as an independent risk factor for female early breast cancer. Four signaling pathways were enriched in the high SKA3 expression group, including mTORC1 signaling pathway, MYC targets v1, mitotic spindle, estrogen response early. Besides, the SKA3 expression level was associate with infiltrating levels of activated CD4 T cells and eosinophils in breast cancer. Conclusion High SKA3 expression correlates with poor prognosis and immune infiltrates in breast cancer. SKA3 may become a biomarker for the prognosis of breast cancer.

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