Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data.

BACKGROUND: Colorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often leading to gastrointestinal symptoms and severe health issues. Nucleotide metabolism, which encompasses the synthesis of DNA and RNA, is a pivotal cellular biochemical process that significantly impacts both the progression and therapeutic strategies of colorectal cancer METHODS: For single-cell RNA sequencing (scRNA-seq), five functions were employed to calculate scores related to nucleotide metabolism. Cell developmental trajectory analysis and intercellular interaction analysis were utilized to explore the metabolic characteristics and communication patterns of different epithelial cells. These findings were further validated using spatial transcriptome RNA sequencing (stRNA-seq). A risk model was constructed using expression profile data from TCGA and GEO cohorts to optimize clinical decision-making. Key nucleotide metabolism-related genes (NMRGs) were functionally validated by further in vitro experiments. RESULTS: In both scRNA-seq and stRNA-seq, colorectal cancer (CRC) exhibited unique cellular heterogeneity, with myeloid cells and epithelial cells in tumor samples displaying higher nucleotide metabolism scores. Analysis of intercellular communication revealed enhanced signaling pathways and ligand-receptor interactions between epithelial cells with high nucleotide metabolism and fibroblasts. Spatial transcriptome sequencing confirmed elevated nucleotide metabolism states in the core region of tumor tissue. After identifying differentially expressed NMRGs in epithelial cells, a risk prognostic model based on four genes effectively predicted overall survival and immunotherapy outcomes in patients. High-risk group patients exhibited an immunosuppressive microenvironment and relatively poorer prognosis and responses to chemotherapy and immunotherapy. Finally, based on data analysis and a series of cellular functional experiments, ACOX1 and CPT2 were identified as novel therapeutic targets for CRC. CONCLUSION: In this study, a comprehensive analysis of NMRGs in CRC was conducted using a combination of single-cell sequencing, spatial transcriptome sequencing, and high-throughput data. The prognostic model constructed with NMRGs shows potential as a standalone prognostic marker for colorectal cancer patients and may significantly influence the development of personalized treatment approaches for CRC.

Deciphering cutaneous melanoma prognosis through LDL metabolism: Single-cell transcriptomics analysis via 101 machine learning algorithms.

Cutaneous melanoma poses a formidable challenge within the field of oncology, marked by its aggressive nature and capacity for metastasis. Despite extensive research uncovering numerous genetic and molecular contributors to cutaneous melanoma development, there remains a critical knowledge gap concerning the role of lipids, notably low-density lipoprotein (LDL), in this lethal skin cancer. This article endeavours to bridge this knowledge gap by delving into the intricate interplay between LDL metabolism and cutaneous melanoma, shedding light on how lipids influence tumour progression, immune responses and potential therapeutic avenues. Genes associated with LDL metabolism were extracted from the GSEA database. We acquired and analysed single-cell sequencing data (GSE215120) and bulk-RNA sequencing data, including the TCGA data set, GSE19234, GSE22153 and GSE65904. Our analysis unveiled the heterogeneity of LDL across various cell types at the single-cell sequencing level. Additionally, we constructed an LDL-related signature (LRS) using machine learning algorithms, incorporating differentially expressed genes and highly correlated genes. The LRS serves as a valuable tool for assessing the prognosis, immunity and mutation status of patients with cutaneous melanoma. Furthermore, we conducted experiments on A375 and WM-115 cells to validate the function of PPP2R1A, a pivotal gene within the LRS. Our comprehensive approach, combining advanced bioinformatics analyses with an extensive review of current literature, presents compelling evidence regarding the significance of LDL within the cutaneous melanoma microenvironment.

Unravelling the metabolic landscape of cutaneous melanoma: Insights from single-cell sequencing analysis and machine learning for prognostic assessment of lactate metabolism.

This manuscript presents a comprehensive investigation into the role of lactate metabolism-related genes as potential prognostic markers in skin cutaneous melanoma (SKCM). Bulk-transcriptome data from The Cancer Genome Atlas (TCGA) and GSE19234, GSE22153, and GSE65904 cohorts from GEO database were processed and harmonized to mitigate batch effects. Lactate metabolism scores were assigned to individual cells using the 'AUCell' package. Weighted Co-expression Network Analysis (WGCNA) was employed to identify gene modules correlated with lactate metabolism. Machine learning algorithms were applied to construct a prognostic model, and its performance was evaluated in multiple cohorts. Immune correlation, mutation analysis, and enrichment analysis were conducted to further characterize the prognostic model's biological implications. Finally, the function of key gene NDUFS7 was verified by cell experiments. Machine learning resulted in an optimal prognostic model, demonstrating significant prognostic value across various cohorts. In the different cohorts, the high-risk group showed a poor prognosis. Immune analysis indicated differences in immune cell infiltration and checkpoint gene expression between risk groups. Mutation analysis identified genes with high mutation loads in SKCM. Enrichment analysis unveiled enriched pathways and biological processes in high-risk SKCM patients. NDUFS7 was found to be a hub gene in the protein-protein interaction network. After the expression of NDUFS7 was reduced by siRNA knockdown, CCK-8, colony formation, transwell and wound healing tests showed that the activity, proliferation and migration of A375 and WM115 cell lines were significantly decreased. This study offers insights into the prognostic significance of lactate metabolism-related genes in SKCM.

PANoptosis-related signature in melanoma: Transcriptomic mapping and clinical prognostication.

Programmed cell death plays a pivotal role in maintaining tissue homeostasis, and recent advancements in cell biology have uncovered PANoptosis-a novel paradigm integrating pyroptosis, apoptosis, and necroptosis. This study investigates the implications of PANoptosis in melanoma, a formidable skin cancer known for its metastatic potential and resistance to conventional therapies. Leveraging bulk and single-cell transcriptome analyses, machine learning modeling, and immune correlation assessments, we unveil the molecular intricacies of PANoptosis in melanoma. Single-cell sequencing identifies diverse cell types involved in PANoptosis, while bulk transcriptome analysis reveals key gene sets correlated with PANoptosis. Machine learning algorithms construct a robust prognostic model, demonstrating consistent predictive power across diverse cohorts. Patients with different cohorts can be divided into high-risk and low-risk groups according to this PANoptosis score, with the high-risk group having a significantly worse prognosis. Immune correlation analyses unveil a link between PANoptosis and immunotherapy response, with potential therapeutic implications. Mutation analysis and enrichment studies provide insights into the mutational landscape associated with PANoptosis. Finally, we used cell experiments to verify the expression and function of key gene PARVA, showing that PARVA was highly expressed in melanoma cell lines, and after PARVA is knocked down, cell invasion, migration, and colony formation ability were significantly decreased. This study advances our understanding of PANoptosis in melanoma, offering a comprehensive framework for targeted therapeutic interventions and personalized medicine strategies in combating this aggressive malignancy.

Exploring the causal relationship between antihypertensive drugs and glioblastoma by combining drug target Mendelian randomization study, eQTL colocalization, and single-cell RNA sequencing.

Recent reports indicate a potential oncogenic role of antihypertensive drugs in common cancers. However, it remains uncertain whether this phenomenon influences the risk of glioblastoma multiforme (GBM). This study aimed to assess the potential causal effects of blood pressure (BP) and antihypertensive drugs on GBM. Genome-wide association study (GWAS) summary statistics for systolic blood pressure (SBP), diastolic blood pressure (DBP), and GBM in Europeans were downloaded. To represent the effects of antihypertensive drugs, we utilized single nucleotide polymorphisms (SNPs) associated with SBP/DBP adjacent to the coding regions of different antihypertensive drugs as instrumental variables to model five antihypertensive drugs, including angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, calcium channel blockers, ß-receptor blockers (BBs), and thiazide diuretics. Positive control studies were performed using GWAS data in chronic heart failure. The primary method for causality estimation was the inverse-variance-weighted method. Mendelian randomization analysis showed that BBs with the ß1-adrenergic receptor (ADRB1) as a therapeutic target could significantly reduce the risk of GBM by mediating DBP (OR = 0.431, 95% CI: 0.267-0.697, p < .001) and that they could also significantly reduce the risk of GBM by mediating SBP (OR = 0.595, 95% CI: 0.422-0.837, p = .003). Sensitivity analysis and colocalization analysis reinforced the robustness of these findings. Finally, the low expression of the ADRB1 gene in malignant gliomas was found by GBM data from TCGA and single-cell RNA sequencing, which most likely contributed to the poor prognosis of GBM patients. In summary, our study provides preliminary evidence of some causal relationship between ADRB1-targeted BBs and glioblastoma development. However, more studies are needed to validate these findings and further reveal the complex relationship between BP and GBM.

Long non-coding RNA PANDAR promoted radiation and cisplatin-induced DNA damage repair through ATR/CHK1 in NSCLC.

BACKGROUND: DNA-damaging agents, including radiation and platinum-based chemotherapy, are indispensable treatments for non-small cell lung cancer (NSCLC) patients. However, cancer cells tend to be resistant to both radiation and chemotherapy, thus resulting in treatment failure or recurrence. The purpose of this study was to explore the effect and mechanism of long non-coding RNA (lncRNA) PANDAR (promoter of CDKN1A antisense DNA damage-activated RNA) on NSCLC sensitivity to radiation and chemotherapy. METHODS: Cell counting kit (CCK-8), colony formation and flow cytometry were respectively performed to determine the cell cycle and apoptosis of NSCLC cells treated with γ-ray radiation and cisplatin. The extent of DNA damage was evaluated using a comet assay and immunofluorescence staining against γH2AX. In addition, we explored the role of PANDAR in DNA damage response pathways through western blot analysis. Finally, a nude mouse subcutaneous xenograft model was established to assess the sensitivity to radiation and chemotherapy in vivo. RESULTS: In cell experiments, PANDAR knockdown can increase the sensitivity of NSCLC cells to radiation and cisplatin. The CCK-8 results showed that cell viability was significantly increased in the overexpression group after radiation and cisplatin treatments. The overexpression group also showed more colonies, less apoptosis and DNA damage, and G2/M phase arrest was aggravated to provide the time necessary for DNA repair. Contrary to PANDAR overexpression, the trends were reversed in the PANDAR knockdown group. Furthermore, PANDAR knockdown inhibited radiation and cisplatin-activated phosphorylation levels of ATR and CHK1 in NSCLC cells. Finally, our in vivo model showed that targeting PANDAR significantly sensitized NSCLC to radiation and cisplatin. CONCLUSION: Our study showed that PANDAR knockdown promoted sensitivity to radiation and cisplatin in NSCLC by regulating the ATR/CHK1 pathway, thus providing a novel understanding as well as a therapeutic target for NSCLC treatment. In NSCLC cells, lncRNA PANDAR negatively regulates sensitivity to radiation and cisplatin. PANDAR can promote the repair of radiation and cisplatin-induced DNA damage and activation of the G2/M checkpoint through the ATR/CHK1 pathway. PANDAR knockdown results in defects in DNA damage repair accompanied by more cell apoptosis.

Expression of hub genes of endothelial cells in glioblastoma-A prognostic model for GBM patients integrating single-cell RNA sequencing and bulk RNA sequencing.

BACKGROUND: This study aimed to use single-cell RNA-seq (scRNA-seq) to discover marker genes in endothelial cells (ECs) and construct a prognostic model for glioblastoma multiforme (GBM) patients in combination with traditional high-throughput RNA sequencing (bulk RNA-seq). METHODS: Bulk RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) and The China Glioma Genome Atlas (CGGA) databases. 10x scRNA-seq data for GBM were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Key modules and differentially expressed genes (DEGs) were identified by weighted gene correlation network analysis (WGCNA). A non-negative matrix decomposition (NMF) algorithm was used to identify the different subtypes based on DEGs, and multivariate cox regression analysis to model the prognosis. Finally, differences in mutational landscape, immune cell abundance, immune checkpoint inhibitors (ICIs)-associated genes, immunotherapy effects, and enriched pathways were investigated between different risk groups. RESULTS: The analysis of scRNA-seq data from eight samples revealed 13 clusters and four cell types. After applying Fisher's exact test, ECs were identified as the most important cell type. The NMF algorithm identified two clusters with different prognostic and immunological features based on DEGs. We finally built a prognostic model based on the expression levels of four key genes. Higher risk scores were significantly associated with poorer survival outcomes, low mutation rates in IDH genes, and upregulation of immune checkpoints such as PD-L1 and CD276. CONCLUSION: We built and validated a 4-gene signature for GBM using 10 scRNA-seq and bulk RNA-seq data in this work.

Application value of intraoperative electrophysiological monitoring in cerebral eloquent area glioma surgery: a retrospective cohort study.

INTRODUCTION: Surgery for gliomas involving eloquent areas is a very challenging microsurgical procedure. Maximizing both the extent of resection (EOR) and preservation of neurological function have always been the focus of attention. Intraoperative neurophysiological monitoring (IONM) is widely used in this kind of surgery. The purpose of this study was to evaluate the efficacy of IONM in eloquent area glioma surgery. METHODS: Sixty-eight glioma patients who underwent surgical treatment from 2014 to 2019 were included in this retrospective cohort study, which focused on eloquent areas. Clinical indicators and IONM data were analysed preoperatively, two weeks after surgery, and at the final follow-up. Logistic regression, Cox regression, and Kaplan‒Meier analyses were performed, and nomograms were then established for predicting prognosis. The diagnostic value of the IONM indicator was evaluated by the receiver operating characteristic (ROC) curve. RESULTS: IONM had no effect on the postoperative outcomes, including EOR, intraoperative bleeding volume, duration of surgery, length of hospital stay, and neurological function status. However, at the three-month follow-up, the percentage of patients who had deteriorated function in the monitored group was significantly lower than that in the unmonitored group (23.3% vs. 52.6%; P < 0.05). Logistic regression analysis showed that IONM was a significant factor in long-term neurological function (OR = 0.23, 95% CI (0.07-0.70). In the survival analysis, long-term neurological deterioration indicated worsened overall survival (OS) and progression-free survival (PFS). A prognostic nomogram was established through Cox regression model analysis, which could predict the probability 3-year survival rate. The concordance index was 0.761 (95% CI 0.734-0.788). The sensitivity and specificity of IONM evoked potential (SSEP and TCeMEP) were 0.875 and 0.909, respectively. In the ROC curve analysis, the area under the curve (AUC) for the SSEP and TCeMEP curves was 0.892 (P < 0.05). CONCLUSIONS: The application of IONM could improve long-term neurological function, which is closely related to prognosis and can be used as an independent prognostic factor. IONM is practical and widely available for predicting postoperative functional deficits in patients with eloquent area glioma.

Corrigendum: Machine learning to construct sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with uveal melanoma.

[This corrects the article DOI: 10.3389/fendo.2022.1056310.].

Identification of peripheral blood immune infiltration signatures and construction of monocyte-associated signatures in ovarian cancer and Alzheimer's disease using single-cell sequencing.

Background: Ovarian cancer (OC) is a common tumor of the female reproductive system, while Alzheimer's disease (AD) is a prevalent neurodegenerative disease that primarily affects cognitive function in the elderly. Monocytes are immune cells in the blood that can enter tissues and transform into macrophages, thus participating in immune and inflammatory responses. Overall, monocytes may play an important role in Alzheimer's disease and ovarian cancer. Methods: The CIBERSORT algorithm results indicate a potential crucial role of monocytes/macrophages in OC and AD. To identify monocyte marker genes, single-cell RNA-seq data of peripheral blood mononuclear cells (PBMCs) from OC and AD patients were analyzed. Enrichment analysis of various cell subpopulations was performed using the "irGSEA" R package. The estimation of cell cycle was conducted with the "tricycle" R package, and intercellular communication networks were analyzed using "CellChat". For 134 monocyte-associated genes (MRGs), bulk RNA-seq data from two diseased tissues were obtained. Cox regression analysis was employed to develop risk models, categorizing patients into high-risk (HR) and low-risk (LR) groups. The model's accuracy was validated using an external GEO cohort. The different risk groups were evaluated in terms of immune cell infiltration, mutational status, signaling pathways, immune checkpoint expression, and immunotherapy. To identify characteristic MRGs in AD, two machine learning algorithms, namely random forest and support vector machine (SVM), were utilized. Results: Based on Cox regression analysis, a risk model consisting of seven genes was developed in OC, indicating a better prognosis for patients in the LR group. The LR group had a higher tumor mutation burden, immune cell infiltration abundance, and immune checkpoint expression. The results of the TIDE algorithm and the IMvigor210 cohort showed that the LR group was more likely to benefit from immunotherapy. Finally, ZFP36L1 and AP1S2 were identified as characteristic MRGs affecting OC and AD progression. Conclusion: The risk profile containing seven genes identified in this study may help further guide clinical management and targeted therapy for OC. ZFP36L1 and AP1S2 may serve as biomarkers and new therapeutic targets for patients with OC and AD.

Advances and future directions in keloid research: Pathogenesis, diagnosis and personalized treatment strategies.

Keloids, which are abnormal manifestations of wound healing, can result in significant functional impairment and aesthetic deformities. The pathogenesis of keloids is multifaceted and complex and influenced by various factors, such as genetics, the environment, and immune responses. The evolution of keloid treatment has progressed from traditional surgical excision to a contemporary combination of therapies including injection and radiation treatments, among others. This article provides a comprehensive review of keloid pathogenesis and treatment, emphasizing the latest advances in the field. Ultimately, this review underscores the necessity for continued research to enhance our understanding of keloid pathogenesis and to devise more effective treatments for this challenging condition.

Crosstalk of disulfidptosis-related subtypes, establishment of a prognostic signature and immune infiltration characteristics in bladder cancer based on a machine learning survival framework.

Background: Bladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response. Methods: The expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts. Results: In the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy. Conclusion: Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.

Applying interpretable machine learning algorithms to predict risk factors for permanent stoma in patients after TME.

Objective: The purpose of this study was to develop a machine learning model to identify preoperative and intraoperative high-risk factors and to predict the occurrence of permanent stoma in patients after total mesorectal excision (TME). Methods: A total of 1,163 patients with rectal cancer were included in the study, including 142 patients with permanent stoma. We collected 24 characteristic variables, including patient demographic characteristics, basic medical history, preoperative examination characteristics, type of surgery, and intraoperative information. Four machine learning algorithms including extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM) and k-nearest neighbor algorithm (KNN) were applied to construct the model and evaluate the model using k-fold cross validation method, ROC curve, calibration curve, decision curve analysis (DCA) and external validation. Results: The XGBoost algorithm showed the best performance among the four prediction models. The ROC curve results showed that XGBoost had a high predictive accuracy with an AUC value of 0.987 in the training set and 0.963 in the validation set. The k-fold cross-validation method was used for internal validation, and the XGBoost model was stable. The calibration curves showed high predictive power of the XGBoost model. DCA curves showed higher benefit rates for patients who received interventional treatment under the XGBoost model. The AUC value for the external validation set was 0.89, indicating that the XGBoost prediction model has good extrapolation. Conclusion: The prediction model for permanent stoma in patients with rectal cancer derived from the XGBoost machine learning algorithm in this study has high prediction accuracy and clinical utility.

Identification of copper metabolism-related subtypes and establishment of the prognostic model in ovarian cancer.

Background: Ovarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with copper metabolism (CMRGs) on the prognosis of OC, discovered various CM clusters, and built a risk model to evaluate patient prognosis, immunological features, and therapy response. Methods: 15 CMRGs affecting the prognosis of OC patients were identified in The Cancer Genome Atlas (TCGA). Consensus Clustering was used to identify two CM clusters. lasso-cox methods were used to establish the copper metabolism-related gene prognostic signature (CMRGPS) based on differentially expressed genes in the two clusters. The GSE63885 cohort was used as an external validation cohort. Expression of CM risk score-associated genes was verified by single-cell sequencing and quantitative real-time PCR (qRT-PCR). Nomograms were used to visually depict the clinical value of CMRGPS. Differences in clinical traits, immune cell infiltration, and tumor mutational load (TMB) between risk groups were also extensively examined. Tumour Immune Dysfunction and Rejection (TIDE) and Immune Phenotype Score (IPS) were used to validate whether CMRGPS could predict response to immunotherapy in OC patients. Results: In the TCGA and GSE63885 cohorts, we identified two CM clusters that differed significantly in terms of overall survival (OS) and tumor microenvironment. We then created a CMRGPS containing 11 genes to predict overall survival and confirmed its reliable predictive power for OC patients. The expression of CM risk score-related genes was validated by qRT-PCR. Patients with OC were divided into low-risk (LR) and high-risk (HR) groups based on the median CM risk score, with better survival in the LR group. The 5-year AUC value reached 0.74. Enrichment analysis showed that the LR group was associated with tumor immune-related pathways. The results of TIDE and IPS showed a better response to immunotherapy in the LR group. Conclusion: Our study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of patients with OC, offering new insights into individualized treatment.

A Macrophage-Related Gene Signature for Identifying COPD Based on Bioinformatics and ex vivo Experiments.

Background: This study aims to investigate the association between immune cells and the development of COPD, while providing a new method for the diagnosis of COPD according to the changes in immune microenvironment. Methods: In this study, the "CIBERSORT" algorithm was used to estimate the tissue infiltration of 22 types of immune cells in GSE20257 and GSE10006. The "limma" package was used for differentially expressed analysis. The key modules associated with vital immune cells were identified using WGCNA. GO and KEGG enrichment analysis revealed the biological functions of the candidate genes. Ultimately, a novel diagnostic prediction model was constructed via machine learning methods and multivariate logistic regression analysis based on GSE20257. Furthermore, we examined the stability of the model on one internal test set (GSE10006), three external test sets (GSE8545, GSE57148 and GSE76925), one single-cell transcriptome dataset (GSE167295), macrophages (THP-M cells) and lung tissue from COPD patients. Results: M0 macrophages (AUC > 0.7 in GSE20257 and GSE10006) were considered as the most important immune cells through exploring the immune microenvironment landscapes in COPD patients and healthy controls. The differentially expressed genes from GSE20257 and GSE10006 were divided into six and five modules via WGCNA, respectively. The green module in GSE20257 (cor = 0.41, P < 0.001) and the brown module in GSE10006 (cor = 0.67, P < 0.001) were highly correlated with M0 macrophages and were selected as key modules. Forty-one intersected genes obtained from two modules were primarily involved in regulation of cytokine production, regulation of innate immune response, specific granule, phagosome, lysosome, ferroptosis, and other biological processes. On the basis of the candidate genetic markers further characterized via the "Boruta" and "LASSO" algorithm for COPD, a diagnostic model comprising CLEC5A, FTL and SLC2A3 was constructed, which could accurately distinguish COPD patients from healthy controls in multiple datasets. GSE20257 as the training set has an AUC of 0.916. The AUCs of the internal test set and three external test sets were 0.873, 0.932, 0.675 and 0.688, respectively. Single-cell sequencing analysis suggested that CLEC5A, FTL and SLC2A3 were expressed in macrophages from COPD patients. The expressions of CLEC5A, FTL and SLC2A3 were up-regulated in THP-M cells and lung tissue from COPD patients. Conclusion: According to the variations of immune microenvironment in COPD patients, we constructed and validated a novel macrophage M0-associated diagnostic model with satisfactory predictive value. CLEC5A, FTL and SLC2A3 are expected to be promising targets of immunotherapy in COPD.

Interaction, immune infiltration characteristics and prognostic modeling of efferocytosis-related subtypes in glioblastoma.

BACKGROUND: Efferocytosis is a biological process in which phagocytes remove apoptotic cells and vesicles from tissues. This process is initiated by the release of inflammatory mediators from apoptotic cells and plays a crucial role in resolving inflammation. The signals associated with efferocytosis have been found to regulate the inflammatory response and the tumor microenvironment (TME), which promotes the immune escape of tumor cells. However, the role of efferocytosis in glioblastoma multiforme (GBM) is not well understood and requires further investigation. METHODS: In this study, we conducted a comprehensive analysis of 22 efferocytosis-related genes (ERGs) by searching for studies related to efferocytosis. Using bulk RNA-Seq and single-cell sequencing data, we analyzed the expression and mutational characteristics of these ERGs. By using an unsupervised clustering algorithm, we obtained ERG clusters from 549 GBM patients and evaluated the immune infiltration characteristics of each cluster. We then identified differential genes (DEGs) in the two ERG clusters and classified GBM patients into different gene clusters using univariate cox analysis and unsupervised clustering algorithms. Finally, we utilized the Boruta algorithm to screen for prognostic genes and reduce dimensionality, and the PCA algorithm was applied to create a novel efferocytosis-related scoring system. RESULTS: Differential expression of ERGs in glioma cell lines and normal cells was analyzed by rt-PCR. Cell function experiments, on the other hand, validated TIMD4 as a tumor risk factor in GBM. We found that different ERG clusters and gene clusters have distinct prognostic and immune infiltration profiles. The ERG signature we developed provides insight into the tumor microenvironment of GBM. Patients with lower ERG scores have a better survival rate and a higher likelihood of benefiting from immunotherapy. CONCLUSIONS: Our novel efferocytosis-related signature has the potential to be used in clinical practice for risk stratification of GBM patients and for selecting individuals who are likely to respond to immunotherapy. This can help clinicians design appropriate targeted therapies before initiating clinical treatment.

Effect of indocyanine green near-infrared light imaging technique guided lymph node dissection on short-term clinical efficacy of minimally invasive radical gastric cancer surgery: a meta-analysis.

Objective: In recent years, the utilization of indocyanine green near-infrared (ICG NIR) light imaging-guided lymph node dissection in the context of minimally invasive radical gastric cancer has emerged as a novel avenue for investigation. The objective of this study was to assess the influence of employing this technique for guiding lymph node dissection on the short-term clinical outcomes of minimally invasive radical gastric cancer surgery. Methods: The present study conducted a comprehensive search for short-term clinical outcomes, comparing the group undergoing ICG NIR light imaging-guided lymph node dissection with the control group, by thoroughly examining relevant literature from the inception to July 2023 in renowned databases such as PubMed, Embase, Web of Science, and Cochrane Library. The primary endpoints encompassed postoperative complications, including abdominal infection, abdominal bleeding, pneumonia, anastomotic fistula, and overall incidence of complications (defined as any morbidity categorized as Clavien-Dindo class I or higher within 30 days post-surgery or during hospitalization). Additionally, secondary outcome measures consisted of the time interval until the initiation of postoperative gas and food intake, as well as various other parameters, namely postoperative hospital stay, operative time, intraoperative blood loss, total number of harvested lymph nodes, and the number of harvested metastatic lymph nodes. To ensure methodological rigor, the Cochrane Collaboration Risk of Bias Tool and the Newcastle-Ottawa Scale (NOS) were employed to assess the quality of the included studies, while statistical analyses were performed using Review Manager 5.4 software and Stata, version 12.0 software. Results: A total of 19 studies including 3103 patients were ultimately included (n=1276 in the ICG group and n=1827 in the non-ICG group). In this meta-analysis, the application of ICG near-infrared light imaging in minimally invasive radical gastric cancer surgery effectively improved the occurrence of postoperative Clavien-Dindo grade II or higher complications in patients (RR=0.72, 95% CI 0.52 to 1.00) with a statistically significant P=0.05; in reducing intraoperative blood loss and shortening While reducing intraoperative blood loss and shortening postoperative hospital stay, it could ensure the thoroughness of lymph node dissection in minimally invasive radical gastric cancer surgery (MD=5.575, 95% CI 3.677-7.473) with significant effect size (Z=5.76, p<0.00001). Conclusion: The utilization of indocyanine green near-infrared light imaging technology in the context of minimally invasive radical gastric cancer surgery demonstrates notable efficacy in mitigating the occurrence of postoperative complications surpassing Clavien-Dindo grade II, while concurrently augmenting both the overall quantity of lymph node dissections and the identification of positive lymph nodes, all the while ensuring the preservation of surgical safety. Furthermore, the implementation of this technique proves particularly advantageous in the realm of robotic-assisted radical gastric cancer surgery, thus bearing significance for enhancing the short-term prognostic outcomes of patients.

Single-cell RNA sequencing identifies a novel proliferation cell type affecting clinical outcome of pancreatic ductal adenocarcinoma.

Background: Pancreatic ductal adenocarcinoma (PDAC) is an extremely deadly neoplasm, with only a 5-year survival rate of around 9%. The tumor and its microenvironment are highly heterogeneous, and it is still unknown which cell types influence patient outcomes. Methods: We used single-cell RNA sequencing (scRNA-seq) and spatial transcriptome (ST) to identify differences in cell types. We then applied the scRNA-seq data to decompose the cell types in bulk RNA sequencing (bulk RNA-seq) data from the Cancer Genome Atlas (TCGA) cohort. We employed unbiased machine learning integration algorithms to develop a prognosis signature based on cell type makers. Lastly, we verified the differential expression of the key gene LY6D using immunohistochemistry and qRT-PCR. Results: In this study, we identified a novel cell type with high proliferative capacity, Prol, enriched with cell cycle and mitosis genes. We observed that the proportion of Prol cells was significantly increased in PDAC, and Prol cells were associated with reduced overall survival (OS) and progression-free survival (PFS). Additionally, the marker genes of Prol cell type, identified from scRNA-seq data, were upregulated and associated with poor prognosis in the bulk RNA-seq data. We further confirmed that mutant KRAS and TP53 were associated with an increased abundance of Prol cells and that these cells were associated with an immunosuppressive and cold tumor microenvironment in PDAC. ST determined the spatial location of Prol cells. Additionally, patients with a lower proportion of Prol cells in PDAC may benefit more from immunotherapy and gemcitabine treatment. Furthermore, we employed unbiased machine learning integration algorithms to develop a Prol signature that can precisely quantify the abundance of Prol cells and accurately predict prognosis. Finally, we confirmed that the LY6D protein and mRNA expression were markedly higher in pancreatic cancer than in normal pancreatic tissue. Conclusions: In summary, by integrating bulk RNA-seq and scRNA-seq, we identified a novel proliferative cell type, Prol, which influences the OS and PFS of PDAC patients.

Revolutionizing anti-tumor therapy: unleashing the potential of B cell-derived exosomes.

B cells occupy a vital role in the functioning of the immune system, working in tandem with T cells to either suppress or promote tumor growth within the tumor microenvironment(TME). In addition to direct cell-to-cell communication, B cells and other cells release exosomes, small membrane vesicles ranging in size from 30-150 nm, that facilitate intercellular signaling. Exosome research is an important development in cancer research, as they have been shown to carry various molecules such as major histocompatibility complex(MHC) molecules and integrins, which regulate the TME. Given the close association between TME and cancer development, targeting substances within the TME has emerged as a promising strategy for cancer therapy. This review aims to present a comprehensive overview of the contributions made by B cells and exosomes to the tumor microenvironment (TME). Additionally, we delve into the potential role of B cell-derived exosomes in the progression of cancer.

A novel T-cell exhaustion-related feature can accurately predict the prognosis of OC patients.

The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.