Multi-modal transcriptomic analysis reveals metabolic dysregulation and immune responses in chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD), a progressive inflammatory condition of the airways, emerges from the complex interplay between genetic predisposition and environmental factors. Notably, its incidence is on the rise, particularly among the elderly demographic. Current research increasingly highlights cellular senescence as a key driver in chronic lung pathologies. Despite this, the detailed mechanisms linking COPD with senescent genomic alterations remain elusive. To address this gap, there is a pressing need for comprehensive bioinformatics methodologies that can elucidate the molecular intricacies of this link. This approach is crucial for advancing our understanding of COPD and its association with cellular aging processes. Utilizing a spectrum of advanced bioinformatics techniques, this research delved into the potential mechanisms linking COPD with aging-related genes, identifying four key genes (EP300, MTOR, NFE2L1, TXN) through machine learning and weighted gene co-expression network analysis (WGCNA) analyses. Subsequently, a precise diagnostic model leveraging an artificial neural network was developed. The study further employed single-cell analysis and molecular docking to investigate senescence-related cell types in COPD tissues, particularly focusing on the interactions between COPD and NFE2L1, thereby enhancing the understanding of COPD's molecular underpinnings. Leveraging artificial neural networks, we developed a robust classification model centered on four genes-EP300, MTOR, NFE2L1, TXN-exhibiting significant predictive capability for COPD and offering novel avenues for its early diagnosis. Furthermore, employing various single-cell analysis techniques, the study intricately unraveled the characteristics of senescence-related cell types in COPD tissues, enriching our understanding of the disease's cellular landscape. This research anticipates offering novel biomarkers and therapeutic targets for early COPD intervention, potentially alleviating the disease's impact on individuals and healthcare systems, and contributing to a reduction in global COPD-related mortality. These findings carry significant clinical and public health ramifications, bolstering the foundation for future research and clinical strategies in managing and understanding COPD.

Deciphering the role of sphingolipid metabolism in the immune microenvironment and prognosis of esophageal cancer via single-cell sequencing and bulk data analysis.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) stands as a significant global health challenge, distinguished by its aggressive progression from the esophageal epithelium. Central to this malignancy is sphingolipid metabolism, a critical pathway that governs key cellular processes, including apoptosis and immune regulation, thereby influencing tumor behavior. The advent of single-cell and transcriptome sequencing technologies has catalyzed significant advancements in oncology research, offering unprecedented insights into the molecular underpinnings of cancer. METHODS: We explored sphingolipid metabolism-related genes in ESCC using scRNA-seq data from GEO and transcriptome data from TCGA. We assessed 97 genes in epithelial cells with AUCell, UCell, and singscore algorithms, followed by bulk RNA-seq and differential analysis to identify prognosis-related genes. Immune infiltration and potential immunotherapeutic strategies were also investigated, and tumor gene mutations and drug treatment strategies were analyzed. RESULT: Our study identified distinct gene expression patterns, highlighting ARSD, CTSA, DEGS1, and PPTQ's roles in later cellular stages. We identified seven independent prognostic genes and created a precise nomogram for prognosis. CONCLUSION: This study integrates single-cell and transcriptomic data to provide a reliable prognostic model associated with sphingolipid metabolism and to inform immunotherapy and pharmacotherapy for ESCC at the genetic level. The findings have significant implications for precision therapy in esophageal cancer.

An overview of multi-omics technologies in rheumatoid arthritis: applications in biomarker and pathway discovery.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease with a complex pathological mechanism involving autoimmune response, local inflammation and bone destruction. Metabolic pathways play an important role in immune-related diseases and their immune responses. The pathogenesis of rheumatoid arthritis may be related to its metabolic dysregulation. Moreover, histological techniques, including genomics, transcriptomics, proteomics and metabolomics, provide powerful tools for comprehensive analysis of molecular changes in biological systems. The present study explores the molecular and metabolic mechanisms of RA, emphasizing the central role of metabolic dysregulation in the RA disease process and highlighting the complexity of metabolic pathways, particularly metabolic remodeling in synovial tissues and its association with cytokine-mediated inflammation. This paper reveals the potential of histological techniques in identifying metabolically relevant therapeutic targets in RA; specifically, we summarize the genetic basis of RA and the dysregulated metabolic pathways, and explore their functional significance in the context of immune cell activation and differentiation. This study demonstrates the critical role of histological techniques in decoding the complex metabolic network of RA and discusses the integration of histological data with other types of biological data.

Acupuncture therapy on myofascial pain syndrome: a systematic review and meta-analysis.

Purpose: Traditional Chinese medicine (TCM) therapies, especially acupuncture, have received increasing attention in the field of pain management. This meta-analysis evaluated the effectiveness of acupuncture in the treatment of myofascial pain syndrome. Methods: A comprehensive search was conducted across a number of databases, including PubMed, Cochrane Library, WOS, CNKI, WANFANG, Sinomed, and VIP. Furthermore, articles of studies published from the inception of these databases until November 22, 2023, were examined. This systematic review and meta-analysis encompassed all randomized controlled trials (RCTs) on acupuncture for myofascial pain syndromes, without language or date restrictions. Based on the mean difference (MD) of symptom change, we critically assessed the outcomes reported in these trials. The quality of evidence was assessed using the Cochrane Risk of Bias Tool. The study is registered with PROSPERO under registration number CRD42023484933. Results: Our analysis included 10 RCTs in which 852 patients were divided into two groups: an acupuncture group (427) and a control group (425). The results of the study showed that acupuncture was significantly more effective than the control group in treating myofascial pain syndromes, which was reflected in a greater decrease in VAS scores (MD = -1.29, 95% [-1.65, -0.94], p < 0.00001). In addition, the improvement in PRI and PPI was more pronounced in the acupuncture group (PRI: MD = -2.04, 95% [-3.76, -0.32], p = 0.02) (PPI: MD = -1.03, 95% [-1.26, -0.79], p < 0.00001) compared to the control group. These results suggest that acupuncture is effective in reducing myofascial pain. It is necessary to further study the optimal acupoints and treatment time to achieve the best therapeutic effect. Systematic review registration: https://www.crd.york.ac.uk/prospero/, identifier CRD42023484933.

Identification of crucial genes through WGCNA in the progression of gastric cancer.

Background: To explore the hub gene closely related to the progression of gastric cancer (GC), so as to provide a theoretical basis for revealing the therapeutic mechanism of GC. Methods: The gene expression profile and clinical data of GSE15459 in Gene Expression Omnibus (GEO) database were downloaded. The weighted gene co-expression network analysis (WGCNA) was used to screen the key modules related to GC progression. Survival analysis was used to assess the influence of hub genes on patients' outcomes. CIBERSORT analysis was used to predict the tissue infiltrating immune cells in patients. Immunohistochemical staining was conducted to further verify the expression of hub genes. Results: Through WGCNA, a total of 26 co-expression modules were constructed, in which salmon module and royalblue module had strong correlation with GC progression. The results of enrichment analysis showed that genes in the two modules were mainly involved in toll-like receptor signaling pathway, cholesterol metabolism and neuroactive ligand-receptor interaction. Six hub genes (C1QA, C1QB, C1QC, FCER1G, FPR3 and TYROBP) related to GC progression were screened. Survival analysis showed overall survival in the high expression group was significantly lower than that in the low expression group. CIBERSORT analysis revealed that immune characteristics difference between patients in early stage and advanced stage. Immunohistochemical results confirmed that C1QB, FCER1G, FPR3 and TYROBP were significantly associated with disease progression in GC. Conclusion: Our study identified that C1QB, FCER1G, FPR3 and TYROBP played important roles in the progression of GC, and their specific mechanisms are worth further study.

Precision unveiled: Synergistic genomic landscapes in breast cancer-Integrating single-cell analysis and decoding drug toxicity for elite prognostication and tailored therapeutics.

BACKGROUND: Globally, breast cancer, with diverse subtypes and prognoses, necessitates tailored therapies for enhanced survival rates. A key focus is glutamine metabolism, governed by select genes. This study explored genes associated with T cells and linked them to glutamine metabolism to construct a prognostic staging index for breast cancer patients for more precise medical treatment. METHODS: Two frameworks, T-cell related genes (TRG) and glutamine metabolism (GM), stratified breast cancer patients. TRG analysis identified key genes via hdWGCNA and machine learning. T-cell communication and spatial transcriptomics emphasized TRG's clinical value. GM was defined using Cox analyses and the Lasso algorithm. Scores categorized patients as TRG_high+GM_high (HH), TRG_high+GM_low (HL), TRG_low+GM_high (LH), or TRG_low+GM_low (LL). Similarities between HL and LH birthed a "Mixed" class and the TRG_GM classifier. This classifier illuminated gene variations, immune profiles, mutations, and drug responses. RESULTS: Utilizing a composite of two distinct criteria, we devised a typification index termed TRG_GM classifier, which exhibited robust prognostic potential for breast cancer patients. Our analysis elucidated distinct immunological attributes across the classifiers. Moreover, by scrutinizing the genetic variations across groups, we illuminated their unique genetic profiles. Insights into drug sensitivity further underscored avenues for tailored therapeutic interventions. CONCLUSION: Utilizing TRG and GM, a robust TRG_GM classifier was developed, integrating clinical indicators to create an accurate predictive diagnostic map. Analysis of enrichment disparities, immune responses, and mutation patterns across different subtypes yields crucial subtype-specific characteristics essential for prognostic assessment, clinical decision-making, and personalized therapies. Further exploration is warranted into multiple fusions between metrics to uncover prognostic presentations across various dimensions.

High-intensity mechanical bowel preparation before curative colorectal surgery is associated with poor long-term prognosis.

PURPOSE: Mechanical bowel preparation (MBP) has been widely used to reduce intestinal feces and bacteria and is considered necessary to prevent surgical infections. However, it is still controversial which intensity level of MBP is the most beneficial for patients before colorectal surgery. Our study aimed to determine the impact of different intensity levels of MBP on the progression-free survival (PFS) and overall survival (OS) for colorectal cancer (CRC) patients. METHODS: We evaluated 694 patients pathologically diagnosed with CRC and underwent MBP before surgery at 4 general hospitals from January 2011 to December 2015. The survival status of patients, the disease progression, and the time of death or progression were obtained through telephone follow-up at the deadline October 10, 2018. Hazard ratios were estimated by Cox proportional hazard models. Survival was assessed using the Kaplan-Meier method followed by the log-rank test. RESULTS: Of 694 patients included, 462 received low-intensity MBP and 232 received high-intensity MBP. A significantly higher PFS in low-intensity MBP was observed (p = 0.009). PFS at 2000 days was 69.331% in the low-intensity arm and 58.717% in the high-intensity arm. Patients who underwent low-intensity MBP also showed higher OS (p = 0.009). Nine patients in the low-intensity MBP group received secondary surgery, and two patients in the high-intensity MBP group received secondary surgery. CONCLUSIONS: In this retrospective cohort, low-intensity MBP was associated with better PFS and OS, which could provide a reference for doctors when choosing the intensity of MBP.

Increased 18F-FDG Uptake in Multiple Muscles in a Patient With Violent Cough.

Increased muscular FDG uptake could be due to various causes. Detailed analysis combined with history might be helpful for image interpretation. We present FDG PET/CT findings of a 69-year-old woman with lung cancer. The images demonstrated intense FDG uptake in multiple muscles, likely due to cough before the PET/CT study. To relieve the patient's cough, codeine was administrated. The second F-FDG PET/CT was performed 2 days later. The images showed that the abnormal muscular activity had become decreased, which was slightly lower than hepatic activity.