Traditional Chinese medicine for gastric cancer: An evidence mapping.

As a complementary and alternative therapy, traditional Chinese medicine (TCM) has been playing a significant role in gastric cancer treatment. Data from individual systematic reviews have not been comprehensively summarized, and the relationship between certain interventions and outcomes are ill-defined. This study aimed to analyze the advantages of TCM interventions for gastric cancer by the method of evidence mapping. We searched PubMed, Embase, Web of Science, China National Knowledge Infrastructure, Chinese Scientific Journals Database, and Wanfang Database for systematic reviews of TCM treating gastric cancer up to December 31, 2023. We used Excel, Endnote 20, and Python software for the analysis of incorporated studies. We assessed the quality of included SRs by AMSTAR-2 and performed evidence mapping including 89 SRs, 1648 RCTs and 122,902 patients, identifying 47 types of interventions and 39 types of outcomes. From a visual overview, we displayed that most SRs reported beneficial effects in improving short- and long-term survival, myelosuppression, and immune function, even though the quality of evidence was generally low. The benefits of Brucea javanica Oil Emulsion Injection, ShenQiFuZheng Injection, XiaoAiPing, Astragalus-Containing TCM and Guben Xiaoji Therapy were found the most solid in corresponding aspects. Our findings suggest that although more rigorous clinical trials and SRs are needed to identify the precise effectiveness, integrating such evidence into clinical care of gastric cancer is expected to be beneficial.

Development and validation of a novel stemness-related prognostic model for neuroblastoma using integrated machine learning and bioinformatics analyses.

Background: Neuroblastoma (NB) is a common solid tumor in children, with a dismal prognosis in high-risk cases. Despite advancements in NB treatment, the clinical need for precise prognostic models remains critical, particularly to address the heterogeneity of cancer stemness which plays a pivotal role in tumor aggressiveness and patient outcomes. By utilizing machine learning (ML) techniques, we aimed to explore the cancer stemness features in NB and identify stemness-related hub genes for future investigation and potential targeted therapy. Methods: The public dataset GSE49710 was employed as the training set for acquire gene expression data and NB sample information, including age, stage, and MYCN amplification status and survival. The messenger RNA (mRNA) expression-based stemness index (mRNAsi) was calculated and patients were grouped according to their mRNAsi value. Stemness-related hub genes were identified from the differentially expressed genes (DEGs) to construct a gene signature. This was followed by evaluating the relationship between cancer stemness and the NB immune microenvironment, and the development of a predictive nomogram. We assessed the prognostic outcomes including overall survival (OS) and event-free survival, employing machine learning methods to measure predictive accuracy through concordance indices and validation in an independent cohort E-MTAB-8248. Results: Based on mRNAsi, we categorized NB patients into two groups to explore the association between varying levels of stemness and their clinical outcomes. High mRNAsi was linked to the advanced International Neuroblastoma Staging System (INSS) stage, amplified MYCN, and elder age. High mRNAsi patients had a significantly poorer prognosis than low mRNAsi cases. According to the multivariate Cox analysis, the mRNAsi was an independent risk factor of prognosis in NB patients. After least absolute shrinkage and selection operator (LASSO) regression analysis, four key genes (ERCC6L, DUXAP10, NCAN, DIRAS3) most related to mRNAsi scores were discovered and a risk model was built. Our model demonstrated a significant prognostic capacity with hazard ratios (HR) ranging from 18.96 to 41.20, P values below 0.0001, and area under the receiver operating characteristic curve (AUC) values of 0.918 in the training set, suggesting high predictive accuracy which was further confirmed by external verification. Individuals with a low four-gene signature score had a favorable outcome and better immune responses. Finally, a nomogram for clinical practice was constructed by integrating the four-gene signature and INSS stage. Conclusions: Our findings confirm the influence of CSC features in NB prognosis. The newly developed NB stemness-related four-gene signature prognostic signature could facilitate the prognostic prediction, and the identified hub genes may serve as promising targets for individualized treatments.

EZH2 regulates pancreatic cancer cells through E2F1, GLI1, CDK3, and Mcm4.

Pancreatic cancer (PC) is one of the most common malignant tumors in digestive tract. To explore the role of epigenetic factor EZH2 in the malignant proliferation of PC, so as to provide effective medical help in PC. Sixty paraffin sections of PC were collected and the expression of EZH2 in PC tissues was detected by immunohistochemical assay. Three normal pancreas tissue samples were used as controls. The regulation of EZH2 gene on proliferation and migration of normal pancreatic cell and PC cell were determined by MTS, colony forming, Ki-67 antibody, scratch and Transwell assays. Through differential gene annotation and differential gene signaling pathway analysis, differentially expressed genes related to cell proliferation were selected and verified by RT-qPCR. EZH2 is mainly expressed in the nuclei of pancreatic tumor cells, but not in normal pancreatic cells. The results of cell function experiments showed that EZH2 overexpression could enhance the proliferation and migration ability of PC cell BXPC-3. Cell proliferation ability increased by 38% compared to the control group. EZH2 knockdown resulted in reduced proliferation and migration ability of cells. Compared with control, proliferation ability of cells reduced by 16%-40%. The results of bioinformatics analysis of transcriptome data and RT-qPCR demonstrated that EZH2 could regulate the expression of E2F1, GLI1, CDK3 and Mcm4 in normal and PC cells. The results revealed that EZH2 might regulate the proliferation of normal pancreatic cell and PC cell through E2F1, GLI1, CDK3 and Mcm4.

Establishment of a typing model for diffuse large B-cell lymphoma based on B-cell receptor repertoire sequencing.

BACKGROUND: The purpose of this study was to construct a new typing model for diffuse large B-cell lymphoma (DLBCL) patients based on the B-cell receptor (BCR) and explore its potential molecular mechanism. METHODS: BCR repertoire sequencing and whole-exome sequencing were performed on formalin-fixed paraffin-embedded samples from 12 DLBCL patients. Subsequently, a typing model was built with cluster analysis, and prognostic indicators between the two groups were compared to verify the typing model. Then, mutation and bioinformatics analyses were conducted to investigate the potential biomarkers of prognostic differences between the two groups. RESULTS: Based on BCR sequencing data, we divided patients into two clusters (cluster 1 and cluster 2); this classification differed from the traditional typing method (GCB and non-GCB), in which cluster 1 included some non-GCB patients. The progression-free survival (PFS), overall survival (OS), metastasis and Shannon diversity index of IGH V-J and survival after chemotherapy were significantly different (P < 0.05) between the two clusters, but no statistical significance was found between the GCB and non-GCB groups. The mutation status of 248 genes was significantly different between cluster 1 and cluster 2. Among them, FTSJ3, MAGED2, and ODF3L2 were the specific mutated genes in all patients in cluster 2, and these genes could be considered critical to the different prognoses of the two clusters of DLBCL patients. CONCLUSION: We constructed a new typing model of DLBCL based on BCR repertoire sequencing that can better predict the survival time after chemotherapy. FTSJ3, MAGED2, and ODF3L2 may represent key genes for the difference in prognosis between the two clusters.