Objective: To analyze the role and influence of the GINS4 gene in breast cancer progression and to explore its expression in triple-negative and non-triple-negative breast cancer cell lines. Methods: Single-gene analy...Objective: To analyze the role and influence of the GINS4 gene in breast cancer progression and to explore its expression in triple-negative and non-triple-negative breast cancer cell lines. Methods: Single-gene analysis of GINS4 was performed by breast cancer RNA transcriptome data from The Cancer Genome Atlas (TCGA). Real-time quantitative polymerase chain reaction (PCR) was used to detect the expression of GINS4 in triple-negative and non-triple-negative breast cancer cell lines. The knockdown effects of GINS4 in MDA-MB-231 and MCF-7 cell lines on the proliferation and invasion of breast cancer cells were examined by cell counting kit 8 (CCK8) and Transwell assays. Results: Bioinformatics analysis showed that the expression of GINS4 in breast cancer was significantly higher than that in normal breast tissues (P > 0.05). At the same time, cell experiments confirmed that GINS4 was highly expressed in human breast cancer cell lines with normal breast cells as reference and in MDA-MB-231 and MCF-7 cell lines as reference, where the ability of proliferation and invasion of MDA-MB-231 and MCF-7 cells decreased after GINS4 knockdown. Conclusion: GINS4 is a gene associated with breast cancer malignancy, which can act as a novel tumor marker and has the potential as a new therapeutic target for breast cancer.展开更多
BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are as...BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making.AIM To explore the diagnostic value of artificial intelligence(AI)automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy.METHODS A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital,University of Chinese Academy of Sciences.These nodules were classified by ultrasound doctors and the AI-SONIC breast system.The diagnostic values of conventional ultrasound,the AI automatic detection system,conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed.RESULTS Among the 107 breast nodules,61 were benign(57.01%),and 46 were malignant(42.99%).The pathology results were considered the gold standard;furthermore,the sensitivity,specificity,accuracy,Youden index,and positive and negative predictive values were 84.78%,67.21%,74.77%,0.5199,66.10%and 85.42%for conventional ultrasound BI-RADS classification diagnosis,86.96%,75.41%,80.37%,0.6237,72.73%,and 88.46%for automatic AI detection,80.43%,90.16%,85.98%,0.7059,86.05%,and 85.94%for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%,67.21%,78.50%,0.6069,68.25%,and 93.18%for adjusted BI-RADS classification,respectively.The biopsy rate,cancer detection rate and malignancy risk were 100%,42.99%and 0%and 67.29%,61.11%,and 1.87%before and after BI-RADS adjustment,respectively.CONCLUSION Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules.Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.展开更多
Breast Imaging Reporting and Data System,also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a ...Breast Imaging Reporting and Data System,also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a wide range of cancer risk since it is divided into 3 categories.Mathematicalmodels play an important role in the diagnosis and treatment of cancer.In this study,data of 42 BI-RADS 4 patients taken fromthe Center for Breast Health,Near East University Hospital is utilized.Regarding the analysis,a mathematical model is constructed by dividing the population into 4 compartments.Sensitivity analysis is applied to the parameters with the desired outcome of a reduced range of cancer risk.Numerical simulations of the parameters are demonstrated.The results of the model have revealed that an increase in the lactation rate and earlymenopause have a negative correlation with the chance of being diagnosed with BI-RADS 4 whereas a positive correlation increase in age,the palpable mass,and family history is distinctive.Furthermore,the negative effects of smoking and late menopause on BI-RADS 4C diagnosis are vehemently outlined.Consequently,the model showed that the percentages of parameters play an important role in the diagnosis of BI-RADS 4 subcategories.All things considered,with the assistance of the most effective parameters,the range of cancer risks in BI-RADS 4 subcategories will decrease.展开更多
As a representative chemotherapeutic drug,docetaxel(DTX)has been used for breast cancer treatment for decades.However,the poor solubility of DTX limits its efficacy,and the DTX based therapy increases the metastasis r...As a representative chemotherapeutic drug,docetaxel(DTX)has been used for breast cancer treatment for decades.However,the poor solubility of DTX limits its efficacy,and the DTX based therapy increases the metastasis risk due to the upregulation of C-X-C chemokine receptor type 4(CXCR4)expression during the treatment.Herein,we conjugated CXCR4 antagonist peptide(CTCE)with DTX(termed CTCE-DTX)as an anti-metastasis agent to treat breast cancer.CTCE-DTX could selfassemble to nanoparticles,targeting CXCR4-upregulated metastatic tumor cells and enhancing the DTX efficacy.Thus,the CTCE-DTX NPs achieved promising efficacy on inhibiting both bonespecific metastasis and lung metastasis of triple-negative breast cancer.Our work provided a rational strategy on designing peptide-drug conjugates with synergistic anti-tumor efficacy.展开更多
文摘Objective: To analyze the role and influence of the GINS4 gene in breast cancer progression and to explore its expression in triple-negative and non-triple-negative breast cancer cell lines. Methods: Single-gene analysis of GINS4 was performed by breast cancer RNA transcriptome data from The Cancer Genome Atlas (TCGA). Real-time quantitative polymerase chain reaction (PCR) was used to detect the expression of GINS4 in triple-negative and non-triple-negative breast cancer cell lines. The knockdown effects of GINS4 in MDA-MB-231 and MCF-7 cell lines on the proliferation and invasion of breast cancer cells were examined by cell counting kit 8 (CCK8) and Transwell assays. Results: Bioinformatics analysis showed that the expression of GINS4 in breast cancer was significantly higher than that in normal breast tissues (P > 0.05). At the same time, cell experiments confirmed that GINS4 was highly expressed in human breast cancer cell lines with normal breast cells as reference and in MDA-MB-231 and MCF-7 cell lines as reference, where the ability of proliferation and invasion of MDA-MB-231 and MCF-7 cells decreased after GINS4 knockdown. Conclusion: GINS4 is a gene associated with breast cancer malignancy, which can act as a novel tumor marker and has the potential as a new therapeutic target for breast cancer.
文摘BACKGROUND The incidence rate of breast cancer has exceeded that of lung cancer,and it has become the most malignant type of cancer in the world.BI-RADS 4 breast nodules have a wide range of malignant risks and are associated with challenging clinical decision-making.AIM To explore the diagnostic value of artificial intelligence(AI)automatic detection systems for BI-RADS 4 breast nodules and to assess whether conventional ultrasound BI-RADS classification with AI automatic detection systems can reduce the probability of BI-RADS 4 biopsy.METHODS A total of 107 BI-RADS breast nodules confirmed by pathology were selected between June 2019 and July 2020 at Hwa Mei Hospital,University of Chinese Academy of Sciences.These nodules were classified by ultrasound doctors and the AI-SONIC breast system.The diagnostic values of conventional ultrasound,the AI automatic detection system,conventional ultrasound combined with the AI automatic detection system and adjusted BI-RADS classification diagnosis were statistically analyzed.RESULTS Among the 107 breast nodules,61 were benign(57.01%),and 46 were malignant(42.99%).The pathology results were considered the gold standard;furthermore,the sensitivity,specificity,accuracy,Youden index,and positive and negative predictive values were 84.78%,67.21%,74.77%,0.5199,66.10%and 85.42%for conventional ultrasound BI-RADS classification diagnosis,86.96%,75.41%,80.37%,0.6237,72.73%,and 88.46%for automatic AI detection,80.43%,90.16%,85.98%,0.7059,86.05%,and 85.94%for conventional ultrasound BI-RADS classification with automatic AI detection and 93.48%,67.21%,78.50%,0.6069,68.25%,and 93.18%for adjusted BI-RADS classification,respectively.The biopsy rate,cancer detection rate and malignancy risk were 100%,42.99%and 0%and 67.29%,61.11%,and 1.87%before and after BI-RADS adjustment,respectively.CONCLUSION Automatic AI detection has high accuracy in determining benign and malignant BI-RADS 4 breast nodules.Conventional ultrasound BI-RADS classification combined with AI automatic detection can reduce the biopsy rate of BI-RADS 4 breast nodules.
文摘Breast Imaging Reporting and Data System,also known as BI-RADS is a universal system used by radiologists and doctors.It constructs a comprehensive language for the diagnosis of breast cancer.BI-RADS 4 category has a wide range of cancer risk since it is divided into 3 categories.Mathematicalmodels play an important role in the diagnosis and treatment of cancer.In this study,data of 42 BI-RADS 4 patients taken fromthe Center for Breast Health,Near East University Hospital is utilized.Regarding the analysis,a mathematical model is constructed by dividing the population into 4 compartments.Sensitivity analysis is applied to the parameters with the desired outcome of a reduced range of cancer risk.Numerical simulations of the parameters are demonstrated.The results of the model have revealed that an increase in the lactation rate and earlymenopause have a negative correlation with the chance of being diagnosed with BI-RADS 4 whereas a positive correlation increase in age,the palpable mass,and family history is distinctive.Furthermore,the negative effects of smoking and late menopause on BI-RADS 4C diagnosis are vehemently outlined.Consequently,the model showed that the percentages of parameters play an important role in the diagnosis of BI-RADS 4 subcategories.All things considered,with the assistance of the most effective parameters,the range of cancer risks in BI-RADS 4 subcategories will decrease.
基金sponsored by the National Natural Science Foundation of China(52173120,21877023,32271391)the Youth Innovation Promotion Association CAS(2021018,China)+1 种基金the Beijing Natural Science Foundation(L222015,China)the Beijing Nova Program(20220484233,China)。
文摘As a representative chemotherapeutic drug,docetaxel(DTX)has been used for breast cancer treatment for decades.However,the poor solubility of DTX limits its efficacy,and the DTX based therapy increases the metastasis risk due to the upregulation of C-X-C chemokine receptor type 4(CXCR4)expression during the treatment.Herein,we conjugated CXCR4 antagonist peptide(CTCE)with DTX(termed CTCE-DTX)as an anti-metastasis agent to treat breast cancer.CTCE-DTX could selfassemble to nanoparticles,targeting CXCR4-upregulated metastatic tumor cells and enhancing the DTX efficacy.Thus,the CTCE-DTX NPs achieved promising efficacy on inhibiting both bonespecific metastasis and lung metastasis of triple-negative breast cancer.Our work provided a rational strategy on designing peptide-drug conjugates with synergistic anti-tumor efficacy.
