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1.
World J Gastrointest Oncol ; 15(1): 90-101, 2023 Jan 15.
Artigo em Inglês | MEDLINE | ID: mdl-36684054

RESUMO

BACKGROUND: Heat-clearing and detoxifying drugs has protective effect on colorectal cancer (CRC). Given the complicated features of Traditional Chinese medicine formulas, network pharmacology is an effective approach for studying the multiple interactions between drugs and diseases. AIM: To systematically explore the anticancer mechanism of heat-clearing and detoxifying drug JC724. METHODS: This study obtained the active compounds and their targets in JC724 from Traditional Chinese Medicine System Pharmacology Database. In addition, the CRC targets were obtained from Drugbank, TTD, DisGeNET and GeneCards databases. We performed transcriptome analysis of differentially expressed genes in CRC treated with JC724. Venn diagram was used to screen the JC724-CRC intersection targets as candidate targets. Core targets were selected by protein-protein interaction network and herb ingredient-target-disease network analysis. The functional and pathway of core targets were analysed by enrichment analysis. RESULTS: We found 174 active ingredients and 283 compound targets from JC724. 940 CRC-related targets were reserved from the four databases and 304 CRC differentially expressed genes were obtained by transcriptome analysis. We constructed the network and found that the five core ingredients were quercetin, ß Beta sitosterol, wogonin, kaempferol and baicalein. The core JC724-CRC targets were CYP1A1, HMOX1, CXCL8, NQO1 and FOSL1. JC724 acts on multiple signaling pathways associated with CRC, including the Nrf2 signaling pathway, oxidative stress, and the IL-17 signaling pathway. CONCLUSION: In this study, we systematically analyzed the active ingredients, core targets and main mechanisms of JC724 in the treatment of CRC. This study could bring a new perspective to the heat-clearing and detoxifying therapy of CRC.

2.
Oncol Lett ; 25(2): 53, 2023 Feb.
Artigo em Inglês | MEDLINE | ID: mdl-36644143

RESUMO

Breast cancer has the highest incidence rate among all cancer types worldwide, seriously threatening women's health. The present retrospective study explored differences in serum lipid contents in different breast cancer (BC) subcategories and their correlation with Ki-67 expression levels in patients with invasive BC with the aim of identifying novel diagnostic and prognostic indicators for personalized BC treatment. The study included 170 patients diagnosed with BC who were diagnosed with invasive BC by postoperative pathological examination. Data on patient age, body mass index and menopausal status were collected, in addition to estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2 (HER2) and antigen Ki-67 expression levels and pathological tumor type. Preoperative circulating lipid levels, specifically the levels of total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG) and apolipoproteins A1 (ApoA1) and B (ApoB) were also obtained. Molecular subcategories of BC were grouped based on their immunohistochemistry. Differences in serum lipid levels between the groups were assessed, and correlations between serum lipid and Ki-67 expression levels were explored. While TC, LDL-C, HDL-C and ApoA1 levels differed significantly among molecular subcategories. TG and ApoB levels did not. Circulating TC and LDL-C levels were considerably higher in patients with triple-negative BC (TNBC) and HER2-positive [hormone receptor (HR)-negative] BC than in those with luminal A and B (HER2-negative) BC. Serum HDL-C levels were significantly diminished in the TNBC and HER2-positive (HR-negative) groups compared with the luminal A and B (HER2-negative) groups. ApoA1 levels were significantly reduced in cases of TNBC and HER2-positive (HR-negative) BC compared with luminal A and B BC. Ki-67 expression levels were positively correlated with circulating TC and LDL-C levels and inversely correlated with circulating HDL-C and ApoA1 levels but exhibited no correlation with serum ApoB and TG levels. The results indicate that elevated TC and LDL-C levels and diminished HDL-C and ApoA1 levels were high-risk factors in patients with TNBC and HER2-positive (HR-negative) BC, but not patients with luminal subcategories of BC. Abnormal serum lipid levels were correlated with Ki-67 expression levels, with elevated circulating TC and LDL-C levels and reduced circulating HDL-C and ApoA1 levels indicating a poor prognosis in patients with BC.

3.
Int J Med Inform ; 161: 104733, 2022 05.
Artigo em Inglês | MEDLINE | ID: mdl-35299099

RESUMO

PURPOSE: To develop and validate machine learning (ML) models for cancer-associated deep vein thrombosis (DVT) and to compare the performance of these models with the Khorana score (KS). METHODS: We randomly extracted data of 2100 patients with cancer between Jan. 1, 2017, and Oct. 31, 2019, and 1035 patients who underwent Doppler ultrasonography were enrolled. Univariate analysis and Lasso regression were applied to select important predictors. Model training and hyperparameter tuning were implemented on 70% of the data using a ten-fold cross-validation method. The remaining 30% of the data were used to compare the performance with seven indicators (area under the receiver operating characteristic curve [AUC], sensitivity, specificity, accuracy, balanced accuracy, Brier score, and calibration curve), among all five ML models (linear discriminant analysis [LDA], logistic regression [LR], classification tree [CT], random forest [RF], and support vector machine [SVM]), and the KS. RESULTS: The incidence of cancer-associated DVT was 22.3%. The top five predictors were D-dimer level, age, Charlson Comorbidity Index (CCI), length of stay (LOS), and previous VTE (venous thromboembolism) history according to RF. Only LDA (AUC = 0.773) and LR (AUC = 0.772) outperformed KS (AUC = 0.642), and combination with D-dimer showed improved performance in all models. A nomogram and web calculator https://webcalculatorofcancerassociateddvt.shinyapps.io/dynnomapp/ were used to visualize the best recommended LR model. CONCLUSION: This study developed and validated cancer-associated DVT predictive models using five ML algorithms and visualized the best recommended model using a nomogram and web calculator. The nomogram and web calculator developed in this study may assist doctors and nurses in evaluating individualized cancer-associated DVT risk and making decisions. However, other prospective cohort studies should be conducted to externally validate the recommended model.


Assuntos
Neoplasias , Trombose Venosa , Humanos , Modelos Logísticos , Aprendizado de Máquina , Neoplasias/complicações , Neoplasias/epidemiologia , Estudos Prospectivos , Trombose Venosa/diagnóstico , Trombose Venosa/epidemiologia , Trombose Venosa/etiologia
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