Several studies have demonstrated the positive impact of non-pharmacological Traditional Chinese Medicine (TCM) therapies on pain, fatigue, sleep quality, and quality of life in cancer survivors. However, no research has compared the effectiveness of these therapies. This study aims to compare various interventions and identify the most effective non-pharmacological TCM therapies to provide evidence-based recommendations for cancer survivors. A systematic search was conducted in PubMed, Embase, Web of Science, CNKI, and Wanfang Data. RCTs investigating the effects of Taichi, Qigong, acupuncture, acupressure, TCM emotional therapy, and mixed therapies as interventions for cancer survivors were screened was conducted. Data from the creation of the database to February 2025 were included. Two independent reviewers evaluated the study quality. A Bayesian Network Meta-analysis was conducted to carry out a random effects model. Seventy-one RCTs involving 6473 patients were included in the analysis. Network meta-analysis showed significance for all five intervention therapies in pain control in cancer patients. The best efficacy was observed for acupressure (SMD=-1.1 [-1.55, -0.66]) and Taichi/Qigong (SMD=-1.08[-1.64, -0.53]), followed by TCM emotional therapy (SMD=-0.93 [-1.42, -0.44]) and acupuncture (SMD=-0.54 [-0.93, -0.15]), with the latter showing comparatively lower efficacy. None of the interventions demonstrated superior efficacy in improving fatigue compared to the control group. Mixed therapies (SMD=-1.36[-2.56, -0.28]) demonstrated the greatest effect in improving sleep quality. Taichi/Qigong (SMD=1.87 [0.96, 2.83]) demonstrated certain advantages in improving quality of life. However, acupuncture and TCM emotional therapy had no significant effect on sleep quality or overall quality of life. The evidence from this study suggests that acupressure and Taichi/Qigong are recommended as the most effective therapies for pain relief and quality of life improvement, respectively. The efficacy of these therapies for fatigue remains inconclusive. However, due to the limited number of included studies and the high risk of bias, these results should be interpreted with caution. Future studies should include more rigorously designed high-quality randomized controlled trials to confirm their long-term efficacy and safety. PROSPERO CRD42024601976.

Background: Previous observational studies have suggested a potential association between Epstein-Barr virus (EBV) infection and the development of multiple myeloma (MM), but this relationship is not clear. Therefore, we conducted a systematic Mendelian randomization (MR) analysis to investigate the causal relationship between EBV infection and the risk of MM, while exploring the possible mediating role of immune cells in this association. Methods: The study first conducted a two-sample MR analysis using the MM R11 dataset from the FinnGen Consortium to evaluate the causal relationship between five EBV infection-related antibodies (AEB-IgG, EA-D, EBNA-1, VCA-p18, and ZEBRA) and MM, with validation in the MM R10 dataset. A reverse MR analysis was then performed. For significant results, multivariable MR (MVMR) was used to adjust for the effects of confounding risk factors. Next, a two-step MR mediation analysis was applied to investigate the potential mediating role of 731 immune cell types between positive exposure and MM. Multiple sensitivity analyses were conducted to assess the robustness of the findings. Results: A two-sample MR study found that EBNA-1 antibodies (OR = 1.36, 95% CI: 1.06–1.73; P = 0.015) were associated with an increased risk of MM, with similar results observed in the FinnGen Consortium R10 replication study. Although the association did not remain statistically significant after false discovery rate (FDR) adjustment (P_fdr = 0.075), further adjustment for relevant confounders using multivariable MR (MVMR) demonstrated that EBNA-1 antibodies (OR = 1.33, 95% CI: 1.01–1.75; P = 0.041) were still significantly associated with an increased risk of MM. Reverse MR analysis indicated no causal effect of MM on EBV-related antibodies. A two-sample MR analysis involving 731 immune cell phenotypes identified 27 potential mediating cell types. Ultimately, two-step MR confirmed that HLA-DR on myeloid dendritic cells (HLA-DR⁺ mDC) serves as a mediating factor, with EBNA-1 antibodies downregulating HLA-DR⁺ mDC, thereby increasing MM risk. Multiple sensitivity analyses supported the robustness of these findings. Conclusion: The findings of this study suggest that EBNA-1 antibodies may increase the risk of MM by downregulating HLA-DR⁺ mDC. This indicates that chronic EBV infection may contribute to an elevated risk of MM. We hope these results provide new insights for future research on the prevention and treatment of MM.

Studies suggest a possible association between Epstein-Barr virus (EBV) infection and bladder cancer (BCa) risk, though this remains unclear. Secreted frizzled-related protein (sFRP) is also linked to BCa, with some DNA viruses potentially regulating its expression. This study used Mendelian randomization (MR) and colocalization analysis to explore the causal relationship between EBV infection, BCa risk, and the mediating role of sFRP. We first performed a two-sample MR study to assess the causal relationship between 5 EBV-related antibodies (AEB-IgG, EA-D, EBNA-1, VCA-p18, ZEBRA) and BCa using the Finnish Consortium’s R11 dataset, validated with R10. Reverse MR analysis followed. For significant results, multivariable MR (MVMR) was applied to adjust for confounding risk factors. A two-step MR explored the potential mediating role of 3 sFRPs (sFRP1, sFRP2, sFRP3) between positive exposures and BCa. Colocalization analysis were conducted for positive exposures, mediators, and BCa, with multiple sensitivity analyses confirming the robustness of the results. The two-sample Mendelian randomization study found that EBNA-1 (OR = 1.15, 95% CI: 1.01–1.30; p = 0.039) and VCA-p18 (OR = 1.36, 95% CI: 1.13–1.64; p = 0.001) may increase BCa risk, with only VCA-p18 (P_fdr = 0.006) showing a significant effect after False Discovery Rate (FDR) correction. The Finnish Consortium R10 replication study yielded similar results, and reverse MR analysis did not suggest reverse causality. After MVMR adjusted for relevant confounders, VCA-p18 (OR = 1.40, 95% CI: 1.13–1.74; p = 0.002) still significantly increased BCa risk. Two-step MR identified sFRP2 as a mediator, with VCA-p18 down-regulating sFRP2 expression to elevate BCa risk. Colocalization analysis suggested a shared causal variant (nearby gene HLA-DQA1) between VCA-p18 and BCa (PPH4 = 65.44%). Multiple sensitivity analyses confirmed the robustness of the results. Our study suggests that EBV infection (VCA-p18 antibody) may increase the risk of BCa by lowering sFRP2 levels. Additionally, EBNA-1 antibodies may also contribute to an elevated risk of BCa. We hope these findings will provide new insights for future research on the association between EBV and BCa.

Previous studies have identified an association between per- and polyfluoroalkyl substances (PFAS) and the occurrence of intrahepatic cholangiocarcinoma (ICC), but the specific mechanisms remain unclear. This study aims to analyze the toxicological mechanisms of PFAS, particularly perfluoroctanoic acid (PFOA) and perfluorooctane sulfonate (), on ICC through network toxicology and bioinformatics. Relevant targets for PFOS and PFOA were retrieved from the CTD, ChEMBL, and Super-PRED databases. Targets related to ICC were gathered from the GeneCards, NCBI, and OMIM databases. Common targets between ICC and PFOS, PFOA were identified and subjected to median filtering in CytoHubba to select core targets, which were then analyzed for protein–protein interaction using the STRING database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on core targets to elucidate the biological processes affected by PFAS in ICC. The intersection of the targets for ICC and those from PFOS/PFOA was compared with the differentially expressed genes in ICC from the TCGA database. LASSO regression was applied to filter prediction targets, and bioinformatics analysis was conducted to assess the differential expression of these targets in ICC, constructing a risk assessment model. Molecular docking was utilized to evaluate the binding affinity of PFOA and PFOS to the predicted targets. A total of 1,838 and 2,616 targets were predicted for PFOA and PFOS, respectively; whereas, for ICC, 2,044 targets were identified, with 345 and 459 common targets for PFOA and PFOS, respectively. GO and KEGG enrichment analyses revealed that these processes were primarily associated with biological functions such as cell proliferation and apoptosis, potentially mediated by the PI3K-Akt and HIF-1 signaling pathways. The integrated analysis of differentially expressed genes from the TCGA database and the intersection of targets from PFOA/PFOS with those from ICC yielded five core genes (TP53, ESR1, CDH1, BCL2, and MMP9) that formed an ICC prognostic score with robust predictive capability for survival, classifying ICC patients into high-risk and low-risk groups (p < 0.05). Molecular docking simulations demonstrated strong binding affinities between PFAS and these core targets, supporting their potential roles in PFAS-induced ICC development. This study explores the potential key mechanisms underlying PFAS-induced ICC and highlights core biomarkers that could serve as therapeutic targets. These results are expected to offer novel perspectives on strategies for the prevention and treatment of ICC.

Purpose: The Jianpi therapeutic strategy in traditional Chinese medicine aims to enhance the spleen’s digestive function and overall wellness. It has shown promise in improving cancer-related fatigue (CRF). This research systematically evaluates the effectiveness of Jianpi therapy in reducing fatigue in cancer patients through a meta-analytic review.Methods: An exhaustive search was performed within PubMed, Embase, Web of Science, Cochrane Library, SinoMed, Wanfang Data, China Science and Technology Journal Database, and China National Knowledge Infrastructure (CNKI) for randomized controlled trials concerning the application of Jianpi therapy to address CRF. The search spanned from the commencement of each database’s records to April 1, 2024. The extracted data were subjected to analysis using Stata (Version 15.1), with the selection of either a random-effects or fixed-effects model based on the heterogeneity among studies. Outcome measures were demonstrated with standardized mean differences (SMDs) or mean differences (MDs), and each complemented by a 95% confidence interval (CI). The Cochrane Risk of Bias Assessment Tool 2.0 was utilized to assess the potential biases within the studies.Results: A comprehensive analysis was performed on 45 eligible studies, all of which were conducted within China and encompassed a total of 3,596 participants. The meta-analysis indicated that Jianpi decoction alone exhibited the most significant improvement in the proportion of CD4 cells (SMD=1.34, 95% CI 0.54 to 2.31, P<0.001) and hemoglobin (MD=7.45, 95% CI 4.18 to 10.72, Z=4.47, P<0.001), while also more significantly reducing Piper Fatigue Scale scores (SMD=-2.05, 95% CI -2.71 to -1.39, P<0.001). The combined therapy, which integrated Jianpi therapy with standard care, demonstrated the greatest advantage in enhancing the proportion of CD3 cells (SMD=1.25, 95% CI 0.46 to 2.04, P<0.001). Furthermore, Jianpi therapy was found to be effective in lowering tumor necrosis factor-alpha levels (MD=-7.79, 95% CI -11.24 to -4.34, P<0.001) and concurrently enhancing interferon-gamma (MD=5.15, 95% CI 3.20 to 7.09, P=0.002), interleukin-2 (MD=8.37, 95% CI 6.14 to 10.59, P<0.001).Conclusion: Our research indicates that Jianpi therapy effectively alleviates CRF, reduces inflammation, and strengthens immune function. However, further high-quality, multicenter randomized controlled trials are essential to confirm these findings and strengthen the evidence.Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024566739.

Background: Vessels encapsulating tumor clusters (VETC) are significantly associated with poor prognosis in hepatocellular carcinoma (HCC). However, identifying VETC early remains challenging. Recently, machine learning has shown promise for VETC detection, but their diagnostic accuracy lacks systematic validation. Objective: This meta-analysis aimed to systematically evaluate the diagnostic accuracy of machine learning models for detecting VETC in patients with HCC. Methods: The Cochrane Library, Embase, Web of Science, and PubMed were searched up to June 21, 2025. Eligible studies focused on machine learning models for HCC VETC diagnosis. Studies that merely analyzed risk factors or lacked outcome measures were excluded. The Prediction Model Risk of Bias Assessment Tool was used to evaluate the risk of bias. A bivariate mixed-effects model was used for a meta-analysis based on 2×2 diagnostic tables. Subgroup analyses were performed according to modeling variables (nonradiomic vs radiomic features) and model types (traditional machine learning vs deep learning). Results: This meta-analysis included 31 studies comprising 6755 patients with HCC (2699 VETC-positive). Nineteen studies used machine learning models based on nonradiomic features, and 12 used radiomic features (including deep learning). In the validation set, the nonradiomic model demonstrated a pooled sensitivity of 0.72 (95% CI 0.66-0.78), specificity of 0.74 (95% CI 0.68-0.80), and an area under the summary receiver operating characteristic curve (SROC AUC) of 0.80 (95% CI 0.76-0.83). The radiomic model showed sensitivity of 0.81 (95% CI 0.73-0.87), specificity of 0.73 (95% CI 0.67-0.79), and SROC AUC of 0.84 (95% CI 0.80-0.87). Traditional machine learning achieved sensitivity of 0.84 (95% CI 0.71-0.92), specificity of 0.75 (95% CI 0.67-0.81), and SROC AUC of 0.83 (95% CI 0.80-0.86). Deep learning exhibited sensitivity of 0.77 (95% CI 0.69-0.84), specificity of 0.70 (95% CI 0.59-0.79), and SROC AUC of 0.81 (95% CI 0.77-0.85). Conclusions: This meta-analysis is the first to quantitatively assess the efficacy of machine learning models in HCC VETC diagnosis, addressing an evidence gap in this field. Unlike previous descriptive reviews, this analysis provides the first quantitative evidence revealing the potential value of machine learning in detecting HCC VETC. The findings provide a foundation for developing and refining subsequent intelligent detection tools. Despite their promising prospects, machine learning models have not yet reached the maturity required for clinical translation, owing to methodological heterogeneity, limited validation, and a high risk of bias. Future research should focus on conducting multicenter, large-sample, standardized, prospective studies to advance clinical translation. Trial Registration: PROSPERO CRD420251084894; https://www.crd.york.ac.uk/PROSPERO/view/CRD420251084894