Acupuncture in Pediatrics: A Scoping Review

Background: The use of acupuncture for common pediatric conditions continues to grow in the United States. Six previous reviews have summarized the research; however, approximately 8 years have accumulated adding to this growing body of literature. Objectives: The aim of this article is to provide a scoping review of acupuncture research in pediatric conditions and a summary of the effectiveness of acupuncture in the pediatric population. Methods: A systematic search was performed to identify clinical trials and reviews published between August 2015 and October 2023. Trials and reviews were excluded if they were not: (1) acupuncture specific, (2) data limited to pediatric patients, and (3) published in English. The literature was assessed and synthesized into a scoping review and commentary. Results: Seventy-one publications were identified (42 clinical trials and 29 reviews) that represented 17 conditions and 10 countries; the top 2 were China and the United States. The top five areas of new research (based on numbers of clinical trials) are pain, cerebral palsy (CP), attention-deficit hyperactivity disorder, autism spectrum disorder, and nausea and vomiting. Conclusions: Clinical trials in pediatric acupuncture represent a small but important subset of acupuncture literature. Data are positive for postoperative nausea and vomiting and promising for CP, nocturnal enuresis, perioperative pain, procedural pain, and tic disorders. Several factors preclude additional firm conclusions from being drawn, including a limited number of trials, small sample size (n < 100) of the majority of randomized controlled trials (78.6%), and heterogeneity of treatment approaches. Continued research on the use of acupuncture for pediatric conditions is warranted, particularly in conditions demonstrating recent promising evidence.

Deciphering T-cell exhaustion in the tumor microenvironment: paving the way for innovative solid tumor therapies

In solid tumors, the tumor microenvironment (TME) is a complex mix of tumor, immune, stromal cells, fibroblasts, and the extracellular matrix. Cytotoxic T lymphocytes (CTLs) constitute a fraction of immune cells that may infiltrate into the TME. The primary function of these T-cells is to detect and eliminate tumor cells. However, due to the immunosuppressive factors present in the TME primarily mediated by Myeloid-Derived Suppressor Cells (MDSCs), Tumor associated macrophages (TAMs), Cancer Associated Fibroblasts (CAFs) as well as the tumor cells themselves, T-cells fail to differentiate into effector cells or become dysfunctional and are unable to eliminate the tumor. In addition, chronic antigen stimulation within the TME also leads to a phenomenon, first identified in chronic lymphocytic choriomeningitis virus (LCMV) infection in mice, where the T-cells become exhausted and lose their effector functions. Exhausted T-cells (Tex) are characterized by the presence of remarkably conserved inhibitory receptors, transcription and signaling factors and the downregulation of key effector molecules. Tex cells have been identified in various malignancies, including melanoma, colorectal and hepatocellular cancers. Recent studies have indicated novel strategies to reverse T-cell exhaustion. These include checkpoint inhibitor blockade targeting programmed cell death protein 1 (PD-1), T-cell immunoglobulin and mucin-domain containing-3 (Tim-3), cytotoxic T-lymphocyte associated protein 4 (CTLA-4), or combinations of different immune checkpoint therapies (ICTs) or combination of ICTs with cytokine co-stimulation. In this review, we discuss aspects of T-cell dysfunction within the TME with a focus on T-cell exhaustion. We believe that gaining insight into the mechanisms of T-cell exhaustion within the TME of human solid tumors will pave the way for developing therapeutic strategies to target and potentially re-invigorate exhausted T-cells in cancer.

MPAC: a computational framework for inferring pathway activities from multi-omic data

Fully capturing cellular state requires examining genomic, epigenomic, transcriptomic, proteomic, and other assays for a biological sample and comprehensive computational modeling to reason with the complex and sometimes conflicting measurements. Modeling these so-called multi-omic data is especially beneficial in disease analysis, where observations across omic data types may reveal unexpected patient groupings and inform clinical outcomes and treatments. We present Multi-omic Pathway Analysis of Cells (MPAC), a computational framework that interprets multi-omic data through prior knowledge from biological pathways. MPAC uses network relationships encoded in pathways using a factor graph to infer consensus activity levels for proteins and associated pathway entities from multi-omic data, runs permutation testing to eliminate spurious activity predictions, and groups biological samples by pathway activities to prioritize proteins with potential clinical relevance. Using DNA copy number alteration and RNA-seq data from head and neck squamous cell carcinoma patients from The Cancer Genome Atlas as an example, we demonstrate that MPAC predicts a patient subgroup related to immune responses not identified by analysis with either input omic data type alone. Key proteins identified via this subgroup have pathway activities related to clinical outcome as well as immune cell compositions. Our MPAC R package, available at https://bioconductor.org/packages/MPAC, enables similar multi-omic analyses on new datasets.

3-(3-(diethylamino)propyl)-2-(4-(methylthio)phenyl)thiazolidin-4-one Attenuates Scopolamine-induced Cognitive Impairment in Rats: Insights Into Neuroprotective Effects

Alzheimer's Disease (AD) is characterized by memory decline, dysregulation in cholinergic and purinergic signaling, neuroinflammation, and oxidative stress. Current treatments are limited, highlighting the need for new compounds to prevent or delay AD progression. Thiazolidinones have emerged as promising candidates due to their antioxidant, anti-inflammatory, and anticholinesterase properties. The aim of this study was to evaluate the effects of 3-(3-(diethylamino)propyl)-2-(4-(methylthio)phenyl)thiazolidin-4-one (DS27) in a rat model of scopolamine-induced memory deficits. Male rats were divided into groups: I - Control, II - Scopolamine (SCO) (1 mg/kg), III - SCO and DS27 (5 mg/kg), IV - SCO and DS27 (10 mg/kg), V - SCO and donepezil (5 mg/kg). The animals were treated orally with DS27 or donepezil for seven days. On the 8th day, they underwent the open field test and inhibitory avoidance training, followed by intraperitoneal administration SCO. Twenty-four hours later, an inhibitory avoidance test was conducted. Acetylcholinesterase (AChE) activity, oxidative stress, and inflammatory and purinergic parameters were analyzed in the cerebral cortex, hippocampus, cerebrospinal fluid, serum, lymphocytes, and liver. DS27 prevented memory deficits, alterations in AChE activity, and oxidative damage induced by SCO in brain structures. Additionally, DS27 prevented SCO-induced decrease in IL-10 levels, and increase in IL-6, and TNF-α expression in the cerebral cortex, and normalized ATP and ADP hydrolysis in cerebrospinal fluid and lymphocytes. DS27 did not induce oxidative stress in the liver or alter serum biochemical parameters. These findings suggest that DS27 has significant neuroprotective properties and could be a promising alternative for treating neurodegenerative diseases like AD.

Leveraging glycosylation for early detection and therapeutic target discovery in pancreatic cancer

Pancreatic cancer (PC) remains one of the most lethal malignancies, primarily due to late-stage diagnosis, limited biomarker specificity, and aggressive metastatic potential. Recent glycoproteomic studies have illuminated the crucial role of glycosylation in PC progression, revealing altered glycosylation patterns that impact cell adhesion, immune evasion, and tumor invasiveness. Biomarkers such as CA19-9 remain the clinical standard, yet limitations in sensitivity and specificity, especially in early disease stages, necessitate the exploration of alternative markers. Emerging glycoproteins-such as mesothelin, thrombospondin-2, and glycan modifications like sialyl-Lewis x-offer diagnostic promise when combined with CA19-9 or used in profiling panels. Furthermore, therapeutic strategies targeting glycosylation processes, including sialylation, and fucosylation, have shown potential in curbing PC metastasis and enhancing immune response. Translational platforms, such as patient-derived xenografts and advanced in vitro models, are pivotal in validating these findings and assessing glycosylation potential therapeutic impact. Continued exploration of glycosylation-driven mechanisms and biomarker discovery in PC can significantly advance early detection and treatment efficacy, offering new hope in the management of this challenging disease.

The immunosuppressive role of MDSCs in HCC: mechanisms and therapeutic opportunities

Hepatocellular carcinoma (HCC) is a prevalent malignancy with a significant global burden. Despite substantial advancements in HCC treatment in recent years, therapeutic efficacy remains constrained by immune evasion mechanisms within the tumor microenvironment (TME). Myeloid-derived suppressor cells (MDSCs), as critical immunosuppressive elements of the TME, have garnered increasing attention for their role in tumor progression. Recent studies emphasize their central involvement in promoting immune evasion, tolerance, and immunosuppression in HCC. This review examines the contributions of MDSCs to HCC pathogenesis, elucidates their underlying mechanisms, and discusses ongoing clinical trials, emphasizing their potential as therapeutic targets for improving clinical outcomes.

Integrating multi-omics and machine learning methods reveals the metabolism of amino acids and derivatives-related signature in colorectal cancer

Objective

The metabolism of amino acids and derivatives (MAAD) is closely related to the occurrence and development of colorectal cancer (CRC), but the specific regulatory mechanisms are not yet clear. This study aims to explore the role of MAAD in the progression of colorectal cancer and ultimately identify key molecules that may become potential therapeutic targets for CRC.

Methods

This study integrates bulk transcriptome and single-cell transcriptome to analyze and identify key MAAD-related genes from multiple levels. Subsequently, numerous machine learning methods were incorporated to construct MAAD-related prognostic models, and the infiltration of immune cells, tumor heterogeneity, tumor mutation burden, and potential pathway changes under different modes were analyzed. Finally, key molecules were identified for experimental validation.

Results

We successfully constructed prognostic models and Nomograms based on key MAAD-related molecules. There was a notable survival benefit observed for low-risk patients when contrasted with their high-risk counterparts. In addition, the high-risk group had a poorer response to immunotherapy and stronger tumor heterogeneity compared with the low-risk group. Further research found that by knocking down the MAAD-related gene. LSM8, the malignant characteristics of colorectal cancer cell lines were significantly alleviated, suggesting that LSM8 may become a potential therapeutic target.

Conclusion

The MAAD-related gene LSM8 is likely involved in the progression of CRC and could be a hopeful target for therapeutic intervention.

Establishment and validation of an immune-related genes diagnostic model and experimental validation of diagnostic biomarkers for autoimmune thyroiditis based on RNA-seq

BACKGROUND

Autoimmune thyroiditis (AIT), a common autoimmune disease, is a complex disease with an increasing incidence and an unknown pathogenesis that awaits the refinement of diagnostic methods and identification of diagnostic biomarkers to improve screening to identify patients at high risk of AIT earlier and provide the potential effective therapeutic drugs.

PATIENTS AND METHODS

All samples for this study were from a cross-sectional survey, which was conducted among adults in two regions of Anhui Province, China. Ten representative samples (nAIT = 5, nControl = 5) were selected for RNA sequencing to build a training-set in order to identify immune-related differentially expressed genes (IRDEGs), and least absolute shrinkage and selection operator (LASSO) regression analysis was subsequently adopted to screen key IRDEGs and construct a diagnostic model. Then, a test-set was created by downloading AIT transcriptome datasets from the Gene Expression Omnibus (GEO) database. The diagnostic model was systematically evaluated in the training-set and test-set by principal component analysis (PCA) analysis, receiver operating characteristic (ROC) curve and immune infiltration analysis. To identify diagnostic biomarkers, quantitative reverse transcription PCR (RT-qPCR) was used to measure the expression levels of the diagnostic genes in 80 samples. The diagnostic and therapeutic values of the diagnostic genes for AIT were investigated using gene set variation analysis (GSVA), ROC curve, logistic regression analysis and drug docking.

RESULTS

The diagnostic model included three diagnostic genes (FGFR2, CCR1, IL1B). All ROC curves (AUC > 0.7) results suggested that the diagnostic model and the diagnostic genes had reliable predictive power. The results of logistic regression analysis showed that the three diagnostic genes were significant for AIT. The results of GSVA and immunoinfiltration analysis demonstrated that the diagnostic genes have significant negative or positive regulatory effect in immune mechanisms of AIT and the diagnostic model implements immune-related prediction algorithms. Finally, the small molecular compounds (Acetaminophen and Albuterol) were screened as the potential therapeutic drugs for AIT.

CONCLUSION

Using machine learning and bioinformatics techniques, this study developed and validated an AIT diagnostic model, explored the diagnostic model's prediction mechanism, verified three potential diagnostic biomarkers by experiments and predicted two small molecule therapeutic drugs.

Identification of mitochondrial permeability transition-related lncRNAs as quantitative biomarkers for the prognosis and therapy of breast cancer

Breast cancer (BC) continues to pose a global health threat and presents challenges for treatment due to its high heterogeneity. Recent advancements in the understanding of mitochondrial permeability transition (MPT) and the regulatory roles of long non-coding RNAs (lncRNAs) offer potential insights for the stratification and personalized treatment of BC. Although the association between MPT and lncRNAs has not been widely studied, a few research studies have indicated a regulatory impact of lncRNAs on MPT, further deepening the understanding of the tumor. To identify reliable biomarkers associated with MPT for managing BC, bulk RNA-seq data of MPT-related lncRNAs acquired from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) project were utilized to assess BC patients. A scoring system, termed the MPT-related score (MPTRscore), was developed using LASSO-Cox regression on data from 1,029 BC patients from TCGA-BRCA. Meanwhile, the superior prognostic accuracy of the MPTRscore was demonstrated by comparing it with biomarkers, including PAM50 subtyping for standardization. Subsequently, a clinical prediction model was created by incorporating the MPTRscore and clinical variables. This analysis revealed two distinct MPTRscore groups characterized by different biomolecular processes, tumor microenvironment (TME) patterns, and clinical outcomes. The MPTRscore was further investigated through unsupervised consensus clustering of TCGA-BRCA based on MPTRscore-related prognostic genes. Additionally, the MPTRscore was identified as an independent prognostic factor for BC and showed guiding utility in immunotherapy and chemotherapy response. Specifically, patients with a low MPTRscore exhibited better prognosis and treatment responses compared to those with a high MPTRscore. Significantly, the relevance of clustering results and MPTRscore was found to be mediated by lncRNA transcript RP11-573D15.8-018. In conclusion, MPTRscore-related clusters were identified in BC, and an integrative score was developed as a biomarker for predicting BC prognosis and therapeutic response. Additionally, molecular interactions underlying the relationship between MPTRscore-related clusters and MPTRscore were uncovered, proving insights for BC stratification. These findings may aid in prognosis determination and therapeutic decision-making for BC patients.

Single-cell sequencing reveals the role of aggrephagy-related patterns in tumor microenvironment, prognosis and immunotherapy in endometrial cancer

Background

As a type of autophagy, aggrephagy degrades the aggregation of misfolded protein in cells and plays an important role in key genetic events for various cancers. However, aggrephagy functions within the tumor microenvironment (TME) in endometrial cancer (EC) remain to be elucidated.

Methods

A total of 36,227 single cells from single-cell RNA-seq data derived from five EC tumor samples were comprehensively analyzed using a nonnegative matrix factorization (NMF) algorithm for 44 aggrephagy-related genes. Bulk RNA-seq cohorts from public repositories were utilized to assess the prognostic value of aggrephagy-related TME clusters and predict immune checkpoint blockade immunotherapeutic response in EC.

Results

Fibroblasts, macrophages, CD8+T cells, and lymphatic endothelial cells were categorized into two to five aggrephagy-related subclusters, respectively. CellChat analysis showed that the aggrephagy-related subtypes of TME cells exhibited extensive interactions with tumor epithelial cells, particularly for macrophages. Moreover, aggrephagy regulators may be significantly associated with the pseudotime trajectories of major TME cell types as well as the clinical and biological features of EC. Bulk-seq analysis showed that these aggrephagy-related subclusters had significant predictive value for the survival and immune checkpoint blockade response in EC patients. Notably, immunohistochemical staining results manifested that the TUBA1A+ macrophage subtype was linked to less lymph node metastasis and longer survival, which were consistent with the bioinformatics analysis findings.

Conclusions

This study provided a novel view of aggrephagy signaling in the EC tumor microenvironment, and intervention of aggrephagy was expected to improve the survival rate of EC patients.

Multifaceted roles of Galectins: from carbohydrate binding to targeted cancer therapy

Galectins play pivotal roles in cellular recognition and signaling processes by interacting with glycoconjugates. Extensive research has highlighted the significance of Galectins in the context of cancer, aiding in the identification of biomarkers for early detection, personalized therapy, and predicting treatment responses. This review offers a comprehensive overview of the structural characteristics, ligand-binding properties, and interacting proteins of Galectins. We delve into their biological functions and examine their roles across various cancer types. Galectins, characterized by a conserved carbohydrate recognition domain (CRD), are divided into prototype, tandem-repeat, and chimera types based on their structural configurations. Prototype Galectins contain a single CRD, tandem-repeat Galectins contain two distinct CRDs linked by a peptide, and the chimera-type Galectin-3 features a unique structural arrangement. The capacity of Galectins to engage in multivalent interactions allows them to regulate a variety of signaling pathways, thereby affecting cell fate and function. In cancer, Galectins contribute to tumor cell transformation, angiogenesis, immune evasion, and metastasis, making them critical targets for therapeutic intervention. This review discusses the multifaceted roles of Galectins in cancer progression and explores current advancements in the development of Galectin-targeted therapies. We also address the challenges and future directions for integrating Galectin research into clinical practice to enhance cancer treatment outcomes. In brief, understanding the complex functions of Galectins in cancer biology opens new avenues for therapeutic strategies. Continued research on Galectin interactions and their pathological roles is essential for developing effective carbohydrate-based treatments and improving clinical interventions for cancer patients.

Transcriptome analysis reveals that the injection of mesenchymal stem cells remodels extracellular matrix and complement components of the brain through PI3K/AKT/FOXO1 signaling pathway in a neuroinflammation mouse model

Neurological disorders are often accompanied by neuroinflammatory responses. Our previous research indicated that mesenchymal stem cells (MSCs) suppressed neuroinflammation in the brain. The mechanism of action remains not fully understood. In this study, we analyzed the impact of injected MSCs on the transcriptome in the brains of neuroinflammatory mouse model (NIM) with bioinformatical methods and conducted experimental validation with qPCR and Western blot. The results showed that the expression of extracellular matrix components changed, and the complement cascade was activated in the NIM brains. Injection of MSCs reversed the expression of ECM components and inhibited complement activation. MSCs may promote the improvement of neuronal synaptic function and alter the infiltration of immune cells into the brain. MSCs regulated the PI3K/AKT/Foxo1 signaling pathway. These findings will be very helpful for the development of MSCs-based therapy and the treatment of neuroinflammation-related diseases.

Targeting tumor-associated macrophages in colon cancer: mechanisms and therapeutic strategies

Colon cancer (CC) remains a primary contributor to cancer-related fatalities worldwide, driven by difficulties in early diagnosis and constrained therapeutic options. Recent studies underscore the importance of the tumor microenvironment (TME), notably tumor-associated macrophages (TAMs), in fostering malignancy progression and therapy resistance. Through their inherent plasticity, TAMs facilitate immunosuppression, angiogenic processes, metastatic spread, and drug tolerance. In contrast to M1 macrophages, which promote inflammatory and tumoricidal responses, M2 macrophages support tumor expansion and dissemination by exerting immunosuppressive and pro-angiogenic influences. Consequently, manipulating TAMs has emerged as a potential avenue to enhance treatment effectiveness. This review outlines the origins, polarization states, and functions of TAMs in CC, highlights their role in driving tumor advancement, and surveys ongoing efforts to target these cells for better patient outcomes. Emerging therapeutic strategies aimed at modulating TAM functions - including depletion strategies, reprogramming approaches that shift M2-polarized TAMs toward an M1 phenotype, and inhibition of key signaling pathways sustaining TAM-mediated immunosuppression-are currently under active investigation. These approaches hold promise in overcoming TAM - induced resistance and improving immunotherapeutic efficacy in CC.

Metabolic reprogramming shapes the immune microenvironment in pancreatic adenocarcinoma: prognostic implications and therapeutic targets

Introduction

Pancreatic adenocarcinoma (PAAD) is characterized by a profoundly immunosuppressive tumor microenvironment (TME) that limits the efficacy of immunotherapy. Emerging evidence suggests that tumor-specific metabolic reprogramming may drive disease progression and shape the immune landscape in PAAD.

Methods

We integrated multi-omics data from TCGA, GEO, and ICGC to identify key metabolism-related genes (MRGs) that influence immune cell infiltration, tumor progression, and patient survival. Based on nine pivotal MRGs (including ANLN, PKMYT1, and HMGA1), we developed and validated a novel metabolic-prognostic index (MPI). Functional enrichment analyses were conducted to elucidate the metabolic pathways associated with different MPI risk groups. In vitro experiments and drug sensitivity analyses were performed to confirm the oncogenic role of selected MRGs and to explore their therapeutic implications.

Results

The MPI effectively stratified patients into high- and low-risk groups. High-MPI scores correlated with poor overall survival, elevated tumor mutation burden (TMB), and an immunosuppressive TME, evidenced by reduced CD8⁺ T-cell infiltration and increased expression of immune checkpoints (PD-L1, TGF-β). Functional enrichment revealed glycolysis and folate biosynthesis as dominant pathways in high-MPI groups, whereas fatty acid metabolism prevailed in low-MPI groups. Experimental validation underscored the role of ANLN in promoting epithelial-mesenchymal transition (EMT) and immune evasion via NF-κB signaling. ANLN knockdown significantly reduced glycolytic activity, tumor cell migration, and immune evasion. Drug sensitivity analyses indicated resistance to gemcitabine but sensitivity to afatinib in high-MPI patients. Although TIDE analysis predicted immune checkpoint inhibitor (ICI) resistance in high-MPI tumors, a subset of patients showed favorable responses to anti-PD-L1 therapy.

Discussion

These findings provide a comprehensive framework for understanding how metabolic reprogramming shapes PAAD's immunosuppressive TME and affects treatment outcomes. By accurately stratifying patients, the MPI serves as a promising tool to guide therapeutic decisions, including targeted therapy selection and immunotherapy prediction, ultimately offering potential for more personalized management of PAAD.

Auricular acupoint therapy for functional gastrointestinal disorders: a systematic review and meta-analysis of randomized clinical trials

Introduction

This study aims to conduct a systematic review and meta-analysis of randomized controlled trials to evaluate the efficacy and safety of auricular acupoint therapy (AAT) for functional gastrointestinal disorders (FGIDs).

Methods

We conducted a thorough search across eight databases, including PubMed, EMBASE, Web of Science, the Cochrane Library, CNKI, Wanfang, VIP, and CBM. The search covered the period from the inception of each database up to June 30, 2024. The authors independently reviewed all the references, evaluated the risk of bias, and extracted the data. GRADEpro software was utilized to calculate overall strength of evidence. A random effects or fixed effects model was selected on the basis of the p-value and I2 . RevMan 5.3, Stata/MP 18.0, R 4.3.1 and R Studio 2023.09.0 were used for data processing. TSA 0.9.5.10 beta software was used to evaluate data stability.

Results

The review included 19 randomized controlled trials with a total of 1,681 patients (895 in the treatment group and 886 in the control group). The treatment duration ranged from 2-12 weeks. The meta-analysis revealed that, compared with the control, AAT was significantly more effective at treating FGIDs (RR: 1.35; 95% CI: 1.21-1.51; p < 0.001), reducing the symptom score (MD: -1.94; 95% CI: -3.06 to -0.85; p < 0.001; five trials), improving the SAS score (MD: -12.47; 95% CI: -13.92 to -11.01; p < 0.001; five trials), and improving the SDS score (MD: -4.97; 95% CI: -9.23 to -0.72; p = 0.02; six trials). A total of two articles mentioned relatively significant adverse reactions (MD: 2.98; 95% CI: 0.51-17.26; p = 0.009). Sensitivity and trial sequential analyses confirmed the stability of these results.

Discussion

While our meta-analysis suggests that AAT may offer benefits for FGIDs, these results must be interpreted with caution due to methodological limitations in the included trials. Further investigations in high-quality trials are warranted.

Systematic review registration

https://clinicaltrials.gov/, identifier CRD42024558786.

Limitations of nomogram models in predicting survival outcomes for glioma patients

Purpose

Glioma represents a prevalent and malignant tumor of the central nervous system (CNS), and it is essential to accurately predict the survival of glioma patients to optimize their subsequent treatment plans. This review outlines the most recent advancements and viewpoints regarding the application of nomograms in glioma prognosis research.

Design

With an emphasis on the precision and external applicability of predictive models, we carried out a comprehensive review of the literature on the application of nomograms in glioma and provided a step-by-step guide for developing and evaluating nomograms.

Results

A summary of thirty-nine articles was produced. The majority of nomogram-building research has used limited patient samples, disregarded the proportional hazards (PH) assumption in Cox regression models, and some of them have failed to incorporate external validation. Furthermore, the predictive capability of nomograms is influenced by the selection of incorporated risk factors. Overall, the current predictive accuracy of nomograms is moderately credible.

Conclusion

The development and validation of nomogram models ought to adhere to a standardized set of criteria, thereby augmenting their worth in clinical decision-making and clinician-patient communication. Prior to the clinical application of a nomogram, it is imperative to thoroughly scrutinize its statistical foundation, rigorously evaluate its accuracy, and, whenever feasible, assess its external applicability utilizing multicenter databases.

Exosomes derived from natural killer cells: transforming immunotherapy for aggressive breast cancer

Natural killer cell-derived exosomes (NK-Exos) hold great promise as immune modulators and immunotherapeutics against cancer due to their intrinsically latent anti-tumor effects. They use these nanosized vesicles to deliver cytotoxic molecules, such as perforin, granzymes, and miRNAs, directly to cancer cells to kill them, avoiding immune suppression. NK-Exos has particular efficacy for treating aggressive breast cancer by modulating the TME to activate the immune response and suppress immunosuppressive factors. Bioengineering advances have extended the therapeutic potential of NK-Exos, which permits precise tumor cell targeting and efficient delivery of therapeutic payloads, including small RNAs and chemotherapeutic agents. In engineered NK-Exos, sensitization of cancer cells to apoptosis, reduction of tumor growth, and resistance to drugs have been demonstrated to be highly effective. When combined, NK-Exos synergizes with radiotherapy, chemotherapy, or checkpoint inhibitors, enhancing therapeutic efficacy, and minimizing systemic toxicity. This review emphasizes the critical role of NK-Exos in breast cancer treatment, their integration into combination therapies, and the need for further research to overcome existing limitations and fully realize their clinical potential.

Downregulated STAT3 and STAT5B are prognostic biomarkers for colorectal cancer and are associated with immune infiltration

BACKGROUND

Colorectal cancer has high incidence and mortality rates. The signal transducer and activator of transcription (STAT) family plays vital roles in the tumorigenesis and development of colorectal cancer. The expression, prognostic value, and immune function of the STAT family are becoming much more clearly.

METHODS

Our study collected data from several public data portals such as TCGA (644 samples) and GTEx database (308 samples) and clinical samples (30 samples, China). Then we systematically assessed the expression level and prognostic value of the STAT family in colorectal cancer samples. Moreover, the immune function and immune infiltration levels of prognosis-related STAT members were explored via single cell RNA-seq and spatial transcriptomics technology data. Several useful portals and tools have been utilized such as CancerSEA and TISIDB in single-cell analysis, CBio Cancer Genomics in multidimensional alterations, MethSurv in DNA methylation, and related R packages.

RESULTS

Our study found that STAT3 and STAT5B were significantly lower in colorectal cancer via multi-omics (P < 0.001). Higher STAT3 and STAT5B level were correlated with better future outcome. Nomograms were developed to predict the distal survival time (C-index = 0.724). The functions of STAT3 and STAT5B are associated with inflammation, the JAK/STAT pathway and the immune response. The major cell types of colorectal cancer were CD4Tconv, CD8T, CD8Tex, Tprolif, Treg and STAT3 and STAT5B widely expressed in these cells. STAT3 and STAT5B both correlated with CD244 and KDR for immune checkpoints.

CONCLUSION

STAT3 and STAT5B are downregulated in colorectal cancer and have great potential as prognostic biomarkers and novel immunotherapy targets.

T lymphocyte-based immune response and therapy in hepatocellular carcinoma: focus on TILs and CAR-T cells

Hepatocellular carcinoma (HCC) is among the leading causes of cancer-related death worldwide. The primary therapies for HCC are liver transplantation, hepatic tumor excision, radiofrequency ablation, and molecular-targeted medicines. An unfavorable prognosis marks HCC and has limited pharmacological response in therapeutic studies. The tumor immune microenvironment (TME) imposes significant selection pressure on HCC, resulting in its evolution and recurrence after various treatments. As the principal cellular constituents of tumor-infiltrating lymphocytes (TILs), T cells have shown both anti-tumor and protumor actions in HCC. T cell-mediated immune responses are pivotal in cancer monitoring and elimination. TILs are recognized for their critical involvement in the progression, prognosis, and immunotherapeutic management of HCC. Foxp3 + , CD8 + , CD3 + , and CD4 + T cells are the extensively researched subtypes of TILs. This article examines the functions and processes of several subtypes of TILs in HCC. Emerging T cell-based therapies, including TILs and chimeric antigen receptor (CAR)-T cell therapy, have shown tumor regression in several clinical and preclinical studies. Herein, it also delves into the existing T cell-based immunotherapies in HCC, with emphasis on TILs and CAR-T cells.

Uncovering the heterogeneity of NK cells on the prognosis of HCC by integrating bulk and single-cell RNA-seq data

Background

The tumor microenvironment (TME) plays a critical role in the development, progression, and clinical outcomes of hepatocellular carcinoma (HCC). Despite the critical role of natural killer (NK) cells in tumor immunity, there is limited research on their status within the tumor microenvironment of HCC. In this study, single-cell RNA sequencing (scRNA-seq) analysis of HCC datasets was performed to identify potential biomarkers and investigate the involvement of natural killer (NK) cells in the TME.

Methods

Single-cell RNA sequencing (scRNA-seq) data were extracted from the GSE149614 dataset and processed for quality control using the "Seurat" package. HCC subtypes from the TCGA dataset were classified through consensus clustering based on differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was employed to construct co-expression networks. Furthermore, univariate and multivariate Cox regression analyses were conducted to identify variables linked to overall survival. The single-sample gene set enrichment analysis (ssGSEA) was used to analyze immune cells and the screened genes.

Result

A total of 715 DEGs from GSE149614 and 864 DEGs from TCGA were identified, with 25 overlapping DEGs found between the two datasets. A prognostic risk score model based on two genes was then established. Significant differences in immune cell infiltration were observed between high-risk and low-risk groups. Immunohistochemistry showed that HRG expression was decreased in HCC compared to normal tissues, whereas TUBA1B expression was elevated in HCC.

Conclusion

Our study identified a two-gene prognostic signature based on NK cell markers and highlighted their role in the TME, which may offer novel insights in immunotherapy strategies. Additionally, we developed an accurate and reliable prognostic model, combining clinical factors to aid clinicians in decision-making.

Metformin-induced E6/E7 inhibition prevents HPV-positive cancer progression through p53 reactivation

The human papillomavirus (HPV) is implicated in multiple lethal cancers, although it is more sensitive to certain therapies than HPV-negative cancers. Therefore, the development of more targeted therapeutic strategies is imperative. The HPV oncogenes E6/E7 are ideal targets for HPV-positive cancer, but there are no clinical strategies that have been proven to effectively target E6/E7. Notably, metformin significantly inhibits E6/E7 expression; however, the underlying mechanism and therapeutic potential remain unclear, limiting its clinical translation. Cell Counting Kit-8, ethynyl-2'-deoxyuridine, and terminal-deoxynucleotidyl transferase-mediated Nick end labeling assays were conducted to evaluate the effects of metformin on cell viability, proliferation, and apoptosis. Quantitative real-time PCR, western blotting, and immunofluorescence assays were performed to determine changes in E6/E7 and p53 expression levels following metformin treatment. Patient-derived organoids and in-vivo xenograft models were constructed to evaluate the anticancer activity of metformin against HPV-positive cancer. Our research demonstrated enhanced sensitivity of HPV-positive cancer cells to metformin. Mechanistic studies have revealed that metformin exerts anticancer effects by inhibiting E6/E7 expression, which is associated with p53 reactivation. Furthermore, we substantiated the anticancer potential of metformin in HPV-positive patient-derived organoids and in-vivo tumor models. Our study focused on the mechanism underlying the enhanced responsiveness of HPV-positive cancer to metformin, highlighting the clinical potential of metformin as a targeted therapeutic strategy for HPV-positive cancer.

How effective is acupuncture in treating hot flashes in breast cancer patients? A systematic review and meta-analysis

Background

Although acupuncture is recommended for managing breast cancer-related hot flashes, the level of evidence is limited. With the updating of randomized controlled trials, it is necessary to reassess its efficacy.

Objective

To assess the effectiveness of acupuncture in the treatment of hot flashes in patients with breast cancer.

Methods

Up to March 2024, we retrieved data from nine databases and used Stata software (version 14.0, version 17.0) and RevMan software (version 5.3) to conduct a meta-analysis. The Cochrane Collaboration's risk of bias assessment tool was used for methodological assessment of the risk of bias, and the GRADEpro GDT online assessment tool was used for evidence evaluation.

Results

In total, 11 randomized controlled trials (RCTs) involved 963 participants were included in the meta-analysis. The result of risk of bias revealed that the included RCTs exhibited a high risk of bias, primarily attributable to deficiencies in randomization and blinding methods. The results of primary meta-analysis indicated that acupuncture can improved the hot flash symptom scale score (SMD, -0.54; 95% CI, -0.83 to -0.24; P < 0.05). However, acupuncture does not reduce the frequency of hot flashes(SMD, -0.20; 95% CI, -0.75 to 0.36; P = 0.48). Further subgroup analyses, including the type of control group and the duration of needle retention, etc. showed different results, highlighting the necessity for further research. Sensitivity analysis confirmed the reliability of these finding. In addition, due to various issues, the level of evidence is low.

Conclusions

Although acupuncture treatment for hot flashes in breast cancer shows potential, the evidence for the efficacy of acupuncture is still lacking due to various factors such as bias risk and significant differences between studies, and more high-quality RCTs are needed to confirm the efficacy of acupuncture.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42024531542.

New progress in imaging diagnosis and immunotherapy of breast cancer

Breast cancer (BC) is a predominant malignancy among women globally, with its etiology remaining largely elusive. Diagnosis primarily relies on invasive histopathological methods, which are often limited by sample representation and processing time. Consequently, non-invasive imaging techniques such as mammography, ultrasound, and Magnetic Resonance Imaging (MRI) are indispensable for BC screening, diagnosis, staging, and treatment monitoring. Recent advancements in imaging technologies and artificial intelligence-driven radiomics have enhanced precision medicine by enabling early detection, accurate molecular subtyping, and personalized therapeutic strategies. Despite reductions in mortality through traditional treatments, challenges like tumor heterogeneity and therapeutic resistance persist. Immunotherapies, particularly PD-1/PD-L1 inhibitors, have emerged as promising alternatives. This review explores recent developments in BC imaging diagnostics and immunotherapeutic approaches, aiming to inform clinical practices and optimize therapeutic outcomes.

Recent update on the development of HPV16 inhibitors for cervical cancer

Persistent infection with human papillomavirus (HPV) can lead to cervical cancer (CC), which is the fourth most commonly diagnosed cancer in women globally. In this review, we have explained the HPV genome and the development of CC. Additionally, we summarized recently discovered small molecules that act as inhibitors of HPV-16. These molecules were identified through experimental and in-silico studies aimed at preventing or treating CC. HPV-16 and HPV-18 are the most common subtypes of HPV that cause CC globally. E6 oncoprotein of HPV-16 is considered the primary cause of CC progression. Therefore, E6 is the most focused targeted protein for developing specific and novel therapeutic inhibitors to treat HPV-related cancers. In recent years, various HPV inhibitors have been identified by means of experimental and in-silico studies. In addition, artificial intelligence-based medical diagnostic tools have grown more popular as they are capable of screening and diagnosing HPV-related cancer.

Nomogram model based on tumor-infiltrating lymphocytes and clinical characteristics to predict prognosis of patients with laryngeal squamous cell carcinoma

Head and neck carcinomas are the sixth most common cancers worldwide, with laryngeal squamous cell carcinoma (LSCC) being the second most prevalent subtype. Improving survival outcomes in LSCC patients remains a critical clinical challenge. This retrospective study aimed to develop a nomogram model integrating tumor-infiltrating lymphocytes (TILs) and clinicopathological characteristics to predict the prognosis of LSCC patients. The nomogram model was constructed using Cox and Lasso regression analyses and was subsequently evaluated through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were utilized for model validation and to further elucidate the role of TILs and immune responses in LSCC. This study cohort included LSCC patients diagnosed by pathological examination between 2011 and 2014 at Xiangya Hospital and Harbin Medical University Cancer Hospital. A total of 412 patients were assigned to the training cohort and 140 patients to the test cohort for validation. The final nomogram model integrated TNM stage, TILs, PLR, BMI, age, differentiation and NLR. The area under the curve (AUC) was 0.745, indicating strong calibration and clinical utility. Kaplan-Meier survival curves demonstrated significant discrimination. TILs were positively correlated with immune cell abundance and the expression of immune-related genes. In conclusion, the nomogram model based on TILs and clinicopathological features effectively predicts the prognosis of LSCC patients.

Exploring FAM13A-N-Myc interactions to uncover potential targets in MYCN-amplified neuroblastoma: a study of protein interactions and molecular dynamics simulations

Neuroblastoma (NB), a common infantile neuroendocrine tumor, presents a substantial therapeutic challenge when MYCN is amplified. Given that the protein structure of N-Myc is disordered, we utilized Alphafold for prediction and GROMACS for optimization of the N-Myc structure, thereby improving the reliability of the predicted structure. The publicly available datasets GSE49710 and GSE73517 were adopted, which contain the transcriptome data of clinical samples from 598 NB patients. Through various machine learning algorithms, FAM13A was identified as a characteristic gene of MYCN. Cell functional experiments, including those on cell proliferation, apoptosis, and cell cycle, also indicate that FAM13A is a potential risk factor. Additionally, Alphafold and GROMACS were employed to predict and optimize the structure of FAM13A. Protein-protein docking and molecular dynamic modeling techniques were then used to validate the enhanced protein stability resulting from the interaction between N-Myc and FAM13A. Consequently, targeting FAM13A holds the potential to reduce the stability of N-Myc, hinder the proliferation of NB cells, and increase the infiltration of immune cells. This multi-faceted approach effectively combats tumor cells, making FAM13A a prospective therapeutic target for MYCN-amplified NB.

Treatment of intermediate-to-advanced unresectable hepatocellular carcinoma is shifting toward a multidisciplinary strategy that includes multiple modalities as needed

In the recent issue of the World Journal of Gastroenterology, Han et al compared the efficacy of and adverse reactions to bevacizumab versus lenvatinib as molecularly targeted agents in combination with interventional therapy and immunotherapy (IMT) to treat intermediate-to-advanced unresectable hepatocellular carcinoma. No significant differences in efficacy or adverse reactions were observed between bevacizumab and lenvatinib. This study is highly promising because in some regions, e.g., Japan, the combination of molecularly targeted therapy with IMT is fixed because of insurance restrictions, and some molecularly targeted agents cannot be combined with IMT. Further studies using these three modalities are expected to be conducted in the future. Additionally, because advanced radiotherapy modalities have recently been established, the number of combinations continues to increase, and further evidence regarding combination therapy, which is the cornerstone of personalized medicine, needs to be accumulated.

Exploring acupuncture as a therapeutic approach for tic disorders: a review of current understanding and potential benefits

Tic disorders (TD) refer to a condition where individuals experience recurring motor movements (e.g., eye blinking) and/or vocalizations (e.g., throat clearing). These disorders vary in terms of duration, cause, and manifestation of symptoms. Tourette's syndrome (TS) involves the presence of ongoing motor and vocal tics for a minimum of 1 year, with fluctuating intensity. Persistent chronic motor or vocal tic disorder is characterized by either motor or vocal tics (not both) present for at least 1 year. Provisional TD presents with either motor or vocal tics (not both) that have been present for less than 12 months. Though medications like Aripiprazole and dopamine receptor blockers are frequently prescribed, their potential unwanted consequences increase, may result in low adherence. In an effort to improve and broaden the care available for children diagnosed with TD, alternative methods such as acupuncture are being investigated and considered. Acupuncture is a method of traditional Chinese medicine that includes the placement of thin needles into particular areas of the body in order to correct any disruptions or irregularities. Research has demonstrated that acupuncture can help regulate abnormal brain function and relieve tic symptoms in individuals with TD. Additional studies are required to fully evaluate the usefulness of complementary treatments in addressing TD in young individuals, despite its common usage. Herein, we summarized the therapeutic effects of acupuncture in the treatment of TD.

Exploring novel biomarkers and immunotherapeutic targets for biofeedback therapies to reveal the tumor-associated immune microenvironment through a multimetric analysis of kidney renal clear cell carcinoma

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) constitutes the primary subtype of renal cell carcinoma, representing 75% to 80% of cases and carrying a substantial cancer-specific mortality rate of up to 24%. Despite advancements in treatment options, KIRC displays notable resistance to conventional therapies, emphasizing the need for innovative targeted immunotherapeutic strategies. Chromatin regulators (CRs), pivotal proteins controlling gene expression and critical biological processes, play a crucial role in the initiation and progression of KIRC. This study employed a multi-omics approach to evaluate the impact of CR-associated genes on KIRC prognosis.

METHODS

The study utilized the TCGA-KIRC dataset and employed LASSO Cox regression to construct and validate a prognostic model that focuses on genes influencing KIRC prognosis. The research investigated interactions among gene characteristics, clinical parameters, the tumor microenvironment, targeted immunotherapy, and drug responsiveness. Experimental validation, encompassing various techniques such as cell culture, transient transfection, qPCR, Transwell assays, confirmed the robust predictive capability of the BRD9 gene.

RESULTS

The analysis identified the risk score of CRs as an independent factor determining KIRC prognosis. Furthermore, the study introduced a predictive Nomogram model that integrates clinical attributes and risk assessment. Significantly, BRD9 exhibited substantially elevated expression within KIRC cells, underscoring its role in driving cancer cell proliferation, invasion, and migration. These findings suggest the potential for tailored immunotherapy targeting BRD9 in the treatment of KIRC.

CONCLUSION

This study presents an innovative prognostic framework for KIRC based on multi-omics approaches, seamlessly incorporating CRs. This model holds promise for improving the accuracy of prognosis prediction for KIRC patients, laying a robust foundation for the development of targeted immunotherapies.

Outcomes of Transarterial Hepatic Embolization versus Yttrium-90 Radioembolization for Treatment of Patients with Hepatocellular Carcinoma >7 cm

PURPOSE

To assess and compare the safety, effectiveness, and outcomes of transarterial hepatic embolization (TAE) and transarterial radioembolization (TARE) for the treatment of patients with unresectable hepatocellular carcinoma (HCC) >7 cm.

MATERIALS AND METHODS

Treatment-naive patients with HCC >7 cm who were treated with TAE or TARE between January 2013 and December 2023 were reviewed in this retrospective study. Nearest neighbor 2:1 propensity score matching was utilized for direct comparison. Radiological treatment response was assessed using the modified Response Evaluation Criteria in Solid Tumours (mRECIST). Kaplan-Meier survival curves were used to estimate progression-free survival (PFS) and overall survival (OS). Log-rank tests were performed to compare survival curves.

RESULTS

A total of 125 patients with HCC >7 cm were treated with TAE (n = 103) or TARE (n = 22). After propensity score matching, 44 patients who underwent TAE and 22 patients who were treated with TARE were compared. The mean tumor sizes were 10.4 cm (SD ± 2.6) in the TAE group and 10.7 cm (SD ± 2.7) in the TARE group (P > .695). TAE and TARE exhibited comparable adverse event (AE) rates (Grade 1 AE in 22 [50%] of 44 in TAE and 6 [27%] of 22 in TARE, P = .999; 1 Grade 2 AE [4.5%] in TAE). Median OS durations were 15.2 and 23.6 months in the TAE and TARE groups, respectively (P = .252). Median local PFS (4.7 vs 21.6 months, P < .001) and PFS (3.6 vs 10.0 months, P = .002) were significantly longer after TARE. TAE and TARE had similar objective response rates (TAE, 88.6% vs TARE, 77.3%; P = .364). Systemic therapy after TAE or TARE was a significant positive prognostic factor associated with disease progression and survival (PFS hazard ratio [HR], 0.58 [P = .047]; OS HR, 0.33 [P < .001]).

CONCLUSIONS

TAE and TARE are both safe transarterial therapies for patients with HCC >7 cm. TARE is associated with a longer time to progression and longer OS.

A novel tertiary lymphoid structure-associated signature accurately predicts patient prognosis and facilitates the selection of personalized treatment strategies for HNSCC

Background

Head and neck squamous cell carcinoma (HNSCC) is the most common type of head and neck cancer and is characterized by its aggressive nature and variable prognosis and response to immunotherapy. Tertiary lymphoid structures (TLSs) play crucial roles in creating a favourable immune microenvironment to control tumour progression. However, the specific impact of these structures on HNSCC has not been thoroughly studied.

Methods

In this study, a comprehensive review of tertiary lymphoid structures was conducted by analysing 9 TLS-associated genes in a cohort of 871 HNSCC patients. Distinct TLS-related subgroups were identified through unsupervised clustering analysis, and the associated genes were explored. Prognostic genes were identified via univariate Cox and Boruta algorithms, and a novel TLS-related scoring system was developed via the GSVA algorithm.

Results

Our study revealed that patients with higher TLS-related scores had improved overall survival and were more likely to benefit from immunotherapy. Furthermore, we observed a significant negative correlation between sensitivity to traditional chemotherapeutic agents and the TLS-related signature score.

Conclusions

Our findings suggest that the TLS-related features of HNSCC patients hold promise as predictive indicators for immunotherapy efficacy and may offer novel insights for tailoring personalized treatment strategies in clinical practice.

EXO1's pan-cancer roles: diagnostic, prognostic, and immunological analyses through bioinformatics

Cancer remains a leading cause of mortality worldwide, with human exonuclease 1 (EXO1) emerging as a key player in DNA repair and damage response pathways, critical for genomic stability and tumor evolution. The aim of this study was to conduct a comprehensive pan-cancer analysis to elucidate the multifaceted roles of EXO1 in various malignancies. Leveraging public databases including TCGA, GTEx, HPA, cBioPortal, UALCAN, STRING, CancerSEA and TISIDB database, we examined EXO1's expression, diagnostic potential, prognostic significance, mutational characteristics, functional roles, and immunological effects across different cancer types. EXO1 was found to be upregulated in multiple cancers, with significant diagnostic potential as indicated by high AUC values in ROC analyses. Elevated EXO1 expression correlated with adverse prognosis in several cancer types, including breast, lung, and pancreatic cancers. Epigenetic alterations, including DNA methylation and mRNA modifications, were also associated with EXO1 expression. Enrichment analyses identified EXO1-related genes involved in DNA recombination, replication, and repair, with GSEA implicating EXO1 in cell cycle regulation and DNA processing pathways. Importantly, immunogenomic analyses revealed EXO1's significant role in modulating the tumor microenvironment, as it is associated with immune cell infiltration and cytokine expression, suggesting its involvement in tumor immunology and immune response regulation. These results implied that EXO1 as a significant biomarker with prognostic and diagnostic potential across various malignancies, suggesting its potential as a therapeutic target and its involvement in immunomodulatory processes within the tumor microenvironment.

Integrative single cell transcriptomic analysis reveals 3p deletion associated tumor microenvironment and chemoresistance in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) remains a prevalent and lethal malignancy, with a five-year survival rate of just 50% for cases of locally advanced disease. Chromosomal aberrations, particularly the deletion of the short arm of chromosome 3 (3p), have been strongly associated with poor prognosis and more aggressive tumor phenotypes. The tumor microenvironment (TME) plays a pivotal role in tumor progression and resistance to therapy. This study aims to elucidate the impact of 3p deletion on the TME, immune cell infiltration, and treatment response in HNSCC, to identify novel therapeutic targets to improve patient outcomes. We analyzed single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and bulk transcriptome data from The Cancer Genome Atlas (TCGA). Pseudo-time trajectory and cell-cell communication analyses were performed with the Monocle and CellChat packages. The Wilcoxon test was used to evaluate the differential gene expression between wild-type (wt) and mutant (mut) groups. Prognostic models were developed using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and Cox regression analyses to find the genes related to survival, with survival analysis conducted via Kaplan-Meier curves. Gene set enrichment analysis (GSEA) was employed to investigate pathway dysregulation, and immune cell infiltration was assessed using various immune scoring methodologies to explore the differences immune environment. The Tumor Immune Dysfunction and Exclusion (TIDE) database was utilized to predict the potential efficacy of immune checkpoint inhibitors. mRNA and protein expression levels of SPP1 were examined by RT-qPCR and Western blotting, while cell proliferation was assessed using the CCK8 assay. The mut group demonstrated significant alterations in cellular composition, characterized by increased endothelial cells and macrophages and decreased fibroblasts and CD8 + T cells, indicative of an immunosuppressive TME. Differential expression analysis revealed downregulation of immune pathways, including antigen processing and presentation, T cell receptor signaling, and B cell receptor signaling pathways in the mut group, along with enhanced metabolic activity in glycolysis and lipid metabolism. The prognostic model identified nine key genes associated with poor survival in HNSCC. The mut group exhibited poorer overall survival and a more immunosuppressive microenvironment compared to the wt group, which correlated with the outcomes observed in high-risk versus low-risk groups. High-risk patients also showed a diminished response to immunotherapy compared to low-risk patients. Additionally, SPP1 emerged as a critical gene associated with chemotherapy resistance and macrophage M2 polarization. This study demonstrates that 3p deletion significantly reshapes the TME, contributing to poor prognosis in HNSCC by fostering an immunosuppressive environment and enhancing chemoresistance. These findings highlight the potential for developing targeted therapies that address the genetic and immunological landscape of HNSCC.

Differential gene expression in PBMCs: Insights into the mechanism how pulmonary tuberculosis increases lung cancer risk

Pre-existing of pulmonary tuberculosis (PTB) poses increased lung cancer risk, yet the molecular mechanisms remain inadequately understood. This study sought to elucidate the potential mechanisms by performing comprehensive analyses of differentially expressed genes (DEGs) in peripheral blood mononuclear cells (PBMCs) from patients with PTB, lung adenocarcinoma (LUAD), and lung squamous cell carcinoma (LUSC). Microarray assays were employed to analyze the DEGs in PBMCs of these patients. The analyses revealed that, compared to healthy controls, the number of differentially expressed LncRNA in PBMCs from patients with PTB, LUAD, and LUSC were 801, 8,541, and 7,796, respectively. Similarly, the differentially expressed mRNA in PBMCs from patients with PTB, LUAD, and LUSC were 629, 4,865, and 4,438, respectively. These differentially expressed transcripts represent significant resources for the identifying diagnostic and differential diagnostic biomarkers for lung cancer and PTB. Pathways enriched by dysregulated mRNAs in patients with PTB, LUAD, and LUSC were identified through GO and KEGG pathway analyses. The results indicated that 9 pathways including the NOD-like receptor signaling pathway, pathways in cancer, and the MAPK signaling pathway were co-enriched across the PTB, LUAD, and LUSC groups, providing insights into the mechanisms by which PTB may increase the risk of cancer development and progression.

Circulating cell-free DNA methylation analysis of pancreatic cancer patients for early noninvasive diagnosis

Background

Aberrant hypermethylation of genomic DNA CpG islands (CGIs) is frequently observed in human pancreatic cancer (PAC). A plasma cell-free DNA (cfDNA) methylation analysis method can be utilized for the early and noninvasive detection of PAC. This study also aimed to differentiate PAC from other cancer types.

Methods

We employed the methylated CpG tandem amplification and sequencing (MCTA-Seq) method, which targets approximately one-third of CGIs, on plasma samples from PAC patients (n = 50) and healthy controls (n = 52), as well as from cancerous and adjacent noncancerous tissue samples (n = 66). The method's efficacy in detecting PAC and distinguishing it from hepatocellular carcinoma (HCC), colorectal cancer (CRC), and gastric cancer (GC) was evaluated. Additionally, a methylation score and typing system for PAC was also established.

Results

We identified a total of 120 cfDNA methylation biomarkers, including IRX4, KCNS2, and RIMS4, for the detection of PAC in blood. A panel comprising these biomarkers achieved a sensitivity of 97% and 86% for patients in the discovery and validation cohorts, respectively, with a specificity of 100% in both cohorts. The methylation scoring and typing systems were clinically applicable. Furthermore, we identified hundreds of differentially methylated cfDNA biomarkers between PAC and HCC, CRC, and GC. Certain combinations of these markers can be used in a highly specific (approximately 100%) algorithm to differentiate PAC from HCC, CRC, and GC in blood.

Conclusions

Our study identified cfDNA methylation markers for PAC, offering a novel approach for the early, noninvasive diagnosis of PAC.

The Role of Wolfiporia cocos (F. A. Wolf) Ryvarden and Gilb. Polysaccharides in Regulating the Gut Microbiota and Its Health Benefits

Wolfiporia cocos (F. A. Wolf) Ryvarden and Gilb. is a widely used herb in China, belonging to the large fungi of the family Polyporaceae. P. cocos; it consists of a variety of biologically active ingredients such as polysaccharides, triterpenes, and sterols, and is considered a treasure in traditional Chinese medicine (TCM). Notably, P. cocos polysaccharides, as the most prominent constituent, are of interest for their superior anti-obesity, anti-tumor, anti-inflammatory, antioxidant, and immunomodulatory activities. P. cocos polysaccharides can be divided into water-soluble polysaccharides and water-insoluble polysaccharides, which may contribute to their diverse biological functions. Numerous scholars have focused on the extraction process, structural identification, and classical pharmacological pathways of P. cocos polysaccharides, but there are few systematic reviews on P. cocos polysaccharides regulating the gut microbiota. Natural products and their active ingredients are closely related to intestinal health, and further exploration of these mechanisms is warranted. This review summarizes the recent cases of P. cocos polysaccharides regulating the gut microbiota to promote health and discusses their relationship with bioactive functions. It aims to provide a basis for exploring the new mechanisms of P. cocos polysaccharides in promoting intestinal health and offers a new vision for the further development of functional products.

Identification and validation of m6A RNA methylation and ferroptosis-related biomarkers in sepsis: transcriptome combined with single-cell RNA sequencing

Background

Sepsis, a systemic inflammatory response syndrome triggered by infection, is associated with high mortality rates and an increasing global incidence. While N 6-methyladenosine (m6A) RNA methylation and ferroptosis are implicated in inflammatory diseases, their specific genes and mechanisms in sepsis remain unclear.

Methods

Transcriptomic datasets of sepsis, along with m6A-related genes (m6A-RGs) and ferroptosis-related genes (FRGs), were sourced from public databases. Differentially expressed genes (DEGs) were identified between the sepsis and control groups, and m6A-RGs were analyzed through weighted gene co-expression network analysis (WGCNA) to uncover m6A module genes. These were then intersected with DEGs and FRGs to identify candidate genes. Biomarkers were identified using two machine learning methods, receiver operating characteristic (ROC) curves, and expression validation, followed by the development of a nomogram. Further in-depth analyses of the biomarkers were performed, including functional enrichment, immune infiltration, drug prediction, and molecular docking. Single-cell analysis was conducted to identify distinct cell clusters and evaluate biomarker expression at the single-cell level. Finally, reverse transcription-quantitative PCR (RT-qPCR) was employed to validate biomarker expression in clinical samples.

Results

DPP4 and TXN were identified as key biomarkers, showing higher expression in control and sepsis samples, respectively. The nomogram incorporating these biomarkers demonstrated strong diagnostic potential. Enrichment analysis highlighted their involvement in spliceosome function and antigen processing and presentation. Differential analysis of immune cell types revealed significant correlations between biomarkers and immune cells, such as macrophages and activated dendritic cells. Drug predictions identified gambogenic acid and valacyclovir as potential treatments, which were successfully docked with the biomarkers. Single-cell analysis revealed that the biomarkers were predominantly expressed in CD4+ memory cells, and CD16+ and CD14+ monocytes. The expression of DPP4 was further validated in clinical samples.

Conclusions

DPP4 and TXN were validated as biomarkers for sepsis, with insights into immune infiltration and therapeutic potential at the single-cell level, offering novel perspectives for sepsis treatment.

Comprehensive analysis of anoikis-related gene signature in ulcerative colitis using machine learning algorithms

Ulcerative colitis (UC) is a chronic inflammatory bowel disease with an idiopathic origin, characterized by persistent mucosal inflammation. Anoikis is a programmed cell death mechanism activated during carcinogenesis to eliminate undetected isolated cells from the extracellular matrix. Although existing evidence indicates that anoikis contributes to the modulation of immune response, the involvement of anoikis-related genes (ARGs) in UC pathogenesis and their interaction with infiltrating immune cells has not been thoroughly explored. The GSE75214, GSE92415, and GSE16879 datasets were acquired and integrated from the GEO database. Additionally, 58 ARGs were identified through the GSEA database. Key anoikis-DEGs in UC were identified using three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO) Cox regression, random forest (RF), and support vector machine (SVM). Receiver operating characteristic (ROC) analysis was utilized to evaluate the diagnostic accuracy of each gene. Subsequently, Single sample GSEA (ssGSEA) was executed to explore the relationships within immune cell infiltration, UC subtypes, and key anoikis-DEGs. Besides, unsupervised cluster analysis was conducted to categorize the UC samples into distinct subgroups, followed by comparing subtype differences. Finally, the upstream regulatory network was constructed and visualized. A comprehensive analysis of the involvement of ARGs in UC was performed, revealing their expression profile, correlation with infiltrating immune cells, and enrichment analyses. We identified five key anoikis-DEGs (PDK4, CEACAM6, CFB, CX3CL1, and HLA-DMA) and demonstrated their high diagnostic accuracy for UC. Moreover, CEACAM6, CFB, CX3CL1, and HLA-DMA exhibited positive associations with infiltrating immune cells in UC, whereas PDK4 displayed a negative correlation with all immune cells. Unsupervised cluster analysis enabled the classification of UC patients into two clusters, both of which exhibited distinct gene expression profiles and immune signaling pathways. Further, based upon the upstream regulatory network, TP53, RARB, RXRB, and CTCF potentially exerted regulatory functions. Our analysis identified five key anoikis-DEGs as characteristic biomarkers of UC. These genes were strongly associated with the infiltration of both innate and adaptive immune cells, as well as immune pathways. This study highlights the role of anoikis genes in UC pathophysiology and offers valuable insights for further elucidating UC pathogenesis and individualized therapy.

Integrated single-cell analysis reveals the regulatory network of disulfidptosis-related lncRNAs in bladder cancer: constructing a prognostic model and predicting treatment response

Background

Disulfidptosis is a newly discovered form of cell death, and long non-coding RNAs (lncRNAs) play a crucial role in tumor cell growth, migration, recurrence, and drug resistance, particularly in bladder cancer (BLCA). This study aims to investigate disulfidptosis-related lncRNAs (DRLs) as potential prognostic markers for BLCA patients.

Methods

Utilizing single-cell sequencing data, RNA sequencing data, and corresponding clinical information sourced from the GEO and TCGA databases, this study conducted cell annotation and intercellular communication analyses to identify differentially expressed disulfide death-related genes (DRGs). Subsequently, Pearson correlation and Cox regression analyses were employed to discern DRLs that correlate with overall survival. A prognostic model was constructed through LASSO regression analysis based on DRLs, complemented by multivariate Cox regression analysis. The performance of this model was rigorously evaluated using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC). Furthermore, this investigation delved into the potential signaling pathways, immune status, tumor mutation burden (TMB), and responses to anticancer therapies associated with varying prognoses in patients with BLCA.

Results

We identified twelve differentially expressed DRGs and elucidated their corresponding intercellular communication relationships. Notably, epithelial cells function as ligands, signaling to other cell types, with the interactions between epithelial cells and both monocytes and endothelial cells exhibiting the strongest connectivity. This study identified six DRLs in BLCA-namely, C1RL-AS1, GK-AS1, AC134349.1, AC104785.1, AC011092.3, and AC009951.6, and constructed a nomogram to improve the predictive accuracy of the model. The DRL features demonstrated significant associations with various clinical variables, diverse immune landscapes, and drug sensitivity profiles in BLCA patients. Furthermore, RT-qPCR validation confirmed the aberrant expression levels of these DRLs in BLCA tissues, affirming the potential of DRL characteristics as prognostic biomarkers.

Conclusion

We established a DRLs model that serves as a predictive tool for the prognosis of BLCA patients, as well as for assessing tumor mutation burden, immune cell infiltration, and responses to immunotherapy and targeted therapies. Collectively, this study contributes valuable insights toward advancing precision medicine within the context of BLCA.

Antitumor Research Based on Drug Delivery Carriers: Reversing the Polarization of Tumor-Associated Macrophages

The development of malignant tumors is a complex process that involves the tumor microenvironment (TME). An immunosuppressive TME presents significant challenges to current cancer therapies, serving as a key mechanism through which tumor cells evade immune detection and play a crucial role in tumor progression and metastasis. This impedes the optimal effectiveness of immunotherapeutic approaches, including cytokines, immune checkpoint inhibitors, and cancer vaccines. Tumor-associated macrophages (TAMs), a major component of tumor-infiltrating immune cells, exhibit dual functionalities: M1-like TAMs suppress tumorigenesis, while M2-like TAMs promote tumor growth and metastasis. Consequently, the development of various nanocarriers aimed at polarizing M2-like TAMs to M1-like phenotypes through distinct mechanisms has emerged as a promising therapeutic strategy to inhibit tumor immune escape and enhance antitumor responses. This Review covers the origin and types of TAMs, common pathways regulating macrophage polarization, the role of TAMs in tumor progression, and therapeutic strategies targeting TAMs, aiming to provide a comprehensive understanding and guidance for future research and clinical applications.

Acupuncture in Dentistry: A Comprehensive Review of Its Applications, Mechanisms, and Clinical Efficacy

The ancient therapeutic practice of acupuncture, which has its roots in traditional Chinese medicine, has drawn a lot of interest lately due to its possible use in contemporary dentistry. Examining the mechanics, clinical effectiveness, and contemporary uses of acupuncture in dentistry is the goal of this review. We evaluate its impact on the healing process, surgical recovery, anxiety management, and dental pain. However, more thorough and well-planned randomized controlled trials are still required to confirm its therapeutic advantages. The aim of this review is to explore acupuncture's mechanisms of action, including endorphin release, autonomic nervous system regulation, and effects on local blood circulation, along with how it may be included in a comprehensive dental care plan.

Improving hemodialysis patient depression outcomes with acupuncture: A randomized controlled trial

OBJECTIVE

To evaluate the efficacy and safety of acupuncture as a supplementary treatment for mild to moderate depression in hemodialysis patients.

METHOD

This multicenter, randomized, controlled, single-masked trial included 64 hemodialysis patients aged 18-75 divided into two groups. One group received genuine acupuncture, while the other received sham acupuncture over 12 weeks. The primary outcome measure was Hamilton Depression Scale (HAMD) scores; an inclusion criterion was HAMD scores of 10-23. Secondary outcomes included life quality improvements and changes in biochemical markers, such as serum albumin and hemoglobin levels. To assess the predictive effects of acupuncture treatment and biochemical parameters on depressive symptoms, a multivariable linear regression analysis was conducted.

RESULTS

Following acupuncture treatment, HAMD scores significantly decreased, quality of life scores improved, and biochemical indicators (serum albumin, hemoglobin, transferrin, and total protein levels) showed some improvement, indicating the effectiveness of acupuncture in alleviating depressive symptoms and enhancing overall health in hemodialysis patients. Multivariable regression analysis showed that acupuncture treatment (P = 0.004) and serum albumin levels (P = 0.03) were significant predictors of improvement in depressive symptoms, with an adjusted R2 of 0.45, indicating that the model explained 45 % of the variance in symptom improvement. Other biochemical indicators, such as hemoglobin, transferrin, and total protein, did not show significant predictive effects (P > 0.05). No serious adverse events were observed during the treatment.

CONCLUSION

Acupuncture is a safe and effective adjunct therapy for alleviating mild to moderate depression in hemodialysis patients. Acupuncture treatment and certain biochemical indicators (such as serum albumin) have significant predictive value for improving depressive symptoms.

Relevance of proteomics and metabolomics approaches to overview the tumorigenesis and better management of cancer

Proteomics and metabolomics, integral combination of OMICs platform are gaining prominence in cancer research to enhance scientific knowledge of bio-molecular interactions occurs in the cellular processes during cancer progression. This approach designed to identify potential tools for addressing the complexities of this multifaceted disease. This analysis focussed on the intricate interplay between proteins and metabolites within cancer cells and their surrounding microenvironment. By reviewing current proteomics and metabolomics studies, we aim to gain invaluable insights into tumour biology, progression, and its implication in therapeutic responses. This study highlights the importance of proteomics and metabolomics in discovering therapeutic targets and diagnostic biomarkers for targeted cancer treatment. Proteomics facilitates the analysis of protein expression, modifications and interactions, exemplified by the identification of HER2 mutations leads to development of breast cancer hence targeted therapies like trastuzumab could be initiated. Metabolomics reveals metabolic alternations such as elevated 2-hydroxyglutarate levels in gliomas linked to cancer progression and treatment resistance. The integration of these approaches clarifies complex signalling network driving oncogenesis and paves the way for innovative cancer therapies, including immune cheque point inhibitors. Proteomics and metabolomics have revolutionised cancer biology by revealing intricate signalling networks, metabolic dysregulations, and unique molecular alterations. This information is crucial for early cancer identification and prognosis, and for designing personalized therapeutic strategies. Innovative technologies like artificial intelligence and high-throughput mass spectrometry further enhance the potential of these studies. Fostering multidisciplinary collaboration and data-sharing is essential for maximising the impact of these approaches to cure as well as better management of the cancer.

Ferroptosis in Pulmonary Disease and Lung Cancer: Molecular Mechanisms, Crosstalk Regulation, and Therapeutic Strategies

Ferroptosis is a distinct form of iron-dependent programmed cell death characterized primarily by intracellular iron accumulation and lipid peroxidation. Multiple cellular processes, including amino acid metabolism, iron metabolism, lipid metabolism, various signaling pathways, and autophagy, have been demonstrated to influence the induction and progression of ferroptosis. Recent investigations have elucidated that ferroptosis plays a crucial role in the pathogenesis of various pulmonary disorders, including lung injury, chronic obstructive pulmonary disease, pulmonary fibrosis, and asthma. Ferroptosis is increasingly recognized as a promising novel strategy for cancer treatment. Various immune cells within the tumor microenvironment, including CD8+ T cells, macrophages, regulatory T cells, natural killer cells, and dendritic cells, have been shown to induce ferroptosis in tumor cells and modulate the process through the regulation of iron and lipid metabolism pathways. Conversely, ferroptosis can reciprocally alter the metabolic environment, leading to the activation or inhibition of immune cell functions, thereby modulating immune responses. This paper reviews the molecular mechanism of ferroptosis and describes the tumor immune microenvironment, discusses the connection between ferroptosis and the tumor microenvironment in lung cancer and pulmonary diseases, and discusses the development prospect of their interaction in the treatment of lung cancer and pulmonary diseases.

Single-cell RNA sequencing in ovarian cancer: Current progress and future prospects

Ovarian cancer is one of the most prevalent gynaecological malignancies. The rapid development of single-cell RNA sequencing (scRNA-seq) has allowed scientists to use this technique to study ovarian cancer development, heterogeneity, and tumour environment. Although multiple original research articles have reported the use of scRNA-seq in understanding ovarian cancer and how therapy resistance occurs, there is a lack of a comprehensive review that could summarize the findings from multiple studies. Therefore, this review aimed to fill this gap by comparing and summarizing the results from different studies that have used scRNA-seq in understanding ovarian cancer development, heterogeneity, tumour microenvironment, and treatment resistance. This review will begin with an overview of scRNA-seq workflow, followed by a discussion of various applications of scRNA-seq in studying ovarian cancer. Next, the limitations and future directions of scRNA-seq in ovarian cancer research will be presented.

Application of Interpretable Machine Learning Models to Predict the Risk Factors of HBV-Related Liver Cirrhosis in CHB Patients Based on Routine Clinical Data: A Retrospective Cohort Study

Chronic hepatitis B (CHB) infection represents a significant global public health issue, often leading to hepatitis B virus (HBV)-related liver cirrhosis (HBV-LC) with poor prognoses. Early identification of HBV-LC risk is essential for timely intervention. This study develops and compares nine machine learning (ML) models to predict HBV-LC risk in CHB patients using routine clinical and laboratory data. A retrospective analysis was conducted involving 777 CHB patients, with 50.45% (392/777) progressing to HBV-LC. Admission data consisted of 52 clinical and laboratory variables, with missing values addressed using multiple imputation. Feature selection utilized Least Absolute Shrinkage and Selection Operator (LASSO) regression and the Boruta algorithm, identifying 24 key variables. The evaluated ML models included XGBoost, logistic regression (LR), LightGBM, random forest (RF), AdaBoost, Gaussian naive Bayes (GNB), multilayer perceptron (MLP), support vector machine (SVM), and k-nearest neighbors (KNN). The data set was partitioned into an 80% training set (n = 621) and a 20% independent testing set (n = 156). Cross-validation (CV) facilitated hyperparameter tuning and internal validation of the optimal model. Performance metrics included the area under the receiver operating characteristic curve (AUC), Brier score, accuracy, sensitivity, specificity, and F1 score. The RF model demonstrated superior performance, with AUCs of 0.992 (training) and 0.907 (validation), while the reconstructed model achieved AUCs of 0.944 (training) and 0.945 (validation), maintaining an AUC of 0.863 in the testing set. Calibration curves confirmed a strong alignment between observed and predicted probabilities. Decision curve analysis indicated that the RF model provided the highest net benefit across threshold probabilities. The SHAP algorithm identified RPR, PLT, HBV DNA, ALT, and TBA as critical predictors. This interpretable ML model enhances early HBV-LC prediction and supports clinical decision-making in resource-limited settings.

The Interplay Between Epilepsy and Parkinson's Disease: Gene Expression Profiling and Functional Analysis

The results of many epidemiological studies suggest a bidirectional causality may exist between epilepsy and Parkinson's disease (PD). However, the underlying molecular landscape linking these two diseases remains largely unknown. This study aimed to explore this possible bidirectional causality by identifying differentially expressed genes (DEGs) in each disease as well as their intersection based on two respective disease-related datasets. We performed enrichment analyses and explored immune cell infiltration based on an intersection of the DEGs. Identifying a protein-protein interaction (PPI) network between epilepsy and PD, and this network was visualised using Cytoscape software to screen key modules and hub genes. Finally, exploring the diagnostic values of the identified hub genes. NetworkAnalyst 3.0 and Cytoscape software were also used to construct and visualise the transcription factor-micro-RNA regulatory and co-regulatory networks, the gene-microRNA interaction network, as well as gene-disease association. Based on the enrichment results, the intersection of the DEGs mainly revealed enrichment in immunity-, phosphorylation-, metabolism-, and inflammation-related pathways. The boxplots revealed similar trends in infiltration of many immune cells in epilepsy and Parkinson's disease, with greater infiltration in patients than in controls. A complex PPI network comprising 186 nodes and 512 edges were constructed. According to node connection degree, top 15 hub genes were considered the kernel targets of epilepsy and PD. The area under curve values of hub gene expression profiles confirmed their excellent diagnostic values. This study is the first to analyse the molecular landscape underlying the epidemiological link between epilepsy and Parkinson's disease. The two diseases are closely linked through immunity-, inflammation-, and metabolism-related pathways. This information was of great help in understanding the pathogenesis, diagnosis, and treatment of the diseases. The present results may provide guidance for further in-depth analysis about molecular mechanisms of epilepsy and PD and novel potential targets.

Machine Learning-Based Glycolipid Metabolism Gene Signature Predicts Prognosis and Immune Landscape in Oesophageal Squamous Cell Carcinoma

Using machine learning approaches, we developed and validated a novel prognostic model for oesophageal squamous cell carcinoma (ESCC) based on glycolipid metabolism-related genes. Through integrated analysis of TCGA and GEO datasets, we established a robust 15-gene signature that effectively stratified patients into distinct risk groups. This signature demonstrated superior prognostic value and revealed significant associations with immune infiltration patterns. High-risk patients exhibited reduced immune cell infiltration, particularly in B cells and NK cells, alongside increased tumour purity. Single-cell RNA sequencing analysis uncovered unique cellular composition patterns and enhanced interaction intensities in the high-risk group, especially within epithelial and smooth muscle cells. Functional validation confirmed MECP2 as a promising therapeutic target, with its knockdown significantly inhibiting tumour progression both in vitro and in vivo. Drug sensitivity analysis identified specific therapeutic agents showing potential efficacy for high-risk patients. Our study provides both a practical prognostic tool and novel insights into the relationship between glycolipid metabolism and tumour immunity in ESCC, offering potential strategies for personalised treatment.

Loss of TNFRSF21 induces cisplatin sensitivity in lung adenocarcinoma

Background

Despite the identification of numerous therapeutic targets in lung cancer, achieving significant efficacy has been challenging. TNFRSF21 plays an important role in various cancers. We investigated the function of TNFRSF21 in lung adenocarcinoma (LUAD).

Methods

The prognostic value of TNFRSF21 expression in lung cancer was evaluated by the GEPIA and Kaplan-Meier Plotter databases. Lung cancer cell viability was assessed by the CCK8 assay. TNFRSF21 expression patterns in lung cancer tissues and cells were examined using RT-PCR assay. Tumor sphere growth was evaluated through tumor sphere formation assays. MtROS contents in lung cancer cells were observed through MitoSOX fluorescent assays.

Result

TNFRSF21 was up-regulated in LUAD patients. TNFRSF21 induction was particularly notable in LUAD, especially in cancerous cells (A549, H1299, H460, and SPC-A1), compared to BEAS-2B cells. Additionally, TNFRSF21 was increased in cisplatin (DDP)-resistant LUAD cells. Loss of TNFRSF21 significantly inhibited LUAD cell growth. It was observed that forced expression of TNFRSF21 contributed to tumor cell proliferation and DDP resistance. The production of ROS was found to participate in the inhibitory effects on lung cancer stem cells (CSCs), with decreased TNFRSF21 restraining ROS contents. Collectively, these findings reveal that the downregulation of TNFRSF21 promotes ROS contents to restrain the lung CSC-like characteristics via modulation of CD44 and CD133.

Conclusions

In conclusion, TNFRSF21 may act as a novel target for lung cancer chemotherapy, particularly for eradicating lung CSCs.

Biological roles and molecular mechanism of circular RNAs in epithelial-mesenchymal transition of gastrointestinal malignancies

Circular RNAs (circRNAs) are formed by splicing of precursor RNAs and covalently linked at the 5' and 3' ends. Dysregulated circRNAs are closely related to the epithelial-mesenchymal transition (EMT) of gastrointestinal malignancies. CircRNAs, including circRNA_0008717, circGOT1, circ-DOCK5, circVPS33B, circPVT1, circMET, circ-OXCT1, circ_67835, circRTN4, circ_0087502, circFNDC38, circ_PTEN1, circPGPEP1, and circ-E-Cad are involved in the EMT process of gastrointestinal malignancies through a variety of mechanisms, such as regulating EMT-inducing transcription factors, signaling pathways, and tumor microenvironments. Gastrointestinal (GI) malignancies are common malignant tumors worldwide, and the heterogeneity and easy metastasis of gastrointestinal malignancies limit the effectiveness of medical treatments. Therefore, investigating the molecular mechanisms involved in the pathogenesis of gastrointestinal malignancies is essential for clinical treatment. This article summarizes the biological roles and molecular mechanism of circRNAs in EMT of gastrointestinal malignancies, providing a theoretical basis for applying EMT-related circRNAs in targeted therapy.