scholarly journals A six-mRNA signature-based model for the prognosis prediction of breast cancer

2020 ◽  
Author(s):  
Yan Huang ◽  
Xia Wan ◽  
Shixiang Wang
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Bioinformatics Analysis ◽  
Independent Predictor ◽  
Gene Signature ◽  
Prognostic Signature ◽  
Important Indicator ◽  
Potential Indicator

Abstract BackgroundDue to the lack of predictors, high mortality and poor prognosis have always been a serious threat to the breast cancer (BC) patients. Accumulating studies have shown that molecular markers which affect the tumor microenvironment (TME) play an important role in the development of cancer. Here, we aim to use machine learning to identify a new prognostic gene signature and independent prognostic factors related to BC survival through comprehensive bioinformatics analysis.Results This 6-mRNA signature-based model is not only an important indicator to predict the prognosis and survival of BC, but also a potential indicator to monitor the clinical therapeutic effect with certain clinical significance.Conclusions A new 6-gene prognostic signature was discovered and it is a promising independent predictor. The 6 feature genes can function as important biomarkers for BC clinical treatment. At the same time, our study also provides a new insight to explore the molecular mechanism of TME and immune cells that influence BC progress.

scholarly journals Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer

2021 ◽  
Author(s):  
E. Amiri Souri ◽  
A. Chenoweth ◽  
A. Cheung ◽  
S. N. Karagiannis ◽  
S. Tsoka
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Clinical Decision Making ◽  
Histological Grade ◽  
Tumour Size ◽  
Clinical Decision ◽  
Gene Signature ◽  
Risk Classification ◽  
Breast Cancers ◽  
Grade 3

Abstract Background Prognostic stratification of breast cancers remains a challenge to improve clinical decision making. We employ machine learning on breast cancer transcriptomics from multiple studies to link the expression of specific genes to histological grade and classify tumours into a more or less aggressive prognostic type. Materials and methods Microarray data of 5031 untreated breast tumours spanning 33 published datasets and corresponding clinical data were integrated. A machine learning model based on gradient boosted trees was trained on histological grade-1 and grade-3 samples. The resulting predictive model (Cancer Grade Model, CGM) was applied on samples of grade-2 and unknown-grade (3029) for prognostic risk classification. Results A 70-gene signature for assessing clinical risk was identified and was shown to be 90% accurate when tested on known histological-grade samples. The predictive framework was validated through survival analysis and showed robust prognostic performance. CGM was cross-referenced with existing genomic tests and demonstrated the competitive predictive power of tumour risk. Conclusions CGM is able to classify tumours into better-defined prognostic categories without employing information on tumour size, stage, or subgroups. The model offers means to improve prognosis and support the clinical decision and precision treatments, thereby potentially contributing to preventing underdiagnosis of high-risk tumours and minimising over-treatment of low-risk disease.

Multicenter Validation of a Gene Expression–Based Prognostic Signature in Lymph Node–Negative Primary Breast Cancer

2006 ◽  
Vol 24 (11) ◽  
pp. 1665-1671 ◽  
Author(s):  
John A. Foekens ◽  
David Atkins ◽  
Yi Zhang ◽  
Fred C.G.J. Sweep ◽  
Nadia Harbeck ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Estrogen Receptor ◽  
Distant Metastasis ◽  
Gene Signature ◽  
Breast Cancer Patients ◽  
Prognostic Signature ◽  
Free Survival ◽  
Node Negative ◽  
Distant Metastasis Free Survival ◽  
Multicenter Validation

Purpose We previously identified in a single-center study a 76-gene prognostic signature for lymph node-negative (LNN) breast cancer patients. The aim of this study was to validate this gene signature in an independent more diverse population of LNN patients from multiple institutions. Patients and Methods Using custom-designed DNA chips we analyzed the expression of the 76 genes in RNA of frozen tumor samples from 180 LNN patients who did not receive adjuvant systemic treatment. Results In this independent validation, the 76-gene signature was highly informative in identifying patients with distant metastasis within 5 years (hazard ratio, [HR], 7.41; 95% CI, 2.63 to 20.9), even when corrected for traditional prognostic factors in multivariate analysis (HR, 11.36; 95% CI, 2.67 to 48.4). The actuarial 5- and 10-year distant metastasis-free survival were 96% (95% CI, 89% to 99%) and 94% (95% CI, 83% to 98%), respectively, for the good profile group and 74% (95% CI, 64% to 81%) and 65% (53% to 74%), respectively for the poor profile group. The sensitivity for 5-yr distant metastasis-free survival was 90%, and the specificity was 50%. The positive and negative predictive values were 38% (95% CI, 29% to 47%) and 94% (95% CI, 86% to 97%), respectively. The 76-gene signature was confirmed as a strong prognostic factor in subgroups of estrogen receptor-positive patients, pre- and postmenopausal patients, and patients with tumor sizes 20 mm or smaller. The subgroup of patients with estrogen receptor-negative tumors was considered too small to perform a separate analysis. Conclusion Our data provide a strong methodologic and clinical multicenter validation of the predefined prognostic 76-gene signature in LNN breast cancer patients.

Activation of a Mir-181-Targeting HOXA-PBX3 Homeobox Gene Signature Is Associated with Adverse Prognosis of Cytogenetically Abnormal Acute Myeloid Leukemia

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 236-236
Author(s):  
Zejuan Li ◽  
Hao Huang ◽  
Yuanyuan Li ◽  
Xi Jiang ◽  
Ping Chen ◽  
...  
Keyword(s):  
Acute Myeloid Leukemia ◽  
Mouse Model ◽  
Myeloid Leukemia ◽  
Poor Prognosis ◽  
Independent Predictor ◽  
Family Members ◽  
Independent Sets ◽  
Gene Signature ◽  
Altered Expression ◽  
Acute Myeloid

Abstract Abstract 236 Altered expression of microRNAs (miRNAs, a class of small regulatory RNAs) is associated with various types of cancers, including acute myeloid leukemia (AML). We showed previously that increased expression of miR-181a with or without miR-181b was associated with a favorable prognosis for patients with cytogenetically normal AML (CN-AML). However, the prognostic value of miR-181 expression in cytogenetically abnormal AML (CA-AML) remains elusive, even though CA-AML represents the majority of human AML. To investigate the association of expression signatures of miR-181 family members and of their potential target genes with outcome in patients with primary CA-AML, we employed two independent sets of 86 CA-AML patients to investigate the association of expression signatures of miR-181 family members with outcome. We also used four independent sets of 454 CA-AML patients to identify and validate a prognostic signature of miR-181 targets. In addition, we investigated the biological functions of miR-181a/b and target(s) in leukemia cell lines and in a leukemia mouse model. As with CN-AML, we found that both miR-181a and miR-181b expression signatures are significantly (P<.05) associated with favorable overall survival (OS) of patients, but only the miR-181b signature is an independent predictor in multivariable model tests. An overexpressing gene signature of potential targets of miR-181a/b, HOXA7, HOXA9, HOXA11, and PBX 3, derived from a meta-analysis of three independent sets of 183 patients, was an independent predictor of adverse OS, which was confirmed in a validation set of 271 patients. Ectopic expression of miR-181b significantly (P<.05) promoted apoptosis and decreased viability of MONOMAC-6/t(9;11), THP-1/t(9;11), and KOCL-48/t(4;11), and delayed leukemogenesis in our mouse model; such effects could be reversed by forced expression of PBX3. Our data suggest that the silencing of miR-181b and thereby the activation of the four homeobox genes likely contributes to the poor prognosis of adverse CA-AML patients. Thus, restoring expression of miR-181b and/or targeting the HOXA/PBX3 pathways in poor prognosis AML patients may provide new strategies to improve outcome substantially. Disclosures: No relevant conflicts of interest to declare.

Molecular characteristics and metastasis predictor genes of triple-negative breast cancer.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 1043-1043
Author(s):  
Wen-Hung Kuo ◽  
Yao-Yin Chang ◽  
Ming-Feng Hou ◽  
Eric Y Chuang ◽  
King-Jen Chang
Keyword(s):  
Breast Cancer ◽  
Triple Negative Breast Cancer ◽  
Triple Negative ◽  
Early Stage ◽  
Gene Signature ◽  
Breast Cancers ◽  
Prognostic Signature ◽  
Characteristic Analysis ◽  
Disease Etiology ◽  
Breast Cancer Outcome

1043 Background: Triple-negative breast cancer(TNBC) is a subtype of breast cancer with aggressive tumor behavior and distinct disease etiology. Due to the lack of an effective targeted medicine, treatment options for triple-negative breast cancer are few and recurrence rates are high. Although various multi-gene prognostic markers have been proposed for the prediction of breast cancer outcome, most of them were proven clinically useful only for estrogen receptor-positive breast cancers. Reliable identification of triple-negative patients with a favorable prognosis is not yet possible. Methods: Clinicopathological information and microarray data from 157 invasive breast carcinomas were collected at National Taiwan University Hospital from 1995 to 2008. Gene expression data of 51 triple-negative and 106 luminal breast cancers were generated with oligonucleotide microarrays. A prognostic 45-gene signature for triple-negative breast cancer was identified using Student’s t test and receiver operating characteristic analysis. Results: Hierarchical clustering analysis revealed that the majority (94%) of triple-negative breast cancers were tightly clustered together carrying strong basal-like characteristics. A novel 45-gene signature giving 98% predictive accuracy in distant metastasis recurrence was identified in our triple-negative patient cohort. External validation of the prognostic signature in an independent microarray dataset of 59 early-stage triple-negative patients also obtained statistical significance (hazard ratio 2.29, 95% CI 1.04-5.06, Cox P = 0.04), outperforming five other published breast cancer prognostic signatures. The prognostic signature was statistically predictive with the node-negative triple-negative patients in the validation cohort. Conclusions: The 45-gene prognostic signature identified in this study revealed that TGF-β signaling in immune/inflammatory regulation may be critically involved in distant metastatic invasion of TNBC. The 45-gene signature, if further validated, may be a clinically useful tool in risk assessment of metastasis recurrence for early-stage triple-negative patients.

scholarly journals The Major Prognostic Features of Nuclear ReceptorNR5A2in Infiltrating Ductal Breast Carcinomas

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Li-Yun Chang ◽  
Li-Yu D. Liu ◽  
Don A. Roth ◽  
Wen-Hung Kuo ◽  
Hsiao-Lin Hwa ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Network Analysis ◽  
Poor Prognosis ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Gene Signature ◽  
Cancer Prognosis ◽  
Transcriptional Networks ◽  
Cancer Subtypes ◽  
Prognostic Features

Background. Gene expression profiles of 181 breast cancer samples were analyzed to identify prognostic features of nuclear receptorsNR5A1andNR5A2based upon their associated transcriptional networks.Methods. A supervised network analysis approach was used to build the NR5A-mediated transcriptional regulatory network. Other bioinformatic tools and statistical methods were utilized to confirm and extend results from the network analysis methodology.Results.NR5A2expression is a negative factor in breast cancer prognosis in both ER(−) and ER(−)/ER(+) mixed cohorts. The clinical and cohort significance ofNR5A2-mediated transcriptional activities indicates that it may have a significant role in attenuating grade development and cancer related signal transduction pathways.NR5A2signature that conditions poor prognosis was identified based upon results from 15 distinct probes. Alternatively, the expression ofNR5A1predicts favorable prognosis when concurrentNR5A2expression is low. A favorable signature of eight transcription factors mediated byNR5A1was also identified.Conclusions. Correlation of poor prognosis andNR5A2activity is identified byNR5A2-mediated 15-gene signature.NR5A2may be a potential drug target for treating a subset of breast cancer tumors across breast cancer subtypes, especially ER(−) breast tumors. The favorable prognostic feature ofNR5A1is predicted byNR5A1-mediated 8-gene signature.

scholarly journals Identification of Prognostic Gene Signature Associated with Tumor Microenvironment of Cholangiocarcinoma

2021 ◽  
Author(s):  
Jixiang Cao ◽  
Xi Chen ◽  
Guang Lu ◽  
Haowei Wang ◽  
Xinyu Zhang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Tumor Microenvironment ◽  
Immune Cells ◽  
Prognostic Model ◽  
Cox Regression ◽  
Gene Signature ◽  
Risk Scores ◽  
Tumor Infiltration ◽  
Prognostic Signature ◽  
Cox Regression Analysis

Abstract Background: Cholangiocarcinoma (CCA) is the most common malignancy of the biliary tract with a dismal prognosis. Increasing evidence suggests that tumor microenvironment (TME) is closely associated with cancer prognosis. However, the prognostic signature for CCA based on TME has not yet been reported. This study aimed to develop a TME-related prognostic signature for accurately predicting the prognosis of patients with CCA. Methods: Based on the TCGA database, we calculated the stromal and immune scores using the ESTIMATE algorithm to assess TME in stromal and immune cells derived from CCA. TME-related differentially expressed genes were identified, followed by functional enrichment analysis and PPI network analysis. Univariate Cox regression analysis, Lasso Cox regression model and multivariable Cox regression analysis were performed to identify and construct the TME-related prognostic gene signature. Gene Set Enrichment Analyses (GSEA) was performed to further investigate the potential molecular mechanisms. The correlations between the risk scores and tumor infiltration immune cells were analyzed using Tumor Immune Estimation Resource (TIMER) database. Results: A total of 784 TME-related differentially expressed genes (DEGs) were identified, which were mainly enriched in immune-related processes and pathways. Among these TME-related DEGs, A novel two‑gene signature (including GAD1 and KLRB1) was constructed for CCA prognosis prediction. The AUC of the prognostic model for predicting the survival of patients at 1-, 2-, and 3- years was 0.811, 0.772, and 0.844, respectively. Cox regression analysis showed that the two‑gene signature was an independent prognostic factor. Based on the risk scores of the prognostic model, CCA patients were divided into high- and low-risk groups, and patients with high-risk score had shorter survival time than those with low-risk score. Furthermore, we found that the risk scores were negatively correlated with TME-scores and the number of several tumor infiltration immune cells, including B cells and CD4+ T cells. Conclusion: Our study established a novel TME-related gene signature to predict the prognosis of patients with CCA. This might provide a new understanding of the potential relationship between TME and CCA prognosis, and serve as a prognosis stratification tool for guiding personalized treatment of CCA patients.

scholarly journals Upregulated CCNE1 Correlates with Poor Prognosis, Tumor Immune Infiltration and Escape in Breast Cancer

2021 ◽  
Author(s):  
Jiaxi Feng ◽  
Yanan Hu ◽  
Dan Liu ◽  
Shanshan Wang ◽  
Mengci Zhang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Immune Cell ◽  
High Expression ◽  
Expression Level ◽  
Immune Cell Infiltration ◽  
Immune Infiltration ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Abstract Background Breast cancer (BC) is the most common malignant tumor in women and widely known for its poor prognosis. More and more research has discovered that cyclin E1 (CCNE1) plays an important role in progression of various types of cancer. But its specific mechanism in BC progression still needs further research to explore.Methods At first, we determined the expression and prognostic value of CCNE1 through The Cancer Genome Atlas (TCGA) database and The Genotype-Tissue Expression (GTEx) data. Then, we predicted the upstream non-coding RNAs of CCNE1 through StarBase, GEPIA, and Kaplan-Meier plotter database. We further studied the correlation of CCNE1 expression with BC immune cell infiltration, biomarkers of immune cells and immune checkpoints expression through TIMER and GEPIA databases.Results The results suggested that CCNE1 was significantly upregulated in BC and its high expression was correlated with poor prognosis in BC patients. Next, we identified long noncoding RNA (lncRNA) LINC00511 / microRNA-195-5p (miR-195-5p) / CCNE1 axis as the most potential pathway that could regulate CCNE1 expression in BC through StarBase, GEPIA, and Kaplan-Meier plotter database. Furthermore, our in-depth research discovered that CCNE1 expression level was significantly correlated with tumor immune cell infiltration, biomarkers of immune cells, and immune checkpoint expression in BC. conclusions In summary, high expression level of CCNE1 was significantly correlated with poor prognosis, tumor immune infiltration and escape in BC.

scholarly journals Sphingosine-1-Phosphate Catabolizing Enzymes Predict Better Prognosis in Triple-Negative Breast Cancer Patients and Correlates With Tumor-Infiltrating Immune Cells

2021 ◽  
Vol 8 ◽  
Author(s):  
Rajeev Nema ◽  
Ashok Kumar
Keyword(s):  
Breast Cancer ◽  
T Cells ◽  
Cd8 T Cells ◽  
Immune Cells ◽  
Poor Prognosis ◽  
Triple Negative ◽  
Strong Positive Correlation ◽  
Breast Cancer Patients ◽  
Sphingosine 1 Phosphate ◽  
S1p Receptor

Background: Triple-negative breast cancer (TNBC) is associated with a poor prognosis. Sphingosine-1-phosphate (S1P), a potent sphingolipid metabolite, has been implicated in many processes that are important for breast cancer (BC). S1P signaling regulates tumorigenesis, and response to chemotherapy and immunotherapy by affecting the trafficking, differentiation or effector function of tumor-infiltrating immune cells (TIICs).Objective: In this study, using bioinformatics tools and publicly available databases, we have analyzed the prognostic value of S1P metabolizing genes and their correlation with TIICs in BC patients.Methods: The expression of S1P metabolizing genes and receptors was evaluated by the UALCAN cancer database. The correlation between mRNA expression of S1P metabolizing genes and receptors and survival outcome of breast cancer patients was analyzed by the Kaplan-Meier plotter database. The association between the gene expression and infiltration of immune cells in the tumors was analyzed by “Tumor-Infiltrating Immune Estimation Resource (TIMER). In silico protein expression analysis was done using the Human Protein Atlas” database.Results: TNBC patients with lower expression of S1P phosphatase 1 (SGPP1) or lipid phosphate phosphatase 3 (PLPP3) have much shorter relapse-free survival than the patients with a higher expression of these genes. SGPP1 and PLPP3 expression show a strong positive correlation with tumor-infiltrating dendritic cells (DCs), CD4+ and CD8+ T cells, neutrophils, and macrophages in the TNBC subtypes. In addition, S1P receptor 4 (S1PR4), an S1P receptor exhibit a strong positive correlation with DCs, CD4+ and CD8+ T cells and neutrophils in TNBC. We, therefore, conclude that low expression of SGPP1 and PLPP3 may hinder the recruitment of immune cells to the tumor environment, resulting in the blockage of cancer cell clearance and a subsequent poor prognosis.

scholarly journals Surgical Treatment for Refractory Aggression after Bacterial Meningitis Complicated by Stroke: Case Report

2022 ◽  
Author(s):  
Pietro Alessandro Vaccario ◽  
Alícia Carolina Rodrigues Rocha ◽  
Ledismar José da Silva
Keyword(s):  
Bacterial Meningitis ◽  
Poor Prognosis ◽  
Independent Predictor ◽  
Scientific Evidence ◽  
Public Health Problem ◽  
Mental Illnesses ◽  
Important Indicator ◽  
Psychiatric Illnesses ◽  
Stereotactic Technique ◽  
History Of

AbstractBacterial meningitis remains a public health problem. One of the complications of this group of diseases is cerebral ischemia, an important indicator of severity and an independent predictor of poor prognosis. It is already known that, in many cases, pathological aggressiveness is the result of brain abnormalities in individuals with mental illnesses. The indication of neurosurgeries for psychiatric disorders (NPDs) relies on numerous studies based on scientific evidence that correlate psychiatric illnesses with the limbic system and the pathophysiology of emotions. The development of sophisticated stereotactic target localization techniques, brain atlases, and imaging methods made stereotaxis possible, a procedure that increased the precision of neurosurgery and reduced brain damage. Nowadays, multiple targets can be treated during NPD, according to the particular characteristics of the patient. Moreover, the combination of lesions leads to more significant improvements compared with isolated procedures. The present study aimed to report the rare case of a patient with a history of bacterial meningitis who developed stroke and chronic pathological aggressiveness refractory to clinical treatment and underwent ablation using the multitarget stereotactic technique.

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