Subtype-specific risk models for accurately predicting the prognosis of breast cancer using differentially expressed autophagy-related genes

Han, Baoai; Zhang, He; Zhu, Yuying; Han, Xingxing; Wang, Zhiyong; Gao, Zicong; Yuan, Yue; Tian, Ruinan; Zhang, Fei; Niu, Ruifang

doi:10.18632/aging.103437

Cited by 7 publications

(5 citation statements)

References 35 publications

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“…Baoai Han et al found that the BIRC5 gene was more strongly expressed in TNBC patients compared to other molecular breast cancer subtypes and control [ 65 ]. According to the available data, it can be assumed that the BIRC5 gene may be a factor involved in tumor formation and the processes of disease invasion and progression [ 66 ].…”

Section: Discussionmentioning

confidence: 99%

The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

Makuch-Kocka

Kocki

Brzozowska

et al. 2021

IJMS

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The BIRC (baculoviral IAP repeat‐containing; BIRC) family genes encode for Inhibitor of Apoptosis (IAP) proteins. The dysregulation of the expression levels of the genes in question in cancer tissue as compared to normal tissue suggests that the apoptosis process in cancer cells was disturbed, which may be associated with the development and chemoresistance of triple negative breast cancer (TNBC). In our study, we determined the expression level of eight genes from the BIRC family using the Real-Time PCR method in patients with TNBC and compared the obtained results with clinical data. Additionally, using bioinformatics tools (Ualcan and The Breast Cancer Gene-Expression Miner v4.5 (bc-GenExMiner v4.5)), we compared our data with the data in the Cancer Genome Atlas (TCGA) database. We observed diverse expression pattern among the studied genes in breast cancer tissue. Comparing the expression level of the studied genes with the clinical data, we found that in patients diagnosed with breast cancer under the age of 50, the expression levels of all studied genes were higher compared to patients diagnosed after the age of 50. We observed that in patients with invasion of neoplastic cells into lymphatic vessels and fat tissue, the expression levels of BIRC family genes were lower compared to patients in whom these features were not noted. Statistically significant differences in gene expression were also noted in patients classified into three groups depending on the basis of the Scarff-Bloom and Richardson (SBR) Grading System.

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Section: Discussionmentioning

confidence: 99%

The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

Makuch-Kocka

Kocki

Brzozowska

et al. 2021

IJMS

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“…The gene at the minimum of the Akaike information criterion (AIC) was calculated and used as the variable to be included in the model, and each patient's gene expression level was used to evaluate the risk score, with the algorithm according to the previous studies [ 14 , 15 ]. The median risk score of each patient is considered the reference standard for classifying the high and low groups, followed by the analysis of the survival of the two groups and drawing the survival curves using the Kaplan-Meier method (K-M method) [ 16 ]. In virtue of the critical parameters and model scores and in combination with various clinical factors, we drew a clinically relevant nomogram to predict the 1-, 3-, and 5-year survival, and the scales on nomograms represented the numerical ranges of each variable [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

A Risk-Scoring Model Based on Evaluation of Ferroptosis-Related Genes in Osteosarcoma

Jiang

Wang

et al. 2022

Journal of Oncology

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Background. Osteosarcoma (OS) is a bone malignancy frequently seen in pediatrics and has high mortality and incidence. Ferroptosis is an important cell death process in regulating the apoptosis and invasion of tumor cells, so constructing the risk-scoring model based on OS ferroptosis-related genes (FRGs) will benefit the evaluation of both treatment and prognosis. Methods. The OS dataset was screened from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database, and OS-related FRGs were found through the Ferroptosis Database (FerrDb) using a multivariate Cox regression model, followed by the generation of the risk scores and a risk-scoring prediction model. Further systematical exploration for immune cell infiltration and assessing the prediction of response to targeted drugs was conducted. Results. Based on OS-related FRGs, a risk-scoring model of FRGs in OS was constructed. The six FRGs played a role in the carbon metabolism, glutathione metabolism, and pentose phosphate pathways. Results from targeted drug sensitivity analyses were concordant to pathway analyses. The response to targeted drugs statistically differed between the two groups with different risks, and the high-risk group presented a high sensitivity to targeted drugs. Conclusions. We identified a 6-ferroptosis-gene-based prognostic signature in OS and created and verified a risk-scoring model to predict the prognosis of OS at 1, 3, and 5 years for OS patients independently.

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“…, where coefx is the regression coefficient obtained from multivariate Cox regression analysis, and Exprx represents the gene expression level of each variable (24,25). The median risk score of each patient was taken as the reference standard for dividing the high and low groups, and then the Kaplan-Meier method (K-M method) was used to analyze the survival of the two groups and draw the survival curves (26). In order to avoid mutual influence between risk factors, we conducted principal component analysis (PCA) and dimension reduction.…”

Section: Model Establishmentmentioning

confidence: 99%

Risk score model of autophagy-related genes in osteosarcoma

Jiang¹,

Fang²,

He³

et al. 2022

Ann Transl Med

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Background: Osteosarcoma (OS) is a common pediatric malignancy with high mortality and disability rates. Autophagy is an essential process in regulating the apoptosis and invasion of tumor cells, so constructing a risk score model of OS autophagy-related genes (ARGs) will bring benefit to the evaluation of both treatment and prognosis.Methods: We downloaded a dataset of OS from the Therapeutically Applicable Research To Generate Effective Treatments (TARGET) database, and found OS-related ARGs through the Human Autophagy Database (HADb). Five hub ARGs (CCL2, AMBRA1, VEGFA, MYC and EGFR) were obtained using a multivariate Cox regression model. We then generated the risk scores and constructed a prediction model.Another dataset obtained from the Gene Expression Omnibus (GEO) was used to test accuracy and validity.The role of immune cell infiltration was systematically explored, and prediction of response to targeted drugs was assessed. Immunohistochemistry was carried out to verify the expression of the key ARGs.Results: Based on the five hub ARGs, we established a risk score model related to OS. High accuracy and validity were demonstrated by datasets downloaded from the GEO. The five ARGs played a role in the PI3K and MAPK pathways. Results from targeted drug sensitivity analyses were consistent with pathway analyses.Immunohistochemistry demonstrated that the expression differences of the five ARGs were significant between the OS group and the paracancerous group. Conclusions:We constructed a risk score model related to autophagy of OS, explored the diagnostic value of ARGs, and present possible therapeutic targets.

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Subtype-specific risk models for accurately predicting the prognosis of breast cancer using differentially expressed autophagy-related genes

Cited by 7 publications

References 35 publications

The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

The BIRC Family Genes Expression in Patients with Triple Negative Breast Cancer

A Risk-Scoring Model Based on Evaluation of Ferroptosis-Related Genes in Osteosarcoma

Risk score model of autophagy-related genes in osteosarcoma

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