1.D_(140) macroreticular adsorbent adsorption properties for epimedii flavonoid from Epimedium brevicornum Maxim
Xiangqun JIN ; Yonggang LIU ; Zhigang SUI ; Wei SUN ; Yantong SUN ; Lei CHEN ;
Chinese Traditional Patent Medicine 1992;0(11):-
60%, and the extraction process is simple and convenient. The treatment of regenarated resin is easy, this method is advisable.
2.Risk model of breast cancer prognosis based on the expression profile of long non-coding RNA
Jinsong WANG ; Chunxiao LI ; Ting WANG ; Jingyao ZHANG ; Yantong ZHOU ; Fangzhou SUN ; Mengjiao CHANG ; Fei MA ; Haijuan WANG ; Haili QIAN
Clinical Medicine of China 2020;36(3):217-222
Objective:To construct a prediction model for the prognosis of breast cancer patients with long non-coding RNA expression characteristics.Methods:To construct a long non-coding RNA(LncRNA) model for predicting the prognosis of breast cancer patients.Methods Analyzing LncRNA expression profiles and clinical characteristics of 1 081 breast cancer patients in the cancer genome atlas (TCGA) database.Performing differential expression analysis and univariate analysis on 112 paired breast cancer and normal breast tissues′ transcriptome sequencing data in the TCGA database, and screened for differentially expressed (DELncRNAs) that significantly correlated with the prognosis of BRCA (To reduce batch effects, sequencing data has been normalized using the DESeq function). One thousand eighty-one breast cancer patients were randomly divided into two groups: training set (541) and validation set (540). Performing Cox proportional hazard regression using DELncRNAs and establishing a multi-LncRNA prognosis model in the training set, followed by proportional hazards assumption test(PH assumption test). Patients were divided into high-risk and low-risk groups based on calculated risk score.Kaplan-Meier method was used for survival analysis, and 540 patients′ data were used for validation.To evaluate the prognostic value of the model in patients with squamous cell carcinoma of the lung and hepatocarcinoma in TCGA database.Gene Set Enrichment Analysis (GSEA) was used to analyze the specific mechanism of lncrna affecting the survival of patients.Results:There were 2815 differentially expressed genes screened by transcriptome sequencing, 91 of which were significantly related to the prognosis of breast cancer patients ( P<0.05). Based on the Cox regression analysis of 91 delncrna expression data from 541 breast cancer patients in training set, a Cox proportional risk regression model was constructed based on 5 LncRNA (training set AUC=0.746, validation set AUC=0.650): AC004551.1, MTOR-AS1, KCNAB1-AS2, FAM230G and LINC01283, and PH assumption test( P=0.388). K-M survival analysis showed that the survival time of high-risk group was significantly worse than that of low-risk group (median survival time: 7.049 and 12.21 years, HR 0.367, 95% CI0.228-0.597, P<0.001), and the survival time of high-risk group was significantly shorter than that of low-risk group (median survival time: 7.57 and 10.85 years, HR 0.412, 95% CI0.214-0.793, P<0.001). Similar prediction results were also obtained in other cancer species of TCGA: lung squamous cell carcinoma ( HR 0.604, 95% CI0.383-0.951, P=0.007) and liver cell carcinoma ( HR 0.551, 95% CI0.307-0.987, P=0.011). GSEA results suggested that the expression patterns of the above five LncRNA were related to the cell cycle regulation of tumor cells. Conclusion:The prognostic model constructed based on expression profile of AC004551.1, MTOR-AS1, KCNAB1-AS2, FAM230G and LINC01283 can be used to predict the prognosis of breast cancer patients, which is helpful to further guide clinical treatment.
3.Establishment of a risk prediction model for postoperative constipation in patients with oral and maxillofacial malignant tumors
ZHU Huixuan ; HE Xingfang ; HUANG Qiuyu ; LIU Manfeng ; LIN Yantong
Journal of Prevention and Treatment for Stomatological Diseases 2022;30(8):564-570
Objective:
To understand the incidence and influencing factors of postoperative constipation in patients with malignant tumors who undergo oral and maxillofacial surgery and construct a constipation risk prediction model to provide a reference for the prevention and treatment of postoperative constipation.
Methods:
The data of 191 patients who underwent oral and maxillofacial malignant tumor surgery at the Affiliated Stomatological Hospital of Sun Yat sen University from June 2019 to June 2020 were analyzed retrospectively. The independent influencing factors were selected via univariate analysis and logistic multivariate regression analysis, a risk prediction nomogram was established, and the prediction model was evaluated by the area under the ROC curve. Both internal and external use the C index to verify the accuracy of the model.
Results :
Among 191 patients, 52 (27.23%) had postoperative constipation. Univariate analysis showed that a preoperative secret history of defecation, total energy intake, tracheotomy, smoking, drinking, operation duration, bleeding volume, bed time, eating homogenate diet, sex, surgical repair method, use of probiotics, T-stage of cancer and food intake may be the influencing factors of postoperative constipation in patients with oral and maxillofacial malignant tumors (P<0.05). Multivariate analysis showed that repair method, bed time and sex were independent risk factors for postoperative constipation in patients with oral and maxillofacial malignant tumors (P<0.05). The repair method was a fibular myocutaneous flap with a long bed time, and male patients were prone to constipation after surgery. The c-index values in the training group and the verification group were 0.882 and 0.953, respectively. The area under the ROC curve of the training group was 0.909 (95%CI: 0.850-0.968), and the area under the ROC curve of the verification group was 0.893 (95%CI: 0.787-0.999). The nomogram showed good discrimination ability.
Conclusion
The repair method, bed time and sex are independent risk factors for postoperative constipation in patients with oral and maxillofacial malignant tumors. The risk prediction model has good discrimination ability.