Health-Related Quality of Life and Utility Scores of Lung Cancer Patients Treated with Traditional Chinese Medicine in China

Chinese Medicine Enhancing Response Rates to Immunosuppressant PD-L1 Inhibitor and Improving the Quality of Life of Hepatocellular Carcinoma-Bearing Mice

Background

Malignant tumors are a significant disease endangering human health. Chinese Medicine (CM) plays an important role in comprehensive and holistic tumor treatment.

Objectives

We aimed to investigate whether CM combined with the immunosuppressant PD-1/PD-L1 inhibitor has a good synergistic effect and can significantly improve response rates for the immunosuppressant.

Methods

We combined CM with immunosuppressant in treating six-week-old hepatocellular carcinoma-bearing mice and compared the outcomes of groups undergoing different interventions: blank group, control group, CM group, PD-L1 inhibitor group, and CM + PD-L1 inhibitor group, with ten mice in each group. The quality of life was evaluated along with the tumor inhibition effects and growth rates.

Results

CM significantly reduced tumor load and improved the quality of life of cancer-bearing mice. The survival rate was 81.8% in the control group, 100% in the CM group, 90.9% in the PD-L1 inhibitor group, and 100% in the combined group in the first week. The survival rate was 45.5% in the control group, 54.5% in the CM group, 81.8% in the PD-L1 inhibitor group, and 81.8% in the combined group in the second week. 38% mice in the CM+PD-L1 inhibitor group with smaller tumor size than the average of the control group, which was much higher than other treatment groups. CM also reduced the expression of JAK2 mRNA and STAT3 mRNA, although not significantly (P > 0.05), and reduced PD-L1 mRNA in tumor tissue compared to the control group (P < 0.05).

Conclusions

CM had a synergistic effect on PD-L1 inhibitors and increased response rates to PD-L1 inhibitor treatment.

Feiyanning downregulating CXCLs/CXCR2 axis to suppress TANs infiltration in the prevention of lung cancer metastasis

ETHNOPHARMACOLOGICAL RELEVANCE

Tumor-associated neutrophils (TANs) play an important role in tumor metastasis. The Traditional Chinese medicine (TCM) Feiyanning (FYN) has been clinically proven to effectively prevent the recurrence and metastasis of lung cancer, improve immunity, and prolong the survival period of lung cancer patients. However, its anti-metastatic immune mechanism has not been fully elucidated. To this end, we studied the mechanism of FYN's regulation of neutrophils infiltration in the tumor microenvironment (TME).

AIM OF THE STUDY

To explore the anti-metastatic mechanism of FYN from the perspective of anti-immunosuppressive phenotype neutrophils infiltration in the TME.

MATERIALS AND METHODS

TCM network pharmacological analysis was used to predict Feiyanning effective target. Flow cytometry was used to detect the proportion of immune cell subsets in the TME. Lung metastases were investigated in C57 mice by tail vein injection. Protein expression was evaluated by immunohistochemistry and Western blot. Gene expression was evaluated by qRT-PCR.

RESULTS

FYN could reshape the tumor immune microenvironment. It prevents Tregs, M2 macrophages, and neutrophils infiltration, as well as recruits T cells, NK cells, and DCs, and improves DCs activation. In addition, FYN could regulate the polarization of TANs, inhibit the infiltration of neutrophils with an immunosuppressive phenotype, downregulate CXCLs/CXCR2 axis and inhibitory factors like Arg-1 and TGF-β, and up-regulate the immune effector molecule ICAM-1. Furthermore, FYN increases anti-tumor immune effects in the TME to prevent tumor cells from spreading to the lungs.

CONCLUSION

This study clarifies the potential mechanism of FYN in regulating neutrophils infiltration and anti-metastasis. FYN may regulate neutrophils infiltration in the TME by regulating CXCLs/CXCR2 axis.

Developing a visual model for predicting depression in patients with lung cancer

AIMS AND OBJECTIVES

To investigate and analyse the prevalence of depression among patients with lung cancer, identify risk factors of depression, and develop a visual, non-invasive, and straightforward clinical prediction model that can be used to predict the risk probability of depression in patients with lung cancer quantitatively.

BACKGROUND

Depression is one of the common concomitant symptoms of patients with lung cancer, which can increase the risk of suicide. However, the current assessment tools cannot combine multiple risk factors to predict the risk probability of depression in patients.

DESIGN

A cross-sectional study.

METHODS

The clinical data from 297 patients with lung cancer in China were collected and analysed in this cross-sectional study. The clinical prediction model was constructed according to the results of the Chi-square test and the logistic regression analysis, evaluated by discrimination, calibration, and decision curve analysis, and visualised by a nomogram. This study was reported using the TRIPOD checklist.

RESULTS

130 patients with lung cancer had depressive symptoms with a prevalence of 43.77%. A visual prediction model was constructed based on age, disease duration, exercise, stigma, and resilience. This model showed good discrimination at an AUC of 0.842. Calibration curve analysis indicated a good agreement between experimental and predicted values, and the decision curve analysis showed a high clinical utility.

CONCLUSIONS

The visual prediction model developed in this study has excellent performance, which can accurately predict the occurrence of depression in patients with lung cancer at an early stage and assist the medical staff in taking targeted preventative measures.

RELEVANCE TO CLINICAL PRACTICE

The visual, non-invasive, and simple nomogram can help clinical medical staff to calculate the risk probability of depression among patients with lung cancer, formulate personalised preventive care measures for high-risk groups as soon as possible, and improve the quality of life of patients.