METHODS: Smaller micro tissues (˂150 μm in diameter) mixed with Matrigel were engrafted subcutaneously into NSG mice to generate the passage 1 (P1) patient-derived xenograft. The micro tumours from P1 patient-derived xenograft were then excised and orthotopically xenografted into another batch of NSG mice to generate a metastatic colorectal cancer patient-derived xenograft, P2. Haematoxylin and eosin and immunohistochemistry staining were performed to compare the characters between patient-derived xenograft tumours and primary tumours.
RESULTS: About 16 out of 18 P1 xenograft models successfully grew a tumour for 50.8 ± 5.1 days (success rate 89.9%). Six out of eight P1 xenograft models originating from metastatic patients successfully grew tumours in the colon and metastasized to liver or lung in the NSG recipients for 60.9 ± 4.5 days (success rate 75%). Histological examination of both P1 and P2 xenografts closely resembled the histological architecture of the original patients' tumours. Immunohistochemical analysis revealed similar biomarker expression levels, including CDH17, Ki-67, active β-catenin, Ki-67 and α smooth muscle actin when compared with the original patients' tumours. The stromal components that support the growth of patient-derived xenograft tumours were of murine origin.
CONCLUSIONS: Metastatic patient-derived xenograft mouse model could be established with shorter time and higher success rate. Although the patient-derived xenograft tumours were supported by the stromal cells of murine origin, they retained the dominant characters of the original patient tumours.
METHOD: Relevant studies detecting SMAD4 expression in cancer patients treated with chemo-drugs up till December 2020 were systematically searched in four common scientific databases using selected keywords. The pooled hazard ratio (HR) was the ratio of hazard rate between SMAD4neg population vs SMAD4pos population. The HRs and risk ratios (RRs) with 95% confidence intervals (CIs) were used to explore the association between SMAD4 expression losses with drug resistance in cancers.
RESULT: After an initial screening according to the inclusion and exclusion criteria, eleven studies were included in the meta-analysis. There were a total of 2092 patients from all the included studies in this analysis. Results obtained indicated that loss of SMAD4 expression was significantly correlated with drug resistance with pooled HRs (95% CI) of 1.23 (1.01-1.45), metastasis with pooled RRs (95% CI) of 1.10 (0.97-1.25) and recurrence with pooled RRs (95% CI) of 1.32 (1.06-1.64). In the subgroup analysis, cancer type, drug type, sample size and antibody brand did not affect the significance of association between loss of SMAD4 expression and drug resistance. In addition, there was no evidence of publication bias as suggested by Begg's test.
CONCLUSION: Findings from our meta-analysis demonstrated that loss of SMAD4 expression was correlated with drug resistance, metastasis and recurrence. Therefore, SMAD4 expression could be potentially used as a molecular marker for cancer resistance.
METHODS: A total of 236 breast cancer patients from China completed the Chinese Version of the Posttraumatic Stress Disorder Symptom Scale (PSS), the Chinese version of the Patient Health Questionnaire (PHQ-9), the Chinese version of the General Anxiety Symptoms Scale (GAD-7). In addition, caregivers of these breast cancer patients were surveyed by the Caregiver Self-Assessment Questionnaire (CSAQ).
RESULTS: Structural equation model showed that our model fitted well [χ2 /df = 1.966, TLI = 0.959, CFI = 0.994, RMSEA (90% CI) = 0.065 (0-0.12)] and revealed that anxiety, but not depression, mediated the relationship between PTSS in breast cancer patients and caregiver burden.
CONCLUSION: The level of PTSS was positively correlated with anxiety and depression in breast cancer patients, and the level of anxiety and depression was positively related to caregiver burden. The PTSS of patients positively predicted caregiver burden and this relationship appears to be mediated by the patient's anxiety.
METHODS: G. lucidum samples from various sources and in varying stages were identified by using δ 13C, δD, δ 18O, δ 15N, C, and N contents combined with chemometric tools. Chemometric approaches, including PCA, OPLS-DA, PLS, and FLDA models, were applied to the obtained data. The established models were used to trace the origin of G. lucidum from various sources or track various stages of G. lucidum.
RESULTS: In the stage model, the δ 13C, δD, δ 18O, δ 15N, C, and N contents were considered meaningful variables to identify various stages of G. lucidum (bud development, growth, and maturing) using PCA and OPLS-DA and the findings were validated by the PLS model rather than by only four variables (δ 13C, δD, δ 18O, and δ 15N). In the origin model, only four variables, namely δ 13C, δD, δ 18O, and δ 15N, were used. PCA divided G. lucidum samples into four clusters: A (Zhejiang), B (Anhui), C (Jilin), and D (Fujian). The OPLS-DA model could be used to classify the origin of G. lucidum. The model was validated by other test samples (Pseudostellaria heterophylla), and the external test (G. lucidum) by PLS and FLDA models demonstrated external verification accuracy of up to 100%.
CONCLUSION: C, H, O, and N stable isotopes and C and N contents combined with chemometric techniques demonstrated considerable potential in the geographic authentication of G. lucidum, providing a promising method to identify stages of G. lucidum.