Systematic review of Buzhong Yiqi method in alleviating cancer-related fatigue: a meta-analysis and exploratory network pharmacology approach

Abstract

Objectives: Cancer-related fatigue (CRF) is a prevalent and distressing symptom experienced by many cancer patients, necessitating effective treatments. This study utilizes meta-analysis and network pharmacology to comprehensively assess the efficacy of the Buzhong Yiqi prescription in alleviating cancer-related fatigue and to preliminarily explore the mechanism of its core drugs.

Methods: We included randomized controlled trials (RCTs) in cancer patients. The inclusion criteria encompassed a diagnosis of cancer-related fatigue, without limitation on cancer type, the experimental group receiving Buzhong Yiqi prescription, the control group receiving conventional treatment, patients awaiting treatment, and articles published in either English or Chinese. We conducted a search through 29 February 2024, across PubMed, Cochrane Database of Systematic Reviews, Cochrane Controlled Clinical Trials (CENTRAL), China Biomedical Literature Service (CBM), China National Knowledge Infrastructure (CNKI), WANFANG Database, and Weipu Database (VIP). Journal articles that met the inclusion criteria were selected for inclusion. Two independent investigators evaluated the quality of the included studies. A meta-analysis was performed utilizing the Stata 12.0 software package, where estimates of cancer-related fatigue were aggregated through the application of a random-effects model. We employed the Cochrane Risk of Bias Tool to evaluate potential biases in RCTs. The primary outcome measures utilized to assess the efficacy and safety of CRF treatment comprised the Revised Piper Fatigue Scale (PFS-R) and the Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). The secondary outcomes encompassed the KPS score, the effective rate, the TCM syndrome score, and an evaluation of adverse reactions. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) was utilized to identify the active ingredients and targets of BZD. Additionally, the Drug bank, Therapeutic Target Database (TTD), DiaGeNET, and GeneCards databases were utilized to retrieve relevant targets for CRC. The Venn diagram was employed to identify overlapping targets. Cytoscape software was utilized to construct a network of "herb-ingredient-target" and identify core targets. GO and KEGG pathway enrichment analyses were performed using R language software.

Results: In comparison to the control group, patients with CRF who received BZYQ prescription exhibited marked improvements in KPS score, QLQ-C30 quality of life score, and effective rate. Conversely, PFS, TCM syndrome score, and adverse reaction assessments significantly decreased. The primary active ingredients in its core drugs may exert a positive therapeutic effect on CRF by targeting molecules such as AKT1, IL6, IL1B, PTGS2, CASP3, ESR1, and BCL2, as well as through signaling pathways including TNF, IL17, TLR, NF-κB, and C-type lectin receptor.

Conclusion: BZYQ demonstrates significant efficacy in treating CRF with minimal adverse reactions. It can serve as a fundamental treatment for CRF in clinical practice, and the medication can be tailored to individual patients for personalized therapy. The potential pharmacological mechanism of BZYQ in treating CRF, as predicted by network pharmacology, offers a molecular foundation for clinical CRF treatment.

Systematic review registration: https://inplasy.com, identifier INPLASY202430025.

Keywords: Buzhong Yiqi; cancer-related fatigue; mechanism; meta-analysis; network pharmacology.

FIGURE 1

Study selection process for the meta-analysis.

FIGURE 2

Risk of bias. Review of authors’ judgements about each risk of bias item for included studies. (A) bar plot of risk of bias. The composition ratio of different risks in each field of all included studies is shown. (B) Summary of risk of bias. The risk of bias of each included study in each field and the grade evaluation results of the overall risk of bias are shown. Note: Each color represents a different level of bias: read for high-risk, green for low-risk, and yellow for unclear-risk of bias.

FIGURE 3

Comparisons of KPS score between experimental and control group. (A) The forest plot shows the comparison of KPS scores between the experimental group and the control group in the baseline period; (B) The forest plot shows a comparison of KPS scores between the experimental and control groups after BZYQ treatment; (C) Funnel plot of KPS score publication bias; (D) Sensitivity analysis for KPS score.

FIGURE 4

Comparisons of Piper scale of cognitive dimensions between experimental and control group. (A) The forest plot shows the comparison of Piper scale of cognitive dimensions between the experimental group and the control group in the baseline period; (B) The forest plot shows a comparison of Piper scale of cognitive dimensions between the experimental and control groups after BZYQ treatment; (C) Funnel plot of Piper scale of cognitive dimensions publication bias; (D) Sensitivity analysis for Piper scale of cognitive dimensions.

FIGURE 5

Comparisons of Piper scale of sensory dimensions between experimental and control group. (A) The forest plot shows the comparison of Piper scale of sensory dimensions between the experimental group and the control group in the baseline period; (B) The forest plot shows a comparison of Piper scale of sensory dimensions between the experimental and control groups after BZYQ treatment; (C) Funnel plot of Piper scale of sensory dimensions publication bias; (D) Sensitivity analysis for Piper scale of sensory dimensions.

FIGURE 6

Comparisons of Piper scale of emotional dimension between experimental and control group. (A) The forest plot shows the comparison of Piper scale of emotional dimension between the experimental group and the control group in the baseline period; (B) The forest plot shows a comparison of Piper scale of emotional dimension between the experimental and control groups after BZYQ treatment; (C) Funnel plot of Piper scale of emotional dimensions publication bias; (D) Sensitivity analysis for Piper scale of emotional dimensions.

FIGURE 7

Comparisons of Piper scale of behavior dimension between experimental and control group. (A) The forest plot shows the comparison of Piper scale of behavior dimension between the experimental group and the control group in the baseline period; (B) The forest plot shows a comparison of Piper scale of behavior dimension between the experimental and control groups after BZYQ treatment; (C) Funnel plot of Piper scale of behavior dimensions publication bias; (D) Sensitivity analysis for Piper scale of behavior dimensions.

FIGURE 8

Comparisons of QLQ-C30 Quality of Life score between experimental and control group. (A) The forest plot shows the comparison of QLQ-C30 quality of life score between the experimental group and the control group in the baseline period; (B) The forest plot shows a comparison of QLQ-C30 Quality of Life score between the experimental and control groups after BZYQ treatment; (C) Funnel plot of QLQ-C30 Quality of Life score publication bias; (D) Sensitivity analysis for QLQ-C30 Quality of Life score.

FIGURE 9

Comparisons of TCM syndrome score between experimental and control group. (A) The forest plot shows the comparison of TCM syndrome score between the experimental group and the control group in the baseline period; (B) The forest plot shows a comparison of TCM syndrome score between the experimental and control groups after BZYQ treatment; (C) Funnel plot of TCM syndrome score publication bias; (D) Sensitivity analysis for TCM syndrome score.

FIGURE 10

Comparisons of effective rate between experimental and control group. (A) The forest plot shows a comparison of effective rate between the experimental and control groups after BZYQ treatment; (B) Funnel plot of effective rate publication bias; (C) Sensitivity analysis for effective rate.

FIGURE 11

Comparisons of adverse reaction between experimental and control group. (A) The forest plot shows a comparison of adverse reaction between the experimental and control groups after BZYQ treatment; (B) Funnel plot of adverse reaction publication bias; (C) Sensitivity analysis for adverse reaction.

FIGURE 12

Drug component-target network. (A) Venn diagram of the targets of the active ingredients of BZYQ and the CRF-related targets; (B) Network diagram of BZYQ components and CRF-related targets plotted with Cytoscape software.

FIGURE 13

PPI network of the common targets of BZYQ and CRF. (A) PPI network diagram plotted on a string network; (B) 20 key targets determined in the PPI network.

FIGURE 14

Top GO and KEGG enriched terms of BZYQ in treating CRF. (A) Top GO enriched terms of BZYQ in treating CRF. The X-coordinate indicates the number of enriched genes, and the color of the dot represents the p-value of the corresponding term. (B) Top KEGG enriched pathways of HQSJZD in treating CRF. A bigger dot indicates that more genes are enriched. A bigger dot indicates that more genes are enriched in that pathway, and a dot with a darker red color represents a smaller p-value.

FIGURE 15

KEGG pathway. (A) TNF signaling pathway (p = 9.73E-17); (B) IL-17 signaling pathway (p = 1.10E-12); (C) Toll-like receptor signaling pathway (p = 7.55E-12); (D) AGE-RAGE signaling pathway in diabetic complications (p = 2.18E-11); (E) C-type lectin receptor signaling pathway (p = 3.97E-10).