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

PeerJ ◽  
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.

scholarly journals Overexpression of MAL2 Correlates with Immune Infiltration and Poor Prognosis in Breast Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Yue Zhong ◽  
Zhenjie Zhuang ◽  
PeiJu Mo ◽  
Qi Shang ◽  
Mandi Lin ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Dendritic Cells ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Plasmacytoid Dendritic Cells ◽  
Chi Square ◽  
Exact Test ◽  
Kaplan Meier ◽  
Chi Square Test ◽  
The Relationship

Background. Myelin and lymphocyte, T cell differentiation protein 2 (MAL2) is highly expressed in various cancers and associated with the development and prognosis of cancer. However, the relationship between MAL2 and breast cancer requires further investigation. This study aimed to explore the prognostic significance of MAL2 in breast cancer. Methods. MAL2 expression was initially assessed using the Oncomine database and The Cancer Genome Atlas (TCGA) database and verified by quantitative real-time polymerase chain reaction (RT-qPCR). The chi-square test or Fisher’s exact test was used to explore the association between clinical characteristics and MAL2 expression. The prognostic value of MAL2 in breast cancer was assessed by the Kaplan–Meier method and Cox regression analysis. Gene set enrichment analysis (GSEA) was performed to identify the biological pathways correlated with MAL2 expression in breast cancer. Besides, a single-sample GSEA (ssGSEA) was used to assess the relationship between the level of immune infiltration and MAL2 in breast cancer. Results. Both bioinformatics and RT-qPCR results showed that MAL2 was expressed at high levels in breast cancer tissues compared with the adjacent tissues. The chi-square test or Fisher’s exact test indicated that MAL2 expression was related to stage, M classification, and vital status. Kaplan–Meier curves implicated that high MAL2 expression was significantly associated with the poor prognosis. Cox regression models showed that high MAL2 expression could be an independent risk factor for breast cancer. GSEA showed that 14 signaling pathways were enriched in the high-MAL2-expression group. Besides, the MAL2 expression level negatively correlated with infiltrating levels of eosinophils and plasmacytoid dendritic cells in breast cancer. Conclusion. Overexpression of MAL2 correlates with poor prognosis and lower immune infiltrating levels of eosinophils and plasmacytoid dendritic cells in breast cancer and may become a biomarker for breast cancer prognosis.

scholarly journals Choosing Wisely – Implication based on Indian data in our patients with breast cancer (INR vs. USD)

2021 ◽  
Vol 6 ◽  
pp. 6-10
Author(s):  
Ajay Bapna ◽  
Nidhi Patni ◽  
Sanjeev Patni
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Metronomic Chemotherapy ◽  
Chi Square ◽  
Exact Test ◽  
Chi Square Test ◽  
Cost Implications ◽  
The Cost ◽  
Indian Data

Objectives: Breast cancer is increasing in India due to aging population, better awareness among general public, willingness to seek treatment of cancers, and easier access to cancers centers. We present our single-center data over a 2-year period and discuss cost implications taking the example of metronomic chemotherapy maintenance and predictive markers in early breast cancer. Material and Methods: Prospectively collected data of all consecutive patients with breast cancer registered between September 2017 and August 2019 were evaluated. Clinical features, stage, receptor status, and other features were tabulated. Statistical analysis was using SAS version 9.4 – Chi-square test and Fisher’s exact test were performed. P ≤ 0.05 was considered as statistically significant. Results: For the 484 consecutive patients, the median age was 50 years. This included EBC (201, 42%), LABC (141, 29%), and MBC (142, 29%). ER expression was seen in 52% of cases (253/484), PR in 47% (229/484), and Her2 was positive in 47% (229/484). Finally, 83 patients (17%) were identified as TNBC. HR-positive Her2-negative EBC constituted 111/484 patients (23%). Discussion: If our 83 TNBC patients were given metronomic maintenance chemotherapy, their 3-year overall survival (OS) is projected to increase from 54% to 100% at a cost of INR 8191/- per patient (equivalent to USD 109/-). If our 111 HR-positive Her2-negative EBC patients were evaluated for risk by biomarker test validated in Indian patients, 76 of these would be spared the toxicity of adjuvant CT. This would also result in saving on the cost of chemotherapy medication of INR 4,035,296/- in India (equivalent to USD 53,699/- if treated in USD). In addition, they would also have better quality of life (QoL). Conclusion: It is possible to identify patients with low risk early breast cancer using Can assist and save them from unnecessary cost and/or toxicity.

scholarly journals Overexpressed XRCC2 as an independent risk factor for poor prognosis in glioma patients

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Zhendong Liu ◽  
Wang Zhang ◽  
Xingbo Cheng ◽  
Hongbo Wang ◽  
Lu Bian ◽  
...  
Keyword(s):  
Risk Factor ◽  
Expression Pattern ◽  
Poor Prognosis ◽  
Independent Risk Factor ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Cellular Mechanisms ◽  
Molecular Features ◽  
Meta Analyses

Abstract Background XRCC2, a homologous recombination-related gene, has been reported to be associated with a variety of cancers. However, its role in glioma has not been reported. This study aimed to find out the role of XRCC2 in glioma and reveal in which glioma-specific biological processes is XRCC2 involved based on thousands of glioma samples, thereby, providing a new perspective in the treatment and prognostic evaluation of glioma. Methods The expression characteristics of XRCC2 in thousands of glioma samples from CGGA and TCGA databases were comprehensively analyzed. Wilcox or Kruskal test was used to analyze the expression pattern of XRCC2 in gliomas with different clinical and molecular features. The effect of XRCC2 on the prognosis of glioma patients was explored by Kaplan–Meier and Cox regression. Gene set enrichment analysis (GSEA) revealed the possible cellular mechanisms involved in XRCC2 in glioma. Connectivity map (CMap) was used to screen small molecule drugs targeting XRCC2 and the expression levels of XRCC2 were verified in glioma cells and tissues by RT-qPCR and immunohistochemical staining. Results We found the overexpression of XRCC2 in glioma. Moreover, the overexpressed XRCC2 was associated with a variety of clinical features related to prognosis. Cox and meta-analyses showed that XRCC2 is an independent risk factor for the poor prognosis of glioma. Furthermore, the results of GSEA indicated that overexpressed XRCC2 could promote malignant progression through involved signaling pathways, such as in the cell cycle. Finally, doxazosin, quinostatin, canavanine, and chrysin were identified to exert anti-glioma effects by targeting XRCC2. Conclusions This study analyzed the expression pattern of XRCC2 in gliomas and its relationship with prognosis using multiple datasets. This is the first study to show that XRCC2, a novel oncogene, is significantly overexpressed in glioma and can lead to poor prognosis in glioma patients. XRCC2 could serve as a new biomarker for glioma diagnosis, treatment, and prognosis evaluation, thus bringing new insight into the management of glioma.

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

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.

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

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.

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

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.

scholarly journals A Signature of Autophagy-Related Long Non-coding RNA to Predict the Prognosis of Breast Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiaoping Li ◽  
Jishang Chen ◽  
Qihe Yu ◽  
Hui Huang ◽  
Zhuangsheng Liu ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Regression Analysis ◽  
High Risk ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Low Risk ◽  
Gene Set Enrichment Analysis ◽  
Lasso Regression ◽  
Cox Regression Analysis ◽  
Risk Scoring

Background: A surge in newly diagnosed breast cancer has overwhelmed the public health system worldwide. Joint effort had beed made to discover the genetic mechanism of these disease globally. Accumulated research has revealed autophagy may act as a vital part in the pathogenesis of breast cancer.Objective: Aim to construct a prognostic model based on autophagy-related lncRNAs and investigate their potential mechanisms in breast cancer.Methods: The transcriptome data and clinical information of patients with breast cancer were obtained from The Cancer Genome Atlas (TCGA) database. Autophagy-related genes were obtained from the Human Autophagy Database (HADb). Long non-coding RNAs (lncRNAs) related to autophagy were acquired through the Pearson correlation analysis. Univariate Cox regression analysis as well as the least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify autophagy-related lncRNAs with prognostic value. We constructed a risk scoring model to assess the prognostic significance of the autophagy-related lncRNAs signatures. The nomogram was then established based on the risk score and clinical indicators. Through the calibration curve, the concordance index (C-index) and receiver operating characteristic (ROC) curve analysis were evaluated to obtain the model's predictive performance. Subgroup analysis was performed to evaluate the differential ability of the model. Subsequently, gene set enrichment analysis was conducted to investigate the potential functions of these lncRNAs.Results: We attained 1,164 breast cancer samples from the TCGA database and 231 autophagy-related genes from the HAD database. Through correlation analysis, 179 autophagy-related lncRNAs were finally identified. Univariate Cox regression analysis and LASSO regression analysis further screened 18 prognosis-associated lncRNAs. The risk scoring model was constructed to divide patients into high-risk and low-risk groups. It was found that the low-risk group had better overall survival (OS) than those of the high-risk group. Then, the nomogram model including age, tumor stage, TNM stage and risk score was established. The evaluation index (C-index: 0.78, 3-year OS AUC: 0.813 and 5-year OS AUC: 0.785) showed that the nomogram had excellent predictive power. Subgroup analysis showed there were difference in OS between high-risk and low-risk patients in different subgroups (stage I-II, ER positive, Her-2 negative and non-TNBC subgroups; all P < 0.05). According to the results of gene set enrichment analysis, these lncRNAs were involved in the regulation of multicellular organismal macromolecule metabolic process in multicellular organisms, nucleotide excision repair, oxidative phosphorylation, and TGF-β signaling pathway.Conclusions: We identified 18 autophagy-related lncRNAs with prognostic value in breast cancer, which may regulate tumor growth and progression in multiple ways.

Prognostic Value of LRG1 in Breast Cancer: A Retrospective Study

2020 ◽  
pp. 1-6
Author(s):  
Yan-Shou Zhang ◽  
Lei Han ◽  
Chao Yang ◽  
Yun-Jiang Liu ◽  
Xiang-Mei Zhang
Keyword(s):  
Breast Cancer ◽  
Early Breast Cancer ◽  
Cox Regression ◽  
Molecular Subtype ◽  
Histological Grade ◽  
Disease Free Survival ◽  
Pathological Stage ◽  
Tissue Samples ◽  
Factors Affecting ◽  
Elevated Expression

<b><i>Background:</i></b> High expression of leucine-rich alpha-2-glycoprotein 1 (LRG1) is closely related to angiogenesis, which may play an important role in promoting invasion and metastasis. However, the current literature has yet to clarify the clinical significance of LRG1 in breast cancer. <b><i>Objectives:</i></b> The purpose of this work was to validate the correlation between LRG1 expression and prognosis in early breast cancer. <b><i>Methods:</i></b> We utilized an LRG1 detection agent in 330 cases of early breast cancer. The correlation of LRG1 expression with clinicopathological features, patient recurrence, and survival was investigated. <b><i>Results:</i></b> Compared with adjacent tissue samples, an elevated expression of LRG1 was observed in breast cancer samples. Moreover, LRG1 expression is associated with the number of lymphatic metastases and TNM pathological stage (<i>p</i> = 0.000, <i>p</i> = 0.000, respectively). For disease-free survival (DFS), the Kaplan-Meier curve indicated a poorer prognosis for the group with high LRG1 levels compared with the low LRG1 group (<i>p</i> = 0.000). A similar result was found for overall survival (OS; <i>p</i> = 0.000). The multivariate Cox regression indicated that LRG1 was still associated with DFS (HR 2.090, 95% CI 1.205–3.625, <i>p</i> = 0.009) and OS (HR 2.112, 95% CI 1.167–3.822, <i>p</i> = 0.013). The histological grade, TNM pathological stage, and molecular subtype were identified as independent risk factors affecting OS. <b><i>Conclusion:</i></b> In the malignant progression of breast cancer, high LRG1 levels are associated with lymphatic metastasis, histological grade, poor DFS, and poor OS. This study validates the use of LRG1 as a potential prognosis biomarker for early breast cancer.

High BMI leads to increased breast cancer risk in postmenopausal women

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Tifanny Tantoso ◽  
Mega Sari Sitorus ◽  
Lita Feriyawati ◽  
Dian Dwi Wahyuni
Keyword(s):  
Breast Cancer ◽  
Medical Records ◽  
Test Results ◽  
Breast Cancer Patients ◽  
Chi Square ◽  
Exact Test ◽  
Cancer Breast ◽  
Chi Square Test ◽  
Control Study ◽  
High Bmi

Obesity is a worldwide problem that has been steadily increasing even in developing countries. Obesity has been linked to various types of cancer, one of which is breast cancer. Breast cancer has been classified into various types based on gene and hormone receptor expressions, which offered new insights to therapies and prognoses. We conducted a case-control study using 42 breast cancer patients and 43 healthy women, all of which are older than 55 years of age and have experienced menopause, and for case subjects, additional immunohistochemistry profiles have been provided. Data were collected by interviews and medical records. For data analysis, we used Pearson’s Chi-Square test and Fisher’s Exact test. Results showed that high BMI is significantly associated with breast cancer, and risk is elevated (p < 0.05, OR = 1.263, 95% CI = 1.007-1.583). No significant association with molecular subtypes was observed.

scholarly journals Low UGP2 Expression Is Associated with Tumour Progression and Predicts Poor Prognosis in Hepatocellular Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Qiuyue Hu ◽  
Shen Shen ◽  
Jianhao Li ◽  
Liwen Liu ◽  
Xin Liu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Poor Prognosis ◽  
Cox Regression ◽  
Tumour Progression ◽  
Enrichment Analysis ◽  
Malignant Tumour ◽  
Diagnostic Value ◽  
Gene Set Enrichment Analysis ◽  
Uridine Diphosphate ◽  
Roc Curve Analysis

Hepatocellular carcinoma (HCC) is a malignant tumour associated with a high mortality rate and poor prognosis worldwide. Uridine diphosphate-glucose pyrophosphorylase 2 (UGP2), a key enzyme in glycogen biosynthesis, has been reported to be associated with the occurrence and development of various cancer types. However, its diagnostic value and prognostic value in HCC remain unclear. The present study observed that UGP2 expression was significantly downregulated at both the mRNA and protein levels in HCC tissues. Receiver operating characteristic (ROC) curve analysis revealed that UGP2 may be an indicator for the diagnosis of HCC. In addition, Kaplan-Meier and Cox regression multivariate analyses indicated that UGP2 is an independent prognostic factor of overall survival (OS) in patients with HCC. Furthermore, gene set enrichment analysis (GSEA) suggested that gene sets negatively correlated with the survival of HCC patients were enriched in the group with low UGP2 expression levels. More importantly, a significant correlation was identified between low UGP2 expression and fatty acid metabolism. In summary, the present study demonstrates that UGP2 may contribute to the progression of HCC, indicating a potential therapeutic target for HCC patients.

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